From 92f77a640f763c0af73554fb810a85a7d4c85e5e Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 11 Dec 2024 14:59:41 +0100
Subject: [PATCH 001/376] ci : pin nodejs to 22.11.0 (#10779)

---
 .github/workflows/server.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 9e66fb68c..671fe595c 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -79,7 +79,7 @@ jobs:
       # Setup nodejs (to be used for verifying bundled index.html)
       - uses: actions/setup-node@v4
         with:
-          node-version: 22
+          node-version: '22.11.0'
 
       - name: Verify bundled index.html
         id: verify_server_index_html

From 1a31d0dc00ba946d448e16ecc915ce5e8355994e Mon Sep 17 00:00:00 2001
From: qingy1337 <qxli2@students.everettcc.edu>
Date: Wed, 11 Dec 2024 07:16:32 -0800
Subject: [PATCH 002/376] Update README.md (#10772)

---
 examples/quantize/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/quantize/README.md b/examples/quantize/README.md
index 5d1e11c67..f9cce7b21 100644
--- a/examples/quantize/README.md
+++ b/examples/quantize/README.md
@@ -81,7 +81,7 @@ Several quantization methods are supported. They differ in the resulting model d
   - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
   - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
   - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
-  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#4996 - k-quants tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
   - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
   - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
   - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)

From 235f6e14bf0ed0211c51aeff14139038ae1000aa Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 11 Dec 2024 20:52:14 +0100
Subject: [PATCH 003/376] server : (UI) add tok/s, get rid of completion.js
 (#10786)

* get rid of completion.js

* extract chat bubble to a component

* add tok/s info

* sync

* fix BASE_URL

* only extract timings when it's enabled

* fix auto scroll
---
 examples/server/public/index.html       | 198 ++++++++++++---------
 examples/server/webui/index.html        | 124 ++++++++-----
 examples/server/webui/package-lock.json |   7 +
 examples/server/webui/package.json      |   1 +
 examples/server/webui/src/completion.js | 225 ------------------------
 examples/server/webui/src/main.js       | 128 +++++++++++---
 6 files changed, 307 insertions(+), 376 deletions(-)
 delete mode 100644 examples/server/webui/src/completion.js

diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 250729a44..9a19c5e83 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -11,84 +11,84 @@
   <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
   <meta name="color-scheme" content="light dark">
   <title>🦙 llama.cpp - chat</title>
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\xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/,hb=Object.freeze(Object.defineProperty({__proto__:null,Any:hf,Cc:pf,Cf:db,P:Po,S:mf,Z:bf},Symbol.toStringTag,{value:"Module"})),pb=new Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\0\0\0\0\0\0ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\\bfms„‹•˜¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\0\0\0͔͂\0Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\0\0ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\0\0ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\0ц\0ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\0ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\0\0ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\0\0֣mallSquare;旼erySmallSquare;斪Ͱֺ\0ֿ\0\0ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\0ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\0ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\0ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\0ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\0ࣃbleBracket;柦nǔࣈ\0࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻"䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\0စbleBracket;柧nǔည\0နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\0\0ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\0ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀  Space;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\0ጬጱ\0\0\0\0\0ጸጽ፷ᎅ\0᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\0጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\0ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\0ᖰᖶᖿ\0\0\0\0ᗆᗛᗫᙟ᙭\0ᚕ᚛ᚲᚹ\0ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\0\0ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\0\0ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\0ᠳƲᠯ\0ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\0᧨ᨑᨕᨲ\0ᨷᩐ\0\0᪴\0\0᫁\0\0ᬡᬮ᭍᭒\0᯽\0ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\0᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\0\0᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\0ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\0\0᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\0ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\0\0᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\0\0ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\0\0ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\0\0ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\0\0ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\0ᾞ\0ᾡᾧ\0\0ῆῌ\0ΐ\0ῦῪ \0 ⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\0\0᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\0ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\0⁐β•‥‧‪‬\0‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\0‶;慔;慖ʴ‾⁁\0\0⁃耻¾䂾;慗;慜5;慘ƶ⁌\0⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\0↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\0⊪\0⊸⋅⋎\0⋕⋳\0\0⋸⌢⍧⍢⍿\0⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\0⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\0⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\0⒪\0⒱\0\0\0\0\0⒵Ⓔ\0ⓆⓈⓍ\0⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐa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di㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\0㪋\0㪐㪛\0\0㪝㪨㪫㪯\0\0㫃㫎\0㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'.split("").map(e=>e.charCodeAt(0))),mb=new 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\xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/,Cb=Object.freeze(Object.defineProperty({__proto__:null,Any:Sf,Cc:Af,Cf:Eb,P:Wo,S:vf,Z:Df},Symbol.toStringTag,{value:"Module"})),kb=new Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\0\0\0\0\0\0ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\\bfms„‹•˜¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\0\0\0͔͂\0Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\0\0ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\0\0ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\0ц\0ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\0ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\0\0ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\0\0֣mallSquare;旼erySmallSquare;斪Ͱֺ\0ֿ\0\0ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\0ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\0ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\0ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\0ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\0ࣃbleBracket;柦nǔࣈ\0࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻"䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\0စbleBracket;柧nǔည\0နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\0\0ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\0ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀  Space;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\0ጬጱ\0\0\0\0\0ጸጽ፷ᎅ\0᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\0጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\0ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\0ᖰᖶᖿ\0\0\0\0ᗆᗛᗫᙟ᙭\0ᚕ᚛ᚲᚹ\0ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\0\0ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\0\0ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\0ᠳƲᠯ\0ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\0᧨ᨑᨕᨲ\0ᨷᩐ\0\0᪴\0\0᫁\0\0ᬡᬮ᭍᭒\0᯽\0ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\0᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\0\0᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\0ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\0\0᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\0ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\0\0᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\0\0ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\0\0ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\0\0ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\0\0ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\0ᾞ\0ᾡᾧ\0\0ῆῌ\0ΐ\0ῦῪ \0 ⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\0\0᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\0ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\0⁐β•‥‧‪‬\0‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\0‶;慔;慖ʴ‾⁁\0\0⁃耻¾䂾;慗;慜5;慘ƶ⁌\0⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\0↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\0⊪\0⊸⋅⋎\0⋕⋳\0\0⋸⌢⍧⍢⍿\0⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\0⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\0⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\0⒪\0⒱\0\0\0\0\0⒵Ⓔ\0ⓆⓈⓍ\0⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐabnoptwz⛩⛴⛷✚✯❁❇❐Ānr⛮⛱g;柬r;懽rëࣁgƀlmr⛿✍✔eftĀar০✇ightá৲apsto;柼ightá৽parrowĀlr✥✩efô⓭ight;憬ƀafl✶✹✽r;榅;쀀𝕝us;樭imes;樴š❋❏st;戗áፎƀ;ef❗❘᠀旊nge»❘arĀ;l❤❥䀨t;榓ʀachmt❳❶❼➅➇ròࢨorneòᶌarĀ;d྘➃;業;怎ri;抿̀achiqt➘➝ੀ➢➮➻quo;怹r;쀀𝓁mƀ;egল➪➬;檍;檏Ābu┪➳oĀ;rฟ➹;怚rok;䅂萀<;cdhilqrࠫ⟒☹⟜⟠⟥⟪⟰Āci⟗⟙;檦r;橹reå◲mes;拉arr;楶uest;橻ĀPi⟵⟹ar;榖ƀ;ef⠀भ᠛旃rĀdu⠇⠍shar;楊har;楦Āen⠗⠡rtneqq;쀀≨︀Å⠞܀Dacdefhilnopsu⡀⡅⢂⢎⢓⢠⢥⢨⣚⣢⣤ઃ⣳⤂Dot;戺Ȁclpr⡎⡒⡣⡽r耻¯䂯Āet⡗⡙;時Ā;e⡞⡟朠se»⡟Ā;sျ⡨toȀ;dluျ⡳⡷⡻owîҌefôएðᏑker;斮Āoy⢇⢌mma;権;䐼ash;怔asuredangle»ᘦr;쀀𝔪o;愧ƀcdn⢯⢴⣉ro耻µ䂵Ȁ;acdᑤ⢽⣀⣄sôᚧir;櫰ot肻·Ƶusƀ;bd⣒ᤃ⣓戒Ā;uᴼ⣘;横ţ⣞⣡p;櫛ò−ðઁĀdp⣩⣮els;抧f;쀀𝕞Āct⣸⣽r;쀀𝓂pos»ᖝƀ;lm⤉⤊⤍䎼timap;抸ఀGLRVabcdefghijlmoprstuvw⥂⥓⥾⦉⦘⧚⧩⨕⨚⩘⩝⪃⪕⪤⪨⬄⬇⭄⭿⮮ⰴⱧⱼ⳩Āgt⥇⥋;쀀⋙̸Ā;v⥐௏쀀≫⃒ƀelt⥚⥲⥶ftĀar⥡⥧rrow;懍ightarrow;懎;쀀⋘̸Ā;v⥻ే쀀≪⃒ightarrow;懏ĀDd⦎⦓ash;抯ash;抮ʀbcnpt⦣⦧⦬⦱⧌la»˞ute;䅄g;쀀∠⃒ʀ;Eiop඄⦼⧀⧅⧈;쀀⩰̸d;쀀≋̸s;䅉roø඄urĀ;a⧓⧔普lĀ;s⧓ସdz⧟\0⧣p肻 ଷmpĀ;e௹ఀʀaeouy⧴⧾⨃⨐⨓ǰ⧹\0⧻;橃on;䅈dil;䅆ngĀ;dൾ⨊ot;쀀⩭̸p;橂;䐽ash;怓΀;Aadqsxஒ⨩⨭⨻⩁⩅⩐rr;懗rĀhr⨳⨶k;椤Ā;oᏲᏰot;쀀≐̸uiöୣĀei⩊⩎ar;椨í஘istĀ;s஠டr;쀀𝔫ȀEest௅⩦⩹⩼ƀ;qs஼⩭௡ƀ;qs஼௅⩴lanô௢ií௪Ā;rஶ⪁»ஷƀAap⪊⪍⪑rò⥱rr;憮ar;櫲ƀ;svྍ⪜ྌĀ;d⪡⪢拼;拺cy;䑚΀AEadest⪷⪺⪾⫂⫅⫶⫹rò⥦;쀀≦̸rr;憚r;急Ȁ;fqs఻⫎⫣⫯tĀar⫔⫙rro÷⫁ightarro÷⪐ƀ;qs఻⪺⫪lanôౕĀ;sౕ⫴»శiíౝĀ;rవ⫾iĀ;eచథiäඐĀpt⬌⬑f;쀀𝕟膀¬;in⬙⬚⬶䂬nȀ;Edvஉ⬤⬨⬮;쀀⋹̸ot;쀀⋵̸ǡஉ⬳⬵;拷;拶iĀ;vಸ⬼ǡಸ⭁⭃;拾;拽ƀaor⭋⭣⭩rȀ;ast୻⭕⭚⭟lleì୻l;쀀⫽⃥;쀀∂̸lint;樔ƀ;ceಒ⭰⭳uåಥĀ;cಘ⭸Ā;eಒ⭽ñಘȀAait⮈⮋⮝⮧rò⦈rrƀ;cw⮔⮕⮙憛;쀀⤳̸;쀀↝̸ghtarrow»⮕riĀ;eೋೖ΀chimpqu⮽⯍⯙⬄୸⯤⯯Ȁ;cerല⯆ഷ⯉uå൅;쀀𝓃ortɭ⬅\0\0⯖ará⭖mĀ;e൮⯟Ā;q൴൳suĀbp⯫⯭å೸åഋƀbcp⯶ⰑⰙȀ;Ees⯿ⰀഢⰄ抄;쀀⫅̸etĀ;eഛⰋqĀ;qണⰀcĀ;eലⰗñസȀ;EesⰢⰣൟⰧ抅;쀀⫆̸etĀ;e൘ⰮqĀ;qൠⰣȀgilrⰽⰿⱅⱇìௗlde耻ñ䃱çృiangleĀlrⱒⱜeftĀ;eచⱚñదightĀ;eೋⱥñ೗Ā;mⱬⱭ䎽ƀ;esⱴⱵⱹ䀣ro;愖p;怇ҀDHadgilrsⲏⲔⲙⲞⲣⲰⲶⳓⳣash;抭arr;椄p;쀀≍⃒ash;抬ĀetⲨⲬ;쀀≥⃒;쀀>⃒nfin;槞ƀAetⲽⳁⳅrr;椂;쀀≤⃒Ā;rⳊⳍ쀀<⃒ie;쀀⊴⃒ĀAtⳘⳜrr;椃rie;쀀⊵⃒im;쀀∼⃒ƀAan⳰⳴ⴂrr;懖rĀhr⳺⳽k;椣Ā;oᏧᏥear;椧ቓ᪕\0\0\0\0\0\0\0\0\0\0\0\0\0ⴭ\0ⴸⵈⵠⵥ⵲ⶄᬇ\0\0ⶍⶫ\0ⷈⷎ\0ⷜ⸙⸫⸾⹃Ācsⴱ᪗ute耻ó䃳ĀiyⴼⵅrĀ;c᪞ⵂ耻ô䃴;䐾ʀabios᪠ⵒⵗLjⵚlac;䅑v;樸old;榼lig;䅓Ācr⵩⵭ir;榿;쀀𝔬ͯ⵹\0\0⵼\0ⶂn;䋛ave耻ò䃲;槁Ābmⶈ෴ar;榵Ȁacitⶕ⶘ⶥⶨrò᪀Āir⶝ⶠr;榾oss;榻nå๒;槀ƀaeiⶱⶵⶹcr;䅍ga;䏉ƀcdnⷀⷅǍron;䎿;榶pf;쀀𝕠ƀaelⷔ⷗ǒr;榷rp;榹΀;adiosvⷪⷫⷮ⸈⸍⸐⸖戨rò᪆Ȁ;efmⷷⷸ⸂⸅橝rĀ;oⷾⷿ愴f»ⷿ耻ª䂪耻º䂺gof;抶r;橖lope;橗;橛ƀclo⸟⸡⸧ò⸁ash耻ø䃸l;折iŬⸯ⸴de耻õ䃵esĀ;aǛ⸺s;樶ml耻ö䃶bar;挽ૡ⹞\0⹽\0⺀⺝\0⺢⺹\0\0⻋ຜ\0⼓\0\0⼫⾼\0⿈rȀ;astЃ⹧⹲຅脀¶;l⹭⹮䂶leìЃɩ⹸\0\0⹻m;櫳;櫽y;䐿rʀcimpt⺋⺏⺓ᡥ⺗nt;䀥od;䀮il;怰enk;怱r;쀀𝔭ƀimo⺨⺰⺴Ā;v⺭⺮䏆;䏕maô੶ne;明ƀ;tv⺿⻀⻈䏀chfork»´;䏖Āau⻏⻟nĀck⻕⻝kĀ;h⇴⻛;愎ö⇴sҀ;abcdemst⻳⻴ᤈ⻹⻽⼄⼆⼊⼎䀫cir;樣ir;樢Āouᵀ⼂;樥;橲n肻±ຝim;樦wo;樧ƀipu⼙⼠⼥ntint;樕f;쀀𝕡nd耻£䂣Ԁ;Eaceinosu່⼿⽁⽄⽇⾁⾉⾒⽾⾶;檳p;檷uå໙Ā;c໎⽌̀;acens່⽙⽟⽦⽨⽾pproø⽃urlyeñ໙ñ໎ƀaes⽯⽶⽺pprox;檹qq;檵im;拨iíໟmeĀ;s⾈ຮ怲ƀEas⽸⾐⽺ð⽵ƀdfp໬⾙⾯ƀals⾠⾥⾪lar;挮ine;挒urf;挓Ā;t໻⾴ï໻rel;抰Āci⿀⿅r;쀀𝓅;䏈ncsp;怈̀fiopsu⿚⋢⿟⿥⿫⿱r;쀀𝔮pf;쀀𝕢rime;恗cr;쀀𝓆ƀaeo⿸〉〓tĀei⿾々rnionóڰnt;樖stĀ;e【】䀿ñἙô༔઀ABHabcdefhilmnoprstux぀けさすムㄎㄫㅇㅢㅲㆎ㈆㈕㈤㈩㉘㉮㉲㊐㊰㊷ƀartぇおがròႳòϝail;検aròᱥar;楤΀cdenqrtとふへみわゔヌĀeuねぱ;쀀∽̱te;䅕iãᅮmptyv;榳gȀ;del࿑らるろ;榒;榥å࿑uo耻»䂻rր;abcfhlpstw࿜ガクシスゼゾダッデナp;極Ā;f࿠ゴs;椠;椳s;椞ë≝ð✮l;楅im;楴l;憣;憝Āaiパフil;椚oĀ;nホボ戶aló༞ƀabrョリヮrò៥rk;杳ĀakンヽcĀekヹ・;䁽;䁝Āes㄂㄄;榌lĀduㄊㄌ;榎;榐Ȁaeuyㄗㄜㄧㄩron;䅙Ādiㄡㄥil;䅗ì࿲âヺ;䑀Ȁclqsㄴㄷㄽㅄa;椷dhar;楩uoĀ;rȎȍh;憳ƀacgㅎㅟངlȀ;ipsླྀㅘㅛႜnåႻarôྩt;断ƀilrㅩဣㅮsht;楽;쀀𝔯ĀaoㅷㆆrĀduㅽㅿ»ѻĀ;l႑ㆄ;楬Ā;vㆋㆌ䏁;䏱ƀgns㆕ㇹㇼht̀ahlrstㆤㆰ㇂㇘㇤㇮rrowĀ;t࿜ㆭaéトarpoonĀduㆻㆿowîㅾp»႒eftĀah㇊㇐rrowó࿪arpoonóՑightarrows;應quigarro÷ニhreetimes;拌g;䋚ingdotseñἲƀahm㈍㈐㈓rò࿪aòՑ;怏oustĀ;a㈞㈟掱che»㈟mid;櫮Ȁabpt㈲㈽㉀㉒Ānr㈷㈺g;柭r;懾rëဃƀafl㉇㉊㉎r;榆;쀀𝕣us;樮imes;樵Āap㉝㉧rĀ;g㉣㉤䀩t;榔olint;樒arò㇣Ȁachq㉻㊀Ⴜ㊅quo;怺r;쀀𝓇Ābu・㊊oĀ;rȔȓƀhir㊗㊛㊠reåㇸmes;拊iȀ;efl㊪ၙᠡ㊫方tri;槎luhar;楨;愞ൡ㋕㋛㋟㌬㌸㍱\0㍺㎤\0\0㏬㏰\0㐨㑈㑚㒭㒱㓊㓱\0㘖\0\0㘳cute;䅛quï➺Ԁ;Eaceinpsyᇭ㋳㋵㋿㌂㌋㌏㌟㌦㌩;檴ǰ㋺\0㋼;檸on;䅡uåᇾĀ;dᇳ㌇il;䅟rc;䅝ƀEas㌖㌘㌛;檶p;檺im;择olint;樓iíሄ;䑁otƀ;be㌴ᵇ㌵担;橦΀Aacmstx㍆㍊㍗㍛㍞㍣㍭rr;懘rĀhr㍐㍒ë∨Ā;oਸ਼਴t耻§䂧i;䀻war;椩mĀin㍩ðnuóñt;朶rĀ;o㍶⁕쀀𝔰Ȁacoy㎂㎆㎑㎠rp;景Āhy㎋㎏cy;䑉;䑈rtɭ㎙\0\0㎜iäᑤaraì⹯耻­䂭Āgm㎨㎴maƀ;fv㎱㎲㎲䏃;䏂Ѐ;deglnprካ㏅㏉㏎㏖㏞㏡㏦ot;橪Ā;q኱ኰĀ;E㏓㏔檞;檠Ā;E㏛㏜檝;檟e;扆lus;樤arr;楲aròᄽȀaeit㏸㐈㐏㐗Āls㏽㐄lsetmé㍪hp;樳parsl;槤Ādlᑣ㐔e;挣Ā;e㐜㐝檪Ā;s㐢㐣檬;쀀⪬︀ƀflp㐮㐳㑂tcy;䑌Ā;b㐸㐹䀯Ā;a㐾㐿槄r;挿f;쀀𝕤aĀdr㑍ЂesĀ;u㑔㑕晠it»㑕ƀcsu㑠㑹㒟Āau㑥㑯pĀ;sᆈ㑫;쀀⊓︀pĀ;sᆴ㑵;쀀⊔︀uĀbp㑿㒏ƀ;esᆗᆜ㒆etĀ;eᆗ㒍ñᆝƀ;esᆨᆭ㒖etĀ;eᆨ㒝ñᆮƀ;afᅻ㒦ְrť㒫ֱ»ᅼaròᅈȀcemt㒹㒾㓂㓅r;쀀𝓈tmîñiì㐕aræᆾĀar㓎㓕rĀ;f㓔ឿ昆Āan㓚㓭ightĀep㓣㓪psiloîỠhé⺯s»⡒ʀbcmnp㓻㕞ሉ㖋㖎Ҁ;Edemnprs㔎㔏㔑㔕㔞㔣㔬㔱㔶抂;櫅ot;檽Ā;dᇚ㔚ot;櫃ult;櫁ĀEe㔨㔪;櫋;把lus;檿arr;楹ƀeiu㔽㕒㕕tƀ;en㔎㕅㕋qĀ;qᇚ㔏eqĀ;q㔫㔨m;櫇Ābp㕚㕜;櫕;櫓c̀;acensᇭ㕬㕲㕹㕻㌦pproø㋺urlyeñᇾñᇳƀaes㖂㖈㌛pproø㌚qñ㌗g;晪ڀ123;Edehlmnps㖩㖬㖯ሜ㖲㖴㗀㗉㗕㗚㗟㗨㗭耻¹䂹耻²䂲耻³䂳;櫆Āos㖹㖼t;檾ub;櫘Ā;dሢ㗅ot;櫄sĀou㗏㗒l;柉b;櫗arr;楻ult;櫂ĀEe㗤㗦;櫌;抋lus;櫀ƀeiu㗴㘉㘌tƀ;enሜ㗼㘂qĀ;qሢ㖲eqĀ;q㗧㗤m;櫈Ābp㘑㘓;櫔;櫖ƀAan㘜㘠㘭rr;懙rĀhr㘦㘨ë∮Ā;oਫ਩war;椪lig耻ß䃟௡㙑㙝㙠ዎ㙳㙹\0㙾㛂\0\0\0\0\0㛛㜃\0㜉㝬\0\0\0㞇ɲ㙖\0\0㙛get;挖;䏄rë๟ƀaey㙦㙫㙰ron;䅥dil;䅣;䑂lrec;挕r;쀀𝔱Ȁeiko㚆㚝㚵㚼Dz㚋\0㚑eĀ4fኄኁaƀ;sv㚘㚙㚛䎸ym;䏑Ācn㚢㚲kĀas㚨㚮pproø዁im»ኬsðኞĀas㚺㚮ð዁rn耻þ䃾Ǭ̟㛆⋧es膀×;bd㛏㛐㛘䃗Ā;aᤏ㛕r;樱;樰ƀeps㛡㛣㜀á⩍Ȁ;bcf҆㛬㛰㛴ot;挶ir;櫱Ā;o㛹㛼쀀𝕥rk;櫚á㍢rime;怴ƀaip㜏㜒㝤dåቈ΀adempst㜡㝍㝀㝑㝗㝜㝟ngleʀ;dlqr㜰㜱㜶㝀㝂斵own»ᶻeftĀ;e⠀㜾ñम;扜ightĀ;e㊪㝋ñၚot;旬inus;樺lus;樹b;槍ime;樻ezium;揢ƀcht㝲㝽㞁Āry㝷㝻;쀀𝓉;䑆cy;䑛rok;䅧Āio㞋㞎xô᝷headĀlr㞗㞠eftarro÷ࡏightarrow»ཝऀAHabcdfghlmoprstuw㟐㟓㟗㟤㟰㟼㠎㠜㠣㠴㡑㡝㡫㢩㣌㣒㣪㣶ròϭar;楣Ācr㟜㟢ute耻ú䃺òᅐrǣ㟪\0㟭y;䑞ve;䅭Āiy㟵㟺rc耻û䃻;䑃ƀabh㠃㠆㠋ròᎭlac;䅱aòᏃĀir㠓㠘sht;楾;쀀𝔲rave耻ù䃹š㠧㠱rĀlr㠬㠮»ॗ»ႃlk;斀Āct㠹㡍ɯ㠿\0\0㡊rnĀ;e㡅㡆挜r»㡆op;挏ri;旸Āal㡖㡚cr;䅫肻¨͉Āgp㡢㡦on;䅳f;쀀𝕦̀adhlsuᅋ㡸㡽፲㢑㢠ownáᎳarpoonĀlr㢈㢌efô㠭ighô㠯iƀ;hl㢙㢚㢜䏅»ᏺon»㢚parrows;懈ƀcit㢰㣄㣈ɯ㢶\0\0㣁rnĀ;e㢼㢽挝r»㢽op;挎ng;䅯ri;旹cr;쀀𝓊ƀdir㣙㣝㣢ot;拰lde;䅩iĀ;f㜰㣨»᠓Āam㣯㣲rò㢨l耻ü䃼angle;榧ހABDacdeflnoprsz㤜㤟㤩㤭㦵㦸㦽㧟㧤㧨㧳㧹㧽㨁㨠ròϷarĀ;v㤦㤧櫨;櫩asèϡĀnr㤲㤷grt;榜΀eknprst㓣㥆㥋㥒㥝㥤㦖appá␕othinçẖƀhir㓫⻈㥙opô⾵Ā;hᎷ㥢ïㆍĀiu㥩㥭gmá㎳Ābp㥲㦄setneqĀ;q㥽㦀쀀⊊︀;쀀⫋︀setneqĀ;q㦏㦒쀀⊋︀;쀀⫌︀Āhr㦛㦟etá㚜iangleĀlr㦪㦯eft»थight»ၑy;䐲ash»ံƀelr㧄㧒㧗ƀ;beⷪ㧋㧏ar;抻q;扚lip;拮Ābt㧜ᑨaòᑩr;쀀𝔳tré㦮suĀbp㧯㧱»ജ»൙pf;쀀𝕧roð໻tré㦴Ācu㨆㨋r;쀀𝓋Ābp㨐㨘nĀEe㦀㨖»㥾nĀEe㦒㨞»㦐igzag;榚΀cefoprs㨶㨻㩖㩛㩔㩡㩪irc;䅵Ādi㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\0㪋\0㪐㪛\0\0㪝㪨㪫㪯\0\0㫃㫎\0㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'.split("").map(e=>e.charCodeAt(0))),Sb=new 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f=o.level,p=0;l.length>0&&l[0].index===0&&s>0&&r[s-1].type==="text_special"&&(l=l.slice(1));for(let d=0;d<l.length;d++){const h=l[d].url,_=e.md.normalizeLink(h);if(!e.md.validateLink(_))continue;let v=l[d].text;l[d].schema?l[d].schema==="mailto:"&&!/^mailto:/i.test(v)?v=e.md.normalizeLinkText("mailto:"+v).replace(/^mailto:/,""):v=e.md.normalizeLinkText(v):v=e.md.normalizeLinkText("http://"+v).replace(/^http:\/\//,"");const A=l[d].index;if(A>p){const x=new e.Token("text","",0);x.content=c.slice(p,A),x.level=f,a.push(x)}const E=new e.Token("link_open","a",1);E.attrs=[["href",_]],E.level=f++,E.markup="linkify",E.info="auto",a.push(E);const m=new e.Token("text","",0);m.content=v,m.level=f,a.push(m);const g=new e.Token("link_close","a",-1);g.level=--f,g.markup="linkify",g.info="auto",a.push(g),p=l[d].lastIndex}if(p<c.length){const d=new e.Token("text","",0);d.content=c.slice(p),d.level=f,a.push(d)}t[u].children=r=yf(r,s,a)}}}}const Cf=/\+-|\.\.|\?\?\?\?|!!!!|,,|--/,eg=/\((c|tm|r)\)/i,tg=/\((c|tm|r)\)/ig,ug={c:"©",r:"®",tm:"™"};function ng(e,t){return ug[t.toLowerCase()]}function rg(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&(n.content=n.content.replace(tg,ng)),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function ig(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&Cf.test(n.content)&&(n.content=n.content.replace(/\+-/g,"±").replace(/\.{2,}/g,"…").replace(/([?!])…/g,"$1..").replace(/([?!]){4,}/g,"$1$1$1").replace(/,{2,}/g,",").replace(/(^|[^-])---(?=[^-]|$)/mg,"$1—").replace(/(^|\s)--(?=\s|$)/mg,"$1–").replace(/(^|[^-\s])--(?=[^-\s]|$)/mg,"$1–")),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function sg(e){let 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v=jn(h)||qn(String.fromCharCode(h)),A=jn(_)||qn(String.fromCharCode(_)),E=Un(h),m=Un(_);if(m?f=!1:A&&(E||v||(f=!1)),E?p=!1:v&&(m||A||(p=!1)),_===34&&a[0]==='"'&&h>=48&&h<=57&&(p=f=!1),f&&p&&(f=v,p=A),!f&&!p){d&&(i.content=mr(i.content,a.index,ll));continue}if(p)for(u=n.length-1;u>=0;u--){let g=n[u];if(n[u].level<s)break;if(g.single===d&&n[u].level===s){g=n[u];let x,k;d?(x=t.md.options.quotes[2],k=t.md.options.quotes[3]):(x=t.md.options.quotes[0],k=t.md.options.quotes[1]),i.content=mr(i.content,a.index,k),e[g.token].content=mr(e[g.token].content,g.pos,x),c+=k.length-1,g.token===r&&(c+=x.length-1),o=i.content,l=o.length,n.length=u;continue e}}f?n.push({token:r,pos:a.index,single:d,level:s}):p&&d&&(i.content=mr(i.content,a.index,ll))}}}function lg(e){if(e.md.options.typographer)for(let t=e.tokens.length-1;t>=0;t--)e.tokens[t].type!=="inline"||!og.test(e.tokens[t].content)||cg(e.tokens[t].children,e)}function ag(e){let t,u;const n=e.tokens,r=n.length;for(let i=0;i<r;i++){if(n[i].type!=="inline")continue;const s=n[i].children,o=s.length;for(t=0;t<o;t++)s[t].type==="text_special"&&(s[t].type="text");for(t=u=0;t<o;t++)s[t].type==="text"&&t+1<o&&s[t+1].type==="text"?s[t+1].content=s[t].content+s[t+1].content:(t!==u&&(s[u]=s[t]),u++);t!==u&&(s.length=u)}}const Zi=[["normalize",Gb],["block",Zb],["inline",Jb],["linkify",Qb],["replacements",sg],["smartquotes",lg],["text_join",ag]];function Vo(){this.ruler=new Je;for(let e=0;e<Zi.length;e++)this.ruler.push(Zi[e][0],Zi[e][1])}Vo.prototype.process=function(e){const t=this.ruler.getRules("");for(let u=0,n=t.length;u<n;u++)t[u](e)};Vo.prototype.State=Ef;function Tt(e,t,u,n){this.src=e,this.md=t,this.env=u,this.tokens=n,this.bMarks=[],this.eMarks=[],this.tShift=[],this.sCount=[],this.bsCount=[],this.blkIndent=0,this.line=0,this.lineMax=0,this.tight=!1,this.ddIndent=-1,this.listIndent=-1,this.parentType="root",this.level=0;const r=this.src;for(let i=0,s=0,o=0,c=0,l=r.length,a=!1;s<l;s++){const f=r.charCodeAt(s);if(!a)if(ae(f)){o++,f===9?c+=4-c%4:c++;continue}else a=!0;(f===10||s===l-1)&&(f!==10&&s++,this.bMarks.push(i),this.eMarks.push(s),this.tShift.push(o),this.sCount.push(c),this.bsCount.push(0),a=!1,o=0,c=0,i=s+1)}this.bMarks.push(r.length),this.eMarks.push(r.length),this.tShift.push(0),this.sCount.push(0),this.bsCount.push(0),this.lineMax=this.bMarks.length-1}Tt.prototype.push=function(e,t,u){const n=new _t(e,t,u);return n.block=!0,u<0&&this.level--,n.level=this.level,u>0&&this.level++,this.tokens.push(n),n};Tt.prototype.isEmpty=function(t){return this.bMarks[t]+this.tShift[t]>=this.eMarks[t]};Tt.prototype.skipEmptyLines=function(t){for(let u=this.lineMax;t<u&&!(this.bMarks[t]+this.tShift[t]<this.eMarks[t]);t++);return t};Tt.prototype.skipSpaces=function(t){for(let u=this.src.length;t<u;t++){const n=this.src.charCodeAt(t);if(!ae(n))break}return t};Tt.prototype.skipSpacesBack=function(t,u){if(t<=u)return t;for(;t>u;)if(!ae(this.src.charCodeAt(--t)))return t+1;return t};Tt.prototype.skipChars=function(t,u){for(let n=this.src.length;t<n&&this.src.charCodeAt(t)===u;t++);return t};Tt.prototype.skipCharsBack=function(t,u,n){if(t<=n)return t;for(;t>n;)if(u!==this.src.charCodeAt(--t))return t+1;return t};Tt.prototype.getLines=function(t,u,n,r){if(t>=u)return"";const i=new Array(u-t);for(let s=0,o=t;o<u;o++,s++){let c=0;const l=this.bMarks[o];let a=l,f;for(o+1<u||r?f=this.eMarks[o]+1:f=this.eMarks[o];a<f&&c<n;){const p=this.src.charCodeAt(a);if(ae(p))p===9?c+=4-(c+this.bsCount[o])%4:c++;else if(a-l<this.tShift[o])c++;else break;a++}c>n?i[s]=new Array(c-n+1).join(" ")+this.src.slice(a,f):i[s]=this.src.slice(a,f)}return i.join("")};Tt.prototype.Token=_t;const fg=65536;function Ji(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];return e.src.slice(u,n)}function al(e){const t=[],u=e.length;let n=0,r=e.charCodeAt(n),i=!1,s=0,o="";for(;n<u;)r===124&&(i?(o+=e.substring(s,n-1),s=n):(t.push(o+e.substring(s,n)),o="",s=n+1)),i=r===92,n++,r=e.charCodeAt(n);return t.push(o+e.substring(s)),t}function dg(e,t,u,n){if(t+2>u)return!1;let r=t+1;if(e.sCount[r]<e.blkIndent||e.sCount[r]-e.blkIndent>=4)return!1;let i=e.bMarks[r]+e.tShift[r];if(i>=e.eMarks[r])return!1;const s=e.src.charCodeAt(i++);if(s!==124&&s!==45&&s!==58||i>=e.eMarks[r])return!1;const o=e.src.charCodeAt(i++);if(o!==124&&o!==45&&o!==58&&!ae(o)||s===45&&ae(o))return!1;for(;i<e.eMarks[r];){const g=e.src.charCodeAt(i);if(g!==124&&g!==45&&g!==58&&!ae(g))return!1;i++}let c=Ji(e,t+1),l=c.split("|");const a=[];for(let g=0;g<l.length;g++){const x=l[g].trim();if(!x){if(g===0||g===l.length-1)continue;return!1}if(!/^:?-+:?$/.test(x))return!1;x.charCodeAt(x.length-1)===58?a.push(x.charCodeAt(0)===58?"center":"right"):x.charCodeAt(0)===58?a.push("left"):a.push("")}if(c=Ji(e,t).trim(),c.indexOf("|")===-1||e.sCount[t]-e.blkIndent>=4)return!1;l=al(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop();const f=l.length;if(f===0||f!==a.length)return!1;if(n)return!0;const p=e.parentType;e.parentType="table";const d=e.md.block.ruler.getRules("blockquote"),h=e.push("table_open","table",1),_=[t,0];h.map=_;const v=e.push("thead_open","thead",1);v.map=[t,t+1];const A=e.push("tr_open","tr",1);A.map=[t,t+1];for(let g=0;g<l.length;g++){const x=e.push("th_open","th",1);a[g]&&(x.attrs=[["style","text-align:"+a[g]]]);const k=e.push("inline","",0);k.content=l[g].trim(),k.children=[],e.push("th_close","th",-1)}e.push("tr_close","tr",-1),e.push("thead_close","thead",-1);let E,m=0;for(r=t+2;r<u&&!(e.sCount[r]<e.blkIndent);r++){let g=!1;for(let k=0,I=d.length;k<I;k++)if(d[k](e,r,u,!0)){g=!0;break}if(g||(c=Ji(e,r).trim(),!c)||e.sCount[r]-e.blkIndent>=4||(l=al(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop(),m+=f-l.length,m>fg))break;if(r===t+2){const k=e.push("tbody_open","tbody",1);k.map=E=[t+2,0]}const x=e.push("tr_open","tr",1);x.map=[r,r+1];for(let k=0;k<f;k++){const I=e.push("td_open","td",1);a[k]&&(I.attrs=[["style","text-align:"+a[k]]]);const w=e.push("inline","",0);w.content=l[k]?l[k].trim():"",w.children=[],e.push("td_close","td",-1)}e.push("tr_close","tr",-1)}return E&&(e.push("tbody_close","tbody",-1),E[1]=r),e.push("table_close","table",-1),_[1]=r,e.parentType=p,e.line=r,!0}function hg(e,t,u){if(e.sCount[t]-e.blkIndent<4)return!1;let n=t+1,r=n;for(;n<u;){if(e.isEmpty(n)){n++;continue}if(e.sCount[n]-e.blkIndent>=4){n++,r=n;continue}break}e.line=r;const i=e.push("code_block","code",0);return i.content=e.getLines(t,r,4+e.blkIndent,!1)+`
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o=[],c=[],l=[],a=[],f=e.md.block.ruler.getRules("blockquote"),p=e.parentType;e.parentType="blockquote";let d=!1,h;for(h=t;h<u;h++){const m=e.sCount[h]<e.blkIndent;if(r=e.bMarks[h]+e.tShift[h],i=e.eMarks[h],r>=i)break;if(e.src.charCodeAt(r++)===62&&!m){let x=e.sCount[h]+1,k,I;e.src.charCodeAt(r)===32?(r++,x++,I=!1,k=!0):e.src.charCodeAt(r)===9?(k=!0,(e.bsCount[h]+x)%4===3?(r++,x++,I=!1):I=!0):k=!1;let w=x;for(o.push(e.bMarks[h]),e.bMarks[h]=r;r<i;){const C=e.src.charCodeAt(r);if(ae(C))C===9?w+=4-(w+e.bsCount[h]+(I?1:0))%4:w++;else break;r++}d=r>=i,c.push(e.bsCount[h]),e.bsCount[h]=e.sCount[h]+1+(k?1:0),l.push(e.sCount[h]),e.sCount[h]=w-x,a.push(e.tShift[h]),e.tShift[h]=r-e.bMarks[h];continue}if(d)break;let g=!1;for(let x=0,k=f.length;x<k;x++)if(f[x](e,h,u,!0)){g=!0;break}if(g){e.lineMax=h,e.blkIndent!==0&&(o.push(e.bMarks[h]),c.push(e.bsCount[h]),a.push(e.tShift[h]),l.push(e.sCount[h]),e.sCount[h]-=e.blkIndent);break}o.push(e.bMarks[h]),c.push(e.bsCount[h]),a.push(e.tShift[h]),l.push(e.sCount[h]),e.sCount[h]=-1}const _=e.blkIndent;e.blkIndent=0;const v=e.push("blockquote_open","blockquote",1);v.markup=">";const A=[t,0];v.map=A,e.md.block.tokenize(e,t,h);const E=e.push("blockquote_close","blockquote",-1);E.markup=">",e.lineMax=s,e.parentType=p,A[1]=e.line;for(let m=0;m<a.length;m++)e.bMarks[m+t]=o[m],e.tShift[m+t]=a[m],e.sCount[m+t]=l[m],e.bsCount[m+t]=c[m];return e.blkIndent=_,!0}function bg(e,t,u,n){const r=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let i=e.bMarks[t]+e.tShift[t];const s=e.src.charCodeAt(i++);if(s!==42&&s!==45&&s!==95)return!1;let o=1;for(;i<r;){const l=e.src.charCodeAt(i++);if(l!==s&&!ae(l))return!1;l===s&&o++}if(o<3)return!1;if(n)return!0;e.line=t+1;const c=e.push("hr","hr",0);return c.map=[t,e.line],c.markup=Array(o+1).join(String.fromCharCode(s)),!0}function fl(e,t){const u=e.eMarks[t];let n=e.bMarks[t]+e.tShift[t];const r=e.src.charCodeAt(n++);if(r!==42&&r!==45&&r!==43)return-1;if(n<u){const i=e.src.charCodeAt(n);if(!ae(i))return-1}return n}function dl(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];let r=u;if(r+1>=n)return-1;let i=e.src.charCodeAt(r++);if(i<48||i>57)return-1;for(;;){if(r>=n)return-1;if(i=e.src.charCodeAt(r++),i>=48&&i<=57){if(r-u>=10)return-1;continue}if(i===41||i===46)break;return-1}return r<n&&(i=e.src.charCodeAt(r),!ae(i))?-1:r}function gg(e,t){const u=e.level+2;for(let n=t+2,r=e.tokens.length-2;n<r;n++)e.tokens[n].level===u&&e.tokens[n].type==="paragraph_open"&&(e.tokens[n+2].hidden=!0,e.tokens[n].hidden=!0,n+=2)}function _g(e,t,u,n){let r,i,s,o,c=t,l=!0;if(e.sCount[c]-e.blkIndent>=4||e.listIndent>=0&&e.sCount[c]-e.listIndent>=4&&e.sCount[c]<e.blkIndent)return!1;let a=!1;n&&e.parentType==="paragraph"&&e.sCount[c]>=e.blkIndent&&(a=!0);let f,p,d;if((d=dl(e,c))>=0){if(f=!0,s=e.bMarks[c]+e.tShift[c],p=Number(e.src.slice(s,d-1)),a&&p!==1)return!1}else if((d=fl(e,c))>=0)f=!1;else return!1;if(a&&e.skipSpaces(d)>=e.eMarks[c])return!1;if(n)return!0;const h=e.src.charCodeAt(d-1),_=e.tokens.length;f?(o=e.push("ordered_list_open","ol",1),p!==1&&(o.attrs=[["start",p]])):o=e.push("bullet_list_open","ul",1);const v=[c,0];o.map=v,o.markup=String.fromCharCode(h);let A=!1;const E=e.md.block.ruler.getRules("list"),m=e.parentType;for(e.parentType="list";c<u;){i=d,r=e.eMarks[c];const g=e.sCount[c]+d-(e.bMarks[c]+e.tShift[c]);let x=g;for(;i<r;){const z=e.src.charCodeAt(i);if(z===9)x+=4-(x+e.bsCount[c])%4;else if(z===32)x++;else break;i++}const k=i;let I;k>=r?I=1:I=x-g,I>4&&(I=1);const w=g+I;o=e.push("list_item_open","li",1),o.markup=String.fromCharCode(h);const C=[c,0];o.map=C,f&&(o.info=e.src.slice(s,d-1));const S=e.tight,R=e.tShift[c],T=e.sCount[c],P=e.listIndent;if(e.listIndent=e.blkIndent,e.blkIndent=w,e.tight=!0,e.tShift[c]=k-e.bMarks[c],e.sCount[c]=x,k>=r&&e.isEmpty(c+1)?e.line=Math.min(e.line+2,u):e.md.block.tokenize(e,c,u,!0),(!e.tight||A)&&(l=!1),A=e.line-c>1&&e.isEmpty(e.line-1),e.blkIndent=e.listIndent,e.listIndent=P,e.tShift[c]=R,e.sCount[c]=T,e.tight=S,o=e.push("list_item_close","li",-1),o.markup=String.fromCharCode(h),c=e.line,C[1]=c,c>=u||e.sCount[c]<e.blkIndent||e.sCount[c]-e.blkIndent>=4)break;let q=!1;for(let z=0,H=E.length;z<H;z++)if(E[z](e,c,u,!0)){q=!0;break}if(q)break;if(f){if(d=dl(e,c),d<0)break;s=e.bMarks[c]+e.tShift[c]}else if(d=fl(e,c),d<0)break;if(h!==e.src.charCodeAt(d-1))break}return f?o=e.push("ordered_list_close","ol",-1):o=e.push("bullet_list_close","ul",-1),o.markup=String.fromCharCode(h),v[1]=c,e.line=c,e.parentType=m,l&&gg(e,_),!0}function yg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t],s=t+1;if(e.sCount[t]-e.blkIndent>=4||e.src.charCodeAt(r)!==91)return!1;function o(E){const m=e.lineMax;if(E>=m||e.isEmpty(E))return null;let g=!1;if(e.sCount[E]-e.blkIndent>3&&(g=!0),e.sCount[E]<0&&(g=!0),!g){const I=e.md.block.ruler.getRules("reference"),w=e.parentType;e.parentType="reference";let C=!1;for(let S=0,R=I.length;S<R;S++)if(I[S](e,E,m,!0)){C=!0;break}if(e.parentType=w,C)return null}const x=e.bMarks[E]+e.tShift[E],k=e.eMarks[E];return e.src.slice(x,k+1)}let c=e.src.slice(r,i+1);i=c.length;let l=-1;for(r=1;r<i;r++){const E=c.charCodeAt(r);if(E===91)return!1;if(E===93){l=r;break}else if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(E===92&&(r++,r<i&&c.charCodeAt(r)===10)){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}}if(l<0||c.charCodeAt(l+1)!==58)return!1;for(r=l+2;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}const a=e.md.helpers.parseLinkDestination(c,r,i);if(!a.ok)return!1;const f=e.md.normalizeLink(a.str);if(!e.md.validateLink(f))return!1;r=a.pos;const p=r,d=s,h=r;for(;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}let _=e.md.helpers.parseLinkTitle(c,r,i);for(;_.can_continue;){const E=o(s);if(E===null)break;c+=E,r=i,i=c.length,s++,_=e.md.helpers.parseLinkTitle(c,r,i,_)}let v;for(r<i&&h!==r&&_.ok?(v=_.str,r=_.pos):(v="",r=p,s=d);r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10&&v)for(v="",r=p,s=d;r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10)return!1;const A=Di(c.slice(1,l));return A?(n||(typeof e.env.references>"u"&&(e.env.references={}),typeof e.env.references[A]>"u"&&(e.env.references[A]={title:v,href:f}),e.line=s),!0):!1}const xg=["address","article","aside","base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","h2","h3","h4","h5","h6","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","nav","noframes","ol","optgroup","option","p","param","search","section","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"],Eg="[a-zA-Z_:][a-zA-Z0-9:._-]*",Cg="[^\"'=<>`\\x00-\\x20]+",kg="'[^']*'",Ag='"[^"]*"',Sg="(?:"+Cg+"|"+kg+"|"+Ag+")",vg="(?:\\s+"+Eg+"(?:\\s*=\\s*"+Sg+")?)",kf="<[A-Za-z][A-Za-z0-9\\-]*"+vg+"*\\s*\\/?>",Af="<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>",Dg="\x3C!---?>|\x3C!--(?:[^-]|-[^-]|--[^>])*-->",Tg="<[?][\\s\\S]*?[?]>",wg="<![A-Za-z][^>]*>",Fg="<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",Ig=new RegExp("^(?:"+kf+"|"+Af+"|"+Dg+"|"+Tg+"|"+wg+"|"+Fg+")"),Og=new RegExp("^(?:"+kf+"|"+Af+")"),Ru=[[/^<(script|pre|style|textarea)(?=(\s|>|$))/i,/<\/(script|pre|style|textarea)>/i,!0],[/^\x3C!--/,/-->/,!0],[/^<\?/,/\?>/,!0],[/^<![A-Z]/,/>/,!0],[/^<!\[CDATA\[/,/\]\]>/,!0],[new RegExp("^</?("+xg.join("|")+")(?=(\\s|/?>|$))","i"),/^$/,!0],[new RegExp(Og.source+"\\s*$"),/^$/,!1]];function Ng(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4||!e.md.options.html||e.src.charCodeAt(r)!==60)return!1;let s=e.src.slice(r,i),o=0;for(;o<Ru.length&&!Ru[o][0].test(s);o++);if(o===Ru.length)return!1;if(n)return Ru[o][2];let c=t+1;if(!Ru[o][1].test(s)){for(;c<u&&!(e.sCount[c]<e.blkIndent);c++)if(r=e.bMarks[c]+e.tShift[c],i=e.eMarks[c],s=e.src.slice(r,i),Ru[o][1].test(s)){s.length!==0&&c++;break}}e.line=c;const l=e.push("html_block","",0);return l.map=[t,c],l.content=e.getLines(t,c,e.blkIndent,!0),!0}function Rg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let s=e.src.charCodeAt(r);if(s!==35||r>=i)return!1;let o=1;for(s=e.src.charCodeAt(++r);s===35&&r<i&&o<=6;)o++,s=e.src.charCodeAt(++r);if(o>6||r<i&&!ae(s))return!1;if(n)return!0;i=e.skipSpacesBack(i,r);const c=e.skipCharsBack(i,35,r);c>r&&ae(e.src.charCodeAt(c-1))&&(i=c),e.line=t+1;const l=e.push("heading_open","h"+String(o),1);l.markup="########".slice(0,o),l.map=[t,e.line];const a=e.push("inline","",0);a.content=e.src.slice(r,i).trim(),a.map=[t,e.line],a.children=[];const f=e.push("heading_close","h"+String(o),-1);return f.markup="########".slice(0,o),!0}function Mg(e,t,u){const n=e.md.block.ruler.getRules("paragraph");if(e.sCount[t]-e.blkIndent>=4)return!1;const r=e.parentType;e.parentType="paragraph";let i=0,s,o=t+1;for(;o<u&&!e.isEmpty(o);o++){if(e.sCount[o]-e.blkIndent>3)continue;if(e.sCount[o]>=e.blkIndent){let d=e.bMarks[o]+e.tShift[o];const h=e.eMarks[o];if(d<h&&(s=e.src.charCodeAt(d),(s===45||s===61)&&(d=e.skipChars(d,s),d=e.skipSpaces(d),d>=h))){i=s===61?1:2;break}}if(e.sCount[o]<0)continue;let p=!1;for(let d=0,h=n.length;d<h;d++)if(n[d](e,o,u,!0)){p=!0;break}if(p)break}if(!i)return!1;const c=e.getLines(t,o,e.blkIndent,!1).trim();e.line=o+1;const l=e.push("heading_open","h"+String(i),1);l.markup=String.fromCharCode(s),l.map=[t,e.line];const a=e.push("inline","",0);a.content=c,a.map=[t,e.line-1],a.children=[];const f=e.push("heading_close","h"+String(i),-1);return f.markup=String.fromCharCode(s),e.parentType=r,!0}function Pg(e,t,u){const n=e.md.block.ruler.getRules("paragraph"),r=e.parentType;let i=t+1;for(e.parentType="paragraph";i<u&&!e.isEmpty(i);i++){if(e.sCount[i]-e.blkIndent>3||e.sCount[i]<0)continue;let l=!1;for(let a=0,f=n.length;a<f;a++)if(n[a](e,i,u,!0)){l=!0;break}if(l)break}const s=e.getLines(t,i,e.blkIndent,!1).trim();e.line=i;const o=e.push("paragraph_open","p",1);o.map=[t,e.line];const c=e.push("inline","",0);return c.content=s,c.map=[t,e.line],c.children=[],e.push("paragraph_close","p",-1),e.parentType=r,!0}const br=[["table",dg,["paragraph","reference"]],["code",hg],["fence",pg,["paragraph","reference","blockquote","list"]],["blockquote",mg,["paragraph","reference","blockquote","list"]],["hr",bg,["paragraph","reference","blockquote","list"]],["list",_g,["paragraph","reference","blockquote"]],["reference",yg],["html_block",Ng,["paragraph","reference","blockquote"]],["heading",Rg,["paragraph","reference","blockquote"]],["lheading",Mg],["paragraph",Pg]];function Ti(){this.ruler=new Je;for(let e=0;e<br.length;e++)this.ruler.push(br[e][0],br[e][1],{alt:(br[e][2]||[]).slice()})}Ti.prototype.tokenize=function(e,t,u){const n=this.ruler.getRules(""),r=n.length,i=e.md.options.maxNesting;let s=t,o=!1;for(;s<u&&(e.line=s=e.skipEmptyLines(s),!(s>=u||e.sCount[s]<e.blkIndent));){if(e.level>=i){e.line=u;break}const c=e.line;let l=!1;for(let a=0;a<r;a++)if(l=n[a](e,s,u,!1),l){if(c>=e.line)throw new Error("block rule didn't increment state.line");break}if(!l)throw new Error("none of the block rules matched");e.tight=!o,e.isEmpty(e.line-1)&&(o=!0),s=e.line,s<u&&e.isEmpty(s)&&(o=!0,s++,e.line=s)}};Ti.prototype.parse=function(e,t,u,n){if(!e)return;const r=new this.State(e,t,u,n);this.tokenize(r,r.line,r.lineMax)};Ti.prototype.State=Tt;function tr(e,t,u,n){this.src=e,this.env=u,this.md=t,this.tokens=n,this.tokens_meta=Array(n.length),this.pos=0,this.posMax=this.src.length,this.level=0,this.pending="",this.pendingLevel=0,this.cache={},this.delimiters=[],this._prev_delimiters=[],this.backticks={},this.backticksScanned=!1,this.linkLevel=0}tr.prototype.pushPending=function(){const e=new _t("text","",0);return e.content=this.pending,e.level=this.pendingLevel,this.tokens.push(e),this.pending="",e};tr.prototype.push=function(e,t,u){this.pending&&this.pushPending();const n=new _t(e,t,u);let r=null;return u<0&&(this.level--,this.delimiters=this._prev_delimiters.pop()),n.level=this.level,u>0&&(this.level++,this._prev_delimiters.push(this.delimiters),this.delimiters=[],r={delimiters:this.delimiters}),this.pendingLevel=this.level,this.tokens.push(n),this.tokens_meta.push(r),n};tr.prototype.scanDelims=function(e,t){const u=this.posMax,n=this.src.charCodeAt(e),r=e>0?this.src.charCodeAt(e-1):32;let i=e;for(;i<u&&this.src.charCodeAt(i)===n;)i++;const s=i-e,o=i<u?this.src.charCodeAt(i):32,c=jn(r)||qn(String.fromCharCode(r)),l=jn(o)||qn(String.fromCharCode(o)),a=Un(r),f=Un(o),p=!f&&(!l||a||c),d=!a&&(!c||f||l);return{can_open:p&&(t||!d||c),can_close:d&&(t||!p||l),length:s}};tr.prototype.Token=_t;function Lg(e){switch(e){case 10:case 33:case 35:case 36:case 37:case 38:case 42:case 43:case 45:case 58:case 60:case 61:case 62:case 64:case 91:case 92:case 93:case 94:case 95:case 96:case 123:case 125:case 126:return!0;default:return!1}}function Bg(e,t){let u=e.pos;for(;u<e.posMax&&!Lg(e.src.charCodeAt(u));)u++;return u===e.pos?!1:(t||(e.pending+=e.src.slice(e.pos,u)),e.pos=u,!0)}const Vg=/(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i;function $g(e,t){if(!e.md.options.linkify||e.linkLevel>0)return!1;const u=e.pos,n=e.posMax;if(u+3>n||e.src.charCodeAt(u)!==58||e.src.charCodeAt(u+1)!==47||e.src.charCodeAt(u+2)!==47)return!1;const r=e.pending.match(Vg);if(!r)return!1;const i=r[1],s=e.md.linkify.matchAtStart(e.src.slice(u-i.length));if(!s)return!1;let o=s.url;if(o.length<=i.length)return!1;o=o.replace(/\*+$/,"");const c=e.md.normalizeLink(o);if(!e.md.validateLink(c))return!1;if(!t){e.pending=e.pending.slice(0,-i.length);const l=e.push("link_open","a",1);l.attrs=[["href",c]],l.markup="linkify",l.info="auto";const a=e.push("text","",0);a.content=e.md.normalizeLinkText(o);const f=e.push("link_close","a",-1);f.markup="linkify",f.info="auto"}return e.pos+=o.length-i.length,!0}function Hg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==10)return!1;const n=e.pending.length-1,r=e.posMax;if(!t)if(n>=0&&e.pending.charCodeAt(n)===32)if(n>=1&&e.pending.charCodeAt(n-1)===32){let i=n-1;for(;i>=1&&e.pending.charCodeAt(i-1)===32;)i--;e.pending=e.pending.slice(0,i),e.push("hardbreak","br",0)}else e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0);else e.push("softbreak","br",0);for(u++;u<r&&ae(e.src.charCodeAt(u));)u++;return e.pos=u,!0}const $o=[];for(let e=0;e<256;e++)$o.push(0);"\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach(function(e){$o[e.charCodeAt(0)]=1});function Ug(e,t){let u=e.pos;const n=e.posMax;if(e.src.charCodeAt(u)!==92||(u++,u>=n))return!1;let r=e.src.charCodeAt(u);if(r===10){for(t||e.push("hardbreak","br",0),u++;u<n&&(r=e.src.charCodeAt(u),!!ae(r));)u++;return e.pos=u,!0}let i=e.src[u];if(r>=55296&&r<=56319&&u+1<n){const o=e.src.charCodeAt(u+1);o>=56320&&o<=57343&&(i+=e.src[u+1],u++)}const s="\\"+i;if(!t){const o=e.push("text_special","",0);r<256&&$o[r]!==0?o.content=i:o.content=s,o.markup=s,o.info="escape"}return e.pos=u+1,!0}function qg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==96)return!1;const r=u;u++;const i=e.posMax;for(;u<i&&e.src.charCodeAt(u)===96;)u++;const s=e.src.slice(r,u),o=s.length;if(e.backticksScanned&&(e.backticks[o]||0)<=r)return t||(e.pending+=s),e.pos+=o,!0;let c=u,l;for(;(l=e.src.indexOf("`",c))!==-1;){for(c=l+1;c<i&&e.src.charCodeAt(c)===96;)c++;const a=c-l;if(a===o){if(!t){const f=e.push("code_inline","code",0);f.markup=s,f.content=e.src.slice(u,l).replace(/\n/g," ").replace(/^ (.+) $/,"$1")}return e.pos=c,!0}e.backticks[a]=l}return e.backticksScanned=!0,t||(e.pending+=s),e.pos+=o,!0}function jg(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==126)return!1;const r=e.scanDelims(e.pos,!0);let i=r.length;const s=String.fromCharCode(n);if(i<2)return!1;let o;i%2&&(o=e.push("text","",0),o.content=s,i--);for(let c=0;c<i;c+=2)o=e.push("text","",0),o.content=s+s,e.delimiters.push({marker:n,length:0,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close});return e.pos+=r.length,!0}function hl(e,t){let u;const n=[],r=t.length;for(let i=0;i<r;i++){const s=t[i];if(s.marker!==126||s.end===-1)continue;const o=t[s.end];u=e.tokens[s.token],u.type="s_open",u.tag="s",u.nesting=1,u.markup="~~",u.content="",u=e.tokens[o.token],u.type="s_close",u.tag="s",u.nesting=-1,u.markup="~~",u.content="",e.tokens[o.token-1].type==="text"&&e.tokens[o.token-1].content==="~"&&n.push(o.token-1)}for(;n.length;){const i=n.pop();let s=i+1;for(;s<e.tokens.length&&e.tokens[s].type==="s_close";)s++;s--,i!==s&&(u=e.tokens[s],e.tokens[s]=e.tokens[i],e.tokens[i]=u)}}function zg(e){const t=e.tokens_meta,u=e.tokens_meta.length;hl(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&hl(e,t[n].delimiters)}const Sf={tokenize:jg,postProcess:zg};function Kg(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==95&&n!==42)return!1;const r=e.scanDelims(e.pos,n===42);for(let i=0;i<r.length;i++){const s=e.push("text","",0);s.content=String.fromCharCode(n),e.delimiters.push({marker:n,length:r.length,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close})}return e.pos+=r.length,!0}function pl(e,t){const u=t.length;for(let n=u-1;n>=0;n--){const r=t[n];if(r.marker!==95&&r.marker!==42||r.end===-1)continue;const i=t[r.end],s=n>0&&t[n-1].end===r.end+1&&t[n-1].marker===r.marker&&t[n-1].token===r.token-1&&t[r.end+1].token===i.token+1,o=String.fromCharCode(r.marker),c=e.tokens[r.token];c.type=s?"strong_open":"em_open",c.tag=s?"strong":"em",c.nesting=1,c.markup=s?o+o:o,c.content="";const l=e.tokens[i.token];l.type=s?"strong_close":"em_close",l.tag=s?"strong":"em",l.nesting=-1,l.markup=s?o+o:o,l.content="",s&&(e.tokens[t[n-1].token].content="",e.tokens[t[r.end+1].token].content="",n--)}}function Wg(e){const t=e.tokens_meta,u=e.tokens_meta.length;pl(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&pl(e,t[n].delimiters)}const vf={tokenize:Kg,postProcess:Wg};function Gg(e,t){let u,n,r,i,s="",o="",c=e.pos,l=!0;if(e.src.charCodeAt(e.pos)!==91)return!1;const a=e.pos,f=e.posMax,p=e.pos+1,d=e.md.helpers.parseLinkLabel(e,e.pos,!0);if(d<0)return!1;let h=d+1;if(h<f&&e.src.charCodeAt(h)===40){for(l=!1,h++;h<f&&(u=e.src.charCodeAt(h),!(!ae(u)&&u!==10));h++);if(h>=f)return!1;if(c=h,r=e.md.helpers.parseLinkDestination(e.src,h,e.posMax),r.ok){for(s=e.md.normalizeLink(r.str),e.md.validateLink(s)?h=r.pos:s="",c=h;h<f&&(u=e.src.charCodeAt(h),!(!ae(u)&&u!==10));h++);if(r=e.md.helpers.parseLinkTitle(e.src,h,e.posMax),h<f&&c!==h&&r.ok)for(o=r.str,h=r.pos;h<f&&(u=e.src.charCodeAt(h),!(!ae(u)&&u!==10));h++);}(h>=f||e.src.charCodeAt(h)!==41)&&(l=!0),h++}if(l){if(typeof e.env.references>"u")return!1;if(h<f&&e.src.charCodeAt(h)===91?(c=h+1,h=e.md.helpers.parseLinkLabel(e,h),h>=0?n=e.src.slice(c,h++):h=d+1):h=d+1,n||(n=e.src.slice(p,d)),i=e.env.references[Di(n)],!i)return e.pos=a,!1;s=i.href,o=i.title}if(!t){e.pos=p,e.posMax=d;const _=e.push("link_open","a",1),v=[["href",s]];_.attrs=v,o&&v.push(["title",o]),e.linkLevel++,e.md.inline.tokenize(e),e.linkLevel--,e.push("link_close","a",-1)}return e.pos=h,e.posMax=f,!0}function Zg(e,t){let u,n,r,i,s,o,c,l,a="";const f=e.pos,p=e.posMax;if(e.src.charCodeAt(e.pos)!==33||e.src.charCodeAt(e.pos+1)!==91)return!1;const d=e.pos+2,h=e.md.helpers.parseLinkLabel(e,e.pos+1,!1);if(h<0)return!1;if(i=h+1,i<p&&e.src.charCodeAt(i)===40){for(i++;i<p&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(i>=p)return!1;for(l=i,o=e.md.helpers.parseLinkDestination(e.src,i,e.posMax),o.ok&&(a=e.md.normalizeLink(o.str),e.md.validateLink(a)?i=o.pos:a=""),l=i;i<p&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(o=e.md.helpers.parseLinkTitle(e.src,i,e.posMax),i<p&&l!==i&&o.ok)for(c=o.str,i=o.pos;i<p&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);else c="";if(i>=p||e.src.charCodeAt(i)!==41)return e.pos=f,!1;i++}else{if(typeof e.env.references>"u")return!1;if(i<p&&e.src.charCodeAt(i)===91?(l=i+1,i=e.md.helpers.parseLinkLabel(e,i),i>=0?r=e.src.slice(l,i++):i=h+1):i=h+1,r||(r=e.src.slice(d,h)),s=e.env.references[Di(r)],!s)return e.pos=f,!1;a=s.href,c=s.title}if(!t){n=e.src.slice(d,h);const _=[];e.md.inline.parse(n,e.md,e.env,_);const v=e.push("image","img",0),A=[["src",a],["alt",""]];v.attrs=A,v.children=_,v.content=n,c&&A.push(["title",c])}return e.pos=i,e.posMax=p,!0}const Jg=/^([a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/,Yg=/^([a-zA-Z][a-zA-Z0-9+.-]{1,31}):([^<>\x00-\x20]*)$/;function Xg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==60)return!1;const n=e.pos,r=e.posMax;for(;;){if(++u>=r)return!1;const s=e.src.charCodeAt(u);if(s===60)return!1;if(s===62)break}const i=e.src.slice(n+1,u);if(Yg.test(i)){const s=e.md.normalizeLink(i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}if(Jg.test(i)){const s=e.md.normalizeLink("mailto:"+i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}return!1}function Qg(e){return/^<a[>\s]/i.test(e)}function e3(e){return/^<\/a\s*>/i.test(e)}function t3(e){const t=e|32;return t>=97&&t<=122}function u3(e,t){if(!e.md.options.html)return!1;const u=e.posMax,n=e.pos;if(e.src.charCodeAt(n)!==60||n+2>=u)return!1;const r=e.src.charCodeAt(n+1);if(r!==33&&r!==63&&r!==47&&!t3(r))return!1;const i=e.src.slice(n).match(Ig);if(!i)return!1;if(!t){const s=e.push("html_inline","",0);s.content=i[0],Qg(s.content)&&e.linkLevel++,e3(s.content)&&e.linkLevel--}return e.pos+=i[0].length,!0}const n3=/^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i,r3=/^&([a-z][a-z0-9]{1,31});/i;function i3(e,t){const u=e.pos,n=e.posMax;if(e.src.charCodeAt(u)!==38||u+1>=n)return!1;if(e.src.charCodeAt(u+1)===35){const i=e.src.slice(u).match(n3);if(i){if(!t){const s=i[1][0].toLowerCase()==="x"?parseInt(i[1].slice(1),16):parseInt(i[1],10),o=e.push("text_special","",0);o.content=Bo(s)?Xr(s):Xr(65533),o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}else{const i=e.src.slice(u).match(r3);if(i){const s=_f(i[0]);if(s!==i[0]){if(!t){const o=e.push("text_special","",0);o.content=s,o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}}return!1}function ml(e){const t={},u=e.length;if(!u)return;let n=0,r=-2;const i=[];for(let s=0;s<u;s++){const o=e[s];if(i.push(0),(e[n].marker!==o.marker||r!==o.token-1)&&(n=s),r=o.token,o.length=o.length||0,!o.close)continue;t.hasOwnProperty(o.marker)||(t[o.marker]=[-1,-1,-1,-1,-1,-1]);const c=t[o.marker][(o.open?3:0)+o.length%3];let l=n-i[n]-1,a=l;for(;l>c;l-=i[l]+1){const f=e[l];if(f.marker===o.marker&&f.open&&f.end<0){let p=!1;if((f.close||o.open)&&(f.length+o.length)%3===0&&(f.length%3!==0||o.length%3!==0)&&(p=!0),!p){const d=l>0&&!e[l-1].open?i[l-1]+1:0;i[s]=s-l+d,i[l]=d,o.open=!1,f.end=s,f.close=!1,a=-1,r=-2;break}}}a!==-1&&(t[o.marker][(o.open?3:0)+(o.length||0)%3]=a)}}function s3(e){const t=e.tokens_meta,u=e.tokens_meta.length;ml(e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&ml(t[n].delimiters)}function o3(e){let t,u,n=0;const r=e.tokens,i=e.tokens.length;for(t=u=0;t<i;t++)r[t].nesting<0&&n--,r[t].level=n,r[t].nesting>0&&n++,r[t].type==="text"&&t+1<i&&r[t+1].type==="text"?r[t+1].content=r[t].content+r[t+1].content:(t!==u&&(r[u]=r[t]),u++);t!==u&&(r.length=u)}const Yi=[["text",Bg],["linkify",$g],["newline",Hg],["escape",Ug],["backticks",qg],["strikethrough",Sf.tokenize],["emphasis",vf.tokenize],["link",Gg],["image",Zg],["autolink",Xg],["html_inline",u3],["entity",i3]],Xi=[["balance_pairs",s3],["strikethrough",Sf.postProcess],["emphasis",vf.postProcess],["fragments_join",o3]];function ur(){this.ruler=new Je;for(let e=0;e<Yi.length;e++)this.ruler.push(Yi[e][0],Yi[e][1]);this.ruler2=new Je;for(let e=0;e<Xi.length;e++)this.ruler2.push(Xi[e][0],Xi[e][1])}ur.prototype.skipToken=function(e){const t=e.pos,u=this.ruler.getRules(""),n=u.length,r=e.md.options.maxNesting,i=e.cache;if(typeof i[t]<"u"){e.pos=i[t];return}let s=!1;if(e.level<r){for(let o=0;o<n;o++)if(e.level++,s=u[o](e,!0),e.level--,s){if(t>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}else e.pos=e.posMax;s||e.pos++,i[t]=e.pos};ur.prototype.tokenize=function(e){const t=this.ruler.getRules(""),u=t.length,n=e.posMax,r=e.md.options.maxNesting;for(;e.pos<n;){const i=e.pos;let s=!1;if(e.level<r){for(let o=0;o<u;o++)if(s=t[o](e,!1),s){if(i>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}if(s){if(e.pos>=n)break;continue}e.pending+=e.src[e.pos++]}e.pending&&e.pushPending()};ur.prototype.parse=function(e,t,u,n){const r=new this.State(e,t,u,n);this.tokenize(r);const i=this.ruler2.getRules(""),s=i.length;for(let o=0;o<s;o++)i[o](r)};ur.prototype.State=tr;function c3(e){const t={};e=e||{},t.src_Any=hf.source,t.src_Cc=pf.source,t.src_Z=bf.source,t.src_P=Po.source,t.src_ZPCc=[t.src_Z,t.src_P,t.src_Cc].join("|"),t.src_ZCc=[t.src_Z,t.src_Cc].join("|");const u="[><｜]";return 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+`;break}return n};an.prototype.render=function(e,t,u){let n="";const r=this.rules;for(let i=0,s=e.length;i<s;i++){const o=e[i].type;o==="inline"?n+=this.renderInline(e[i].children,t,u):typeof r[o]<"u"?n+=r[o](e,i,t,u,this):n+=this.renderToken(e,i,t,u)}return n};function Je(){this.__rules__=[],this.__cache__=null}Je.prototype.__find__=function(e){for(let t=0;t<this.__rules__.length;t++)if(this.__rules__[t].name===e)return t;return-1};Je.prototype.__compile__=function(){const e=this,t=[""];e.__rules__.forEach(function(u){u.enabled&&u.alt.forEach(function(n){t.indexOf(n)<0&&t.push(n)})}),e.__cache__={},t.forEach(function(u){e.__cache__[u]=[],e.__rules__.forEach(function(n){n.enabled&&(u&&n.alt.indexOf(u)<0||e.__cache__[u].push(n.fn))})})};Je.prototype.at=function(e,t,u){const n=this.__find__(e),r=u||{};if(n===-1)throw new Error("Parser rule not found: "+e);this.__rules__[n].fn=t,this.__rules__[n].alt=r.alt||[],this.__cache__=null};Je.prototype.before=function(e,t,u,n){const r=this.__find__(e),i=n||{};if(r===-1)throw new Error("Parser rule not found: "+e);this.__rules__.splice(r,0,{name:t,enabled:!0,fn:u,alt:i.alt||[]}),this.__cache__=null};Je.prototype.after=function(e,t,u,n){const r=this.__find__(e),i=n||{};if(r===-1)throw new Error("Parser rule not found: "+e);this.__rules__.splice(r+1,0,{name:t,enabled:!0,fn:u,alt:i.alt||[]}),this.__cache__=null};Je.prototype.push=function(e,t,u){const n=u||{};this.__rules__.push({name:e,enabled:!0,fn:t,alt:n.alt||[]}),this.__cache__=null};Je.prototype.enable=function(e,t){Array.isArray(e)||(e=[e]);const u=[];return e.forEach(function(n){const r=this.__find__(n);if(r<0){if(t)return;throw new Error("Rules manager: invalid rule name "+n)}this.__rules__[r].enabled=!0,u.push(n)},this),this.__cache__=null,u};Je.prototype.enableOnly=function(e,t){Array.isArray(e)||(e=[e]),this.__rules__.forEach(function(u){u.enabled=!1}),this.enable(e,t)};Je.prototype.disable=function(e,t){Array.isArray(e)||(e=[e]);const u=[];return e.forEach(function(n){const r=this.__find__(n);if(r<0){if(t)return;throw new Error("Rules manager: invalid rule name "+n)}this.__rules__[r].enabled=!1,u.push(n)},this),this.__cache__=null,u};Je.prototype.getRules=function(e){return this.__cache__===null&&this.__compile__(),this.__cache__[e]||[]};function _t(e,t,u){this.type=e,this.tag=t,this.attrs=null,this.map=null,this.nesting=u,this.level=0,this.children=null,this.content="",this.markup="",this.info="",this.meta=null,this.block=!1,this.hidden=!1}_t.prototype.attrIndex=function(t){if(!this.attrs)return-1;const u=this.attrs;for(let n=0,r=u.length;n<r;n++)if(u[n][0]===t)return n;return-1};_t.prototype.attrPush=function(t){this.attrs?this.attrs.push(t):this.attrs=[t]};_t.prototype.attrSet=function(t,u){const n=this.attrIndex(t),r=[t,u];n<0?this.attrPush(r):this.attrs[n]=r};_t.prototype.attrGet=function(t){const u=this.attrIndex(t);let n=null;return u>=0&&(n=this.attrs[u][1]),n};_t.prototype.attrJoin=function(t,u){const n=this.attrIndex(t);n<0?this.attrPush([t,u]):this.attrs[n][1]=this.attrs[n][1]+" "+u};function Of(e,t,u){this.src=e,this.env=u,this.tokens=[],this.inlineMode=!1,this.md=t}Of.prototype.Token=_t;const tg=/\r\n?|\n/g,ug=/\0/g;function ng(e){let t;t=e.src.replace(tg,`
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f=o.level,h=0;l.length>0&&l[0].index===0&&s>0&&r[s-1].type==="text_special"&&(l=l.slice(1));for(let d=0;d<l.length;d++){const p=l[d].url,_=e.md.normalizeLink(p);if(!e.md.validateLink(_))continue;let D=l[d].text;l[d].schema?l[d].schema==="mailto:"&&!/^mailto:/i.test(D)?D=e.md.normalizeLinkText("mailto:"+D).replace(/^mailto:/,""):D=e.md.normalizeLinkText(D):D=e.md.normalizeLinkText("http://"+D).replace(/^http:\/\//,"");const S=l[d].index;if(S>h){const x=new e.Token("text","",0);x.content=c.slice(h,S),x.level=f,a.push(x)}const E=new e.Token("link_open","a",1);E.attrs=[["href",_]],E.level=f++,E.markup="linkify",E.info="auto",a.push(E);const m=new e.Token("text","",0);m.content=D,m.level=f,a.push(m);const g=new e.Token("link_close","a",-1);g.level=--f,g.markup="linkify",g.info="auto",a.push(g),h=l[d].lastIndex}if(h<c.length){const d=new e.Token("text","",0);d.content=c.slice(h),d.level=f,a.push(d)}t[u].children=r=Ff(r,s,a)}}}}const Nf=/\+-|\.\.|\?\?\?\?|!!!!|,,|--/,lg=/\((c|tm|r)\)/i,ag=/\((c|tm|r)\)/ig,fg={c:"©",r:"®",tm:"™"};function dg(e,t){return fg[t.toLowerCase()]}function hg(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&(n.content=n.content.replace(ag,dg)),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function pg(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&Nf.test(n.content)&&(n.content=n.content.replace(/\+-/g,"±").replace(/\.{2,}/g,"…").replace(/([?!])…/g,"$1..").replace(/([?!]){4,}/g,"$1$1$1").replace(/,{2,}/g,",").replace(/(^|[^-])---(?=[^-]|$)/mg,"$1—").replace(/(^|\s)--(?=\s|$)/mg,"$1–").replace(/(^|[^-\s])--(?=[^-\s]|$)/mg,"$1–")),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function mg(e){let t;if(e.md.options.typographer)for(t=e.tokens.length-1;t>=0;t--)e.tokens[t].type==="inline"&&(lg.test(e.tokens[t].content)&&hg(e.tokens[t].children),Nf.test(e.tokens[t].content)&&pg(e.tokens[t].children))}const bg=/['"]/,yl=/['"]/g,xl="’";function Cr(e,t,u){return e.slice(0,t)+u+e.slice(t+1)}function gg(e,t){let u;const n=[];for(let r=0;r<e.length;r++){const i=e[r],s=e[r].level;for(u=n.length-1;u>=0&&!(n[u].level<=s);u--);if(n.length=u+1,i.type!=="text")continue;let o=i.content,c=0,l=o.length;e:for(;c<l;){yl.lastIndex=c;const a=yl.exec(o);if(!a)break;let f=!0,h=!0;c=a.index+1;const d=a[0]==="'";let p=32;if(a.index-1>=0)p=o.charCodeAt(a.index-1);else for(u=r-1;u>=0&&!(e[u].type==="softbreak"||e[u].type==="hardbreak");u--)if(e[u].content){p=e[u].content.charCodeAt(e[u].content.length-1);break}let _=32;if(c<l)_=o.charCodeAt(c);else for(u=r+1;u<e.length&&!(e[u].type==="softbreak"||e[u].type==="hardbreak");u++)if(e[u].content){_=e[u].content.charCodeAt(0);break}const D=Zn(p)||Gn(String.fromCharCode(p)),S=Zn(_)||Gn(String.fromCharCode(_)),E=Wn(p),m=Wn(_);if(m?f=!1:S&&(E||D||(f=!1)),E?h=!1:D&&(m||S||(h=!1)),_===34&&a[0]==='"'&&p>=48&&p<=57&&(h=f=!1),f&&h&&(f=D,h=S),!f&&!h){d&&(i.content=Cr(i.content,a.index,xl));continue}if(h)for(u=n.length-1;u>=0;u--){let g=n[u];if(n[u].level<s)break;if(g.single===d&&n[u].level===s){g=n[u];let x,k;d?(x=t.md.options.quotes[2],k=t.md.options.quotes[3]):(x=t.md.options.quotes[0],k=t.md.options.quotes[1]),i.content=Cr(i.content,a.index,k),e[g.token].content=Cr(e[g.token].content,g.pos,x),c+=k.length-1,g.token===r&&(c+=x.length-1),o=i.content,l=o.length,n.length=u;continue e}}f?n.push({token:r,pos:a.index,single:d,level:s}):h&&d&&(i.content=Cr(i.content,a.index,xl))}}}function _g(e){if(e.md.options.typographer)for(let t=e.tokens.length-1;t>=0;t--)e.tokens[t].type!=="inline"||!bg.test(e.tokens[t].content)||gg(e.tokens[t].children,e)}function yg(e){let t,u;const n=e.tokens,r=n.length;for(let i=0;i<r;i++){if(n[i].type!=="inline")continue;const s=n[i].children,o=s.length;for(t=0;t<o;t++)s[t].type==="text_special"&&(s[t].type="text");for(t=u=0;t<o;t++)s[t].type==="text"&&t+1<o&&s[t+1].type==="text"?s[t+1].content=s[t].content+s[t+1].content:(t!==u&&(s[u]=s[t]),u++);t!==u&&(s.length=u)}}const is=[["normalize",ng],["block",rg],["inline",ig],["linkify",cg],["replacements",mg],["smartquotes",_g],["text_join",yg]];function Jo(){this.ruler=new Je;for(let e=0;e<is.length;e++)this.ruler.push(is[e][0],is[e][1])}Jo.prototype.process=function(e){const t=this.ruler.getRules("");for(let u=0,n=t.length;u<n;u++)t[u](e)};Jo.prototype.State=Of;function Tt(e,t,u,n){this.src=e,this.md=t,this.env=u,this.tokens=n,this.bMarks=[],this.eMarks=[],this.tShift=[],this.sCount=[],this.bsCount=[],this.blkIndent=0,this.line=0,this.lineMax=0,this.tight=!1,this.ddIndent=-1,this.listIndent=-1,this.parentType="root",this.level=0;const r=this.src;for(let i=0,s=0,o=0,c=0,l=r.length,a=!1;s<l;s++){const f=r.charCodeAt(s);if(!a)if(ae(f)){o++,f===9?c+=4-c%4:c++;continue}else a=!0;(f===10||s===l-1)&&(f!==10&&s++,this.bMarks.push(i),this.eMarks.push(s),this.tShift.push(o),this.sCount.push(c),this.bsCount.push(0),a=!1,o=0,c=0,i=s+1)}this.bMarks.push(r.length),this.eMarks.push(r.length),this.tShift.push(0),this.sCount.push(0),this.bsCount.push(0),this.lineMax=this.bMarks.length-1}Tt.prototype.push=function(e,t,u){const n=new _t(e,t,u);return n.block=!0,u<0&&this.level--,n.level=this.level,u>0&&this.level++,this.tokens.push(n),n};Tt.prototype.isEmpty=function(t){return this.bMarks[t]+this.tShift[t]>=this.eMarks[t]};Tt.prototype.skipEmptyLines=function(t){for(let u=this.lineMax;t<u&&!(this.bMarks[t]+this.tShift[t]<this.eMarks[t]);t++);return t};Tt.prototype.skipSpaces=function(t){for(let u=this.src.length;t<u;t++){const n=this.src.charCodeAt(t);if(!ae(n))break}return t};Tt.prototype.skipSpacesBack=function(t,u){if(t<=u)return t;for(;t>u;)if(!ae(this.src.charCodeAt(--t)))return t+1;return t};Tt.prototype.skipChars=function(t,u){for(let n=this.src.length;t<n&&this.src.charCodeAt(t)===u;t++);return t};Tt.prototype.skipCharsBack=function(t,u,n){if(t<=n)return t;for(;t>n;)if(u!==this.src.charCodeAt(--t))return t+1;return t};Tt.prototype.getLines=function(t,u,n,r){if(t>=u)return"";const i=new Array(u-t);for(let s=0,o=t;o<u;o++,s++){let c=0;const l=this.bMarks[o];let a=l,f;for(o+1<u||r?f=this.eMarks[o]+1:f=this.eMarks[o];a<f&&c<n;){const h=this.src.charCodeAt(a);if(ae(h))h===9?c+=4-(c+this.bsCount[o])%4:c++;else if(a-l<this.tShift[o])c++;else break;a++}c>n?i[s]=new Array(c-n+1).join(" ")+this.src.slice(a,f):i[s]=this.src.slice(a,f)}return i.join("")};Tt.prototype.Token=_t;const xg=65536;function ss(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];return e.src.slice(u,n)}function El(e){const t=[],u=e.length;let n=0,r=e.charCodeAt(n),i=!1,s=0,o="";for(;n<u;)r===124&&(i?(o+=e.substring(s,n-1),s=n):(t.push(o+e.substring(s,n)),o="",s=n+1)),i=r===92,n++,r=e.charCodeAt(n);return t.push(o+e.substring(s)),t}function Eg(e,t,u,n){if(t+2>u)return!1;let r=t+1;if(e.sCount[r]<e.blkIndent||e.sCount[r]-e.blkIndent>=4)return!1;let i=e.bMarks[r]+e.tShift[r];if(i>=e.eMarks[r])return!1;const s=e.src.charCodeAt(i++);if(s!==124&&s!==45&&s!==58||i>=e.eMarks[r])return!1;const o=e.src.charCodeAt(i++);if(o!==124&&o!==45&&o!==58&&!ae(o)||s===45&&ae(o))return!1;for(;i<e.eMarks[r];){const g=e.src.charCodeAt(i);if(g!==124&&g!==45&&g!==58&&!ae(g))return!1;i++}let c=ss(e,t+1),l=c.split("|");const a=[];for(let g=0;g<l.length;g++){const x=l[g].trim();if(!x){if(g===0||g===l.length-1)continue;return!1}if(!/^:?-+:?$/.test(x))return!1;x.charCodeAt(x.length-1)===58?a.push(x.charCodeAt(0)===58?"center":"right"):x.charCodeAt(0)===58?a.push("left"):a.push("")}if(c=ss(e,t).trim(),c.indexOf("|")===-1||e.sCount[t]-e.blkIndent>=4)return!1;l=El(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop();const f=l.length;if(f===0||f!==a.length)return!1;if(n)return!0;const h=e.parentType;e.parentType="table";const d=e.md.block.ruler.getRules("blockquote"),p=e.push("table_open","table",1),_=[t,0];p.map=_;const D=e.push("thead_open","thead",1);D.map=[t,t+1];const S=e.push("tr_open","tr",1);S.map=[t,t+1];for(let g=0;g<l.length;g++){const x=e.push("th_open","th",1);a[g]&&(x.attrs=[["style","text-align:"+a[g]]]);const k=e.push("inline","",0);k.content=l[g].trim(),k.children=[],e.push("th_close","th",-1)}e.push("tr_close","tr",-1),e.push("thead_close","thead",-1);let E,m=0;for(r=t+2;r<u&&!(e.sCount[r]<e.blkIndent);r++){let g=!1;for(let k=0,I=d.length;k<I;k++)if(d[k](e,r,u,!0)){g=!0;break}if(g||(c=ss(e,r).trim(),!c)||e.sCount[r]-e.blkIndent>=4||(l=El(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop(),m+=f-l.length,m>xg))break;if(r===t+2){const k=e.push("tbody_open","tbody",1);k.map=E=[t+2,0]}const x=e.push("tr_open","tr",1);x.map=[r,r+1];for(let k=0;k<f;k++){const 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+`,i.map=[t,e.line],!0}function kg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4||r+3>i)return!1;const s=e.src.charCodeAt(r);if(s!==126&&s!==96)return!1;let o=r;r=e.skipChars(r,s);let c=r-o;if(c<3)return!1;const l=e.src.slice(o,r),a=e.src.slice(r,i);if(s===96&&a.indexOf(String.fromCharCode(s))>=0)return!1;if(n)return!0;let f=t,h=!1;for(;f++,!(f>=u||(r=o=e.bMarks[f]+e.tShift[f],i=e.eMarks[f],r<i&&e.sCount[f]<e.blkIndent));)if(e.src.charCodeAt(r)===s&&!(e.sCount[f]-e.blkIndent>=4)&&(r=e.skipChars(r,s),!(r-o<c)&&(r=e.skipSpaces(r),!(r<i)))){h=!0;break}c=e.sCount[t],e.line=f+(h?1:0);const d=e.push("fence","code",0);return d.info=a,d.content=e.getLines(t+1,f,c,!0),d.markup=l,d.map=[t,e.line],!0}function Sg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];const s=e.lineMax;if(e.sCount[t]-e.blkIndent>=4||e.src.charCodeAt(r)!==62)return!1;if(n)return!0;const o=[],c=[],l=[],a=[],f=e.md.block.ruler.getRules("blockquote"),h=e.parentType;e.parentType="blockquote";let d=!1,p;for(p=t;p<u;p++){const m=e.sCount[p]<e.blkIndent;if(r=e.bMarks[p]+e.tShift[p],i=e.eMarks[p],r>=i)break;if(e.src.charCodeAt(r++)===62&&!m){let x=e.sCount[p]+1,k,I;e.src.charCodeAt(r)===32?(r++,x++,I=!1,k=!0):e.src.charCodeAt(r)===9?(k=!0,(e.bsCount[p]+x)%4===3?(r++,x++,I=!1):I=!0):k=!1;let w=x;for(o.push(e.bMarks[p]),e.bMarks[p]=r;r<i;){const C=e.src.charCodeAt(r);if(ae(C))C===9?w+=4-(w+e.bsCount[p]+(I?1:0))%4:w++;else break;r++}d=r>=i,c.push(e.bsCount[p]),e.bsCount[p]=e.sCount[p]+1+(k?1:0),l.push(e.sCount[p]),e.sCount[p]=w-x,a.push(e.tShift[p]),e.tShift[p]=r-e.bMarks[p];continue}if(d)break;let g=!1;for(let x=0,k=f.length;x<k;x++)if(f[x](e,p,u,!0)){g=!0;break}if(g){e.lineMax=p,e.blkIndent!==0&&(o.push(e.bMarks[p]),c.push(e.bsCount[p]),a.push(e.tShift[p]),l.push(e.sCount[p]),e.sCount[p]-=e.blkIndent);break}o.push(e.bMarks[p]),c.push(e.bsCount[p]),a.push(e.tShift[p]),l.push(e.sCount[p]),e.sCount[p]=-1}const _=e.blkIndent;e.blkIndent=0;const D=e.push("blockquote_open","blockquote",1);D.markup=">";const S=[t,0];D.map=S,e.md.block.tokenize(e,t,p);const E=e.push("blockquote_close","blockquote",-1);E.markup=">",e.lineMax=s,e.parentType=h,S[1]=e.line;for(let m=0;m<a.length;m++)e.bMarks[m+t]=o[m],e.tShift[m+t]=a[m],e.sCount[m+t]=l[m],e.bsCount[m+t]=c[m];return e.blkIndent=_,!0}function Ag(e,t,u,n){const r=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let i=e.bMarks[t]+e.tShift[t];const s=e.src.charCodeAt(i++);if(s!==42&&s!==45&&s!==95)return!1;let o=1;for(;i<r;){const l=e.src.charCodeAt(i++);if(l!==s&&!ae(l))return!1;l===s&&o++}if(o<3)return!1;if(n)return!0;e.line=t+1;const c=e.push("hr","hr",0);return c.map=[t,e.line],c.markup=Array(o+1).join(String.fromCharCode(s)),!0}function Cl(e,t){const u=e.eMarks[t];let n=e.bMarks[t]+e.tShift[t];const r=e.src.charCodeAt(n++);if(r!==42&&r!==45&&r!==43)return-1;if(n<u){const i=e.src.charCodeAt(n);if(!ae(i))return-1}return n}function kl(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];let r=u;if(r+1>=n)return-1;let i=e.src.charCodeAt(r++);if(i<48||i>57)return-1;for(;;){if(r>=n)return-1;if(i=e.src.charCodeAt(r++),i>=48&&i<=57){if(r-u>=10)return-1;continue}if(i===41||i===46)break;return-1}return r<n&&(i=e.src.charCodeAt(r),!ae(i))?-1:r}function vg(e,t){const u=e.level+2;for(let n=t+2,r=e.tokens.length-2;n<r;n++)e.tokens[n].level===u&&e.tokens[n].type==="paragraph_open"&&(e.tokens[n+2].hidden=!0,e.tokens[n].hidden=!0,n+=2)}function Dg(e,t,u,n){let r,i,s,o,c=t,l=!0;if(e.sCount[c]-e.blkIndent>=4||e.listIndent>=0&&e.sCount[c]-e.listIndent>=4&&e.sCount[c]<e.blkIndent)return!1;let a=!1;n&&e.parentType==="paragraph"&&e.sCount[c]>=e.blkIndent&&(a=!0);let f,h,d;if((d=kl(e,c))>=0){if(f=!0,s=e.bMarks[c]+e.tShift[c],h=Number(e.src.slice(s,d-1)),a&&h!==1)return!1}else if((d=Cl(e,c))>=0)f=!1;else return!1;if(a&&e.skipSpaces(d)>=e.eMarks[c])return!1;if(n)return!0;const p=e.src.charCodeAt(d-1),_=e.tokens.length;f?(o=e.push("ordered_list_open","ol",1),h!==1&&(o.attrs=[["start",h]])):o=e.push("bullet_list_open","ul",1);const D=[c,0];o.map=D,o.markup=String.fromCharCode(p);let S=!1;const E=e.md.block.ruler.getRules("list"),m=e.parentType;for(e.parentType="list";c<u;){i=d,r=e.eMarks[c];const g=e.sCount[c]+d-(e.bMarks[c]+e.tShift[c]);let x=g;for(;i<r;){const z=e.src.charCodeAt(i);if(z===9)x+=4-(x+e.bsCount[c])%4;else if(z===32)x++;else break;i++}const k=i;let I;k>=r?I=1:I=x-g,I>4&&(I=1);const w=g+I;o=e.push("list_item_open","li",1),o.markup=String.fromCharCode(p);const C=[c,0];o.map=C,f&&(o.info=e.src.slice(s,d-1));const A=e.tight,R=e.tShift[c],T=e.sCount[c],P=e.listIndent;if(e.listIndent=e.blkIndent,e.blkIndent=w,e.tight=!0,e.tShift[c]=k-e.bMarks[c],e.sCount[c]=x,k>=r&&e.isEmpty(c+1)?e.line=Math.min(e.line+2,u):e.md.block.tokenize(e,c,u,!0),(!e.tight||S)&&(l=!1),S=e.line-c>1&&e.isEmpty(e.line-1),e.blkIndent=e.listIndent,e.listIndent=P,e.tShift[c]=R,e.sCount[c]=T,e.tight=A,o=e.push("list_item_close","li",-1),o.markup=String.fromCharCode(p),c=e.line,C[1]=c,c>=u||e.sCount[c]<e.blkIndent||e.sCount[c]-e.blkIndent>=4)break;let q=!1;for(let z=0,H=E.length;z<H;z++)if(E[z](e,c,u,!0)){q=!0;break}if(q)break;if(f){if(d=kl(e,c),d<0)break;s=e.bMarks[c]+e.tShift[c]}else if(d=Cl(e,c),d<0)break;if(p!==e.src.charCodeAt(d-1))break}return f?o=e.push("ordered_list_close","ol",-1):o=e.push("bullet_list_close","ul",-1),o.markup=String.fromCharCode(p),D[1]=c,e.line=c,e.parentType=m,l&&vg(e,_),!0}function Tg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t],s=t+1;if(e.sCount[t]-e.blkIndent>=4||e.src.charCodeAt(r)!==91)return!1;function o(E){const m=e.lineMax;if(E>=m||e.isEmpty(E))return null;let g=!1;if(e.sCount[E]-e.blkIndent>3&&(g=!0),e.sCount[E]<0&&(g=!0),!g){const I=e.md.block.ruler.getRules("reference"),w=e.parentType;e.parentType="reference";let C=!1;for(let A=0,R=I.length;A<R;A++)if(I[A](e,E,m,!0)){C=!0;break}if(e.parentType=w,C)return null}const x=e.bMarks[E]+e.tShift[E],k=e.eMarks[E];return e.src.slice(x,k+1)}let c=e.src.slice(r,i+1);i=c.length;let l=-1;for(r=1;r<i;r++){const E=c.charCodeAt(r);if(E===91)return!1;if(E===93){l=r;break}else if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(E===92&&(r++,r<i&&c.charCodeAt(r)===10)){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}}if(l<0||c.charCodeAt(l+1)!==58)return!1;for(r=l+2;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}const a=e.md.helpers.parseLinkDestination(c,r,i);if(!a.ok)return!1;const f=e.md.normalizeLink(a.str);if(!e.md.validateLink(f))return!1;r=a.pos;const h=r,d=s,p=r;for(;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}let _=e.md.helpers.parseLinkTitle(c,r,i);for(;_.can_continue;){const E=o(s);if(E===null)break;c+=E,r=i,i=c.length,s++,_=e.md.helpers.parseLinkTitle(c,r,i,_)}let D;for(r<i&&p!==r&&_.ok?(D=_.str,r=_.pos):(D="",r=h,s=d);r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10&&D)for(D="",r=h,s=d;r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10)return!1;const S=Mi(c.slice(1,l));return S?(n||(typeof e.env.references>"u"&&(e.env.references={}),typeof e.env.references[S]>"u"&&(e.env.references[S]={title:D,href:f}),e.line=s),!0):!1}const wg=["address","article","aside","base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","h2","h3","h4","h5","h6","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","nav","noframes","ol","optgroup","option","p","param","search","section","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"],Fg="[a-zA-Z_:][a-zA-Z0-9:._-]*",Ig="[^\"'=<>`\\x00-\\x20]+",Og="'[^']*'",Ng='"[^"]*"',Rg="(?:"+Ig+"|"+Og+"|"+Ng+")",Mg="(?:\\s+"+Fg+"(?:\\s*=\\s*"+Rg+")?)",Rf="<[A-Za-z][A-Za-z0-9\\-]*"+Mg+"*\\s*\\/?>",Mf="<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>",Pg="\x3C!---?>|\x3C!--(?:[^-]|-[^-]|--[^>])*-->",Lg="<[?][\\s\\S]*?[?]>",Bg="<![A-Za-z][^>]*>",Vg="<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",$g=new RegExp("^(?:"+Rf+"|"+Mf+"|"+Pg+"|"+Lg+"|"+Bg+"|"+Vg+")"),Hg=new RegExp("^(?:"+Rf+"|"+Mf+")"),Lu=[[/^<(script|pre|style|textarea)(?=(\s|>|$))/i,/<\/(script|pre|style|textarea)>/i,!0],[/^\x3C!--/,/-->/,!0],[/^<\?/,/\?>/,!0],[/^<![A-Z]/,/>/,!0],[/^<!\[CDATA\[/,/\]\]>/,!0],[new RegExp("^</?("+wg.join("|")+")(?=(\\s|/?>|$))","i"),/^$/,!0],[new RegExp(Hg.source+"\\s*$"),/^$/,!1]];function Ug(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4||!e.md.options.html||e.src.charCodeAt(r)!==60)return!1;let s=e.src.slice(r,i),o=0;for(;o<Lu.length&&!Lu[o][0].test(s);o++);if(o===Lu.length)return!1;if(n)return Lu[o][2];let c=t+1;if(!Lu[o][1].test(s)){for(;c<u&&!(e.sCount[c]<e.blkIndent);c++)if(r=e.bMarks[c]+e.tShift[c],i=e.eMarks[c],s=e.src.slice(r,i),Lu[o][1].test(s)){s.length!==0&&c++;break}}e.line=c;const l=e.push("html_block","",0);return l.map=[t,c],l.content=e.getLines(t,c,e.blkIndent,!0),!0}function qg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let s=e.src.charCodeAt(r);if(s!==35||r>=i)return!1;let o=1;for(s=e.src.charCodeAt(++r);s===35&&r<i&&o<=6;)o++,s=e.src.charCodeAt(++r);if(o>6||r<i&&!ae(s))return!1;if(n)return!0;i=e.skipSpacesBack(i,r);const c=e.skipCharsBack(i,35,r);c>r&&ae(e.src.charCodeAt(c-1))&&(i=c),e.line=t+1;const l=e.push("heading_open","h"+String(o),1);l.markup="########".slice(0,o),l.map=[t,e.line];const a=e.push("inline","",0);a.content=e.src.slice(r,i).trim(),a.map=[t,e.line],a.children=[];const f=e.push("heading_close","h"+String(o),-1);return f.markup="########".slice(0,o),!0}function jg(e,t,u){const n=e.md.block.ruler.getRules("paragraph");if(e.sCount[t]-e.blkIndent>=4)return!1;const r=e.parentType;e.parentType="paragraph";let i=0,s,o=t+1;for(;o<u&&!e.isEmpty(o);o++){if(e.sCount[o]-e.blkIndent>3)continue;if(e.sCount[o]>=e.blkIndent){let d=e.bMarks[o]+e.tShift[o];const p=e.eMarks[o];if(d<p&&(s=e.src.charCodeAt(d),(s===45||s===61)&&(d=e.skipChars(d,s),d=e.skipSpaces(d),d>=p))){i=s===61?1:2;break}}if(e.sCount[o]<0)continue;let h=!1;for(let d=0,p=n.length;d<p;d++)if(n[d](e,o,u,!0)){h=!0;break}if(h)break}if(!i)return!1;const c=e.getLines(t,o,e.blkIndent,!1).trim();e.line=o+1;const l=e.push("heading_open","h"+String(i),1);l.markup=String.fromCharCode(s),l.map=[t,e.line];const a=e.push("inline","",0);a.content=c,a.map=[t,e.line-1],a.children=[];const f=e.push("heading_close","h"+String(i),-1);return f.markup=String.fromCharCode(s),e.parentType=r,!0}function zg(e,t,u){const n=e.md.block.ruler.getRules("paragraph"),r=e.parentType;let i=t+1;for(e.parentType="paragraph";i<u&&!e.isEmpty(i);i++){if(e.sCount[i]-e.blkIndent>3||e.sCount[i]<0)continue;let l=!1;for(let a=0,f=n.length;a<f;a++)if(n[a](e,i,u,!0)){l=!0;break}if(l)break}const s=e.getLines(t,i,e.blkIndent,!1).trim();e.line=i;const o=e.push("paragraph_open","p",1);o.map=[t,e.line];const c=e.push("inline","",0);return c.content=s,c.map=[t,e.line],c.children=[],e.push("paragraph_close","p",-1),e.parentType=r,!0}const kr=[["table",Eg,["paragraph","reference"]],["code",Cg],["fence",kg,["paragraph","reference","blockquote","list"]],["blockquote",Sg,["paragraph","reference","blockquote","list"]],["hr",Ag,["paragraph","reference","blockquote","list"]],["list",Dg,["paragraph","reference","blockquote"]],["reference",Tg],["html_block",Ug,["paragraph","reference","blockquote"]],["heading",qg,["paragraph","reference","blockquote"]],["lheading",jg],["paragraph",zg]];function Pi(){this.ruler=new Je;for(let e=0;e<kr.length;e++)this.ruler.push(kr[e][0],kr[e][1],{alt:(kr[e][2]||[]).slice()})}Pi.prototype.tokenize=function(e,t,u){const n=this.ruler.getRules(""),r=n.length,i=e.md.options.maxNesting;let s=t,o=!1;for(;s<u&&(e.line=s=e.skipEmptyLines(s),!(s>=u||e.sCount[s]<e.blkIndent));){if(e.level>=i){e.line=u;break}const c=e.line;let l=!1;for(let a=0;a<r;a++)if(l=n[a](e,s,u,!1),l){if(c>=e.line)throw new Error("block rule didn't increment state.line");break}if(!l)throw new Error("none of the block rules matched");e.tight=!o,e.isEmpty(e.line-1)&&(o=!0),s=e.line,s<u&&e.isEmpty(s)&&(o=!0,s++,e.line=s)}};Pi.prototype.parse=function(e,t,u,n){if(!e)return;const r=new this.State(e,t,u,n);this.tokenize(r,r.line,r.lineMax)};Pi.prototype.State=Tt;function cr(e,t,u,n){this.src=e,this.env=u,this.md=t,this.tokens=n,this.tokens_meta=Array(n.length),this.pos=0,this.posMax=this.src.length,this.level=0,this.pending="",this.pendingLevel=0,this.cache={},this.delimiters=[],this._prev_delimiters=[],this.backticks={},this.backticksScanned=!1,this.linkLevel=0}cr.prototype.pushPending=function(){const e=new _t("text","",0);return e.content=this.pending,e.level=this.pendingLevel,this.tokens.push(e),this.pending="",e};cr.prototype.push=function(e,t,u){this.pending&&this.pushPending();const n=new _t(e,t,u);let r=null;return u<0&&(this.level--,this.delimiters=this._prev_delimiters.pop()),n.level=this.level,u>0&&(this.level++,this._prev_delimiters.push(this.delimiters),this.delimiters=[],r={delimiters:this.delimiters}),this.pendingLevel=this.level,this.tokens.push(n),this.tokens_meta.push(r),n};cr.prototype.scanDelims=function(e,t){const u=this.posMax,n=this.src.charCodeAt(e),r=e>0?this.src.charCodeAt(e-1):32;let i=e;for(;i<u&&this.src.charCodeAt(i)===n;)i++;const s=i-e,o=i<u?this.src.charCodeAt(i):32,c=Zn(r)||Gn(String.fromCharCode(r)),l=Zn(o)||Gn(String.fromCharCode(o)),a=Wn(r),f=Wn(o),h=!f&&(!l||a||c),d=!a&&(!c||f||l);return{can_open:h&&(t||!d||c),can_close:d&&(t||!h||l),length:s}};cr.prototype.Token=_t;function Kg(e){switch(e){case 10:case 33:case 35:case 36:case 37:case 38:case 42:case 43:case 45:case 58:case 60:case 61:case 62:case 64:case 91:case 92:case 93:case 94:case 95:case 96:case 123:case 125:case 126:return!0;default:return!1}}function Wg(e,t){let u=e.pos;for(;u<e.posMax&&!Kg(e.src.charCodeAt(u));)u++;return u===e.pos?!1:(t||(e.pending+=e.src.slice(e.pos,u)),e.pos=u,!0)}const Gg=/(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i;function Zg(e,t){if(!e.md.options.linkify||e.linkLevel>0)return!1;const u=e.pos,n=e.posMax;if(u+3>n||e.src.charCodeAt(u)!==58||e.src.charCodeAt(u+1)!==47||e.src.charCodeAt(u+2)!==47)return!1;const r=e.pending.match(Gg);if(!r)return!1;const i=r[1],s=e.md.linkify.matchAtStart(e.src.slice(u-i.length));if(!s)return!1;let o=s.url;if(o.length<=i.length)return!1;o=o.replace(/\*+$/,"");const c=e.md.normalizeLink(o);if(!e.md.validateLink(c))return!1;if(!t){e.pending=e.pending.slice(0,-i.length);const l=e.push("link_open","a",1);l.attrs=[["href",c]],l.markup="linkify",l.info="auto";const a=e.push("text","",0);a.content=e.md.normalizeLinkText(o);const f=e.push("link_close","a",-1);f.markup="linkify",f.info="auto"}return e.pos+=o.length-i.length,!0}function Jg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==10)return!1;const n=e.pending.length-1,r=e.posMax;if(!t)if(n>=0&&e.pending.charCodeAt(n)===32)if(n>=1&&e.pending.charCodeAt(n-1)===32){let i=n-1;for(;i>=1&&e.pending.charCodeAt(i-1)===32;)i--;e.pending=e.pending.slice(0,i),e.push("hardbreak","br",0)}else e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0);else e.push("softbreak","br",0);for(u++;u<r&&ae(e.src.charCodeAt(u));)u++;return e.pos=u,!0}const Yo=[];for(let e=0;e<256;e++)Yo.push(0);"\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach(function(e){Yo[e.charCodeAt(0)]=1});function Yg(e,t){let u=e.pos;const n=e.posMax;if(e.src.charCodeAt(u)!==92||(u++,u>=n))return!1;let r=e.src.charCodeAt(u);if(r===10){for(t||e.push("hardbreak","br",0),u++;u<n&&(r=e.src.charCodeAt(u),!!ae(r));)u++;return e.pos=u,!0}let i=e.src[u];if(r>=55296&&r<=56319&&u+1<n){const o=e.src.charCodeAt(u+1);o>=56320&&o<=57343&&(i+=e.src[u+1],u++)}const s="\\"+i;if(!t){const o=e.push("text_special","",0);r<256&&Yo[r]!==0?o.content=i:o.content=s,o.markup=s,o.info="escape"}return e.pos=u+1,!0}function Xg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==96)return!1;const r=u;u++;const i=e.posMax;for(;u<i&&e.src.charCodeAt(u)===96;)u++;const s=e.src.slice(r,u),o=s.length;if(e.backticksScanned&&(e.backticks[o]||0)<=r)return t||(e.pending+=s),e.pos+=o,!0;let c=u,l;for(;(l=e.src.indexOf("`",c))!==-1;){for(c=l+1;c<i&&e.src.charCodeAt(c)===96;)c++;const a=c-l;if(a===o){if(!t){const f=e.push("code_inline","code",0);f.markup=s,f.content=e.src.slice(u,l).replace(/\n/g," ").replace(/^ (.+) $/,"$1")}return e.pos=c,!0}e.backticks[a]=l}return e.backticksScanned=!0,t||(e.pending+=s),e.pos+=o,!0}function Qg(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==126)return!1;const r=e.scanDelims(e.pos,!0);let i=r.length;const s=String.fromCharCode(n);if(i<2)return!1;let o;i%2&&(o=e.push("text","",0),o.content=s,i--);for(let c=0;c<i;c+=2)o=e.push("text","",0),o.content=s+s,e.delimiters.push({marker:n,length:0,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close});return e.pos+=r.length,!0}function Sl(e,t){let u;const n=[],r=t.length;for(let i=0;i<r;i++){const s=t[i];if(s.marker!==126||s.end===-1)continue;const o=t[s.end];u=e.tokens[s.token],u.type="s_open",u.tag="s",u.nesting=1,u.markup="~~",u.content="",u=e.tokens[o.token],u.type="s_close",u.tag="s",u.nesting=-1,u.markup="~~",u.content="",e.tokens[o.token-1].type==="text"&&e.tokens[o.token-1].content==="~"&&n.push(o.token-1)}for(;n.length;){const i=n.pop();let s=i+1;for(;s<e.tokens.length&&e.tokens[s].type==="s_close";)s++;s--,i!==s&&(u=e.tokens[s],e.tokens[s]=e.tokens[i],e.tokens[i]=u)}}function e3(e){const t=e.tokens_meta,u=e.tokens_meta.length;Sl(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&Sl(e,t[n].delimiters)}const Pf={tokenize:Qg,postProcess:e3};function t3(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==95&&n!==42)return!1;const r=e.scanDelims(e.pos,n===42);for(let i=0;i<r.length;i++){const s=e.push("text","",0);s.content=String.fromCharCode(n),e.delimiters.push({marker:n,length:r.length,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close})}return e.pos+=r.length,!0}function Al(e,t){const u=t.length;for(let n=u-1;n>=0;n--){const r=t[n];if(r.marker!==95&&r.marker!==42||r.end===-1)continue;const i=t[r.end],s=n>0&&t[n-1].end===r.end+1&&t[n-1].marker===r.marker&&t[n-1].token===r.token-1&&t[r.end+1].token===i.token+1,o=String.fromCharCode(r.marker),c=e.tokens[r.token];c.type=s?"strong_open":"em_open",c.tag=s?"strong":"em",c.nesting=1,c.markup=s?o+o:o,c.content="";const l=e.tokens[i.token];l.type=s?"strong_close":"em_close",l.tag=s?"strong":"em",l.nesting=-1,l.markup=s?o+o:o,l.content="",s&&(e.tokens[t[n-1].token].content="",e.tokens[t[r.end+1].token].content="",n--)}}function u3(e){const t=e.tokens_meta,u=e.tokens_meta.length;Al(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&Al(e,t[n].delimiters)}const Lf={tokenize:t3,postProcess:u3};function n3(e,t){let u,n,r,i,s="",o="",c=e.pos,l=!0;if(e.src.charCodeAt(e.pos)!==91)return!1;const a=e.pos,f=e.posMax,h=e.pos+1,d=e.md.helpers.parseLinkLabel(e,e.pos,!0);if(d<0)return!1;let p=d+1;if(p<f&&e.src.charCodeAt(p)===40){for(l=!1,p++;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);if(p>=f)return!1;if(c=p,r=e.md.helpers.parseLinkDestination(e.src,p,e.posMax),r.ok){for(s=e.md.normalizeLink(r.str),e.md.validateLink(s)?p=r.pos:s="",c=p;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);if(r=e.md.helpers.parseLinkTitle(e.src,p,e.posMax),p<f&&c!==p&&r.ok)for(o=r.str,p=r.pos;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);}(p>=f||e.src.charCodeAt(p)!==41)&&(l=!0),p++}if(l){if(typeof e.env.references>"u")return!1;if(p<f&&e.src.charCodeAt(p)===91?(c=p+1,p=e.md.helpers.parseLinkLabel(e,p),p>=0?n=e.src.slice(c,p++):p=d+1):p=d+1,n||(n=e.src.slice(h,d)),i=e.env.references[Mi(n)],!i)return e.pos=a,!1;s=i.href,o=i.title}if(!t){e.pos=h,e.posMax=d;const _=e.push("link_open","a",1),D=[["href",s]];_.attrs=D,o&&D.push(["title",o]),e.linkLevel++,e.md.inline.tokenize(e),e.linkLevel--,e.push("link_close","a",-1)}return e.pos=p,e.posMax=f,!0}function r3(e,t){let u,n,r,i,s,o,c,l,a="";const f=e.pos,h=e.posMax;if(e.src.charCodeAt(e.pos)!==33||e.src.charCodeAt(e.pos+1)!==91)return!1;const d=e.pos+2,p=e.md.helpers.parseLinkLabel(e,e.pos+1,!1);if(p<0)return!1;if(i=p+1,i<h&&e.src.charCodeAt(i)===40){for(i++;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(i>=h)return!1;for(l=i,o=e.md.helpers.parseLinkDestination(e.src,i,e.posMax),o.ok&&(a=e.md.normalizeLink(o.str),e.md.validateLink(a)?i=o.pos:a=""),l=i;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(o=e.md.helpers.parseLinkTitle(e.src,i,e.posMax),i<h&&l!==i&&o.ok)for(c=o.str,i=o.pos;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);else c="";if(i>=h||e.src.charCodeAt(i)!==41)return e.pos=f,!1;i++}else{if(typeof e.env.references>"u")return!1;if(i<h&&e.src.charCodeAt(i)===91?(l=i+1,i=e.md.helpers.parseLinkLabel(e,i),i>=0?r=e.src.slice(l,i++):i=p+1):i=p+1,r||(r=e.src.slice(d,p)),s=e.env.references[Mi(r)],!s)return e.pos=f,!1;a=s.href,c=s.title}if(!t){n=e.src.slice(d,p);const _=[];e.md.inline.parse(n,e.md,e.env,_);const D=e.push("image","img",0),S=[["src",a],["alt",""]];D.attrs=S,D.children=_,D.content=n,c&&S.push(["title",c])}return e.pos=i,e.posMax=h,!0}const i3=/^([a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/,s3=/^([a-zA-Z][a-zA-Z0-9+.-]{1,31}):([^<>\x00-\x20]*)$/;function o3(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==60)return!1;const n=e.pos,r=e.posMax;for(;;){if(++u>=r)return!1;const s=e.src.charCodeAt(u);if(s===60)return!1;if(s===62)break}const i=e.src.slice(n+1,u);if(s3.test(i)){const s=e.md.normalizeLink(i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}if(i3.test(i)){const s=e.md.normalizeLink("mailto:"+i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}return!1}function c3(e){return/^<a[>\s]/i.test(e)}function l3(e){return/^<\/a\s*>/i.test(e)}function a3(e){const t=e|32;return t>=97&&t<=122}function f3(e,t){if(!e.md.options.html)return!1;const u=e.posMax,n=e.pos;if(e.src.charCodeAt(n)!==60||n+2>=u)return!1;const r=e.src.charCodeAt(n+1);if(r!==33&&r!==63&&r!==47&&!a3(r))return!1;const i=e.src.slice(n).match($g);if(!i)return!1;if(!t){const s=e.push("html_inline","",0);s.content=i[0],c3(s.content)&&e.linkLevel++,l3(s.content)&&e.linkLevel--}return e.pos+=i[0].length,!0}const d3=/^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i,h3=/^&([a-z][a-z0-9]{1,31});/i;function p3(e,t){const u=e.pos,n=e.posMax;if(e.src.charCodeAt(u)!==38||u+1>=n)return!1;if(e.src.charCodeAt(u+1)===35){const i=e.src.slice(u).match(d3);if(i){if(!t){const s=i[1][0].toLowerCase()==="x"?parseInt(i[1].slice(1),16):parseInt(i[1],10),o=e.push("text_special","",0);o.content=Zo(s)?si(s):si(65533),o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}else{const i=e.src.slice(u).match(h3);if(i){const s=wf(i[0]);if(s!==i[0]){if(!t){const o=e.push("text_special","",0);o.content=s,o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}}return!1}function vl(e){const t={},u=e.length;if(!u)return;let n=0,r=-2;const i=[];for(let s=0;s<u;s++){const o=e[s];if(i.push(0),(e[n].marker!==o.marker||r!==o.token-1)&&(n=s),r=o.token,o.length=o.length||0,!o.close)continue;t.hasOwnProperty(o.marker)||(t[o.marker]=[-1,-1,-1,-1,-1,-1]);const c=t[o.marker][(o.open?3:0)+o.length%3];let l=n-i[n]-1,a=l;for(;l>c;l-=i[l]+1){const f=e[l];if(f.marker===o.marker&&f.open&&f.end<0){let h=!1;if((f.close||o.open)&&(f.length+o.length)%3===0&&(f.length%3!==0||o.length%3!==0)&&(h=!0),!h){const d=l>0&&!e[l-1].open?i[l-1]+1:0;i[s]=s-l+d,i[l]=d,o.open=!1,f.end=s,f.close=!1,a=-1,r=-2;break}}}a!==-1&&(t[o.marker][(o.open?3:0)+(o.length||0)%3]=a)}}function m3(e){const t=e.tokens_meta,u=e.tokens_meta.length;vl(e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&vl(t[n].delimiters)}function b3(e){let t,u,n=0;const r=e.tokens,i=e.tokens.length;for(t=u=0;t<i;t++)r[t].nesting<0&&n--,r[t].level=n,r[t].nesting>0&&n++,r[t].type==="text"&&t+1<i&&r[t+1].type==="text"?r[t+1].content=r[t].content+r[t+1].content:(t!==u&&(r[u]=r[t]),u++);t!==u&&(r.length=u)}const os=[["text",Wg],["linkify",Zg],["newline",Jg],["escape",Yg],["backticks",Xg],["strikethrough",Pf.tokenize],["emphasis",Lf.tokenize],["link",n3],["image",r3],["autolink",o3],["html_inline",f3],["entity",p3]],cs=[["balance_pairs",m3],["strikethrough",Pf.postProcess],["emphasis",Lf.postProcess],["fragments_join",b3]];function lr(){this.ruler=new Je;for(let e=0;e<os.length;e++)this.ruler.push(os[e][0],os[e][1]);this.ruler2=new Je;for(let e=0;e<cs.length;e++)this.ruler2.push(cs[e][0],cs[e][1])}lr.prototype.skipToken=function(e){const t=e.pos,u=this.ruler.getRules(""),n=u.length,r=e.md.options.maxNesting,i=e.cache;if(typeof i[t]<"u"){e.pos=i[t];return}let s=!1;if(e.level<r){for(let o=0;o<n;o++)if(e.level++,s=u[o](e,!0),e.level--,s){if(t>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}else e.pos=e.posMax;s||e.pos++,i[t]=e.pos};lr.prototype.tokenize=function(e){const t=this.ruler.getRules(""),u=t.length,n=e.posMax,r=e.md.options.maxNesting;for(;e.pos<n;){const i=e.pos;let s=!1;if(e.level<r){for(let o=0;o<u;o++)if(s=t[o](e,!1),s){if(i>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}if(s){if(e.pos>=n)break;continue}e.pending+=e.src[e.pos++]}e.pending&&e.pushPending()};lr.prototype.parse=function(e,t,u,n){const r=new this.State(e,t,u,n);this.tokenize(r);const i=this.ruler2.getRules(""),s=i.length;for(let o=0;o<s;o++)i[o](r)};lr.prototype.State=cr;function g3(e){const t={};e=e||{},t.src_Any=Sf.source,t.src_Cc=Af.source,t.src_Z=Df.source,t.src_P=Wo.source,t.src_ZPCc=[t.src_Z,t.src_P,t.src_Cc].join("|"),t.src_ZCc=[t.src_Z,t.src_Cc].join("|");const u="[><｜]";return 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         <div class="text-right sticky top-4 mb-2 mr-2 h-0">
-          <button class="badge btn-mini" onclick="copyStr(${U3(JSON.stringify(o))})">📋 Copy</button>
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t=document.getElementById("messages-list"),u=t.scrollHeight-t.scrollTop-t.clientHeight;(!e||u<100)&&setTimeout(()=>t.scrollTo({top:t.scrollHeight}),1)},Vf=Ur({components:{VueMarkdown:W3,SettingsModalShortInput:G3},data(){return{conversations:he.getAllConversations(),messages:[],viewingConvId:he.getNewConvId(),inputMsg:"",isGenerating:!1,pendingMsg:null,stopGeneration:()=>{},selectedTheme:he.getTheme(),config:he.getConfig(),showConfigDialog:!1,editingMsg:null,themes:K3,configDefault:{...ei},configInfo:{...j3}}},computed:{},mounted(){document.getElementById("app").classList.remove("opacity-0");const e=document.getElementById("pending-msg");new ResizeObserver(()=>{this.isGenerating&&fn(!0)}).observe(e)},methods:{hideSidebar(){document.getElementById("toggle-drawer").checked=!1},setSelectedTheme(e){this.selectedTheme=e,he.setTheme(e)},newConversation(){this.isGenerating||(this.viewingConvId=he.getNewConvId(),this.editingMsg=null,this.fetchMessages(),fn(),this.hideSidebar())},setViewingConv(e){this.isGenerating||(this.viewingConvId=e,this.editingMsg=null,this.fetchMessages(),fn(),this.hideSidebar())},deleteConv(e){this.isGenerating||window.confirm("Are you sure to delete this conversation?")&&(he.remove(e),this.viewingConvId===e&&(this.viewingConvId=he.getNewConvId(),this.editingMsg=null),this.fetchConversation(),this.fetchMessages())},downloadConv(e){const t=he.getOneConversation(e);if(!t){alert("Conversation not found.");return}const u=JSON.stringify(t,null,2),n=new Blob([u],{type:"application/json"}),r=URL.createObjectURL(n),i=document.createElement("a");i.href=r,i.download=`conversation_${e}.json`,document.body.appendChild(i),i.click(),document.body.removeChild(i),URL.revokeObjectURL(r)},async sendMessage(){if(!this.inputMsg)return;const e=this.viewingConvId;he.appendMsg(e,{id:Date.now(),role:"user",content:this.inputMsg}),this.fetchConversation(),this.fetchMessages(),this.inputMsg="",this.editingMsg=null,this.generateMessage(e),fn()},async generateMessage(e){if(!this.isGenerating){this.pendingMsg={id:Date.now()+1,role:"assistant",content:null},this.isGenerating=!0,this.editingMsg=null;try{const t=new AbortController;this.stopGeneration=()=>t.abort();const u={messages:[{role:"system",content:this.config.systemMessage},...this.messages],stream:!0,cache_prompt:!0,samplers:this.config.samplers,temperature:this.config.temperature,dynatemp_range:this.config.dynatemp_range,dynatemp_exponent:this.config.dynatemp_exponent,top_k:this.config.top_k,top_p:this.config.top_p,min_p:this.config.min_p,typical_p:this.config.typical_p,xtc_probability:this.config.xtc_probability,xtc_threshold:this.config.xtc_threshold,repeat_last_n:this.config.repeat_last_n,repeat_penalty:this.config.repeat_penalty,presence_penalty:this.config.presence_penalty,frequency_penalty:this.config.frequency_penalty,dry_multiplier:this.config.dry_multiplier,dry_base:this.config.dry_base,dry_allowed_length:this.config.dry_allowed_length,dry_penalty_last_n:this.config.dry_penalty_last_n,max_tokens:this.config.max_tokens,...this.config.custom.length?JSON.parse(this.config.custom):{},...this.config.apiKey?{api_key:this.config.apiKey}:{}},n={controller:t,api_url:q3,endpoint:"/chat/completions"};for await(const r of eb(prompt,u,n)){const i=r.data.stop,s=r.data.choices[0].delta.content,o=this.pendingMsg.content||"";s&&(this.pendingMsg={id:this.pendingMsg.id,role:"assistant",content:o+s})}he.appendMsg(e,this.pendingMsg),this.fetchConversation(),this.fetchMessages(),setTimeout(()=>document.getElementById("msg-input").focus(),1)}catch(t){if(t.name==="AbortError")he.appendMsg(e,this.pendingMsg),this.fetchConversation(),this.fetchMessages();else{console.error(t),alert(t);const u=he.popMsg(e);this.inputMsg=u?u.content:""}}this.pendingMsg=null,this.isGenerating=!1,this.stopGeneration=()=>{},this.fetchMessages(),fn()}},regenerateMsg(e){if(this.isGenerating)return;const t=this.viewingConvId;he.filterAndKeepMsgs(t,u=>u.id<e.id),this.fetchConversation(),this.fetchMessages(),this.generateMessage(t)},copyMsg(e){Bf(e.content)},editUserMsgAndRegenerate(e){if(this.isGenerating)return;const t=this.viewingConvId,u=e.content;this.editingMsg=null,he.filterAndKeepMsgs(t,n=>n.id<e.id),he.appendMsg(t,{id:Date.now(),role:"user",content:u}),this.fetchConversation(),this.fetchMessages(),this.generateMessage(t)},closeAndSaveConfigDialog(){try{this.config.custom.length&&JSON.parse(this.config.custom)}catch{alert("Invalid JSON for custom config. 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     Failed to start app. Please try clearing localStorage and try again.<br/>
     <br/>
     <button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
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24.16}:root:has(input.theme-controller[value=autumn]:checked){color-scheme:light;--b2: 89.1077% 0 0;--b3: 82.4006% 0 0;--bc: 19.1629% 0 0;--pc: 88.1446% .032232 17.530175;--sc: 12.3353% .033821 23.865865;--ac: 14.6851% .018999 60.729616;--nc: 90.8734% .007475 51.902819;--inc: 13.8449% .019596 207.284192;--suc: 12.199% .016032 174.616213;--wac: 14.0163% .032982 56.844303;--erc: 90.614% .0482 24.16;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 40.7232% .16116 17.530175;--s: 61.6763% .169105 23.865865;--a: 73.4253% .094994 60.729616;--n: 54.3672% .037374 51.902819;--b1: 95.8147% 0 0;--in: 69.2245% .097979 207.284192;--su: 60.9951% .080159 174.616213;--wa: 70.0817% .164909 56.844303;--er: 53.07% .241 24.16}[data-theme=business]{color-scheme:dark;--b2: 22.6487% 0 0;--b3: 20.944% 0 0;--bc: 84.8707% 0 0;--pc: 88.3407% .019811 251.473931;--sc: 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240.033697;--suc: 14.0454% .018919 156.59611;--wac: 15.4965% .023141 81.519177;--erc: 90.3221% .029356 29.674507;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 41.7036% .099057 251.473931;--s: 64.0924% .027405 229.389418;--a: 67.271% .167726 35.791525;--n: 27.441% .01325 253.041249;--b1: 24.3535% 0 0;--in: 62.6163% .143511 240.033697;--su: 70.2268% .094594 156.59611;--wa: 77.4824% .115704 81.519177;--er: 51.6105% .14678 29.674507;--rounded-box: .25rem;--rounded-btn: .125rem;--rounded-badge: .125rem}[data-theme=acid]{color-scheme:light;--b2: 91.6146% 0 0;--b3: 84.7189% 0 0;--bc: 19.7021% 0 0;--pc: 14.38% .0714 330.759573;--sc: 14.674% .0448 48.250878;--ac: 18.556% .0528 122.962951;--nc: 84.262% .0256 278.68;--inc: 12.144% .0454 252.05;--suc: 17.144% .0532 158.53;--wac: 18.202% .0424 100.5;--erc: 12.968% .0586 29.349188;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 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100.5;--er: 64.84% .293 29.349188;--rounded-box: 1.25rem;--rounded-btn: 1rem;--rounded-badge: 1rem;--tab-radius: .7rem}[data-theme=lemonade]{color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}:root:has(input.theme-controller[value=lemonade]:checked){color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}[data-theme=night]{color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}:root:has(input.theme-controller[value=night]:checked){color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}[data-theme=coffee]{color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}:root:has(input.theme-controller[value=coffee]:checked){color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}[data-theme=winter]{color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}:root:has(input.theme-controller[value=winter]:checked){color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}[data-theme=dim]{color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}:root:has(input.theme-controller[value=dim]:checked){color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}[data-theme=nord]{color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}:root:has(input.theme-controller[value=nord]:checked){color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}[data-theme=sunset]{color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 74.7039% .158278 39.94703;--s: 72.5375% .177654 2.72034;--a: 71.2947% .166678 299.844533;--n: 26% .019 237.69;--nc: 70% .019 237.69;--b1: 22% .019 237.69;--b2: 20% .019 237.69;--b3: 18% .019 237.69;--bc: 77.3835% .043586 245.096534;--in: 85.5596% .085271 206.015183;--su: 85.5609% .08586 144.77874;--wa: 85.5695% .084806 74.427797;--er: 85.5116% .07889 16.886379;--rounded-box: 1.2rem;--rounded-btn: .8rem;--rounded-badge: .4rem;--tab-radius: .7rem}:root:has(input.theme-controller[value=sunset]:checked){color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 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.chat-start .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e")}.chat-end{place-items:end;grid-template-columns:1fr auto}.chat-end .chat-header,.chat-end .chat-footer{grid-column-start:1}.chat-end .chat-image{grid-column-start:2}.chat-end .chat-bubble{grid-column-start:1;border-end-end-radius:0px}.chat-end .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 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.25rem}.input{flex-shrink:1;-webkit-appearance:none;-moz-appearance:none;appearance:none;height:3rem;padding-left:1rem;padding-right:1rem;font-size:1rem;line-height:2;line-height:1.5rem;border-radius:var(--rounded-btn, .5rem);border-width:1px;border-color:transparent;--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)))}.input[type=number]::-webkit-inner-spin-button,.input-md[type=number]::-webkit-inner-spin-button{margin-top:-1rem;margin-bottom:-1rem;margin-inline-end:-1rem}.join :where(.join-item){border-start-end-radius:0;border-end-end-radius:0;border-end-start-radius:0;border-start-start-radius:0}.join .join-item:not(:first-child):not(:last-child),.join *:not(:first-child):not(:last-child) .join-item{border-start-end-radius:0;border-end-end-radius:0;border-end-start-radius:0;border-start-start-radius:0}.join .join-item:first-child:not(:last-child),.join *:first-child:not(:last-child) 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24.16}:root:has(input.theme-controller[value=autumn]:checked){color-scheme:light;--b2: 89.1077% 0 0;--b3: 82.4006% 0 0;--bc: 19.1629% 0 0;--pc: 88.1446% .032232 17.530175;--sc: 12.3353% .033821 23.865865;--ac: 14.6851% .018999 60.729616;--nc: 90.8734% .007475 51.902819;--inc: 13.8449% .019596 207.284192;--suc: 12.199% .016032 174.616213;--wac: 14.0163% .032982 56.844303;--erc: 90.614% .0482 24.16;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 40.7232% .16116 17.530175;--s: 61.6763% .169105 23.865865;--a: 73.4253% .094994 60.729616;--n: 54.3672% .037374 51.902819;--b1: 95.8147% 0 0;--in: 69.2245% .097979 207.284192;--su: 60.9951% .080159 174.616213;--wa: 70.0817% .164909 56.844303;--er: 53.07% .241 24.16}[data-theme=business]{color-scheme:dark;--b2: 22.6487% 0 0;--b3: 20.944% 0 0;--bc: 84.8707% 0 0;--pc: 88.3407% .019811 251.473931;--sc: 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240.033697;--suc: 14.0454% .018919 156.59611;--wac: 15.4965% .023141 81.519177;--erc: 90.3221% .029356 29.674507;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 41.7036% .099057 251.473931;--s: 64.0924% .027405 229.389418;--a: 67.271% .167726 35.791525;--n: 27.441% .01325 253.041249;--b1: 24.3535% 0 0;--in: 62.6163% .143511 240.033697;--su: 70.2268% .094594 156.59611;--wa: 77.4824% .115704 81.519177;--er: 51.6105% .14678 29.674507;--rounded-box: .25rem;--rounded-btn: .125rem;--rounded-badge: .125rem}[data-theme=acid]{color-scheme:light;--b2: 91.6146% 0 0;--b3: 84.7189% 0 0;--bc: 19.7021% 0 0;--pc: 14.38% .0714 330.759573;--sc: 14.674% .0448 48.250878;--ac: 18.556% .0528 122.962951;--nc: 84.262% .0256 278.68;--inc: 12.144% .0454 252.05;--suc: 17.144% .0532 158.53;--wac: 18.202% .0424 100.5;--erc: 12.968% .0586 29.349188;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 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100.5;--er: 64.84% .293 29.349188;--rounded-box: 1.25rem;--rounded-btn: 1rem;--rounded-badge: 1rem;--tab-radius: .7rem}[data-theme=lemonade]{color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}:root:has(input.theme-controller[value=lemonade]:checked){color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}[data-theme=night]{color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}:root:has(input.theme-controller[value=night]:checked){color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}[data-theme=coffee]{color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}:root:has(input.theme-controller[value=coffee]:checked){color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}[data-theme=winter]{color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}:root:has(input.theme-controller[value=winter]:checked){color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}[data-theme=dim]{color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}:root:has(input.theme-controller[value=dim]:checked){color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}[data-theme=nord]{color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}:root:has(input.theme-controller[value=nord]:checked){color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}[data-theme=sunset]{color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 74.7039% .158278 39.94703;--s: 72.5375% .177654 2.72034;--a: 71.2947% .166678 299.844533;--n: 26% .019 237.69;--nc: 70% .019 237.69;--b1: 22% .019 237.69;--b2: 20% .019 237.69;--b3: 18% .019 237.69;--bc: 77.3835% .043586 245.096534;--in: 85.5596% .085271 206.015183;--su: 85.5609% .08586 144.77874;--wa: 85.5695% .084806 74.427797;--er: 85.5116% .07889 16.886379;--rounded-box: 1.2rem;--rounded-btn: .8rem;--rounded-badge: .4rem;--tab-radius: .7rem}:root:has(input.theme-controller[value=sunset]:checked){color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 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.chat-start .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e")}.chat-end{place-items:end;grid-template-columns:1fr auto}.chat-end .chat-header,.chat-end .chat-footer{grid-column-start:1}.chat-end .chat-image{grid-column-start:2}.chat-end .chat-bubble{grid-column-start:1;border-end-end-radius:0px}.chat-end .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 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.25rem}.input{flex-shrink:1;-webkit-appearance:none;-moz-appearance:none;appearance:none;height:3rem;padding-left:1rem;padding-right:1rem;font-size:1rem;line-height:2;line-height:1.5rem;border-radius:var(--rounded-btn, .5rem);border-width:1px;border-color:transparent;--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)))}.input[type=number]::-webkit-inner-spin-button,.input-md[type=number]::-webkit-inner-spin-button{margin-top:-1rem;margin-bottom:-1rem;margin-inline-end:-1rem}.join :where(.join-item){border-start-end-radius:0;border-end-end-radius:0;border-end-start-radius:0;border-start-start-radius:0}.join .join-item:not(:first-child):not(:last-child),.join *:not(:first-child):not(:last-child) .join-item{border-start-end-radius:0;border-end-end-radius:0;border-end-start-radius:0;border-start-start-radius:0}.join .join-item:first-child:not(:last-child),.join *:first-child:not(:last-child) 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li:not(.menu-title,.disabled)>*:not(ul,details,.menu-title)):is(summary):not(.active,.btn):focus-visible,:where(.menu li:not(.menu-title,.disabled)>details>summary:not(.menu-title)):not(summary,.active,.btn).focus,:where(.menu li:not(.menu-title,.disabled)>details>summary:not(.menu-title)):not(summary,.active,.btn):focus,:where(.menu li:not(.menu-title,.disabled)>details>summary:not(.menu-title)):is(summary):not(.active,.btn):focus-visible{cursor:pointer;background-color:var(--fallback-bc,oklch(var(--bc)/.1));--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)));outline:2px solid transparent;outline-offset:2px}.menu li>*:not(ul,.menu-title,details,.btn):active,.menu li>*:not(ul,.menu-title,details,.btn).active,.menu li>details>summary:active{--tw-bg-opacity: 1;background-color:var(--fallback-n,oklch(var(--n)/var(--tw-bg-opacity)));--tw-text-opacity: 1;color:var(--fallback-nc,oklch(var(--nc)/var(--tw-text-opacity)))}.menu :where(li>details>summary)::-webkit-details-marker{display:none}.menu :where(li>details>summary):after,.menu :where(li>.menu-dropdown-toggle):after{justify-self:end;display:block;margin-top:-.5rem;height:.5rem;width:.5rem;transform:rotate(45deg);transition-property:transform,margin-top;transition-duration:.3s;transition-timing-function:cubic-bezier(.4,0,.2,1);content:"";transform-origin:75% 75%;box-shadow:2px 2px;pointer-events:none}.menu :where(li>details[open]>summary):after,.menu :where(li>.menu-dropdown-toggle.menu-dropdown-show):after{transform:rotate(225deg);margin-top:0}.mockup-browser .mockup-browser-toolbar .input{position:relative;margin-left:auto;margin-right:auto;display:block;height:1.75rem;width:24rem;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;--tw-bg-opacity: 1;background-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-bg-opacity)));padding-left:2rem;direction:ltr}.mockup-browser .mockup-browser-toolbar .input:before{content:"";position:absolute;left:.5rem;top:50%;aspect-ratio:1 / 1;height:.75rem;--tw-translate-y: -50%;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-radius:9999px;border-width:2px;border-color:currentColor;opacity:.6}.mockup-browser .mockup-browser-toolbar .input:after{content:"";position:absolute;left:1.25rem;top:50%;height:.5rem;--tw-translate-y: 25%;--tw-rotate: -45deg;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-radius:9999px;border-width:1px;border-color:currentColor;opacity:.6}.modal:not(dialog:not(.modal-open)),.modal::backdrop{background-color:#0006;animation:modal-pop .2s ease-out}.modal-open .modal-box,.modal-toggle:checked+.modal .modal-box,.modal:target .modal-box,.modal[open] .modal-box{--tw-translate-y: 0px;--tw-scale-x: 1;--tw-scale-y: 1;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y))}.modal-action>:not([hidden])~:not([hidden]){--tw-space-x-reverse: 0;margin-right:calc(.5rem * var(--tw-space-x-reverse));margin-left:calc(.5rem * calc(1 - var(--tw-space-x-reverse)))}@keyframes modal-pop{0%{opacity:0}}@keyframes progress-loading{50%{background-position-x:-115%}}.radio:focus{box-shadow:none}.radio:focus-visible{outline-style:solid;outline-width:2px;outline-offset:2px;outline-color:var(--fallback-bc,oklch(var(--bc)/1))}.radio:checked,.radio[aria-checked=true]{--tw-bg-opacity: 1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));background-image:none;animation:radiomark var(--animation-input, .2s) ease-out;box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}.radio:disabled{cursor:not-allowed;opacity:.2}@keyframes radiomark{0%{box-shadow:0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset}50%{box-shadow:0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset}to{box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}}.range:focus-visible::-webkit-slider-thumb{--focus-shadow: 0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset, 0 0 0 2rem var(--range-shdw) inset}.range:focus-visible::-moz-range-thumb{--focus-shadow: 0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset, 0 0 0 2rem var(--range-shdw) inset}.range::-webkit-slider-runnable-track{height:.5rem;width:100%;border-radius:var(--rounded-box, 1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-moz-range-track{height:.5rem;width:100%;border-radius:var(--rounded-box, 1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-webkit-slider-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box, 1rem);border-style:none;--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));-moz-appearance:none;appearance:none;-webkit-appearance:none;top:50%;color:var(--range-shdw);transform:translateY(-50%);--filler-size: 100rem;--filler-offset: .6rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow, 0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}.range::-moz-range-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box, 1rem);border-style:none;--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));top:50%;color:var(--range-shdw);--filler-size: 100rem;--filler-offset: .5rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow, 0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}@keyframes rating-pop{0%{transform:translateY(-.125em)}40%{transform:translateY(-.125em)}to{transform:translateY(0)}}@keyframes skeleton{0%{background-position:150%}to{background-position:-50%}}.textarea-bordered{border-color:var(--fallback-bc,oklch(var(--bc)/.2))}.textarea:focus{box-shadow:none;border-color:var(--fallback-bc,oklch(var(--bc)/.2));outline-style:solid;outline-width:2px;outline-offset:2px;outline-color:var(--fallback-bc,oklch(var(--bc)/.2))}.textarea-disabled,.textarea:disabled,.textarea[disabled]{cursor:not-allowed;--tw-border-opacity: 1;border-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-border-opacity)));--tw-bg-opacity: 1;background-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-bg-opacity)));color:var(--fallback-bc,oklch(var(--bc)/.4))}.textarea-disabled::-moz-placeholder,.textarea:disabled::-moz-placeholder,.textarea[disabled]::-moz-placeholder{color:var(--fallback-bc,oklch(var(--bc)/var(--tw-placeholder-opacity)));--tw-placeholder-opacity: .2}.textarea-disabled::placeholder,.textarea:disabled::placeholder,.textarea[disabled]::placeholder{color:var(--fallback-bc,oklch(var(--bc)/var(--tw-placeholder-opacity)));--tw-placeholder-opacity: .2}@keyframes toast-pop{0%{transform:scale(.9);opacity:0}to{transform:scale(1);opacity:1}}.glass,.glass.btn-active{border:none;-webkit-backdrop-filter:blur(var(--glass-blur, 40px));backdrop-filter:blur(var(--glass-blur, 40px));background-color:transparent;background-image:linear-gradient(135deg,rgb(255 255 255 / var(--glass-opacity, 30%)),#0000),linear-gradient(var(--glass-reflex-degree, 100deg),rgb(255 255 255 / var(--glass-reflex-opacity, 10%)) 25%,rgb(0 0 0 / 0%) 25%);box-shadow:0 0 0 1px rgb(255 255 255 / var(--glass-border-opacity, 10%)) inset,0 0 0 2px #0000000d;text-shadow:0 1px rgb(0 0 0 / var(--glass-text-shadow-opacity, 5%))}@media (hover: hover){.glass.btn-active{border:none;-webkit-backdrop-filter:blur(var(--glass-blur, 40px));backdrop-filter:blur(var(--glass-blur, 40px));background-color:transparent;background-image:linear-gradient(135deg,rgb(255 255 255 / var(--glass-opacity, 30%)),#0000),linear-gradient(var(--glass-reflex-degree, 100deg),rgb(255 255 255 / var(--glass-reflex-opacity, 10%)) 25%,rgb(0 0 0 / 0%) 25%);box-shadow:0 0 0 1px rgb(255 255 255 / var(--glass-border-opacity, 10%)) inset,0 0 0 2px #0000000d;text-shadow:0 1px rgb(0 0 0 / var(--glass-text-shadow-opacity, 5%))}}.btn-sm{height:2rem;min-height:2rem;padding-left:.75rem;padding-right:.75rem;font-size:.875rem}.btn-block{width:100%}.btn-square:where(.btn-sm){height:2rem;width:2rem;padding:0}.btn-circle:where(.btn-sm){height:2rem;width:2rem;border-radius:9999px;padding:0}.drawer-open>.drawer-toggle{display:none}.drawer-open>.drawer-toggle~.drawer-side{pointer-events:auto;visibility:visible;position:sticky;display:block;width:auto;overscroll-behavior:auto}.drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}[dir=rtl] .drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}.drawer-open>.drawer-toggle:checked~.drawer-side{pointer-events:auto;visibility:visible}.drawer-open>.drawer-side{overflow-y:auto}html:has(.drawer-toggle:checked){overflow-y:hidden;scrollbar-gutter:stable}.join.join-vertical .join-item:first-child:not(:last-child),.join.join-vertical *:first-child:not(:last-child) .join-item{border-end-start-radius:0;border-end-end-radius:0;border-start-start-radius:inherit;border-start-end-radius:inherit}.join.join-vertical .join-item:last-child:not(:first-child),.join.join-vertical *:last-child:not(:first-child) .join-item{border-start-start-radius:0;border-start-end-radius:0;border-end-start-radius:inherit;border-end-end-radius:inherit}.join.join-horizontal .join-item:first-child:not(:last-child),.join.join-horizontal *:first-child:not(:last-child) .join-item{border-end-end-radius:0;border-start-end-radius:0;border-end-start-radius:inherit;border-start-start-radius:inherit}.join.join-horizontal .join-item:last-child:not(:first-child),.join.join-horizontal *:last-child:not(:first-child) .join-item{border-end-start-radius:0;border-start-start-radius:0;border-end-end-radius:inherit;border-start-end-radius:inherit}.drawer-open>.drawer-toggle~.drawer-side>.drawer-overlay{cursor:default;background-color:transparent}.join.join-vertical>:where(*:not(:first-child)):is(.btn){margin-top:calc(var(--border-btn) * -1)}.join.join-horizontal>:where(*:not(:first-child)):is(.btn){margin-inline-start:calc(var(--border-btn) * -1);margin-top:0}.modal-top :where(.modal-box){width:100%;max-width:none;--tw-translate-y: -2.5rem;--tw-scale-x: 1;--tw-scale-y: 1;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-bottom-right-radius:var(--rounded-box, 1rem);border-bottom-left-radius:var(--rounded-box, 1rem);border-top-left-radius:0;border-top-right-radius:0}.modal-middle :where(.modal-box){width:91.666667%;max-width:32rem;--tw-translate-y: 0px;--tw-scale-x: .9;--tw-scale-y: .9;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-top-left-radius:var(--rounded-box, 1rem);border-top-right-radius:var(--rounded-box, 1rem);border-bottom-right-radius:var(--rounded-box, 1rem);border-bottom-left-radius:var(--rounded-box, 1rem)}.modal-bottom :where(.modal-box){width:100%;max-width:none;--tw-translate-y: 2.5rem;--tw-scale-x: 1;--tw-scale-y: 1;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skew(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));border-top-left-radius:var(--rounded-box, 1rem);border-top-right-radius:var(--rounded-box, 1rem);border-bottom-right-radius:0;border-bottom-left-radius:0}.collapse{visibility:collapse}.relative{position:relative}.sticky{position:sticky}.top-4{top:1rem}.z-10{z-index:10}.z-50{z-index:50}.z-\[1\]{z-index:1}.m-1{margin:.25rem}.mx-4{margin-left:1rem;margin-right:1rem}.mx-auto{margin-left:auto;margin-right:auto}.my-4{margin-top:1rem;margin-bottom:1rem}.mb-2{margin-bottom:.5rem}.mb-4{margin-bottom:1rem}.mb-6{margin-bottom:1.5rem}.ml-2{margin-left:.5rem}.ml-4{margin-left:1rem}.mr-2{margin-right:.5rem}.mt-2{margin-top:.5rem}.mt-4{margin-top:1rem}.mt-6{margin-top:1.5rem}.mt-8{margin-top:2rem}.mt-auto{margin-top:auto}.inline{display:inline}.flex{display:flex}.h-0{height:0px}.h-24{height:6rem}.h-80{height:20rem}.h-\[calc\(90vh-12rem\)\]{height:calc(90vh - 12rem)}.h-screen{height:100vh}.min-h-full{min-height:100%}.w-52{width:13rem}.w-64{width:16rem}.w-\[calc\(90vw-8em\)\]{width:calc(90vw - 8em)}.w-full{width:100%}.w-screen{width:100vw}.max-w-64{max-width:16rem}.grow{flex-grow:1}.cursor-pointer{cursor:pointer}.flex-row{flex-direction:row}.flex-col{flex-direction:column}.items-center{align-items:center}.justify-start{justify-content:flex-start}.justify-center{justify-content:center}.justify-between{justify-content:space-between}.gap-2{gap:.5rem}.overflow-visible{overflow:visible}.overflow-y-auto{overflow-y:auto}.truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.rounded-box{border-radius:var(--rounded-box, 1rem)}.bg-base-100{--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity, 1)))}.bg-base-200{--tw-bg-opacity: 1;background-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-bg-opacity, 1)))}.bg-base-300{--tw-bg-opacity: 1;background-color:var(--fallback-b3,oklch(var(--b3)/var(--tw-bg-opacity, 1)))}.p-2{padding:.5rem}.px-4{padding-left:1rem;padding-right:1rem}.py-4{padding-top:1rem;padding-bottom:1rem}.text-center{text-align:center}.text-right{text-align:right}.text-2xl{font-size:1.5rem;line-height:2rem}.text-lg{font-size:1.125rem;line-height:1.75rem}.text-sm{font-size:.875rem;line-height:1.25rem}.text-xs{font-size:.75rem;line-height:1rem}.font-bold{font-weight:700}.font-normal{font-weight:400}.font-semibold{font-weight:600}.text-base-content{--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity, 1)))}.text-error{--tw-text-opacity: 1;color:var(--fallback-er,oklch(var(--er)/var(--tw-text-opacity, 1)))}.underline{text-decoration-line:underline}.opacity-0{opacity:0}.opacity-40{opacity:.4}.opacity-60{opacity:.6}.shadow{--tw-shadow: 0 1px 3px 0 rgb(0 0 0 / .1), 0 1px 2px -1px rgb(0 0 0 / .1);--tw-shadow-colored: 0 1px 3px 0 var(--tw-shadow-color), 0 1px 2px -1px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.shadow-2xl{--tw-shadow: 0 25px 50px -12px rgb(0 0 0 / .25);--tw-shadow-colored: 0 25px 50px -12px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.filter{filter:var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow)}.markdown h1,.markdown h2,.markdown h3,.markdown h4,.markdown h5,.markdown h6,.markdown ul,.markdown ol,.markdown li{all:revert}.markdown pre{white-space:pre-wrap}.markdown pre{border-radius:.5rem}.markdown pre{padding:.5rem}.markdown pre{border:1px solid currentColor}@media (min-width: 768px){.show-on-hover{opacity:0}.group:hover .show-on-hover{opacity:1}}.btn-mini{cursor:pointer}.btn-mini:hover{--tw-shadow: 0 4px 6px -1px rgb(0 0 0 / .1), 0 2px 4px -2px rgb(0 0 0 / .1);--tw-shadow-colored: 0 4px 6px -1px var(--tw-shadow-color), 0 2px 4px -2px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.chat-screen{max-width:900px}.chat-bubble-base-300{--tw-bg-opacity: 1;background-color:var(--fallback-b3,oklch(var(--b3)/var(--tw-bg-opacity, 1)));--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity, 1)))}@media (min-width: 1024px){.lg\:drawer-open>.drawer-toggle{display:none}.lg\:drawer-open>.drawer-toggle~.drawer-side{pointer-events:auto;visibility:visible;position:sticky;display:block;width:auto;overscroll-behavior:auto}.lg\:drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}[dir=rtl] .lg\:drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}.lg\:drawer-open>.drawer-toggle:checked~.drawer-side{pointer-events:auto;visibility:visible}.lg\:drawer-open>.drawer-side{overflow-y:auto}html:has(.lg\:drawer-open.lg\:drawer-open){overflow-y:auto;scrollbar-gutter:auto}.lg\:drawer-open>.drawer-toggle~.drawer-side>.drawer-overlay{cursor:default;background-color:transparent}.lg\:hidden{display:none}.lg\:h-screen{height:100vh}.lg\:w-96{width:24rem}.lg\:max-w-64{max-width:16rem}}</style>
 </head>
 
 <body>
@@ -99,7 +99,7 @@ Server rendered element contains fewer child nodes than client vdom.`),T=!0),au(
       <!-- sidebar -->
       <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
         <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
-        <div class="flex flex-col bg-base-200 min-h-full max-w-[calc(100vw-2em)] py-4 px-4">
+        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
           <div class="flex flex-row items-center justify-between mb-4 mt-4">
             <h2 class="font-bold ml-4">Conversations</h2>
 
@@ -204,51 +204,25 @@ Server rendered element contains fewer child nodes than client vdom.`),T=!0),au(
             {{ messages.length === 0 ? 'Send a message to start' : '' }}
           </div>
           <div v-for="msg in messages" class="group">
-            <div :class="{
-              'chat': true,
-              'chat-start': msg.role !== 'user',
-              'chat-end': msg.role === 'user',
-            }">
-              <div :class="{
-                'chat-bubble markdown': true,
-                'chat-bubble-base-300': msg.role !== 'user',
-              }">
-                <!-- textarea for editing message -->
-                <template v-if="editingMsg && editingMsg.id === msg.id">
-                  <textarea
-                    class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
-                    v-model="msg.content"></textarea>
-                  <br/>
-                  <button class="btn btn-ghost mt-2 mr-2" @click="editingMsg = null">Cancel</button>
-                  <button class="btn mt-2" @click="editUserMsgAndRegenerate(msg)">Submit</button>
-                </template>
-                <!-- render message as markdown -->
-                <vue-markdown v-else :source="msg.content" />
-              </div>
-            </div>
-
-            <!-- actions for each message -->
-            <div :class="{'text-right': msg.role === 'user'}" class="mx-4 mt-2 mb-2">
-              <!-- user message -->
-              <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingMsg = msg" :disabled="isGenerating">
-                ✍️ Edit
-              </button>
-              <!-- assistant message -->
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-                🔄 Regenerate
-              </button>
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg(msg)" :disabled="isGenerating">
-                📋 Copy
-              </button>
-            </div>
+            <message-bubble
+              :config="config"
+              :msg="msg"
+              :key="msg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
+              :regenerate-msg="regenerateMsg"></message-bubble>
           </div>
 
           <!-- pending (ongoing) assistant message -->
-          <div id="pending-msg" class="chat chat-start">
-            <div v-if="pendingMsg" class="chat-bubble markdown chat-bubble-base-300">
-              <span v-if="!pendingMsg.content" class="loading loading-dots loading-md"></span>
-              <vue-markdown v-else :source="pendingMsg.content" />
-            </div>
+          <div id="pending-msg" class="group">
+            <message-bubble
+              v-if="pendingMsg"
+              :config="config"
+              :msg="pendingMsg"
+              :key="pendingMsg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="() => {}"
+              :regenerate-msg="() => {}"></message-bubble>
           </div>
         </div>
 
@@ -311,6 +285,10 @@ Server rendered element contains fewer child nodes than client vdom.`),T=!0),au(
           <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
             <summary class="collapse-title font-bold">Advanced config</summary>
             <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
+                <span class="ml-4">Show tokens per second</span>
+              </div>
               <label class="form-control mb-2">
                 <!-- Custom parameters input -->
                 <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
@@ -331,6 +309,66 @@ Server rendered element contains fewer child nodes than client vdom.`),T=!0),au(
 
   </div>
 
+
+  <!-- Template to be used as message bubble -->
+  <template id="message-bubble">
+    <div :class="{
+      'chat': true,
+      'chat-start': msg.role !== 'user',
+      'chat-end': msg.role === 'user',
+    }">
+      <div :class="{
+        'chat-bubble markdown': true,
+        'chat-bubble-base-300': msg.role !== 'user',
+      }">
+        <!-- textarea for editing message -->
+        <template v-if="editingContent !== null">
+          <textarea
+            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+            v-model="editingContent"></textarea>
+          <br/>
+          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
+          <button class="btn mt-2" @click="editMsg()">Submit</button>
+        </template>
+        <template v-else>
+          <!-- show loading dots for pending message -->
+          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
+          <!-- render message as markdown -->
+          <vue-markdown v-else :source="msg.content"></vue-markdown>
+          <!-- render timings if enabled -->
+          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
+            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
+            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
+              <b>Prompt</b><br/>
+              - Tokens: {{ timings.prompt_n }}<br/>
+              - Time: {{ timings.prompt_ms }} ms<br/>
+              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
+              <b>Generation</b><br/>
+              - Tokens: {{ timings.predicted_n }}<br/>
+              - Time: {{ timings.predicted_ms }} ms<br/>
+              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
+            </div>
+          </div>
+        </template>
+      </div>
+    </div>
+    <!-- actions for each message -->
+    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
+      <!-- user message -->
+      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
+        ✍️ Edit
+      </button>
+      <!-- assistant message -->
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
+        🔄 Regenerate
+      </button>
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
+        📋 Copy
+      </button>
+    </div>
+  </template>
+
+
   <!-- Template to be used by settings modal -->
   <template id="settings-modal-short-input">
     <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index c7e18b45e..9d29fe005 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -15,7 +15,7 @@
       <!-- sidebar -->
       <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
         <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
-        <div class="flex flex-col bg-base-200 min-h-full max-w-[calc(100vw-2em)] py-4 px-4">
+        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
           <div class="flex flex-row items-center justify-between mb-4 mt-4">
             <h2 class="font-bold ml-4">Conversations</h2>
 
@@ -120,51 +120,25 @@
             {{ messages.length === 0 ? 'Send a message to start' : '' }}
           </div>
           <div v-for="msg in messages" class="group">
-            <div :class="{
-              'chat': true,
-              'chat-start': msg.role !== 'user',
-              'chat-end': msg.role === 'user',
-            }">
-              <div :class="{
-                'chat-bubble markdown': true,
-                'chat-bubble-base-300': msg.role !== 'user',
-              }">
-                <!-- textarea for editing message -->
-                <template v-if="editingMsg && editingMsg.id === msg.id">
-                  <textarea
-                    class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
-                    v-model="msg.content"></textarea>
-                  <br/>
-                  <button class="btn btn-ghost mt-2 mr-2" @click="editingMsg = null">Cancel</button>
-                  <button class="btn mt-2" @click="editUserMsgAndRegenerate(msg)">Submit</button>
-                </template>
-                <!-- render message as markdown -->
-                <vue-markdown v-else :source="msg.content" />
-              </div>
-            </div>
-
-            <!-- actions for each message -->
-            <div :class="{'text-right': msg.role === 'user'}" class="mx-4 mt-2 mb-2">
-              <!-- user message -->
-              <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingMsg = msg" :disabled="isGenerating">
-                ✍️ Edit
-              </button>
-              <!-- assistant message -->
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-                🔄 Regenerate
-              </button>
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg(msg)" :disabled="isGenerating">
-                📋 Copy
-              </button>
-            </div>
+            <message-bubble
+              :config="config"
+              :msg="msg"
+              :key="msg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
+              :regenerate-msg="regenerateMsg"></message-bubble>
           </div>
 
           <!-- pending (ongoing) assistant message -->
-          <div id="pending-msg" class="chat chat-start">
-            <div v-if="pendingMsg" class="chat-bubble markdown chat-bubble-base-300">
-              <span v-if="!pendingMsg.content" class="loading loading-dots loading-md"></span>
-              <vue-markdown v-else :source="pendingMsg.content" />
-            </div>
+          <div id="pending-msg" class="group">
+            <message-bubble
+              v-if="pendingMsg"
+              :config="config"
+              :msg="pendingMsg"
+              :key="pendingMsg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="() => {}"
+              :regenerate-msg="() => {}"></message-bubble>
           </div>
         </div>
 
@@ -227,6 +201,10 @@
           <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
             <summary class="collapse-title font-bold">Advanced config</summary>
             <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
+                <span class="ml-4">Show tokens per second</span>
+              </div>
               <label class="form-control mb-2">
                 <!-- Custom parameters input -->
                 <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
@@ -247,6 +225,66 @@
 
   </div>
 
+
+  <!-- Template to be used as message bubble -->
+  <template id="message-bubble">
+    <div :class="{
+      'chat': true,
+      'chat-start': msg.role !== 'user',
+      'chat-end': msg.role === 'user',
+    }">
+      <div :class="{
+        'chat-bubble markdown': true,
+        'chat-bubble-base-300': msg.role !== 'user',
+      }">
+        <!-- textarea for editing message -->
+        <template v-if="editingContent !== null">
+          <textarea
+            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+            v-model="editingContent"></textarea>
+          <br/>
+          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
+          <button class="btn mt-2" @click="editMsg()">Submit</button>
+        </template>
+        <template v-else>
+          <!-- show loading dots for pending message -->
+          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
+          <!-- render message as markdown -->
+          <vue-markdown v-else :source="msg.content"></vue-markdown>
+          <!-- render timings if enabled -->
+          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
+            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
+            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
+              <b>Prompt</b><br/>
+              - Tokens: {{ timings.prompt_n }}<br/>
+              - Time: {{ timings.prompt_ms }} ms<br/>
+              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
+              <b>Generation</b><br/>
+              - Tokens: {{ timings.predicted_n }}<br/>
+              - Time: {{ timings.predicted_ms }} ms<br/>
+              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
+            </div>
+          </div>
+        </template>
+      </div>
+    </div>
+    <!-- actions for each message -->
+    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
+      <!-- user message -->
+      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
+        ✍️ Edit
+      </button>
+      <!-- assistant message -->
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
+        🔄 Regenerate
+      </button>
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
+        📋 Copy
+      </button>
+    </div>
+  </template>
+
+
   <!-- Template to be used by settings modal -->
   <template id="settings-modal-short-input">
     <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json
index 6b93090f0..f9104f65f 100644
--- a/examples/server/webui/package-lock.json
+++ b/examples/server/webui/package-lock.json
@@ -13,6 +13,7 @@
         "markdown-it": "^14.1.0",
         "postcss": "^8.4.49",
         "tailwindcss": "^3.4.15",
+        "textlinestream": "^1.1.1",
         "vite-plugin-singlefile": "^2.0.3",
         "vue": "^3.5.13"
       },
@@ -2677,6 +2678,12 @@
         "node": ">=14.0.0"
       }
     },
+    "node_modules/textlinestream": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/textlinestream/-/textlinestream-1.1.1.tgz",
+      "integrity": "sha512-iBHbi7BQxrFmwZUQJsT0SjNzlLLsXhvW/kg7EyOMVMBIrlnj/qYofwo1LVLZi+3GbUEo96Iu2eqToI2+lZoAEQ==",
+      "license": "MIT"
+    },
     "node_modules/uc.micro": {
       "version": "2.1.0",
       "resolved": "https://registry.npmjs.org/uc.micro/-/uc.micro-2.1.0.tgz",
diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json
index 2a45ece14..d656a841d 100644
--- a/examples/server/webui/package.json
+++ b/examples/server/webui/package.json
@@ -17,6 +17,7 @@
     "markdown-it": "^14.1.0",
     "postcss": "^8.4.49",
     "tailwindcss": "^3.4.15",
+    "textlinestream": "^1.1.1",
     "vite-plugin-singlefile": "^2.0.3",
     "vue": "^3.5.13"
   }
diff --git a/examples/server/webui/src/completion.js b/examples/server/webui/src/completion.js
deleted file mode 100644
index 54a0f22f5..000000000
--- a/examples/server/webui/src/completion.js
+++ /dev/null
@@ -1,225 +0,0 @@
-const paramDefaults = {
-  stream: true,
-  temperature: 0.2,
-};
-
-let generation_settings = null;
-
-export class CompletionError extends Error {
-  constructor(message, name, data) {
-    super(message);
-    this.name = name;
-  }
-};
-
-// Completes the prompt as a generator. Recommended for most use cases.
-//
-// Example:
-//
-//    import { llama } from '/completion.js'
-//
-//    const request = llama("Tell me a joke", {n_predict: 800})
-//    for await (const chunk of request) {
-//      document.write(chunk.data.content)
-//    }
-//
-export async function* llama(prompt, params = {}, config = {}) {
-  let controller = config.controller;
-  const api_url = config.api_url?.replace(/\/+$/, '') || "";
-
-  if (!controller) {
-    controller = new AbortController();
-  }
-
-  const completionParams = { ...paramDefaults, ...params, prompt };
-
-  const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
-    method: 'POST',
-    body: JSON.stringify(completionParams),
-    headers: {
-      'Connection': 'keep-alive',
-      'Content-Type': 'application/json',
-      'Accept': 'text/event-stream',
-      ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
-    },
-    signal: controller.signal,
-  });
-
-  const status = response.status;
-  if (status !== 200) {
-    try {
-      const body = await response.json();
-      if (body && body.error && body.error.message) {
-        throw new CompletionError(body.error.message, 'ServerError');
-      }
-    } catch (err) {
-      throw new CompletionError(err.message, 'ServerError');
-    }
-  }
-
-  const reader = response.body.getReader();
-  const decoder = new TextDecoder();
-
-  let content = "";
-  let leftover = ""; // Buffer for partially read lines
-
-  try {
-    let cont = true;
-
-    while (cont) {
-      const result = await reader.read();
-      if (result.done) {
-        break;
-      }
-
-      // Add any leftover data to the current chunk of data
-      const text = leftover + decoder.decode(result.value);
-
-      // Check if the last character is a line break
-      const endsWithLineBreak = text.endsWith('\n');
-
-      // Split the text into lines
-      let lines = text.split('\n');
-
-      // If the text doesn't end with a line break, then the last line is incomplete
-      // Store it in leftover to be added to the next chunk of data
-      if (!endsWithLineBreak) {
-        leftover = lines.pop();
-      } else {
-        leftover = ""; // Reset leftover if we have a line break at the end
-      }
-
-      // Parse all sse events and add them to result
-      const regex = /^(\S+):\s(.*)$/gm;
-      for (const line of lines) {
-        const match = regex.exec(line);
-        if (match) {
-          result[match[1]] = match[2];
-          if (result.data === '[DONE]') {
-            cont = false;
-            break;
-          }
-
-          // since we know this is llama.cpp, let's just decode the json in data
-          if (result.data) {
-            result.data = JSON.parse(result.data);
-            content += result.data.content;
-
-            // yield
-            yield result;
-
-            // if we got a stop token from server, we will break here
-            if (result.data.stop) {
-              if (result.data.generation_settings) {
-                generation_settings = result.data.generation_settings;
-              }
-              cont = false;
-              break;
-            }
-          }
-          if (result.error) {
-            try {
-              result.error = JSON.parse(result.error);
-              if (result.error.message.includes('slot unavailable')) {
-                // Throw an error to be caught by upstream callers
-                throw new Error('slot unavailable');
-              } else {
-                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
-              }
-            } catch(e) {
-              console.error(`llama.cpp error ${result.error}`)
-            }
-          }
-        }
-      }
-    }
-  } catch (e) {
-    if (e.name !== 'AbortError') {
-      console.error("llama error: ", e);
-    }
-    throw e;
-  }
-  finally {
-    controller.abort();
-  }
-
-  return content;
-}
-
-// Call llama, return an event target that you can subscribe to
-//
-// Example:
-//
-//    import { llamaEventTarget } from '/completion.js'
-//
-//    const conn = llamaEventTarget(prompt)
-//    conn.addEventListener("message", (chunk) => {
-//      document.write(chunk.detail.content)
-//    })
-//
-export const llamaEventTarget = (prompt, params = {}, config = {}) => {
-  const eventTarget = new EventTarget();
-  (async () => {
-    let content = "";
-    for await (const chunk of llama(prompt, params, config)) {
-      if (chunk.data) {
-        content += chunk.data.content;
-        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
-      }
-      if (chunk.data.generation_settings) {
-        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
-      }
-      if (chunk.data.timings) {
-        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
-      }
-    }
-    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
-  })();
-  return eventTarget;
-}
-
-// Call llama, return a promise that resolves to the completed text. This does not support streaming
-//
-// Example:
-//
-//     llamaPromise(prompt).then((content) => {
-//       document.write(content)
-//     })
-//
-//     or
-//
-//     const content = await llamaPromise(prompt)
-//     document.write(content)
-//
-export const llamaPromise = (prompt, params = {}, config = {}) => {
-  return new Promise(async (resolve, reject) => {
-    let content = "";
-    try {
-      for await (const chunk of llama(prompt, params, config)) {
-        content += chunk.data.content;
-      }
-      resolve(content);
-    } catch (error) {
-      reject(error);
-    }
-  });
-};
-
-/**
- * (deprecated)
- */
-export const llamaComplete = async (params, controller, callback) => {
-  for await (const chunk of llama(params.prompt, params, { controller })) {
-    callback(chunk);
-  }
-}
-
-// Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async (config = {}) => {
-  if (!generation_settings) {
-    const api_url = config.api_url?.replace(/\/+$/, '') || "";
-    const props = await fetch(`${api_url}/props`).then(r => r.json());
-    generation_settings = props.default_generation_settings;
-  }
-  return generation_settings;
-}
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 9b5b12329..f1f35481d 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -1,21 +1,25 @@
 import './styles.css';
 import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
-import { llama } from './completion.js';
 import MarkdownIt from 'markdown-it';
+import TextLineStream from 'textlinestream';
+
+const isDev = import.meta.env.MODE === 'development';
 
 // utility functions
 const isString = (x) => !!x.toLowerCase;
-const isNumeric = (n) => !isString(n) && !isNaN(n);
+const isBoolean = (x) => x === true || x === false;
+const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
 const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
 const copyStr = (str) => navigator.clipboard.writeText(str);
 
 // constants
 const BASE_URL = localStorage.getItem('base') // for debugging
-  || (new URL('.', document.baseURI).href).toString(); // for production
+  || (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
 const CONFIG_DEFAULT = {
   // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
   apiKey: '',
   systemMessage: 'You are a helpful assistant.',
+  showTokensPerSecond: false,
   // make sure these default values are in sync with `common.h`
   samplers: 'dkypmxt',
   temperature: 0.8,
@@ -101,6 +105,48 @@ const SettingsModalShortInput = defineComponent({
   },
 });
 
+// message bubble component
+const MessageBubble = defineComponent({
+  components: {
+    VueMarkdown
+  },
+  template: document.getElementById('message-bubble').innerHTML,
+  props: {
+    config: Object,
+    msg: Object,
+    isGenerating: Boolean,
+    editUserMsgAndRegenerate: Function,
+    regenerateMsg: Function,
+  },
+  data() {
+    return {
+      editingContent: null,
+    };
+  },
+  computed: {
+    timings() {
+      if (!this.msg.timings) return null;
+      return {
+        ...this.msg.timings,
+        prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
+        predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
+      };
+    }
+  },
+  methods: {
+    copyMsg() {
+      copyStr(this.msg.content);
+    },
+    editMsg() {
+      this.editUserMsgAndRegenerate({
+        ...this.msg,
+        content: this.editingContent,
+      });
+      this.editingContent = null;
+    },
+  },
+});
+
 // coversations is stored in localStorage
 // format: { [convId]: { id: string, lastModified: number, messages: [...] } }
 // convId is a string prefixed with 'conv-'
@@ -192,10 +238,29 @@ const chatScrollToBottom = (requiresNearBottom) => {
   }
 };
 
+// wrapper for SSE
+async function* sendSSEPostRequest(url, fetchOptions) {
+  const res = await fetch(url, fetchOptions);
+  const lines = res.body
+    .pipeThrough(new TextDecoderStream())
+    .pipeThrough(new TextLineStream());
+  for await (const line of lines) {
+    if (isDev) console.log({line});
+    if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
+      const data = JSON.parse(line.slice(5));
+      yield data;
+    } else if (line.startsWith('error:')) {
+      const data = JSON.parse(line.slice(6));
+      throw new Error(data.message || 'Unknown error');
+    }
+  }
+};
+
 const mainApp = createApp({
   components: {
     VueMarkdown,
     SettingsModalShortInput,
+    MessageBubble,
   },
   data() {
     return {
@@ -209,7 +274,6 @@ const mainApp = createApp({
       selectedTheme: StorageUtils.getTheme(),
       config: StorageUtils.getConfig(),
       showConfigDialog: false,
-      editingMsg: null,
       // const
       themes: THEMES,
       configDefault: {...CONFIG_DEFAULT},
@@ -226,6 +290,15 @@ const mainApp = createApp({
     });
     resizeObserver.observe(pendingMsgElem);
   },
+  watch: {
+    viewingConvId: function(val, oldVal) {
+      if (val != oldVal) {
+        this.fetchMessages();
+        chatScrollToBottom();
+        this.hideSidebar();
+      }
+    }
+  },
   methods: {
     hideSidebar() {
       document.getElementById('toggle-drawer').checked = false;
@@ -237,18 +310,10 @@ const mainApp = createApp({
     newConversation() {
       if (this.isGenerating) return;
       this.viewingConvId = StorageUtils.getNewConvId();
-      this.editingMsg = null;
-      this.fetchMessages();
-      chatScrollToBottom();
-      this.hideSidebar();
     },
     setViewingConv(convId) {
       if (this.isGenerating) return;
       this.viewingConvId = convId;
-      this.editingMsg = null;
-      this.fetchMessages();
-      chatScrollToBottom();
-      this.hideSidebar();
     },
     deleteConv(convId) {
       if (this.isGenerating) return;
@@ -256,7 +321,6 @@ const mainApp = createApp({
         StorageUtils.remove(convId);
         if (this.viewingConvId === convId) {
           this.viewingConvId = StorageUtils.getNewConvId();
-          this.editingMsg = null;
         }
         this.fetchConversation();
         this.fetchMessages();
@@ -291,7 +355,6 @@ const mainApp = createApp({
       this.fetchConversation();
       this.fetchMessages();
       this.inputMsg = '';
-      this.editingMsg = null;
       this.generateMessage(currConvId);
       chatScrollToBottom();
     },
@@ -299,7 +362,6 @@ const mainApp = createApp({
       if (this.isGenerating) return;
       this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
       this.isGenerating = true;
-      this.editingMsg = null;
 
       try {
         const abortController = new AbortController();
@@ -330,17 +392,21 @@ const mainApp = createApp({
           dry_allowed_length: this.config.dry_allowed_length,
           dry_penalty_last_n: this.config.dry_penalty_last_n,
           max_tokens: this.config.max_tokens,
+          timings_per_token: !!this.config.showTokensPerSecond,
           ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}),
-          ...(this.config.apiKey ? { api_key: this.config.apiKey } : {}),
         };
-        const config = {
-          controller: abortController,
-          api_url: BASE_URL,
-          endpoint: '/chat/completions',
-        };
-        for await (const chunk of llama(prompt, params, config)) {
-          const stop = chunk.data.stop;
-          const addedContent = chunk.data.choices[0].delta.content;
+        const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': this.config.apiKey ? `Bearer ${this.config.apiKey}` : undefined,
+          },
+          body: JSON.stringify(params),
+          signal: abortController.signal,
+        });
+        for await (const chunk of chunks) {
+          const stop = chunk.stop;
+          const addedContent = chunk.choices[0].delta.content;
           const lastContent = this.pendingMsg.content || '';
           if (addedContent) {
             this.pendingMsg = {
@@ -349,6 +415,16 @@ const mainApp = createApp({
               content: lastContent + addedContent,
             };
           }
+          const timings = chunk.timings;
+          if (timings && this.config.showTokensPerSecond) {
+            // only extract what's really needed, to save some space
+            this.pendingMsg.timings = {
+              prompt_n: timings.prompt_n,
+              prompt_ms: timings.prompt_ms,
+              predicted_n: timings.predicted_n,
+              predicted_ms: timings.predicted_ms,
+            };
+          }
         }
 
         StorageUtils.appendMsg(currConvId, this.pendingMsg);
@@ -387,14 +463,10 @@ const mainApp = createApp({
       this.fetchMessages();
       this.generateMessage(currConvId);
     },
-    copyMsg(msg) {
-      copyStr(msg.content);
-    },
     editUserMsgAndRegenerate(msg) {
       if (this.isGenerating) return;
       const currConvId = this.viewingConvId;
       const newContent = msg.content;
-      this.editingMsg = null;
       StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
       StorageUtils.appendMsg(currConvId, {
         id: Date.now(),

From fb18934a97425c42c8b32a1baaa94f0080eb051d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 11 Dec 2024 23:13:31 +0200
Subject: [PATCH 004/376] gguf-py : bump version to 0.11.0

---
 gguf-py/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 33cfe26b7..10e94876c 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.10.0"
+version = "0.11.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

From 5555c0c1f66d766c544c30699190dec0285bcbfc Mon Sep 17 00:00:00 2001
From: CentricStorm <CentricStorm@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:40:40 +0000
Subject: [PATCH 005/376] docs: update server streaming mode documentation
 (#9519)

Provide more documentation for streaming mode.
---
 examples/server/README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 2bd23d1a6..686be7baf 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -303,23 +303,23 @@ mkdir llama-client
 cd llama-client
 ```
 
-Create a index.js file and put this inside:
+Create an index.js file and put this inside:
 
 ```javascript
-const prompt = `Building a website can be done in 10 simple steps:`;
+const prompt = "Building a website can be done in 10 simple steps:"
 
-async function Test() {
+async function test() {
     let response = await fetch("http://127.0.0.1:8080/completion", {
-        method: 'POST',
+        method: "POST",
         body: JSON.stringify({
             prompt,
-            n_predict: 512,
+            n_predict: 64,
         })
     })
     console.log((await response.json()).content)
 }
 
-Test()
+test()
 ```
 
 And run it:
@@ -381,7 +381,7 @@ Multiple prompts are also supported. In this case, the completion result will be
 `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
 By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
-`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
+`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.
 
 `stop`: Specify a JSON array of stopping strings.
 These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
@@ -446,7 +446,7 @@ These words will not be included in the completion, so make sure to add them to
 
 **Response format**
 
-- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
+- Note: In streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
 

From 9fdb1243049aa7e8211693f116daf2052d47507d Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 12 Dec 2024 16:57:32 +0100
Subject: [PATCH 006/376] common : add missing env var for speculative (#10801)

---
 common/arg.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 49af31682..b27567f3b 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2083,35 +2083,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.speculative.n_max = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX"));
     add_opt(common_arg(
         {"--draft-min", "--draft-n-min"}, "N",
         string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
         [](common_params & params, int value) {
             params.speculative.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN"));
     add_opt(common_arg(
         {"--draft-p-split"}, "P",
         string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
         [](common_params & params, const std::string & value) {
             params.speculative.p_split = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT"));
     add_opt(common_arg(
         {"--draft-p-min"}, "P",
         string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
         [](common_params & params, const std::string & value) {
             params.speculative.p_min = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
     add_opt(common_arg(
         {"-cd", "--ctx-size-draft"}, "N",
         string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
         [](common_params & params, int value) {
             params.speculative.n_ctx = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
     add_opt(common_arg(
         {"-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -2131,14 +2131,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
     add_opt(common_arg(
         {"-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.model = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
 
     return ctx_arg;
 }

From dc5301d565b7d0b2c691f7df13099aab3997479c Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Thu, 12 Dec 2024 18:35:37 +0100
Subject: [PATCH 007/376] Vulkan: Add VK_EXT_subgroup_size_control support to
 ensure full subgroups for coopmats (#10721)

* Vulkan: Add VK_EXT_subgroup_size_control support to ensure full subgroups for coopmats

* Fix subgroup size control extension support check

Add accf32 and accf16 checks for coopmats

* Also disable coopmats on amdvlk
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 182 ++++++++++++++++++++-------
 1 file changed, 139 insertions(+), 43 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a8ae58ee2..74d08815e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -163,7 +163,11 @@ struct vk_device_struct {
     uint32_t shader_core_count;
     bool uma;
     bool float_controls_rte_fp16;
-    bool coopmat2;
+
+    bool subgroup_size_control;
+    uint32_t subgroup_min_size;
+    uint32_t subgroup_max_size;
+    bool subgroup_require_full_support;
 
     bool coopmat_support;
     bool coopmat_acc_f32_support;
@@ -171,6 +175,7 @@ struct vk_device_struct {
     uint32_t coopmat_m;
     uint32_t coopmat_n;
     uint32_t coopmat_k;
+    bool coopmat2;
 
     size_t idx;
 
@@ -749,8 +754,12 @@ static uint32_t compile_count = 0;
 static std::mutex compile_count_mutex;
 static std::condition_variable compile_count_cond;
 
-static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants, uint32_t align, bool disable_robustness) {
-    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")");
+static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint,
+                                         uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants,
+                                         uint32_t align, bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) {
+    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size <<
+                 ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align <<
+                 ", " << disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")");
     GGML_ASSERT(parameter_count > 0);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
@@ -809,14 +818,28 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         specialization_constants.data()
     );
 
+    vk::PipelineShaderStageCreateFlags pipeline_shader_stage_create_flags{};
+
+    if (device->subgroup_require_full_support && require_full_subgroups) {
+        pipeline_shader_stage_create_flags |= vk::PipelineShaderStageCreateFlagBits::eRequireFullSubgroupsEXT;
+    }
+
     vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
-            vk::PipelineShaderStageCreateFlags(),
+            pipeline_shader_stage_create_flags,
             vk::ShaderStageFlagBits::eCompute,
             pipeline->shader_module,
             entrypoint.c_str(),
             &specialization_info);
+
+    vk::PipelineShaderStageRequiredSubgroupSizeCreateInfoEXT pipeline_shader_stage_required_subgroup_size_create_info;
+    pipeline_shader_stage_required_subgroup_size_create_info.requiredSubgroupSize = required_subgroup_size;
+    if (device->subgroup_size_control && required_subgroup_size > 0) {
+        GGML_ASSERT(device->subgroup_min_size <= required_subgroup_size && required_subgroup_size <= device->subgroup_max_size);
+        pipeline_shader_create_info.setPNext(&pipeline_shader_stage_required_subgroup_size_create_info);
+    }
+
     vk::ComputePipelineCreateInfo compute_pipeline_create_info(
-        vk::PipelineCreateFlags(),
+        vk::PipelineCreateFlags{},
         pipeline_shader_create_info,
         pipeline->layout);
 
@@ -1496,7 +1519,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
     device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
 
     std::vector<std::future<void>> compiles;
-    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants, uint32_t align, bool disable_robustness = false) {
+    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint,
+                                              uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
+                                              uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
         {
             // wait until fewer than N compiles are in progress
             uint32_t N = std::max(1u, std::thread::hardware_concurrency());
@@ -1506,7 +1531,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
             }
             compile_count++;
         }
-        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness));
+        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint,
+                                      parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness, require_full_subgroups, required_subgroup_size));
     };
 
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
@@ -1612,40 +1638,59 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _m) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _s) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _l) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
         if (device->mul_mat ## ID ## _m) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
         if (device->mul_mat ## ID ## _s) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \
 
         // Create 2 variants, {f16,f32} accumulator
 #define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        if (device->coopmat_acc_f16_support) { \
+            CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        } \
+        if (device->coopmat_acc_f32_support) { \
+            CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        } \
 
         CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        if (device->coopmat_acc_f16_support) {
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        } else {
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        }
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
         if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
@@ -1653,19 +1698,35 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
             CREATE_MM2(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            if (device->coopmat_acc_f16_support) {
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            } else {
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            }
         }
+#undef CREATE_MM2
 #undef CREATE_MM
     } else if (device->fp16) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
@@ -1683,6 +1744,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
         if (device->mul_mat ## ID ## _s) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
 
+        // Create 2 variants, {f16,f32} accumulator
+#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+
         CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
@@ -1720,6 +1786,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
+#undef CREATE_MM2
 #undef CREATE_MM
     } else {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
@@ -1774,7 +1841,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
-#undef CREATE_MM2
 #undef CREATE_MM
     }
 
@@ -1998,6 +2064,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 amd_shader_core_properties2 = true;
             } else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) {
                 pipeline_robustness = true;
+            } else if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) {
+                device->subgroup_size_control = true;
             } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 &&
                        !getenv("GGML_VK_DISABLE_COOPMAT")) {
                 device->coopmat_support = true;
@@ -2018,6 +2086,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
         vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props;
         vk::PhysicalDeviceShaderCoreProperties2AMD amd_shader_core_properties2_props;
         vk::PhysicalDeviceVulkan12Properties vk12_props;
+        vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props;
+
         props2.pNext = &props3;
         props3.pNext = &subgroup_props;
         subgroup_props.pNext = &driver_props;
@@ -2037,6 +2107,10 @@ static vk_device ggml_vk_get_device(size_t idx) {
             last_struct->pNext = (VkBaseOutStructure *)&amd_shader_core_properties2_props;
             last_struct = (VkBaseOutStructure *)&amd_shader_core_properties2_props;
         }
+        if (device->subgroup_size_control) {
+            last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_props;
+            last_struct = (VkBaseOutStructure *)&subgroup_size_control_props;
+        }
 
 #if defined(VK_NV_cooperative_matrix2)
         vk::PhysicalDeviceCooperativeMatrix2PropertiesNV coopmat2_props;
@@ -2075,7 +2149,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
 
-        if (device->vendor_id == VK_VENDOR_ID_INTEL || (props2.properties.vendorID == VK_VENDOR_ID_AMD && driver_props.driverID == vk::DriverId::eAmdProprietary)) {
+        if (device->vendor_id == VK_VENDOR_ID_INTEL || (device->vendor_id == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
             // Intel drivers don't support coopmat properly yet
             // Only RADV supports coopmat properly on AMD
             device->coopmat_support = false;
@@ -2131,6 +2205,17 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device_extensions.push_back("VK_EXT_pipeline_robustness");
         }
 
+        VkPhysicalDeviceSubgroupSizeControlFeaturesEXT subgroup_size_control_features;
+        subgroup_size_control_features.pNext = nullptr;
+        subgroup_size_control_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_SIZE_CONTROL_FEATURES_EXT;
+        subgroup_size_control_features.computeFullSubgroups = false;
+        subgroup_size_control_features.subgroupSizeControl = false;
+
+        if (device->subgroup_size_control) {
+            last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_features;
+            last_struct = (VkBaseOutStructure *)&subgroup_size_control_features;
+        }
+
         VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features;
         coopmat_features.pNext = nullptr;
         coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR;
@@ -2158,6 +2243,17 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->pipeline_robustness = pl_robustness_features.pipelineRobustness;
 
+        device->subgroup_size_control = device->subgroup_size_control &&
+                (subgroup_size_control_props.requiredSubgroupSizeStages & vk::ShaderStageFlagBits::eCompute) &&
+                subgroup_size_control_features.subgroupSizeControl;
+
+        if (device->subgroup_size_control) {
+            device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
+            device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
+            device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;
+            device_extensions.push_back("VK_EXT_subgroup_size_control");
+        }
+
         device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix;
 
         if (coopmat2_support) {
@@ -2307,7 +2403,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 }
             }
 
-            if (device->coopmat_m == 0) {
+            if (device->coopmat_m == 0 || !device->coopmat_acc_f32_support) {
                 // No suitable matmul mode found
                 GGML_LOG_DEBUG("ggml_vulkan: WARNING: No suitable matrix core mode found. Disabling matrix cores.\n");
                 device->coopmat_support = false;
@@ -2440,7 +2536,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
         }
     }
 
-    if (props2.properties.vendorID == VK_VENDOR_ID_INTEL || (props2.properties.vendorID == VK_VENDOR_ID_AMD && driver_props.driverID == vk::DriverId::eAmdProprietary)) {
+    if (props2.properties.vendorID == VK_VENDOR_ID_INTEL || (props2.properties.vendorID == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
         // Intel drivers don't support coopmat properly yet
         // Only RADV supports coopmat properly on AMD
         coopmat_support = false;
@@ -2727,7 +2823,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
     if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
         return ctx->device->pipeline_matmul_f32_f16;
     }
-    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16) {
+    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) {
         if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
             return ctx->device->pipeline_matmul_f16_f32.f16acc;
         }
@@ -2802,7 +2898,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
     if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
         return ctx->device->pipeline_matmul_id_f32;
     }
-    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16) {
+    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) {
         if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
             return ctx->device->pipeline_matmul_id_f16_f32.f16acc;
         }

From 4064c0e3b6c27440f5d12b7caaf90b4088c28c61 Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Thu, 12 Dec 2024 18:36:00 +0100
Subject: [PATCH 008/376] Vulkan: Use improved q4_k and q5_k dequant code in
 dequant shaders (#10798)

---
 .../vulkan-shaders/dequant_q4_k.comp          | 64 ++++++++++-------
 .../vulkan-shaders/dequant_q5_k.comp          | 68 +++++++++++--------
 2 files changed, 78 insertions(+), 54 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp
index 92acb7540..987f113a3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp
@@ -9,8 +9,8 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
 void main() {
     [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
+        const uint ib = gl_WorkGroupID.x * 256 + wgy;
+        if (ib >= p.M * p.K / QUANT_K) {
             return;
         }
 
@@ -20,37 +20,49 @@ void main() {
         const uint is = 2 * il;
         const uint n = 4;
 
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib].d.y);
 
-        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
+        const uint y_idx = ib * QUANT_K + 64 * il + n * ir;
         const uint qs_idx = 32*il + n * ir;
 
-        uint8_t sc;
-        uint8_t m;
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is] & 63);
-            m  = uint8_t(data_a[i].scales[is + 4] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
-        }
-        const FLOAT_TYPE d1 = dall * sc;
-        const FLOAT_TYPE m1 = dmin * m;
+        uint scidx0 = (is < 4) ? is : (is + 4);
+        uint scidx1 = (is < 4) ? is : (is - 4);
+        uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        uint scidxshift1 = (is < 4) ? 0 : 2;
+        uint mbidx0 = is + 4;
+        uint mbidx1 = (is < 4) ? is + 4 : is;
+        uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+        uint mbidxshift0 = (is < 4) ? 0 : 4;
+        uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        uint mbidxshift1 = (is < 4) ? 0 : 2;
+
+        uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+        uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * mbyte;
+
+        scidx0 = (is < 4) ? is + 1 : (is + 5);
+        scidx1 = (is < 4) ? is + 1 : (is - 3);
+        scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        scidxshift1 = (is < 4) ? 0 : 2;
+        mbidx0 = is + 5;
+        mbidx1 = (is < 4) ? is + 5 : is + 1;
+        mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+        mbidxshift0 = (is < 4) ? 0 : 4;
+        mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        mbidxshift1 = (is < 4) ? 0 : 2;
+
+        sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+        mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
 
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is + 1] & 63);
-            m  = uint8_t(data_a[i].scales[is + 5] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
-        }
         const FLOAT_TYPE d2 = dall * sc;
-        const FLOAT_TYPE m2 = dmin * m;
+        const FLOAT_TYPE m2 = dmin * mbyte;
 
         [[unroll]] for (uint l = 0; l < n; ++l) {
-            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
-            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
+            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[ib].qs[qs_idx + l] & 0xF) - m1);
+            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[ib].qs[qs_idx + l] >>  4) - m2);
         }
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp
index f314a76d1..6db5403b6 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp
@@ -9,8 +9,8 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 
 void main() {
     [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
+        const uint ib = gl_WorkGroupID.x * 256 + wgy;
+        if (ib >= p.M * p.K / QUANT_K) {
             return;
         }
 
@@ -19,40 +19,52 @@ void main() {
         const uint ir = tid % 16;
         const uint is = 2 * il;
 
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib].d.y);
 
-        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
+        const uint y_idx = ib * QUANT_K + 64 * il + 2 * ir;
         const uint qs_idx = 32*il + 2 * ir;
         const uint qh_idx = 2 * ir;
 
-        uint8_t sc;
-        uint8_t m;
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is] & 63);
-            m  = uint8_t(data_a[i].scales[is + 4] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
-        }
-        const FLOAT_TYPE d1 = dall * sc;
-        const FLOAT_TYPE m1 = dmin * m;
+        uint scidx0 = (is < 4) ? is : (is + 4);
+        uint scidx1 = (is < 4) ? is : (is - 4);
+        uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        uint scidxshift1 = (is < 4) ? 0 : 2;
+        uint mbidx0 = is + 4;
+        uint mbidx1 = (is < 4) ? is + 4 : is;
+        uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+        uint mbidxshift0 = (is < 4) ? 0 : 4;
+        uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        uint mbidxshift1 = (is < 4) ? 0 : 2;
+
+        uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+        uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * mbyte;
+
+        scidx0 = (is < 4) ? is + 1 : (is + 5);
+        scidx1 = (is < 4) ? is + 1 : (is - 3);
+        scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        scidxshift1 = (is < 4) ? 0 : 2;
+        mbidx0 = is + 5;
+        mbidx1 = (is < 4) ? is + 5 : is + 1;
+        mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+        mbidxshift0 = (is < 4) ? 0 : 4;
+        mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+        mbidxshift1 = (is < 4) ? 0 : 2;
+
+        sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+        mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
 
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is + 1] & 63);
-            m  = uint8_t(data_a[i].scales[is + 5] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
-        }
         const FLOAT_TYPE d2 = dall * sc;
-        const FLOAT_TYPE m2 = dmin * m;
+        const FLOAT_TYPE m2 = dmin * mbyte;
 
         const uint8_t hm1 = uint8_t(1 << (2 * il    ));
         const uint8_t hm2 = uint8_t(1 << (2 * il + 1));
-        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx    ] & 0xF) + (((data_a[i].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
-        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
-        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx    ]  >> 4) + (((data_a[i].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
-        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1]  >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
+        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx    ] & 0xF) + (((data_a[ib].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1] & 0xF) + (((data_a[ib].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx    ]  >> 4) + (((data_a[ib].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
+        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1]  >> 4) + (((data_a[ib].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
     }
 }

From cb13ef85a444eb52a3f1b82dce198ceb25606583 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Thu, 12 Dec 2024 19:02:49 +0100
Subject: [PATCH 009/376] remove CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS (#10797)

other windows build fixes
---
 examples/CMakeLists.txt              | 12 ++++-
 examples/gguf-split/gguf-split.cpp   |  4 +-
 examples/llama-bench/llama-bench.cpp | 10 ++---
 examples/retrieval/retrieval.cpp     |  2 +-
 examples/tokenize/tokenize.cpp       |  2 +-
 ggml/CMakeLists.txt                  |  7 +++
 ggml/src/CMakeLists.txt              |  5 ---
 ggml/src/ggml-cpu/amx/amx.cpp        |  2 +-
 ggml/src/ggml-cpu/ggml-cpu.c         |  5 +--
 ggml/src/ggml-impl.h                 |  8 ++--
 ggml/src/ggml-threading.h            |  6 ++-
 src/CMakeLists.txt                   |  7 ---
 src/llama.cpp                        |  6 +--
 tests/CMakeLists.txt                 | 65 +++++++++++++++-------------
 14 files changed, 76 insertions(+), 65 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 9210e9fea..b2df4f2fb 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -20,7 +20,12 @@ else()
     add_subdirectory(batched)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
-    add_subdirectory(gbnf-validator)
+
+    if (NOT WIN32)
+        # disabled on Windows because it uses internal functions not exported with LLAMA_API
+        add_subdirectory(gbnf-validator)
+    endif()
+
     add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
     add_subdirectory(gguf)
@@ -51,7 +56,10 @@ else()
         add_subdirectory(convert-llama2c-to-ggml)
         add_subdirectory(cvector-generator)
         add_subdirectory(export-lora)
-        add_subdirectory(quantize-stats)
+        if (NOT WIN32)
+            # disabled on Windows because it uses internal functions not exported with LLAMA_API
+            add_subdirectory(quantize-stats)
+        endif()
         add_subdirectory(llava)
         if (GGML_RPC)
             add_subdirectory(rpc)
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
index 7e62657e1..75f63f938 100644
--- a/examples/gguf-split/gguf-split.cpp
+++ b/examples/gguf-split/gguf-split.cpp
@@ -287,7 +287,7 @@ struct split_strategy {
     }
 
     void print_info() {
-        printf("n_split: %ld\n", ctx_outs.size());
+        printf("n_split: %zu\n", ctx_outs.size());
         int i_split = 0;
         for (auto & ctx_out : ctx_outs) {
             // re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
@@ -297,7 +297,7 @@ struct split_strategy {
                 total_size += ggml_nbytes(t);
             }
             total_size = total_size / 1000 / 1000; // convert to megabytes
-            printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            printf("split %05d: n_tensors = %d, total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
             i_split++;
         }
     }
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index bac606f47..2338ad106 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -1521,7 +1521,7 @@ int main(int argc, char ** argv) {
     for (const auto & inst : params_instances) {
         params_idx++;
         if (params.progress) {
-            fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count);
+            fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count);
         }
         // keep the same model between tests when possible
         if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
@@ -1573,14 +1573,14 @@ int main(int argc, char ** argv) {
         // warmup run
         if (t.n_prompt > 0) {
             if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
             }
             //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
             test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count);
             }
             test_gen(ctx, 1, t.n_threads);
         }
@@ -1592,14 +1592,14 @@ int main(int argc, char ** argv) {
 
             if (t.n_prompt > 0) {
                 if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count,
                             i + 1, params.reps);
                 }
                 test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
             }
             if (t.n_gen > 0) {
                 if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count,
                             i + 1, params.reps);
                 }
                 test_gen(ctx, t.n_gen, t.n_threads);
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index e78a8596d..23ff4db27 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -143,7 +143,7 @@ int main(int argc, char ** argv) {
         std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
         chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
     }
-    LOG_INF("Number of chunks: %ld\n", chunks.size());
+    LOG_INF("Number of chunks: %zu\n", chunks.size());
 
     llama_backend_init();
     llama_numa_init(params.numa);
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 12ad54256..c97e22724 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -394,7 +394,7 @@ int main(int raw_argc, char ** raw_argv) {
     }
 
     if (show_token_count) {
-        printf("Total number of tokens: %ld\n", tokens.size());
+        printf("Total number of tokens: %zu\n", tokens.size());
     }
     // silence valgrind
     llama_free(ctx);
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 1b3d98967..c91e93163 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -32,6 +32,13 @@ else()
     endif()
 endif()
 
+# remove the lib prefix on win32 mingw
+if (WIN32)
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_MODULE_PREFIX  "")
+endif()
+
 option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
 option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
 
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index a267a8b59..349f4c57f 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -194,11 +194,6 @@ endif()
 
 if (WIN32)
     add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-
-    if (BUILD_SHARED_LIBS)
-        # TODO: should not use this
-        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-    endif()
 endif()
 
 # ggml
diff --git a/ggml/src/ggml-cpu/amx/amx.cpp b/ggml/src/ggml-cpu/amx/amx.cpp
index b9074cb3a..5ec5263ce 100644
--- a/ggml/src/ggml-cpu/amx/amx.cpp
+++ b/ggml/src/ggml-cpu/amx/amx.cpp
@@ -122,7 +122,7 @@ static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_ty
 }
 
 static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * data = aligned_alloc(TENSOR_ALIGNMENT, size);
+    void * data = ggml_aligned_malloc(size);
     if (data == NULL) {
         fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
         return NULL;
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index ea17d6077..52881737c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -126,8 +126,7 @@ struct ggml_arm_arch_features_type {
 #endif
 #include <windows.h>
 
-
-#if !defined(__clang__)
+#if defined(_MSC_VER) && !defined(__clang__)
 #define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE))
 
 typedef volatile LONG atomic_int;
@@ -12945,7 +12944,7 @@ static thread_ret_t ggml_graph_compute_secondary_thread(void* data);
 #include "windows.h"
 
 // TODO: support > 64 CPUs
-bool ggml_thread_apply_affinity(bool * mask) {
+static bool ggml_thread_apply_affinity(bool * mask) {
     HANDLE    h = GetCurrentThread();
     uint64_t  bitmask = 0ULL;
 
diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index 00a1546a7..f961134ed 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -74,8 +74,8 @@ static inline int ggml_up(int n, int m) {
 //
 
 GGML_ATTRIBUTE_FORMAT(2, 3)
-void ggml_log_internal        (enum ggml_log_level level, const char * format, ...);
-void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data);
+GGML_API void ggml_log_internal        (enum ggml_log_level level, const char * format, ...);
+GGML_API void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data);
 
 #define GGML_LOG(...)       ggml_log_internal(GGML_LOG_LEVEL_NONE , __VA_ARGS__)
 #define GGML_LOG_INFO(...)  ggml_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
@@ -304,8 +304,8 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
 
 // Memory allocation
 
-void * ggml_aligned_malloc(size_t size);
-void ggml_aligned_free(void * ptr, size_t size);
+GGML_API void * ggml_aligned_malloc(size_t size);
+GGML_API void ggml_aligned_free(void * ptr, size_t size);
 
 // FP16 to FP32 conversion
 
diff --git a/ggml/src/ggml-threading.h b/ggml/src/ggml-threading.h
index ce975d880..dec2c8840 100644
--- a/ggml/src/ggml-threading.h
+++ b/ggml/src/ggml-threading.h
@@ -1,11 +1,13 @@
 #pragma once
 
+#include "ggml.h"
+
 #ifdef __cplusplus
 extern "C" {
 #endif
 
-void ggml_critical_section_start(void);
-void ggml_critical_section_end(void);
+GGML_API void ggml_critical_section_start(void);
+GGML_API void ggml_critical_section_end(void);
 
 #ifdef __cplusplus
 }
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index f3b3908b1..2d3ea0994 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -1,10 +1,3 @@
-# TODO: should not use this
-if (WIN32)
-    if (BUILD_SHARED_LIBS)
-        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-    endif()
-endif()
-
 llama_add_compile_flags()
 
 #
diff --git a/src/llama.cpp b/src/llama.cpp
index cae3f76ad..49ef5b78a 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1794,7 +1794,7 @@ private:
         DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
                                     NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
         if (!bufLen) {
-            ret = format("Win32 error code: %s", error_code);
+            ret = format("Win32 error code: %lx", error_code);
         } else {
             ret = lpMsgBuf;
             LocalFree(lpMsgBuf);
@@ -2132,7 +2132,7 @@ struct llama_mmap {
             HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
 
             // may fail on pre-Windows 8 systems
-            pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
+            pPrefetchVirtualMemory = (decltype(pPrefetchVirtualMemory))(void *) GetProcAddress(hKernel32, "PrefetchVirtualMemory");
 
             if (pPrefetchVirtualMemory) {
                 // advise the kernel to preload the mapped memory
@@ -21577,7 +21577,7 @@ float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
                 throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
             }
         } else if ((size_t) i >= ctx->output_ids.size()) {
-            throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+            throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size()));
         } else {
             j = ctx->output_ids[i];
         }
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 82373ff4e..daeed4564 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -84,38 +84,50 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
 
-# build test-tokenizer-1-bpe target once and add many tests
-add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
-target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
-install(TARGETS test-tokenizer-1-bpe RUNTIME)
 
-# TODO: disabled due to slowness
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+if (NOT WIN32)
+    # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API
+    llama_target_and_test(test-sampling.cpp)
+    llama_target_and_test(test-grammar-parser.cpp)
+    llama_target_and_test(test-grammar-integration.cpp)
+    llama_target_and_test(test-llama-grammar.cpp)
+    # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
+    if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+        llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+        target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+    endif()
 
-# build test-tokenizer-1-spm target once and add many tests
-add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp)
-target_link_libraries(test-tokenizer-1-spm PRIVATE common)
-install(TARGETS test-tokenizer-1-spm RUNTIME)
 
-llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
-#llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-baichuan  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
+    # build test-tokenizer-1-bpe target once and add many tests
+    add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
+    target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
+    install(TARGETS test-tokenizer-1-bpe RUNTIME)
+
+    # TODO: disabled due to slowness
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+    #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+
+    # build test-tokenizer-1-spm target once and add many tests
+    add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp)
+    target_link_libraries(test-tokenizer-1-spm PRIVATE common)
+    install(TARGETS test-tokenizer-1-spm RUNTIME)
+
+    llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
+    #llama_test(test-tokenizer-1-spm  NAME test-tokenizer-1-baichuan  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
+
+    # llama_target_and_test(test-double-float.cpp) # SLOW
+endif()
 
-# llama_target_and_test(test-double-float.cpp) # SLOW
 llama_target_and_test(test-log.cpp)
 llama_target_and_test(test-arg-parser.cpp)
-llama_target_and_test(test-sampling.cpp)
 llama_target_and_test(test-chat-template.cpp)
 
-llama_target_and_test(test-grammar-parser.cpp)
-llama_target_and_test(test-grammar-integration.cpp)
-llama_target_and_test(test-llama-grammar.cpp)
 # llama_target_and_test(test-opt.cpp) # SLOW
 llama_target_and_test(test-backend-ops.cpp)
 
@@ -130,11 +142,6 @@ if (NOT GGML_BACKEND_DL)
     llama_target_and_test(test-rope.cpp)
 endif()
 
-# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
-if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
-    llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
-    target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
-endif()
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)

From 8faa1d4dd42f6cb26088ce7f5bbca5996b921685 Mon Sep 17 00:00:00 2001
From: a3sh <38979186+A3shTnT@users.noreply.github.com>
Date: Fri, 13 Dec 2024 02:09:50 +0800
Subject: [PATCH 010/376] CUDA: faster non-contiguous concat (#10760)

* faster uncontiguous concat

* Use a lambda to avoid code duplication

Co-authored-by: Diego Devesa <slarengh@gmail.com>

* Update ggml/src/ggml-cuda/concat.cu

* add constexpr  and static assert

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 ggml/src/ggml-cuda/concat.cu | 53 ++++++++++++++++++++++++++----------
 1 file changed, 39 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu
index dac10ec36..2f42b8a95 100644
--- a/ggml/src/ggml-cuda/concat.cu
+++ b/ggml/src/ggml-cuda/concat.cu
@@ -94,7 +94,9 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, int n
 }
 
 // non-contiguous kernel (slow)
-static __global__ void concat_f32_non_cont(
+template <int dim>
+static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
+    concat_f32_non_cont(
         const char * src0,
         const char * src1,
               char * dst,
@@ -121,22 +123,28 @@ static __global__ void concat_f32_non_cont(
           uint64_t   nb0,
           uint64_t   nb1,
           uint64_t   nb2,
-          uint64_t   nb3,
-          int32_t   dim) {
+          uint64_t   nb3){
+    static_assert(dim >= 0 && dim <= 3);
+
     const int64_t i3 = blockIdx.z;
     const int64_t i2 = blockIdx.y;
     const int64_t i1 = blockIdx.x;
 
-    int64_t o[4] = {0, 0, 0, 0};
-    o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
-
     const float * x;
 
-    for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
+    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
         if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
             x = (const float *)(src0 + (i3       )*nb03 + (i2       )*nb02 + (i1       )*nb01 + (i0       )*nb00);
         } else {
-            x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
+            if constexpr (dim == 0) {
+                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + i1 * nb11 + (i0 - ne00) * nb10);
+            } else if constexpr (dim == 1) {
+                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + (i1 - ne01) * nb11 + i0 * nb10);
+            } else if constexpr (dim == 2) {
+                x = (const float *) (src1 + i3 * nb13 + (i2 - ne02) * nb12 + i1 * nb11 + i0 * nb10);
+            } else if constexpr (dim == 3) {
+                x = (const float *) (src1 + (i3 - ne03) * nb13 + i2 * nb12 + i1 * nb11 + i0 * nb10);
+            }
         }
 
         float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@@ -182,15 +190,32 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         }
     } else {
         dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
-        concat_f32_non_cont<<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
-                (const char *)src0->data,
-                (const char *)src1->data,
-                (      char *)dst->data,
+        auto launch_kernel = [&](auto dim) {
+            concat_f32_non_cont<dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
+                (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
                 src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
                 src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
                 src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
                 src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
-                dst->ne[0],  dst->ne[1],  dst->ne[2],  dst->ne[3],
-                dst->nb[0],  dst->nb[1],  dst->nb[2],  dst->nb[3], dim);
+                dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+                dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3]);
+        };
+        switch (dim) {
+            case 0:
+                launch_kernel(std::integral_constant<int, 0>{});
+                break;
+            case 1:
+                launch_kernel(std::integral_constant<int, 1>{});
+                break;
+            case 2:
+                launch_kernel(std::integral_constant<int, 2>{});
+                break;
+            case 3:
+                launch_kernel(std::integral_constant<int, 3>{});
+                break;
+            default:
+                GGML_ABORT("Invalid dim: %d", dim);
+                break;
+        }
     }
 }

From 274ec65af6e54039eb95cb44904af5c945dca1fa Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 12 Dec 2024 20:52:28 +0100
Subject: [PATCH 011/376] contrib : add ngxson as codeowner (#10804)

---
 CODEOWNERS | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 88ab6de4f..adeba5395 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,3 +1,5 @@
 # collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
 
-ci/ @ggerganov
+/ci/ @ggerganov
+/.devops/ @ngxson
+/examples/server/ @ngxson

From adffa6ffd59997da59f62b72d2b79fc37e085e84 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 12 Dec 2024 22:53:05 +0100
Subject: [PATCH 012/376] common : improve -ctv -ctk CLI arguments (#10806)

* common : improve ctv ctk cli argument

* regenerate docs

* even better approach

* use std::vector
---
 common/arg.cpp            | 51 ++++++++++++++++++++++++++++++++++-----
 common/common.cpp         | 36 ++-------------------------
 common/common.h           |  4 +--
 examples/CMakeLists.txt   |  1 +
 examples/server/README.md | 19 ++++++++-------
 5 files changed, 60 insertions(+), 51 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index b27567f3b..39bc874c8 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -145,6 +145,35 @@ static void common_params_handle_model_default(common_params & params) {
     }
 }
 
+const std::vector<ggml_type> kv_cache_types = {
+    GGML_TYPE_F32,
+    GGML_TYPE_F16,
+    GGML_TYPE_BF16,
+    GGML_TYPE_Q8_0,
+    GGML_TYPE_Q4_0,
+    GGML_TYPE_Q4_1,
+    GGML_TYPE_IQ4_NL,
+    GGML_TYPE_Q5_0,
+    GGML_TYPE_Q5_1,
+};
+
+static ggml_type kv_cache_type_from_str(const std::string & s) {
+    for (const auto & type : kv_cache_types) {
+        if (ggml_type_name(type) == s) {
+            return type;
+        }
+    }
+    throw std::runtime_error("Unsupported cache type: " + s);
+}
+
+static std::string get_all_kv_cache_types() {
+    std::ostringstream msg;
+    for (const auto & type : kv_cache_types) {
+        msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", ");
+    }
+    return msg.str();
+}
+
 //
 // CLI argument parsing functions
 //
@@ -1174,18 +1203,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
-        string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+        string_format(
+            "KV cache data type for K\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_k)
+        ),
         [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_k = value;
+            params.cache_type_k = kv_cache_type_from_str(value);
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
     add_opt(common_arg(
         {"-ctv", "--cache-type-v"}, "TYPE",
-        string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+        string_format(
+            "KV cache data type for V\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_v)
+        ),
         [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_v = value;
+            params.cache_type_v = kv_cache_type_from_str(value);
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
     add_opt(common_arg(
diff --git a/common/common.cpp b/common/common.cpp
index 6143516d2..3cd43ecdf 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1015,38 +1015,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     return mparams;
 }
 
-static ggml_type kv_cache_type_from_str(const std::string & s) {
-    if (s == "f32") {
-        return GGML_TYPE_F32;
-    }
-    if (s == "f16") {
-        return GGML_TYPE_F16;
-    }
-    if (s == "bf16") {
-        return GGML_TYPE_BF16;
-    }
-    if (s == "q8_0") {
-        return GGML_TYPE_Q8_0;
-    }
-    if (s == "q4_0") {
-        return GGML_TYPE_Q4_0;
-    }
-    if (s == "q4_1") {
-        return GGML_TYPE_Q4_1;
-    }
-    if (s == "iq4_nl") {
-        return GGML_TYPE_IQ4_NL;
-    }
-    if (s == "q5_0") {
-        return GGML_TYPE_Q5_0;
-    }
-    if (s == "q5_1") {
-        return GGML_TYPE_Q5_1;
-    }
-
-    throw std::runtime_error("Unsupported cache type: " + s);
-}
-
 struct llama_context_params common_context_params_to_llama(const common_params & params) {
     auto cparams = llama_context_default_params();
 
@@ -1081,8 +1049,8 @@ struct llama_context_params common_context_params_to_llama(const common_params &
         cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
     }
 
-    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
-    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
+    cparams.type_k = params.cache_type_k;
+    cparams.type_v = params.cache_type_v;
 
     return cparams;
 }
diff --git a/common/common.h b/common/common.h
index 95d20401d..0481720ab 100644
--- a/common/common.h
+++ b/common/common.h
@@ -286,8 +286,8 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
 
-    std::string cache_type_k = "f16"; // KV cache data type for the K
-    std::string cache_type_v = "f16"; // KV cache data type for the V
+    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
+    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index b2df4f2fb..21b31392e 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -51,6 +51,7 @@ else()
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
     add_subdirectory(tokenize)
+    add_subdirectory(gen-docs)
     if (NOT GGML_BACKEND_DL)
         # these examples use the backends directly and cannot be built with dynamic loading
         add_subdirectory(convert-llama2c-to-ggml)
diff --git a/examples/server/README.md b/examples/server/README.md
index 686be7baf..a9443e56b 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -62,8 +62,8 @@ The project is under active development, and we are [looking for feedback and co
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
 | `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
-| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
-| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
+| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
+| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
@@ -138,6 +138,7 @@ The project is under active development, and we are [looking for feedback and co
 | -------- | ----------- |
 | `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
 | `-sp, --special` | special tokens output enabled (default: false) |
+| `--no-warmup` | skip warming up the model with an empty run |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
@@ -146,7 +147,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
-| `--no-webui` | disable the Web UI<br/>(env: LLAMA_ARG_NO_WEBUI) |
+| `--no-webui` | Disable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_NO_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -164,13 +165,13 @@ The project is under active development, and we are [looking for feedback and co
 | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>list of built-in templates:<br/>chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
-| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16) |
-| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5) |
-| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9) |
-| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model) |
+| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
+| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
+| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
+| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
 | `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model |
-| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused) |
+| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
 
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.

From d583cd03f663701858ba152a334cbb467fabe342 Mon Sep 17 00:00:00 2001
From: Karol Kontny <82021046+kkontny@users.noreply.github.com>
Date: Fri, 13 Dec 2024 01:04:19 +0100
Subject: [PATCH 013/376] ggml : Fix compilation issues on ARM platform when
 building without fp16 (#10811)

---
 ggml/src/ggml-cpu/ggml-cpu.c | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 52881737c..92df6fdda 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -454,21 +454,21 @@ const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type
 #define GGML_F32x4_ADD          vaddq_f32
 #define GGML_F32x4_MUL          vmulq_f32
 #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
-#define GGML_F32x4_REDUCE(res, x)                  \
-{                                                  \
-    int offset = GGML_F32_ARR >> 1;                \
-    for (int i = 0; i < offset; ++i) {             \
-        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \
-    }                                              \
-    (res) = GGML_F32x4_REDUCE_ONE((x)[0]);         \
+#define GGML_F32x4_REDUCE(res, x)                       \
+{                                                       \
+    int offset = GGML_F32_ARR >> 1;                     \
+    for (int i = 0; i < offset; ++i) {                  \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]);      \
+    }                                                   \
+    offset >>= 1;                                       \
+    for (int i = 0; i < offset; ++i) {                  \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]);      \
+    }                                                   \
+    offset >>= 1;                                       \
+    for (int i = 0; i < offset; ++i) {                  \
+        (x)[i] = vaddq_f32((x)[i], (x)[offset+i]);      \
+    }                                                   \
+    (res) = (ggml_float) GGML_F32x4_REDUCE_ONE((x)[0]); \
 }
 
 #define GGML_F32_VEC        GGML_F32x4
@@ -2395,7 +2395,7 @@ static void ggml_init_arm_arch_features(void) {
     uint32_t hwcap2 = getauxval(AT_HWCAP2);
 
     ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD);
-    ggml_arm_arch_features.has_dotprod = !!(hwcap && HWCAP_ASIMDDP);
+    ggml_arm_arch_features.has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
     ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM);
     ggml_arm_arch_features.has_sve  = !!(hwcap & HWCAP_SVE);
 

From 83ed24a97b500ccdb32b90b94e6f9621ad8db79e Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Fri, 13 Dec 2024 12:12:15 +0530
Subject: [PATCH 014/376] SYCL: Reduce most of the compiler warnings (#10748)

* Try to reduce some unused and typecast warnings

* Reduce compiler warnings step 2

* add a newline at the end of the file

* Initialize nreduce as size_t

* [SYCL] Remove pragma directives from mmq.cpp

* SYCL: mmq add condition to prevent blocks_per_tile_x_row variable from becoming 0

* SYCL softmax: Initialize nreduce as size_t

* ggml-sycl.cpp: fix some trailing whitespaces

* SYCL: remove the unused variables instead of commenting it out

* SYCL poo2d kernel: set NAN for invalid pooling op

* SYCL gemm.hpp: remove pragma directives

* SYCL gemm.hpp: use const cast to properly support dnnl::memory

* SYCL: wkv6 remove a comment

* SYCL: clean comments step 2

* SYCL: clean comments and variables step 3

* SYCL: Use GGML_UNUSED for unused variables

* SYCL: remove extra empty lines and a comment

* Remove TODO

* cleanup spaces

* add a stdout for unsupported op

* use sycl printf over fprintf

* remove prints for CI

* SYCL ggml-sycl: pool2D use sycl::nan and remove if-else block

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
---
 ggml/src/ggml-sycl/common.cpp       |  13 +--
 ggml/src/ggml-sycl/common.hpp       |   1 +
 ggml/src/ggml-sycl/concat.cpp       |   4 +-
 ggml/src/ggml-sycl/convert.cpp      |   2 +-
 ggml/src/ggml-sycl/dmmv.cpp         |  10 +-
 ggml/src/ggml-sycl/dpct/helper.hpp  |   2 +-
 ggml/src/ggml-sycl/element_wise.cpp | 141 ++++++++++++++++------------
 ggml/src/ggml-sycl/gemm.hpp         |   8 +-
 ggml/src/ggml-sycl/ggml-sycl.cpp    | 134 ++++++++++++++------------
 ggml/src/ggml-sycl/im2col.cpp       |   5 +-
 ggml/src/ggml-sycl/mmq.cpp          |  12 +--
 ggml/src/ggml-sycl/mmvq.cpp         |  26 ++---
 ggml/src/ggml-sycl/norm.cpp         |   7 +-
 ggml/src/ggml-sycl/rope.cpp         |   7 +-
 ggml/src/ggml-sycl/softmax.cpp      |  14 +--
 ggml/src/ggml-sycl/tsembd.cpp       |   1 +
 ggml/src/ggml-sycl/wkv6.cpp         |   5 +-
 17 files changed, 205 insertions(+), 187 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
index 97ab2003c..a9ee40491 100644
--- a/ggml/src/ggml-sycl/common.cpp
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -11,6 +11,7 @@
 //
 
 #include "common.hpp"
+#include "ggml-impl.h"
 
 int get_current_device_id() {
   return dpct::dev_mgr::instance().current_device_id();
@@ -28,11 +29,7 @@ void* ggml_sycl_host_malloc(size_t size) try {
 
   if (err != 0) {
     // clear the error
-    fprintf(
-        stderr,
-        "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-        size / 1024.0 / 1024.0,
-        "syclGetErrorString is not supported");
+    GGML_LOG_ERROR("WARNING: failed to allocate %.2f MB of pinned memory: %s\n", size / 1024.0 / 1024.0,    "syclGetErrorString is not supported");
     return nullptr;
   }
 
@@ -66,18 +63,12 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
 void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                  const ggml_tensor *src1, ggml_tensor *dst,
                                  const ggml_sycl_op_flatten_t op) try {
-    const int64_t nrows0 = ggml_nrows(src0);
 
     const bool use_src1 = src1 != nullptr;
-    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
     GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
-    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
-    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
-
     // dd = data device
     float * src0_ddf = (float *) src0->data;
     float * src1_ddf = use_src1 ? (float *) src1->data : nullptr;
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 4549fa5e9..c1582f610 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -626,6 +626,7 @@ struct bin_bcast_sycl {
                     });
             }
         }
+        GGML_UNUSED(ctx);
     }
 };
 
diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp
index c90c452d8..a240968ad 100644
--- a/ggml/src/ggml-sycl/concat.cpp
+++ b/ggml/src/ggml-sycl/concat.cpp
@@ -47,7 +47,7 @@ static void concat_f32_dim1(const float *x, const float *y, float *dst,
   // operation
   int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
                    item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-  if (item_ct1.get_group(1) < ne01) { // src0
+  if (item_ct1.get_group(1) < (size_t) ne01) { // src0
     int offset_src =
         nidx + item_ct1.get_group(1) * ne0 + item_ct1.get_group(0) * ne0 * ne01;
     dst[offset_dst] = x[offset_src];
@@ -70,7 +70,7 @@ static void concat_f32_dim2(const float *x, const float *y, float *dst,
   // operation
   int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
                    item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-  if (item_ct1.get_group(0) < ne02) { // src0
+  if (item_ct1.get_group(0) < (size_t) ne02) { // src0
     int offset_src = nidx + item_ct1.get_group(1) * ne0 +
                      item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
     dst[offset_dst] = x[offset_src];
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index 5fd15e6cd..05b01db2d 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -424,7 +424,7 @@ static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y,
     const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
 
     // make each work-item deal with more elements since sycl global range can not exceed max int
-    const src_t * x = (src_t *) vx;
+    const src_t * x = (const src_t *) vx;
     for (int64_t i = global_id; i < k; i += work_group_size * item_ct1.get_group_range(2)) {
         y[i] = x[i];
     }
diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp
index 0c3dfaa37..0d097357c 100644
--- a/ggml/src/ggml-sycl/dmmv.cpp
+++ b/ggml/src/ggml-sycl/dmmv.cpp
@@ -1015,9 +1015,9 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
             break;
     }
 
-    (void) src1;
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_ncols);
+    GGML_UNUSED(src1_padded_row_size);
 }
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index d1b5dd87c..e167948e7 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -1237,7 +1237,7 @@ namespace dpct
 
             std::map<byte_t *, allocation>::iterator get_map_iterator(const void *ptr)
             {
-                auto it = m_map.upper_bound((byte_t *)ptr);
+                auto it = m_map.upper_bound(const_cast<byte_t *>(reinterpret_cast<const byte_t *>(ptr)));
                 if (it == m_map.end())
                 {
                     // Not a virtual pointer.
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index e5cd736eb..d05a51f80 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -237,7 +237,7 @@ void upscale_f32(const float  *x, float *dst, const int nb00, const int nb01,
     int i02 = i12 / sf2;
     int i03 = i13 / sf3;
 
-    dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
+    dst[index] = *(const float *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
 }
 
 void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
@@ -251,8 +251,7 @@ void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const i
     // operation
     int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
                      item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
-        item_ct1.get_group(0) < ne02) {
+    if (nidx < ne00 && item_ct1.get_group(1) < (size_t) ne01 && item_ct1.get_group(0) < (size_t) ne02) {
         int offset_src = nidx + item_ct1.get_group(1) * ne00 +
                          item_ct1.get_group(0) * ne00 * ne01;
             dst[offset_dst] = x[offset_src];
@@ -520,9 +519,10 @@ inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -535,9 +535,10 @@ inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                     const ggml_tensor *src1, ggml_tensor *dst,
@@ -550,9 +551,10 @@ inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_
 
     gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -564,9 +566,10 @@ inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
     tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -579,9 +582,10 @@ inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -595,9 +599,10 @@ inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml
 
     hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -610,9 +615,10 @@ inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_t
 
     hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -625,9 +631,10 @@ inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     exp_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -640,9 +647,10 @@ inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     log_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -655,9 +663,10 @@ inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_ten
 
     sigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -670,9 +679,10 @@ inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     sqrt_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -685,9 +695,10 @@ inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     sin_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -700,9 +711,10 @@ inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     cos_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -715,9 +727,10 @@ inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     step_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -730,9 +743,10 @@ inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     neg_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -749,9 +763,10 @@ inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_
 
     leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -764,9 +779,10 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -787,9 +803,10 @@ inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_ten
                      dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3,
                      main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -805,9 +822,10 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor
         src0->ne[0], src0->ne[1], src0->ne[2],
         dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -827,7 +845,8 @@ inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
 
-    (void) dst;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
diff --git a/ggml/src/ggml-sycl/gemm.hpp b/ggml/src/ggml-sycl/gemm.hpp
index 2ad9b36f4..3f0f34ad6 100644
--- a/ggml/src/ggml-sycl/gemm.hpp
+++ b/ggml/src/ggml-sycl/gemm.hpp
@@ -51,8 +51,8 @@ public:
         const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
         const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
         const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
-        auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
-        auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
+        auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
+        auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));
         auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
         auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
 
@@ -79,8 +79,8 @@ public:
         const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
         const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
         const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
-        auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
-        auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
+        auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
+        auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));
         auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
         auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
 
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index ae3baedc7..6b9f0b0d9 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -47,7 +47,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
 
     info.device_count = dpct::dev_mgr::instance().device_count();
     if (info.device_count == 0) {
-        GGML_LOG_ERROR("%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__);
+        GGML_LOG_ERROR("%s: failed to initialize: %s\n", GGML_SYCL_NAME, __func__);
         return info;
     }
 
@@ -64,7 +64,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
 #else
     GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__);
 #endif
-    GGML_LOG_INFO("%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count);
+    GGML_LOG_INFO("%s: found %d %s devices:\n", __func__, info.device_count, GGML_SYCL_NAME);
 
     for (int i = 0; i < info.device_count; ++i) {
         info.devices[i].vmm = 0;
@@ -137,7 +137,6 @@ void ggml_backend_sycl_print_sycl_devices() {
 
     for (int id = 0; id < device_count; ++id) {
       sycl::device device = dpct::dev_mgr::instance().get_device(id);
-      sycl::backend backend = device.get_backend();
       std::string backend_type = get_device_backend_and_type(device);
       int type_id = DeviceNums[backend_type]++;
       std::stringstream device_type;
@@ -420,13 +419,11 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
         return true;
     }
     return false;
+    GGML_UNUSED(buffer);
+} catch (const sycl::exception & exc) {
+    std::cerr << exc.what() << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl;
+    std::exit(1);
 }
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
 
 static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
                                            uint8_t value) try {
@@ -1092,10 +1089,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
     ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {};
     size_t pool_size = 0;
 
-    explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) :
-        qptr(qptr_),
-        device(device_) {
-    }
+    explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : device(device_), qptr(qptr_) {}
 
     ~ggml_sycl_pool_leg() {
         for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
@@ -1238,7 +1232,7 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy,
         zeros[i] = 0.f;
         qzeros[i] = 0;
     }
-    const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
+    const TC xi = ix < kx ? *(const TC *)&x[iy * kx + ix] : zeros;
     float sum = xi[0];
     float amax = sycl::fabs(xi[0]);
 #pragma unroll
@@ -1799,6 +1793,9 @@ static  void pool2d_nchw_kernel(
         switch (op) {
             case GGML_OP_POOL_AVG: res = 0; break;
             case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+            default:
+                res      = (To) sycl::nan(uint32_t(0));
+                break;
         }
 
         for (int i = bh; i < eh; i += 1) {
@@ -1817,6 +1814,9 @@ static  void pool2d_nchw_kernel(
                 switch (op) {
                     case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
                     case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
+                    default:
+                        res = (To) sycl::nan(uint32_t(0));
+                        break;
                 }
             }
         }
@@ -1855,7 +1855,8 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
                                  s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
                          });
 
-    (void) dst;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(ctx);
 }
 
 template <typename src0_t>
@@ -1893,10 +1894,10 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
             });
     }
 
-    (void) dst;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(ctx);
 }
 
-
 static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
                                    const int ky, const int kx_padded,
                                    queue_ptr stream) {
@@ -2464,8 +2465,8 @@ static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tens
 
     ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_repeat>>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
 
-    (void) src1;
-    (void) src1_d;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src1_d);
 }
 
 
@@ -2484,17 +2485,18 @@ inline void ggml_sycl_op_mul_mat_sycl(
     const int64_t ne00 = src0->ne[0];
     const int64_t ne10 = src1->ne[0];
 
-    const int64_t ne0 = dst->ne[0];
 
     const int64_t row_diff = row_high - row_low;
 
     int id;
     SYCL_CHECK(
         CHECK_TRY_ERROR(id = get_current_device_id()));
-
+#if !GGML_SYCL_DNNL
+    const int64_t ne0 = dst->ne[0];
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
     int ldc = id == ctx.device ? ne0 : row_diff;
+#endif
 
 #ifdef GGML_SYCL_F16
     bool use_fp16 = true;  // TODO(Yu) SYCL capability check
@@ -2531,9 +2533,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
                                          : src1_as_f16.get();
         ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
 
-        const sycl::half alpha_f16 = 1.0f;
-        const sycl::half beta_f16 = 0.0f;
 #if !GGML_SYCL_DNNL
+        const sycl::half alpha_f16 = 1.0f;
+        const sycl::half beta_f16  = 0.0f;
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
             *stream, oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
@@ -2570,9 +2572,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
         const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
         const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
 
-        const float alpha = 1.0f;
-        const float beta = 0.0f;
 #if !GGML_SYCL_DNNL
+        const float alpha = 1.0f;
+        const float beta  = 0.0f;
 #    ifdef GGML_SYCL_NVIDIA
         SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
             oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ *stream }, oneapi::mkl::transpose::trans,
@@ -2590,9 +2592,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
             src0_ddf_i, DnnlGemmWrapper::to_dt<float>(), dst_dd_i, DnnlGemmWrapper::to_dt<float>());
 #endif
     }
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_padded_row_size;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_padded_row_size);
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -2638,8 +2640,9 @@ static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tens
                                item_ct1);
         });
 
-    (void) src1;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2654,9 +2657,10 @@ inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor
 
     sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2673,9 +2677,10 @@ inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_te
 
     sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2694,9 +2699,10 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_ten
 
     argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2713,9 +2719,10 @@ inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tens
 
     argmax_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2735,9 +2742,10 @@ inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const gg
 
     diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -2758,9 +2766,10 @@ inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tenso
     */
     SYCL_CHECK(0);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -2783,9 +2792,10 @@ inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tenso
     */
     SYCL_CHECK(0);
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
 
 static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) {
@@ -2862,7 +2872,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
 
     ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
 
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
@@ -3289,7 +3298,6 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
-    const int64_t ne_dst = ggml_nelements(dst);
 
     SYCL_CHECK(ggml_sycl_set_device(ctx.device));
     queue_ptr main_stream = ctx.stream();;
@@ -3397,6 +3405,7 @@ catch (sycl::exception const &exc) {
 
 inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
     // TODO: accuracy issues in MMQ
+    GGML_UNUSED(type);
     return false;
 }
 
@@ -3772,7 +3781,7 @@ static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
         GGML_ABORT("fatal error");
     }
 
-    (void) dst;
+    GGML_UNUSED(dst);
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -3783,7 +3792,7 @@ catch (sycl::exception const &exc) {
 static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     // TODO: why do we pass dst as src1 here?
     ggml_sycl_cpy(ctx, src0, dst, nullptr);
-    (void) src1;
+    GGML_UNUSED(src1);
 }
 
 static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -3828,13 +3837,16 @@ static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor
 }
 
 static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    (void) src0;
-    (void) src1;
-    (void) dst;
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(ctx);
 }
 
 void ggml_sycl_set_main_device(const int main_device) try {
-    if (dpct::get_current_device_id() == main_device) return;
+    if (dpct::get_current_device_id() == static_cast<unsigned int> (main_device)) {
+        return;
+    }
     check_allow_gpu_index(main_device);
     dpct::select_device(main_device);
 
@@ -4202,6 +4214,7 @@ try
 {
     ggml_backend_sycl_context *sycl_ctx =
         (ggml_backend_sycl_context *)backend->context;
+
     sycl::event *sycl_event = static_cast<sycl::event *>(event->context);
 
     const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0);
@@ -4216,7 +4229,7 @@ catch (sycl::exception const &exc)
 }
 
 static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try {
-    ggml_backend_sycl_context* sycl_ctx = static_cast<ggml_backend_sycl_context*>(backend->context);
+
     sycl::event* sycl_event = static_cast<sycl::event*>(event->context);
 
     if (ggml_backend_is_sycl(backend)) {
@@ -4624,6 +4637,7 @@ static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, cons
     // SYCL doesn't support registering host memory, left here for reference
     // "ggml_backend_register_host_buffer"
     // "ggml_backend_unregister_host_buffer"
+    GGML_UNUSED(name);
     return nullptr;
 }
 
diff --git a/ggml/src/ggml-sycl/im2col.cpp b/ggml/src/ggml-sycl/im2col.cpp
index 6a0a0fcd0..6146a99ed 100644
--- a/ggml/src/ggml-sycl/im2col.cpp
+++ b/ggml/src/ggml-sycl/im2col.cpp
@@ -120,6 +120,7 @@ void ggml_sycl_op_im2col(
         im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
     }
 
-    (void) src0;
-    (void) src0_dd;
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src0_dd);
+    GGML_UNUSED(ctx);
 }
diff --git a/ggml/src/ggml-sycl/mmq.cpp b/ggml/src/ggml-sycl/mmq.cpp
index e952533d3..8ea82c940 100644
--- a/ggml/src/ggml-sycl/mmq.cpp
+++ b/ggml/src/ggml-sycl/mmq.cpp
@@ -813,7 +813,7 @@ load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql,
         x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    constexpr int blocks_per_tile_x_row = QI4_K > WARP_SIZE ? 1 : WARP_SIZE / QI4_K; // == 1 if QK_K == 256
     const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
 
 #pragma unroll
@@ -961,7 +961,7 @@ load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql,
         x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    constexpr int blocks_per_tile_x_row = QI5_K > WARP_SIZE ? 1 : WARP_SIZE / QI5_K; // == 1 if QK_K == 256
     const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
 
 #pragma unroll
@@ -1109,7 +1109,7 @@ load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql,
                                                  dpct::sub_sat());
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    constexpr int blocks_per_tile_x_row = QI6_K > WARP_SIZE ? 1 : WARP_SIZE / QI6_K; // == 1 if QK_K == 256
     const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
     float * x_dmf = (float *) x_dm;
 
@@ -3020,9 +3020,9 @@ void ggml_sycl_op_mul_mat_q(
             break;
     }
 
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
 }
 catch (sycl::exception const &exc) {
   std::cerr << exc.what() << "Exception caught at file:" << __FILE__
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
index 7b10cf688..221f65c21 100644
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -753,11 +753,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
     {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
+        stream->submit([&](sycl::handler & cgh) {
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
@@ -780,9 +776,6 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
     {
 
         stream->submit([&](sycl::handler &cgh) {
-            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
@@ -805,9 +798,6 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
     {
 
         stream->submit([&](sycl::handler &cgh) {
-            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
@@ -830,8 +820,6 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
     {
 
         stream->submit([&](sycl::handler &cgh) {
-            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
-
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
@@ -854,9 +842,6 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
     {
 
         stream->submit([&](sycl::handler &cgh) {
-            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
@@ -954,7 +939,7 @@ void ggml_sycl_op_mul_mat_vec_q(
     const size_t q8_1_bs = QK8_1;
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
+
     for (int i = 0; i < src1_ncols; i++)
     {
         const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
@@ -1023,7 +1008,8 @@ void ggml_sycl_op_mul_mat_vec_q(
             break;
         }
     }
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+    GGML_UNUSED(ctx);
 }
diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
index 72d8fdb87..9cf2be155 100644
--- a/ggml/src/ggml-sycl/norm.cpp
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -31,7 +31,7 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep
         */
         item_ct1.barrier(sycl::access::fence_space::local_space);
         mean_var = 0.f;
-        int nreduce = nwarps / WARP_SIZE;
+        size_t nreduce = nwarps / WARP_SIZE;
         for (size_t i = 0; i < nreduce; i += 1)
         {
             mean_var += s_sum[lane_id + i * WARP_SIZE];
@@ -55,7 +55,7 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
     const int nthreads = item_ct1.get_local_range(2);
     const int nwarps = nthreads / WARP_SIZE;
     start += item_ct1.get_local_id(2);
-    int nreduce = nwarps / WARP_SIZE;
+    size_t nreduce = nwarps / WARP_SIZE;
 
     if (end >= ne_elements) {
         end = ne_elements;
@@ -163,7 +163,7 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa
         converged control flow. You may need to adjust the code.
         */
         item_ct1.barrier(sycl::access::fence_space::local_space);
-        int nreduce = nwarps / WARP_SIZE;
+        size_t nreduce = nwarps / WARP_SIZE;
         tmp = 0.f;
         for (size_t i = 0; i < nreduce; i += 1)
         {
@@ -352,6 +352,7 @@ void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor*
     (void)src1;
     (void)dst;
     (void)src1_dd;
+    GGML_UNUSED(ctx);
 }
 
 void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp
index 1f06f78fa..1244b231a 100644
--- a/ggml/src/ggml-sycl/rope.cpp
+++ b/ggml/src/ggml-sycl/rope.cpp
@@ -269,7 +269,8 @@ void ggml_sycl_op_rope(
         }
     }
 
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_dd);
+    GGML_UNUSED(ctx);
 }
diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index 17a542e49..a9b3fce0d 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -16,7 +16,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
     const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
     const int nthreads = block_size;
     const int nwarps = nthreads / WARP_SIZE;
-    int nreduce = nwarps / WARP_SIZE;
+    size_t nreduce = nwarps / WARP_SIZE;
     float slope = 1.0f;
 
     // ALiBi
@@ -53,8 +53,9 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
     if (block_size > WARP_SIZE) {
         if (warp_id == 0) {
             buf[lane_id] = -INFINITY;
-            for (size_t i = 1; i < nreduce; i += 1)
+            for (size_t i = 1; i < nreduce; i += 1) {
                 buf[lane_id + i * WARP_SIZE] = -INFINITY;
+            }
         }
         item_ct1.barrier(sycl::access::fence_space::local_space);
 
@@ -63,8 +64,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         }
         item_ct1.barrier(sycl::access::fence_space::local_space);
         max_val = buf[lane_id];
-        for (size_t i = 1; i < nreduce; i += 1)
-        {
+        for (size_t i = 1; i < nreduce; i += 1) {
             max_val = std::max(max_val, buf[lane_id + i * WARP_SIZE]);
         }
         max_val = warp_reduce_max(max_val, item_ct1);
@@ -89,8 +89,9 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         item_ct1.barrier(sycl::access::fence_space::local_space);
         if (warp_id == 0) {
             buf[lane_id] = 0.f;
-            for (size_t i = 1; i < nreduce; i += 1)
+            for (size_t i = 1; i < nreduce; i += 1) {
                 buf[lane_id + i * WARP_SIZE] = 0.f;
+            }
         }
         item_ct1.barrier(sycl::access::fence_space::local_space);
 
@@ -100,8 +101,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         item_ct1.barrier(sycl::access::fence_space::local_space);
 
         tmp = buf[lane_id];
-        for (size_t i = 1; i < nreduce; i += 1)
-        {
+        for (size_t i = 1; i < nreduce; i += 1) {
             tmp += buf[lane_id + i * WARP_SIZE];
         }
         tmp = warp_reduce_sum(tmp, item_ct1);
diff --git a/ggml/src/ggml-sycl/tsembd.cpp b/ggml/src/ggml-sycl/tsembd.cpp
index d5c227cd1..2ffe3cca9 100644
--- a/ggml/src/ggml-sycl/tsembd.cpp
+++ b/ggml/src/ggml-sycl/tsembd.cpp
@@ -68,4 +68,5 @@ void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml
     const int max_period = dst->op_params[1];
 
     timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+    GGML_UNUSED(src1);
 }
diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp
index 4c737f4bf..75ddfb86a 100644
--- a/ggml/src/ggml-sycl/wkv6.cpp
+++ b/ggml/src/ggml-sycl/wkv6.cpp
@@ -59,7 +59,7 @@ static void rwkv_wkv_f32_kernel(
         float y = 0;
 
         // Process in chunks of 4 for better vectorization
-        sycl::float4 k4, r4, tf4, td4, s4, kv4;
+        sycl::float4 k4, r4, tf4, td4, s4;
         #pragma unroll
         for (int j = 0; j < head_size; j += 4) {
             // Load data in vec4 chunks
@@ -135,4 +135,7 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* s
                 );
             });
     });
+
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
 }

From 64ae0655114f84f11a724bc6878c6f8f4a55560b Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Fri, 13 Dec 2024 08:42:04 +0000
Subject: [PATCH 015/376] vulkan: small mul_mat_vec optimizations (#10665)

* double the number of rows per workgroup

* Update ggml-vulkan.cpp

* Vulkan: Add VK_EXT_subgroup_size_control support to ensure full subgroups for coopmats

* only increase the number of rows for amd and subgroup size 64

* fix missing NUM_ROWS for mul_mat_vec_iq4_nl_f16_f32, untested

* use subgroup min and max to check for gcn (requires https://github.com/ggerganov/llama.cpp/pull/10721)

* manual merge ggml-vulkan.cpp

* set min and max subgroup size in any case

* Also double the number of rows for Intel GPUs
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 51 +++++++++-------
 .../vulkan-shaders/dequant_funcs.comp         | 58 ++++++++++---------
 .../vulkan-shaders/get_rows_quant.comp        |  2 +
 .../vulkan-shaders/mul_mat_vec.comp           | 55 ++++++++----------
 4 files changed, 86 insertions(+), 80 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 74d08815e..515d66b39 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1845,48 +1845,54 @@ static void ggml_vk_load_shaders(vk_device& device) {
     }
 
     // mul mat vec
-    // computing two rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0.
+
+    // AMD GCN and Intel graphics cards perform best when the number of rows per shader is doubled
+    uint32_t rm = 1;
+    if ((device->vendor_id == VK_VENDOR_ID_AMD && device->subgroup_min_size == 64 && device->subgroup_max_size == 64) || device->vendor_id == VK_VENDOR_ID_INTEL)
+        rm = 2;
+
+    // computing additional rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0.
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32",  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -2243,13 +2249,16 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->pipeline_robustness = pl_robustness_features.pipelineRobustness;
 
+        if (device->subgroup_size_control) {
+            device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
+            device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
+        }
+
         device->subgroup_size_control = device->subgroup_size_control &&
                 (subgroup_size_control_props.requiredSubgroupSizeStages & vk::ShaderStageFlagBits::eCompute) &&
                 subgroup_size_control_features.subgroupSizeControl;
 
         if (device->subgroup_size_control) {
-            device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
-            device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
             device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;
             device_extensions.push_back("VK_EXT_subgroup_size_control");
         }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
index 5fc1ba4ad..91bb8f8db 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
@@ -25,92 +25,94 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
 
 #if defined(DATA_A_Q4_0)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+    return (vec2(vui & 0xF, vui >> 4) - 8.0f);
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
     const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
-    return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) - 8.0f) * d;
+    return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, vui >> 12) - 8.0f);
 }
 #endif
 
 #if defined(DATA_A_Q4_1)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const float m = float(data_a[a_offset + ib].m);
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2(vui & 0xF, vui >> 4) * d + m;
+    return vec2(vui & 0xF, vui >> 4);
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
-    const float m = float(data_a_packed16[a_offset + ib].m);
     const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
-    return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) * d + m;
+    return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, vui >> 12);
 }
 #endif
 
 #if defined(DATA_A_Q5_0)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
     const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0];
     const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f);
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
     const uint uint_qh = uint(data_a_packed16[a_offset + ib].qh[1]) << 16 | data_a_packed16[a_offset + ib].qh[0];
     const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
     const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10);
     const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
-    return (vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) - 16.0f) * d;
+    return (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f);
 }
 #endif
 
 #if defined(DATA_A_Q5_1)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const float m = float(data_a[a_offset + ib].m);
     const uint uint_qh = data_a[a_offset + ib].qh;
     const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y);
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
-    const float m = float(data_a_packed16[a_offset + ib].m);
     const uint uint_qh = data_a_packed16[a_offset + ib].qh;
     const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
     const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10);
     const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
-    return vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * d + m;
+    return vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y);
 }
 #endif
 
 #if defined(DATA_A_Q8_0)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
+    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1]));
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
     uint32_t v0 = data_a_packed16[a_offset + ib].qs[iqs/2];
     uint32_t v1 = data_a_packed16[a_offset + ib].qs[iqs/2 + 1];
-    return vec4(int8_t(v0 & 0xFF), int8_t((v0 >> 8) & 0xFF), int8_t(v1 & 0xFF), int8_t((v1 >> 8) & 0xFF)) * d;
+    return vec4(int8_t(v0 & 0xFF), int8_t(v0 >> 8), int8_t(v1 & 0xFF), int8_t(v1 >> 8));
 }
 #endif
 
 #if defined(DATA_A_IQ4_NL)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
+    return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]);
 }
 vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a_packed16[a_offset + ib].d);
     const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
-    return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[(vui >> 12) & 0xF]) * d;
+    return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[vui >> 12]);
+}
+#endif
+
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(0, 0);
+}
+#endif
+
+#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ4_NL)
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(float(data_a[a_offset + ib].d), 0);
+}
+#endif
+
+#if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(float(data_a[a_offset + ib].d), float(data_a[a_offset + ib].m));
 }
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
index 7f608315b..1426fde65 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
@@ -31,6 +31,8 @@ void main() {
     const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 
     vec2 v = dequantize(ib, iqs, 0);
+    const vec2 dm = get_dm(ib, 0);
+    v = v * dm.x + dm.y;
 
     data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
     data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
index 2d5b8e466..187c31916 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
@@ -31,27 +31,13 @@ void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_
 
 #if K_PER_ITER == 8
 #if QUANT_R == 2
-    B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4];
-    B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4];
-    FLOAT_TYPE b0 = FLOAT_TYPE(bv02.x);
-    FLOAT_TYPE b1 = FLOAT_TYPE(bv13.x);
-    FLOAT_TYPE b2 = FLOAT_TYPE(bv02.y);
-    FLOAT_TYPE b3 = FLOAT_TYPE(bv13.y);
-    FLOAT_TYPE b4 = FLOAT_TYPE(bv02.z);
-    FLOAT_TYPE b5 = FLOAT_TYPE(bv13.z);
-    FLOAT_TYPE b6 = FLOAT_TYPE(bv02.w);
-    FLOAT_TYPE b7 = FLOAT_TYPE(bv13.w);
+    const B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4];
+    const B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4];
+    const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y);
+    const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w);
 #else
-    B_TYPE_VEC4 bv0 = data_b_v4[(b_offset + iybs + iqs) / 4];
-    B_TYPE_VEC4 bv1 = data_b_v4[(b_offset + iybs + iqs) / 4 + 1];
-    FLOAT_TYPE b0 = FLOAT_TYPE(bv0.x);
-    FLOAT_TYPE b1 = FLOAT_TYPE(bv0.y);
-    FLOAT_TYPE b2 = FLOAT_TYPE(bv0.z);
-    FLOAT_TYPE b3 = FLOAT_TYPE(bv0.w);
-    FLOAT_TYPE b4 = FLOAT_TYPE(bv1.x);
-    FLOAT_TYPE b5 = FLOAT_TYPE(bv1.y);
-    FLOAT_TYPE b6 = FLOAT_TYPE(bv1.z);
-    FLOAT_TYPE b7 = FLOAT_TYPE(bv1.w);
+    const vec4 bv0 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4]);
+    const vec4 bv1 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4 + 1]);
 #endif
 #else
     // Check if the second of the pair of elements is OOB, and don't fetch B or
@@ -67,22 +53,29 @@ void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_
         b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
     }
 #endif
+    uint ibi = first_row*p.ncols;
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib = ((first_row + n)*p.ncols + col)/QUANT_K; // block index
+        const uint ib = (ibi + col)/QUANT_K; // block index
+        ibi += p.ncols;
 
 #if K_PER_ITER == 8
-        const vec4 v = dequantize4(ib, iqs, a_offset);
-        const vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset);
+        vec4 v = dequantize4(ib, iqs, a_offset);
+        vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset);
+
+        const vec2 dm = get_dm(ib, a_offset);
+        if (dm.y != 0) { // quant has min component
+            v = v * dm.x + dm.y;
+            v2 = v2 * dm.x + dm.y;
+        }
 
         // matrix multiplication
-        temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v.z), b2, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v.w), b3, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v2.x), b4, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v2.y), b5, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v2.z), b6, temp[n]);
-        temp[n] = fma(FLOAT_TYPE(v2.w), b7, temp[n]);
+        FLOAT_TYPE rowtmp = dot(bv0, v);
+        rowtmp += dot(bv1, v2);
+
+        if (dm.y == 0)
+            rowtmp *= dm.x;
+
+        temp[n] += rowtmp;
 #else
         const vec2 v = dequantize(ib, iqs, a_offset);
 

From 9f35e44592a7646a5803620eb6a3f0ed5ac90553 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E4=B9=83=E9=97=BB?=
 <sienaiwun@users.noreply.github.com>
Date: Fri, 13 Dec 2024 12:56:07 +0000
Subject: [PATCH 016/376] Fix crash caused by ggml_backend_load_all when
 launching on Android Activity (#10812)

* Fix crash caused by ggml_backend_load_all when launching on AndroidActivity.

Details:
Calling ggml_backend_load_all during initialization in the AndroidActivity project leads to a crash with the error:
terminating with uncaught exception of type std::__ndk1::__fs::filesystem::filesystem_error: filesystem error: in directory_iterator::directory_iterator(...): Permission denied [./].
This issue occurs because AndroidActivity restricts file access due to sandboxing.

Reproduction:
In the example folder, the LlamaAndroid project can reproduce the crash by calling ggml_backend_load_all first in Java_android_llama_cpp_LLamaAndroid_backend_1init.

* Update ggml/src/ggml-backend-reg.cpp

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 ggml/src/ggml-backend-reg.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 2e7340145..b2eded903 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -473,7 +473,8 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         if (!fs::exists(search_path)) {
             continue;
         }
-        for (const auto & entry : fs::directory_iterator(search_path)) {
+        fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
+        for (const auto & entry : dir_it) {
             if (entry.is_regular_file()) {
                 std::string filename = entry.path().filename().string();
                 std::string ext = entry.path().extension().string();

From 4601a8bb6784d2ab8b4b605354b51979fbeea1d3 Mon Sep 17 00:00:00 2001
From: Jett Janiak <jettjaniak@gmail.com>
Date: Fri, 13 Dec 2024 15:48:44 +0100
Subject: [PATCH 017/376] gguf-py : numpy 2 newbyteorder fix (#9772)

---
 gguf-py/gguf/gguf_reader.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py
index e8e61abf8..e17a4e831 100644
--- a/gguf-py/gguf/gguf_reader.py
+++ b/gguf-py/gguf/gguf_reader.py
@@ -145,11 +145,10 @@ class GGUFReader:
         count = int(count)
         itemsize = int(np.empty([], dtype = dtype).itemsize)
         end_offs = offset + itemsize * count
-        return (
-            self.data[offset:end_offs]
-            .view(dtype = dtype)[:count]
-            .newbyteorder(override_order or self.byte_order)
-        )
+        arr = self.data[offset:end_offs].view(dtype=dtype)[:count]
+        if override_order is None:
+            return arr
+        return arr.view(arr.dtype.newbyteorder(override_order))
 
     def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
         if field.name in self.fields:

From 11e07fd63bac1cb642380a7a3eac03fd8703e948 Mon Sep 17 00:00:00 2001
From: Corentin REGAL <corentin.regal@gmail.com>
Date: Fri, 13 Dec 2024 18:23:50 +0100
Subject: [PATCH 018/376] fix: graceful shutdown for Docker images (#10815)

---
 .devops/tools.sh | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.devops/tools.sh b/.devops/tools.sh
index 24dcfd350..9a86e6ea0 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -8,11 +8,11 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert_hf_to_gguf.py "$@"
+    exec python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    ./llama-quantize "$@"
+    exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    ./llama-cli "$@"
+    exec ./llama-cli "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -20,11 +20,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
             echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
+            exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    ./llama-server "$@"
+    exec ./llama-server "$@"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "

From c27ac678dd393af0da9b8acf10266e760c8a0912 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Fri, 13 Dec 2024 18:34:25 +0000
Subject: [PATCH 019/376] Opt class for positional argument handling (#10508)

Added support for positional arguments `model` and `prompt`. Added
functionality to download via strings like:

  llama-run llama3
  llama-run ollama://granite-code
  llama-run ollama://granite-code:8b
  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf
  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf
  llama-run https://example.com/some-file1.gguf
  llama-run some-file2.gguf
  llama-run file://some-file3.gguf

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 README.md                   |  14 +
 common/CMakeLists.txt       |   2 +-
 common/common.cpp           |   6 -
 common/common.h             |  11 +-
 examples/run/CMakeLists.txt |   2 +-
 examples/run/README.md      |  42 ++-
 examples/run/run.cpp        | 628 +++++++++++++++++++++++++++---------
 7 files changed, 542 insertions(+), 163 deletions(-)

diff --git a/README.md b/README.md
index 6fdd8d9ee..54466c250 100644
--- a/README.md
+++ b/README.md
@@ -433,6 +433,20 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
+## [`llama-run`](examples/run)
+
+#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
+
+- <details>
+    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
+
+    ```bash
+    llama-run granite-code
+    ```
+
+    </details>
+
+[^3]: [https://github.com/containers/ramalama](RamaLama)
 
 ## [`llama-simple`](examples/simple)
 
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 89862fe11..df1cdf9a5 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -81,7 +81,7 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info)
 # Use curl to download model url
 if (LLAMA_CURL)
     find_package(CURL REQUIRED)
-    add_definitions(-DLLAMA_USE_CURL)
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
     include_directories(${CURL_INCLUDE_DIRS})
     find_library(CURL_LIBRARY curl REQUIRED)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
diff --git a/common/common.cpp b/common/common.cpp
index 3cd43ecdf..3adfb0329 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1076,12 +1076,6 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2
 
-
-static bool starts_with(const std::string & str, const std::string & prefix) {
-    // While we wait for C++20's std::string::starts_with...
-    return str.rfind(prefix, 0) == 0;
-}
-
 static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
     int remaining_attempts = max_attempts;
 
diff --git a/common/common.h b/common/common.h
index 0481720ab..9e47b70a4 100644
--- a/common/common.h
+++ b/common/common.h
@@ -37,9 +37,9 @@ using llama_tokens = std::vector<llama_token>;
 
 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern char const * LLAMA_COMMIT;
-extern char const * LLAMA_COMPILER;
-extern char const * LLAMA_BUILD_TARGET;
+extern const char * LLAMA_COMMIT;
+extern const char * LLAMA_COMPILER;
+extern const char * LLAMA_BUILD_TARGET;
 
 struct common_control_vector_load_info;
 
@@ -437,6 +437,11 @@ std::vector<std::string> string_split<std::string>(const std::string & input, ch
     return parts;
 }
 
+static bool string_starts_with(const std::string & str,
+                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
 
diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt
index 52add51ef..0686d6305 100644
--- a/examples/run/CMakeLists.txt
+++ b/examples/run/CMakeLists.txt
@@ -1,5 +1,5 @@
 set(TARGET llama-run)
 add_executable(${TARGET} run.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/run/README.md b/examples/run/README.md
index 6e926811f..6162658e9 100644
--- a/examples/run/README.md
+++ b/examples/run/README.md
@@ -3,5 +3,45 @@
 The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models.
 
 ```bash
-./llama-run Meta-Llama-3.1-8B-Instruct.gguf
+llama-run granite-code
+...
+
+```bash
+llama-run -h
+Description:
+  Runs a llm
+
+Usage:
+  llama-run [options] model [prompt]
+
+Options:
+  -c, --context-size <value>
+      Context size (default: 2048)
+  -n, --ngl <value>
+      Number of GPU layers (default: 0)
+  -h, --help
+      Show help message
+
+Commands:
+  model
+      Model is a string with an optional prefix of
+      huggingface:// (hf://), ollama://, https:// or file://.
+      If no protocol is specified and a file exists in the specified
+      path, file:// is assumed, otherwise if a file does not exist in
+      the specified path, ollama:// is assumed. Models that are being
+      pulled are downloaded with .partial extension while being
+      downloaded and then renamed as the file without the .partial
+      extension when complete.
+
+Examples:
+  llama-run llama3
+  llama-run ollama://granite-code
+  llama-run ollama://smollm:135m
+  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf
+  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf
+  llama-run https://example.com/some-file1.gguf
+  llama-run some-file2.gguf
+  llama-run file://some-file3.gguf
+  llama-run --ngl 99 some-file4.gguf
+  llama-run --ngl 99 some-file5.gguf Hello World
 ...
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index cac2faefc..834ea8f7b 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -1,128 +1,350 @@
 #if defined(_WIN32)
-#include <windows.h>
+#    include <windows.h>
 #else
-#include <unistd.h>
+#    include <unistd.h>
 #endif
 
-#include <climits>
+#if defined(LLAMA_USE_CURL)
+#    include <curl/curl.h>
+#endif
+
+#include <cstdarg>
 #include <cstdio>
 #include <cstring>
+#include <filesystem>
 #include <iostream>
 #include <sstream>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
+#include "common.h"
+#include "json.hpp"
 #include "llama-cpp.h"
 
-typedef std::unique_ptr<char[]> char_array_ptr;
+#define printe(...)                   \
+    do {                              \
+        fprintf(stderr, __VA_ARGS__); \
+    } while (0)
 
-struct Argument {
-    std::string flag;
-    std::string help_text;
-};
+class Opt {
+  public:
+    int init(int argc, const char ** argv) {
+        construct_help_str_();
+        // Parse arguments
+        if (parse(argc, argv)) {
+            printe("Error: Failed to parse arguments.\n");
+            help();
+            return 1;
+        }
 
-struct Options {
-    std::string model_path, prompt_non_interactive;
-    int ngl = 99;
-    int n_ctx = 2048;
-};
+        // If help is requested, show help and exit
+        if (help_) {
+            help();
+            return 2;
+        }
 
-class ArgumentParser {
-   public:
-    ArgumentParser(const char * program_name) : program_name(program_name) {}
-
-    void add_argument(const std::string & flag, std::string & var, const std::string & help_text = "") {
-        string_args[flag] = &var;
-        arguments.push_back({flag, help_text});
+        return 0;  // Success
     }
 
-    void add_argument(const std::string & flag, int & var, const std::string & help_text = "") {
-        int_args[flag] = &var;
-        arguments.push_back({flag, help_text});
+    std::string model_;
+    std::string user_;
+    int         context_size_ = 2048, ngl_ = -1;
+
+  private:
+    std::string help_str_;
+    bool        help_ = false;
+
+    void construct_help_str_() {
+        help_str_ =
+            "Description:\n"
+            "  Runs a llm\n"
+            "\n"
+            "Usage:\n"
+            "  llama-run [options] model [prompt]\n"
+            "\n"
+            "Options:\n"
+            "  -c, --context-size <value>\n"
+            "      Context size (default: " +
+            std::to_string(context_size_);
+        help_str_ +=
+            ")\n"
+            "  -n, --ngl <value>\n"
+            "      Number of GPU layers (default: " +
+            std::to_string(ngl_);
+        help_str_ +=
+            ")\n"
+            "  -h, --help\n"
+            "      Show help message\n"
+            "\n"
+            "Commands:\n"
+            "  model\n"
+            "      Model is a string with an optional prefix of \n"
+            "      huggingface:// (hf://), ollama://, https:// or file://.\n"
+            "      If no protocol is specified and a file exists in the specified\n"
+            "      path, file:// is assumed, otherwise if a file does not exist in\n"
+            "      the specified path, ollama:// is assumed. Models that are being\n"
+            "      pulled are downloaded with .partial extension while being\n"
+            "      downloaded and then renamed as the file without the .partial\n"
+            "      extension when complete.\n"
+            "\n"
+            "Examples:\n"
+            "  llama-run llama3\n"
+            "  llama-run ollama://granite-code\n"
+            "  llama-run ollama://smollm:135m\n"
+            "  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n"
+            "  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
+            "  llama-run https://example.com/some-file1.gguf\n"
+            "  llama-run some-file2.gguf\n"
+            "  llama-run file://some-file3.gguf\n"
+            "  llama-run --ngl 99 some-file4.gguf\n"
+            "  llama-run --ngl 99 some-file5.gguf Hello World\n";
     }
 
     int parse(int argc, const char ** argv) {
+        int positional_args_i = 0;
         for (int i = 1; i < argc; ++i) {
-            std::string arg = argv[i];
-            if (string_args.count(arg)) {
-                if (i + 1 < argc) {
-                    *string_args[arg] = argv[++i];
-                } else {
-                    fprintf(stderr, "error: missing value for %s\n", arg.c_str());
-                    print_usage();
+            if (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0) {
+                if (i + 1 >= argc) {
                     return 1;
                 }
-            } else if (int_args.count(arg)) {
-                if (i + 1 < argc) {
-                    if (parse_int_arg(argv[++i], *int_args[arg]) != 0) {
-                        fprintf(stderr, "error: invalid value for %s: %s\n", arg.c_str(), argv[i]);
-                        print_usage();
-                        return 1;
-                    }
-                } else {
-                    fprintf(stderr, "error: missing value for %s\n", arg.c_str());
-                    print_usage();
+
+                context_size_ = std::atoi(argv[++i]);
+            } else if (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0) {
+                if (i + 1 >= argc) {
                     return 1;
                 }
+
+                ngl_ = std::atoi(argv[++i]);
+            } else if (strcmp(argv[i], "-h") == 0 || strcmp(argv[i], "--help") == 0) {
+                help_ = true;
+                return 0;
+            } else if (!positional_args_i) {
+                ++positional_args_i;
+                model_ = argv[i];
+            } else if (positional_args_i == 1) {
+                ++positional_args_i;
+                user_ = argv[i];
             } else {
-                fprintf(stderr, "error: unrecognized argument %s\n", arg.c_str());
-                print_usage();
-                return 1;
+                user_ += " " + std::string(argv[i]);
             }
         }
 
-        if (string_args["-m"]->empty()) {
-            fprintf(stderr, "error: -m is required\n");
-            print_usage();
+        return model_.empty();  // model_ is the only required value
+    }
+
+    void help() const { printf("%s", help_str_.c_str()); }
+};
+
+struct progress_data {
+    size_t file_size = 0;
+    std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now();
+    bool   printed   = false;
+};
+
+struct FileDeleter {
+    void operator()(FILE * file) const {
+        if (file) {
+            fclose(file);
+        }
+    }
+};
+
+typedef std::unique_ptr<FILE, FileDeleter> FILE_ptr;
+
+#ifdef LLAMA_USE_CURL
+class CurlWrapper {
+  public:
+    int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+             const bool progress, std::string * response_str = nullptr) {
+        std::string output_file_partial;
+        curl = curl_easy_init();
+        if (!curl) {
             return 1;
         }
 
+        progress_data data;
+        FILE_ptr      out;
+        if (!output_file.empty()) {
+            output_file_partial = output_file + ".partial";
+            out.reset(fopen(output_file_partial.c_str(), "ab"));
+        }
+
+        set_write_options(response_str, out);
+        data.file_size = set_resume_point(output_file_partial);
+        set_progress_options(progress, data);
+        set_headers(headers);
+        perform(url);
+        if (!output_file.empty()) {
+            std::filesystem::rename(output_file_partial, output_file);
+        }
+
         return 0;
     }
 
-   private:
-    const char * program_name;
-    std::unordered_map<std::string, std::string *> string_args;
-    std::unordered_map<std::string, int *> int_args;
-    std::vector<Argument> arguments;
+    ~CurlWrapper() {
+        if (chunk) {
+            curl_slist_free_all(chunk);
+        }
 
-    int parse_int_arg(const char * arg, int & value) {
-        char * end;
-        const long val = std::strtol(arg, &end, 10);
-        if (*end == '\0' && val >= INT_MIN && val <= INT_MAX) {
-            value = static_cast<int>(val);
+        if (curl) {
+            curl_easy_cleanup(curl);
+        }
+    }
+
+  private:
+    CURL *              curl  = nullptr;
+    struct curl_slist * chunk = nullptr;
+
+    void set_write_options(std::string * response_str, const FILE_ptr & out) {
+        if (response_str) {
+            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data);
+            curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str);
+        } else {
+            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data);
+            curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.get());
+        }
+    }
+
+    size_t set_resume_point(const std::string & output_file) {
+        size_t file_size = 0;
+        if (std::filesystem::exists(output_file)) {
+            file_size = std::filesystem::file_size(output_file);
+            curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast<curl_off_t>(file_size));
+        }
+
+        return file_size;
+    }
+
+    void set_progress_options(bool progress, progress_data & data) {
+        if (progress) {
+            curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+            curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data);
+            curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, progress_callback);
+        }
+    }
+
+    void set_headers(const std::vector<std::string> & headers) {
+        if (!headers.empty()) {
+            if (chunk) {
+                curl_slist_free_all(chunk);
+                chunk = 0;
+            }
+
+            for (const auto & header : headers) {
+                chunk = curl_slist_append(chunk, header.c_str());
+            }
+
+            curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk);
+        }
+    }
+
+    void perform(const std::string & url) {
+        CURLcode res;
+        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+        curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+        curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
+        curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
+        res = curl_easy_perform(curl);
+        if (res != CURLE_OK) {
+            printe("curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
+        }
+    }
+
+    static std::string human_readable_time(double seconds) {
+        int hrs  = static_cast<int>(seconds) / 3600;
+        int mins = (static_cast<int>(seconds) % 3600) / 60;
+        int secs = static_cast<int>(seconds) % 60;
+
+        std::ostringstream out;
+        if (hrs > 0) {
+            out << hrs << "h " << std::setw(2) << std::setfill('0') << mins << "m " << std::setw(2) << std::setfill('0')
+                << secs << "s";
+        } else if (mins > 0) {
+            out << mins << "m " << std::setw(2) << std::setfill('0') << secs << "s";
+        } else {
+            out << secs << "s";
+        }
+
+        return out.str();
+    }
+
+    static std::string human_readable_size(curl_off_t size) {
+        static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" };
+        char         length   = sizeof(suffix) / sizeof(suffix[0]);
+        int          i        = 0;
+        double       dbl_size = size;
+        if (size > 1024) {
+            for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) {
+                dbl_size = size / 1024.0;
+            }
+        }
+
+        std::ostringstream out;
+        out << std::fixed << std::setprecision(2) << dbl_size << " " << suffix[i];
+        return out.str();
+    }
+
+    static int progress_callback(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
+                                 curl_off_t) {
+        progress_data * data = static_cast<progress_data *>(ptr);
+        if (total_to_download <= 0) {
             return 0;
         }
-        return 1;
-    }
 
-    void print_usage() const {
-        printf("\nUsage:\n");
-        printf("  %s [OPTIONS]\n\n", program_name);
-        printf("Options:\n");
-        for (const auto & arg : arguments) {
-            printf("  %-10s %s\n", arg.flag.c_str(), arg.help_text.c_str());
+        total_to_download += data->file_size;
+        const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size;
+        const curl_off_t percentage                    = (now_downloaded_plus_file_size * 100) / total_to_download;
+        const curl_off_t pos                           = (percentage / 5);
+        std::string progress_bar;
+        for (int i = 0; i < 20; ++i) {
+            progress_bar.append((i < pos) ? "█" : " ");
         }
 
-        printf("\n");
+        // Calculate download speed and estimated time to completion
+        const auto                          now             = std::chrono::steady_clock::now();
+        const std::chrono::duration<double> elapsed_seconds = now - data->start_time;
+        const double                        speed           = now_downloaded / elapsed_seconds.count();
+        const double                        estimated_time  = (total_to_download - now_downloaded) / speed;
+        printe("\r%ld%% |%s| %s/%s  %.2f MB/s  %s      ", percentage, progress_bar.c_str(),
+               human_readable_size(now_downloaded).c_str(), human_readable_size(total_to_download).c_str(),
+               speed / (1024 * 1024), human_readable_time(estimated_time).c_str());
+        fflush(stderr);
+        data->printed = true;
+
+        return 0;
+    }
+
+    // Function to write data to a file
+    static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+        FILE * out = static_cast<FILE *>(stream);
+        return fwrite(ptr, size, nmemb, out);
+    }
+
+    // Function to capture data into a string
+    static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+        std::string * str = static_cast<std::string *>(stream);
+        str->append(static_cast<char *>(ptr), size * nmemb);
+        return size * nmemb;
     }
 };
+#endif
 
 class LlamaData {
-   public:
-    llama_model_ptr model;
-    llama_sampler_ptr sampler;
-    llama_context_ptr context;
+  public:
+    llama_model_ptr                 model;
+    llama_sampler_ptr               sampler;
+    llama_context_ptr               context;
     std::vector<llama_chat_message> messages;
+    std::vector<std::string>        msg_strs;
+    std::vector<char>               fmtted;
 
-    int init(const Options & opt) {
-        model = initialize_model(opt.model_path, opt.ngl);
+    int init(Opt & opt) {
+        model = initialize_model(opt);
         if (!model) {
             return 1;
         }
 
-        context = initialize_context(model, opt.n_ctx);
+        context = initialize_context(model, opt.context_size_);
         if (!context) {
             return 1;
         }
@@ -131,15 +353,123 @@ class LlamaData {
         return 0;
     }
 
-   private:
-    // Initializes the model and returns a unique pointer to it
-    llama_model_ptr initialize_model(const std::string & model_path, const int ngl) {
-        llama_model_params model_params = llama_model_default_params();
-        model_params.n_gpu_layers = ngl;
+  private:
+#ifdef LLAMA_USE_CURL
+    int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+                 const bool progress, std::string * response_str = nullptr) {
+        CurlWrapper curl;
+        if (curl.init(url, headers, output_file, progress, response_str)) {
+            return 1;
+        }
 
-        llama_model_ptr model(llama_load_model_from_file(model_path.c_str(), model_params));
+        return 0;
+    }
+#else
+    int download(const std::string &, const std::vector<std::string> &, const std::string &, const bool,
+                 std::string * = nullptr) {
+        printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+        return 1;
+    }
+#endif
+
+    int huggingface_dl(const std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+        // Find the second occurrence of '/' after protocol string
+        size_t pos = model.find('/');
+        pos        = model.find('/', pos + 1);
+        if (pos == std::string::npos) {
+            return 1;
+        }
+
+        const std::string hfr = model.substr(0, pos);
+        const std::string hff = model.substr(pos + 1);
+        const std::string url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
+        return download(url, headers, bn, true);
+    }
+
+    int ollama_dl(std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+        if (model.find('/') == std::string::npos) {
+            model = "library/" + model;
+        }
+
+        std::string model_tag = "latest";
+        size_t      colon_pos = model.find(':');
+        if (colon_pos != std::string::npos) {
+            model_tag = model.substr(colon_pos + 1);
+            model     = model.substr(0, colon_pos);
+        }
+
+        std::string manifest_url = "https://registry.ollama.ai/v2/" + model + "/manifests/" + model_tag;
+        std::string manifest_str;
+        const int   ret = download(manifest_url, headers, "", false, &manifest_str);
+        if (ret) {
+            return ret;
+        }
+
+        nlohmann::json manifest = nlohmann::json::parse(manifest_str);
+        std::string    layer;
+        for (const auto & l : manifest["layers"]) {
+            if (l["mediaType"] == "application/vnd.ollama.image.model") {
+                layer = l["digest"];
+                break;
+            }
+        }
+
+        std::string blob_url = "https://registry.ollama.ai/v2/" + model + "/blobs/" + layer;
+        return download(blob_url, headers, bn, true);
+    }
+
+    std::string basename(const std::string & path) {
+        const size_t pos = path.find_last_of("/\\");
+        if (pos == std::string::npos) {
+            return path;
+        }
+
+        return path.substr(pos + 1);
+    }
+
+    int remove_proto(std::string & model_) {
+        const std::string::size_type pos = model_.find("://");
+        if (pos == std::string::npos) {
+            return 1;
+        }
+
+        model_ = model_.substr(pos + 3);  // Skip past "://"
+        return 0;
+    }
+
+    int resolve_model(std::string & model_) {
+        const std::string              bn      = basename(model_);
+        const std::vector<std::string> headers = { "--header",
+                                                   "Accept: application/vnd.docker.distribution.manifest.v2+json" };
+        int                            ret     = 0;
+        if (string_starts_with(model_, "file://") || std::filesystem::exists(bn)) {
+            remove_proto(model_);
+        } else if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
+            remove_proto(model_);
+            ret = huggingface_dl(model_, headers, bn);
+        } else if (string_starts_with(model_, "ollama://")) {
+            remove_proto(model_);
+            ret = ollama_dl(model_, headers, bn);
+        } else if (string_starts_with(model_, "https://")) {
+            download(model_, headers, bn, true);
+        } else {
+            ret = ollama_dl(model_, headers, bn);
+        }
+
+        model_ = bn;
+
+        return ret;
+    }
+
+    // Initializes the model and returns a unique pointer to it
+    llama_model_ptr initialize_model(Opt & opt) {
+        ggml_backend_load_all();
+        llama_model_params model_params = llama_model_default_params();
+        model_params.n_gpu_layers       = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
+        resolve_model(opt.model_);
+        llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
         if (!model) {
-            fprintf(stderr, "%s: error: unable to load model\n", __func__);
+            printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
 
         return model;
@@ -148,12 +478,11 @@ class LlamaData {
     // Initializes the context with the specified parameters
     llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
         llama_context_params ctx_params = llama_context_default_params();
-        ctx_params.n_ctx = n_ctx;
-        ctx_params.n_batch = n_ctx;
-
+        ctx_params.n_ctx                = n_ctx;
+        ctx_params.n_batch              = n_ctx;
         llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
         if (!context) {
-            fprintf(stderr, "%s: error: failed to create the llama_context\n", __func__);
+            printe("%s: error: failed to create the llama_context\n", __func__);
         }
 
         return context;
@@ -170,23 +499,22 @@ class LlamaData {
     }
 };
 
-// Add a message to `messages` and store its content in `owned_content`
-static void add_message(const char * role, const std::string & text, LlamaData & llama_data,
-                        std::vector<char_array_ptr> & owned_content) {
-    char_array_ptr content(new char[text.size() + 1]);
-    std::strcpy(content.get(), text.c_str());
-    llama_data.messages.push_back({role, content.get()});
-    owned_content.push_back(std::move(content));
+// Add a message to `messages` and store its content in `msg_strs`
+static void add_message(const char * role, const std::string & text, LlamaData & llama_data) {
+    llama_data.msg_strs.push_back(std::move(text));
+    llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() });
 }
 
 // Function to apply the chat template and resize `formatted` if needed
-static int apply_chat_template(const LlamaData & llama_data, std::vector<char> & formatted, const bool append) {
-    int result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
-                                           llama_data.messages.size(), append, formatted.data(), formatted.size());
-    if (result > static_cast<int>(formatted.size())) {
-        formatted.resize(result);
+static int apply_chat_template(LlamaData & llama_data, const bool append) {
+    int result = llama_chat_apply_template(
+        llama_data.model.get(), nullptr, llama_data.messages.data(), llama_data.messages.size(), append,
+        append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
+    if (append && result > static_cast<int>(llama_data.fmtted.size())) {
+        llama_data.fmtted.resize(result);
         result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
-                                           llama_data.messages.size(), append, formatted.data(), formatted.size());
+                                           llama_data.messages.size(), append, llama_data.fmtted.data(),
+                                           llama_data.fmtted.size());
     }
 
     return result;
@@ -199,7 +527,8 @@ static int tokenize_prompt(const llama_model_ptr & model, const std::string & pr
     prompt_tokens.resize(n_prompt_tokens);
     if (llama_tokenize(model.get(), prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
                        true) < 0) {
-        GGML_ABORT("failed to tokenize the prompt\n");
+        printe("failed to tokenize the prompt\n");
+        return -1;
     }
 
     return n_prompt_tokens;
@@ -207,11 +536,11 @@ static int tokenize_prompt(const llama_model_ptr & model, const std::string & pr
 
 // Check if we have enough space in the context to evaluate this batch
 static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx      = llama_n_ctx(ctx.get());
     const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
     if (n_ctx_used + batch.n_tokens > n_ctx) {
         printf("\033[0m\n");
-        fprintf(stderr, "context size exceeded\n");
+        printe("context size exceeded\n");
         return 1;
     }
 
@@ -221,9 +550,10 @@ static int check_context_size(const llama_context_ptr & ctx, const llama_batch &
 // convert the token to a string
 static int convert_token_to_string(const llama_model_ptr & model, const llama_token token_id, std::string & piece) {
     char buf[256];
-    int n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
+    int  n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
     if (n < 0) {
-        GGML_ABORT("failed to convert token to piece\n");
+        printe("failed to convert token to piece\n");
+        return 1;
     }
 
     piece = std::string(buf, n);
@@ -238,19 +568,19 @@ static void print_word_and_concatenate_to_response(const std::string & piece, st
 
 // helper function to evaluate a prompt and generate a response
 static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) {
-    std::vector<llama_token> prompt_tokens;
-    const int n_prompt_tokens = tokenize_prompt(llama_data.model, prompt, prompt_tokens);
-    if (n_prompt_tokens < 0) {
+    std::vector<llama_token> tokens;
+    if (tokenize_prompt(llama_data.model, prompt, tokens) < 0) {
         return 1;
     }
 
     // prepare a batch for the prompt
-    llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
+    llama_batch batch = llama_batch_get_one(tokens.data(), tokens.size());
     llama_token new_token_id;
     while (true) {
         check_context_size(llama_data.context, batch);
         if (llama_decode(llama_data.context.get(), batch)) {
-            GGML_ABORT("failed to decode\n");
+            printe("failed to decode\n");
+            return 1;
         }
 
         // sample the next token, check is it an end of generation?
@@ -273,22 +603,9 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
     return 0;
 }
 
-static int parse_arguments(const int argc, const char ** argv, Options & opt) {
-    ArgumentParser parser(argv[0]);
-    parser.add_argument("-m", opt.model_path, "model");
-    parser.add_argument("-p", opt.prompt_non_interactive, "prompt");
-    parser.add_argument("-c", opt.n_ctx, "context_size");
-    parser.add_argument("-ngl", opt.ngl, "n_gpu_layers");
-    if (parser.parse(argc, argv)) {
-        return 1;
-    }
-
-    return 0;
-}
-
 static int read_user_input(std::string & user) {
     std::getline(std::cin, user);
-    return user.empty();  // Indicate an error or empty input
+    return user.empty();  // Should have data in happy path
 }
 
 // Function to generate a response based on the prompt
@@ -296,7 +613,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
     // Set response color
     printf("\033[33m");
     if (generate(llama_data, prompt, response)) {
-        fprintf(stderr, "failed to generate response\n");
+        printe("failed to generate response\n");
         return 1;
     }
 
@@ -306,11 +623,10 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
 }
 
 // Helper function to apply the chat template and handle errors
-static int apply_chat_template_with_error_handling(const LlamaData & llama_data, std::vector<char> & formatted,
-                                                   const bool is_user_input, int & output_length) {
-    const int new_len = apply_chat_template(llama_data, formatted, is_user_input);
+static int apply_chat_template_with_error_handling(LlamaData & llama_data, const bool append, int & output_length) {
+    const int new_len = apply_chat_template(llama_data, append);
     if (new_len < 0) {
-        fprintf(stderr, "failed to apply the chat template\n");
+        printe("failed to apply the chat template\n");
         return -1;
     }
 
@@ -319,56 +635,63 @@ static int apply_chat_template_with_error_handling(const LlamaData & llama_data,
 }
 
 // Helper function to handle user input
-static bool handle_user_input(std::string & user_input, const std::string & prompt_non_interactive) {
-    if (!prompt_non_interactive.empty()) {
-        user_input = prompt_non_interactive;
-        return true;  // No need for interactive input
+static int handle_user_input(std::string & user_input, const std::string & user_) {
+    if (!user_.empty()) {
+        user_input = user_;
+        return 0;  // No need for interactive input
     }
 
-    printf("\033[32m> \033[0m");
-    return !read_user_input(user_input);  // Returns false if input ends the loop
+    printf(
+        "\r                                                                       "
+        "\r\033[32m> \033[0m");
+    return read_user_input(user_input);  // Returns true if input ends the loop
 }
 
 // Function to tokenize the prompt
-static int chat_loop(LlamaData & llama_data, std::string & prompt_non_interactive) {
-    std::vector<char_array_ptr> owned_content;
-    std::vector<char> fmtted(llama_n_ctx(llama_data.context.get()));
+static int chat_loop(LlamaData & llama_data, const std::string & user_) {
     int prev_len = 0;
-
+    llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
     while (true) {
         // Get user input
         std::string user_input;
-        if (!handle_user_input(user_input, prompt_non_interactive)) {
-            break;
+        while (handle_user_input(user_input, user_)) {
         }
 
-        add_message("user", prompt_non_interactive.empty() ? user_input : prompt_non_interactive, llama_data,
-                    owned_content);
-
+        add_message("user", user_.empty() ? user_input : user_, llama_data);
         int new_len;
-        if (apply_chat_template_with_error_handling(llama_data, fmtted, true, new_len) < 0) {
+        if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
             return 1;
         }
 
-        std::string prompt(fmtted.begin() + prev_len, fmtted.begin() + new_len);
+        std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len);
         std::string response;
         if (generate_response(llama_data, prompt, response)) {
             return 1;
         }
+
+        if (!user_.empty()) {
+            break;
+        }
+
+        add_message("assistant", response, llama_data);
+        if (apply_chat_template_with_error_handling(llama_data, false, prev_len) < 0) {
+            return 1;
+        }
     }
+
     return 0;
 }
 
 static void log_callback(const enum ggml_log_level level, const char * text, void *) {
     if (level == GGML_LOG_LEVEL_ERROR) {
-        fprintf(stderr, "%s", text);
+        printe("%s", text);
     }
 }
 
 static bool is_stdin_a_terminal() {
 #if defined(_WIN32)
     HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
-    DWORD mode;
+    DWORD  mode;
     return GetConsoleMode(hStdin, &mode);
 #else
     return isatty(STDIN_FILENO);
@@ -382,17 +705,20 @@ static std::string read_pipe_data() {
 }
 
 int main(int argc, const char ** argv) {
-    Options opt;
-    if (parse_arguments(argc, argv, opt)) {
+    Opt       opt;
+    const int ret = opt.init(argc, argv);
+    if (ret == 2) {
+        return 0;
+    } else if (ret) {
         return 1;
     }
 
     if (!is_stdin_a_terminal()) {
-        if (!opt.prompt_non_interactive.empty()) {
-            opt.prompt_non_interactive += "\n\n";
+        if (!opt.user_.empty()) {
+            opt.user_ += "\n\n";
         }
 
-        opt.prompt_non_interactive += read_pipe_data();
+        opt.user_ += read_pipe_data();
     }
 
     llama_log_set(log_callback, nullptr);
@@ -401,7 +727,7 @@ int main(int argc, const char ** argv) {
         return 1;
     }
 
-    if (chat_loop(llama_data, opt.prompt_non_interactive)) {
+    if (chat_loop(llama_data, opt.user_)) {
         return 1;
     }
 

From a76c56fa1a3d27467eb97468d8c3b2fe1243b61a Mon Sep 17 00:00:00 2001
From: lhez <quic_lih@quicinc.com>
Date: Fri, 13 Dec 2024 12:23:52 -0800
Subject: [PATCH 020/376] Introducing experimental OpenCL backend with support
 for Qualcomm Adreno GPUs (#10693)

* [cl][adreno] Add Adreno GPU support

Add new OpenCL backend to support Adreno GPUs

---------

Co-authored-by: Skyler Szot <quic_sszot@quicinc.com>
Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>
Co-authored-by: Alexander Angus <quic_aangus@quicinc.com>
Co-authored-by: Hongqiang Wang <quic_wangh@quicinc.com>
Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>

* [cl][ci] Add workflow for CL

* [cl][adreno] Fix memory leak for non SMALL_ALLOC path

* opencl: integrate backend dyn.load interface and fix compiler and format warnings

* opencl: remove small-alloc support and fix build errors for non-opencl platforms

* opencl: fixed merge conflict (MUSA added twice in cmake)

* opencl-ci: use RUNNER_TEMP instead of github.workspace

* opencl: fix embed tool invocation with python3

* opencl: CI workflow fixes

* opencl: Clean up small-alloc in CMake files

* opencl: cleanup ggml-opencl2 header file

* opencl: use ulong for offsets and strides in ADD kernel

* opencl: use cl_ulong for all offsets

* opencl: use cl_ulong for sizes and strides

* opencl: use `GGML_LOG_xxx` instead of `fprintf(stderr, ...)`

* opencl: rename backend `opencl2` -> `opencl`

* opencl: rename kernel files `ggml-opencl2` -> `ggml-opencl`

* opencl: make OpenCL required, remove redundant lib and inc directories

* `ggml-base`, `..` and `.` are added by `ggml_add_backend_library`

* opencl: rename backend - funcs, structs, etc `opencl2` -> `opencl`

* opencl: remove copyright marker since main license already covers

* opencl: replace some more OPENCL2 leftovers

* opencl: remove limits on `tensor_extra`

* opencl: use pools for `tensor_extra`

* opencl: fix compiler warnings with GCC and Clang

Still getting the warning about clCreateCmdQueue being obsolete.
Will fix that separately.

* opencl: fail gracefully if opencl devices are not available

Also for unsupported GPUs.

* opencl: fix MSVC builds (string length error)

* opencl: check for various requirements, allow deprecated API

* opencl: update log message for unsupported GPUs

---------

Co-authored-by: Skyler Szot <quic_sszot@quicinc.com>
Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>
Co-authored-by: Alexander Angus <quic_aangus@quicinc.com>
Co-authored-by: Hongqiang Wang <quic_wangh@quicinc.com>
Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
---
 .github/workflows/build.yml                   |   26 +-
 ggml/CMakeLists.txt                           |    5 +
 ggml/include/ggml-opencl.h                    |   26 +
 ggml/src/CMakeLists.txt                       |    1 +
 ggml/src/ggml-backend-reg.cpp                 |    8 +
 ggml/src/ggml-opencl/CMakeLists.txt           |  147 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 4004 +++++++++++++++++
 ggml/src/ggml-opencl/kernels/embed_kernel.py  |   26 +
 ggml/src/ggml-opencl/kernels/ggml-opencl.cl   | 2683 +++++++++++
 .../ggml-opencl/kernels/ggml-opencl_cvt.cl    |  106 +
 .../kernels/ggml-opencl_gemv_noshuffle.cl     |  265 ++
 .../ggml-opencl_gemv_noshuffle_general.cl     |  271 ++
 .../src/ggml-opencl/kernels/ggml-opencl_mm.cl | 1225 +++++
 .../kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl  |  130 +
 .../kernels/ggml-opencl_transpose_16.cl       |   32 +
 .../kernels/ggml-opencl_transpose_32.cl       |   25 +
 .../kernels/ggml-opencl_transpose_32_16.cl    |   35 +
 17 files changed, 9014 insertions(+), 1 deletion(-)
 create mode 100644 ggml/include/ggml-opencl.h
 create mode 100644 ggml/src/ggml-opencl/CMakeLists.txt
 create mode 100644 ggml/src/ggml-opencl/ggml-opencl.cpp
 create mode 100644 ggml/src/ggml-opencl/kernels/embed_kernel.py
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_cvt.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle_general.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_mm.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_16.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32_16.cl

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 886d33d2d..022d31fb2 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -662,6 +662,8 @@ jobs:
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'msvc-arm64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'llvm-arm64-opencl-adreno'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
 
     steps:
       - name: Clone
@@ -703,6 +705,28 @@ jobs:
         run: |
           choco install ninja
 
+      - name: Install OpenCL Headers and Libs
+        id: install_opencl
+        if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }}
+        run: |
+          git clone https://github.com/KhronosGroup/OpenCL-Headers
+          cd OpenCL-Headers
+          mkdir build && cd build
+          cmake .. `
+            -DBUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install
+          git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+          cd OpenCL-ICD-Loader
+          mkdir build-arm64-release && cd build-arm64-release
+          cmake .. `
+            -A arm64 `
+            -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install --config release
+
       - name: Build
         id: cmake_build
         run: |
@@ -732,7 +756,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index c91e93163..3442142ad 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -179,6 +179,11 @@ set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
 set   (GGML_SYCL_DEVICE_ARCH "" CACHE STRING
                                             "ggml: sycl device architecture")
 
+option(GGML_OPENCL                          "ggml: use OpenCL"                                OFF)
+option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increases overhead)" OFF)
+option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
+option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
+
 # extra artifacts
 option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
 option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
diff --git a/ggml/include/ggml-opencl.h b/ggml/include/ggml-opencl.h
new file mode 100644
index 000000000..6b6177135
--- /dev/null
+++ b/ggml/include/ggml-opencl.h
@@ -0,0 +1,26 @@
+#ifndef GGML_OPENCL_H
+#define GGML_OPENCL_H
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+//
+GGML_BACKEND_API ggml_backend_t ggml_backend_opencl_init(void);
+GGML_BACKEND_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_opencl_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
+
+#endif // GGML_OPENCL_H
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 349f4c57f..bf5ee5fc2 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -308,6 +308,7 @@ ggml_add_backend(MUSA)
 ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
+ggml_add_backend(OpenCL)
 
 foreach (target ggml-base ggml)
     target_include_directories(${target} PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index b2eded903..66927148a 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -46,6 +46,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef GGML_USE_OPENCL
+#include "ggml-opencl.h"
+#endif
+
 #ifdef GGML_USE_BLAS
 #include "ggml-blas.h"
 #endif
@@ -146,6 +150,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_VULKAN
         register_backend(ggml_backend_vk_reg());
 #endif
+#ifdef GGML_USE_OPENCL
+        register_backend(ggml_backend_opencl_reg());
+#endif
 #ifdef GGML_USE_CANN
         register_backend(ggml_backend_cann_reg());
 #endif
@@ -539,6 +546,7 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
     ggml_backend_load_best("rpc", silent, dir_path);
     ggml_backend_load_best("sycl", silent, dir_path);
     ggml_backend_load_best("vulkan", silent, dir_path);
+    ggml_backend_load_best("opencl", silent, dir_path);
     ggml_backend_load_best("musa", silent, dir_path);
     ggml_backend_load_best("cpu", silent, dir_path);
 }
diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
new file mode 100644
index 000000000..45328a657
--- /dev/null
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -0,0 +1,147 @@
+find_package(OpenCL REQUIRED)
+find_package(Python3 REQUIRED)
+
+set(TARGET_NAME ggml-opencl)
+
+ggml_add_backend_library(${TARGET_NAME}
+                         ggml-opencl.cpp
+                         ../../include/ggml-opencl.h)
+target_link_libraries(${TARGET_NAME} PRIVATE ${OpenCL_LIBRARIES})
+target_include_directories(${TARGET_NAME} PRIVATE ${OpenCL_INCLUDE_DIRS})
+
+if (GGML_OPENCL_PROFILING)
+    message(STATUS "OpenCL profiling enabled (increases CPU overhead)")
+    add_compile_definitions(GGML_OPENCL_PROFILING)
+endif ()
+
+add_compile_definitions(GGML_OPENCL_SOA_Q)
+
+if (GGML_OPENCL_USE_ADRENO_KERNELS)
+    message(STATUS "OpenCL will use matmul kernels optimized for Adreno")
+    add_compile_definitions(GGML_OPENCL_USE_ADRENO_KERNELS)
+endif ()
+
+if (GGML_OPENCL_EMBED_KERNELS)
+    add_compile_definitions(GGML_OPENCL_EMBED_KERNELS)
+
+    set(OPENCL_CL_SOURCE_EMBED         "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl.cl.h")
+    set(OPENCL_MM_CL_SOURCE_EMBED      "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_mm.cl.h")
+    set(OPENCL_CVT_CL_SOURCE_EMBED     "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_cvt.cl.h")
+
+    set(OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED             "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_gemv_noshuffle.cl.h")
+    set(OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED     "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_gemv_noshuffle_general.cl.h")
+    set(OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED          "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_mul_mat_Ab_Bi_8x4.cl.h")
+    set(OPENCL_TRANSPOSE_16_SOURCE_EMBED               "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_16.cl.h")
+    set(OPENCL_TRANSPOSE_32_SOURCE_EMBED               "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_32.cl.h")
+    set(OPENCL_TRANSPOSE_32_16_SOURCE_EMBED            "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_32_16.cl.h")
+
+    set(EMBED_KERNEL_SCRIPT             "${CMAKE_CURRENT_SOURCE_DIR}/kernels/embed_kernel.py")
+    file(MAKE_DIRECTORY                 "${CMAKE_BINARY_DIR}/autogenerated")
+
+    include_directories("${CMAKE_BINARY_DIR}/autogenerated")
+
+    # Python must be accessible from command line
+    add_custom_command(
+        OUTPUT ${OPENCL_CL_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl.cl
+            ${OPENCL_CL_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_MM_CL_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_mm.cl
+            ${OPENCL_MM_CL_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_mm.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_mm.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_CVT_CL_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_cvt.cl
+            ${OPENCL_CVT_CL_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_cvt.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_cvt.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_gemv_noshuffle.cl
+            ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_gemv_noshuffle.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_gemv_noshuffle.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_gemv_noshuffle_general.cl
+            ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_gemv_noshuffle_general.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_gemv_noshuffle_general.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl
+            ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_mul_mat_Ab_Bi_8x4.cl.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_16.cl
+            ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_transpose_16.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_transpose_16.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_32.cl
+            ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_transpose_32.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_transpose_32.cl.h"
+    )
+
+    add_custom_command(
+        OUTPUT ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED}
+        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
+            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_32_16.cl
+            ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED}
+        DEPENDS kernels/ggml-opencl_transpose_32_16.cl ${EMBED_KERNEL_SCRIPT}
+        COMMENT "Generate ggml-opencl_transpose_32_16.cl.h"
+    )
+
+    target_sources(${TARGET_NAME} PRIVATE
+                   ${OPENCL_CL_SOURCE_EMBED}
+                   ${OPENCL_MM_CL_SOURCE_EMBED}
+                   ${OPENCL_CVT_CL_SOURCE_EMBED}
+                   ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
+                   ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
+                   ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
+                   ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
+                   ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
+                   ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED})
+else ()
+    # copy ggml-opencl.cl to bin directory
+    configure_file(kernels/ggml-opencl.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_mm.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_mm.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_cvt.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_cvt.cl COPYONLY)
+
+    configure_file(kernels/ggml-opencl_gemv_noshuffle.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_gemv_noshuffle.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_gemv_noshuffle_general.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_gemv_noshuffle_general.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_mul_mat_Ab_Bi_8x4.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_transpose_16.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_16.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_transpose_32.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_32.cl COPYONLY)
+    configure_file(kernels/ggml-opencl_transpose_32_16.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_32_16.cl COPYONLY)
+endif ()
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
new file mode 100644
index 000000000..c77d629f0
--- /dev/null
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -0,0 +1,4004 @@
+#define CL_TARGET_OPENCL_VERSION 220
+#define CL_USE_DEPRECATED_OPENCL_1_2_APIS
+
+// suppress warnings in CL headers for GCC and Clang
+#pragma GCC diagnostic ignored "-Woverlength-strings"
+#ifdef __clang__
+#pragma GCC diagnostic ignored "-Wgnu-anonymous-struct"
+#endif
+
+#include "ggml-opencl.h"
+#include "ggml-backend.h"
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+#include "ggml.h"
+
+#include <CL/cl.h>
+
+#include <string.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <atomic>
+#include <fstream>
+#include <limits>
+#include <vector>
+#include <string>
+#include <cmath>
+
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+#define UNUSED(x) (void)(x)
+
+#define CL_CHECK(err)                                               \
+    do {                                                            \
+        cl_int err_ = (err);                                        \
+        if (err_ != CL_SUCCESS) {                                   \
+            GGML_LOG_ERROR("ggml_opencl: %s error %d at %s:%d\n",  \
+                #err, err_, __FILE__, __LINE__);                    \
+            GGML_ASSERT(0);                                         \
+        }                                                           \
+    } while (0)
+
+//------------------------------------------------------------------------------
+// OpenCL
+//------------------------------------------------------------------------------
+
+bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor);
+
+enum GPU_FAMILY {
+    ADRENO,
+    INTEL,
+    UNKNOWN,
+};
+
+enum ADRENO_GPU_GEN {
+    ADRENO_UNKNOWN,
+    A7X,
+    A8X,
+    X1E,
+};
+
+static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) {
+    if (strstr(device_name, "730") ||
+        strstr(device_name, "740") ||
+        strstr(device_name, "750")) {
+        return ADRENO_GPU_GEN::A7X;
+    }
+
+    if (strstr(device_name, "830")) {
+        return ADRENO_GPU_GEN::A8X;
+    }
+
+    if (strstr(device_name, "X1")) {
+        return ADRENO_GPU_GEN::X1E;
+    }
+
+    return ADRENO_GPU_GEN::ADRENO_UNKNOWN;
+}
+
+static int get_adreno_cl_compiler_version(const char *driver_version) {
+    std::string driver_ver_str(driver_version);
+    size_t compiler_ver_pos = driver_ver_str.find("E031");
+    size_t compiler_ver_len = 13;
+    size_t compiler_ver_offset = 5;
+
+    if (compiler_ver_pos == std::string::npos) {
+        compiler_ver_pos = driver_ver_str.find("DX");
+        if (compiler_ver_pos == std::string::npos) {
+            return -1;
+        }
+        compiler_ver_len = 11;
+        compiler_ver_offset = 3;
+    }
+
+    std::string compiler_ver_str = driver_ver_str.substr(compiler_ver_pos, compiler_ver_len);
+    std::string major_ver_str = compiler_ver_str.substr(compiler_ver_offset, 2);
+    return std::atoi(major_ver_str.c_str());
+}
+
+// backend device context
+struct ggml_backend_opencl_device_context {
+    cl_platform_id platform;
+    std::string platform_name;
+
+    cl_device_id device;
+    std::string device_name;
+};
+
+// backend context
+struct ggml_backend_opencl_context {
+    cl_device_id device;
+    std::string device_name;
+
+    std::string driver_version;
+
+    GPU_FAMILY gpu_family;
+    ADRENO_GPU_GEN adreno_gen;
+
+    cl_int alignment;
+    size_t max_alloc_size;
+    bool fp16_support;
+
+    int adreno_wave_size;
+
+    cl_context context;
+    cl_command_queue queue;
+
+    cl_program program;
+    cl_program program_1;
+    cl_program program_2;
+
+    cl_kernel kernel_add, kernel_add_row;
+    cl_kernel kernel_mul, kernel_mul_row;
+    cl_kernel kernel_scale;
+    cl_kernel kernel_silu, kernel_silu_4;
+    cl_kernel kernel_gelu, kernel_gelu_4;
+    cl_kernel kernel_relu;
+    cl_kernel kernel_clamp;
+    cl_kernel kernel_norm;
+    cl_kernel kernel_rms_norm;
+    cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
+    cl_kernel kernel_soft_max, kernel_soft_max_4;
+    cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
+    cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
+    cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
+    cl_kernel kernel_mul_mat_f32_f32;
+    cl_kernel kernel_mul_mat_f16_f16;
+    cl_kernel kernel_mul_mat_f16_f32_1row;
+    cl_kernel kernel_mul_mat_f16_f32;
+    cl_kernel kernel_mul_mat_f16_f32_l4;
+    cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
+    cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0, kernel_mul_mat_q4_0_f32_flat;
+    cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
+    cl_kernel kernel_convert_block_q4_0_noshuffle, kernel_mul_mat_q4_0_f32_flat_v0,
+              kernel_mul_mat_q4_0_f32_flat_img_v0;
+    cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
+    cl_kernel kernel_mul_mv_q6_K_f32;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    // Transpose kernels
+    cl_program program_transpose_32;
+    cl_program program_transpose_32_16;
+    cl_program program_transpose_16;
+    cl_kernel kernel_transpose_32;
+    cl_kernel kernel_transpose_32_16;
+    cl_kernel kernel_transpose_16;
+
+    cl_mem A_s_d_max;            // max scale buffer size for transpose
+    cl_mem A_q_d_max;            // max weight buffer size for transpose
+    cl_mem B_d_max;              // max activation buffer size for transpose
+
+    // Gemm and Gemv related programs, kernels, etc
+    cl_program program_CL_gemm;
+    cl_program program_CL_gemv_general;
+    cl_program program_CL_gemv_4096_1_11008;
+    cl_program program_CL_gemv_4096_1_4096;
+    cl_program program_CL_gemv_11008_1_4096;
+    cl_program program_CL_gemv_32000_1_4096;
+    cl_kernel CL_mul_mat_Ab_Bi_8x4;
+    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
+    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
+    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
+    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
+    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+};
+
+static ggml_backend_device                 g_ggml_backend_opencl_device;
+static ggml_backend_opencl_device_context  g_ggml_ctx_dev_main {
+    /*.platform         =*/ nullptr,
+    /*.platform_nane    =*/ "",
+    /*.device           =*/ nullptr,
+    /*.device_name      =*/ "",
+};
+
+static int ggml_backend_opencl_n_devices = 0;
+
+// Profiling
+#ifdef GGML_OPENCL_PROFILING
+struct ProfilingInfo {
+    std::string op_name;
+    std::string kernel_name;
+    // Kernel execution time in nanoseconds.
+    cl_ulong duration_ns;
+    // Global and local work sizes.
+    size_t global_size[3];
+    size_t local_size[3];
+    // Op output size.
+    size_t output_size[4];
+};
+
+std::vector<ProfilingInfo> g_profiling_info;
+#endif
+
+inline std::string read_file(const std::string &path) {
+  std::ifstream ifs(path);
+  if (!ifs) {
+    return "";
+  }
+  std::string text;
+  ifs.seekg(0, std::ios::end);
+  text.resize(ifs.tellg());
+  ifs.seekg(0, std::ios::beg);
+  ifs.read(&text[0], text.size());
+  return text;
+}
+
+static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) {
+    cl_program p;
+    char *program_log;
+    size_t program_size;
+    size_t log_size;
+    int err;
+
+    program_size = strlen(program_buffer);
+
+    p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
+    if(err < 0) {
+        GGML_LOG_ERROR("OpenCL error creating program");
+        exit(1);
+    }
+
+    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
+    if(err < 0) {
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
+        program_log = (char*) malloc(log_size + 1);
+        program_log[log_size] = '\0';
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
+        GGML_LOG_ERROR("ggml_opencl: kernel compile error:\n\n%s\n", program_log);
+        free(program_log);
+        exit(1);
+    }
+
+    return p;
+}
+
+static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
+    static bool initialized = false;
+    static ggml_backend_opencl_context *backend_ctx = nullptr;
+
+    if (initialized) {
+        return backend_ctx;
+    }
+
+    ggml_backend_opencl_device_context *dev_ctx = (ggml_backend_opencl_device_context *)dev->context;
+    GGML_ASSERT(dev_ctx);
+    GGML_ASSERT(dev_ctx->platform == nullptr);
+    GGML_ASSERT(dev_ctx->device == nullptr);
+    GGML_ASSERT(backend_ctx == nullptr);
+
+    initialized = true;
+    backend_ctx = new ggml_backend_opencl_context();
+    backend_ctx->gpu_family = GPU_FAMILY::UNKNOWN;
+
+    cl_int err;
+
+#ifdef GGML_PROFILE_OPENCL
+    GGML_LOG_INFO("ggml_opencl: OpenCL profiling enabled\n");
+#endif
+
+    struct cl_device;
+    struct cl_platform {
+        cl_platform_id id;
+        unsigned number;
+        char name[128];
+        char vendor[128];
+        struct cl_device * devices;
+        unsigned n_devices;
+        struct cl_device * default_device;
+    };
+
+    struct cl_device {
+        struct cl_platform * platform;
+        cl_device_id id;
+        unsigned number;
+        cl_device_type type;
+        char name[128];
+    };
+
+    enum { NPLAT = 16, NDEV = 16 };
+
+    struct cl_platform platforms[NPLAT];
+    unsigned n_platforms = 0;
+    struct cl_device devices[NDEV];
+    unsigned n_devices = 0;
+    struct cl_device * default_device = NULL;
+
+    cl_platform_id platform_ids[NPLAT];
+    if (clGetPlatformIDs(NPLAT, platform_ids, &n_platforms) != CL_SUCCESS) {
+        GGML_LOG_ERROR("ggml_opencl: plaform IDs not available.\n");
+        return backend_ctx;
+    }
+
+    for (unsigned i = 0; i < n_platforms; i++) {
+        struct cl_platform * p = &platforms[i];
+        p->number = i;
+        p->id = platform_ids[i];
+        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
+        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
+
+        cl_device_id device_ids[NDEV];
+        cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
+        if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
+            p->n_devices = 0;
+        } else {
+            CL_CHECK(clGetDeviceIDsError);
+        }
+        p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
+        p->default_device = NULL;
+
+        for (unsigned j = 0; j < p->n_devices; j++) {
+            struct cl_device * d = &devices[n_devices];
+            d->number = n_devices++;
+            d->id = device_ids[j];
+            d->platform = p;
+            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
+            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
+
+            if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
+                p->default_device = d;
+            }
+        }
+
+        if (default_device == NULL && p->default_device != NULL) {
+            default_device = p->default_device;
+        }
+    }
+
+    if (n_devices == 0) {
+        GGML_LOG_ERROR("ggml_opencl: could find any OpenCL devices.\n");
+        return backend_ctx;
+    }
+
+    char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
+    char * user_device_string = getenv("GGML_OPENCL_DEVICE");
+    int user_platform_number = -1;
+    int user_device_number = -1;
+
+    unsigned n;
+    if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
+        user_platform_number = (int)n;
+    }
+    if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
+        user_device_number = (int)n;
+    }
+    if (user_platform_number != -1 && user_device_number != -1) {
+        cl_platform* platform = &platforms[user_platform_number];
+        if ((unsigned)user_device_number >= platform->n_devices) {
+            GGML_LOG_ERROR("ggml_opencl: invalid device number %d\n", user_device_number);
+            exit(1);
+        }
+        default_device = &platform->devices[user_device_number];
+    } else {
+
+        struct cl_device * selected_devices = devices;
+        unsigned n_selected_devices = n_devices;
+
+        if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
+            for (unsigned i = 0; i < n_platforms; i++) {
+                struct cl_platform * p = &platforms[i];
+                if (strstr(p->name, user_platform_string) != NULL ||
+                    strstr(p->vendor, user_platform_string) != NULL) {
+                    user_platform_number = (int)i;
+                    break;
+                }
+            }
+            if (user_platform_number == -1) {
+                GGML_LOG_ERROR("ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
+                exit(1);
+            }
+        }
+        if (user_platform_number != -1) {
+            struct cl_platform * p = &platforms[user_platform_number];
+            selected_devices = p->devices;
+            n_selected_devices = p->n_devices;
+            default_device = p->default_device;
+            if (n_selected_devices == 0) {
+                GGML_LOG_ERROR("ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
+                exit(1);
+            }
+        }
+
+        if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
+            for (unsigned i = 0; i < n_selected_devices; i++) {
+                struct cl_device * d = &selected_devices[i];
+                if (strstr(d->name, user_device_string) != NULL) {
+                    user_device_number = d->number;
+                    break;
+                }
+            }
+            if (user_device_number == -1) {
+                GGML_LOG_ERROR("ggml_opencl: no device matching '%s' was found.\n", user_device_string);
+                exit(1);
+            }
+        }
+        if (user_device_number != -1) {
+            selected_devices = &devices[user_device_number];
+            n_selected_devices = 1;
+            default_device = &selected_devices[0];
+        }
+
+        GGML_ASSERT(n_selected_devices > 0);
+
+        if (default_device == NULL) {
+            default_device = &selected_devices[0];
+        }
+    }
+
+    GGML_LOG_INFO("ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
+    GGML_LOG_INFO("ggml_opencl: selecting device: '%s'\n", default_device->name);
+    if (default_device->type != CL_DEVICE_TYPE_GPU) {
+        GGML_LOG_WARN("ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
+    }
+
+    dev_ctx->platform = default_device->platform->id;
+    dev_ctx->device = default_device->id;
+    backend_ctx->device = default_device->id;
+
+    if (strstr(default_device->name, "Adreno")) {
+        backend_ctx->gpu_family = GPU_FAMILY::ADRENO;
+        backend_ctx->adreno_gen = get_adreno_gpu_gen(default_device->name);
+
+        // Default wave size is 128, A8x uses 64.
+        if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A8X) {
+            backend_ctx->adreno_wave_size = 64;
+        } else if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X ||
+                   backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) {
+            backend_ctx->adreno_wave_size = 128;
+        } else {
+            backend_ctx->adreno_wave_size = 128;
+            GGML_LOG_WARN("ggml_opencl: Unsupported Adreno GPU: %s, "
+                "using wave size %d, "
+                "may not work as expected\n",
+                backend_ctx->device_name.c_str(), backend_ctx->adreno_wave_size);
+        }
+    } else if (strstr(default_device->name, "Intel")) {
+        backend_ctx->gpu_family = GPU_FAMILY::INTEL;
+    } else {
+        GGML_LOG_ERROR("Unsupported GPU: %s\n", default_device->name);
+        backend_ctx->gpu_family = GPU_FAMILY::UNKNOWN;
+        return backend_ctx;
+    }
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) {
+        GGML_LOG_ERROR("ggml_opencl: Adreno-specific kernels should not be enabled for non-Adreno GPUs; "
+            "run on an Adreno GPU or recompile with CMake option `-DGGML_OPENCL_USE_ADRENO_KERNELS=OFF`\n");
+        return backend_ctx;
+    }
+#endif
+
+    // Populate backend device name
+    dev_ctx->platform_name = default_device->platform->name;
+    dev_ctx->device_name = default_device->name;
+    backend_ctx->device_name = default_device->name;
+
+    // A local ref of cl_device_id for convenience
+    cl_device_id device = backend_ctx->device;
+
+    // Check device OpenCL version, OpenCL 2.0 or above is required
+    size_t device_ver_str_size;
+    clGetDeviceInfo(device, CL_DEVICE_VERSION, 0, NULL, &device_ver_str_size);
+    char *device_ver_buffer = (char *)alloca(device_ver_str_size + 1);
+    clGetDeviceInfo(device, CL_DEVICE_VERSION, device_ver_str_size, device_ver_buffer, NULL);
+    device_ver_buffer[device_ver_str_size] = '\0';
+    GGML_LOG_INFO("ggml_opencl: device OpenCL version: %s\n", device_ver_buffer);
+
+    if (strstr(device_ver_buffer, "OpenCL 2") == NULL &&
+        strstr(device_ver_buffer, "OpenCL 3") == NULL) {
+        GGML_LOG_ERROR("ggml_opencl: OpenCL 2.0 or above is required\n");
+        return backend_ctx;
+    }
+
+    // Check driver version
+    size_t driver_version_str_size;
+    clGetDeviceInfo(device, CL_DRIVER_VERSION, 0, NULL, &driver_version_str_size);
+    char *driver_version = (char *)alloca(driver_version_str_size + 1);
+    clGetDeviceInfo(device, CL_DRIVER_VERSION, driver_version_str_size, driver_version, NULL);
+    driver_version[driver_version_str_size] = '\0';
+    GGML_LOG_INFO("ggml_opencl: OpenCL driver: %s\n", driver_version);
+    backend_ctx->driver_version = driver_version;
+
+    int adreno_cl_compiler_version = get_adreno_cl_compiler_version(driver_version);
+    bool has_vector_subgroup_broadcast =
+        adreno_cl_compiler_version >= 47 || adreno_cl_compiler_version == 17;
+    GGML_LOG_INFO("ggml_opencl: vector subgroup broadcast support: %s\n",
+        has_vector_subgroup_broadcast ? "true" : "false");
+
+    size_t ext_str_size;
+    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
+    char *ext_buffer = (char *)alloca(ext_str_size + 1);
+    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
+    ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
+    // Check if ext_buffer contains cl_khr_fp16
+    backend_ctx->fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
+    GGML_LOG_INFO("ggml_opencl: device FP16 support: %s\n", backend_ctx->fp16_support ? "true" : "false");
+
+    // fp16 is required
+    if (!backend_ctx->fp16_support) {
+        GGML_LOG_ERROR("ggml_opencl: device does not support FP16\n");
+        return backend_ctx;
+    }
+
+    // If OpenCL 3.0 is supported, then check for cl_khr_subgroups, which becomes
+    // optional in OpenCL 3.0 (cl_khr_subgroup is mandatory in OpenCL 2.x)
+    if (strstr(device_ver_buffer, "OpenCL 3") &&
+        strstr(ext_buffer, "cl_khr_subgroups") == NULL &&
+        strstr(ext_buffer, "cl_intel_subgroups") == NULL) {
+        GGML_LOG_ERROR("ggml_opencl: device does not support subgroups (cl_khr_subgroups or cl_intel_subgroups) "
+            "(note that subgroups is an optional feature in OpenCL 3.0)\n");
+        return backend_ctx;
+    }
+
+    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &backend_ctx->alignment, NULL));
+    GGML_LOG_INFO("ggml_opencl: mem base addr align: %u\n", backend_ctx->alignment);
+
+    clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &backend_ctx->max_alloc_size, NULL);
+    GGML_LOG_INFO("ggml_opencl: max mem alloc size: %zu MB\n", backend_ctx->max_alloc_size/1024/1024);
+
+    // Check SVM.
+    cl_device_svm_capabilities svm_caps;
+    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_SVM_CAPABILITIES, sizeof(cl_device_svm_capabilities), &svm_caps, 0));
+    GGML_LOG_INFO("ggml_opencl: SVM coarse grain buffer support: %s\n",
+        svm_caps & CL_DEVICE_SVM_COARSE_GRAIN_BUFFER ? "true" : "false");
+    GGML_LOG_INFO("ggml_opencl: SVM fine grain buffer support: %s\n",
+        svm_caps & CL_DEVICE_SVM_FINE_GRAIN_BUFFER ? "true" : "false");
+    GGML_LOG_INFO("ggml_opencl: SVM fine grain system support: %s\n",
+        svm_caps & CL_DEVICE_SVM_FINE_GRAIN_SYSTEM ? "true" : "false");
+    GGML_LOG_INFO("ggml_opencl: SVM atomics support: %s\n",
+        svm_caps & CL_DEVICE_SVM_ATOMICS ? "true" : "false");
+
+    // Print out configurations
+#ifdef GGML_OPENCL_SOA_Q
+    GGML_LOG_INFO("ggml_opencl: flattening quantized weights representation as struct of arrays (GGML_OPENCL_SOA_Q)\n");
+#endif // GGML_OPENCL_SOA_Q
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_LOG_INFO("ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)\n");
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+    cl_context_properties properties[] = {
+        (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)dev_ctx->platform, 0
+    };
+
+    CL_CHECK((backend_ctx->context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
+
+    // A local ref of cl_context for convenience
+    cl_context context = backend_ctx->context;
+
+    //CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
+    //    (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err :
+    //    (queue = clCreateCommandQueue(context, device, 0, &err), err)
+    //)));
+    cl_command_queue_properties command_queue_props = 0;
+#ifdef GGML_OPENCL_PROFILING
+    command_queue_props |= CL_QUEUE_PROFILING_ENABLE;
+#endif
+    CL_CHECK((backend_ctx->queue = clCreateCommandQueue(context, device, command_queue_props, &err), err));
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src {
+        #include "ggml-opencl.cl.h"
+    };
+#else
+    const std::string kernel_src = read_file("ggml-opencl.cl");
+#endif
+
+    std::string compile_opts =
+        "-cl-std=CL2.0 -cl-mad-enable -cl-unsafe-math-optimizations "
+        "-cl-finite-math-only -cl-fast-relaxed-math ";
+    backend_ctx->program = build_program_from_source(context, device, kernel_src.c_str(), compile_opts);
+
+    // Non matmul kernels.
+    CL_CHECK((backend_ctx->kernel_get_rows_f32       = clCreateKernel(backend_ctx->program, "kernel_get_rows_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_get_rows_f16       = clCreateKernel(backend_ctx->program, "kernel_get_rows_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_get_rows_q4_0      = clCreateKernel(backend_ctx->program, "kernel_get_rows_q4_0", &err), err));
+    CL_CHECK((backend_ctx->kernel_add                = clCreateKernel(backend_ctx->program, "kernel_add", &err), err));
+    CL_CHECK((backend_ctx->kernel_add_row            = clCreateKernel(backend_ctx->program, "kernel_add_row", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul                = clCreateKernel(backend_ctx->program, "kernel_mul", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_row            = clCreateKernel(backend_ctx->program, "kernel_mul_row", &err), err));
+    CL_CHECK((backend_ctx->kernel_scale              = clCreateKernel(backend_ctx->program, "kernel_scale", &err), err));
+    CL_CHECK((backend_ctx->kernel_silu               = clCreateKernel(backend_ctx->program, "kernel_silu", &err), err));
+    CL_CHECK((backend_ctx->kernel_silu_4             = clCreateKernel(backend_ctx->program, "kernel_silu_4", &err), err));
+    CL_CHECK((backend_ctx->kernel_gelu               = clCreateKernel(backend_ctx->program, "kernel_gelu", &err), err));
+    CL_CHECK((backend_ctx->kernel_gelu_4             = clCreateKernel(backend_ctx->program, "kernel_gelu_4", &err), err));
+    CL_CHECK((backend_ctx->kernel_relu               = clCreateKernel(backend_ctx->program, "kernel_relu", &err), err));
+    CL_CHECK((backend_ctx->kernel_clamp              = clCreateKernel(backend_ctx->program, "kernel_clamp", &err), err));
+    CL_CHECK((backend_ctx->kernel_norm               = clCreateKernel(backend_ctx->program, "kernel_norm", &err), err));
+    CL_CHECK((backend_ctx->kernel_rms_norm           = clCreateKernel(backend_ctx->program, "kernel_rms_norm", &err), err));
+    CL_CHECK((backend_ctx->kernel_diag_mask_inf      = clCreateKernel(backend_ctx->program, "kernel_diag_mask_inf", &err), err));
+    CL_CHECK((backend_ctx->kernel_diag_mask_inf_8    = clCreateKernel(backend_ctx->program, "kernel_diag_mask_inf_8", &err), err));
+    CL_CHECK((backend_ctx->kernel_soft_max           = clCreateKernel(backend_ctx->program, "kernel_soft_max", &err), err));
+    CL_CHECK((backend_ctx->kernel_soft_max_4         = clCreateKernel(backend_ctx->program, "kernel_soft_max_4", &err), err));
+    CL_CHECK((backend_ctx->kernel_rope_norm_f32      = clCreateKernel(backend_ctx->program, "kernel_rope_norm_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_rope_norm_f16      = clCreateKernel(backend_ctx->program, "kernel_rope_norm_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_rope_neox_f32      = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_rope_neox_f16      = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_cpy_f16_f16        = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_cpy_f16_f32        = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_cpy_f32_f16        = clCreateKernel(backend_ctx->program, "kernel_cpy_f32_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_cpy_f32_f32        = clCreateKernel(backend_ctx->program, "kernel_cpy_f32_f32", &err), err));
+
+    // Matmul kernels.
+    CL_CHECK((backend_ctx->kernel_mul_mat_f32_f32        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f32_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f16        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f16", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_1row   = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32_1row", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4     = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32_l4", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32       = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_v     = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_v", &err), err));
+
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat  = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_flat", &err), err));
+    CL_CHECK((backend_ctx->kernel_convert_block_q4_0     = clCreateKernel(backend_ctx->program, "kernel_convert_block_q4_0", &err), err));
+    CL_CHECK((backend_ctx->kernel_restore_block_q4_0     = clCreateKernel(backend_ctx->program, "kernel_restore_block_q4_0", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_8x_flat", &err), err));
+
+    // Load additional mulmat kernels.
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src_1 {
+        #include "ggml-opencl_mm.cl.h"
+    };
+#else
+    const std::string kernel_src_1 = read_file("ggml-opencl_mm.cl");
+#endif
+    backend_ctx->program_1 = build_program_from_source(context, device, kernel_src_1.c_str(), compile_opts);
+
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat      = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_1d_8x_flat", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat     = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_1d_16x_flat", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32                  = clCreateKernel(backend_ctx->program_1, "kernel_mul_mv_q6_K_f32", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat_v0         = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_flat_v0", &err), err));
+    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat_img_v0     = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_flat_img_v0", &err), err));
+
+    // Load additional data conversion kernels.
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src_2 {
+        #include "ggml-opencl_cvt.cl.h"
+    };
+#else
+    const std::string kernel_src_2 = read_file("ggml-opencl_cvt.cl");
+#endif
+    backend_ctx->program_2 = build_program_from_source(context, device, kernel_src_2.c_str(), compile_opts);
+
+    CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle     = clCreateKernel(backend_ctx->program_2, "kernel_convert_block_q4_0_noshuffle", &err), err));
+
+    // Kernels for Adreno
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string transpose_32_src {
+        #include "ggml-opencl_transpose_32.cl.h"
+    };
+#else
+    const std::string transpose_32_src = read_file("ggml-opencl_transpose_32.cl");
+#endif
+    backend_ctx->program_transpose_32 = build_program_from_source(context, device, transpose_32_src.c_str(), compile_opts);
+    CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose_32, "kernel_transpose_32", &err), err));
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string transpose_32_16_src {
+        #include "ggml-opencl_transpose_32_16.cl.h"
+    };
+#else
+    const std::string transpose_32_16_src = read_file("ggml-opencl_transpose_32_16.cl");
+#endif
+    backend_ctx->program_transpose_32_16 = build_program_from_source(context, device, transpose_32_16_src.c_str(), compile_opts);
+    CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose_32_16, "kernel_transpose_32_16", &err), err));
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string transpose_16_src {
+        #include "ggml-opencl_transpose_16.cl.h"
+    };
+#else
+    const std::string transpose_16_src = read_file("ggml-opencl_transpose_16.cl");
+#endif
+    backend_ctx->program_transpose_16 = build_program_from_source(context, device, transpose_16_src.c_str(), compile_opts);
+    CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose_16, "kernel_transpose_16", &err), err));
+
+    // Gemv general
+    std::string CL_gemv_compile_opts =
+        " -cl-std=CL2.0 "
+        " -cl-mad-enable "
+        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    if (has_vector_subgroup_broadcast) {
+        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+    }
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src_CL_gemv_general {
+        #include "ggml-opencl_gemv_noshuffle_general.cl.h"
+    };
+#else
+    const std::string kernel_src_CL_gemv_general = read_file("ggml-opencl_gemv_noshuffle_general.cl");
+#endif
+
+    backend_ctx->program_CL_gemv_general = build_program_from_source(
+        context, device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general = clCreateKernel(backend_ctx->program_CL_gemv_general, "kernel_gemv_noshuffle", &err), err));
+
+    // Gemv 2048, 16384
+    CL_gemv_compile_opts =
+        " -cl-std=CL2.0 "
+        " -cl-mad-enable "
+        " -DLINE_STRIDE_A=2048 "
+        " -DBLOCK_STRIDE_A=16384 "
+        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    if (has_vector_subgroup_broadcast) {
+        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+    }
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src_CL_gemv {
+        #include "ggml-opencl_gemv_noshuffle.cl.h"
+    };
+#else
+    const std::string kernel_src_CL_gemv = read_file("ggml-opencl_gemv_noshuffle.cl");
+#endif
+
+    backend_ctx->program_CL_gemv_4096_1_4096 = build_program_from_source(
+        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_4096, "kernel_gemv_noshuffle", &err), err));
+
+    // Gemv 2048, 16384
+    CL_gemv_compile_opts =
+        " -cl-std=CL2.0 "
+        " -cl-mad-enable "
+        " -DLINE_STRIDE_A=2048 "
+        " -DBLOCK_STRIDE_A=16384 "
+        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    if (has_vector_subgroup_broadcast) {
+        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+    }
+
+    backend_ctx->program_CL_gemv_4096_1_11008 = build_program_from_source(
+        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_11008, "kernel_gemv_noshuffle", &err), err));
+
+    // Gemv 5504, 44032
+    CL_gemv_compile_opts =
+        " -cl-std=CL2.0 "
+        " -cl-mad-enable "
+        " -DLINE_STRIDE_A=5504 "
+        " -DBLOCK_STRIDE_A=44032 "
+        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    if (has_vector_subgroup_broadcast) {
+        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+    }
+
+    backend_ctx->program_CL_gemv_11008_1_4096 = build_program_from_source(
+        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_11008_1_4096, "kernel_gemv_noshuffle", &err), err));
+
+    // Gemv 16000, 128000
+    CL_gemv_compile_opts =
+        " -cl-std=CL2.0 "
+        " -cl-mad-enable "
+        " -DLINE_STRIDE_A=16000 "
+        " -DBLOCK_STRIDE_A=128000 "
+        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    if (has_vector_subgroup_broadcast) {
+        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+    }
+
+    backend_ctx->program_CL_gemv_32000_1_4096 = build_program_from_source(context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_32000_1_4096, "kernel_gemv_noshuffle", &err), err));
+
+    // Gemm
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    const std::string kernel_src_CL_gemm {
+        #include "ggml-opencl_mul_mat_Ab_Bi_8x4.cl.h"
+    };
+#else
+    const std::string kernel_src_CL_gemm = read_file("ggml-opencl_mul_mat_Ab_Bi_8x4.cl");
+#endif
+    backend_ctx->program_CL_gemm = build_program_from_source(context, device, kernel_src_CL_gemm.c_str(), compile_opts);
+    CL_CHECK((backend_ctx->CL_mul_mat_Ab_Bi_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mat_Ab_Bi_8x4", &err), err));
+
+    // Allocate intermediate buffers and images
+    size_t max_A_q_d_bytes = 311164928;
+    size_t max_A_s_d_bytes = 38895616;
+    size_t max_B_d_bytes = 45088768;
+
+    CL_CHECK((backend_ctx->A_q_d_max = clCreateBuffer(context, 0, max_A_q_d_bytes, NULL, &err), err));
+    CL_CHECK((backend_ctx->A_s_d_max = clCreateBuffer(context, 0, max_A_s_d_bytes, NULL, &err), err));
+    CL_CHECK((backend_ctx->B_d_max   = clCreateBuffer(context, 0, max_B_d_bytes,   NULL, &err), err));
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+    // For now we support a single devices
+    ggml_backend_opencl_n_devices = 1;
+
+    return backend_ctx;
+}
+
+static void ggml_cl2_free(void) {
+#ifdef GGML_OPENCL_PROFILING
+    FILE * fperf = fopen("cl_profiling.csv", "w");
+    if (!fperf) {
+        GGML_LOG_ERROR("Failed to open cl_profiling.csv\n");
+        return;
+    }
+
+    float total_kernel_time = 0;
+    fprintf(fperf, "op name, kernel name, duration (ms), global size, local size, output size\n");
+    for (const ProfilingInfo & info : g_profiling_info) {
+        total_kernel_time += info.duration_ns/1.e6f;
+        fprintf(fperf, "%s,%s,%f,%zux%zux%zu,%zux%zux%zu,%zux%zux%zux%zu\n",
+            info.op_name.c_str(), info.kernel_name.c_str(), info.duration_ns/1.e6f,
+            info.global_size[0], info.global_size[1], info.global_size[2],
+            info.local_size[0], info.local_size[2], info.local_size[2],
+            info.output_size[0], info.output_size[1], info.output_size[2], info.output_size[3]);
+    }
+    fclose(fperf);
+
+    GGML_LOG_INFO("ggml_opencl: total kernel time: %f\n", total_kernel_time);
+#endif
+}
+
+//------------------------------------------------------------------------------
+// Tensor extra management
+//------------------------------------------------------------------------------
+struct ggml_tensor_extra_cl {
+    // The buffer object that holds the data.
+    cl_mem data_device;
+    // The offset into the buffer object. This is primarily for scratch buffer
+    // and view operation.
+    // NB: this offset no longer includes view offset (view_offs). Whenever this
+    // offset is used, view_offs should be considered.
+    cl_ulong offset;
+    // The actual size of the cl_mem object. This is needed when returning the
+    // block to the pool.
+    size_t actual_size;
+
+    void reset() {
+        data_device = nullptr;
+        offset = 0;
+        actual_size = 0;
+    }
+};
+
+// Additional tensor extra structs for quantized tensors.
+// These tensors are loaded from files and should not be allocated in scratch --
+// they should always be allocated from the pool. Hence, they do not have an
+// `offset`, which indicate their locations in the scratch buffer.
+struct ggml_tensor_extra_cl_q4_0 {
+    // Quantized values.
+    cl_mem q = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem q_img = nullptr;
+    // Scales.
+    cl_mem d = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem d_img = nullptr;
+    // Size of quantized values.
+    size_t size_q = 0;
+    // Size of scales.
+    size_t size_d = 0;
+
+    ~ggml_tensor_extra_cl_q4_0() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q));
+            q = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
+        // enabled. They point to the images in ggml_backend_opencl_buffer_context.
+        // So, there is no need to release them here.
+        // TODO: initialize them for non SMALL_PATH path, or remove them.
+        q_img = nullptr;
+        d_img = nullptr;
+        size_q = 0;
+        size_d = 0;
+    }
+};
+
+//------------------------------------------------------------------------------
+// Backend API
+//------------------------------------------------------------------------------
+
+//
+// backend
+//
+static const char * ggml_backend_opencl_name(ggml_backend_t backend) {
+    return "OpenCL";
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_opencl_free(ggml_backend_t backend) {
+    ggml_cl2_free();
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_opencl_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_UNUSED(backend);
+    GGML_UNUSED(tensor);
+    GGML_UNUSED(data);
+    GGML_UNUSED(offset);
+    GGML_UNUSED(size);
+}
+
+static void ggml_backend_opencl_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_UNUSED(backend);
+    GGML_UNUSED(tensor);
+    GGML_UNUSED(data);
+    GGML_UNUSED(offset);
+    GGML_UNUSED(size);
+}
+
+static bool ggml_backend_opencl_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src);
+    GGML_UNUSED(dst);
+    return false;
+}
+
+static void ggml_backend_opencl_synchronize(ggml_backend_t backend) {
+    GGML_UNUSED(backend);
+}
+
+static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+
+        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+            continue;
+        }
+
+        bool ok = ggml_cl_compute_forward(backend, node);
+        if (!ok) {
+            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
+    GGML_UNUSED(dev);
+
+    switch (op->op) {
+        case GGML_OP_NONE:
+            return true;
+        case GGML_OP_GET_ROWS:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    return true;
+                case GGML_TYPE_Q4_0:
+#ifdef GGML_OPENCL_SOA_Q
+                    // We do not support flattened Q4_0 (and possibly other Q's)
+                    return false;
+#else // GGML_OPENCL_SOA_Q
+                    return true;
+#endif // GGML_OPENCL_SOA_Q
+                default:
+                    return false;
+            }
+        case GGML_OP_CPY:
+        case GGML_OP_DUP:
+        case GGML_OP_CONT:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                    switch (op->type) {
+                        case GGML_TYPE_F16:
+                        case GGML_TYPE_F32:
+                            return true;
+                        default:
+                            return false;
+                    }
+                case GGML_TYPE_F16:
+                    switch (op->type) {
+                        case GGML_TYPE_F16:
+                        case GGML_TYPE_F32:
+                            return true;
+                        default:
+                            return false;
+                    }
+                default:
+                    return false;
+            }
+        case GGML_OP_ADD:
+        case GGML_OP_SCALE:
+        case GGML_OP_MUL:
+            return true;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                   return ggml_is_contiguous(op->src[0]);
+                default:
+                    return false;
+            }
+        case GGML_OP_CLAMP:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_NORM:
+        case GGML_OP_RMS_NORM:
+            return true;
+        case GGML_OP_MUL_MAT:
+            if (op->src[0]->type == GGML_TYPE_F16) {
+                return true;
+            } else if (op->src[0]->type == GGML_TYPE_F32) {
+                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
+            } else if (op->src[0]->type == GGML_TYPE_Q4_0 ||
+                       op->src[0]->type == GGML_TYPE_Q6_K) {
+                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
+            }
+            return false;
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            return true;
+        case GGML_OP_DIAG_MASK_INF:
+            return op->ne[3] == 1;
+        case GGML_OP_ROPE:
+            return true;
+        default:
+            return false;
+    }
+}
+
+// Forward declaration - implementation appears later in the file.
+static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer_type_t buffer_type);
+
+static ggml_guid_t ggml_backend_opencl_guid() {
+    static ggml_guid guid = { 0xde, 0xe0, 0x70, 0xa2, 0x73, 0x4e, 0x4d, 0xbc, 0xb0, 0xc7, 0x4f, 0xd4, 0x6d, 0x4e, 0x90, 0xfe };
+    return &guid;
+}
+
+static ggml_backend_i ggml_backend_opencl_i = {
+    /* .get_name                = */ ggml_backend_opencl_name,
+    /* .free                    = */ ggml_backend_opencl_free,
+    /* .set_tensor_async        = */ NULL,  /* ggml_backend_opencl_set_tensor_async */
+    /* .get_tensor_async        = */ NULL,  /* ggml_backend_opencl_get_tensor_async */
+    /* .cpy_tensor_async        = */ NULL,  /* ggml_backend_opencl_cpy_tensor_async */
+    /* .synchronize             = */ NULL,  /* ggml_backend_opencl_synchronize */
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_opencl_graph_compute,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+};
+
+ggml_backend_t ggml_backend_opencl_init(void) {
+    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_opencl_reg(), 0);
+    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(dev);
+
+    ggml_backend_t backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_opencl_guid(),
+        /* .interface = */ ggml_backend_opencl_i,
+        /* .device    = */ dev,
+        /* .context   = */ backend_ctx
+    };
+
+    return backend;
+}
+
+bool ggml_backend_is_opencl(ggml_backend_t backend) {
+    return backend && backend->iface.get_name == ggml_backend_opencl_name;
+}
+
+//
+// buffer
+//
+struct ggml_backend_opencl_buffer_context {
+    // A buffer context can hold multiple cl_mem objects. This is for flattening
+    // quantized weights and should be used with GGML_OPENCL_SMALL_ALLOC where
+    // each tensor is allocated a separate buffer. When flattening is enabled
+    // with small allocation, each tensor is backed by two cl_mem objects (for
+    // quants and scales) packed into a backend_opencl_buffer.
+    ggml_backend_opencl_buffer_context(cl_mem buf)
+        : name("OpenCL") {
+        buffer.push_back(buf);
+    }
+
+    ~ggml_backend_opencl_buffer_context() {
+        for (cl_mem buf : buffer) {
+            CL_CHECK(clReleaseMemObject(buf));
+        }
+        for (cl_mem im : img) {
+            CL_CHECK(clReleaseMemObject(im));
+        }
+
+        // Delete all extras to trigger their destructors
+        for (ggml_tensor_extra_cl * e : temp_tensor_extras) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
+            delete e;
+        }
+    }
+
+    ggml_tensor_extra_cl * ggml_opencl_alloc_temp_tensor_extra() {
+        ggml_tensor_extra_cl * extra;
+        if (temp_tensor_extras.empty()) {
+            extra = new ggml_tensor_extra_cl();
+        } else {
+            extra = temp_tensor_extras.back();
+            temp_tensor_extras.pop_back();
+        }
+
+        temp_tensor_extras_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
+    ggml_tensor_extra_cl_q4_0 * ggml_opencl_alloc_temp_tensor_extra_q4_0() {
+        ggml_tensor_extra_cl_q4_0 * extra;
+        if (temp_tensor_extras_q4_0.empty()) {
+            extra = new ggml_tensor_extra_cl_q4_0();
+        } else {
+            extra = temp_tensor_extras_q4_0.back();
+            temp_tensor_extras_q4_0.pop_back();
+        }
+
+        temp_tensor_extras_q4_0_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
+    void reset() {
+        for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
+            temp_tensor_extras.push_back(e);
+        }
+        temp_tensor_extras_in_use.clear();
+
+        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
+            temp_tensor_extras_q4_0.push_back(e);
+        }
+        temp_tensor_extras_q4_0_in_use.clear();
+    }
+
+    // Pools for extras. Available extras are in `temp_tensor_extras`. Extras
+    // being used are in `temp_tensor_extras_in_use`. At the first run, new
+    // extras get created and put in `in_use`. When the buffer is reset via
+    // the `reset` callback, all extras in `in_use` get moved to available extras
+    // for reuse.
+    std::vector<ggml_tensor_extra_cl *> temp_tensor_extras;
+    std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
+    std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
+    std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
+
+    // The buffer_context is initially created by ggml_backend_buft_alloc_buffer
+    // before any tensor is initialized (at the beginning of alloc_tensor_range).
+    // Hence, there is alway a buffer object in this vector. When each tensor is
+    // being initialized, this original buffer object will be released if both
+    // flattening and small allocation are enabled, and additional buffer
+    // objects will be created in init_tensor to represent flattened quantized
+    // weights.
+    std::vector<cl_mem> buffer;
+    // These are image1d_buffer_t objects that wrap around the quants and scales.
+    // For Q4_0 quantization, there should be two of them - one for quants and
+    // one for scales. They should be populated only when flattening and small
+    // allocation are enabled.
+    std::vector<cl_mem> img;
+    std::string name;
+};
+
+static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;
+
+static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    delete ctx;
+}
+
+static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return cl_ptr_base;
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+
+    ggml_cl2_init(buffer->buft->device);
+
+    if (tensor->view_src != nullptr) {
+        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
+
+        ggml_tensor_extra_cl * view_extra = (ggml_tensor_extra_cl *) tensor->view_src->extra;
+        GGML_ASSERT(view_extra && "view_extra is nullptr?");
+
+        // Reuse extra of the parent tensor. The offset of this view tensor
+        // becomes `extra->offset + view_offs` and needs to be calculated when
+        // it is used. This changes is needed because of the change to
+        // ggml_alloc.c in https://github.com/ggerganov/llama.cpp/pull/7640.
+        // `buffer` passed in here will always be `tensor->buffer`. It is OK
+        // to allocate extras from the same buffer context for ordinary
+        // intermediate tensors. But for views into kv cache tensors, doing so
+        // would mess up the extras used by kv cache.
+        // Before #7640, `buffer` is for intermediate tensors, which is always
+        // different from that of kv cache tensors.
+        //
+        // NB: now extra->offset no longer accounts for view_offs.
+        // NB: this should not apply to weight tensors (for end-to-end runs, but
+        //     may apply for test-backend-ops).
+        // FIXME: if any unexpected results are seen, double check the offset -
+        // there could be other places that need fix.
+        tensor->extra = view_extra;
+    } else {
+        {
+            size_t offset = (char *)tensor->data - (char *)cl_ptr_base;
+
+            ggml_tensor_extra_cl * extra = ctx->ggml_opencl_alloc_temp_tensor_extra();
+            extra->offset = offset;
+            extra->data_device = ctx->buffer[0];
+            extra->actual_size = ggml_nbytes(tensor);
+
+            tensor->extra = extra;
+        }
+    }
+}
+
+// The optimized gemm and gemv kernels are used for large matrices without batch.
+// tensor is the quantized weights matrix.
+inline bool use_adreno_kernels(const ggml_tensor *tensor) {
+    return tensor->ne[0] >= 512 && tensor->ne[1] >= 512 &&
+            tensor->ne[2] == 1 && tensor->ne[3] == 1;
+}
+
+static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device);
+
+    cl_context context = backend_ctx->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+#ifdef GGML_OPENCL_SOA_Q
+    // We separate the quantized bits and scale from block_q4_0 by using an
+    // additional kernel, where each thread handles a block. We first read the
+    // original weights into a temporary buffer, then create two separate
+    // buffers for quantized bits and scales, which are then populated by the
+    // conversion kernel.
+    if (tensor->type == GGML_TYPE_Q4_0) {
+        // Tensors should have been preallocated, therefore they should
+        // already have ggml_tensor_extra_cl as extra.
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_q4_0 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_0();
+
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        // We consider the specified offset arg as always, although For weights
+        // the offset arg should be 0 (we do not assert this).
+        //GGML_ASSERT(offset == 0);
+
+        // We create subbuffers from the original tensor buffer for scales and
+        // quants - i.e., scales and quants are aliases into the buffer obejct
+        // that backs the original tensor. This is a cleaner way to adapt to the
+        // new memory management.
+        // In the old code, we allocate new buffers for scales and quants
+        // respectively, which could still be done but would result in double
+        // allocation; properly deallocating the preallocated buffer that backs
+        // the tensors is tricky and would leak the backend specific information
+        // into the general backend code.
+        // Does this create misaligned subbuffers (alignment is 1024) in certain
+        // cases ?
+        cl_buffer_region region;
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, then quants.
+        // Create subbuffer for scales.
+        region.origin = extra_orig->offset + tensor->view_offs + offset;
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+        // Create subbuffer for quants.
+        region.origin = extra_orig->offset + tensor->view_offs + offset + size_d;
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+        //cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
+    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
+
+        // The optimized kernels need weights in natural order, so unshuffle.
+        if (use_adreno_kernels(tensor)) {
+            kernel = backend_ctx->kernel_convert_block_q4_0_noshuffle;
+        }
+    #else
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
+    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+
+        // transpose the weights and scales
+    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Only do transpose for large, non batched matrix
+        // TODO: use preallocated images instead of sub-buffer then image
+        if (use_adreno_kernels(tensor)) {
+        // <----------------------------------------------------------------------------------> //
+        // start transpose
+        // <----------------------------------------------------------------------------------> //
+        int M = tensor->ne[1];   // ne01
+        int K = tensor->ne[0];   // ne00
+
+        // transpose is out of place, so we need to allocate transposed buffers
+        // <----------------------------------------------------------------------------------> //
+        // use sub_buffer of max buffer size instead
+
+        size_t q_size_bytes = K * M / 8 * sizeof(float);
+        cl_buffer_region region;
+        region.origin = 0;
+        region.size = q_size_bytes;
+        cl_mem qT_d = clCreateSubBuffer(
+            backend_ctx->A_q_d_max,
+            0,
+            CL_BUFFER_CREATE_TYPE_REGION,
+            &region,
+            &err);
+        // cl_mem qT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, q_size_bytes, NULL, &err);
+        CL_CHECK(err);
+
+        // size_t d_size_bytes = M * (K / 32) / 2 * sizeof(float);
+        size_t d_size_bytes = M * (K / 32) * 2;
+        region.origin = 0;
+        region.size = d_size_bytes;
+        cl_mem dT_d = clCreateSubBuffer(
+            backend_ctx->A_s_d_max,
+            0,
+            CL_BUFFER_CREATE_TYPE_REGION,
+            &region,
+            &err);
+        // cl_mem dT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, d_size_bytes, NULL, &err);
+        CL_CHECK(err);
+
+        // <----------------------------------------------------------------------------------> //
+
+
+        // create images from the buffers
+        // <----------------------------------------------------------------------------------> //
+        cl_mem q_d_image1D;
+        cl_mem d_d_image1D;
+        cl_mem qT_d_image1D;
+        cl_mem dT_d_image1D;
+
+        cl_image_format img_fmt_1d = { CL_RGBA, CL_FLOAT };
+        cl_image_desc img_desc_1d;
+
+        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc_1d.image_width = M * K / 8 / 4;
+        img_desc_1d.buffer = extra->q;
+        q_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
+        CL_CHECK(err);
+
+        img_fmt_1d = { CL_RGBA, CL_FLOAT };
+        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc_1d.image_width = M * K / 8 / 4;
+        img_desc_1d.buffer = qT_d;
+        qT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
+        CL_CHECK(err);
+
+        img_fmt_1d = { CL_RGBA, CL_FLOAT };
+        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc_1d.image_width = M * K / 32 / 4 / 2;
+        img_desc_1d.buffer = extra->d;
+        d_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
+        CL_CHECK(err);
+
+        img_fmt_1d = { CL_RGBA, CL_FLOAT };
+        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc_1d.image_width = M * K / 32 / 4 / 2;
+        img_desc_1d.buffer = dT_d;
+        dT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
+        CL_CHECK(err);
+        // <----------------------------------------------------------------------------------> //
+
+        // set up and call the transpose kernels
+        // <----------------------------------------------------------------------------------> //
+        // weights
+        int height_q = M / 8;
+        int width_q = K / 8 / 4;
+        kernel = backend_ctx->kernel_transpose_16;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_d_image1D));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &qT_d_image1D));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_q));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_q));
+
+        size_t local_size_q[3] = {4, 16, 1};
+        size_t global_size_q[3] = {static_cast<size_t>(width_q), static_cast<size_t>(height_q), 1};
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_q, local_size_q, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+
+        // scales
+        int height_s = M / 8;
+        int width_s = K / 32 / 8;
+
+        kernel = backend_ctx->kernel_transpose_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &d_d_image1D));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &dT_d_image1D));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_s));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_s));
+
+        size_t local_size_s[3] = {4, 16, 1};
+        size_t global_size_s[3] = {static_cast<size_t>(width_s), static_cast<size_t>(height_s), 1};
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_s, local_size_s, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        // <----------------------------------------------------------------------------------> //
+
+        // copy transposed buffer contents to original buffers
+        // <----------------------------------------------------------------------------------> //
+        // weights
+        CL_CHECK(clEnqueueCopyBuffer(queue, qT_d, extra->q, 0, 0, q_size_bytes, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+
+        // scales
+        CL_CHECK(clEnqueueCopyBuffer(queue, dT_d, extra->d, 0, 0, d_size_bytes, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        // <----------------------------------------------------------------------------------> //
+
+        // deallocate transpose buffers
+        // <----------------------------------------------------------------------------------> //
+        CL_CHECK(clReleaseMemObject(qT_d));
+        CL_CHECK(clReleaseMemObject(dT_d));
+
+        // deallocate temporary images
+        CL_CHECK(clReleaseMemObject(q_d_image1D));
+        CL_CHECK(clReleaseMemObject(d_d_image1D));
+        CL_CHECK(clReleaseMemObject(qT_d_image1D));
+        CL_CHECK(clReleaseMemObject(dT_d_image1D));
+        // <----------------------------------------------------------------------------------> //
+        // end transpose
+        // <----------------------------------------------------------------------------------> //
+        }
+    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+        return;
+    }
+#endif // GGML_OPENCL_SOA_Q
+
+    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
+    GGML_ASSERT(extra);
+
+    CL_CHECK(clEnqueueWriteBuffer(
+        queue, extra->data_device, CL_TRUE, extra->offset + offset,
+        size, data, 0, NULL, NULL));
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(tensor->extra);
+
+    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device);
+
+    cl_context context = backend_ctx->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    // Make sure all previously submitted commands are finished.
+    CL_CHECK(clFinish(queue));
+
+#ifdef GGML_OPENCL_SOA_Q
+    // In end-to-end runs, get_tensor is usually used to get back the logits,
+    // where we can simply do clEnqueueReadBuffer since they are f32.
+    // However, in test-backend-ops, the GPU graph is copied to the CPU backend,
+    // which requires reading back quantized weight tensors.
+    // To properly support this, we need to restore block_q4_0 struct arrays
+    // from the flattened buffers.
+    if (tensor->type == GGML_TYPE_Q4_0) {
+        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)tensor->extra;
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_restore_block_q4_0;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+            global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(
+            queue, data_device, CL_TRUE, offset,
+            size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    }
+#endif // GGML_OPENCL_SOA_Q
+
+    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
+
+    CL_CHECK(clEnqueueReadBuffer(
+        queue, extra->data_device, CL_TRUE, extra->offset + tensor->view_offs + offset,
+        size, data, 0, NULL, NULL));
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_dev_t dev = buffer->buft->device;
+    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(dev);
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    for (cl_mem buf : ctx->buffer) {
+        CL_CHECK(clEnqueueFillBuffer(queue, buf, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL));
+    }
+    CL_CHECK(clFinish(queue));
+}
+
+static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    ctx->reset();
+}
+
+static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = {
+    /* .free_buffer     = */ ggml_backend_opencl_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_opencl_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_opencl_buffer_init_tensor,
+    /* .memset_tensor   = */ NULL,
+    /* .set_tensor      = */ ggml_backend_opencl_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_opencl_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_opencl_buffer_clear,
+    /* .reset           = */ ggml_backend_opencl_buffer_reset,
+};
+
+//
+// buffer type
+//
+
+static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer_type_t buffer_type) {
+    return "OpenCL";
+
+    GGML_UNUSED(buffer_type);
+}
+
+static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) {
+    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer_type->device);
+
+    // clCreateBuffer returns -61 for size 0
+    size = std::max(size, (size_t)1);
+
+    cl_int err;
+    cl_mem mem = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, size, NULL, &err);
+    if (err != CL_SUCCESS) {
+        GGML_LOG_INFO("%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
+        return nullptr;
+    }
+
+    ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context(mem);
+
+    return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size);
+}
+
+static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
+    // FIXME: not thread safe, device may not be initialized yet
+    static cl_uint alignment = -1;
+    if (alignment == (cl_uint)-1) {
+        ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(buffer_type->device);
+        alignment = backend_ctx->alignment;
+    }
+    return alignment;
+}
+
+static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
+    static size_t max_size = -1;
+    if (max_size == (size_t)-1) {
+        ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(buffer_type->device);
+        max_size = backend_ctx->max_alloc_size;
+    }
+    return max_size;
+}
+
+static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    return ggml_backend_is_opencl(backend);
+
+    UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_opencl_buffer_type_get_name,
+    /* .alloc_buffer     = */ ggml_backend_opencl_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_opencl_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_opencl_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ NULL,
+    /* .is_host          = */ NULL,
+};
+
+ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() {
+    static ggml_backend_buffer_type buffer_type = {
+        /* .iface   = */ ggml_backend_opencl_buffer_type_interface,
+        /* .device  = */ &g_ggml_backend_opencl_device,
+        /* .context = */ nullptr,
+    };
+
+    return &buffer_type;
+}
+
+//
+// backend device
+//
+
+static const char * ggml_backend_opencl_device_get_name(ggml_backend_dev_t dev) {
+    return "GPUOpenCL";
+
+    GGML_UNUSED(dev);
+}
+
+static const char * ggml_backend_opencl_device_get_description(ggml_backend_dev_t dev) {
+    ggml_backend_opencl_device_context *dev_ctx = (ggml_backend_opencl_device_context *) dev->context;
+    return dev_ctx->device_name.c_str();
+}
+
+static void ggml_backend_opencl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    *free = 1;
+    *total = 1;
+
+    GGML_UNUSED(dev);
+}
+
+static enum ggml_backend_dev_type ggml_backend_opencl_device_get_type(ggml_backend_dev_t dev) {
+    return GGML_BACKEND_DEVICE_TYPE_GPU;
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_opencl_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_opencl_device_get_name(dev);
+    props->description = ggml_backend_opencl_device_get_description(dev);
+    props->type        = ggml_backend_opencl_device_get_type(dev);
+    ggml_backend_opencl_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = ggml_backend_dev_caps {
+        /* .async                 = */ false,
+        /* .host_buffer           = */ false,
+        /* .buffer_from_host_ptr  = */ false,
+        /* .events                = */ false,
+    };
+}
+
+static ggml_backend_t ggml_backend_opencl_device_init(ggml_backend_dev_t dev, const char * params) {
+    ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(dev);
+
+    ggml_backend_t backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_opencl_guid(),
+        /* .interface = */ ggml_backend_opencl_i,
+        /* .device    = */ dev,
+        /* .context   = */ backend_ctx,
+    };
+
+    return backend;
+
+    GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_opencl_device_get_buffer_type(ggml_backend_dev_t dev) {
+    return ggml_backend_opencl_buffer_type();
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_opencl_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+    GGML_UNUSED(dev);
+    GGML_UNUSED(ptr);
+    GGML_UNUSED(size);
+    GGML_UNUSED(max_tensor_size);
+    return nullptr;
+}
+
+static bool ggml_backend_opencl_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
+    return ggml_opencl_supports_op(dev, op);
+}
+
+static bool ggml_backend_opencl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_opencl_buffer_type_get_name;
+
+    GGML_UNUSED(dev);
+}
+
+static struct ggml_backend_device_i ggml_backend_opencl_device_i = {
+    /* .get_name             = */ ggml_backend_opencl_device_get_name,
+    /* .get_description      = */ ggml_backend_opencl_device_get_description,
+    /* .get_memory           = */ ggml_backend_opencl_device_get_memory,
+    /* .get_type             = */ ggml_backend_opencl_device_get_type,
+    /* .get_props            = */ ggml_backend_opencl_device_get_props,
+    /* .init_backend         = */ ggml_backend_opencl_device_init,
+    /* .get_buffer_type      = */ ggml_backend_opencl_device_get_buffer_type,
+    /* .get_host_buffer_type = */ NULL,
+    /* .buffer_from_host_ptr = */ ggml_backend_opencl_device_buffer_from_ptr,
+    /* .supports_op          = */ ggml_backend_opencl_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_opencl_device_supports_buft,
+    /* .offload_op           = */ NULL,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
+};
+
+// Backend registry
+
+static const char * ggml_backend_opencl_reg_get_name(ggml_backend_reg_t reg) {
+    return "OpenCL";
+
+    GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_opencl_reg_device_count(ggml_backend_reg_t reg) {
+    return ggml_backend_opencl_n_devices;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_opencl_reg_device_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+
+    return &g_ggml_backend_opencl_device;
+
+    GGML_UNUSED(reg);
+    GGML_UNUSED(index);
+}
+
+static struct ggml_backend_reg_i ggml_backend_opencl_reg_i = {
+    /* .get_name         = */ ggml_backend_opencl_reg_get_name,
+    /* .device_count     = */ ggml_backend_opencl_reg_device_count,
+    /* .device_get       = */ ggml_backend_opencl_reg_device_get,
+    /* .get_proc_address = */ NULL,
+};
+
+ggml_backend_reg_t ggml_backend_opencl_reg(void) {
+    // TODO: make this thread-safe somehow?
+    static ggml_backend_reg reg;
+    static bool initialized = false;
+
+    if (!initialized) {
+        reg = ggml_backend_reg {
+            /* .api_version = */ GGML_BACKEND_API_VERSION,
+            /* .iface   = */ ggml_backend_opencl_reg_i,
+            /* .context = */ NULL,
+        };
+
+        g_ggml_backend_opencl_device = ggml_backend_device {
+            /* .iface   = */ ggml_backend_opencl_device_i,
+            /* .reg     = */ &reg,
+            /* .context = */ &g_ggml_ctx_dev_main,
+        };
+
+        ggml_cl2_init(&g_ggml_backend_opencl_device);
+
+        initialized = true;
+    }
+
+    return &reg;
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_opencl_reg)
+
+//------------------------------------------------------------------------------
+// Debugging utils
+//------------------------------------------------------------------------------
+#if 0
+#define QK4_0 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    uint8_t qs[QK4_0 / 2];  // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2,
+    "wrong q4_0 block size/padding");
+
+#include <math.h>
+#ifdef __cplusplus
+#include "half.hpp"
+#endif
+
+static void dump_tensor(ggml_backend_t backend, const struct ggml_tensor * tensor) {
+    void * buf = malloc(ggml_nbytes(tensor));
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+#ifdef GGML_OPENCL_SOA_Q
+    void * buf_q;
+    void * buf_d;
+#endif
+
+#ifdef GGML_USE_OPENCL
+    // Make sure everything is done.
+    CL_CHECK(clFinish(queue));
+
+#ifdef GGML_OPENCL_SOA_Q
+    if (tensor->type == GGML_TYPE_Q4_0) {
+        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *) tensor->extra;
+        GGML_ASSERT(extra);
+
+        size_t size_q = ggml_nelements(tensor)/QK4_0 * QK4_0/2;
+        size_t size_d = ggml_nelements(tensor)/QK4_0 * sizeof(ggml_fp16_t);
+        GGML_ASSERT(size_q + size_d == ggml_nbytes(tensor));
+        buf_q = malloc(size_q);
+        buf_d = malloc(size_d);
+
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->q, CL_TRUE, 0, size_q, buf_q, 0, NULL, NULL));
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->d, CL_TRUE, 0, size_d, buf_d, 0, NULL, NULL));
+        CL_CHECK(clFinish(queue));
+    } else {
+        // Read out the tensor from GPU memory.
+        ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
+        GGML_ASSERT(extra);
+
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->data_device, CL_TRUE,
+        extra->offset, ggml_nbytes(tensor), buf, 0, NULL, NULL));
+        CL_CHECK(clFinish(queue));
+    }
+#else
+    // Read out the tensor from GPU memory.
+    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
+    GGML_ASSERT(extra);
+
+    CL_CHECK(clEnqueueReadBuffer(queue, extra->data_device, CL_TRUE,
+        extra->offset, ggml_nbytes(tensor), buf, 0, NULL, NULL));
+    CL_CHECK(clFinish(queue));
+#endif // GGML_OPENCL_SOA_Q
+#endif // GGML_USE_OPENCL
+
+    // Open file and dump.
+    char fname[512];
+    sprintf(fname, "./tensor-dumps/%s.txt", tensor->name);
+    FILE * f = fopen(fname, "w");
+    if (!f) {
+        printf("Failed to open %s\n", fname);
+        return;
+    }
+
+    if (tensor->type == GGML_TYPE_F32) {
+        float * data = (float *) buf;
+        for (int i = 0; i < ggml_nelements(tensor); ++i) {
+            if (isnan(data[i])) {
+                printf("NaN found: %s\n", tensor->name);
+                break;
+            }
+            fprintf(f, "%f\n", data[i]);
+        }
+    } else if (tensor->type == GGML_TYPE_I32) {
+        int * data = (int *) buf;
+        for (int i = 0; i < ggml_nelements(tensor); ++i) {
+            if (isnan(data[i])) {
+                printf("NaN found: %s\n", tensor->name);
+                break;
+            }
+            fprintf(f, "%d\n", data[i]);
+        }
+    } else if (tensor->type == GGML_TYPE_F16) {
+#ifdef __cplusplus
+        half_float::half * data = (half_float::half *) buf;
+        for (int i = 0; i < ggml_nelements(tensor); ++i) {
+            if (std::isnan(data[i])) {
+                printf("NaN found: %s\n", tensor->name);
+                break;
+            }
+            fprintf(f, "%f\n", float(data[i]));
+        }
+#endif
+    } else if (tensor->type == GGML_TYPE_Q4_0) {
+#ifdef GGML_OPENCL_SOA_Q
+        ggml_fp16_t * data_d = (ggml_fp16_t *)buf_d;
+        unsigned char * data_q = (unsigned char *)buf_q;
+
+        for (int i = 0; i < ggml_nelements(tensor)/QK4_0; ++i) {
+            fprintf(f, "%04x, ", data_d[i]);
+            for (int k = 0; k < QK4_0/2; ++k) {
+                fprintf(f, "%02x, ", data_q[k]);
+            }
+            fprintf(f, "\n");
+            data_q += QK4_0/2;
+        }
+        free(buf_d);
+        free(buf_q);
+#else
+        block_q4_0 * data = (block_q4_0 *) buf;
+        for (int i = 0; i < ggml_nelements(tensor)/QK4_0; ++i) {
+            fprintf(f, "%04x, ", data[i].d);
+            for (int k = 0; k < QK4_0/2; ++k) {
+                fprintf(f, "%02x, ", data[i].qs[k]);
+            }
+            fprintf(f, "\n");
+        }
+#endif // GGML_OPENCL_SOA_Q
+    }
+    free(buf);
+    fflush(f);
+    fclose(f);
+}
+#else
+#define dump_tensor(tensor)
+#endif
+
+//------------------------------------------------------------------------------
+// Profiling utility
+//------------------------------------------------------------------------------
+#ifdef GGML_OPENCL_PROFILING
+void populateProfilingInfo(
+        ProfilingInfo& info, cl_event evt, cl_kernel kernel,
+        size_t global_size[3], size_t local_size[3],
+        const ggml_tensor * tensor) {
+    cl_ulong start;
+    cl_ulong end;
+    CL_CHECK(clWaitForEvents(1, &evt));
+    CL_CHECK(clGetEventProfilingInfo(
+        evt, CL_PROFILING_COMMAND_START, sizeof(cl_ulong), &start, NULL));
+    CL_CHECK(clGetEventProfilingInfo(
+        evt, CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &end, NULL));
+
+    char kernel_name[512];
+    CL_CHECK(clGetKernelInfo(kernel, CL_KERNEL_FUNCTION_NAME,
+        sizeof(kernel_name), kernel_name, NULL));
+
+    info.duration_ns = end - start;
+    info.op_name = tensor->name;
+    info.kernel_name = kernel_name;
+    info.local_size[0]  = local_size[0];
+    info.local_size[1]  = local_size[1];
+    info.local_size[2]  = local_size[2];
+    info.global_size[0] = global_size[0];
+    info.global_size[1] = global_size[1];
+    info.global_size[2] = global_size[2];
+    info.output_size[0] = tensor->ne[0];
+    info.output_size[1] = tensor->ne[1];
+    info.output_size[2] = tensor->ne[2];
+    info.output_size[3] = tensor->ne[3];
+}
+#endif
+
+//------------------------------------------------------------------------------
+// Ops
+//------------------------------------------------------------------------------
+
+static bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+            src1->type == GGML_TYPE_F32 &&
+             dst->type == GGML_TYPE_F32 &&
+            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
+}
+
+static void ggml_cl_nop(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    UNUSED(backend);
+    UNUSED(src0);
+    UNUSED(src1);
+    UNUSED(dst);
+}
+
+static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const int      ne00 = src0 ? src0->ne[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
+    const int      ne10 = src1 ? src1->ne[0] : 0;
+    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
+    const int      ne11 = src1 ? src1->ne[1] : 0;
+    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
+    const cl_ulong nb1  = dst  ?  dst->nb[1] : 0;
+    const cl_ulong nb2  = dst  ?  dst->nb[2] : 0;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            kernel = backend_ctx->kernel_get_rows_f32;
+            break;
+        case GGML_TYPE_F16:
+            kernel = backend_ctx->kernel_get_rows_f16;
+            break;
+        case GGML_TYPE_Q4_0:
+            kernel = backend_ctx->kernel_get_rows_q4_0;
+            break;
+        default:
+            GGML_ASSERT(false && "not implemented");
+    }
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb10));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb2));
+
+    size_t global_work_size[] = {(size_t)ne10, (size_t)ne11, 1};
+    size_t local_work_size[] = {1, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const int  ne00 = src0 ? src0->ne[0] : 0;
+    const int  ne01 = src0 ? src0->ne[1] : 0;
+    const int  ne02 = src0 ? src0->ne[2] : 0;
+    const int  ne03 = src0 ? src0->ne[3] : 0;
+
+    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
+    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+
+    const int  ne10 = src1 ? src1->ne[0] : 0;
+    const int  ne11 = src1 ? src1->ne[1] : 0;
+    const int  ne12 = src1 ? src1->ne[2] : 0;
+    const int  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+
+    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
+    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
+    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
+    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+
+    const int  ne0  = dst ? dst->ne[0] : 0;
+    const int  ne1  = dst ? dst->ne[1] : 0;
+    const int  ne2  = dst ? dst->ne[2] : 0;
+    const int  ne3  = dst ? dst->ne[3] : 0;
+
+    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
+    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
+    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
+    const cl_ulong nb3  = dst ? dst->nb[3] : 0;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    bool bcast_row = false;
+    cl_kernel kernel;
+
+    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+
+        // src1 is a row
+        GGML_ASSERT(ne11 == 1);
+
+        bcast_row = true;
+        int ne = ne00 / 4;
+        kernel = backend_ctx->kernel_add_row;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
+    } else {
+        kernel = backend_ctx->kernel_add;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
+        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
+        CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
+        CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
+        CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
+        CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
+        CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
+    }
+
+    if (bcast_row) {
+        int n = ggml_nelements(dst)/4;
+        size_t global_work_size[] = {(size_t)n, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        unsigned int nth = MIN(64, ne0);
+        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
+        size_t local_work_size[] = {nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
+static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const int ne00 = src0 ? src0->ne[0] : 0;
+    const int ne01 = src0 ? src0->ne[1] : 0;
+    const int ne02 = src0 ? src0->ne[2] : 0;
+    const int ne03 = src0 ? src0->ne[3] : 0;
+
+    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
+    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+
+    const int ne10 = src1 ? src1->ne[0] : 0;
+    const int ne11 = src1 ? src1->ne[1] : 0;
+    const int ne12 = src1 ? src1->ne[2] : 0;
+    const int ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+
+    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
+    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
+    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
+    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+
+    const int ne0  = dst ? dst->ne[0] : 0;
+    const int ne1  = dst ? dst->ne[1] : 0;
+    const int ne2  = dst ? dst->ne[2] : 0;
+    const int ne3  = dst ? dst->ne[3] : 0;
+
+    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
+    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
+    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
+    const cl_ulong nb3  = dst ? dst->nb[3] : 0;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    bool bcast_row = false;
+    cl_kernel kernel;
+
+    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+
+        // src1 is a row
+        GGML_ASSERT(ne11 == 1);
+
+        bcast_row = true;
+        int ne = ne00 / 4;
+        kernel = backend_ctx->kernel_mul_row;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
+    } else {
+        kernel = backend_ctx->kernel_mul;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
+        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
+        CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
+        CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
+        CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
+        CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
+        CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
+    }
+
+    if (bcast_row) {
+        int n = ggml_nelements(dst)/4;
+        size_t global_work_size[] = {(size_t)n, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        unsigned int nth = MIN(64, ne0);
+        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
+        size_t local_work_size[] = {nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
+static void ggml_cl_gelu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+
+    int n = ggml_nelements(dst);
+
+    if (n % 4 == 0) {
+        kernel = backend_ctx->kernel_gelu_4;
+        n /= 4;
+    } else {
+        kernel = backend_ctx->kernel_gelu;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt);
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL);
+#endif
+}
+
+static void ggml_cl_silu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+
+    int n = ggml_nelements(dst);
+
+    if (n % 4 == 0) {
+        kernel = backend_ctx->kernel_silu_4;
+        n /= 4;
+    } else {
+        kernel = backend_ctx->kernel_silu;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_relu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel = backend_ctx->kernel_relu;
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+
+    const int64_t n = ggml_nelements(dst);
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_clamp(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    float min;
+    float max;
+    memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
+    memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));
+
+    cl_kernel kernel = backend_ctx->kernel_clamp;
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(float),    &min));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(float),    &max));
+
+    const int64_t n = ggml_nelements(dst);
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    const int ne00 = src0 ? src0->ne[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+
+    const int nth = MIN(64, ne00);
+
+    cl_kernel kernel = backend_ctx->kernel_norm;
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),    &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong),  &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),    &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong),  &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),       &ne00));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong),  &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float),     &eps));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth, NULL));
+
+    const int64_t nrows = ggml_nrows(src0);
+
+    size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_rms_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_backend_opencl_device_context * dev_ctx =
+        (ggml_backend_opencl_device_context *)backend->device->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    const int ne00 = src0 ? src0->ne[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+
+    GGML_ASSERT(ne00 % 4 == 0);
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+
+    const int nth = MIN(64, ne00);
+
+    const int64_t nrows = ggml_nrows(src0);
+
+    size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+    cl_kernel kernel = backend_ctx->kernel_rms_norm;
+
+    // Note, this kernel declares local memory in kernel args and the size
+    // depends on subgroup size.
+    // Retrieve subgroup size.
+    // Note, this requires OpenCL 2.1 and above
+    size_t sgs;
+    CL_CHECK(clGetKernelSubGroupInfo(kernel, dev_ctx->device,
+        CL_KERNEL_MAX_SUB_GROUP_SIZE_FOR_NDRANGE,
+        sizeof(local_work_size), local_work_size,
+        sizeof(size_t), &sgs, NULL));
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),    &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong),  &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),    &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong),  &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),       &ne00));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong),  &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float),     &eps));
+    // This is local memory - the size depends on subgroup size.
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth/sgs,  NULL));
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+#ifdef GGML_OPENCL_SOA_Q
+    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+#endif
+
+    const int  ne00 = src0 ? src0->ne[0] : 0;
+    const int  ne01 = src0 ? src0->ne[1] : 0;
+    const int  ne02 = src0 ? src0->ne[2] : 0;
+    const int  ne03 = src0 ? src0->ne[3] : 0;
+
+    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
+    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+
+    const int  ne10 = src1 ? src1->ne[0] : 0;
+    const int  ne11 = src1 ? src1->ne[1] : 0;
+    const int  ne12 = src1 ? src1->ne[2] : 0;
+    const int  ne13 = src1 ? src1->ne[3] : 0;
+
+    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
+    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
+    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
+    const cl_ulong nb13 = src1 ? src1->nb[3] : 0;
+
+    const int  ne0 = dst ? dst->ne[0] : 0;
+    const int  ne1 = dst ? dst->ne[1] : 0;
+
+    int r2 = ne12/ne02;
+    int r3 = ne13/ne03;
+
+    GGML_ASSERT(ne00 == ne10);
+
+    int nth0 = 32;
+    int nth1 = 1;
+    int nrows = 1;
+    // The number of values produced by each subgroup
+    int ndst = 4;
+
+    cl_kernel kernel;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    cl_context context = backend_ctx->context;
+
+    if (ne01 && ne1 && use_adreno_kernels(src0)) {
+
+    // init CL objects
+    // <--------------------------------------------> //
+    cl_int              status;
+    cl_image_format     img_fmt_1d;
+    cl_image_desc       img_desc_1d;
+    cl_buffer_region    region;
+    cl_mem              A_image1d;
+    cl_mem              B_image1d;
+    cl_mem              B_sub_buffer;
+    cl_mem              C_d;
+    // for B transpose
+    cl_mem B_d;
+    cl_mem B_d_input_image;
+    // <--------------------------------------------> //
+
+    // define matrix dimensions
+    // <--------------------------------------------> //
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+    int padding;
+    // <--------------------------------------------> //
+
+    // q4_0 x fp32
+    if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
+        // TODO: remove duplicate definitions of image description + format -- move to top
+
+        // create an image for A
+        // <--------------------------------------------> //
+        if (N == 1) {
+            img_fmt_1d = { CL_R, CL_UNSIGNED_INT32};
+        } else {
+            img_fmt_1d = { CL_R, CL_FLOAT};
+        }
+        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc_1d.image_width = M * K / 2 / 4;    // Divide by 4 for char -> float
+        img_desc_1d.buffer = extra0_q4_0->q;
+        A_image1d = clCreateImage(
+            context,
+            CL_MEM_READ_ONLY,
+            &img_fmt_1d,
+            &img_desc_1d,
+            NULL,
+            &status);
+        CL_CHECK(status);
+        // <--------------------------------------------> //
+
+
+        // create a sub_buffer for B
+        // <--------------------------------------------> //
+        region.origin = (extra1->offset);
+        region.size = K * N * sizeof(float);
+        B_sub_buffer = clCreateSubBuffer(
+            extra1->data_device,
+            0,
+            CL_BUFFER_CREATE_TYPE_REGION,
+            &region,
+            &status);
+        CL_CHECK(status);
+        // <--------------------------------------------> //
+
+        // transpose activation for Skyler's gemm
+        if (N != 1) {
+            //how many extra elements beyond multiple of 8
+            int extra_elements = N % 8;
+
+            //how much padding to add
+            padding = 0;
+            if (extra_elements > 0){
+                padding = 8 - extra_elements;
+            }
+
+            // Specify the starting offset (in bytes)
+            region.origin = 0;
+            // Specify the size of the sub-buffer (divide by 2 for FP16)
+            region.size = K * (N + padding) * sizeof(float)/2;
+            B_d = clCreateSubBuffer(
+                backend_ctx->B_d_max,
+                0,
+                CL_BUFFER_CREATE_TYPE_REGION,
+                &region,
+                &status);
+            CL_CHECK(status);
+
+            cl_image_format image_format_B_d_input = { CL_RGBA, CL_FLOAT };
+            cl_image_desc image_desc_B_d_input = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(K * N / 4),
+                0, 0, 0, 0, 0, 0, 0, { B_sub_buffer }
+            };
+            B_d_input_image = clCreateImage(
+                context,
+                0,
+                &image_format_B_d_input,
+                &image_desc_B_d_input,
+                NULL,
+                &status);
+            CL_CHECK(status);
+
+            cl_image_format image_format_B_d_output = { CL_RGBA, CL_HALF_FLOAT }; //(CL_HALF_FLOAT for FP16)
+            cl_image_desc image_desc_B_d_output = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(K * (N + padding)/4),
+                0, 0, 0, 0, 0, 0, 0, { B_d }
+            };
+            B_image1d = clCreateImage(
+                context,
+                0,
+                &image_format_B_d_output,
+                &image_desc_B_d_output,
+                NULL,
+                &status);
+            CL_CHECK(status);
+
+            int height_B = N/4;
+            int width_B = K/4;
+            int padded_height_B = (N + padding)/4;
+
+            kernel = backend_ctx->kernel_transpose_32_16;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &B_d_input_image));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &B_image1d));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+            size_t local_size_t[2] = { 1, 16 };
+            //WGS tuning
+            if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
+                local_size_t[0]=4;
+                local_size_t[1]=8;
+            } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
+                local_size_t[0]=2;
+                local_size_t[1]=8;
+            } else if(ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
+                local_size_t[0]=1;
+                local_size_t[1]=8;
+            } else if(ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
+                local_size_t[0]=2;
+                local_size_t[1]=8;
+            }
+
+            size_t global_size_t[2] = {
+                static_cast<size_t>(width_B),
+                static_cast<size_t>(padded_height_B)
+            };
+
+            #ifdef GGML_OPENCL_PROFILING
+                cl_event evt;
+                CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_size_t, local_size_t, 0, NULL, &evt));
+
+                g_profiling_info.emplace_back();
+                populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_size_t, local_size_t, dst);
+            #else
+                CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_size_t, local_size_t, 0, NULL, NULL));
+            #endif
+        } else {
+            // no need to transpose B in other cases
+            // create an image for B from sub_buffer
+            // <--------------------------------------------> //
+            img_fmt_1d = {CL_RGBA, CL_FLOAT};
+
+            memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+            img_desc_1d.image_width = K * N / 4;
+            img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+            img_desc_1d.buffer = B_sub_buffer;
+            B_image1d = clCreateImage(
+                context,
+                CL_MEM_READ_ONLY,
+                &img_fmt_1d,
+                &img_desc_1d,
+                NULL,
+                &status);
+            CL_CHECK(status);
+            // <--------------------------------------------> //
+        }
+
+        // choose gemm or gemv kernel
+        // <--------------------------------------------> //
+        if (N == 1) {
+            kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
+            if (M == 4096 && K == 4096) {
+                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
+            } else if (M == 4096 && K == 11008) {
+                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
+            } else if (M == 11008 && K == 4096) {
+                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
+            } else if (M == 32000 && K == 4096) {
+                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
+            }
+        } else {
+            kernel = backend_ctx->CL_mul_mat_Ab_Bi_8x4;
+        }
+        // <--------------------------------------------> //
+
+        // set kernel args
+        // <--------------------------------------------> //
+        cl_uint k_arg = 0;
+
+        if (N == 1) {
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &A_image1d));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extra0_q4_0->d));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &B_image1d));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extra1->offset));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extrad->offset));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r3));
+        } else {
+            region.origin = extrad->offset; // Specify the starting offset (in bytes)
+            region.size = M * N * sizeof(float); // Specify the size of the sub-buffer
+            C_d = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+            CL_CHECK(status);
+
+            int padded_N = ne1 + padding;
+
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_0->q)); //A_q_dextra0_q4_0->q
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_0->d)); //A_s_d
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &B_image1d)); //B_d
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &C_d)); //C_d
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &ne01)); //M
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),    &padded_N)); //N with padding
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),    &ne00)); //K
+            CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),    &ne1)); //N without padding
+        }
+        // <--------------------------------------------> //
+
+        // choose workgroup size
+        // <--------------------------------------------> //
+        size_t global_work_size[3] = {
+            64, static_cast<size_t>((M+63)/64), static_cast<size_t>((N+31)/32)};
+        size_t local_work_size[3] = {64, 2, 4};
+
+        global_work_size[0] = (size_t)(ceil((float)ne1/8));
+        global_work_size[1] = (size_t)(ne01/4);
+        global_work_size[2] = (size_t)(1);
+
+        local_work_size[0]  = (size_t)(1); //4x32 for FP32
+        local_work_size[1]  = (size_t)(128);
+        local_work_size[2]  = (size_t)(1);
+
+        //WGS tuning
+        if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 1;
+            local_work_size[1] = 128;
+        } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        } else if (ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        } else if (ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        }
+
+        if (N == 1) {
+            local_work_size[0] = backend_ctx->adreno_wave_size; // localsize
+            local_work_size[1] = 4; // reduce factor
+            local_work_size[2] = 1;
+
+            global_work_size[0] = M / 2;
+            global_work_size[1] = 4; // reduce factor
+            global_work_size[2] = 1;
+        }
+        // <--------------------------------------------> //
+
+        // enqueue kernel with profiling
+        // <--------------------------------------------> //
+    #ifdef GGML_OPENCL_PROFILING
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+        // enqueue kernel without profiling
+    #else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+    #endif
+        // <--------------------------------------------> //
+
+        // deallocate sub buffers and images
+        // <--------------------------------------------> //
+        CL_CHECK(clReleaseMemObject(A_image1d));
+        CL_CHECK(clReleaseMemObject(B_sub_buffer));
+        CL_CHECK(clReleaseMemObject(B_image1d));
+
+        if (N != 1) {
+            CL_CHECK(clReleaseMemObject(B_d));
+            CL_CHECK(clReleaseMemObject(B_d_input_image));
+            CL_CHECK(clReleaseMemObject(C_d));
+        }
+        // <--------------------------------------------> //
+
+        return;
+    }
+    } // if (ne01 && ne1)
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+    if (!ggml_is_transposed(src0) &&
+        !ggml_is_transposed(src1) &&
+        src1t == GGML_TYPE_F32 &&
+        ne00%32 == 0 &&
+        ne11 > 2) {
+#ifdef GGML_OPENCL_SOA_Q
+        // Set up kernel.
+        switch(src0t) {
+            case GGML_TYPE_Q4_0:
+                // This should have been satisfied.
+                GGML_ASSERT(ne11 == ne1);
+                GGML_ASSERT(ne01 == ne0);
+
+                if (backend_ctx->gpu_family == INTEL) {
+                    nth0 = 16;
+                    nth1 = 1;
+
+                    kernel = backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat;
+                } else if (backend_ctx->gpu_family == ADRENO) {
+                    nth0 = 64;
+                    nth1 = 1;
+
+                    kernel = backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat;
+                } else {
+                    GGML_ASSERT(false && "TODO: Unknown GPU");
+                }
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+                break;
+            default:
+                break;
+        }
+
+        // Launch kernel.
+        if (src0t == GGML_TYPE_Q4_0) {
+            size_t global_work_size[] = {(size_t)(ne01 + 7)/8*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
+            size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+            if (backend_ctx->gpu_family == INTEL) {
+                // Set global size for Intel. It uses 16x output values.
+                global_work_size[0] = (size_t)(ne01 + 15)/16*nth0;
+                global_work_size[1] = (size_t)ne11*nth1;
+                global_work_size[2] = (size_t)ne12*ne13;
+            }
+
+#ifdef GGML_OPENCL_PROFILING
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+            g_profiling_info.emplace_back();
+            populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+            return;
+        }
+#else // GGML_OPENCL_SOA_Q
+        // TODO: add block_q4_0 variant.
+#endif // GGML_OPENCL_SOA_Q
+    }
+
+    // use custom matrix x vector kernel
+    switch (src0t) {
+        case GGML_TYPE_F32:
+            //GGML_ASSERT(ne02 == ne12);
+            GGML_ASSERT(src1t == GGML_TYPE_F32);
+            kernel = backend_ctx->kernel_mul_mat_f32_f32;
+            nrows = 4;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 32;
+                nth1 = 1;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb00));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
+            break;
+        case GGML_TYPE_F16:
+            //GGML_ASSERT(ne02 == ne12);
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 32;
+                nth1 = 1;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            if (src1t == GGML_TYPE_F32) {
+                if (ne11 * ne12 < 4) {
+                    kernel = backend_ctx->kernel_mul_mat_f16_f32_1row;
+                } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
+                    kernel = backend_ctx->kernel_mul_mat_f16_f32_l4;
+                    nrows = ne11;
+                } else {
+                    kernel = backend_ctx->kernel_mul_mat_f16_f32;
+                    nrows = 4;
+                }
+            } else {
+                kernel = backend_ctx->kernel_mul_mat_f16_f16;
+                nrows = 4;
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb00));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
+            break;
+        case GGML_TYPE_Q4_0:
+            // This should have been satisfied.
+            GGML_ASSERT(ne11 == ne1);
+            GGML_ASSERT(ne01 == ne0);
+
+#ifdef GGML_OPENCL_SOA_Q
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+
+                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat;
+                ndst = 8;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+
+                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat;
+                ndst =8;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+#else // GGML_OPENCL_SOA_Q
+            if (backend_ctx->gpu_family == INTEL) {
+                // Use 1D local size. Each workgroup is a SIMD group. Each SIMD
+                // group produces N_DST (4 for Q4_0 kernel) values in the result.
+                // The number of workgroups on dim 0 (the leading dimension) is
+                // the nearest multiple of 4 that covers ne0 (equals ne01).
+                nth0 = 16;
+                nth1 = 1;
+
+                kernel = backend_ctx->kernel_mul_mat_q4_0_f32;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+
+                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_v;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+#endif // GGML_OPENCL_SOA_Q
+            break;
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            kernel = backend_ctx->kernel_mul_mv_q6_K_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 2;
+                nth1 = 16;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 2;
+                nth1 = 64;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+            break;
+        default:
+            GGML_ASSERT(false && "not implemented");
+    }
+
+    if (src0t == GGML_TYPE_Q4_0 ||
+        src0t == GGML_TYPE_Q4_1 ||
+        src0t == GGML_TYPE_Q8_0 ||
+        src0t == GGML_TYPE_Q2_K) {
+        // Each SIMD group produces N_DST values in the result. Assuming each
+        // workgroup has N_SIMDGROUP SIMD groups, then each workgroup will
+        // produce N_DST*N_SIMDGROUP values in the result. Hence, the grid size
+        // (number of workgroups) will be a nearest multiple of
+        // N_DST*N_SIMDGROUP to cover the size of the dimension. Below, 4 is
+        // N_DST*N_SIMDGROUP (see the kernel for Q4_0 matmul).
+        size_t global_work_size[] = {(size_t)(ne01 + ndst-1)/ndst*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
+        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else if (src0t == GGML_TYPE_Q4_K) {
+        GGML_ASSERT(false && "not implemented");
+    } else if (src0t == GGML_TYPE_Q3_K) {
+        GGML_ASSERT(false && "not implemented");
+    } else if (src0t == GGML_TYPE_Q5_K) {
+        GGML_ASSERT(false && "not implemented");
+    } else if (src0t == GGML_TYPE_Q6_K) {
+        size_t global_work_size[] = {(size_t)(ne01+1)/2*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
+        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        int64_t ny = (ne11 + nrows - 1)/nrows;
+
+        size_t global_work_size[] = {(size_t)ne01*nth0, (size_t)ny*nth1, (size_t)ne12*ne13};
+        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
+static void ggml_cl_scale(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+    GGML_UNUSED(src1);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(scale));
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel = backend_ctx->kernel_scale;
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(float),    &scale));
+
+    int n = ggml_nelements(dst)/4;
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_cpy(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+
+    // GGML_OP_CPY happens between src0 and src1.
+    // GGML_OP_DUP and GGML_OP_CONT happen between src0 and dst.
+    UNUSED(dst);
+
+    const int ne00 = src0 ? src0->ne[0] : 0;
+    const int ne01 = src0 ? src0->ne[1] : 0;
+    const int ne02 = src0 ? src0->ne[2] : 0;
+    const int ne03 = src0 ? src0->ne[3] : 0;
+
+    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
+    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
+    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
+    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+
+    const int ne10 = src1 ? src1->ne[0] : 0;
+    const int ne11 = src1 ? src1->ne[1] : 0;
+    const int ne12 = src1 ? src1->ne[2] : 0;
+    const int ne13 = src1 ? src1->ne[3] : 0;
+
+    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
+    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
+    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
+    const cl_ulong nb13 = src1 ? src1->nb[3] : 0;
+
+    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+
+    cl_kernel kernel;
+
+    switch (src0t) {
+        case GGML_TYPE_F32:
+            switch (src1t) {
+                case GGML_TYPE_F16:
+                    kernel = backend_ctx->kernel_cpy_f32_f16;
+                    break;
+                case GGML_TYPE_F32:
+                    kernel = backend_ctx->kernel_cpy_f32_f32;
+                    break;
+                default:
+                    GGML_ASSERT(false && "not implemented");
+            }
+            break;
+        case GGML_TYPE_F16:
+            switch (src1t) {
+                case GGML_TYPE_F16:
+                    kernel = backend_ctx->kernel_cpy_f16_f16;
+                    break;
+                case GGML_TYPE_F32:
+                    kernel = backend_ctx->kernel_cpy_f16_f32;
+                    break;
+                default:
+                    GGML_ASSERT(false && "not implemented");
+            }
+            break;
+        default:
+            GGML_ASSERT(false && "not implemented");
+    }
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne03));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb00));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne11));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));
+
+    const int nth = MIN(64, ne00);
+
+    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, src1);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_dup(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cl_cpy(backend, src0, dst, nullptr);
+    UNUSED(src1);
+}
+
+static void ggml_cl_diag_mask_inf(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    int n_past = ((int32_t *)(dst->op_params))[0];
+
+    const int  ne00 = src0 ? src0->ne[0] : 0;
+    const int  ne01 = src0 ? src0->ne[1] : 0;
+    const int  ne02 = src0 ? src0->ne[2] : 0;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+
+    if (ne00%8 == 0) {
+        kernel = backend_ctx->kernel_diag_mask_inf_8;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &n_past));
+
+        size_t global_work_size[] = {(size_t)ne00*ne01*ne02/8, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        kernel = backend_ctx->kernel_diag_mask_inf;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &n_past));
+
+        size_t global_work_size[] = {(size_t)ne00, (size_t)ne01, (size_t)ne02};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
+static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    // Softmax can now fuse KQ mask and KQ scale, which used to be two additional
+    // ops before softmax. It now also fuses alibi if `max_bias > 0`. For llama,
+    // alibi is not used; however, for some other models, it is used.
+    // KQ_mask
+    if (src1) {
+        GGML_ASSERT(src1);
+        GGML_ASSERT(src1->extra);
+    }
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    ggml_tensor_extra_cl * extra1 = src1 ? (ggml_tensor_extra_cl *)src1->extra : nullptr;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_ulong offset1 = extra1 ? extra1->offset + src1->view_offs : offset0;
+
+    const int  ne00 = src0 ? src0->ne[0] : 0;
+    const int  ne01 = src0 ? src0->ne[1] : 0;
+    const int  ne02 = src0 ? src0->ne[2] : 0;
+    const int  ne03 = src0 ? src0->ne[3] : 0;
+
+    float scale, max_bias;
+    memcpy(&scale,    dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, dst->op_params + 1, sizeof(float));
+
+    const int nrows_x = ggml_nrows(src0);
+    const int nrows_y = src0->ne[1];
+
+    const int n_head      = nrows_x/nrows_y;
+    const int n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    // Local size must be wave size. Each workgroup is a wave, working on a row,
+    // where a row corresponds to leading dimension.
+    int nth = MIN(32, ne00);
+
+    if (backend_ctx->gpu_family == INTEL) {
+        // This is the same as the initial value.
+        nth = MIN(32, ne00);
+    }
+    else if (backend_ctx->gpu_family == ADRENO) {
+        nth = 64;
+    } else {
+        GGML_ASSERT(false && "TODO: Unknown GPU");
+    }
+
+    cl_kernel kernel;
+
+    if (ne00%4 == 0) {
+        kernel = backend_ctx->kernel_soft_max_4;
+    } else {
+        kernel = backend_ctx->kernel_soft_max;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   extra1 ? &extra1->data_device : &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(float),    &scale));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(float),    &max_bias));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float),    &m0));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float),    &m1));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &n_head_log2));
+
+    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    ggml_tensor * src2 = dst->src[2];
+    ggml_tensor_extra_cl * extra2 = src2 ? (ggml_tensor_extra_cl *)src2->extra : nullptr;
+
+    cl_ulong offset2 = extra2 ? extra2->offset + src2->view_offs : offset0;
+
+    const int  ne00 = src0 ? src0->ne[0] : 0;
+    const int  ne01 = src0 ? src0->ne[1] : 0;
+    const int  ne02 = src0 ? src0->ne[2] : 0;
+    const int  ne03 = src0 ? src0->ne[3] : 0;
+
+    const int  nb00 = src0 ? src0->nb[0] : 0;
+    const int  nb01 = src0 ? src0->nb[1] : 0;
+    const int  nb02 = src0 ? src0->nb[2] : 0;
+    const int  nb03 = src0 ? src0->nb[3] : 0;
+
+    const int ne10 = src1 ? src1->ne[0] : 0;
+    const int ne11 = src1 ? src1->ne[1] : 0; UNUSED(ne11);
+    const int ne12 = src1 ? src1->ne[2] : 0; UNUSED(ne12);
+    const int ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+
+    const int  ne0 = dst ? dst->ne[0] : 0;
+    const int  ne1 = dst ? dst->ne[1] : 0;
+    const int  ne2 = dst ? dst->ne[2] : 0;
+    const int  ne3 = dst ? dst->ne[3] : 0;
+
+    const int  nb0 = dst ? dst->nb[0] : 0;
+    const int  nb1 = dst ? dst->nb[1] : 0;
+    const int  nb2 = dst ? dst->nb[2] : 0;
+    const int  nb3 = dst ? dst->nb[3] : 0;
+
+    GGML_ASSERT(ne10 == ne02);
+
+    int nth = MIN(64, ne00);
+
+    const int n_past     = ((int *) dst->op_params)[0];
+    const int n_dims     = ((int *) dst->op_params)[1];
+    const int mode       = ((int *) dst->op_params)[2];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+
+    memcpy(&freq_base,   (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const bool is_neox = mode & 2;
+
+    cl_kernel kernel;
+
+    if (!is_neox) {
+        switch (src0->type) {
+            case GGML_TYPE_F32:
+                kernel = backend_ctx->kernel_rope_norm_f32;
+                break;
+            case GGML_TYPE_F16:
+                kernel = backend_ctx->kernel_rope_norm_f16;
+                break;
+            default:
+                GGML_ASSERT(false);
+        };
+    } else {
+        switch (src0->type) {
+            case GGML_TYPE_F32:
+                kernel = backend_ctx->kernel_rope_neox_f32;
+                break;
+            case GGML_TYPE_F16:
+                kernel = backend_ctx->kernel_rope_neox_f16;
+                break;
+            default:
+                GGML_ASSERT(false);
+        };
+    }
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   extra2 ? &extra2->data_device : &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne03));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb00));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne0));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne1));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne2));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &ne3));
+    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb0));
+    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &nb2));
+    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &nb3));
+    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &n_past));
+    CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &n_dims));
+    CL_CHECK(clSetKernelArg(kernel, 26, sizeof(int),      &n_ctx_orig));
+    CL_CHECK(clSetKernelArg(kernel, 27, sizeof(float),    &freq_base));
+    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(float),    &freq_scale));
+    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(float),    &ext_factor));
+    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(float),    &attn_factor));
+    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(float),    &beta_fast));
+    CL_CHECK(clSetKernelArg(kernel, 32, sizeof(float),    &beta_slow));
+
+    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
+    size_t local_work_size[] = {(size_t)nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+//------------------------------------------------------------------------------
+// Op offloading
+//------------------------------------------------------------------------------
+
+typedef void (*ggml_cl_func_t)(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+
+bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor) {
+    ggml_cl_func_t func = nullptr;
+
+    ggml_tensor * src0 = tensor->src[0];
+    ggml_tensor * src1 = tensor->src[1];
+
+    const bool any_on_device = tensor->extra
+        || (src0 != nullptr && src0->extra)
+        || (src1 != nullptr && src1->extra);
+
+    switch (tensor->op) {
+        case GGML_OP_GET_ROWS:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_get_rows;
+            break;
+        case GGML_OP_CPY:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_cpy;
+            break;
+        case GGML_OP_DUP:
+        case GGML_OP_CONT:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_dup;
+            break;
+        case GGML_OP_ADD:
+            if (!any_on_device) {
+                return false;
+            }
+            GGML_ASSERT(ggml_is_contiguous(src0));
+            GGML_ASSERT(ggml_is_contiguous(src1));
+            func = ggml_cl_add;
+            break;
+        case GGML_OP_MUL:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_mul;
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(tensor)) {
+                case GGML_UNARY_OP_GELU:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_gelu;
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_silu;
+                    break;
+                case GGML_UNARY_OP_RELU:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_relu;
+                    break;
+                default:
+                    return false;
+            } break;
+        case GGML_OP_CLAMP:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_clamp;
+            break;
+        case GGML_OP_NORM:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_norm;
+            break;
+        case GGML_OP_RMS_NORM:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_rms_norm;
+            break;
+        case GGML_OP_MUL_MAT:
+            if (!any_on_device && !ggml_cl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
+                return false;
+            }
+            func = ggml_cl_mul_mat;
+            break;
+        case GGML_OP_SCALE:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_scale;
+            break;
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_nop;
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_diag_mask_inf;
+            break;
+        case GGML_OP_SOFT_MAX:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_soft_max;
+            break;
+        case GGML_OP_ROPE:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_rope;
+            break;
+        default:
+            return false;
+    }
+
+    func(backend, tensor->src[0], tensor->src[1], tensor);
+    return true;
+}
diff --git a/ggml/src/ggml-opencl/kernels/embed_kernel.py b/ggml/src/ggml-opencl/kernels/embed_kernel.py
new file mode 100644
index 000000000..b5d1d7242
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/embed_kernel.py
@@ -0,0 +1,26 @@
+#
+
+import sys
+import logging
+logger = logging.getLogger("opencl-embed-kernel")
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+
+    if len(sys.argv) != 3:
+        logger.info("Usage: python embed_kernel.py <input_file> <output_file>")
+        sys.exit(1)
+
+    ifile = open(sys.argv[1], "r")
+    ofile = open(sys.argv[2], "w")
+
+    for i in ifile:
+        ofile.write('R"({})"\n'.format(i))
+
+    ifile.close()
+    ofile.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl.cl
new file mode 100644
index 000000000..d1cdf709b
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl.cl
@@ -0,0 +1,2683 @@
+#ifdef cl_khr_fp16
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#elif defined(cl_amd_fp16)
+#pragma OPENCL EXTENSION cl_amd_fp16 : enable
+#else
+#error "Half precision floating point not supportedby OpenCL implementation on your device."
+#endif
+
+#ifdef cl_khr_subgroups
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#elif defined(cl_intel_subgroups)
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#error "Subgroup not supported on your device."
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+// Always use subgroup size of 32 on Intel.
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+// Always use subgroups size of 64 on Adreno.
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#else
+// TODO: do not know how to choose subgroup size on other GPUs.
+#error "Selecting subgroup size is not supported on your device."
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+//------------------------------------------------------------------------------
+// block_q4_1
+//------------------------------------------------------------------------------
+struct block_q4_1
+{
+    half d;
+    half m;
+    uint8_t qs[QK4_1 / 2];
+};
+
+//------------------------------------------------------------------------------
+// block_q5_0
+//------------------------------------------------------------------------------
+struct block_q5_0
+{
+    half d;
+    uint32_t qh;
+    uint8_t qs[QK5_0 / 2];
+};
+
+//------------------------------------------------------------------------------
+// block_q5_1
+//------------------------------------------------------------------------------
+struct block_q5_1
+{
+    half d;
+    half m;
+    uint32_t qh;
+    uint8_t qs[QK5_1 / 2];
+};
+
+//------------------------------------------------------------------------------
+// block_q8_0
+//------------------------------------------------------------------------------
+struct block_q8_0
+{
+    half d;
+    int8_t qs[QK8_0];
+};
+
+//------------------------------------------------------------------------------
+// block_q2_K
+//------------------------------------------------------------------------------
+struct block_q2_K
+{
+    uint8_t scales[16];
+    uint8_t qs[64];
+    half d;
+    half dmin;
+};
+
+//------------------------------------------------------------------------------
+// block_q3_K
+//------------------------------------------------------------------------------
+struct block_q3_K
+{
+    uint8_t hmask[32];
+    uint8_t qs[64];
+    uint8_t scales[12];
+    half d;
+};
+
+//------------------------------------------------------------------------------
+// block_q4_K
+//------------------------------------------------------------------------------
+struct block_q4_K
+{
+    half d;
+    half dmin;
+    uint8_t scales[12];
+    uint8_t qs[128];
+};
+
+//------------------------------------------------------------------------------
+// block_q5_K
+//------------------------------------------------------------------------------
+struct block_q5_K
+{
+    half d;
+    half dmin;
+    uint8_t scales[12];
+    uint8_t qh[32];
+    uint8_t qs[128];
+};
+
+//------------------------------------------------------------------------------
+// block_q6_K
+//------------------------------------------------------------------------------
+struct block_q6_K
+{
+    uint8_t ql[128];
+    uint8_t qh[64];
+    int8_t scales[16];
+    half d;
+};
+
+//------------------------------------------------------------------------------
+// dequantize_q4_0_f32, dequantize_q4_0_f16
+//------------------------------------------------------------------------------
+void dequantize_q4_0_f32(global struct block_q4_0 * xb, short il, float16 * reg) {
+    global ushort * qs = ((global ushort *)xb + 1);
+    float d1 = il ? (xb->d / 16.h) : xb->d;
+    float d2 = d1 / 256.f;
+    float md = -8.h * xb->d;
+    ushort mask0 = il ? 0x00F0 : 0x000F;
+    ushort mask1 = mask0 << 8;
+
+    reg->s0 = d1 * (qs[0] & mask0) + md;
+    reg->s1 = d2 * (qs[0] & mask1) + md;
+
+    reg->s2 = d1 * (qs[1] & mask0) + md;
+    reg->s3 = d2 * (qs[1] & mask1) + md;
+
+    reg->s4 = d1 * (qs[2] & mask0) + md;
+    reg->s5 = d2 * (qs[2] & mask1) + md;
+
+    reg->s6 = d1 * (qs[3] & mask0) + md;
+    reg->s7 = d2 * (qs[3] & mask1) + md;
+
+    reg->s8 = d1 * (qs[4] & mask0) + md;
+    reg->s9 = d2 * (qs[4] & mask1) + md;
+
+    reg->sa = d1 * (qs[5] & mask0) + md;
+    reg->sb = d2 * (qs[5] & mask1) + md;
+
+    reg->sc = d1 * (qs[6] & mask0) + md;
+    reg->sd = d2 * (qs[6] & mask1) + md;
+
+    reg->se = d1 * (qs[7] & mask0) + md;
+    reg->sf = d2 * (qs[7] & mask1) + md;
+}
+
+void dequantize_q4_0_f16(global struct block_q4_0 * xb, short il, half16 * reg) {
+    global ushort * qs = ((global ushort *)xb + 1);
+    half d1 = il ? (xb->d / 16.h) : xb->d;
+    half d2 = d1 / 256.h;
+    half md = -8.h * xb->d;
+    ushort mask0 = il ? 0x00F0 : 0x000F;
+    ushort mask1 = mask0 << 8;
+
+    reg->s0 = d1 * (qs[0] & mask0) + md;
+    reg->s1 = d2 * (qs[0] & mask1) + md;
+
+    reg->s2 = d1 * (qs[1] & mask0) + md;
+    reg->s3 = d2 * (qs[1] & mask1) + md;
+
+    reg->s4 = d1 * (qs[2] & mask0) + md;
+    reg->s5 = d2 * (qs[2] & mask1) + md;
+
+    reg->s6 = d1 * (qs[3] & mask0) + md;
+    reg->s7 = d2 * (qs[3] & mask1) + md;
+
+    reg->s8 = d1 * (qs[4] & mask0) + md;
+    reg->s9 = d2 * (qs[4] & mask1) + md;
+
+    reg->sa = d1 * (qs[5] & mask0) + md;
+    reg->sb = d2 * (qs[5] & mask1) + md;
+
+    reg->sc = d1 * (qs[6] & mask0) + md;
+    reg->sd = d2 * (qs[6] & mask1) + md;
+
+    reg->se = d1 * (qs[7] & mask0) + md;
+    reg->sf = d2 * (qs[7] & mask1) + md;
+}
+
+//------------------------------------------------------------------------------
+// add
+//------------------------------------------------------------------------------
+
+// general-purpose kernel for addition of two tensors
+// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
+// cons: not very efficient
+kernel void kernel_add(
+        global char * src0,
+        ulong  offset0,
+        global char * src1,
+        ulong  offset1,
+        global char * dst,
+        ulong  offsetd,
+        int   ne00,
+        int   ne01,
+        int   ne02,
+        int   ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int   ne10,
+        int   ne11,
+        int   ne12,
+        int   ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int   ne0,
+        int   ne1,
+        int   ne2,
+        int   ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) + *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_add_row(
+        global float4 * src0,
+        ulong  offset0,
+        global float4 * src1,
+        ulong  offset1,
+        global float4 * dst,
+        ulong  offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] + src1[idx1];
+}
+
+//------------------------------------------------------------------------------
+// mul
+//------------------------------------------------------------------------------
+kernel void kernel_mul(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) * *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_mul_row(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * src1,
+        ulong offset1,
+        global float4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] * src1[idx1];
+}
+
+//------------------------------------------------------------------------------
+// scale
+//------------------------------------------------------------------------------
+kernel void kernel_scale(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd,
+        float scale
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+    dst[get_global_id(0)] = src0[get_global_id(0)] * scale;
+}
+
+//------------------------------------------------------------------------------
+// gelu
+//------------------------------------------------------------------------------
+#define GELU_COEF_A     0.044715f
+#define SQRT_2_OVER_PI  0.79788456080286535587989211986876f
+
+kernel void kernel_gelu(
+    global float * src0,
+    ulong offset0,
+    global float * dst,
+    ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    float x = src0[get_global_id(0)];
+
+    dst[get_global_id(0)] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+kernel void kernel_gelu_4(
+    global float4 * src0,
+    ulong offset0,
+    global float4 * dst,
+    ulong offsetd
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    float4 x = src0[get_global_id(0)];
+
+    dst[get_global_id(0)] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+//------------------------------------------------------------------------------
+// silu
+//------------------------------------------------------------------------------
+kernel void kernel_silu(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    float x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x / (1.0f + exp(-x));
+}
+
+kernel void kernel_silu_4(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    float4 x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x / (1.0f + exp(-x));
+}
+
+//------------------------------------------------------------------------------
+// relu
+//------------------------------------------------------------------------------
+kernel void kernel_relu(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = fmax(0.0f, src0[get_global_id(0)]);
+}
+
+//------------------------------------------------------------------------------
+// clamp
+//------------------------------------------------------------------------------
+kernel void kernel_clamp(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        float min,
+        float max
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = src0[get_global_id(0)] < min ?
+        min :
+        (src0[get_global_id(0)] > max ? max : src0[get_global_id(0)]);
+}
+
+//------------------------------------------------------------------------------
+// norm
+//------------------------------------------------------------------------------
+kernel void kernel_norm(
+        global void * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        float eps,
+        local float * sum
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    dst = (global void*)((global char*)dst + offsetd);
+
+    global float * x = (global float *) ((global char *) src0 + get_group_id(0)*nb01);
+
+    // MEAN
+    // parallel sum
+    sum[get_local_id(0)] = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        sum[get_local_id(0)] += x[i00];
+    }
+    // reduce
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (uint i = get_local_size(0)/2; i > 0; i /= 2) {
+        if (get_local_id(0) < i) {
+            sum[get_local_id(0)] += sum[get_local_id(0) + i];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    float mean  = sum[0] / ne00;
+
+    // recenter and VARIANCE
+    barrier(CLK_LOCAL_MEM_FENCE);
+    global float * y = dst + get_group_id(0)*ne00;
+    sum[get_local_id(0)] = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        y[i00] = x[i00] - mean;
+        sum[get_local_id(0)] += y[i00] * y[i00];
+    }
+
+    // reduce
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (uint i = get_local_size(0)/2; i > 0; i /= 2) {
+        if (get_local_id(0) < i) {
+            sum[get_local_id(0)] += sum[get_local_id(0) + i];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    float variance = sum[0] / ne00;
+
+    float scale = 1.0f/sqrt(variance + eps);
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        y[i00] = y[i00] * scale;
+    }
+}
+
+//------------------------------------------------------------------------------
+// rms_norm
+//------------------------------------------------------------------------------
+// This kernel depends on subgroup size.
+kernel void kernel_rms_norm(
+        global void * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        float eps,
+        local float * sum // Note, the size depends on number of subgroups
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    global float4 * x = (global float4 *) ((global char *) src0 + get_group_id(0)*nb01);
+    global float * x_scalar = (global float *) x;
+    float4 sumf = 0;
+    float all_sum = 0;
+
+    // parallel sum
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        sumf += x[i00] * x[i00];
+    }
+    all_sum = sumf.s0 + sumf.s1 + sumf.s2 + sumf.s3;
+    all_sum = sub_group_reduce_add(all_sum);
+    if (get_sub_group_local_id() == 0) {
+        sum[get_sub_group_id()] = all_sum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    // broadcast
+    for (uint i = get_local_size(0) / get_max_sub_group_size() / 2; i > 0; i /= 2) {
+       if (get_local_id(0) < i) {
+           sum[get_local_id(0)] += sum[get_local_id(0) + i];
+       }
+    }
+    if (get_local_id(0) == 0) {
+        for (int i = 4 * (ne00 / 4); i < ne00; i++) {
+            sum[0] += x_scalar[i];
+        }
+        sum[0] /= ne00;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float mean  = sum[0];
+    const float scale = 1.0f/sqrt(mean + eps);
+
+    global float4 * y = (global float4 *) (dst + get_group_id(0)*ne00);
+    global float * y_scalar = (global float *) y;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        y[i00] = x[i00] * scale;
+    }
+    if (get_local_id(0) == 0) {
+        for (int i00 = 4 * (ne00 / 4); i00 < ne00; i00++) {
+            y_scalar[i00] = x_scalar[i00] * scale;
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// diag_mask_inf kernels
+//------------------------------------------------------------------------------
+kernel void kernel_diag_mask_inf(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int n_past
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i02 = get_global_id(2);
+    int i01 = get_global_id(1);
+    int i00 = get_global_id(0);
+
+    if (i00 > n_past + i01) {
+        dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY;
+    } else {
+        dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00];
+    }
+}
+
+kernel void kernel_diag_mask_inf_8(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int n_past
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    int i = 2*get_global_id(0);
+
+    dst[i+0] = src0[i+0];
+    dst[i+1] = src0[i+1];
+    int i4 = 4*i;
+    int i02 = i4/(ne00*ne01); i4 -= i02*ne00*ne01;
+    int i01 = i4/(ne00);      i4 -= i01*ne00;
+    int i00 = i4;
+    for (int k = 3; k >= 0; --k) {
+        if (i00 + 4 + k <= n_past + i01) {
+            break;
+        }
+        (&dst[i+1])[k] = -INFINITY;
+        if (i00 + k > n_past + i01) {
+            (&dst[i])[k] = -INFINITY;
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// softmax
+//------------------------------------------------------------------------------
+kernel void kernel_soft_max(
+        global float * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    global float * pmask = src1 != src0 ? src1 + i01*ne00 : 0;
+    global float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float lmax = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
+    }
+    float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float lsum = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max);
+        lsum += exp_psrc0;
+        // Remember the result of exp here. exp is expensive, so we really do not
+        // wish to compute it twice.
+        pdst[i00] = exp_psrc0;
+    }
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        pdst[i00] /= sum;
+    }
+}
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_soft_max_4(
+        global float * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float4 * psrc4 = (global float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    global float4 * pmask = src1 != src0 ? (global float4 *)(src1 + i01*ne00) : 0;
+    global float4 * pdst4 = (global float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float4 lmax4 = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
+    }
+    float lmax = fmax(fmax(lmax4.s0, lmax4.s1), fmax(lmax4.s2, lmax4.s3));
+
+    const float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float4 lsum4 = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max);
+        lsum4 += exp_psrc4;
+        pdst4[i00] = exp_psrc4;
+    }
+    float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        pdst4[i00] /= sum;
+    }
+}
+
+//------------------------------------------------------------------------------
+// kernel_rope
+//------------------------------------------------------------------------------
+float rope_yarn_ramp(float low, float high, int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+float2 rope_yarn(
+    float theta_extrap, float freq_scale, float2 corr_dims, int i0, float ext_factor, float mscale
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.s0, corr_dims.s1, i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
+    }
+    return (float2)(cos(theta) * mscale, sin(theta) * mscale);
+}
+
+// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
+// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
+float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
+    return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base));
+}
+
+float2 rope_yarn_corr_dims(
+    int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow
+) {
+    // start and end correction dims
+    return (float2)(
+        max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))),
+        min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)))
+    );
+}
+
+kernel void kernel_rope_norm_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global float * src       = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data  = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            float x0 = src[0];
+            float x1 = src[1];
+
+            dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[1] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_norm_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global half * src       = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            float x0 = src[0];
+            float x1 = src[1];
+
+            dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[1] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global half * src      = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_neox_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global float * const src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data  = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_neox_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global half * src       = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global half * const src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// cpy
+//------------------------------------------------------------------------------
+
+kernel void kernel_cpy_f16_f16(
+        global half * src0,
+        ulong offset0,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global half*)((global char*)src0 + offset0);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global half * dst_data = (global half *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const half * src = (global half *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f16_f32(
+        global half * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+
+    src0 = (global half*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global float * dst_data = (global float *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global half * src = (global half *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f32_f16(
+        global float * src0,
+        ulong offset0,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global half * dst_data = (global half *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const float * src = (global float *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f32_f32(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global float * dst_data = (global float *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const float * src = (global float *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        dst_data[i00] = src[0];
+    }
+}
+
+//------------------------------------------------------------------------------
+// get_rows
+//------------------------------------------------------------------------------
+kernel void kernel_get_rows_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00; ind += get_local_size(0)) {
+        ((global float *) ((global char *) dst + i11*nb2 + i10*nb1))[ind] =
+            ((global float *) ((global char *) src0 + r*nb01 + i02*nb02))[ind];
+    }
+}
+
+kernel void kernel_get_rows_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int32_t *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00; ind += get_local_size(0)) {
+        ((global float *) ((global char *) dst + i11*nb2 + i10*nb1))[ind] =
+            ((global half *) ((global char *) src0 + r*nb01 + i02*nb02))[ind];
+    }
+}
+
+kernel void kernel_get_rows_q4_0(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    const int NL = 2;
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int32_t *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00/16; ind += get_local_size(0)) {
+        float16 temp;
+        dequantize_q4_0_f32(
+            ((global struct block_q4_0 *) ((global char *) src0 + r*nb01 + i02*nb02)) + ind/NL, ind%NL, &temp);
+        *(((global float16 *) ((global char *) dst + i11*nb2 + i10*nb1)) + ind) = temp;
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_mat_f32_f32
+//------------------------------------------------------------------------------
+#define N_F32_F32 4
+
+kernel void kernel_mul_mat_f32_f32(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F32_F32;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global float * x = (global float *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float * y = (global float *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += (float) x[i] * (float) y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global float4 * x4 = (global float4 *)x;
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float  * y  = (global float  *) (src1 + offset_src1);
+            global float4 * y4 = (global float4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += (float) x4[i].s0 * y4[i].s0;
+                sumf += (float) x4[i].s1 * y4[i].s1;
+                sumf += (float) x4[i].s2 * y4[i].s2;
+                sumf += (float) x4[i].s3 * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (float) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_mat_f16_f16
+//------------------------------------------------------------------------------
+#define N_F16_F16 4
+
+kernel void kernel_mul_mat_f16_f16(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3)
+{
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F16_F16;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half * x = (global half *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F16_F16; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global half * y = (global half *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += (half) x[i] * (half) y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global half4 * x4 = (global half4 *)x;
+        for (int row = 0; row < N_F16_F16; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global half  * y  = (global half  *) (src1 + offset_src1);
+            global half4 * y4 = (global half4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += (half) x4[i].s0 * y4[i].s0;
+                sumf += (half) x4[i].s1 * y4[i].s1;
+                sumf += (half) x4[i].s2 * y4[i].s2;
+                sumf += (half) x4[i].s3 * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (half) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_mat_f16_f32_1row
+//------------------------------------------------------------------------------
+kernel void kernel_mul_mat_f16_f32_1row(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global half  * x = (global half  *) (src0 + offset_src0);
+    global float * y = (global float *) (src1 + offset_src1);
+
+    float sumf = 0;
+    if (ne00 < 128) {
+        for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+            sumf += (float) x[i] * (float) y[i];
+        }
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    } else {
+        global half4  * x4 = (global half4  *) x;
+        global float4 * y4 = (global float4 *) y;
+        for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+            sumf += (float) x4[i].s0 * y4[i].s0;
+            sumf += (float) x4[i].s1 * y4[i].s1;
+            sumf += (float) x4[i].s2 * y4[i].s2;
+            sumf += (float) x4[i].s3 * y4[i].s3;
+        }
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            for (int i = 4*(ne00/4); i < ne00; ++i) {
+                all_sum += (float) x[i] * y[i];
+            }
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
+
+}
+
+//------------------------------------------------------------------------------
+// mul_mat_f16_f32
+//------------------------------------------------------------------------------
+#define N_F16_F32 4
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F16_F32;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half * x = (global half *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float * y = (global float *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += convert_float(x[i]) * y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global half4 * x4 = (global half4 *)x;
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float  * y  = (global float  *) (src1 + offset_src1);
+            global float4 * y4 = (global float4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += convert_float(x4[i].s0) * y4[i].s0;
+                sumf += convert_float(x4[i].s1) * y4[i].s1;
+                sumf += convert_float(x4[i].s2) * y4[i].s2;
+                sumf += convert_float(x4[i].s3) * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (float) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_mat_f16_f32_l4
+//------------------------------------------------------------------------------
+// Assumes row size (ne00) is a multiple of 4
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int nrows = ne11;
+    int r0 = get_group_id(0);
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half4 * x4 = (global half4 *) (src0 + offset_src0);
+
+    for (int r1 = 0; r1 < nrows; ++r1) {
+        ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+        global float4 * y4 = (global float4 *) (src1 + offset_src1);
+
+        float sumf = 0;
+        for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+            sumf += convert_float(x4[i].s0) * y4[i].s0;
+            sumf += convert_float(x4[i].s1) * y4[i].s1;
+            sumf += convert_float(x4[i].s2) * y4[i].s2;
+            sumf += convert_float(x4[i].s3) * y4[i].s3;
+        }
+
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_vec_q_n_f32
+//------------------------------------------------------------------------------
+// function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i])
+// il indicates where the q4 quants begin (0 or QK4_0/4)
+// we assume that the yl's have been multiplied with the appropriate scale factor
+// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
+inline float block_q_4_0_dot_y(
+        global struct block_q4_0 * qb_curr,
+        float sumy,
+        private float * yl,
+        int il
+) {
+    float d = qb_curr->d;
+    float2 acc = 0.f;
+    global ushort * qs = ((global ushort *)qb_curr + 1 + il/2);
+    for (int i = 0; i < 8; i+=2) {
+        acc.s0 += yl[i + 0] * (qs[i / 2] & 0x000F)
+                + yl[i + 1] * (qs[i / 2] & 0x0F00);
+        acc.s1 += yl[i + 8] * (qs[i / 2] & 0x00F0)
+                + yl[i + 9] * (qs[i / 2] & 0xF000);
+    }
+    return d * (sumy * -8.f + acc.s0 + acc.s1);
+}
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is essenatially the linear global
+    // id of a SIMD group in the grid.
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_0 * x = (global struct block_q4_0 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];       // src1 vector cache
+    float sumf[N_DST]={0.f};
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_0 + il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+        for (int i = 0; i < 8; i += 2) {
+            sumy += yb[i] + yb[i+1];
+            yl[i+0] = yb[i+ 0];
+            yl[i+1] = yb[i+ 1]/256.f;
+            sumy += yb[i+16] + yb[i+17];
+            yl[i+8] = yb[i+16]/16.f;
+            yl[i+9] = yb[i+17]/4096.f;
+        }
+
+        for (int row = 0; row < N_DST; row++) {
+            sumf[row] += block_q_4_0_dot_y(x+ib+row*nb, sumy, yl, il);
+        }
+
+        // One thread in a SIMD group (i.e., subgroup) handles a half block,
+        // hence then entire SIMD group handles SIMDWIDTH/2 blocks.
+        // y points to the activation matrix (of type float). Therefore for
+        // one thread, the # of blocks y should advance is SIMDWIDTH/2 (because
+        // SIMDWIDTH/2 blocks are processed by a SIMD group) - in terms of
+        // floats, it is QK4_0 * (SIMDWIDTH/2), where QK4_0 is the block size.
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    // The above does not work for Adreno - it produces incorrect results for
+    // row = 1, 2, 3 and only row = 0 gives the correct result.
+    // If N_DST is changed, the below array must be initialized accordingly.
+    // This also seems to perform better on Intel.
+    float tot[N_DST] = {
+        sub_group_reduce_add(sumf[0]), sub_group_reduce_add(sumf[1]),
+        sub_group_reduce_add(sumf[2]), sub_group_reduce_add(sumf[3])};
+    for (int row = 0; row < N_DST; ++row) {
+        if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot[row];
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
+
+//
+// This variant unrolls the loops and uses vector types instead of pointers.
+// It improves performance on Adreno but not so much on Intel.
+//
+inline float block_q_4_0_dot_y_v(
+        global struct block_q4_0 * qb_curr,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float d = qb_curr->d;
+    float acc = 0.f;
+    global ushort * qs = ((global ushort *)qb_curr + 1 + il/2);
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_v(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is essenatially the linear global
+    // id of a SIMD group in the grid.
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_0 * x = (global struct block_q4_0 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;       // src1 vector cache
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_0 + il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_v(x+ib+0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_v(x+ib+1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_v(x+ib+2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_v(x+ib+3*nb, sumy, yl, il);
+
+        // One thread in a SIMD group (i.e., subgroup) handles a half block,
+        // hence then entire SIMD group handles SIMDWIDTH/2 blocks.
+        // y points to the activation matrix (of type float). Therefore for
+        // one thread, the # of blocks y should advance is SIMDWIDTH/2 (because
+        // SIMDWIDTH/2 blocks are processed by a SIMD group) - in terms of
+        // floats, it is QK4_0 * (SIMDWIDTH/2), where QK4_0 is the block size.
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    // The above does not work for Adreno - it produces incorrect results for
+    // row = 1, 2, 3 and only row = 0 gives the correct result.
+    // If N_DST is changed, the below array must be initialized accordingly.
+    // This also seems to perform better on Intel.
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_v(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_v(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
+
+//------------------------------------------------------------------------------
+// kernel_convert_block_q4_0
+// Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA).
+// This kernel does not deshuffle the bits.
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_q4_0(
+    global struct block_q4_0 * src0,
+    global uchar * dst_q,
+    global half  * dst_d
+) {
+    global struct block_q4_0 * b = (global struct block_q4_0 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_0/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+
+    for (int i = 0; i < QK4_0/2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_q4_0(
+    global uchar * src_q,
+    global half  * src_d,
+    global struct block_q4_0 * dst
+) {
+    global struct block_q4_0 * b = (global struct block_q4_0 *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_0/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+
+    b->d = *d;
+    for (int i = 0; i < QK4_0/2; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
+//------------------------------------------------------------------------------
+// mul_vec_q_n_f32_flat
+//
+// This variation uses flat arrays (struct of arrays, SOA) representation for
+// quant tensors.
+//------------------------------------------------------------------------------
+
+// This function requires the original shuffled weights.
+// As a reminder, the original weights are shuffled so that (q[0], q[16]) are
+// packed together in a byte, so are (q[1], q[17]) and so on.
+inline float block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 32
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
+
+//
+// This variant outputs 8 values.
+//
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each SIMD group works on 8 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 32
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float8 sumf = 0.f;
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        sumf.s4 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 4*nb*QK4_0/2, d + ib + 4*nb, sumy, yl, il);
+        sumf.s5 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 5*nb*QK4_0/2, d + ib + 5*nb, sumy, yl, il);
+        sumf.s6 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 6*nb*QK4_0/2, d + ib + 6*nb, sumy, yl, il);
+        sumf.s7 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 7*nb*QK4_0/2, d + ib + 7*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_cvt.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_cvt.cl
new file mode 100644
index 000000000..e2024332f
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_cvt.cl
@@ -0,0 +1,106 @@
+//------------------------------------------------------------------------------
+// This file is contains additional kernels for data conversion.
+// These kernels are used when loading the model, so its performance is less
+// important.
+//------------------------------------------------------------------------------
+#ifdef cl_khr_fp16
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#elif defined(cl_amd_fp16)
+#pragma OPENCL EXTENSION cl_amd_fp16 : enable
+#else
+#error "Half precision floating point not supportedby OpenCL implementation on your device."
+#endif
+
+#ifdef cl_khr_subgroups
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#elif defined(cl_intel_subgroups)
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#error "Subgroup not supported on your device."
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+// Always use subgroup size of 32 on Intel.
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+// Always use subgroups size of 64 on Adreno.
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#else
+// TODO: do not know how to choose subgroup size on other GPUs.
+#error "Selecting subgroup size is not supported on your device."
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+//------------------------------------------------------------------------------
+// mul_vec_q_n_f32_flat_noshuffle
+//
+// This variation uses flat arrays (struct of arrays, SOA) representation for
+// quant tensors. It also uses non shuffled bit order for weights.
+//
+// The shuffled version is kept in the original file because moving it here
+// seems to result in worse performance for adreno.
+//------------------------------------------------------------------------------
+
+kernel void kernel_convert_block_q4_0_noshuffle(
+    global struct block_q4_0 * src0,
+    global uchar * dst_q,
+    global half  * dst_d
+) {
+    global struct block_q4_0 * b = (global struct block_q4_0 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_0/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+    for (int i = 0; i < QK4_0/4; ++i) {
+        uchar x0 = b->qs[2*i + 0];
+        uchar x1 = b->qs[2*i + 1];
+
+        q[i + 0      ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        q[i + QK4_0/4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+
+#ifdef ADRENO_GPU
+        // Workaround for adreno - must have the following printf statement for
+        // the kernel to work properly. Otherwise it produces incorrect result.
+        // convert_uchar above also seems necessary.
+        // Compare against a large number so that it does not print anything.
+        // get_sub_group_local_id() also works.
+        if (get_global_id(0) == 65536*4096) {
+            printf("%04x - %02x\n", *(global ushort*)d, ((x0 & 0xF0) >> 4) | (x1 & 0xF0));
+        }
+#endif
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle.cl
new file mode 100644
index 000000000..5e195411d
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle.cl
@@ -0,0 +1,265 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+// assume
+#define QK4_0 32
+#define N_SIMDGROUP 4
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+
+
+__attribute__((qcom_reqd_sub_group_size("full")))
+__kernel void kernel_gemv_noshuffle(
+        __read_only  image1d_buffer_t src0_q,  // quantized A
+        global half2  * src0_d,  // A scales
+        __read_only  image1d_buffer_t src1,    // B
+        ulong offset1,            // offset to B (0)
+        global float * dst,     // C
+        ulong offsetd,            // offset to C (0)
+        uint K,               // K
+        int ne01,               // M
+        int ne02,               // 1
+        int ne10,               // K
+        int ne12,               // 1
+        int ne0,                // M
+        int ne1,                // N
+        int r2,                 // 1
+        int r3)
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid    = get_sub_group_local_id();
+
+    __private uint4     regA;
+    __private half2     regS;
+    __private float8    regB;
+
+    __private float2 totalSum = (float2)(0.0f);
+
+    // loop along K in block granularity, skip 4 blocks every iter
+    for (uint k = groupId; k < (K / QK4_0); k += N_SIMDGROUP) {
+        regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows
+        // first 4 fibers in each wave load 8 B values to its private scope
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        // load half weights for two blocks in consecutive rows
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+    }
+
+    // reduction in local memory, assumes #wave=4
+    __local float2 reduceLM[SIMDGROUP_WIDTH * 3];
+    if (groupId == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = totalSum;
+    if (groupId == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = totalSum;
+    if (groupId == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = totalSum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 2 outputs per fiber in wave 0
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(totalSum, 0, &(dst[gid * 2]));
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle_general.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle_general.cl
new file mode 100644
index 000000000..5bdd4d067
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_gemv_noshuffle_general.cl
@@ -0,0 +1,271 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+// assume
+#define QK4_0 32
+#define N_SIMDGROUP 4
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) - 8) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F) - 8) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) - 8) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) - 8) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) - 8) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) - 8) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) - 8) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) - 8) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) - 8) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) - 8) * scale.s1 * shared_y.s7; \
+
+
+__attribute__((qcom_reqd_sub_group_size("full")))
+__kernel void kernel_gemv_noshuffle(
+        __read_only  image1d_buffer_t src0_q,  // quantized A
+        global half2  * src0_d,  // A scales
+        __read_only  image1d_buffer_t src1,    // B
+        ulong offset1,            // offset to B (0)
+        global float * dst,     // C
+        ulong offsetd,            // offset to C (0)
+        int ne00,               // K
+        int ne01,               // M
+        int ne02,               // 1
+        int ne10,               // K
+        int ne12,               // 1
+        int ne0,                // M
+        int ne1,                // N
+        int r2,                 // 1
+        int r3)
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid    = get_sub_group_local_id();
+
+    uint K = ne00;
+    uint M = ne01;
+
+    uint LINE_STRIDE_A = M / 2;
+    uint BLOCK_STRIDE_A = N_SIMDGROUP * M;
+
+    __private uint4     regA;
+    __private half2     regS;
+    __private float8    regB;
+
+    __private float2 totalSum = (float2)(0.0f);
+
+    // loop along K in block granularity, skip 4 blocks every iter
+    for (uint k = groupId; k < (K / QK4_0); k += N_SIMDGROUP) {
+        regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows
+        // first 4 fibers in each wave load 8 B values to its private scope
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        // load half weights for two blocks in consecutive rows
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+    }
+
+    // reduction in local memory, assumes #wave=4
+    __local float2 reduceLM[SIMDGROUP_WIDTH * 3];
+    if (groupId == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = totalSum;
+    if (groupId == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = totalSum;
+    if (groupId == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = totalSum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 2 outputs per fiber in wave 0
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(totalSum, 0, &(dst[gid * 2]));
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_mm.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_mm.cl
new file mode 100644
index 000000000..e19e9a2f4
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_mm.cl
@@ -0,0 +1,1225 @@
+//------------------------------------------------------------------------------
+// This file is contains additional mulmat kernels
+// (and potentially other kernels).
+//------------------------------------------------------------------------------
+#ifdef cl_khr_fp16
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#elif defined(cl_amd_fp16)
+#pragma OPENCL EXTENSION cl_amd_fp16 : enable
+#else
+#error "Half precision floating point not supportedby OpenCL implementation on your device."
+#endif
+
+#ifdef cl_khr_subgroups
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#elif defined(cl_intel_subgroups)
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#error "Subgroup not supported on your device."
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+// Always use subgroup size of 32 on Intel.
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+// Always use subgroups size of 64 on Adreno.
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#else
+// TODO: do not know how to choose subgroup size on other GPUs.
+#error "Selecting subgroup size is not supported on your device."
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+//------------------------------------------------------------------------------
+// block_q6_K
+//------------------------------------------------------------------------------
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    half d;             // super-block scale
+} block_q6_K;
+
+//------------------------------------------------------------------------------
+// These are the variant for matmatmul, based on the matvecmul kernel with
+// flattened block_q4_0.
+//------------------------------------------------------------------------------
+
+// Common dot prod.
+inline float mm_block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each SIMD group works on 8 rows (in weights matrix)
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+//
+// This variant performs 1d blocking with 8x output.
+// Eeach simdgroup outputs 8 values on `n0` dim (row in the output matrix).
+//
+inline void mul_mat_q_n_f32_1d_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float8 sumf = (float8)(0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        sumf.s4 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 4*nb*QK4_0/2, d + ib + 4*nb, sumy, yl, il);
+        sumf.s5 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 5*nb*QK4_0/2, d + ib + 5*nb, sumy, yl, il);
+        sumf.s6 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 6*nb*QK4_0/2, d + ib + 6*nb, sumy, yl, il);
+        sumf.s7 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 7*nb*QK4_0/2, d + ib + 7*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_1d_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_mat_q_n_f32_1d_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 16 // each SIMD group works on 8 rows (in weights matrix)
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 16
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+//
+// This variant performs 1d blocking with 16x output.
+// Eeach simdgroup outputs 16 values on `n0` dim (row in the output matrix).
+//
+inline void mul_mat_q_n_f32_1d_16x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float16 sumf = (float16)(0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+                             0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  0*nb*QK4_0/2, d + ib +  0*nb, sumy, yl, il);
+        sumf.s1 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  1*nb*QK4_0/2, d + ib +  1*nb, sumy, yl, il);
+        sumf.s2 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  2*nb*QK4_0/2, d + ib +  2*nb, sumy, yl, il);
+        sumf.s3 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  3*nb*QK4_0/2, d + ib +  3*nb, sumy, yl, il);
+
+        sumf.s4 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  4*nb*QK4_0/2, d + ib +  4*nb, sumy, yl, il);
+        sumf.s5 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  5*nb*QK4_0/2, d + ib +  5*nb, sumy, yl, il);
+        sumf.s6 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  6*nb*QK4_0/2, d + ib +  6*nb, sumy, yl, il);
+        sumf.s7 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  7*nb*QK4_0/2, d + ib +  7*nb, sumy, yl, il);
+
+        sumf.s8 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  8*nb*QK4_0/2, d + ib +  8*nb, sumy, yl, il);
+        sumf.s9 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  9*nb*QK4_0/2, d + ib +  9*nb, sumy, yl, il);
+        sumf.sa += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 10*nb*QK4_0/2, d + ib + 10*nb, sumy, yl, il);
+        sumf.sb += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 11*nb*QK4_0/2, d + ib + 11*nb, sumy, yl, il);
+
+        sumf.sc += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 12*nb*QK4_0/2, d + ib + 12*nb, sumy, yl, il);
+        sumf.sd += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 13*nb*QK4_0/2, d + ib + 13*nb, sumy, yl, il);
+        sumf.se += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 14*nb*QK4_0/2, d + ib + 14*nb, sumy, yl, il);
+        sumf.sf += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 15*nb*QK4_0/2, d + ib + 15*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float16 tot = (float16)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7),
+
+        sub_group_reduce_add(sumf.s8), sub_group_reduce_add(sumf.s9),
+        sub_group_reduce_add(sumf.sa), sub_group_reduce_add(sumf.sb),
+        sub_group_reduce_add(sumf.sc), sub_group_reduce_add(sumf.sd),
+        sub_group_reduce_add(sumf.se), sub_group_reduce_add(sumf.sf)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+
+        if (first_row + 8 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 8] = tot.s8;
+        }
+        if (first_row + 9 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 9] = tot.s9;
+        }
+        if (first_row + 10 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 10] = tot.sa;
+        }
+        if (first_row + 11 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 11] = tot.sb;
+        }
+
+        if (first_row + 12 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 12] = tot.sc;
+        }
+        if (first_row + 13 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 13] = tot.sd;
+        }
+        if (first_row + 14 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 14] = tot.se;
+        }
+        if (first_row + 15 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 15] = tot.sf;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_1d_16x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_mat_q_n_f32_1d_16x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
+
+//------------------------------------------------------------------------------
+// kernel_mul_mat_q4_0_f32_flat_v0
+//------------------------------------------------------------------------------
+inline float block_q_4_0_dot_y_flat_v2(
+    half   x,
+    half   d,
+    float  sumy,
+    float4 yl
+) {
+    uchar2 q = as_uchar2(x);
+    float acc = 0.0f;
+
+    acc += (q.s0 & 0x0F) * yl.s0;
+    acc += (q.s1 & 0x0F) * yl.s1;
+
+    acc += (q.s0 & 0xF0) * yl.s2;
+    acc += (q.s1 & 0xF0) * yl.s3;
+
+    return d * (sumy * -8.f + acc);;
+}
+
+inline float block_q_4_0_dot_y_flat_v4(
+    float  x,
+    half   d,
+    float  sumy,
+    float8 yl
+) {
+    uchar4 q = as_uchar4(x);
+    float acc = 0.0f;
+
+    acc += (q.s0 & 0x0F) * yl.s0;
+    acc += (q.s1 & 0x0F) * yl.s1;
+    acc += (q.s2 & 0x0F) * yl.s2;
+    acc += (q.s3 & 0x0F) * yl.s3;
+
+    acc += (q.s0 & 0xF0) * yl.s4;
+    acc += (q.s1 & 0xF0) * yl.s5;
+    acc += (q.s2 & 0xF0) * yl.s6;
+    acc += (q.s3 & 0xF0) * yl.s7;
+
+    return d * (sumy * -8.f + acc);;
+}
+
+inline float block_q_4_0_dot_y_flat_v8(
+    float2  x,
+    half    d,
+    float   sumy,
+    float16 yl
+) {
+    uchar8 q = as_uchar8(x);
+    float acc = 0.0f;
+
+    acc += (q.s0 & 0x0F) * yl.s0;
+    acc += (q.s1 & 0x0F) * yl.s1;
+    acc += (q.s2 & 0x0F) * yl.s2;
+    acc += (q.s3 & 0x0F) * yl.s3;
+    acc += (q.s4 & 0x0F) * yl.s4;
+    acc += (q.s5 & 0x0F) * yl.s5;
+    acc += (q.s6 & 0x0F) * yl.s6;
+    acc += (q.s7 & 0x0F) * yl.s7;
+
+    acc += (q.s0 & 0xF0) * yl.s8;
+    acc += (q.s1 & 0xF0) * yl.s9;
+    acc += (q.s2 & 0xF0) * yl.sa;
+    acc += (q.s3 & 0xF0) * yl.sb;
+    acc += (q.s4 & 0xF0) * yl.sc;
+    acc += (q.s5 & 0xF0) * yl.sd;
+    acc += (q.s6 & 0xF0) * yl.se;
+    acc += (q.s7 & 0xF0) * yl.sf;
+
+    return d * (sumy * -8.f + acc);;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define THREADS_PER_BLK 4   // Number of threads per block, or each thread process 1/THREADS_PER_BLK of a block
+#define N_DST           4
+#define N_SIMDGROUP     1
+#define N_SIMDWIDTH     16
+#elif defined (ADRENO_GPU)
+#define THREADS_PER_BLK 4
+#define N_DST           4
+#define N_SIMDGROUP     1
+#define N_SIMDWIDTH     64
+#endif
+
+#if THREADS_PER_BLK == 2                // Each thread processes 1/2 block
+#   define ACT_TY                       float16
+#   define Q_BLK_LD_TY                  float2
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v8
+#elif THREADS_PER_BLK == 4              // Each thread processes 1/4 block
+#   define ACT_TY                       float8
+#   define Q_BLK_LD_TY                  float
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v4
+#elif THREADS_PER_BLK == 8              // Each thread processes 1/8 block
+#   define ACT_TY                       float4
+#   define Q_BLK_LD_TY                  half
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v2
+#endif
+
+#define BTYES_PER_THREAD_IN_BLK         (QK4_0/2/THREADS_PER_BLK)
+
+#if N_DST == 2
+#   define  SUM_TY                      float2
+#elif N_DST == 4
+#   define  SUM_TY                      float4
+#elif N_DST == 8
+#   define  SUM_TY                      float8
+#elif N_DST == 16
+#   define  SUM_TY                      float16
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_flat_v0(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    int ix = get_sub_group_local_id()/THREADS_PER_BLK;
+    int il = get_sub_group_local_id()%THREADS_PER_BLK;
+
+    global float * yb = y + ix*QK4_0 + BTYES_PER_THREAD_IN_BLK*il;
+
+    // Registers for caching activation
+    ACT_TY yl = 0.f;
+
+    // Registers for caching quants
+    Q_BLK_LD_TY q_blk_0 = 0, q_blk_1 = 0;
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+    Q_BLK_LD_TY q_blk_2 = 0, q_blk_3 = 0;
+#endif
+#if N_DST == 8 || N_DST == 16
+    Q_BLK_LD_TY q_blk_4 = 0, q_blk_5 = 0, q_blk_6 = 0, q_blk_7 = 0;
+#endif
+
+    // Partial sum
+    SUM_TY sumf = 0.f;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/THREADS_PER_BLK) {
+        float sumy = 0.f;
+
+        q_blk_0 = *(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 0*nb*QK4_0/2);
+        q_blk_1 = *(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 1*nb*QK4_0/2);
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        q_blk_2 = *(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 2*nb*QK4_0/2);
+        q_blk_3 = *(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 3*nb*QK4_0/2);
+#endif
+#if N_DST == 8 || N_DST == 16
+        q_blk_4 = (*(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 4*nb*QK4_0/2));
+        q_blk_5 = (*(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 5*nb*QK4_0/2));
+        q_blk_6 = (*(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 6*nb*QK4_0/2));
+        q_blk_7 = (*(global Q_BLK_LD_TY*)(x + ib*QK4_0/2 + BTYES_PER_THREAD_IN_BLK*il + 7*nb*QK4_0/2));
+#endif
+
+        // Load activation
+#if THREADS_PER_BLK == 2    // Each thread processes 1/2 block
+        yl.s01234567 = *(global float8 *)(yb);
+        yl.s89abcdef = *(global float8 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2;
+        sumy += yl.s3;
+        sumy += yl.s4;
+        sumy += yl.s5;
+        sumy += yl.s6;
+        sumy += yl.s7;
+        sumy += yl.s8; yl.s8 /= 16.f;
+        sumy += yl.s9; yl.s9 /= 16.f;
+        sumy += yl.sa; yl.sa /= 16.f;
+        sumy += yl.sb; yl.sb /= 16.f;
+        sumy += yl.sc; yl.sc /= 16.f;
+        sumy += yl.sd; yl.sd /= 16.f;
+        sumy += yl.se; yl.se /= 16.f;
+        sumy += yl.sf; yl.sf /= 16.f;
+#elif THREADS_PER_BLK == 4  // Each thread processes 1/4 block
+        yl.s0123 = *(global float4 *)(yb);
+        yl.s4567 = *(global float4 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2;
+        sumy += yl.s3;
+        sumy += yl.s4; yl.s4 /= 16.f;
+        sumy += yl.s5; yl.s5 /= 16.f;
+        sumy += yl.s6; yl.s6 /= 16.f;
+        sumy += yl.s7; yl.s7 /= 16.f;
+#elif THREADS_PER_BLK == 8  // Each thread processes 1/8 block
+        yl.s01 = *(global float2 *)(yb);
+        yl.s23 = *(global float2 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2; yl.s2 /= 16.f;
+        sumy += yl.s3; yl.s3 /= 16.f;
+#endif
+
+        sumf.s0 += block_q_4_0_dot_y_flat(q_blk_0, *(d + ib + 0*nb), sumy, yl);
+        sumf.s1 += block_q_4_0_dot_y_flat(q_blk_1, *(d + ib + 1*nb), sumy, yl);
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        sumf.s2 += block_q_4_0_dot_y_flat(q_blk_2, *(d + ib + 2*nb), sumy, yl);
+        sumf.s3 += block_q_4_0_dot_y_flat(q_blk_3, *(d + ib + 3*nb), sumy, yl);
+#endif
+#if N_DST == 8 || N_DST == 16
+        sumf.s4 += block_q_4_0_dot_y_flat(q_blk_4, *(d + ib + 4*nb), sumy, yl);
+        sumf.s5 += block_q_4_0_dot_y_flat(q_blk_5, *(d + ib + 5*nb), sumy, yl);
+        sumf.s6 += block_q_4_0_dot_y_flat(q_blk_6, *(d + ib + 6*nb), sumy, yl);
+        sumf.s7 += block_q_4_0_dot_y_flat(q_blk_7, *(d + ib + 7*nb), sumy, yl);
+#endif
+
+        yb += QK4_0 * (N_SIMDWIDTH/THREADS_PER_BLK);
+    }
+
+    SUM_TY tot = (SUM_TY)(
+          sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1)
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        , sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+#endif
+#if N_DST == 8 || N_DST == 16
+        , sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5)
+        , sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+#endif
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+#endif
+#if N_DST == 8 || N_DST == 16
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+#endif
+    }
+}
+
+//------------------------------------------------------------------------------
+// Using image1d_buffer_t
+
+#if defined(cl_qcom_subgroup_shuffle)
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+float qcom_sub_group_reduce_add(float sum) {
+    sum += qcom_sub_group_shuffle_down(sum, 32, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    sum += qcom_sub_group_shuffle_down(sum, 16, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    sum += qcom_sub_group_shuffle_down(sum,  8, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    sum += qcom_sub_group_shuffle_down(sum,  4, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    sum += qcom_sub_group_shuffle_down(sum,  2, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    sum += qcom_sub_group_shuffle_down(sum,  1, CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.f);
+    return sum;
+}
+#define sub_group_reduce_add qcom_sub_group_reduce_add
+#else
+#define sub_group_reduce_add sub_group_reduce_add
+#endif
+
+#undef THREADS_PER_BLK
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define THREADS_PER_BLK 4   // Number of threads per block, or each thread process 1/THREADS_PER_BLK of a block
+#define N_DST           4
+#define N_SIMDGROUP     1
+#define N_SIMDWIDTH     16
+#elif defined (ADRENO_GPU)
+#define THREADS_PER_BLK 4
+#define N_DST           4
+#define N_SIMDGROUP     1
+#define N_SIMDWIDTH     64
+#endif
+
+#if THREADS_PER_BLK == 2                // Each thread processes 1/2 block
+#   define ACT_TY                       float16
+#   define Q_BLK_LD_TY                  float2
+#   define EXTRACT_BLK_DATA(tmp, part)  *((float2*)&tmp + part)
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v8
+#elif THREADS_PER_BLK == 4              // Each thread processes 1/4 block
+#   define ACT_TY                       float8
+#   define Q_BLK_LD_TY                  float
+#   define EXTRACT_BLK_DATA(tmp, part)  *((float*)&tmp + part)
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v4
+#elif THREADS_PER_BLK == 8              // Each thread processes 1/8 block
+#   define ACT_TY                       float4
+#   define Q_BLK_LD_TY                  half
+#   define EXTRACT_BLK_DATA(tmp, part)  *((half*)&tmp + part)
+#   define block_q_4_0_dot_y_flat       block_q_4_0_dot_y_flat_v2
+#endif
+
+#define BTYES_PER_THREAD_IN_BLK         (QK4_0/2/THREADS_PER_BLK)
+
+#if N_DST == 2
+#   define  SUM_TY                      float2
+#elif N_DST == 4
+#   define  SUM_TY                      float4
+#elif N_DST == 8
+#   define  SUM_TY                      float8
+#elif N_DST == 16
+#   define  SUM_TY                      float16
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_flat_img_v0(
+        read_only image1d_buffer_t src0_q,
+        read_only image1d_buffer_t src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    int ix = get_sub_group_local_id()/THREADS_PER_BLK;
+    int il = get_sub_group_local_id()%THREADS_PER_BLK;
+
+    global float * yb = y + ix*QK4_0 + BTYES_PER_THREAD_IN_BLK*il;
+
+    // Registers for caching activation
+    ACT_TY yl = 0.f;
+
+    // Registers for caching quants
+    Q_BLK_LD_TY q_blk_0 = 0, q_blk_1 = 0;
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+    Q_BLK_LD_TY q_blk_2 = 0, q_blk_3 = 0;
+#endif
+#if N_DST == 8 || N_DST == 16
+    Q_BLK_LD_TY q_blk_4 = 0, q_blk_5 = 0, q_blk_6 = 0, q_blk_7 = 0;
+#endif
+
+    // Partial sum
+    SUM_TY sumf = 0.f;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/THREADS_PER_BLK) {
+        float sumy = 0.f;;
+
+        float4 tmp;
+        tmp = read_imagef(src0_q, offset0_q + ib + 0*nb);
+        q_blk_0 = EXTRACT_BLK_DATA(tmp, il);
+        tmp = read_imagef(src0_q, offset0_q + ib + 1*nb);
+        q_blk_1 = EXTRACT_BLK_DATA(tmp, il);
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        tmp = read_imagef(src0_q, offset0_q + ib + 2*nb);
+        q_blk_2 = EXTRACT_BLK_DATA(tmp, il);
+        tmp = read_imagef(src0_q, offset0_q + ib + 3*nb);
+        q_blk_3 = EXTRACT_BLK_DATA(tmp, il);
+#endif
+#if N_DST == 8 || N_DST == 16
+        tmp = read_imagef(src0_q, offset0_q + ib + 4*nb);
+        q_blk_4 = EXTRACT_BLK_DATA(tmp, il);
+        tmp = read_imagef(src0_q, offset0_q + ib + 5*nb);
+        q_blk_5 = EXTRACT_BLK_DATA(tmp, il);
+        tmp = read_imagef(src0_q, offset0_q + ib + 6*nb);
+        q_blk_6 = EXTRACT_BLK_DATA(tmp, il);
+        tmp = read_imagef(src0_q, offset0_q + ib + 7*nb);
+        q_blk_7 = EXTRACT_BLK_DATA(tmp, il);
+#endif
+
+        // Load activation
+#if THREADS_PER_BLK == 2    // Each thread processes 1/2 block
+        yl.s01234567 = *(global float8 *)(yb);
+        yl.s89abcdef = *(global float8 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2;
+        sumy += yl.s3;
+        sumy += yl.s4;
+        sumy += yl.s5;
+        sumy += yl.s6;
+        sumy += yl.s7;
+        sumy += yl.s8; yl.s8 /= 16.f;
+        sumy += yl.s9; yl.s9 /= 16.f;
+        sumy += yl.sa; yl.sa /= 16.f;
+        sumy += yl.sb; yl.sb /= 16.f;
+        sumy += yl.sc; yl.sc /= 16.f;
+        sumy += yl.sd; yl.sd /= 16.f;
+        sumy += yl.se; yl.se /= 16.f;
+        sumy += yl.sf; yl.sf /= 16.f;
+#elif THREADS_PER_BLK == 4  // Each thread processes 1/4 block
+        yl.s0123 = *(global float4 *)(yb);
+        yl.s4567 = *(global float4 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2;
+        sumy += yl.s3;
+        sumy += yl.s4; yl.s4 /= 16.f;
+        sumy += yl.s5; yl.s5 /= 16.f;
+        sumy += yl.s6; yl.s6 /= 16.f;
+        sumy += yl.s7; yl.s7 /= 16.f;
+#elif THREADS_PER_BLK == 8  // Each thread processes 1/8 block
+        yl.s01 = *(global float2 *)(yb);
+        yl.s23 = *(global float2 *)(yb + 16);
+
+        sumy += yl.s0;
+        sumy += yl.s1;
+        sumy += yl.s2; yl.s2 /= 16.f;
+        sumy += yl.s3; yl.s3 /= 16.f;
+#endif
+
+        sumf.s0 += block_q_4_0_dot_y_flat(q_blk_0, read_imageh(src0_d, offset0_d + ib + 0*nb).s0, sumy, yl);
+        sumf.s1 += block_q_4_0_dot_y_flat(q_blk_1, read_imageh(src0_d, offset0_d + ib + 1*nb).s0, sumy, yl);
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        sumf.s2 += block_q_4_0_dot_y_flat(q_blk_2, read_imageh(src0_d, offset0_d + ib + 2*nb).s0, sumy, yl);
+        sumf.s3 += block_q_4_0_dot_y_flat(q_blk_3, read_imageh(src0_d, offset0_d + ib + 3*nb).s0, sumy, yl);
+#endif
+#if N_DST == 8 || N_DST == 16
+        sumf.s4 += block_q_4_0_dot_y_flat(q_blk_4, read_imageh(src0_d, offset0_d + ib + 4*nb).s0, sumy, yl);
+        sumf.s5 += block_q_4_0_dot_y_flat(q_blk_5, read_imageh(src0_d, offset0_d + ib + 5*nb).s0, sumy, yl);
+        sumf.s6 += block_q_4_0_dot_y_flat(q_blk_6, read_imageh(src0_d, offset0_d + ib + 6*nb).s0, sumy, yl);
+        sumf.s7 += block_q_4_0_dot_y_flat(q_blk_7, read_imageh(src0_d, offset0_d + ib + 7*nb).s0, sumy, yl);
+#endif
+
+        yb += QK4_0 * (N_SIMDWIDTH/THREADS_PER_BLK);
+    }
+
+    SUM_TY tot = (SUM_TY)(
+          sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1)
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        , sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+#endif
+#if N_DST == 8 || N_DST == 16
+        , sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5)
+        , sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+#endif
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+#if N_DST == 4 || N_DST == 8 || N_DST == 16
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+#endif
+#if N_DST == 8 || N_DST == 16
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+#endif
+    }
+}
+
+//------------------------------------------------------------------------------
+// kernel_mul_mv_q6_K_f32
+//------------------------------------------------------------------------------
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 1 // number of rows each SIMD group works on
+#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // SIMD group size
+#elif defined (ADRENO_GPU)
+#define N_DST 1
+#define N_SIMDGROUP 2
+#define N_SIMDWIDTH 64
+#endif
+
+#define BLOCK_STRIDE (N_SIMDWIDTH/16) // number of blocks each subgroup processes
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q6_K_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    uchar kmask1 = 0x03;
+    uchar kmask2 = 0x0C;
+    uchar kmask3 = 0x30;
+    uchar kmask4 = 0xC0;
+
+    int nb = ne00/QK_K;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int row = N_SIMDGROUP * r0 + get_sub_group_id();
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global block_q6_K * x = (global block_q6_K *) src0 + row*nb + offset_src0;
+    global float      * yy = (global float     *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float sumf = 0;
+
+    // For Q6_K quantization, 16 values forms a subblock, 16 subblock forms a
+    // block. Values in a subblock shares a scale that is quantized with 8 bits;
+    // the entire block shares a single floating point scale.
+    // For work distribution, each thread processes a subblock (16 weights), hence
+    // 16 threads process a (super) block -- a subgroup thus handles SIMDWIDTH/16
+    // (super) blocks -- this is the block stride.
+    // The 16 threads that process a (super) block are split into 2 portions, each has
+    // 8 threads; each portion works on 8 subblocks.
+    // For subgroup of 16 threads, the entire subgroup works on a single (super) block
+    // before moving to the next (super) block. Thread0 - thread7 work on the
+    // first 8 subblocks; thread8 - thread15 works on the last 8 subblocks.
+    // Thread0 - thread3 work on subblocks 0, 2, 4, 6; thread4 - thread7 work on
+    // subblocks 1, 3, 5, 7. Each thread does not work on an entire subblock, but
+    // works on a total of 16 weight values.
+    int tid  = get_sub_group_local_id()/BLOCK_STRIDE; // first block_stride groups have tid=0
+    int ix   = get_sub_group_local_id()%BLOCK_STRIDE; // first block is 0..block_stride-1
+    int ip   = tid/8;   // first or second half of (super) block (0 or 1)
+    int il   = tid%8;   // each half has 8 parts, one per scale
+    int n    = 4;       // 4 scales at a time (and 4 sums)
+    int l0   = n*il;    // offset into half-block, 0..28
+    int is   = 8*ip + l0/16; // 0, 1, 8, 9
+
+    int y_offset = 128*ip + l0;
+    int q_offset_l = 64*ip + l0;
+    int q_offset_h = 32*ip + l0;
+
+    for (int i = ix; i < nb; i += BLOCK_STRIDE) {
+
+        global uint8_t * q1 = x[i].ql + q_offset_l;
+        global uint8_t * q2 = q1 + QK_K/8;
+        global uint8_t * qh = x[i].qh + q_offset_h;
+        global int8_t  * sc = x[i].scales + is;
+
+        global float * y = yy + i * QK_K + y_offset;
+
+        float dall = x[i].d;
+
+        float4 sums = {0.f, 0.f, 0.f, 0.f};
+
+        sums.s0 += y[0+ 0] * ((float)((q1[0] & 0xF) | ((qh[0] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[0+32] * ((float)((q2[0] & 0xF) | ((qh[0] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[0+64] * ((float)((q1[0]  >> 4) | ((qh[0] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[0+96] * ((float)((q2[0]  >> 4) | ((qh[0] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[1+ 0] * ((float)((q1[1] & 0xF) | ((qh[1] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[1+32] * ((float)((q2[1] & 0xF) | ((qh[1] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[1+64] * ((float)((q1[1]  >> 4) | ((qh[1] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[1+96] * ((float)((q2[1]  >> 4) | ((qh[1] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[2+ 0] * ((float)((q1[2] & 0xF) | ((qh[2] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[2+32] * ((float)((q2[2] & 0xF) | ((qh[2] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[2+64] * ((float)((q1[2]  >> 4) | ((qh[2] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[2+96] * ((float)((q2[2]  >> 4) | ((qh[2] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[3+ 0] * ((float)((q1[3] & 0xF) | ((qh[3] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[3+32] * ((float)((q2[3] & 0xF) | ((qh[3] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[3+64] * ((float)((q1[3]  >> 4) | ((qh[3] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[3+96] * ((float)((q2[3]  >> 4) | ((qh[3] & kmask4) >> 2)) - 32.f);
+
+        sumf += dall * (sums.s0 * sc[0] + sums.s1 * sc[2] + sums.s2 * sc[4] + sums.s3 * sc[6]);
+    }
+
+    float tot = sub_group_reduce_add(sumf);
+    if (get_sub_group_local_id() == 0) {
+        dst[r1*ne0 + im*ne0*ne1 + row] = tot;
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl
new file mode 100644
index 000000000..57768c803
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl
@@ -0,0 +1,130 @@
+// src0_q, src0_d, src1 are transposed as a preprocessing step
+// 4-bit weights are transposed in groups of 4 (unsigned short int)
+// consider weights originally "next to each other", now "on top of each other"
+// each fiber computes a 8x4 tile of output elements
+// using unshuffled weights
+
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+__attribute__((qcom_reqd_sub_group_size("full")))
+kernel void kernel_mul_mat_Ab_Bi_8x4(
+        global const ushort * src0_q,       // quantized A
+        global const half  * src0_d,        // A scales
+        __read_only image1d_buffer_t src1,  // B (1d image)
+        global float * dst,                 // C
+        int m,                              // M
+        int n,                              // N with padding
+        int k,                              // K
+        int n_no_padding                    // N without padding
+) {
+
+    int m_4 = m >> 2;
+    int n_4 = n >> 2;
+
+    int gy = get_global_id(0);
+    int gx = get_global_id(1);
+    int gx_2 = gx << 2;
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0; // 8x4 output elements
+    half8 B; // registers for activations
+    half4 dequantized_weights; // registers for dequantized weights
+    __global const ushort* weight_ptr = src0_q + gx_2; // pointer for weights
+    __global const half* scale_ptr = src0_d + gx_2; // pointer for scales
+
+    for(int i=0; i<k; i+=4){ //loop through K dimension
+
+        B.s0123 = read_imageh(src1, gy*2 + (i)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i)*(n_4)+1);
+
+        // keep (i/4) and (i/32) in parenthesis, rounds down
+        // load 4 consecutive groups of 4 weights
+        ushort4 bits4 = vload4(0, weight_ptr + (i/4)*(m)); // (i/4) because weights grouped in 4s
+
+        // load 4 consecutive scales
+        half4 scale = vload4(0, scale_ptr + (i/32)*(m));// (i/32) because 1 scale per 32 elements
+
+        // j=0
+        dequantized_weights.s0 = ((bits4.s0 & (0x000F)) - 8) * scale.s0; // dequantize a row of the 16 weights
+        dequantized_weights.s1 = ((bits4.s1 & (0x000F)) - 8) * scale.s1;
+        dequantized_weights.s2 = ((bits4.s2 & (0x000F)) - 8) * scale.s2;
+        dequantized_weights.s3 = ((bits4.s3 & (0x000F)) - 8) * scale.s3;
+        c0 += B * dequantized_weights.s0; // vector-scalar multiplication to accumulate
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=1
+        B.s0123 = read_imageh(src1, gy*2 + (i+1)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+1)*(n_4)+1);
+        dequantized_weights.s0 = (((bits4.s0 & (0x00F0)) >> 4) - 8) * scale.s0; // dequantize a row of the 16 weights
+        dequantized_weights.s1 = (((bits4.s1 & (0x00F0)) >> 4) - 8) * scale.s1;
+        dequantized_weights.s2 = (((bits4.s2 & (0x00F0)) >> 4) - 8) * scale.s2;
+        dequantized_weights.s3 = (((bits4.s3 & (0x00F0)) >> 4) - 8) * scale.s3;
+        c0 += B * dequantized_weights.s0; //vector-scalar multiplication to accumulate
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=2
+        B.s0123 = read_imageh(src1, gy*2 + (i+2)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+2)*(n_4)+1);
+        dequantized_weights.s0 = (((bits4.s0 & (0x0F00)) >> 8) - 8) * scale.s0; // dequantize a row of the 16 weights
+        dequantized_weights.s1 = (((bits4.s1 & (0x0F00)) >> 8) - 8) * scale.s1;
+        dequantized_weights.s2 = (((bits4.s2 & (0x0F00)) >> 8) - 8) * scale.s2;
+        dequantized_weights.s3 = (((bits4.s3 & (0x0F00)) >> 8) - 8) * scale.s3;
+        c0 += B * dequantized_weights.s0; // vector-scalar multiplication to accumulate
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=3
+        B.s0123 = read_imageh(src1, gy*2 + (i+3)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+3)*(n_4)+1);
+        dequantized_weights.s0 = (((bits4.s0 & (0xF000)) >> 12) - 8) * scale.s0; // dequantize a row of the 16 weights
+        dequantized_weights.s1 = (((bits4.s1 & (0xF000)) >> 12) - 8) * scale.s1;
+        dequantized_weights.s2 = (((bits4.s2 & (0xF000)) >> 12) - 8) * scale.s2;
+        dequantized_weights.s3 = (((bits4.s3 & (0xF000)) >> 12) - 8) * scale.s3;
+        c0 += B * dequantized_weights.s0; // vector-scalar multiplication to accumulate
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+    }
+
+    int idx = (gy<<3)*m + (gx<<2); // vectorized store 16 elements
+
+    // conditional check if store is to a valid location. Required when N is not a multiple of 8
+    // if statements allow registers to be reused for each store
+    // provides a performance boost due to reduced register footprint, which increases number of concurrent waves
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_16.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_16.cl
new file mode 100644
index 000000000..d59a0c05d
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_16.cl
@@ -0,0 +1,32 @@
+// 16-bit transpose, loading/storing an 8x8 tile of elements
+
+kernel void kernel_transpose_16(
+    __read_only image1d_buffer_t input,
+    __write_only image1d_buffer_t output,
+    const uint rows,
+    const uint cols
+) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_3 = i<<3;
+    const int j_3 = j<<3;
+
+    ushort8 temp0 = as_ushort8(read_imagef(input, (j_3+0)*cols+i));
+    ushort8 temp1 = as_ushort8(read_imagef(input, (j_3+1)*cols+i));
+    ushort8 temp2 = as_ushort8(read_imagef(input, (j_3+2)*cols+i));
+    ushort8 temp3 = as_ushort8(read_imagef(input, (j_3+3)*cols+i));
+    ushort8 temp4 = as_ushort8(read_imagef(input, (j_3+4)*cols+i));
+    ushort8 temp5 = as_ushort8(read_imagef(input, (j_3+5)*cols+i));
+    ushort8 temp6 = as_ushort8(read_imagef(input, (j_3+6)*cols+i));
+    ushort8 temp7 = as_ushort8(read_imagef(input, (j_3+7)*cols+i));
+
+    write_imagef(output, (i_3+0)*rows+j, as_float4((ushort8)(temp0.s0, temp1.s0, temp2.s0, temp3.s0, temp4.s0, temp5.s0, temp6.s0, temp7.s0)));
+    write_imagef(output, (i_3+1)*rows+j, as_float4((ushort8)(temp0.s1, temp1.s1, temp2.s1, temp3.s1, temp4.s1, temp5.s1, temp6.s1, temp7.s1)));
+    write_imagef(output, (i_3+2)*rows+j, as_float4((ushort8)(temp0.s2, temp1.s2, temp2.s2, temp3.s2, temp4.s2, temp5.s2, temp6.s2, temp7.s2)));
+    write_imagef(output, (i_3+3)*rows+j, as_float4((ushort8)(temp0.s3, temp1.s3, temp2.s3, temp3.s3, temp4.s3, temp5.s3, temp6.s3, temp7.s3)));
+    write_imagef(output, (i_3+4)*rows+j, as_float4((ushort8)(temp0.s4, temp1.s4, temp2.s4, temp3.s4, temp4.s4, temp5.s4, temp6.s4, temp7.s4)));
+    write_imagef(output, (i_3+5)*rows+j, as_float4((ushort8)(temp0.s5, temp1.s5, temp2.s5, temp3.s5, temp4.s5, temp5.s5, temp6.s5, temp7.s5)));
+    write_imagef(output, (i_3+6)*rows+j, as_float4((ushort8)(temp0.s6, temp1.s6, temp2.s6, temp3.s6, temp4.s6, temp5.s6, temp6.s6, temp7.s6)));
+    write_imagef(output, (i_3+7)*rows+j, as_float4((ushort8)(temp0.s7, temp1.s7, temp2.s7, temp3.s7, temp4.s7, temp5.s7, temp6.s7, temp7.s7)));
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32.cl
new file mode 100644
index 000000000..914ec0193
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32.cl
@@ -0,0 +1,25 @@
+// 32-bit transpose, loading/storing a 4x4 tile of elements
+
+kernel void kernel_transpose_32(
+    __read_only image1d_buffer_t input,
+    __write_only image1d_buffer_t output,
+    const uint rows,
+    const uint cols
+) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_2 = i<<2;
+    const int j_2 = j<<2;
+
+    float4 temp0 = read_imagef(input, (j_2+0)*cols+i);
+    float4 temp1 = read_imagef(input, (j_2+1)*cols+i);
+    float4 temp2 = read_imagef(input, (j_2+2)*cols+i);
+    float4 temp3 = read_imagef(input, (j_2+3)*cols+i);
+
+    write_imagef(output, (i_2+0)*rows+j, (float4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0));
+    write_imagef(output, (i_2+1)*rows+j, (float4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
+    write_imagef(output, (i_2+2)*rows+j, (float4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
+    write_imagef(output, (i_2+3)*rows+j, (float4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32_16.cl b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32_16.cl
new file mode 100644
index 000000000..d3bd1fabb
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32_16.cl
@@ -0,0 +1,35 @@
+// 32-bit transpose, loading/storing a 4x4 tile of elements
+// Only used for activations
+// converts to FP16
+// also adds zero padding for non multiple of 8 prompt lengths
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+kernel void kernel_transpose_32_16(__read_only image1d_buffer_t input, __write_only image1d_buffer_t output, const uint rows, const uint cols, const uint padded_rows) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_2 = i<<2;
+    const int j_2 = j<<2;
+    half4 temp0 = {0,0,0,0}; // initialize outputs to 0
+    half4 temp1 = {0,0,0,0};
+    half4 temp2 = {0,0,0,0};
+    half4 temp3 = {0,0,0,0};
+
+    if((j_2+0)*cols+i*4+3 < rows*cols*16){ // only load from a valid location. Otherwise keep register data as 0
+        temp0 = read_imageh(input, (j_2+0)*cols+i);
+    }
+    if((j_2+1)*cols+i*4+3 < rows*cols*16){
+        temp1 = read_imageh(input, (j_2+1)*cols+i);
+    }
+    if((j_2+2)*cols+i*4+3 < rows*cols*16){
+        temp2 = read_imageh(input, (j_2+2)*cols+i);
+    }
+    if((j_2+3)*cols+i*4+3 < rows*cols*16){
+        temp3 = read_imageh(input, (j_2+3)*cols+i);
+    }
+
+    write_imageh(output, (i_2+0)*padded_rows+j, (half4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0)); // no conditionals for output, includes zero padding
+    write_imageh(output, (i_2+1)*padded_rows+j, (half4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
+    write_imageh(output, (i_2+2)*padded_rows+j, (half4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
+    write_imageh(output, (i_2+3)*padded_rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
+}

From 56eea0781cbd2608ed8ff524955495569b9264be Mon Sep 17 00:00:00 2001
From: cduk <19917266+cduk@users.noreply.github.com>
Date: Fri, 13 Dec 2024 23:21:49 +0100
Subject: [PATCH 021/376] Removes spurious \r in output that causes logging in
 journalctl to treat lines as binary and therefore hidden by default (#10771)

Signed-off-by: Charles Darke <s.cduk@toodevious.com>
Co-authored-by: Charles Darke <s.cduk@toodevious.com>
---
 examples/server/server.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 8cb992470..210c36e01 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1079,9 +1079,9 @@ struct server_slot {
 
         SLT_INF(*this,
                 "\n"
-                "\rprompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
-                "\r       eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
-                "\r      total time = %10.2f ms / %5d tokens\n",
+                "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
+                "       eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
+                "      total time = %10.2f ms / %5d tokens\n",
                 t_prompt_processing, n_prompt_tokens_processed, t_prompt, n_prompt_second,
                 t_token_generation, n_decoded, t_gen, n_gen_second,
                 t_prompt_processing + t_token_generation, n_prompt_tokens_processed + n_decoded);

From ba1cb19cdd0d92e012e0f6e009e0620f854b6afd Mon Sep 17 00:00:00 2001
From: HimariO <dsfhe49854@gmail.com>
Date: Sat, 14 Dec 2024 20:43:46 +0800
Subject: [PATCH 022/376] llama : add Qwen2VL support + multimodal RoPE
 (#10361)

* Barebone Qwen2VL LLM convertor

* Add Qwen2VL cli entrypoint

* [WIP] add qwen2vl arch

* Verify m-rope output

* Add vl-rope/2d-rope support for qwen2vl ViT

* update qwen2vl cli tool

* update 5D tensor op workaround

* [WIP] qwen2vl vision model

* make batch and clip utils compatible with qwen2vl

* [WIP] create inference workflow, gguf convert script but fix

* correcting vision-rope behavior, add the missing last layer back to ViT

* add arg parser to qwen2vl_surgery

* replace variable size array with vector

* cuda-gdb cmake preset

* add fp32 mrope, vision rope kernel

* add fp16 support for qwen2vl and m-rope

* add `GGML_ROPE_TYPE_MROPE`, `GGML_ROPE_TYPE_VISION`

* fix rope op mode switching, out dated func args

* update `llama_hparams`

* update to keep up stream changes

* resolve linter, test errors

* add makefile entry, update speical image padding token

* add mrope unit test, fix few compiler warnings

* rename `mrope` related function, params

* minor updates on debug util, bug fixs

* add `m-rope` testcase to `test-backend-ops`

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* fix traililng whitespce

* store `llama_hparams.rope_sections` with fixed size array

* update position id tensor size check in GGML_OP_ROPE

* minor updates

* update `ggml_backend_*_supports_op` of unsupported backends

* remote old `rope_section` compare operator

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 Makefile                               |   9 +
 README.md                              |   1 +
 convert_hf_to_gguf.py                  |  23 +
 examples/llava/CMakeLists.txt          |   7 +
 examples/llava/clip.cpp                | 230 ++++++++--
 examples/llava/clip.h                  |  10 +-
 examples/llava/llava.cpp               |  37 +-
 examples/llava/qwen2_vl_surgery.py     | 158 +++++++
 examples/llava/qwen2vl-cli.cpp         | 581 +++++++++++++++++++++++++
 ggml/include/ggml.h                    |  20 +-
 ggml/src/ggml-cann/ggml-cann.cpp       |   9 +
 ggml/src/ggml-cpu/ggml-cpu.c           | 244 +++++++++--
 ggml/src/ggml-cuda/rope.cu             | 231 +++++++++-
 ggml/src/ggml-kompute/ggml-kompute.cpp |  12 +-
 ggml/src/ggml-metal/ggml-metal.m       |  16 +-
 ggml/src/ggml-sycl/ggml-sycl.cpp       |  11 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp   |  11 +-
 ggml/src/ggml.c                        |  52 ++-
 gguf-py/gguf/constants.py              |  17 +
 gguf-py/gguf/gguf_writer.py            |   3 +
 include/llama.h                        |   8 +-
 src/llama.cpp                          | 178 +++++++-
 tests/test-backend-ops.cpp             |  38 +-
 tests/test-rope.cpp                    |  81 +++-
 24 files changed, 1873 insertions(+), 114 deletions(-)
 create mode 100644 examples/llava/qwen2_vl_surgery.py
 create mode 100644 examples/llava/qwen2vl-cli.cpp

diff --git a/Makefile b/Makefile
index bcea450e4..19ae0d5f1 100644
--- a/Makefile
+++ b/Makefile
@@ -22,6 +22,7 @@ BUILD_TARGETS = \
 	llama-infill \
 	llama-llava-cli \
 	llama-minicpmv-cli\
+	llama-qwen2vl-cli\
 	llama-lookahead \
 	llama-lookup \
 	llama-lookup-create \
@@ -1404,6 +1405,14 @@ llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
+llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
diff --git a/README.md b/README.md
index 54466c250..49095acc8 100644
--- a/README.md
+++ b/README.md
@@ -110,6 +110,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
+- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
 
 </details>
 
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c63d929c1..206ab502e 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2001,6 +2001,29 @@ class Qwen2Model(Model):
                 self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
 
 
+@Model.register("Qwen2VLForConditionalGeneration")
+class Qwen2VLModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        mrope_section = self.hparams["rope_scaling"]["mrope_section"]
+        mrope_section += [0] * max(0, 4 - len(mrope_section))
+        self.gguf_writer.add_rope_dimension_sections(mrope_section)
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        for name, data in super().get_tensors():
+            if name.startswith("visual."):
+                continue
+            yield name, data
+
+
 @Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
     model_arch = gguf.MODEL_ARCH.QWEN2MOE
diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt
index 5d32f377f..3ce0d60c8 100644
--- a/examples/llava/CMakeLists.txt
+++ b/examples/llava/CMakeLists.txt
@@ -43,3 +43,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+set(TARGET llama-qwen2vl-cli)
+add_executable(${TARGET} qwen2vl-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index d7c94352b..ba28c07c6 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -102,7 +102,9 @@ static std::string format(const char * fmt, ...) {
 #define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
 #define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
+#define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #define KEY_USE_GELU            "clip.use_gelu"
+#define KEY_USE_SILU            "clip.use_silu"
 #define KEY_N_EMBD              "clip.%s.embedding_length"
 #define KEY_N_FF                "clip.%s.feed_forward_length"
 #define KEY_N_BLOCK             "clip.%s.block_count"
@@ -129,7 +131,8 @@ static std::string format(const char * fmt, ...) {
 #define TN_TOKEN_EMBD      "%s.token_embd.weight"
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
-#define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_EMBD      "v.patch_embd.weight"  // not rename tensor with ".0" postfix for backwrad compat
+#define TN_PATCH_EMBD_1    "v.patch_embd.weight.1"
 #define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
@@ -163,6 +166,7 @@ enum projector_type {
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_RESAMPLER,
+    PROJECTOR_TYPE_MERGER,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -171,6 +175,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LDP, "ldp" },
     { PROJECTOR_TYPE_LDPV2, "ldpv2"},
     { PROJECTOR_TYPE_RESAMPLER, "resampler"},
+    { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
 };
 
 
@@ -463,7 +468,8 @@ struct clip_vision_model {
 
     // embeddings
     struct ggml_tensor * class_embedding;
-    struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_embeddings_0;
+    struct ggml_tensor * patch_embeddings_1;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
     struct ggml_tensor * patch_bias;
     struct ggml_tensor * position_embeddings;
 
@@ -553,6 +559,7 @@ struct clip_ctx {
     bool has_vision_encoder  = false;
     bool has_llava_projector = false;
     bool has_minicpmv_projector = false;
+    bool has_qwen2vl_merger = false;
     int minicpmv_version = 2;
 
     struct clip_vision_model vision_model;
@@ -561,6 +568,7 @@ struct clip_ctx {
     float image_mean[3];
     float image_std[3];
     bool use_gelu = false;
+    bool use_silu = false;
     int32_t ftype = 1;
 
     bool has_class_embedding = true;
@@ -606,14 +614,26 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             image_size_height = imgs->data->ny;
         }
     }
+    else if (ctx->has_qwen2vl_merger) {
+        // use the image's native resolution when image is avaible
+        if (is_inf) {
+        // if (imgs->data->nx && imgs->data->ny) {
+            image_size_width  = imgs->data->nx;
+            image_size_height = imgs->data->ny;
+        }
+    }
     const int patch_size           = hparams.patch_size;
     const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
+    const int patches_w            = image_size_width / patch_size;
+    const int patches_h            = image_size_height / patch_size;
     const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
+    const int num_position_ids     = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions;
     const int hidden_size          = hparams.hidden_size;
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
     int n_layer                    = hparams.n_layer;
     const float eps                = hparams.eps;
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
 
     const int batch_size = imgs->size;
 
@@ -634,10 +654,30 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     ggml_set_name(inp_raw, "inp_raw");
     ggml_set_input(inp_raw);
 
-    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
 
-    inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
-    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    if (ctx->has_qwen2vl_merger) {
+        GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
+        GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
+
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, patches_h, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
+        inp = ggml_reshape_3d(
+            ctx0, inp,
+            hidden_size, patches_w * patches_h, batch_size);
+    }
+    else {
+        inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    }
 
     if (ctx->has_patch_bias) {
         // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
@@ -659,12 +699,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         }
     }
 
-    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
+    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
     ggml_set_name(positions, "positions");
     ggml_set_input(positions);
 
-    embeddings =
-        ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding
+        embeddings =
+            ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    }
 
     if (ctx->has_minicpmv_projector) {
         int pos_w = image_size_width/patch_size;
@@ -688,7 +730,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // loop over layers
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
+        // TODO: figure out why we doing thing in this way ???
         n_layer += 1;
     }
     for (int il = 0; il < n_layer - 1; il++) {
@@ -710,8 +753,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             struct ggml_tensor * Q =
                 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
 
-            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
             Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                Q = ggml_rope_multi(
+                    ctx0, Q, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
+            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
             Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
             Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
 
@@ -719,6 +767,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
 
             K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                K = ggml_rope_multi(
+                    ctx0, K, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
             K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
             K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
 
@@ -758,6 +811,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
 
         if (ctx->use_gelu) {
             cur = ggml_gelu_inplace(ctx0, cur);
+        } else if (ctx->use_silu) {
+            cur = ggml_silu_inplace(ctx0, cur);
         } else {
             cur = ggml_gelu_quick_inplace(ctx0, cur);
         }
@@ -769,6 +824,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         cur = ggml_add(ctx0, embeddings, cur);
 
         embeddings = cur;
+
     }
 
     // post-layernorm
@@ -1030,6 +1086,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             GGML_ASSERT(false);
         }
     }
+    else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
+
+        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+
+        // GELU activation
+        embeddings = ggml_gelu(ctx0, embeddings);
+
+        // Second linear layer
+        embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
+    }
 
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
@@ -1206,6 +1275,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
         }
 
+        idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
+        if (idx != -1) {
+            new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
+        }
         // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
 
         GGML_ASSERT(new_clip->has_vision_encoder);
@@ -1214,6 +1287,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         idx = get_key_idx(ctx, KEY_USE_GELU);
         new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
 
+        try {
+            idx = get_key_idx(ctx, KEY_USE_SILU);
+            new_clip->use_silu = gguf_get_val_bool(ctx, idx);
+        } catch (std::runtime_error & /*e*/) {
+            new_clip->use_silu = false;
+        }
+
         if (verbosity >= 1) {
             LOG_INF("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
             LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
@@ -1389,11 +1469,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         try {
-            vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+            vision_model.patch_embeddings_0    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
         } catch(const std::exception& /*e*/) {
             LOG_ERR("%s: failed to load vision model tensors\n", __func__);
         }
+        try {
+            vision_model.patch_embeddings_1    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1);
+        } catch(const std::exception& /*e*/) {
+            new_clip->has_qwen2vl_merger = false;
+        }
 
         // LLaVA projection
         if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
@@ -1481,6 +1566,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
             vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
         }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
+            vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+            vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+            vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+            vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+        }
         else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1519,6 +1610,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
         clip_image_f32_batch batch;
         batch.size = 1;
+        batch.data = nullptr;
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
@@ -1532,6 +1624,10 @@ void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size
     ctx_clip->load_image_size = load_image_size;
 }
 
+struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) {
+    return ctx_clip->load_image_size;
+}
+
 struct clip_image_size * clip_image_size_init() {
     struct clip_image_size * load_image_size = new struct clip_image_size();
     load_image_size->width = 448;
@@ -1984,6 +2080,23 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         }
         return true;
     }
+    else if (ctx->has_qwen2vl_merger) {
+        clip_image_u8 * resized = clip_image_u8_init();
+        auto patch_size = clip_patch_size(ctx) * 2;
+        int nx = ceil((float)img->nx / patch_size) * patch_size;
+        int ny = ceil((float)img->ny / patch_size) * patch_size;
+        bicubic_resize(*img, *resized, nx, ny);
+
+        res_imgs->data = new clip_image_f32[1];
+        // clip_image_f32 * res = clip_image_f32_init();
+        normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std);
+        // res_imgs->data[0] = *res;
+        res_imgs->size = 1;
+
+        // clip_image_f32_free(res);
+        clip_image_u8_free(resized);
+        return true;
+    }
 
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
@@ -2173,6 +2286,13 @@ size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
     return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
+size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
+    clip_image_f32 img;
+    img.nx = img_w;
+    img.ny = img_h;
+    return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
+}
+
 int32_t clip_image_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.image_size;
 }
@@ -2194,6 +2314,13 @@ const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
 }
 
 int clip_n_patches(const struct clip_ctx * ctx) {
+    clip_image_f32 img;
+    img.nx = ctx->vision_model.hparams.image_size;
+    img.ny = ctx->vision_model.hparams.image_size;
+    return clip_n_patches_by_img(ctx, &img);
+}
+
+int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->vision_model.hparams;
 
     int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
@@ -2207,6 +2334,11 @@ int clip_n_patches(const struct clip_ctx * ctx) {
         else if (ctx->minicpmv_version == 3) {
             n_patches = 64;
         }
+    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        int patch_size = params.patch_size * 2;
+        int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
+        int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
+        n_patches = x_patch * y_patch;
     }
 
     return n_patches;
@@ -2335,7 +2467,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     const int image_size = hparams.image_size;
     int image_size_width  = image_size;
     int image_size_height = image_size;
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) {
         image_size_width  = imgs->data[0].nx;
         image_size_height = imgs->data[0].ny;
     }
@@ -2355,7 +2487,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         for (size_t i = 0; i < imgs->size; i++) {
             const int nx = imgs->data[i].nx;
             const int ny = imgs->data[i].ny;
-            if (!ctx->has_minicpmv_projector) {
+            if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) {
                 GGML_ASSERT(nx == image_size && ny == image_size);
             }
 
@@ -2413,9 +2545,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
 
             float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
-            for(int i=0;i<pos_w * pos_h;++i){
-                for(int j=0;j<embed_dim;++j){
-                    pos_embed_data[i*embed_dim+j]=pos_embed_t[i][j];
+            for(int i=0;i < pos_w * pos_h; ++i){
+                for(int j=0; j < embed_dim; ++j){
+                    pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j];
                 }
             }
 
@@ -2435,7 +2567,34 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             }
         }
 
-        {
+        if (ctx->has_qwen2vl_merger) {
+            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+
+            const int pw = image_size_width / patch_size;
+            const int ph = image_size_height / patch_size;
+            int* positions_data = (int*)malloc(ggml_nbytes(positions));
+
+            int ptr = 0;
+            for (int y = 0; y < ph; y+=2)
+            {
+                for (int x = 0; x < pw; x+=2)
+                {
+                    for (int dy = 0; dy < 2; dy++) {
+                        for (int dx = 0; dx < 2; dx++) {
+                            positions_data[ptr]                 = y + dy;
+                            positions_data[num_patches + ptr]     = x + dx;
+                            positions_data[num_patches * 2 + ptr] = y + dy;
+                            positions_data[num_patches * 3 + ptr] = x + dx;
+                            ptr++;
+                        }
+                    }
+                }
+            }
+
+            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+            free(positions_data);
+        }
+        else {
             struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
 
             int* positions_data = (int*)malloc(ggml_nbytes(positions));
@@ -2444,16 +2603,16 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             }
             ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
             free(positions_data);
-        }
 
-        {
-            struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
-            int* patches_data = (int*)malloc(ggml_nbytes(patches));
-            for (int i = 0; i < num_patches; i++) {
-                patches_data[i] = i + 1;
+            {
+                struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
+                int* patches_data = (int*)malloc(ggml_nbytes(patches));
+                for (int i = 0; i < num_patches; i++) {
+                    patches_data[i] = i + 1;
+                }
+                ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
+                free(patches_data);
             }
-            ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-            free(patches_data);
         }
     }
 
@@ -2626,6 +2785,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return 3584;
         }
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        return ctx->vision_model.mm_1_b->ne[0];
+    }
 
     std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
     throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -2637,3 +2799,21 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
     }
     return 0;
 }
+
+bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
+    return ctx->has_qwen2vl_merger;
+}
+
+
+bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
+    clip_image_f32 clip_img;
+    clip_img.buf.resize(h * w * 3);
+    for (int i = 0; i < h*w*3; i++)
+    {
+        clip_img.buf[i] = img[i];
+    }
+    clip_img.nx = w;
+    clip_img.ny = h;
+    clip_image_encode(ctx, n_threads, &clip_img, vec);
+    return true;
+}
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index 78588bdf1..1603edd26 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -45,6 +45,7 @@ CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity
 CLIP_API void clip_free(struct clip_ctx * ctx);
 
 CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
+CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
 
 CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
 CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
@@ -55,11 +56,13 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 
 CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
 
-CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches        (const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
+CLIP_API int clip_n_mmproj_embd    (const struct clip_ctx * ctx);
 
 CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
 CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
+CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
 
 CLIP_API struct clip_image_size * clip_image_size_init();
 CLIP_API struct clip_image_u8  * clip_image_u8_init ();
@@ -86,6 +89,9 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons
 CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
 
 CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
+CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
+
+CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
 
 #ifdef __cplusplus
 }
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 4ca53a0b8..16f30c56c 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -259,25 +259,33 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
     const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
 
-    if (clip_is_minicpmv(ctx_clip)) {
+    if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
         std::vector<float *> image_embd_v;
         image_embd_v.resize(img_res_v.size);
         struct clip_image_size * load_image_size = clip_image_size_init();
+
         for (size_t i = 0; i < img_res_v.size; i++) {
             const int64_t t_img_enc_step_start_us = ggml_time_us();
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
             int patch_size=14;
             load_image_size->width = img_res_v.data[i].nx;
             load_image_size->height = img_res_v.data[i].ny;
             clip_add_load_image_size(ctx_clip, load_image_size);
+
             bool encoded = false;
-            int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
-            if (has_minicpmv_projector == 2) {
-                encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
-            }
-            else if (has_minicpmv_projector == 3) {
+            if (clip_is_qwen2vl(ctx_clip)) {
                 encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
             }
+            else {
+                int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
+                if (has_minicpmv_projector == 2) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
+                }
+                else if (has_minicpmv_projector == 3) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
+                }
+            }
+
             if (!encoded) {
                 LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                 return false;
@@ -290,8 +298,11 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
         int n_img_pos_out = 0;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
-            std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
-            n_img_pos_out += clip_n_patches(ctx_clip);
+            std::memcpy(
+                image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
+                image_embd_v[i],
+                clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
+            n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]);
         }
         *n_img_pos = n_img_pos_out;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
@@ -387,7 +398,13 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
     if (clip_is_minicpmv(ctx_clip)) {
         num_max_patches = 10;
     }
-    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    float * image_embd;
+    if (clip_is_qwen2vl(ctx_clip)) {
+        // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
+        image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
+    } else {
+        image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    }
     if (!image_embd) {
         LOG_ERR("Unable to allocate memory for image embeddings\n");
         return false;
diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py
new file mode 100644
index 000000000..464ab80d3
--- /dev/null
+++ b/examples/llava/qwen2_vl_surgery.py
@@ -0,0 +1,158 @@
+import argparse
+from typing import Dict
+
+import torch
+import numpy as np
+from gguf import *
+from transformers import (
+    Qwen2VLForConditionalGeneration,
+    Qwen2VLProcessor,
+    AutoProcessor,
+    Qwen2VLConfig
+)
+
+
+VISION = "clip.vision"
+
+
+def k(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def to_gguf_name(name: str) -> str:
+    og = name
+    name = name.replace("text_model", "t").replace("vision_model", "v")
+    name = name.replace("blocks", "blk").replace("embeddings.", "")
+    name = name.replace("attn.", "attn_")
+    name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
+    # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
+    name = name.replace("norm1", "ln1").replace("norm2", "ln2")
+    name = name.replace("merger.mlp", 'mm')
+    print(f"[to_gguf_name] {og} --> {name}")
+    return name
+
+
+def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]:
+    vision_model = qwen2vl.visual
+    tensor_map = {}
+    for name, ten in vision_model.state_dict().items():
+        ten = ten.numpy()
+        if 'qkv' in name:
+            if ten.ndim == 2: # weight
+                c3, _ = ten.shape
+            else:             # bias
+                c3 = ten.shape[0]
+            assert c3 % 3 == 0
+            c = c3 // 3
+            wq = ten[:c]
+            wk = ten[c: c * 2]
+            wv = ten[c * 2:]
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
+        elif 'merger' in name:
+            if name.endswith("ln_q.weight"):
+                tensor_map['v.post_ln.weight'] = ten
+            elif name.endswith("ln_q.bias"):
+                tensor_map['v.post_ln.bias'] = ten
+            else:
+                # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
+                tensor_map[to_gguf_name(name)] = ten
+        elif 'patch_embed.proj.weight' in name:
+            # NOTE: split Conv3D into Conv2Ds
+            c1, c2, kt, kh, kw = ten.shape
+            assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
+            tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
+            tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
+        else:
+            tensor_map[to_gguf_name(f"vision_model.{name}")] = ten
+
+    for new_name, ten in tensor_map.items():
+        if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
+            tensor_map[new_name] = ten.astype(np.float32)
+        else:
+            tensor_map[new_name] = ten.astype(dtype)
+    tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32)  # dummy tensor, just here as a placeholder
+    return tensor_map
+
+
+def main(args):
+    if args.data_type == 'fp32':
+        dtype = torch.float32
+        np_dtype = np.float32
+        ftype = 0
+    elif args.data_type == 'fp16':
+        dtype = torch.float32
+        np_dtype = np.float16
+        ftype = 1
+    else:
+        raise ValueError()
+
+    model_name = args.model_name
+    print("model_name: ", model_name)
+    qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
+        model_name, torch_dtype=dtype, device_map="cpu"
+    )
+    cfg: Qwen2VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
+    vcfg = cfg.vision_config
+
+    if os.path.isdir(model_name):
+        if model_name.endswith(os.sep):
+            model_name = model_name[:-1]
+        model_name = os.path.basename(model_name)
+    fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf"
+
+    fout = GGUFWriter(path=fname_out, arch="clip")
+    fout.add_description("image encoder for Qwen2VL")
+
+    fout.add_file_type(ftype)
+    fout.add_bool("clip.has_text_encoder", False)
+    fout.add_bool("clip.has_vision_encoder", True)
+    fout.add_bool("clip.has_qwen2vl_merger", True)
+    fout.add_string("clip.projector_type", "qwen2vl_merger")
+
+    print(cfg.vision_config)
+    if 'silu' in cfg.vision_config.hidden_act.lower():
+        fout.add_bool("clip.use_silu", True)
+        fout.add_bool("clip.use_gelu", False)
+    elif 'gelu' in cfg.vision_config.hidden_act.lower():
+        fout.add_bool("clip.use_silu", False)
+        fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower())
+    else:
+        raise ValueError()
+
+    tensor_map = find_vision_tensors(qwen2vl, np_dtype)
+    for name, data in tensor_map.items():
+        fout.add_tensor(name, data)
+
+    fout.add_uint32("clip.vision.patch_size", vcfg.patch_size)
+    fout.add_uint32("clip.vision.image_size", 14 * 40)  # some reasonable size that is divable by (14*2)
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
+    fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads)
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
+    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth)
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0)  # not sure what this does, put 0 here as a placeholder
+    fout.add_name(model_name)
+    """
+    HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig,
+            it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`.
+    """
+
+    processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
+    fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue]
+    fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue]
+
+    fout.write_header_to_file()
+    fout.write_kv_data_to_file()
+    fout.write_tensors_to_file()
+    fout.close()
+    print("save model as: ", fname_out)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct")
+    parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32")
+    args = parser.parse_args()
+    main(args)
diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp
new file mode 100644
index 000000000..e86a60280
--- /dev/null
+++ b/examples/llava/qwen2vl-cli.cpp
@@ -0,0 +1,581 @@
+#include "arg.h"
+#include "base64.hpp"
+#include "log.h"
+#include "common.h"
+#include "sampling.h"
+#include "clip.h"
+#include "llava.h"
+#include "llama.h"
+#include "ggml.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+#ifdef NDEBUG
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#endif
+
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+
+
+static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
+                                     int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+    const int patch_size = 14 * 2;
+    const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
+    const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
+    auto img_tokens = image_embed->n_image_pos;
+    // llama_pos mrope_pos[img_tokens * 4];
+    std::vector<llama_pos> mrope_pos;
+    mrope_pos.resize(img_tokens * 4);
+
+    for (int y = 0; y < ph; y++)
+    {
+        for (int x = 0; x < pw; x++)
+        {
+            int i = y * pw + x;
+            mrope_pos[i] = *st_pos_id;
+            mrope_pos[i + img_tokens] = *st_pos_id + y;
+            mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
+            mrope_pos[i + img_tokens * 3] = 0;
+        }
+    }
+    *st_pos_id += std::max(pw, ph);
+
+    int processed = 0;
+    std::vector<llama_pos> batch_mrope_pos;
+    batch_mrope_pos.resize(img_tokens * 4);
+
+    for (int i = 0; i < img_tokens; i += n_batch) {
+        int n_eval = img_tokens - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+
+        // llama_pos batch_mrope_pos[n_eval * 4];
+        std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
+        memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
+
+        llama_batch batch = {
+            int32_t(n_eval),                // n_tokens
+            nullptr,                        // token
+            (image_embed->embed+i*n_embd),  // embed
+            batch_mrope_pos.data(),         // pos
+            nullptr,  // n_seq_id
+            nullptr,  // seq_id
+            nullptr,  // logits
+        };
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+        processed += n_eval;
+    }
+    return true;
+}
+
+
+static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
+    int N = (int) tokens.size();
+    std::vector<llama_pos> pos;
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        auto batch = llama_batch_get_one(&tokens[i], n_eval);
+        // TODO: add mrope pos ids somewhere else
+        pos.resize(batch.n_tokens * 4);
+        std::fill(pos.begin(), pos.end(), 0);
+        for (int j = 0; j < batch.n_tokens * 3; j ++) {
+            pos[j] = *st_pos_id + (j % batch.n_tokens);
+        }
+        batch.pos = pos.data();
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            return false;
+        }
+        *n_past += n_eval;
+        *st_pos_id += n_eval;
+    }
+    return true;
+}
+
+static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
+}
+
+static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
+    eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
+    return true;
+}
+
+static const char * sample(struct common_sampler * smpl,
+                           struct llama_context * ctx_llama,
+                           int * n_past, int * st_pos_id) {
+    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
+    common_sampler_accept(smpl, id, true);
+    static std::string ret;
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+        ret = "</s>";
+    } else {
+        ret = common_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past, st_pos_id);
+    return ret.c_str();
+}
+
+static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
+static const char* IMG_BASE64_TAG_END = "\">";
+
+static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
+    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
+    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
+}
+
+static bool prompt_contains_image(const std::string& prompt) {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    return (begin != std::string::npos);
+}
+
+// replaces the base64 image tag in the prompt with `replacement`
+static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
+    size_t img_base64_str_start, img_base64_str_end;
+    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
+    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
+        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        return NULL;
+    }
+
+    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
+    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
+    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
+
+    auto required_bytes = base64::required_encode_size(base64_str.size());
+    auto img_bytes = std::vector<unsigned char>(required_bytes);
+    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
+
+    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
+    if (!embed) {
+        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
+        return NULL;
+    }
+
+    return embed;
+}
+
+static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    if (begin == std::string::npos || end == std::string::npos) {
+        return prompt;
+    }
+    auto pre = prompt.substr(0, begin);
+    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
+    return pre + replacement + post;
+}
+
+struct llava_context {
+    struct clip_ctx * ctx_clip = NULL;
+    struct llama_context * ctx_llama = NULL;
+    struct llama_model * model = NULL;
+};
+
+static void print_usage(int, char ** argv) {
+    LOG("\n example usage:\n");
+    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
+}
+
+static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
+
+    // load and preprocess the image
+    llava_image_embed * embed = NULL;
+    auto prompt = params->prompt;
+    if (prompt_contains_image(prompt)) {
+        if (!params->image.empty()) {
+            LOG_INF("using base64 encoded image instead of command line image path\n");
+        }
+        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
+        if (!embed) {
+            LOG_ERR("%s: can't load image from prompt\n", __func__);
+            return NULL;
+        }
+        params->prompt = remove_image_from_prompt(prompt);
+    } else {
+        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
+        if (!embed) {
+            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
+            return NULL;
+        }
+    }
+
+    return embed;
+}
+
+static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
+    int n_past = 0;
+    int cur_pos_id = 0;
+
+    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
+
+    std::string system_prompt, user_prompt;
+    size_t image_pos = prompt.find("<|vision_start|>");
+    if (image_pos != std::string::npos) {
+        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
+        system_prompt = prompt.substr(0, image_pos);
+        user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
+        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    } else {
+        // llava-1.5 native mode
+        system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
+        user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    }
+
+    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
+    if (image_embed != nullptr) {
+        auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
+        qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
+    }
+    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
+
+    // generate the response
+
+    LOG("\n");
+
+    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
+    if (!smpl) {
+        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
+        exit(1);
+    }
+
+    std::string response = "";
+    for (int i = 0; i < max_tgt_len; i++) {
+        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
+        response += tmp;
+        if (strcmp(tmp, "</s>") == 0) break;
+        if (strstr(tmp, "###")) break; // Yi-VL behavior
+        LOG("%s", tmp);
+        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
+        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
+        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
+
+        fflush(stdout);
+    }
+
+    common_sampler_free(smpl);
+    LOG("\n");
+}
+
+static struct llama_model * llava_init(common_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);
+
+    llama_model_params model_params = common_model_params_to_llama(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_ERR("%s: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
+    const char * clip_path = params->mmproj.c_str();
+
+    auto prompt = params->prompt;
+    if (prompt.empty()) {
+        prompt = "describe the image in detail.";
+    }
+
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+
+
+    llama_context_params ctx_params = common_context_params_to_llama(*params);
+    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
+        return NULL;
+    }
+
+    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
+
+    ctx_llava->ctx_llama = ctx_llama;
+    ctx_llava->ctx_clip = ctx_clip;
+    ctx_llava->model = model;
+    return ctx_llava;
+}
+
+static void llava_free(struct llava_context * ctx_llava) {
+    if (ctx_llava->ctx_clip) {
+        clip_free(ctx_llava->ctx_clip);
+        ctx_llava->ctx_clip = NULL;
+    }
+
+    llama_free(ctx_llava->ctx_llama);
+    llama_free_model(ctx_llava->model);
+    llama_backend_free();
+}
+
+#ifndef NDEBUG
+
+static void debug_test_mrope_2d() {
+    // 1. Initialize backend
+    ggml_backend_t backend = NULL;
+    std::string backend_name = "";
+#ifdef GGML_USE_CUDA
+    fprintf(stderr, "%s: using CUDA backend\n", __func__);
+    backend = ggml_backend_cuda_init(0); // init device 0
+    backend_name = "cuda";
+    if (!backend) {
+        fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+    }
+#endif
+    // if there aren't GPU Backends fallback to CPU backend
+    if (!backend) {
+        backend = ggml_backend_cpu_init();
+        backend_name = "cpu";
+    }
+
+    // Calculate the size needed to allocate
+    size_t ctx_size = 0;
+    ctx_size += 2 * ggml_tensor_overhead(); // tensors
+    // no need to allocate anything else!
+
+    // 2. Allocate `ggml_context` to store tensor data
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx_size,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
+    };
+    struct ggml_context * ctx = ggml_init(params);
+
+    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+
+    struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
+    ggml_set_name(pos, "pos");
+    ggml_set_input(pos);
+
+    std::vector<float> dummy_q;
+    dummy_q.resize(128 * 12 * 30);
+    std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
+    // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
+
+    std::vector<int> pos_id;
+    pos_id.resize(30 * 4);
+    for (int i = 0; i < 30; i ++) {
+        pos_id[i] = i;
+        pos_id[i + 30] = i + 10;
+        pos_id[i + 60] = i + 20;
+        pos_id[i + 90] = i + 30;
+    }
+    int sections[4] = {32, 32, 0, 0};
+
+    // 4. Allocate a `ggml_backend_buffer` to store all tensors
+    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+
+    // 5. Copy tensor data from main memory (RAM) to backend buffer
+    ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
+    ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
+
+    // 6. Create a `ggml_cgraph` for mul_mat operation
+    struct ggml_cgraph * gf = NULL;
+    struct ggml_context * ctx_cgraph = NULL;
+
+    // create a temporally context to build the graph
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
+    };
+    ctx_cgraph = ggml_init(params0);
+    gf = ggml_new_graph(ctx_cgraph);
+
+    struct ggml_tensor * result0 = ggml_rope_multi(
+        ctx_cgraph, inp_raw, pos, nullptr,
+        128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
+        0, 1, 32, 1);
+
+    // Add "result" tensor and all of its dependencies to the cgraph
+    ggml_build_forward_expand(gf, result0);
+
+    // 7. Create a `ggml_gallocr` for cgraph computation
+    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+    ggml_gallocr_alloc_graph(allocr, gf);
+
+    // 9. Run the computation
+    int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
+    if (ggml_backend_is_cpu(backend)) {
+        ggml_backend_cpu_set_n_threads(backend, n_threads);
+    }
+    ggml_backend_graph_compute(backend, gf);
+
+    // 10. Retrieve results (output tensors)
+    // in this example, output tensor is always the last tensor in the graph
+    struct ggml_tensor * result = result0;
+    // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
+    float * result_data = (float *)malloc(ggml_nbytes(result));
+    // because the tensor data is stored in device buffer, we need to copy it back to RAM
+    ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
+    const std::string bin_file = "mrope_2d_" + backend_name +".bin";
+    std::ofstream outFile(bin_file, std::ios::binary);
+
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
+        outFile.close();
+        std::cout << "Data successfully written to " + bin_file << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+
+    free(result_data);
+    // 11. Free memory and exit
+    ggml_free(ctx_cgraph);
+    ggml_gallocr_free(allocr);
+    ggml_free(ctx);
+    ggml_backend_buffer_free(buffer);
+    ggml_backend_free(backend);
+}
+
+static void debug_dump_img_embed(struct llava_context * ctx_llava) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
+    int ne = n_embd * 4;
+    float vals[56 * 56 * 3];
+    // float embd[ne];
+    std::vector<float> embd;
+    embd.resize(ne);
+
+    for (int i = 0; i < 56*56; i++)
+    {
+        for (int c = 0; c < 3; c++)
+            vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
+    }
+
+    clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
+
+    std::ofstream outFile("img_embed.bin", std::ios::binary);
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
+
+        outFile.close();
+        std::cout << "Data successfully written to mrope.bin" << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+}
+
+#endif
+
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
+        print_usage(argc, argv);
+        return 1;
+    }
+
+    auto * model = llava_init(&params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
+        return 1;
+    }
+
+    if (prompt_contains_image(params.prompt)) {
+        auto * ctx_llava = llava_init_context(&params, model);
+
+        auto * image_embed = load_image(ctx_llava, &params, "");
+
+        // process the prompt
+        process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        llava_image_embed_free(image_embed);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#ifndef NDEBUG
+    } else if (params.image[0].empty()) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        debug_test_mrope_2d();
+        debug_dump_img_embed(ctx_llava);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#endif
+    } else {
+        for (auto & image : params.image) {
+            auto * ctx_llava = llava_init_context(&params, model);
+
+            auto * image_embed = load_image(ctx_llava, &params, image);
+            if (!image_embed) {
+                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
+                return 1;
+            }
+
+            // process the prompt
+            process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+            llama_perf_context_print(ctx_llava->ctx_llama);
+            llava_image_embed_free(image_embed);
+            ctx_llava->model = NULL;
+            llava_free(ctx_llava);
+        }
+    }
+
+    llama_free_model(model);
+
+    return 0;
+}
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 386d5a15d..b0c1ac9ce 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -237,7 +237,9 @@
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1
 
-#define GGML_ROPE_TYPE_NEOX 2
+#define GGML_ROPE_TYPE_NEOX   2
+#define GGML_ROPE_TYPE_MROPE  8
+#define GGML_ROPE_TYPE_VISION 24
 
 #define GGUF_MAGIC "GGUF"
 
@@ -1443,6 +1445,22 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    GGML_API struct ggml_tensor * ggml_rope_multi(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[4],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
             struct ggml_context * ctx,
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index fa04ab84f..d410c0244 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1747,6 +1747,15 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if (*ext_factor != 0) {
                 return false;
             }
+
+            const int mode = ((const int32_t *) op->op_params)[2];
+            if (mode & GGML_ROPE_TYPE_MROPE) {
+                return false;
+            }
+            if (mode & GGML_ROPE_TYPE_VISION) {
+                return false;
+            }
+
             return true;
         }
         case GGML_OP_UPSCALE: {
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 92df6fdda..67e67a089 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -9133,6 +9133,64 @@ static void ggml_rope_cache_init(
     }
 }
 
+static void ggml_mrope_cache_init(
+     float theta_base_t, float theta_base_h, float theta_base_w, float theta_base_e, int sections[4], bool indep_sects,
+     float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
+     float * cache, float sin_sign, float theta_scale) {
+    // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
+    float theta_t = theta_base_t;
+    float theta_h = theta_base_h;
+    float theta_w = theta_base_w;
+    float theta_e = theta_base_e;  // extra position id for vision encoder
+    int sect_dims = sections[0] + sections[1] + sections[2] + sections[3];
+    int sec_w = sections[1] + sections[0];
+    int sec_e = sections[2] + sec_w;
+    GGML_ASSERT(sect_dims <= ne0);
+
+    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+        const float ff = freq_factors ? freq_factors[i0/2] : 1.0f;
+
+        int sector = (i0 / 2) % sect_dims;
+        if (indep_sects) {
+            // compute theta independently for each dim sections
+            // (i.e. reset corresponding theta when `i0` go from one section to another)
+            if (sector == 0) {
+                theta_t = theta_base_t;
+            }
+            else if (sector == sections[0]) {
+                theta_h = theta_base_h;;
+            }
+            else if (sector == sec_w) {
+                theta_w = theta_base_w;
+            }
+            else if (sector == sec_e) {
+                theta_e = theta_base_e;
+            }
+        }
+
+        float theta = theta_t;
+        if (sector >= sections[0] && sector < sec_w) {
+            theta = theta_h;
+        }
+        else if (sector >= sec_w && sector < sec_w + sections[2]) {
+            theta = theta_w;
+        }
+        else if (sector >= sec_w + sections[2]) {
+            theta = theta_e;
+        }
+
+        rope_yarn(
+            theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
+        );
+        cache[i0 + 1] *= sin_sign;
+
+        theta_t *= theta_scale;
+        theta_w *= theta_scale;
+        theta_h *= theta_scale;
+        theta_e *= theta_scale;
+    }
+}
+
 static void ggml_compute_forward_rope_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst,
@@ -9143,6 +9201,7 @@ static void ggml_compute_forward_rope_f32(
     const struct ggml_tensor * src2 = dst->src[2];
 
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    int sections[4];
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
@@ -9156,6 +9215,7 @@ static void ggml_compute_forward_rope_f32(
     memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections,    (int32_t *) dst->op_params + 11, sizeof(int)*4);
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
@@ -9188,6 +9248,16 @@ static void ggml_compute_forward_rope_f32(
     ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;  // ggml_rope_multi, multimodal rotary position embedding
+    const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
+
+    if (is_mrope) {
+        GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
+    }
+
+    if (is_vision) {
+        GGML_ASSERT(n_dims == ne0/2);
+    }
 
     const float * freq_factors = NULL;
     if (src2 != NULL) {
@@ -9203,18 +9273,63 @@ static void ggml_compute_forward_rope_f32(
 
     const int32_t * pos = (const int32_t *) src1->data;
 
-    for (int64_t i3 = 0; i3 < ne3; i3++) {
-        for (int64_t i2 = 0; i2 < ne2; i2++) {
-            const int64_t p = pos[i2];
+    for (int64_t i3 = 0; i3 < ne3; i3++) { // batch
+        for (int64_t i2 = 0; i2 < ne2; i2++) { // seq-len
 
             float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
-            ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            if (!is_mrope) {
+                const int64_t p = pos[i2];
+                ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
+            else {
+                const int64_t p_t = pos[i2];
+                const int64_t p_h = pos[i2 + ne2];
+                const int64_t p_w = pos[i2 + ne2 * 2];
+                const int64_t p_e = pos[i2 + ne2 * 3];
+                ggml_mrope_cache_init(
+                    p_t, p_h, p_w, p_e, sections, is_vision,
+                    freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
 
-            for (int64_t i1 = 0; i1 < ne1; i1++) {
+            for (int64_t i1 = 0; i1 < ne1; i1++) { // attn-heads
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                if (!is_neox) {
+                if (is_neox || is_mrope) {
+                    if (is_vision){
+                        for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                            const int64_t ic = i0/2;
+
+                            const float cos_theta = cache[i0 + 0];
+                            const float sin_theta = cache[i0 + 1];
+
+                            const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                            float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+                            const float x0 = src[0];
+                            const float x1 = src[n_dims];
+
+                            dst_data[0]      = x0*cos_theta - x1*sin_theta;
+                            dst_data[n_dims] = x0*sin_theta + x1*cos_theta;
+                        }
+                    } else {
+                        for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                            const int64_t ic = i0/2;
+
+                            const float cos_theta = cache[i0 + 0];
+                            const float sin_theta = cache[i0 + 1];
+
+                            const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                            float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+                            const float x0 = src[0];
+                            const float x1 = src[n_dims/2];
+
+                            dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                            dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+                        }
+                    }
+                } else {
                     for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
                         const float cos_theta = cache[i0 + 0];
                         const float sin_theta = cache[i0 + 1];
@@ -9228,8 +9343,10 @@ static void ggml_compute_forward_rope_f32(
                         dst_data[0] = x0*cos_theta - x1*sin_theta;
                         dst_data[1] = x0*sin_theta + x1*cos_theta;
                     }
-                } else {
-                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                }
+
+                if (is_vision) {
+                    for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
                         const int64_t ic = i0/2;
 
                         const float cos_theta = cache[i0 + 0];
@@ -9239,19 +9356,20 @@ static void ggml_compute_forward_rope_f32(
                         float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
 
                         const float x0 = src[0];
-                        const float x1 = src[n_dims/2];
+                        const float x1 = src[n_dims];
 
-                        dst_data[0]        = x0*cos_theta - x1*sin_theta;
-                        dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+                        dst_data[0]      = x0*cos_theta - x1*sin_theta;
+                        dst_data[n_dims] = x0*sin_theta + x1*cos_theta;
                     }
-                }
+                } else {
+                    // fill the remain channels with data from src tensor
+                    for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+                        const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                        float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
-                for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
-                    const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                    float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
-
-                    dst_data[0] = src[0];
-                    dst_data[1] = src[1];
+                        dst_data[0] = src[0];
+                        dst_data[1] = src[1];
+                    }
                 }
             }
         }
@@ -9269,6 +9387,7 @@ static void ggml_compute_forward_rope_f16(
     const struct ggml_tensor * src2 = dst->src[2];
 
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    int sections[4];
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
@@ -9281,6 +9400,8 @@ static void ggml_compute_forward_rope_f16(
     memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections,    (int32_t *) dst->op_params + 11, sizeof(int)*4);
+
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
@@ -9313,6 +9434,16 @@ static void ggml_compute_forward_rope_f16(
     ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
+    const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
+
+    if (is_mrope) {
+        GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
+    }
+
+    if (is_vision) {
+        GGML_ASSERT(n_dims == ne0/2);
+    }
 
     const float * freq_factors = NULL;
     if (src2 != NULL) {
@@ -9330,16 +9461,61 @@ static void ggml_compute_forward_rope_f16(
 
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = 0; i2 < ne2; i2++) {
-            const int64_t p = pos[i2];
 
             float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
-            ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            if (!is_mrope) {
+                const int64_t p = pos[i2];
+                ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
+            else {
+                const int64_t p_t = pos[i2];
+                const int64_t p_h = pos[i2 + ne2];
+                const int64_t p_w = pos[i2 + ne2 * 2];
+                const int64_t p_e = pos[i2 + ne2 * 3];
+                ggml_mrope_cache_init(
+                    p_t, p_h, p_w, p_e, sections, is_vision,
+                    freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
 
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                if (!is_neox) {
+                if (is_neox || is_mrope) {
+                    if (is_vision) {
+                        for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                            const int64_t ic = i0/2;
+
+                            const float cos_theta = cache[i0 + 0];
+                            const float sin_theta = cache[i0 + 1];
+
+                            const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                            ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+                            const float x0 = GGML_FP16_TO_FP32(src[0]);
+                            const float x1 = GGML_FP16_TO_FP32(src[n_dims]);
+
+                            dst_data[0]      = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                            dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        }
+                    } else {
+                        for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                            const int64_t ic = i0/2;
+
+                            const float cos_theta = cache[i0 + 0];
+                            const float sin_theta = cache[i0 + 1];
+
+                            const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                            ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+                            const float x0 = GGML_FP16_TO_FP32(src[0]);
+                            const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+
+                            dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                            dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        }
+                    }
+                } else {
                     for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
                         const float cos_theta = cache[i0 + 0];
                         const float sin_theta = cache[i0 + 1];
@@ -9353,8 +9529,10 @@ static void ggml_compute_forward_rope_f16(
                         dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
                         dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
                     }
-                } else {
-                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                }
+
+                if (is_vision) {
+                    for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
                         const int64_t ic = i0/2;
 
                         const float cos_theta = cache[i0 + 0];
@@ -9364,19 +9542,19 @@ static void ggml_compute_forward_rope_f16(
                         ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
 
                         const float x0 = GGML_FP16_TO_FP32(src[0]);
-                        const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+                        const float x1 = GGML_FP16_TO_FP32(src[n_dims]);
 
-                        dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
-                        dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        dst_data[0]      = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[n_dims] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
                     }
-                }
+                } else {
+                    for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                        ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
-                for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
-                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                    ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
-
-                    dst_data[0] = src[0];
-                    dst_data[1] = src[1];
+                        dst_data[0] = src[0];
+                        dst_data[1] = src[1];
+                    }
                 }
             }
         }
diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu
index 88f586d68..2c84778d2 100644
--- a/ggml/src/ggml-cuda/rope.cu
+++ b/ggml/src/ggml-cuda/rope.cu
@@ -4,6 +4,11 @@ struct rope_corr_dims {
     float v[2];
 };
 
+
+struct mrope_sections {
+    int v[4];
+};
+
 static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
@@ -108,6 +113,105 @@ static __global__ void rope_neox(
     dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
+template<typename T, bool has_ff>
+static __global__ void rope_multi(
+    const T * x, T * dst, int ne0, int ne2, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, mrope_sections sections) {
+    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ne0 + i0/2;
+    const int i2 = row/p_delta_rows;
+
+    int sect_dims = sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3];
+    int sec_w = sections.v[1] + sections.v[0];
+    int sector = (i0 / 2) % sect_dims;
+
+    float theta_base = 0.0;
+    if (sector < sections.v[0]) {
+        theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    }
+    else if (sector >= sections.v[0] && sector < sec_w) {
+        theta_base = pos[i2 + ne2 * 1]*powf(theta_scale, i0/2.0f);
+    }
+    else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
+        theta_base = pos[i2 + ne2 * 2]*powf(theta_scale, i0/2.0f);
+    }
+    else if (sector >= sec_w + sections.v[2]) {
+        theta_base = pos[i2 + ne2 * 3]*powf(theta_scale, i0/2.0f);
+    }
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_ff>
+static __global__ void rope_vision(
+    const T * x, T * dst, int ne0, int ne2, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, mrope_sections sections) {
+    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    const int i  = row*ne0 + i0/2;
+    const int i2 = row/p_delta_rows; // i2-th tokens
+
+    int sect_dims = sections.v[0] + sections.v[1];
+    int sec_w = sections.v[1] + sections.v[0];
+    int sector = (i0 / 2) % sect_dims;
+
+    float theta_base = 0.0;
+    if (sector < sections.v[0]) {
+        const int p = sector;
+        theta_base = pos[i2]*powf(theta_scale, p);
+    }
+    else if (sector >= sections.v[0] && sector < sec_w) {
+        const int p = sector - sections.v[0];
+        theta_base = pos[i2 + ne2]*powf(theta_scale, p);
+    }
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims];
+
+    dst[i + 0]      = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims] = x0*sin_theta + x1*cos_theta;
+}
+
 template<typename T>
 static void rope_norm_cuda(
     const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
@@ -156,6 +260,56 @@ static void rope_neox_cuda(
     }
 }
 
+template<typename T>
+static void rope_multi_cuda(
+    const T * x, T * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nr, n_blocks_x, 1);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    if (freq_factors == nullptr) {
+        rope_multi<T, false><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors, sections
+                );
+    } else {
+        rope_multi<T, true><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors, sections
+                );
+    }
+}
+
+template<typename T>
+static void rope_vision_cuda(
+    const T * x, T * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nr, n_blocks_x, 1);
+    // break down (head_dim, heads, seq) into (CUDA_ROPE_BLOCK_SIZE, x, heads * seq)
+    // where x ~= ceil(head_dim / CUDA_ROPE_BLOCK_SIZE);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    if (freq_factors == nullptr) {
+        rope_vision<T, false><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors, sections
+                );
+    } else {
+        rope_vision<T, true><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors, sections
+                );
+    }
+}
+
 static void rope_norm_cuda_f16(
     const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
     float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
@@ -185,6 +339,38 @@ static void rope_neox_cuda_f32(
     rope_neox_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
 }
 
+static void rope_multi_cuda_f16(
+    const half * x, half * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
+) {
+
+    rope_multi_cuda<half>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+}
+
+static void rope_multi_cuda_f32(
+    const float * x, float * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
+) {
+
+    rope_multi_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+}
+
+static void rope_vision_cuda_f16(
+    const half * x, half * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
+) {
+
+    rope_vision_cuda<half>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+}
+
+static void rope_vision_cuda_f32(
+    const float * x, float * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
+) {
+
+    rope_vision_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+}
+
 void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
@@ -201,8 +387,9 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
     GGML_ASSERT(src0->type == dst->type);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
+    const int64_t ne00 = src0->ne[0]; // head dims
+    const int64_t ne01 = src0->ne[1]; // num heads
+    const int64_t ne02 = src0->ne[2]; // num heads
     const int64_t nr = ggml_nrows(src0);
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
@@ -210,6 +397,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int mode       = ((int32_t *) dst->op_params)[2];
     //const int n_ctx      = ((int32_t *) dst->op_params)[3];
     const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+    mrope_sections sections;
 
     // RoPE alteration for extended context
     float freq_base;
@@ -225,8 +413,19 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections.v,  (int32_t *) dst->op_params + 11, sizeof(int)*4);
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
+    const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
+
+    if (is_mrope) {
+        GGML_ASSERT(sections.v[0] > 0 || sections.v[1] > 0 || sections.v[2] > 0);
+    }
+
+    if (is_vision) {
+        GGML_ASSERT(n_dims == ne00/2);
+    }
 
     const int32_t * pos = (const int32_t *) src1_d;
 
@@ -253,6 +452,34 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         } else {
             GGML_ABORT("fatal error");
         }
+    } else if (is_mrope && !is_vision) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_multi_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, sections, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_multi_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, sections, stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else if (is_vision) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_vision_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, sections, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_vision_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, sections, stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
     } else {
         if (src0->type == GGML_TYPE_F32) {
             rope_norm_cuda_f32(
diff --git a/ggml/src/ggml-kompute/ggml-kompute.cpp b/ggml/src/ggml-kompute/ggml-kompute.cpp
index 28ceecfc4..505792271 100644
--- a/ggml/src/ggml-kompute/ggml-kompute.cpp
+++ b/ggml/src/ggml-kompute/ggml-kompute.cpp
@@ -1419,8 +1419,18 @@ static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_SOFT_MAX:
         case GGML_OP_RMS_NORM:
         case GGML_OP_NORM:
-        case GGML_OP_ROPE:
             return true;
+        case GGML_OP_ROPE:
+            {
+                const int mode = ((const int32_t *) op->op_params)[2];
+                if (mode & GGML_ROPE_TYPE_MROPE) {
+                    return false;
+                }
+                if (mode & GGML_ROPE_TYPE_VISION) {
+                    return false;
+                }
+                return true;
+            }
         case GGML_OP_DUP:
         case GGML_OP_CPY:
         case GGML_OP_CONT:
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 34fe5778e..28f590f92 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1125,8 +1125,18 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
             return has_simdgroup_reduction && (op->ne[0] % 4 == 0);
         case GGML_OP_ARGMAX:
         case GGML_OP_NORM:
-        case GGML_OP_ROPE:
             return true;
+        case GGML_OP_ROPE:
+            {
+                const int mode = ((const int32_t *) op->op_params)[2];
+                if (mode & GGML_ROPE_TYPE_MROPE) {
+                    return false;
+                }
+                if (mode & GGML_ROPE_TYPE_VISION) {
+                    return false;
+                }
+                return true;
+            }
         case GGML_OP_IM2COL:
             return op->src[0]->type == GGML_TYPE_F16;
         case GGML_OP_POOL_1D:
@@ -3026,7 +3036,9 @@ static void ggml_metal_encode_node(
             } break;
         case GGML_OP_ROPE:
             {
-                GGML_ASSERT(ne10 == ne02);
+                // make sure we have one or more position id(ne10) per token(ne02)
+                GGML_ASSERT(ne10 % ne02 == 0);
+                GGML_ASSERT(ne10 >= ne02);
 
                 const int nth = MIN(1024, ne00);
 
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 6b9f0b0d9..84f1328e7 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4488,7 +4488,16 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SOFT_MAX:
             return true;
         case GGML_OP_ROPE:
-            return ggml_is_contiguous(op->src[0]);
+            {
+                const int mode = ((const int32_t *) op->op_params)[2];
+                if (mode & GGML_ROPE_TYPE_MROPE) {
+                    return false;
+                }
+                if (mode & GGML_ROPE_TYPE_VISION) {
+                    return false;
+                }
+                return ggml_is_contiguous(op->src[0]);
+            }
         case GGML_OP_IM2COL:
             // TODO: add support for the new F32 operations
             return op->src[0]->type == GGML_TYPE_F16;
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 515d66b39..a26a8fe09 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -7687,7 +7687,16 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_REPEAT:
             return ggml_type_size(op->type) == sizeof(float) && ggml_type_size(op->src[0]->type) == sizeof(float);
         case GGML_OP_ROPE:
-            return ggml_is_contiguous(op->src[0]);
+            {
+                const int mode = ((const int32_t *) op->op_params)[2];
+                if (mode & GGML_ROPE_TYPE_MROPE) {
+                    return false;
+                }
+                if (mode & GGML_ROPE_TYPE_VISION) {
+                    return false;
+                }
+                return ggml_is_contiguous(op->src[0]);
+            }
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 058941c7a..51cc85662 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3517,15 +3517,18 @@ static struct ggml_tensor * ggml_rope_impl(
         GGML_ASSERT(c->ne[0] >= n_dims / 2);
     }
 
+    int sections[4] = {0, 0, 0, 0};
+
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
+    int32_t params[15] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
     memcpy(params +  5, &freq_base,    sizeof(float));
     memcpy(params +  6, &freq_scale,   sizeof(float));
     memcpy(params +  7, &ext_factor,   sizeof(float));
     memcpy(params +  8, &attn_factor,  sizeof(float));
     memcpy(params +  9, &beta_fast,    sizeof(float));
     memcpy(params + 10, &beta_slow,    sizeof(float));
+    memcpy(params + 11, &sections,     sizeof(int)*4);
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op     = GGML_OP_ROPE;
@@ -3547,6 +3550,53 @@ struct ggml_tensor * ggml_rope(
     );
 }
 
+struct ggml_tensor * ggml_rope_multi(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
+        int                   n_dims,
+        int                   sections[4],
+        int                   mode,
+        int                   n_ctx_orig,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    // Multimodal Rotary Position Embedding
+    GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
+
+    GGML_ASSERT(ggml_is_vector(b));
+    GGML_ASSERT(b->type == GGML_TYPE_I32);
+    GGML_ASSERT(a->ne[2] * 4 == b->ne[0]); // mrope expecting 4 position ids per token
+
+    if (c) {
+        GGML_ASSERT(c->type == GGML_TYPE_F32);
+        GGML_ASSERT(c->ne[0] >= n_dims / 2);
+    }
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    int32_t params[11 + 4] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
+    memcpy(params +  5, &freq_base,    sizeof(float));
+    memcpy(params +  6, &freq_scale,   sizeof(float));
+    memcpy(params +  7, &ext_factor,   sizeof(float));
+    memcpy(params +  8, &attn_factor,  sizeof(float));
+    memcpy(params +  9, &beta_fast,    sizeof(float));
+    memcpy(params + 10, &beta_slow,    sizeof(float));
+    memcpy(&params[11], sections,      sizeof(int)*4);
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_ROPE;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = c;
+
+    return result;
+}
+
 struct ggml_tensor * ggml_rope_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 4c8710b39..0cf939429 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -131,6 +131,7 @@ class Keys:
 
     class Rope:
         DIMENSION_COUNT         = "{arch}.rope.dimension_count"
+        DIMENSION_SECTIONS      = "{arch}.rope.dimension_sections"
         FREQ_BASE               = "{arch}.rope.freq_base"
         SCALING_TYPE            = "{arch}.rope.scaling.type"
         SCALING_FACTOR          = "{arch}.rope.scaling.factor"
@@ -226,6 +227,7 @@ class MODEL_ARCH(IntEnum):
     QWEN         = auto()
     QWEN2        = auto()
     QWEN2MOE     = auto()
+    QWEN2VL      = auto()
     PHI2         = auto()
     PHI3         = auto()
     PLAMO        = auto()
@@ -388,6 +390,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN:           "qwen",
     MODEL_ARCH.QWEN2:          "qwen2",
     MODEL_ARCH.QWEN2MOE:       "qwen2moe",
+    MODEL_ARCH.QWEN2VL:        "qwen2vl",
     MODEL_ARCH.PHI2:           "phi2",
     MODEL_ARCH.PHI3:           "phi3",
     MODEL_ARCH.PLAMO:          "plamo",
@@ -772,6 +775,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.QWEN2VL: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.QWEN2MOE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 7a55d1296..65a64e10d 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -751,6 +751,9 @@ class GGUFWriter:
     def add_rope_dimension_count(self, count: int) -> None:
         self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
 
+    def add_rope_dimension_sections(self, dims: Sequence[int]) -> None:
+        self.add_array(Keys.Rope.DIMENSION_SECTIONS.format(arch=self.arch), dims)
+
     def add_rope_freq_base(self, value: float) -> None:
         self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
 
diff --git a/include/llama.h b/include/llama.h
index eebbacb80..c67988a33 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -108,9 +108,11 @@ extern "C" {
     };
 
     enum llama_rope_type {
-        LLAMA_ROPE_TYPE_NONE = -1,
-        LLAMA_ROPE_TYPE_NORM = 0,
-        LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
+        LLAMA_ROPE_TYPE_NONE   = -1,
+        LLAMA_ROPE_TYPE_NORM   = 0,
+        LLAMA_ROPE_TYPE_NEOX   = GGML_ROPE_TYPE_NEOX,
+        LLAMA_ROPE_TYPE_MROPE  = GGML_ROPE_TYPE_MROPE,
+        LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION,
     };
 
     enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
diff --git a/src/llama.cpp b/src/llama.cpp
index 49ef5b78a..abc1252e7 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -163,6 +163,7 @@ enum llm_arch {
     LLM_ARCH_QWEN,
     LLM_ARCH_QWEN2,
     LLM_ARCH_QWEN2MOE,
+    LLM_ARCH_QWEN2VL,
     LLM_ARCH_PHI2,
     LLM_ARCH_PHI3,
     LLM_ARCH_PLAMO,
@@ -217,6 +218,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN,            "qwen"         },
     { LLM_ARCH_QWEN2,           "qwen2"        },
     { LLM_ARCH_QWEN2MOE,        "qwen2moe"     },
+    { LLM_ARCH_QWEN2VL,         "qwen2vl"      },
     { LLM_ARCH_PHI2,            "phi2"         },
     { LLM_ARCH_PHI3,            "phi3"         },
     { LLM_ARCH_PLAMO,           "plamo"        },
@@ -308,6 +310,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_SCALE,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
+    LLM_KV_ROPE_DIMENSION_SECTIONS,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
@@ -424,6 +427,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SCALE,                  "%s.attention.scale"                  },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,             "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_SECTIONS,          "%s.rope.dimension_sections"              },
     { LLM_KV_ROPE_FREQ_BASE,                   "%s.rope.freq_base"                       },
     { LLM_KV_ROPE_SCALE_LINEAR,                "%s.rope.scale_linear"                    },
     { LLM_KV_ROPE_SCALING_TYPE,                "%s.rope.scaling.type"                    },
@@ -898,6 +902,23 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_QWEN2VL,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_QWEN2MOE,
         {
@@ -2474,11 +2495,12 @@ struct llama_hparams {
     uint32_t time_decay_extra_dim = 0;
     uint32_t wkv_head_size = 0;
 
-    float    rope_attn_factor = 1.0f;
-    float    rope_freq_base_train;
-    float    rope_freq_scale_train;
-    uint32_t n_ctx_orig_yarn;
-    float    rope_yarn_log_mul;
+    float     rope_attn_factor = 1.0f;
+    float     rope_freq_base_train;
+    float     rope_freq_scale_train;
+    uint32_t  n_ctx_orig_yarn;
+    float     rope_yarn_log_mul;
+    int       rope_sections[4];
 
     // for State Space Models
     uint32_t ssm_d_conv  = 0;
@@ -2535,6 +2557,9 @@ struct llama_hparams {
 
         if (this->rope_finetuned  != other.rope_finetuned)  return true;
         if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true;
+        if (std::equal(std::begin(this->rope_sections),
+                       std::end(this->rope_sections),
+                       std::begin(other.rope_sections)))    return true;
 
         if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
         if (this->ssm_d_inner != other.ssm_d_inner) return true;
@@ -3378,6 +3403,11 @@ struct llama_context {
     // whether we are computing encoder output or decoder output
     bool is_encoding = false;
 
+    // TODO: find a better way to accommodate mutli-dimension position encoding methods
+    // number of position id each token get, 1 for each token in most cases.
+    // when using m-rope, it will be 3 position ids per token to representing 3 dimension coordinate.
+    int n_pos_per_token = 1;
+
     // output of the encoder part of the encoder-decoder models
     std::vector<float> embd_enc;
     std::vector<std::set<llama_seq_id>> seq_ids_enc;
@@ -5747,6 +5777,13 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN2VL:
+            {
+                std::array<int, 4> section_dims;
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, section_dims, 4, true);
+                std::copy(section_dims.begin(), section_dims.begin() + 4, std::begin(hparams.rope_sections));
+            }
+            // fall through
         case LLM_ARCH_QWEN2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -8167,6 +8204,7 @@ static bool llm_load_tensors(
                     }
                 } break;
             case LLM_ARCH_QWEN2:
+            case LLM_ARCH_QWEN2VL:
                 {
                     model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -12556,6 +12594,124 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_qwen2vl() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens * 4);
+        cb(lctx.inp_pos, "inp_pos", -1);
+        ggml_set_input(lctx.inp_pos);
+        struct ggml_tensor * inp_pos = lctx.inp_pos;
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        int sections[4];
+        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_multi(
+                    ctx0,
+                    ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_multi(
+                    ctx0,
+                    ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_qwen2moe() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
@@ -16657,6 +16813,11 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_qwen2();
             } break;
+        case LLM_ARCH_QWEN2VL:
+            {
+                lctx.n_pos_per_token = 4;
+                result = llm.build_qwen2vl();
+            } break;
         case LLM_ARCH_QWEN2MOE:
             {
                 result = llm.build_qwen2moe();
@@ -16875,8 +17036,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch)
 
     if (ubatch.pos && lctx.inp_pos) {
         const int64_t n_tokens = ubatch.n_tokens;
-
-        ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
+        auto n_pos = lctx.n_pos_per_token;
+        ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*n_pos*ggml_element_size(lctx.inp_pos));
     }
 
     if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
@@ -20009,6 +20170,9 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_MINICPM3:
             return LLAMA_ROPE_TYPE_NEOX;
 
+        case LLM_ARCH_QWEN2VL:
+            return LLAMA_ROPE_TYPE_MROPE;
+
         // all model arches should be listed explicitly here
         case LLM_ARCH_UNKNOWN:
             GGML_ABORT("unknown architecture");
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 9dd41260a..b9454ba59 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2201,7 +2201,15 @@ struct test_rope : public test_case {
             ggml_set_name(a, "a");
         }
 
-        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]);
+        const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
+        const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
+
+        ggml_tensor * pos;
+        if (is_mrope || is_vision) {
+            pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2] * 4);
+        } else {
+            pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]);
+        }
         ggml_set_name(pos, "pos");
 
         ggml_tensor * freq = nullptr;
@@ -2210,7 +2218,20 @@ struct test_rope : public test_case {
             ggml_set_name(freq, "freq");
         }
 
-        ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+        ggml_tensor * out;
+        if (is_mrope) {
+            if (is_vision) {
+                GGML_ASSERT(n_dims/4 > 0);
+                int rope_sections[4] = {n_dims/4, n_dims/4, 0, 0}; // Vision-RoPE only use first two dimension for image (x, y) coordinate
+                out = ggml_rope_multi(ctx, a, pos, freq, n_dims/2, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+            } else {
+                GGML_ASSERT(n_dims/3 > 0);
+                int rope_sections[4] = {n_dims/3, n_dims/3, n_dims/3, 0};
+                out = ggml_rope_multi(ctx, a, pos, freq, n_dims, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+            }
+        } else {
+            out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+        }
         ggml_set_name(out, "out");
 
         return out;
@@ -2220,11 +2241,12 @@ struct test_rope : public test_case {
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             if (t->type == GGML_TYPE_I32) {
                 // pos
-                std::vector<int> data(ne_a[2]);
-                for (int i = 0; i < ne_a[2]; i++) {
+                const int num_pos_ids = (mode & GGML_ROPE_TYPE_MROPE) ? ne_a[2] * 4 : ne_a[2];
+                std::vector<int> data(num_pos_ids);
+                for (int i = 0; i < num_pos_ids; i++) {
                     data[i] = rand() % n_ctx;
                 }
-                ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int));
+                ggml_backend_tensor_set(t, data.data(), 0, num_pos_ids * sizeof(int));
             } else {
                 if (t->ne[0] == n_dims/2) {
                     // frequency factors in the range [0.9f, 1.1f]
@@ -3813,6 +3835,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                     test_cases.emplace_back(new test_rope(type, { 80,  32, 2, 1},  32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2)
                                 }
 
+                                if (all) {
+                                    test_cases.emplace_back(new test_rope(type, {128,  12, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl 2B)
+                                    test_cases.emplace_back(new test_rope(type, {128,  28, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  16, 2, 1},  80, GGML_ROPE_TYPE_VISION, 512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl ViT)
+                                }
+
                                 test_cases.emplace_back(new test_rope(type, { 64, 128, 2, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
                             }
                         }
diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp
index 4656b30f0..322b8bb99 100644
--- a/tests/test-rope.cpp
+++ b/tests/test-rope.cpp
@@ -138,7 +138,7 @@ int main(int /*argc*/, const char ** /*argv*/) {
     struct ggml_tensor * x;
 
     // rope f32
-    for (int m = 0; m < 3; ++m) {
+    for (int m = 0; m < 5; ++m) {
         const int ndims = 4;
 
         const int64_t n_rot = 128;
@@ -147,28 +147,69 @@ int main(int /*argc*/, const char ** /*argv*/) {
         const int n_past_0 = 100;
         const int n_past_2 = 33;
 
-        struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
-        struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
-        struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
-
-        for (int i = 0; i < ne[2]; ++i) {
-            ((int32_t *) p0->data)[i] = n_past_0 + i;
-            ((int32_t *) p1->data)[i] = n_past_2 - n_past_0;
-            ((int32_t *) p2->data)[i] = n_past_2 + i;
-        }
-
-        // test mode 0, 2, 4 (standard, GPT-NeoX, GLM)
-        const int mode = m == 0 ? 0 : m == 1 ? 2 : 4;
-
+        struct ggml_tensor * r0;
+        struct ggml_tensor * r1;
+        struct ggml_tensor * r2;
         x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
+        int mode = -1;
 
-        // 100, 101, 102, ..., 172
-        struct ggml_tensor * r0 = ggml_rope(ctx0, x,  p0, n_rot, mode);
-        // -67, -67, -67, ..., -67
-        struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
+        if (m < 3) {
+            struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
+            struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
+            struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
 
-        //  33,  34,  35, ..., 105
-        struct ggml_tensor * r2 = ggml_rope(ctx0, x,  p2, n_rot, mode);
+            for (int i = 0; i < ne[2]; ++i) {
+                ((int32_t *) p0->data)[i] = n_past_0 + i;
+                ((int32_t *) p1->data)[i] = n_past_2 - n_past_0;
+                ((int32_t *) p2->data)[i] = n_past_2 + i;
+            }
+            // test mode 0, 2, 4 (standard, GPT-NeoX, GLM)
+            mode = m == 0 ? 0 : m == 1 ? 2 : 4;
+
+            // 100, 101, 102, ..., 172
+            r0 = ggml_rope(ctx0, x,  p0, n_rot, mode);
+            // -67, -67, -67, ..., -67
+            r1 = ggml_rope(ctx0, r0, p1, n_rot, mode); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
+
+            //  33,  34,  35, ..., 105
+            r2 = ggml_rope(ctx0, x,  p2, n_rot, mode);
+        } else {
+            // testing multi-dimension rope position embedding mode
+            struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2] * 4);
+            struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2] * 4);
+            struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2] * 4);
+
+            int sections[4] = {16, 24, 24, 0};
+            mode = (m == 3) ? GGML_ROPE_TYPE_MROPE : GGML_ROPE_TYPE_VISION;
+
+            for (int i = 0; i < ne[2]; ++i) {
+                for (int j = 0; j < 4; ++j) {
+                    ((int32_t *) p0->data)[i + ne[2] * j] = n_past_0 + i + j;
+                    ((int32_t *) p1->data)[i + ne[2] * j] = n_past_2 - n_past_0;
+                    ((int32_t *) p2->data)[i + ne[2] * j] = n_past_2 + i + j;
+                }
+            }
+
+            // [[100, 101, 102, ..., 172],
+            // [101, 102, 103, ..., 173],
+            // [102, 103, 104, ..., 174]]
+            r0 = ggml_rope_multi(
+                ctx0, x, p0, nullptr,
+                n_rot, sections, mode, 32768, 1000000, 1, 0, 1, 32, 1);
+            // [[-67, -67, -67, ..., -67]
+            // [-67, -67, -67, ..., -67]
+            // [-67, -67, -67, ..., -67]]
+            r1 = ggml_rope_multi(
+                ctx0, r0, p1, nullptr,
+                n_rot, sections, mode, 32768, 1000000, 1, 0, 1, 32, 1);
+
+            //  [[33,  34,  35, ..., 105]
+            //  [34,  35,  36, ..., 106]
+            //  [35,  36,  37, ..., 107]]
+            r2 = ggml_rope_multi(
+                ctx0, x, p2, nullptr,
+                n_rot, sections, mode, 32768, 1000000, 1, 0, 1, 32, 1);
+        }
 
         ggml_cgraph * gf = ggml_new_graph(ctx0);
 

From e52aba537a34d51a65cddec6bc6dafc9031edc63 Mon Sep 17 00:00:00 2001
From: Evgeny Kurnevsky <kurnevsky@gmail.com>
Date: Sat, 14 Dec 2024 18:17:36 +0000
Subject: [PATCH 023/376] nix: allow to override rocm gpu targets (#10794)

This allows to reduce compile time when you are building for a single GPU.
---
 .devops/nix/package.nix | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index b88e6ca80..043c4364b 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -31,6 +31,7 @@
   # Increases the runtime closure size by ~700M
   useMpi ? false,
   useRocm ? config.rocmSupport,
+  rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
   enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -188,7 +189,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ]
     ++ optionals useRocm [
       (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
+      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets)
     ]
     ++ optionals useMetalKit [
       (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")

From 89d604f2c87af9db657d8a27a1528bc4b7579c29 Mon Sep 17 00:00:00 2001
From: Michelle Tan <41475767+MichelleTanPY@users.noreply.github.com>
Date: Sat, 14 Dec 2024 22:29:45 +0000
Subject: [PATCH 024/376] server: Fix `has_next_line` in JSON response (#10818)

* Update server JSON response.

* Add unit test to check `has_new_line` JSON response

* Remove `has_new_line` unit test changes.

* Address code review comment: type check for `has_new_line` in unit test
---
 examples/server/server.cpp                    | 2 +-
 examples/server/tests/unit/test_completion.py | 2 ++
 examples/server/utils.hpp                     | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 210c36e01..5cc86bf7c 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -459,7 +459,7 @@ struct server_task_result_cmpl_final : server_task_result {
     int32_t n_decoded;
     int32_t n_prompt_tokens;
     int32_t n_tokens_cached;
-    int32_t has_new_line;
+    bool has_new_line;
     std::string stopping_word;
     stop_type stop = STOP_TYPE_NONE;
 
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index 7f4f9cd03..062ebcd4a 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -25,6 +25,7 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int,
     assert res.body["timings"]["prompt_n"] == n_prompt
     assert res.body["timings"]["predicted_n"] == n_predicted
     assert res.body["truncated"] == truncated
+    assert type(res.body["has_new_line"]) == bool
     assert match_regex(re_content, res.body["content"])
 
 
@@ -48,6 +49,7 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp
             assert data["timings"]["predicted_n"] == n_predicted
             assert data["truncated"] == truncated
             assert data["stop_type"] == "limit"
+            assert type(data["has_new_line"]) == bool
             assert "generation_settings" in data
             assert server.n_predict is not None
             assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict)
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 2fcb895ab..c6f08bf21 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -22,7 +22,7 @@
 #include <vector>
 #include <memory>
 
-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo"
 
 using json = nlohmann::ordered_json;
 

From b5ae1ddff93403300fc79c7ab5ee73b8cfbb3457 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 15 Dec 2024 13:16:42 +0200
Subject: [PATCH 025/376] gguf-py : bump to v0.13.0

---
 gguf-py/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 10e94876c..9c3956256 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.11.0"
+version = "0.13.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

From 5478bbcd173e7027af7689493c7421719f5c43df Mon Sep 17 00:00:00 2001
From: Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com>
Date: Sun, 15 Dec 2024 05:55:54 -0600
Subject: [PATCH 026/376] server: (UI) add syntax highlighting and latex math
 rendering (#10808)

* add code highlighting and math formatting

* code cleanup

* build public/index.html

* rebuild public/index.html

* fixed coding style

* fixed coding style

* style fixes

* highlight: smaller bundle size, fix light & dark theme

* remove katex

* add bundle size check

* add more languages

* add php

* reuse some langs

* use gzip

* Revert "remove katex"

This reverts commit c0e5046accd10be3f83018cffdc29a652849fc61.

* use better maintained @vscode/markdown-it-katex

* fix gzip non deterministic

* ability to add a demo conversation for dev

* fix latex rendering

* add comment

* latex codeblock as code

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/CMakeLists.txt                |   2 +-
 examples/server/public/index.html             | 389 -------------
 examples/server/public/index.html.gz          | Bin 0 -> 1205102 bytes
 examples/server/server.cpp                    |  11 +-
 examples/server/webui/index.html              |   4 +
 examples/server/webui/package-lock.json       | 512 ++++++++++++++++++
 examples/server/webui/package.json            |   7 +-
 .../webui/public/demo-conversation.json       |  33 ++
 examples/server/webui/src/highlight-config.js |  60 ++
 examples/server/webui/src/katex-gpt.js        |  66 +++
 examples/server/webui/src/main.js             |  64 ++-
 examples/server/webui/src/styles.css          |  26 -
 examples/server/webui/src/styles.scss         |  48 ++
 examples/server/webui/vite.config.js          |  59 +-
 14 files changed, 838 insertions(+), 443 deletions(-)
 delete mode 100644 examples/server/public/index.html
 create mode 100644 examples/server/public/index.html.gz
 create mode 100644 examples/server/webui/public/demo-conversation.json
 create mode 100644 examples/server/webui/src/highlight-config.js
 create mode 100644 examples/server/webui/src/katex-gpt.js
 delete mode 100644 examples/server/webui/src/styles.css
 create mode 100644 examples/server/webui/src/styles.scss

diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index 63fca1d59..a27597cbc 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -15,7 +15,7 @@ set(TARGET_SRCS
     httplib.h
 )
 set(PUBLIC_ASSETS
-    index.html
+    index.html.gz
     loading.html
 )
 
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
deleted file mode 100644
index 9a19c5e83..000000000
--- a/examples/server/public/index.html
+++ /dev/null
@@ -1,389 +0,0 @@
-<!--
-  This is a single file build of the frontend.
-  It is automatically generated by the build process.
-  Do not edit this file directly.
-  To make changes, refer to the "Web UI" section in the README.
--->
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
-  <meta name="color-scheme" content="light dark">
-  <title>🦙 llama.cpp - chat</title>
-  <script type="module" crossorigin>var nc=e=>{throw TypeError(e)};var Hi=(e,t,u)=>t.has(e)||nc("Cannot "+u);var qt=(e,t,u)=>(Hi(e,t,"read from private field"),u?u.call(e):t.get(e)),Ru=(e,t,u)=>t.has(e)?nc("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(e):t.set(e,u),hn=(e,t,u,n)=>(Hi(e,t,"write to private field"),n?n.call(e,u):t.set(e,u),u),Ui=(e,t,u)=>(Hi(e,t,"access private method"),u);(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const r of document.querySelectorAll('link[rel="modulepreload"]'))n(r);new MutationObserver(r=>{for(const i of r)if(i.type==="childList")for(const s of i.addedNodes)s.tagName==="LINK"&&s.rel==="modulepreload"&&n(s)}).observe(document,{childList:!0,subtree:!0});function u(r){const i={};return r.integrity&&(i.integrity=r.integrity),r.referrerPolicy&&(i.referrerPolicy=r.referrerPolicy),r.crossOrigin==="use-credentials"?i.credentials="include":r.crossOrigin==="anonymous"?i.credentials="omit":i.credentials="same-origin",i}function n(r){if(r.ep)return;r.ep=!0;const i=u(r);fetch(r.href,i)}})();/**
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Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\0\0\0\0\0\0ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\\bfms„‹•˜¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\0\0\0͔͂\0Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\0\0ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\0\0ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\0ц\0ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\0ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\0\0ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\0\0֣mallSquare;旼erySmallSquare;斪Ͱֺ\0ֿ\0\0ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\0ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\0ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\0ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\0ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\0ࣃbleBracket;柦nǔࣈ\0࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻"䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\0စbleBracket;柧nǔည\0နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\0\0ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\0ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀  Space;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\0ጬጱ\0\0\0\0\0ጸጽ፷ᎅ\0᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\0጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\0ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\0ᖰᖶᖿ\0\0\0\0ᗆᗛᗫᙟ᙭\0ᚕ᚛ᚲᚹ\0ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\0\0ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\0\0ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\0ᠳƲᠯ\0ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\0᧨ᨑᨕᨲ\0ᨷᩐ\0\0᪴\0\0᫁\0\0ᬡᬮ᭍᭒\0᯽\0ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\0᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\0\0᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\0ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\0\0᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\0ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\0\0᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\0\0ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\0\0ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\0\0ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\0\0ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\0ᾞ\0ᾡᾧ\0\0ῆῌ\0ΐ\0ῦῪ \0 ⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\0\0᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\0ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\0⁐β•‥‧‪‬\0‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\0‶;慔;慖ʴ‾⁁\0\0⁃耻¾䂾;慗;慜5;慘ƶ⁌\0⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\0↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\0⊪\0⊸⋅⋎\0⋕⋳\0\0⋸⌢⍧⍢⍿\0⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\0⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\0⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\0⒪\0⒱\0\0\0\0\0⒵Ⓔ\0ⓆⓈⓍ\0⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐabnoptwz⛩⛴⛷✚✯❁❇❐Ānr⛮⛱g;柬r;懽rëࣁgƀlmr⛿✍✔eftĀar০✇ightá৲apsto;柼ightá৽parrowĀlr✥✩efô⓭ight;憬ƀafl✶✹✽r;榅;쀀𝕝us;樭imes;樴š❋❏st;戗áፎƀ;ef❗❘᠀旊nge»❘arĀ;l❤❥䀨t;榓ʀachmt❳❶❼➅➇ròࢨorneòᶌarĀ;d྘➃;業;怎ri;抿̀achiqt➘➝ੀ➢➮➻quo;怹r;쀀𝓁mƀ;egল➪➬;檍;檏Ābu┪➳oĀ;rฟ➹;怚rok;䅂萀<;cdhilqrࠫ⟒☹⟜⟠⟥⟪⟰Āci⟗⟙;檦r;橹reå◲mes;拉arr;楶uest;橻ĀPi⟵⟹ar;榖ƀ;ef⠀भ᠛旃rĀdu⠇⠍shar;楊har;楦Āen⠗⠡rtneqq;쀀≨︀Å⠞܀Dacdefhilnopsu⡀⡅⢂⢎⢓⢠⢥⢨⣚⣢⣤ઃ⣳⤂Dot;戺Ȁclpr⡎⡒⡣⡽r耻¯䂯Āet⡗⡙;時Ā;e⡞⡟朠se»⡟Ā;sျ⡨toȀ;dluျ⡳⡷⡻owîҌefôएðᏑker;斮Āoy⢇⢌mma;権;䐼ash;怔asuredangle»ᘦr;쀀𝔪o;愧ƀcdn⢯⢴⣉ro耻µ䂵Ȁ;acdᑤ⢽⣀⣄sôᚧir;櫰ot肻·Ƶusƀ;bd⣒ᤃ⣓戒Ā;uᴼ⣘;横ţ⣞⣡p;櫛ò−ðઁĀdp⣩⣮els;抧f;쀀𝕞Āct⣸⣽r;쀀𝓂pos»ᖝƀ;lm⤉⤊⤍䎼timap;抸ఀGLRVabcdefghijlmoprstuvw⥂⥓⥾⦉⦘⧚⧩⨕⨚⩘⩝⪃⪕⪤⪨⬄⬇⭄⭿⮮ⰴⱧⱼ⳩Āgt⥇⥋;쀀⋙̸Ā;v⥐௏쀀≫⃒ƀelt⥚⥲⥶ftĀar⥡⥧rrow;懍ightarrow;懎;쀀⋘̸Ā;v⥻ే쀀≪⃒ightarrow;懏ĀDd⦎⦓ash;抯ash;抮ʀbcnpt⦣⦧⦬⦱⧌la»˞ute;䅄g;쀀∠⃒ʀ;Eiop඄⦼⧀⧅⧈;쀀⩰̸d;쀀≋̸s;䅉roø඄urĀ;a⧓⧔普lĀ;s⧓ସdz⧟\0⧣p肻 ଷmpĀ;e௹ఀʀaeouy⧴⧾⨃⨐⨓ǰ⧹\0⧻;橃on;䅈dil;䅆ngĀ;dൾ⨊ot;쀀⩭̸p;橂;䐽ash;怓΀;Aadqsxஒ⨩⨭⨻⩁⩅⩐rr;懗rĀhr⨳⨶k;椤Ā;oᏲᏰot;쀀≐̸uiöୣĀei⩊⩎ar;椨í஘istĀ;s஠டr;쀀𝔫ȀEest௅⩦⩹⩼ƀ;qs஼⩭௡ƀ;qs஼௅⩴lanô௢ií௪Ā;rஶ⪁»ஷƀAap⪊⪍⪑rò⥱rr;憮ar;櫲ƀ;svྍ⪜ྌĀ;d⪡⪢拼;拺cy;䑚΀AEadest⪷⪺⪾⫂⫅⫶⫹rò⥦;쀀≦̸rr;憚r;急Ȁ;fqs఻⫎⫣⫯tĀar⫔⫙rro÷⫁ightarro÷⪐ƀ;qs఻⪺⫪lanôౕĀ;sౕ⫴»శiíౝĀ;rవ⫾iĀ;eచథiäඐĀpt⬌⬑f;쀀𝕟膀¬;in⬙⬚⬶䂬nȀ;Edvஉ⬤⬨⬮;쀀⋹̸ot;쀀⋵̸ǡஉ⬳⬵;拷;拶iĀ;vಸ⬼ǡಸ⭁⭃;拾;拽ƀaor⭋⭣⭩rȀ;ast୻⭕⭚⭟lleì୻l;쀀⫽⃥;쀀∂̸lint;樔ƀ;ceಒ⭰⭳uåಥĀ;cಘ⭸Ā;eಒ⭽ñಘȀAait⮈⮋⮝⮧rò⦈rrƀ;cw⮔⮕⮙憛;쀀⤳̸;쀀↝̸ghtarrow»⮕riĀ;eೋೖ΀chimpqu⮽⯍⯙⬄୸⯤⯯Ȁ;cerല⯆ഷ⯉uå൅;쀀𝓃ortɭ⬅\0\0⯖ará⭖mĀ;e൮⯟Ā;q൴൳suĀbp⯫⯭å೸åഋƀbcp⯶ⰑⰙȀ;Ees⯿ⰀഢⰄ抄;쀀⫅̸etĀ;eഛⰋqĀ;qണⰀcĀ;eലⰗñസȀ;EesⰢⰣൟⰧ抅;쀀⫆̸etĀ;e൘ⰮqĀ;qൠⰣȀgilrⰽⰿⱅⱇìௗlde耻ñ䃱çృiangleĀlrⱒⱜeftĀ;eచⱚñదightĀ;eೋⱥñ೗Ā;mⱬⱭ䎽ƀ;esⱴⱵⱹ䀣ro;愖p;怇ҀDHadgilrsⲏⲔⲙⲞⲣⲰⲶⳓⳣash;抭arr;椄p;쀀≍⃒ash;抬ĀetⲨⲬ;쀀≥⃒;쀀>⃒nfin;槞ƀAetⲽⳁⳅrr;椂;쀀≤⃒Ā;rⳊⳍ쀀<⃒ie;쀀⊴⃒ĀAtⳘⳜrr;椃rie;쀀⊵⃒im;쀀∼⃒ƀAan⳰⳴ⴂrr;懖rĀhr⳺⳽k;椣Ā;oᏧᏥear;椧ቓ᪕\0\0\0\0\0\0\0\0\0\0\0\0\0ⴭ\0ⴸⵈⵠⵥ⵲ⶄᬇ\0\0ⶍⶫ\0ⷈⷎ\0ⷜ⸙⸫⸾⹃Ācsⴱ᪗ute耻ó䃳ĀiyⴼⵅrĀ;c᪞ⵂ耻ô䃴;䐾ʀabios᪠ⵒⵗLjⵚlac;䅑v;樸old;榼lig;䅓Ācr⵩⵭ir;榿;쀀𝔬ͯ⵹\0\0⵼\0ⶂn;䋛ave耻ò䃲;槁Ābmⶈ෴ar;榵Ȁacitⶕ⶘ⶥⶨrò᪀Āir⶝ⶠr;榾oss;榻nå๒;槀ƀaeiⶱⶵⶹcr;䅍ga;䏉ƀcdnⷀⷅǍron;䎿;榶pf;쀀𝕠ƀaelⷔ⷗ǒr;榷rp;榹΀;adiosvⷪⷫⷮ⸈⸍⸐⸖戨rò᪆Ȁ;efmⷷⷸ⸂⸅橝rĀ;oⷾⷿ愴f»ⷿ耻ª䂪耻º䂺gof;抶r;橖lope;橗;橛ƀclo⸟⸡⸧ò⸁ash耻ø䃸l;折iŬⸯ⸴de耻õ䃵esĀ;aǛ⸺s;樶ml耻ö䃶bar;挽ૡ⹞\0⹽\0⺀⺝\0⺢⺹\0\0⻋ຜ\0⼓\0\0⼫⾼\0⿈rȀ;astЃ⹧⹲຅脀¶;l⹭⹮䂶leìЃɩ⹸\0\0⹻m;櫳;櫽y;䐿rʀcimpt⺋⺏⺓ᡥ⺗nt;䀥od;䀮il;怰enk;怱r;쀀𝔭ƀimo⺨⺰⺴Ā;v⺭⺮䏆;䏕maô੶ne;明ƀ;tv⺿⻀⻈䏀chfork»´;䏖Āau⻏⻟nĀck⻕⻝kĀ;h⇴⻛;愎ö⇴sҀ;abcdemst⻳⻴ᤈ⻹⻽⼄⼆⼊⼎䀫cir;樣ir;樢Āouᵀ⼂;樥;橲n肻±ຝim;樦wo;樧ƀipu⼙⼠⼥ntint;樕f;쀀𝕡nd耻£䂣Ԁ;Eaceinosu່⼿⽁⽄⽇⾁⾉⾒⽾⾶;檳p;檷uå໙Ā;c໎⽌̀;acens່⽙⽟⽦⽨⽾pproø⽃urlyeñ໙ñ໎ƀaes⽯⽶⽺pprox;檹qq;檵im;拨iíໟmeĀ;s⾈ຮ怲ƀEas⽸⾐⽺ð⽵ƀdfp໬⾙⾯ƀals⾠⾥⾪lar;挮ine;挒urf;挓Ā;t໻⾴ï໻rel;抰Āci⿀⿅r;쀀𝓅;䏈ncsp;怈̀fiopsu⿚⋢⿟⿥⿫⿱r;쀀𝔮pf;쀀𝕢rime;恗cr;쀀𝓆ƀaeo⿸〉〓tĀei⿾々rnionóڰnt;樖stĀ;e【】䀿ñἙô༔઀ABHabcdefhilmnoprstux぀けさすムㄎㄫㅇㅢㅲㆎ㈆㈕㈤㈩㉘㉮㉲㊐㊰㊷ƀartぇおがròႳòϝail;検aròᱥar;楤΀cdenqrtとふへみわゔヌĀeuねぱ;쀀∽̱te;䅕iãᅮmptyv;榳gȀ;del࿑らるろ;榒;榥å࿑uo耻»䂻rր;abcfhlpstw࿜ガクシスゼゾダッデナp;極Ā;f࿠ゴs;椠;椳s;椞ë≝ð✮l;楅im;楴l;憣;憝Āaiパフil;椚oĀ;nホボ戶aló༞ƀabrョリヮrò៥rk;杳ĀakンヽcĀekヹ・;䁽;䁝Āes㄂㄄;榌lĀduㄊㄌ;榎;榐Ȁaeuyㄗㄜㄧㄩron;䅙Ādiㄡㄥil;䅗ì࿲âヺ;䑀Ȁclqsㄴㄷㄽㅄa;椷dhar;楩uoĀ;rȎȍh;憳ƀacgㅎㅟངlȀ;ipsླྀㅘㅛႜnåႻarôྩt;断ƀilrㅩဣㅮsht;楽;쀀𝔯ĀaoㅷㆆrĀduㅽㅿ»ѻĀ;l႑ㆄ;楬Ā;vㆋㆌ䏁;䏱ƀgns㆕ㇹㇼht̀ahlrstㆤㆰ㇂㇘㇤㇮rrowĀ;t࿜ㆭaéトarpoonĀduㆻㆿowîㅾp»႒eftĀah㇊㇐rrowó࿪arpoonóՑightarrows;應quigarro÷ニhreetimes;拌g;䋚ingdotseñἲƀahm㈍㈐㈓rò࿪aòՑ;怏oustĀ;a㈞㈟掱che»㈟mid;櫮Ȁabpt㈲㈽㉀㉒Ānr㈷㈺g;柭r;懾rëဃƀafl㉇㉊㉎r;榆;쀀𝕣us;樮imes;樵Āap㉝㉧rĀ;g㉣㉤䀩t;榔olint;樒arò㇣Ȁachq㉻㊀Ⴜ㊅quo;怺r;쀀𝓇Ābu・㊊oĀ;rȔȓƀhir㊗㊛㊠reåㇸmes;拊iȀ;efl㊪ၙᠡ㊫方tri;槎luhar;楨;愞ൡ㋕㋛㋟㌬㌸㍱\0㍺㎤\0\0㏬㏰\0㐨㑈㑚㒭㒱㓊㓱\0㘖\0\0㘳cute;䅛quï➺Ԁ;Eaceinpsyᇭ㋳㋵㋿㌂㌋㌏㌟㌦㌩;檴ǰ㋺\0㋼;檸on;䅡uåᇾĀ;dᇳ㌇il;䅟rc;䅝ƀEas㌖㌘㌛;檶p;檺im;择olint;樓iíሄ;䑁otƀ;be㌴ᵇ㌵担;橦΀Aacmstx㍆㍊㍗㍛㍞㍣㍭rr;懘rĀhr㍐㍒ë∨Ā;oਸ਼਴t耻§䂧i;䀻war;椩mĀin㍩ðnuóñt;朶rĀ;o㍶⁕쀀𝔰Ȁacoy㎂㎆㎑㎠rp;景Āhy㎋㎏cy;䑉;䑈rtɭ㎙\0\0㎜iäᑤaraì⹯耻­䂭Āgm㎨㎴maƀ;fv㎱㎲㎲䏃;䏂Ѐ;deglnprካ㏅㏉㏎㏖㏞㏡㏦ot;橪Ā;q኱ኰĀ;E㏓㏔檞;檠Ā;E㏛㏜檝;檟e;扆lus;樤arr;楲aròᄽȀaeit㏸㐈㐏㐗Āls㏽㐄lsetmé㍪hp;樳parsl;槤Ādlᑣ㐔e;挣Ā;e㐜㐝檪Ā;s㐢㐣檬;쀀⪬︀ƀflp㐮㐳㑂tcy;䑌Ā;b㐸㐹䀯Ā;a㐾㐿槄r;挿f;쀀𝕤aĀdr㑍ЂesĀ;u㑔㑕晠it»㑕ƀcsu㑠㑹㒟Āau㑥㑯pĀ;sᆈ㑫;쀀⊓︀pĀ;sᆴ㑵;쀀⊔︀uĀbp㑿㒏ƀ;esᆗᆜ㒆etĀ;eᆗ㒍ñᆝƀ;esᆨᆭ㒖etĀ;eᆨ㒝ñᆮƀ;afᅻ㒦ְrť㒫ֱ»ᅼaròᅈȀcemt㒹㒾㓂㓅r;쀀𝓈tmîñiì㐕aræᆾĀar㓎㓕rĀ;f㓔ឿ昆Āan㓚㓭ightĀep㓣㓪psiloîỠhé⺯s»⡒ʀbcmnp㓻㕞ሉ㖋㖎Ҁ;Edemnprs㔎㔏㔑㔕㔞㔣㔬㔱㔶抂;櫅ot;檽Ā;dᇚ㔚ot;櫃ult;櫁ĀEe㔨㔪;櫋;把lus;檿arr;楹ƀeiu㔽㕒㕕tƀ;en㔎㕅㕋qĀ;qᇚ㔏eqĀ;q㔫㔨m;櫇Ābp㕚㕜;櫕;櫓c̀;acensᇭ㕬㕲㕹㕻㌦pproø㋺urlyeñᇾñᇳƀaes㖂㖈㌛pproø㌚qñ㌗g;晪ڀ123;Edehlmnps㖩㖬㖯ሜ㖲㖴㗀㗉㗕㗚㗟㗨㗭耻¹䂹耻²䂲耻³䂳;櫆Āos㖹㖼t;檾ub;櫘Ā;dሢ㗅ot;櫄sĀou㗏㗒l;柉b;櫗arr;楻ult;櫂ĀEe㗤㗦;櫌;抋lus;櫀ƀeiu㗴㘉㘌tƀ;enሜ㗼㘂qĀ;qሢ㖲eqĀ;q㗧㗤m;櫈Ābp㘑㘓;櫔;櫖ƀAan㘜㘠㘭rr;懙rĀhr㘦㘨ë∮Ā;oਫ਩war;椪lig耻ß䃟௡㙑㙝㙠ዎ㙳㙹\0㙾㛂\0\0\0\0\0㛛㜃\0㜉㝬\0\0\0㞇ɲ㙖\0\0㙛get;挖;䏄rë๟ƀaey㙦㙫㙰ron;䅥dil;䅣;䑂lrec;挕r;쀀𝔱Ȁeiko㚆㚝㚵㚼Dz㚋\0㚑eĀ4fኄኁaƀ;sv㚘㚙㚛䎸ym;䏑Ācn㚢㚲kĀas㚨㚮pproø዁im»ኬsðኞĀas㚺㚮ð዁rn耻þ䃾Ǭ̟㛆⋧es膀×;bd㛏㛐㛘䃗Ā;aᤏ㛕r;樱;樰ƀeps㛡㛣㜀á⩍Ȁ;bcf҆㛬㛰㛴ot;挶ir;櫱Ā;o㛹㛼쀀𝕥rk;櫚á㍢rime;怴ƀaip㜏㜒㝤dåቈ΀adempst㜡㝍㝀㝑㝗㝜㝟ngleʀ;dlqr㜰㜱㜶㝀㝂斵own»ᶻeftĀ;e⠀㜾ñम;扜ightĀ;e㊪㝋ñၚot;旬inus;樺lus;樹b;槍ime;樻ezium;揢ƀcht㝲㝽㞁Āry㝷㝻;쀀𝓉;䑆cy;䑛rok;䅧Āio㞋㞎xô᝷headĀlr㞗㞠eftarro÷ࡏightarrow»ཝऀAHabcdfghlmoprstuw㟐㟓㟗㟤㟰㟼㠎㠜㠣㠴㡑㡝㡫㢩㣌㣒㣪㣶ròϭar;楣Ācr㟜㟢ute耻ú䃺òᅐrǣ㟪\0㟭y;䑞ve;䅭Āiy㟵㟺rc耻û䃻;䑃ƀabh㠃㠆㠋ròᎭlac;䅱aòᏃĀir㠓㠘sht;楾;쀀𝔲rave耻ù䃹š㠧㠱rĀlr㠬㠮»ॗ»ႃlk;斀Āct㠹㡍ɯ㠿\0\0㡊rnĀ;e㡅㡆挜r»㡆op;挏ri;旸Āal㡖㡚cr;䅫肻¨͉Āgp㡢㡦on;䅳f;쀀𝕦̀adhlsuᅋ㡸㡽፲㢑㢠ownáᎳarpoonĀlr㢈㢌efô㠭ighô㠯iƀ;hl㢙㢚㢜䏅»ᏺon»㢚parrows;懈ƀcit㢰㣄㣈ɯ㢶\0\0㣁rnĀ;e㢼㢽挝r»㢽op;挎ng;䅯ri;旹cr;쀀𝓊ƀdir㣙㣝㣢ot;拰lde;䅩iĀ;f㜰㣨»᠓Āam㣯㣲rò㢨l耻ü䃼angle;榧ހABDacdeflnoprsz㤜㤟㤩㤭㦵㦸㦽㧟㧤㧨㧳㧹㧽㨁㨠ròϷarĀ;v㤦㤧櫨;櫩asèϡĀnr㤲㤷grt;榜΀eknprst㓣㥆㥋㥒㥝㥤㦖appá␕othinçẖƀhir㓫⻈㥙opô⾵Ā;hᎷ㥢ïㆍĀiu㥩㥭gmá㎳Ābp㥲㦄setneqĀ;q㥽㦀쀀⊊︀;쀀⫋︀setneqĀ;q㦏㦒쀀⊋︀;쀀⫌︀Āhr㦛㦟etá㚜iangleĀlr㦪㦯eft»थight»ၑy;䐲ash»ံƀelr㧄㧒㧗ƀ;beⷪ㧋㧏ar;抻q;扚lip;拮Ābt㧜ᑨaòᑩr;쀀𝔳tré㦮suĀbp㧯㧱»ജ»൙pf;쀀𝕧roð໻tré㦴Ācu㨆㨋r;쀀𝓋Ābp㨐㨘nĀEe㦀㨖»㥾nĀEe㦒㨞»㦐igzag;榚΀cefoprs㨶㨻㩖㩛㩔㩡㩪irc;䅵Ādi㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\0㪋\0㪐㪛\0\0㪝㪨㪫㪯\0\0㫃㫎\0㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'.split("").map(e=>e.charCodeAt(0))),Sb=new 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f=o.level,h=0;l.length>0&&l[0].index===0&&s>0&&r[s-1].type==="text_special"&&(l=l.slice(1));for(let d=0;d<l.length;d++){const p=l[d].url,_=e.md.normalizeLink(p);if(!e.md.validateLink(_))continue;let D=l[d].text;l[d].schema?l[d].schema==="mailto:"&&!/^mailto:/i.test(D)?D=e.md.normalizeLinkText("mailto:"+D).replace(/^mailto:/,""):D=e.md.normalizeLinkText(D):D=e.md.normalizeLinkText("http://"+D).replace(/^http:\/\//,"");const S=l[d].index;if(S>h){const x=new e.Token("text","",0);x.content=c.slice(h,S),x.level=f,a.push(x)}const E=new e.Token("link_open","a",1);E.attrs=[["href",_]],E.level=f++,E.markup="linkify",E.info="auto",a.push(E);const m=new e.Token("text","",0);m.content=D,m.level=f,a.push(m);const g=new e.Token("link_close","a",-1);g.level=--f,g.markup="linkify",g.info="auto",a.push(g),h=l[d].lastIndex}if(h<c.length){const d=new e.Token("text","",0);d.content=c.slice(h),d.level=f,a.push(d)}t[u].children=r=Ff(r,s,a)}}}}const Nf=/\+-|\.\.|\?\?\?\?|!!!!|,,|--/,lg=/\((c|tm|r)\)/i,ag=/\((c|tm|r)\)/ig,fg={c:"©",r:"®",tm:"™"};function dg(e,t){return fg[t.toLowerCase()]}function hg(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&(n.content=n.content.replace(ag,dg)),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function pg(e){let t=0;for(let u=e.length-1;u>=0;u--){const n=e[u];n.type==="text"&&!t&&Nf.test(n.content)&&(n.content=n.content.replace(/\+-/g,"±").replace(/\.{2,}/g,"…").replace(/([?!])…/g,"$1..").replace(/([?!]){4,}/g,"$1$1$1").replace(/,{2,}/g,",").replace(/(^|[^-])---(?=[^-]|$)/mg,"$1—").replace(/(^|\s)--(?=\s|$)/mg,"$1–").replace(/(^|[^-\s])--(?=[^-\s]|$)/mg,"$1–")),n.type==="link_open"&&n.info==="auto"&&t--,n.type==="link_close"&&n.info==="auto"&&t++}}function mg(e){let 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D=Zn(p)||Gn(String.fromCharCode(p)),S=Zn(_)||Gn(String.fromCharCode(_)),E=Wn(p),m=Wn(_);if(m?f=!1:S&&(E||D||(f=!1)),E?h=!1:D&&(m||S||(h=!1)),_===34&&a[0]==='"'&&p>=48&&p<=57&&(h=f=!1),f&&h&&(f=D,h=S),!f&&!h){d&&(i.content=Cr(i.content,a.index,xl));continue}if(h)for(u=n.length-1;u>=0;u--){let g=n[u];if(n[u].level<s)break;if(g.single===d&&n[u].level===s){g=n[u];let x,k;d?(x=t.md.options.quotes[2],k=t.md.options.quotes[3]):(x=t.md.options.quotes[0],k=t.md.options.quotes[1]),i.content=Cr(i.content,a.index,k),e[g.token].content=Cr(e[g.token].content,g.pos,x),c+=k.length-1,g.token===r&&(c+=x.length-1),o=i.content,l=o.length,n.length=u;continue e}}f?n.push({token:r,pos:a.index,single:d,level:s}):h&&d&&(i.content=Cr(i.content,a.index,xl))}}}function _g(e){if(e.md.options.typographer)for(let t=e.tokens.length-1;t>=0;t--)e.tokens[t].type!=="inline"||!bg.test(e.tokens[t].content)||gg(e.tokens[t].children,e)}function yg(e){let t,u;const n=e.tokens,r=n.length;for(let i=0;i<r;i++){if(n[i].type!=="inline")continue;const s=n[i].children,o=s.length;for(t=0;t<o;t++)s[t].type==="text_special"&&(s[t].type="text");for(t=u=0;t<o;t++)s[t].type==="text"&&t+1<o&&s[t+1].type==="text"?s[t+1].content=s[t].content+s[t+1].content:(t!==u&&(s[u]=s[t]),u++);t!==u&&(s.length=u)}}const is=[["normalize",ng],["block",rg],["inline",ig],["linkify",cg],["replacements",mg],["smartquotes",_g],["text_join",yg]];function Jo(){this.ruler=new Je;for(let e=0;e<is.length;e++)this.ruler.push(is[e][0],is[e][1])}Jo.prototype.process=function(e){const t=this.ruler.getRules("");for(let u=0,n=t.length;u<n;u++)t[u](e)};Jo.prototype.State=Of;function Tt(e,t,u,n){this.src=e,this.md=t,this.env=u,this.tokens=n,this.bMarks=[],this.eMarks=[],this.tShift=[],this.sCount=[],this.bsCount=[],this.blkIndent=0,this.line=0,this.lineMax=0,this.tight=!1,this.ddIndent=-1,this.listIndent=-1,this.parentType="root",this.level=0;const r=this.src;for(let i=0,s=0,o=0,c=0,l=r.length,a=!1;s<l;s++){const f=r.charCodeAt(s);if(!a)if(ae(f)){o++,f===9?c+=4-c%4:c++;continue}else a=!0;(f===10||s===l-1)&&(f!==10&&s++,this.bMarks.push(i),this.eMarks.push(s),this.tShift.push(o),this.sCount.push(c),this.bsCount.push(0),a=!1,o=0,c=0,i=s+1)}this.bMarks.push(r.length),this.eMarks.push(r.length),this.tShift.push(0),this.sCount.push(0),this.bsCount.push(0),this.lineMax=this.bMarks.length-1}Tt.prototype.push=function(e,t,u){const n=new _t(e,t,u);return n.block=!0,u<0&&this.level--,n.level=this.level,u>0&&this.level++,this.tokens.push(n),n};Tt.prototype.isEmpty=function(t){return this.bMarks[t]+this.tShift[t]>=this.eMarks[t]};Tt.prototype.skipEmptyLines=function(t){for(let u=this.lineMax;t<u&&!(this.bMarks[t]+this.tShift[t]<this.eMarks[t]);t++);return t};Tt.prototype.skipSpaces=function(t){for(let u=this.src.length;t<u;t++){const n=this.src.charCodeAt(t);if(!ae(n))break}return t};Tt.prototype.skipSpacesBack=function(t,u){if(t<=u)return t;for(;t>u;)if(!ae(this.src.charCodeAt(--t)))return t+1;return t};Tt.prototype.skipChars=function(t,u){for(let n=this.src.length;t<n&&this.src.charCodeAt(t)===u;t++);return t};Tt.prototype.skipCharsBack=function(t,u,n){if(t<=n)return t;for(;t>n;)if(u!==this.src.charCodeAt(--t))return t+1;return t};Tt.prototype.getLines=function(t,u,n,r){if(t>=u)return"";const i=new Array(u-t);for(let s=0,o=t;o<u;o++,s++){let c=0;const l=this.bMarks[o];let a=l,f;for(o+1<u||r?f=this.eMarks[o]+1:f=this.eMarks[o];a<f&&c<n;){const h=this.src.charCodeAt(a);if(ae(h))h===9?c+=4-(c+this.bsCount[o])%4:c++;else if(a-l<this.tShift[o])c++;else break;a++}c>n?i[s]=new Array(c-n+1).join(" ")+this.src.slice(a,f):i[s]=this.src.slice(a,f)}return i.join("")};Tt.prototype.Token=_t;const xg=65536;function ss(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];return e.src.slice(u,n)}function El(e){const t=[],u=e.length;let n=0,r=e.charCodeAt(n),i=!1,s=0,o="";for(;n<u;)r===124&&(i?(o+=e.substring(s,n-1),s=n):(t.push(o+e.substring(s,n)),o="",s=n+1)),i=r===92,n++,r=e.charCodeAt(n);return t.push(o+e.substring(s)),t}function Eg(e,t,u,n){if(t+2>u)return!1;let r=t+1;if(e.sCount[r]<e.blkIndent||e.sCount[r]-e.blkIndent>=4)return!1;let i=e.bMarks[r]+e.tShift[r];if(i>=e.eMarks[r])return!1;const s=e.src.charCodeAt(i++);if(s!==124&&s!==45&&s!==58||i>=e.eMarks[r])return!1;const o=e.src.charCodeAt(i++);if(o!==124&&o!==45&&o!==58&&!ae(o)||s===45&&ae(o))return!1;for(;i<e.eMarks[r];){const g=e.src.charCodeAt(i);if(g!==124&&g!==45&&g!==58&&!ae(g))return!1;i++}let c=ss(e,t+1),l=c.split("|");const a=[];for(let g=0;g<l.length;g++){const x=l[g].trim();if(!x){if(g===0||g===l.length-1)continue;return!1}if(!/^:?-+:?$/.test(x))return!1;x.charCodeAt(x.length-1)===58?a.push(x.charCodeAt(0)===58?"center":"right"):x.charCodeAt(0)===58?a.push("left"):a.push("")}if(c=ss(e,t).trim(),c.indexOf("|")===-1||e.sCount[t]-e.blkIndent>=4)return!1;l=El(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop();const f=l.length;if(f===0||f!==a.length)return!1;if(n)return!0;const h=e.parentType;e.parentType="table";const d=e.md.block.ruler.getRules("blockquote"),p=e.push("table_open","table",1),_=[t,0];p.map=_;const D=e.push("thead_open","thead",1);D.map=[t,t+1];const S=e.push("tr_open","tr",1);S.map=[t,t+1];for(let g=0;g<l.length;g++){const x=e.push("th_open","th",1);a[g]&&(x.attrs=[["style","text-align:"+a[g]]]);const k=e.push("inline","",0);k.content=l[g].trim(),k.children=[],e.push("th_close","th",-1)}e.push("tr_close","tr",-1),e.push("thead_close","thead",-1);let E,m=0;for(r=t+2;r<u&&!(e.sCount[r]<e.blkIndent);r++){let g=!1;for(let k=0,I=d.length;k<I;k++)if(d[k](e,r,u,!0)){g=!0;break}if(g||(c=ss(e,r).trim(),!c)||e.sCount[r]-e.blkIndent>=4||(l=El(c),l.length&&l[0]===""&&l.shift(),l.length&&l[l.length-1]===""&&l.pop(),m+=f-l.length,m>xg))break;if(r===t+2){const k=e.push("tbody_open","tbody",1);k.map=E=[t+2,0]}const x=e.push("tr_open","tr",1);x.map=[r,r+1];for(let k=0;k<f;k++){const I=e.push("td_open","td",1);a[k]&&(I.attrs=[["style","text-align:"+a[k]]]);const w=e.push("inline","",0);w.content=l[k]?l[k].trim():"",w.children=[],e.push("td_close","td",-1)}e.push("tr_close","tr",-1)}return E&&(e.push("tbody_close","tbody",-1),E[1]=r),e.push("table_close","table",-1),_[1]=r,e.parentType=h,e.line=r,!0}function Cg(e,t,u){if(e.sCount[t]-e.blkIndent<4)return!1;let n=t+1,r=n;for(;n<u;){if(e.isEmpty(n)){n++;continue}if(e.sCount[n]-e.blkIndent>=4){n++,r=n;continue}break}e.line=r;const i=e.push("code_block","code",0);return i.content=e.getLines(t,r,4+e.blkIndent,!1)+`
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o=[],c=[],l=[],a=[],f=e.md.block.ruler.getRules("blockquote"),h=e.parentType;e.parentType="blockquote";let d=!1,p;for(p=t;p<u;p++){const m=e.sCount[p]<e.blkIndent;if(r=e.bMarks[p]+e.tShift[p],i=e.eMarks[p],r>=i)break;if(e.src.charCodeAt(r++)===62&&!m){let x=e.sCount[p]+1,k,I;e.src.charCodeAt(r)===32?(r++,x++,I=!1,k=!0):e.src.charCodeAt(r)===9?(k=!0,(e.bsCount[p]+x)%4===3?(r++,x++,I=!1):I=!0):k=!1;let w=x;for(o.push(e.bMarks[p]),e.bMarks[p]=r;r<i;){const C=e.src.charCodeAt(r);if(ae(C))C===9?w+=4-(w+e.bsCount[p]+(I?1:0))%4:w++;else break;r++}d=r>=i,c.push(e.bsCount[p]),e.bsCount[p]=e.sCount[p]+1+(k?1:0),l.push(e.sCount[p]),e.sCount[p]=w-x,a.push(e.tShift[p]),e.tShift[p]=r-e.bMarks[p];continue}if(d)break;let g=!1;for(let x=0,k=f.length;x<k;x++)if(f[x](e,p,u,!0)){g=!0;break}if(g){e.lineMax=p,e.blkIndent!==0&&(o.push(e.bMarks[p]),c.push(e.bsCount[p]),a.push(e.tShift[p]),l.push(e.sCount[p]),e.sCount[p]-=e.blkIndent);break}o.push(e.bMarks[p]),c.push(e.bsCount[p]),a.push(e.tShift[p]),l.push(e.sCount[p]),e.sCount[p]=-1}const _=e.blkIndent;e.blkIndent=0;const D=e.push("blockquote_open","blockquote",1);D.markup=">";const S=[t,0];D.map=S,e.md.block.tokenize(e,t,p);const E=e.push("blockquote_close","blockquote",-1);E.markup=">",e.lineMax=s,e.parentType=h,S[1]=e.line;for(let m=0;m<a.length;m++)e.bMarks[m+t]=o[m],e.tShift[m+t]=a[m],e.sCount[m+t]=l[m],e.bsCount[m+t]=c[m];return e.blkIndent=_,!0}function Ag(e,t,u,n){const r=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let i=e.bMarks[t]+e.tShift[t];const s=e.src.charCodeAt(i++);if(s!==42&&s!==45&&s!==95)return!1;let o=1;for(;i<r;){const l=e.src.charCodeAt(i++);if(l!==s&&!ae(l))return!1;l===s&&o++}if(o<3)return!1;if(n)return!0;e.line=t+1;const c=e.push("hr","hr",0);return c.map=[t,e.line],c.markup=Array(o+1).join(String.fromCharCode(s)),!0}function Cl(e,t){const u=e.eMarks[t];let n=e.bMarks[t]+e.tShift[t];const r=e.src.charCodeAt(n++);if(r!==42&&r!==45&&r!==43)return-1;if(n<u){const i=e.src.charCodeAt(n);if(!ae(i))return-1}return n}function kl(e,t){const u=e.bMarks[t]+e.tShift[t],n=e.eMarks[t];let r=u;if(r+1>=n)return-1;let i=e.src.charCodeAt(r++);if(i<48||i>57)return-1;for(;;){if(r>=n)return-1;if(i=e.src.charCodeAt(r++),i>=48&&i<=57){if(r-u>=10)return-1;continue}if(i===41||i===46)break;return-1}return r<n&&(i=e.src.charCodeAt(r),!ae(i))?-1:r}function vg(e,t){const u=e.level+2;for(let n=t+2,r=e.tokens.length-2;n<r;n++)e.tokens[n].level===u&&e.tokens[n].type==="paragraph_open"&&(e.tokens[n+2].hidden=!0,e.tokens[n].hidden=!0,n+=2)}function Dg(e,t,u,n){let r,i,s,o,c=t,l=!0;if(e.sCount[c]-e.blkIndent>=4||e.listIndent>=0&&e.sCount[c]-e.listIndent>=4&&e.sCount[c]<e.blkIndent)return!1;let a=!1;n&&e.parentType==="paragraph"&&e.sCount[c]>=e.blkIndent&&(a=!0);let f,h,d;if((d=kl(e,c))>=0){if(f=!0,s=e.bMarks[c]+e.tShift[c],h=Number(e.src.slice(s,d-1)),a&&h!==1)return!1}else if((d=Cl(e,c))>=0)f=!1;else return!1;if(a&&e.skipSpaces(d)>=e.eMarks[c])return!1;if(n)return!0;const p=e.src.charCodeAt(d-1),_=e.tokens.length;f?(o=e.push("ordered_list_open","ol",1),h!==1&&(o.attrs=[["start",h]])):o=e.push("bullet_list_open","ul",1);const D=[c,0];o.map=D,o.markup=String.fromCharCode(p);let S=!1;const E=e.md.block.ruler.getRules("list"),m=e.parentType;for(e.parentType="list";c<u;){i=d,r=e.eMarks[c];const g=e.sCount[c]+d-(e.bMarks[c]+e.tShift[c]);let x=g;for(;i<r;){const z=e.src.charCodeAt(i);if(z===9)x+=4-(x+e.bsCount[c])%4;else if(z===32)x++;else break;i++}const k=i;let I;k>=r?I=1:I=x-g,I>4&&(I=1);const w=g+I;o=e.push("list_item_open","li",1),o.markup=String.fromCharCode(p);const C=[c,0];o.map=C,f&&(o.info=e.src.slice(s,d-1));const A=e.tight,R=e.tShift[c],T=e.sCount[c],P=e.listIndent;if(e.listIndent=e.blkIndent,e.blkIndent=w,e.tight=!0,e.tShift[c]=k-e.bMarks[c],e.sCount[c]=x,k>=r&&e.isEmpty(c+1)?e.line=Math.min(e.line+2,u):e.md.block.tokenize(e,c,u,!0),(!e.tight||S)&&(l=!1),S=e.line-c>1&&e.isEmpty(e.line-1),e.blkIndent=e.listIndent,e.listIndent=P,e.tShift[c]=R,e.sCount[c]=T,e.tight=A,o=e.push("list_item_close","li",-1),o.markup=String.fromCharCode(p),c=e.line,C[1]=c,c>=u||e.sCount[c]<e.blkIndent||e.sCount[c]-e.blkIndent>=4)break;let q=!1;for(let z=0,H=E.length;z<H;z++)if(E[z](e,c,u,!0)){q=!0;break}if(q)break;if(f){if(d=kl(e,c),d<0)break;s=e.bMarks[c]+e.tShift[c]}else if(d=Cl(e,c),d<0)break;if(p!==e.src.charCodeAt(d-1))break}return f?o=e.push("ordered_list_close","ol",-1):o=e.push("bullet_list_close","ul",-1),o.markup=String.fromCharCode(p),D[1]=c,e.line=c,e.parentType=m,l&&vg(e,_),!0}function Tg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t],s=t+1;if(e.sCount[t]-e.blkIndent>=4||e.src.charCodeAt(r)!==91)return!1;function o(E){const m=e.lineMax;if(E>=m||e.isEmpty(E))return null;let g=!1;if(e.sCount[E]-e.blkIndent>3&&(g=!0),e.sCount[E]<0&&(g=!0),!g){const I=e.md.block.ruler.getRules("reference"),w=e.parentType;e.parentType="reference";let C=!1;for(let A=0,R=I.length;A<R;A++)if(I[A](e,E,m,!0)){C=!0;break}if(e.parentType=w,C)return null}const x=e.bMarks[E]+e.tShift[E],k=e.eMarks[E];return e.src.slice(x,k+1)}let c=e.src.slice(r,i+1);i=c.length;let l=-1;for(r=1;r<i;r++){const E=c.charCodeAt(r);if(E===91)return!1;if(E===93){l=r;break}else if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(E===92&&(r++,r<i&&c.charCodeAt(r)===10)){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}}if(l<0||c.charCodeAt(l+1)!==58)return!1;for(r=l+2;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}const a=e.md.helpers.parseLinkDestination(c,r,i);if(!a.ok)return!1;const f=e.md.normalizeLink(a.str);if(!e.md.validateLink(f))return!1;r=a.pos;const h=r,d=s,p=r;for(;r<i;r++){const E=c.charCodeAt(r);if(E===10){const m=o(s);m!==null&&(c+=m,i=c.length,s++)}else if(!ae(E))break}let _=e.md.helpers.parseLinkTitle(c,r,i);for(;_.can_continue;){const E=o(s);if(E===null)break;c+=E,r=i,i=c.length,s++,_=e.md.helpers.parseLinkTitle(c,r,i,_)}let D;for(r<i&&p!==r&&_.ok?(D=_.str,r=_.pos):(D="",r=h,s=d);r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10&&D)for(D="",r=h,s=d;r<i;){const E=c.charCodeAt(r);if(!ae(E))break;r++}if(r<i&&c.charCodeAt(r)!==10)return!1;const S=Mi(c.slice(1,l));return S?(n||(typeof e.env.references>"u"&&(e.env.references={}),typeof e.env.references[S]>"u"&&(e.env.references[S]={title:D,href:f}),e.line=s),!0):!1}const wg=["address","article","aside","base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","h2","h3","h4","h5","h6","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","nav","noframes","ol","optgroup","option","p","param","search","section","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"],Fg="[a-zA-Z_:][a-zA-Z0-9:._-]*",Ig="[^\"'=<>`\\x00-\\x20]+",Og="'[^']*'",Ng='"[^"]*"',Rg="(?:"+Ig+"|"+Og+"|"+Ng+")",Mg="(?:\\s+"+Fg+"(?:\\s*=\\s*"+Rg+")?)",Rf="<[A-Za-z][A-Za-z0-9\\-]*"+Mg+"*\\s*\\/?>",Mf="<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>",Pg="\x3C!---?>|\x3C!--(?:[^-]|-[^-]|--[^>])*-->",Lg="<[?][\\s\\S]*?[?]>",Bg="<![A-Za-z][^>]*>",Vg="<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",$g=new RegExp("^(?:"+Rf+"|"+Mf+"|"+Pg+"|"+Lg+"|"+Bg+"|"+Vg+")"),Hg=new RegExp("^(?:"+Rf+"|"+Mf+")"),Lu=[[/^<(script|pre|style|textarea)(?=(\s|>|$))/i,/<\/(script|pre|style|textarea)>/i,!0],[/^\x3C!--/,/-->/,!0],[/^<\?/,/\?>/,!0],[/^<![A-Z]/,/>/,!0],[/^<!\[CDATA\[/,/\]\]>/,!0],[new RegExp("^</?("+wg.join("|")+")(?=(\\s|/?>|$))","i"),/^$/,!0],[new RegExp(Hg.source+"\\s*$"),/^$/,!1]];function Ug(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4||!e.md.options.html||e.src.charCodeAt(r)!==60)return!1;let s=e.src.slice(r,i),o=0;for(;o<Lu.length&&!Lu[o][0].test(s);o++);if(o===Lu.length)return!1;if(n)return Lu[o][2];let c=t+1;if(!Lu[o][1].test(s)){for(;c<u&&!(e.sCount[c]<e.blkIndent);c++)if(r=e.bMarks[c]+e.tShift[c],i=e.eMarks[c],s=e.src.slice(r,i),Lu[o][1].test(s)){s.length!==0&&c++;break}}e.line=c;const l=e.push("html_block","",0);return l.map=[t,c],l.content=e.getLines(t,c,e.blkIndent,!0),!0}function qg(e,t,u,n){let r=e.bMarks[t]+e.tShift[t],i=e.eMarks[t];if(e.sCount[t]-e.blkIndent>=4)return!1;let s=e.src.charCodeAt(r);if(s!==35||r>=i)return!1;let o=1;for(s=e.src.charCodeAt(++r);s===35&&r<i&&o<=6;)o++,s=e.src.charCodeAt(++r);if(o>6||r<i&&!ae(s))return!1;if(n)return!0;i=e.skipSpacesBack(i,r);const c=e.skipCharsBack(i,35,r);c>r&&ae(e.src.charCodeAt(c-1))&&(i=c),e.line=t+1;const l=e.push("heading_open","h"+String(o),1);l.markup="########".slice(0,o),l.map=[t,e.line];const a=e.push("inline","",0);a.content=e.src.slice(r,i).trim(),a.map=[t,e.line],a.children=[];const f=e.push("heading_close","h"+String(o),-1);return f.markup="########".slice(0,o),!0}function jg(e,t,u){const n=e.md.block.ruler.getRules("paragraph");if(e.sCount[t]-e.blkIndent>=4)return!1;const r=e.parentType;e.parentType="paragraph";let i=0,s,o=t+1;for(;o<u&&!e.isEmpty(o);o++){if(e.sCount[o]-e.blkIndent>3)continue;if(e.sCount[o]>=e.blkIndent){let d=e.bMarks[o]+e.tShift[o];const p=e.eMarks[o];if(d<p&&(s=e.src.charCodeAt(d),(s===45||s===61)&&(d=e.skipChars(d,s),d=e.skipSpaces(d),d>=p))){i=s===61?1:2;break}}if(e.sCount[o]<0)continue;let h=!1;for(let d=0,p=n.length;d<p;d++)if(n[d](e,o,u,!0)){h=!0;break}if(h)break}if(!i)return!1;const c=e.getLines(t,o,e.blkIndent,!1).trim();e.line=o+1;const l=e.push("heading_open","h"+String(i),1);l.markup=String.fromCharCode(s),l.map=[t,e.line];const a=e.push("inline","",0);a.content=c,a.map=[t,e.line-1],a.children=[];const f=e.push("heading_close","h"+String(i),-1);return f.markup=String.fromCharCode(s),e.parentType=r,!0}function zg(e,t,u){const n=e.md.block.ruler.getRules("paragraph"),r=e.parentType;let i=t+1;for(e.parentType="paragraph";i<u&&!e.isEmpty(i);i++){if(e.sCount[i]-e.blkIndent>3||e.sCount[i]<0)continue;let l=!1;for(let a=0,f=n.length;a<f;a++)if(n[a](e,i,u,!0)){l=!0;break}if(l)break}const s=e.getLines(t,i,e.blkIndent,!1).trim();e.line=i;const o=e.push("paragraph_open","p",1);o.map=[t,e.line];const c=e.push("inline","",0);return c.content=s,c.map=[t,e.line],c.children=[],e.push("paragraph_close","p",-1),e.parentType=r,!0}const kr=[["table",Eg,["paragraph","reference"]],["code",Cg],["fence",kg,["paragraph","reference","blockquote","list"]],["blockquote",Sg,["paragraph","reference","blockquote","list"]],["hr",Ag,["paragraph","reference","blockquote","list"]],["list",Dg,["paragraph","reference","blockquote"]],["reference",Tg],["html_block",Ug,["paragraph","reference","blockquote"]],["heading",qg,["paragraph","reference","blockquote"]],["lheading",jg],["paragraph",zg]];function Pi(){this.ruler=new Je;for(let e=0;e<kr.length;e++)this.ruler.push(kr[e][0],kr[e][1],{alt:(kr[e][2]||[]).slice()})}Pi.prototype.tokenize=function(e,t,u){const n=this.ruler.getRules(""),r=n.length,i=e.md.options.maxNesting;let s=t,o=!1;for(;s<u&&(e.line=s=e.skipEmptyLines(s),!(s>=u||e.sCount[s]<e.blkIndent));){if(e.level>=i){e.line=u;break}const c=e.line;let l=!1;for(let a=0;a<r;a++)if(l=n[a](e,s,u,!1),l){if(c>=e.line)throw new Error("block rule didn't increment state.line");break}if(!l)throw new Error("none of the block rules matched");e.tight=!o,e.isEmpty(e.line-1)&&(o=!0),s=e.line,s<u&&e.isEmpty(s)&&(o=!0,s++,e.line=s)}};Pi.prototype.parse=function(e,t,u,n){if(!e)return;const r=new this.State(e,t,u,n);this.tokenize(r,r.line,r.lineMax)};Pi.prototype.State=Tt;function cr(e,t,u,n){this.src=e,this.env=u,this.md=t,this.tokens=n,this.tokens_meta=Array(n.length),this.pos=0,this.posMax=this.src.length,this.level=0,this.pending="",this.pendingLevel=0,this.cache={},this.delimiters=[],this._prev_delimiters=[],this.backticks={},this.backticksScanned=!1,this.linkLevel=0}cr.prototype.pushPending=function(){const e=new _t("text","",0);return e.content=this.pending,e.level=this.pendingLevel,this.tokens.push(e),this.pending="",e};cr.prototype.push=function(e,t,u){this.pending&&this.pushPending();const n=new _t(e,t,u);let r=null;return u<0&&(this.level--,this.delimiters=this._prev_delimiters.pop()),n.level=this.level,u>0&&(this.level++,this._prev_delimiters.push(this.delimiters),this.delimiters=[],r={delimiters:this.delimiters}),this.pendingLevel=this.level,this.tokens.push(n),this.tokens_meta.push(r),n};cr.prototype.scanDelims=function(e,t){const u=this.posMax,n=this.src.charCodeAt(e),r=e>0?this.src.charCodeAt(e-1):32;let i=e;for(;i<u&&this.src.charCodeAt(i)===n;)i++;const s=i-e,o=i<u?this.src.charCodeAt(i):32,c=Zn(r)||Gn(String.fromCharCode(r)),l=Zn(o)||Gn(String.fromCharCode(o)),a=Wn(r),f=Wn(o),h=!f&&(!l||a||c),d=!a&&(!c||f||l);return{can_open:h&&(t||!d||c),can_close:d&&(t||!h||l),length:s}};cr.prototype.Token=_t;function Kg(e){switch(e){case 10:case 33:case 35:case 36:case 37:case 38:case 42:case 43:case 45:case 58:case 60:case 61:case 62:case 64:case 91:case 92:case 93:case 94:case 95:case 96:case 123:case 125:case 126:return!0;default:return!1}}function Wg(e,t){let u=e.pos;for(;u<e.posMax&&!Kg(e.src.charCodeAt(u));)u++;return u===e.pos?!1:(t||(e.pending+=e.src.slice(e.pos,u)),e.pos=u,!0)}const Gg=/(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i;function Zg(e,t){if(!e.md.options.linkify||e.linkLevel>0)return!1;const u=e.pos,n=e.posMax;if(u+3>n||e.src.charCodeAt(u)!==58||e.src.charCodeAt(u+1)!==47||e.src.charCodeAt(u+2)!==47)return!1;const r=e.pending.match(Gg);if(!r)return!1;const i=r[1],s=e.md.linkify.matchAtStart(e.src.slice(u-i.length));if(!s)return!1;let o=s.url;if(o.length<=i.length)return!1;o=o.replace(/\*+$/,"");const c=e.md.normalizeLink(o);if(!e.md.validateLink(c))return!1;if(!t){e.pending=e.pending.slice(0,-i.length);const l=e.push("link_open","a",1);l.attrs=[["href",c]],l.markup="linkify",l.info="auto";const a=e.push("text","",0);a.content=e.md.normalizeLinkText(o);const f=e.push("link_close","a",-1);f.markup="linkify",f.info="auto"}return e.pos+=o.length-i.length,!0}function Jg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==10)return!1;const n=e.pending.length-1,r=e.posMax;if(!t)if(n>=0&&e.pending.charCodeAt(n)===32)if(n>=1&&e.pending.charCodeAt(n-1)===32){let i=n-1;for(;i>=1&&e.pending.charCodeAt(i-1)===32;)i--;e.pending=e.pending.slice(0,i),e.push("hardbreak","br",0)}else e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0);else e.push("softbreak","br",0);for(u++;u<r&&ae(e.src.charCodeAt(u));)u++;return e.pos=u,!0}const Yo=[];for(let e=0;e<256;e++)Yo.push(0);"\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach(function(e){Yo[e.charCodeAt(0)]=1});function Yg(e,t){let u=e.pos;const n=e.posMax;if(e.src.charCodeAt(u)!==92||(u++,u>=n))return!1;let r=e.src.charCodeAt(u);if(r===10){for(t||e.push("hardbreak","br",0),u++;u<n&&(r=e.src.charCodeAt(u),!!ae(r));)u++;return e.pos=u,!0}let i=e.src[u];if(r>=55296&&r<=56319&&u+1<n){const o=e.src.charCodeAt(u+1);o>=56320&&o<=57343&&(i+=e.src[u+1],u++)}const s="\\"+i;if(!t){const o=e.push("text_special","",0);r<256&&Yo[r]!==0?o.content=i:o.content=s,o.markup=s,o.info="escape"}return e.pos=u+1,!0}function Xg(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==96)return!1;const r=u;u++;const i=e.posMax;for(;u<i&&e.src.charCodeAt(u)===96;)u++;const s=e.src.slice(r,u),o=s.length;if(e.backticksScanned&&(e.backticks[o]||0)<=r)return t||(e.pending+=s),e.pos+=o,!0;let c=u,l;for(;(l=e.src.indexOf("`",c))!==-1;){for(c=l+1;c<i&&e.src.charCodeAt(c)===96;)c++;const a=c-l;if(a===o){if(!t){const f=e.push("code_inline","code",0);f.markup=s,f.content=e.src.slice(u,l).replace(/\n/g," ").replace(/^ (.+) $/,"$1")}return e.pos=c,!0}e.backticks[a]=l}return e.backticksScanned=!0,t||(e.pending+=s),e.pos+=o,!0}function Qg(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==126)return!1;const r=e.scanDelims(e.pos,!0);let i=r.length;const s=String.fromCharCode(n);if(i<2)return!1;let o;i%2&&(o=e.push("text","",0),o.content=s,i--);for(let c=0;c<i;c+=2)o=e.push("text","",0),o.content=s+s,e.delimiters.push({marker:n,length:0,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close});return e.pos+=r.length,!0}function Sl(e,t){let u;const n=[],r=t.length;for(let i=0;i<r;i++){const s=t[i];if(s.marker!==126||s.end===-1)continue;const o=t[s.end];u=e.tokens[s.token],u.type="s_open",u.tag="s",u.nesting=1,u.markup="~~",u.content="",u=e.tokens[o.token],u.type="s_close",u.tag="s",u.nesting=-1,u.markup="~~",u.content="",e.tokens[o.token-1].type==="text"&&e.tokens[o.token-1].content==="~"&&n.push(o.token-1)}for(;n.length;){const i=n.pop();let s=i+1;for(;s<e.tokens.length&&e.tokens[s].type==="s_close";)s++;s--,i!==s&&(u=e.tokens[s],e.tokens[s]=e.tokens[i],e.tokens[i]=u)}}function e3(e){const t=e.tokens_meta,u=e.tokens_meta.length;Sl(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&Sl(e,t[n].delimiters)}const Pf={tokenize:Qg,postProcess:e3};function t3(e,t){const u=e.pos,n=e.src.charCodeAt(u);if(t||n!==95&&n!==42)return!1;const r=e.scanDelims(e.pos,n===42);for(let i=0;i<r.length;i++){const s=e.push("text","",0);s.content=String.fromCharCode(n),e.delimiters.push({marker:n,length:r.length,token:e.tokens.length-1,end:-1,open:r.can_open,close:r.can_close})}return e.pos+=r.length,!0}function Al(e,t){const u=t.length;for(let n=u-1;n>=0;n--){const r=t[n];if(r.marker!==95&&r.marker!==42||r.end===-1)continue;const i=t[r.end],s=n>0&&t[n-1].end===r.end+1&&t[n-1].marker===r.marker&&t[n-1].token===r.token-1&&t[r.end+1].token===i.token+1,o=String.fromCharCode(r.marker),c=e.tokens[r.token];c.type=s?"strong_open":"em_open",c.tag=s?"strong":"em",c.nesting=1,c.markup=s?o+o:o,c.content="";const l=e.tokens[i.token];l.type=s?"strong_close":"em_close",l.tag=s?"strong":"em",l.nesting=-1,l.markup=s?o+o:o,l.content="",s&&(e.tokens[t[n-1].token].content="",e.tokens[t[r.end+1].token].content="",n--)}}function u3(e){const t=e.tokens_meta,u=e.tokens_meta.length;Al(e,e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&Al(e,t[n].delimiters)}const Lf={tokenize:t3,postProcess:u3};function n3(e,t){let u,n,r,i,s="",o="",c=e.pos,l=!0;if(e.src.charCodeAt(e.pos)!==91)return!1;const a=e.pos,f=e.posMax,h=e.pos+1,d=e.md.helpers.parseLinkLabel(e,e.pos,!0);if(d<0)return!1;let p=d+1;if(p<f&&e.src.charCodeAt(p)===40){for(l=!1,p++;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);if(p>=f)return!1;if(c=p,r=e.md.helpers.parseLinkDestination(e.src,p,e.posMax),r.ok){for(s=e.md.normalizeLink(r.str),e.md.validateLink(s)?p=r.pos:s="",c=p;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);if(r=e.md.helpers.parseLinkTitle(e.src,p,e.posMax),p<f&&c!==p&&r.ok)for(o=r.str,p=r.pos;p<f&&(u=e.src.charCodeAt(p),!(!ae(u)&&u!==10));p++);}(p>=f||e.src.charCodeAt(p)!==41)&&(l=!0),p++}if(l){if(typeof e.env.references>"u")return!1;if(p<f&&e.src.charCodeAt(p)===91?(c=p+1,p=e.md.helpers.parseLinkLabel(e,p),p>=0?n=e.src.slice(c,p++):p=d+1):p=d+1,n||(n=e.src.slice(h,d)),i=e.env.references[Mi(n)],!i)return e.pos=a,!1;s=i.href,o=i.title}if(!t){e.pos=h,e.posMax=d;const _=e.push("link_open","a",1),D=[["href",s]];_.attrs=D,o&&D.push(["title",o]),e.linkLevel++,e.md.inline.tokenize(e),e.linkLevel--,e.push("link_close","a",-1)}return e.pos=p,e.posMax=f,!0}function r3(e,t){let u,n,r,i,s,o,c,l,a="";const f=e.pos,h=e.posMax;if(e.src.charCodeAt(e.pos)!==33||e.src.charCodeAt(e.pos+1)!==91)return!1;const d=e.pos+2,p=e.md.helpers.parseLinkLabel(e,e.pos+1,!1);if(p<0)return!1;if(i=p+1,i<h&&e.src.charCodeAt(i)===40){for(i++;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(i>=h)return!1;for(l=i,o=e.md.helpers.parseLinkDestination(e.src,i,e.posMax),o.ok&&(a=e.md.normalizeLink(o.str),e.md.validateLink(a)?i=o.pos:a=""),l=i;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);if(o=e.md.helpers.parseLinkTitle(e.src,i,e.posMax),i<h&&l!==i&&o.ok)for(c=o.str,i=o.pos;i<h&&(u=e.src.charCodeAt(i),!(!ae(u)&&u!==10));i++);else c="";if(i>=h||e.src.charCodeAt(i)!==41)return e.pos=f,!1;i++}else{if(typeof e.env.references>"u")return!1;if(i<h&&e.src.charCodeAt(i)===91?(l=i+1,i=e.md.helpers.parseLinkLabel(e,i),i>=0?r=e.src.slice(l,i++):i=p+1):i=p+1,r||(r=e.src.slice(d,p)),s=e.env.references[Mi(r)],!s)return e.pos=f,!1;a=s.href,c=s.title}if(!t){n=e.src.slice(d,p);const _=[];e.md.inline.parse(n,e.md,e.env,_);const D=e.push("image","img",0),S=[["src",a],["alt",""]];D.attrs=S,D.children=_,D.content=n,c&&S.push(["title",c])}return e.pos=i,e.posMax=h,!0}const i3=/^([a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/,s3=/^([a-zA-Z][a-zA-Z0-9+.-]{1,31}):([^<>\x00-\x20]*)$/;function o3(e,t){let u=e.pos;if(e.src.charCodeAt(u)!==60)return!1;const n=e.pos,r=e.posMax;for(;;){if(++u>=r)return!1;const s=e.src.charCodeAt(u);if(s===60)return!1;if(s===62)break}const i=e.src.slice(n+1,u);if(s3.test(i)){const s=e.md.normalizeLink(i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}if(i3.test(i)){const s=e.md.normalizeLink("mailto:"+i);if(!e.md.validateLink(s))return!1;if(!t){const o=e.push("link_open","a",1);o.attrs=[["href",s]],o.markup="autolink",o.info="auto";const c=e.push("text","",0);c.content=e.md.normalizeLinkText(i);const l=e.push("link_close","a",-1);l.markup="autolink",l.info="auto"}return e.pos+=i.length+2,!0}return!1}function c3(e){return/^<a[>\s]/i.test(e)}function l3(e){return/^<\/a\s*>/i.test(e)}function a3(e){const t=e|32;return t>=97&&t<=122}function f3(e,t){if(!e.md.options.html)return!1;const u=e.posMax,n=e.pos;if(e.src.charCodeAt(n)!==60||n+2>=u)return!1;const r=e.src.charCodeAt(n+1);if(r!==33&&r!==63&&r!==47&&!a3(r))return!1;const i=e.src.slice(n).match($g);if(!i)return!1;if(!t){const s=e.push("html_inline","",0);s.content=i[0],c3(s.content)&&e.linkLevel++,l3(s.content)&&e.linkLevel--}return e.pos+=i[0].length,!0}const d3=/^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i,h3=/^&([a-z][a-z0-9]{1,31});/i;function p3(e,t){const u=e.pos,n=e.posMax;if(e.src.charCodeAt(u)!==38||u+1>=n)return!1;if(e.src.charCodeAt(u+1)===35){const i=e.src.slice(u).match(d3);if(i){if(!t){const s=i[1][0].toLowerCase()==="x"?parseInt(i[1].slice(1),16):parseInt(i[1],10),o=e.push("text_special","",0);o.content=Zo(s)?si(s):si(65533),o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}else{const i=e.src.slice(u).match(h3);if(i){const s=wf(i[0]);if(s!==i[0]){if(!t){const o=e.push("text_special","",0);o.content=s,o.markup=i[0],o.info="entity"}return e.pos+=i[0].length,!0}}}return!1}function vl(e){const t={},u=e.length;if(!u)return;let n=0,r=-2;const i=[];for(let s=0;s<u;s++){const o=e[s];if(i.push(0),(e[n].marker!==o.marker||r!==o.token-1)&&(n=s),r=o.token,o.length=o.length||0,!o.close)continue;t.hasOwnProperty(o.marker)||(t[o.marker]=[-1,-1,-1,-1,-1,-1]);const c=t[o.marker][(o.open?3:0)+o.length%3];let l=n-i[n]-1,a=l;for(;l>c;l-=i[l]+1){const f=e[l];if(f.marker===o.marker&&f.open&&f.end<0){let h=!1;if((f.close||o.open)&&(f.length+o.length)%3===0&&(f.length%3!==0||o.length%3!==0)&&(h=!0),!h){const d=l>0&&!e[l-1].open?i[l-1]+1:0;i[s]=s-l+d,i[l]=d,o.open=!1,f.end=s,f.close=!1,a=-1,r=-2;break}}}a!==-1&&(t[o.marker][(o.open?3:0)+(o.length||0)%3]=a)}}function m3(e){const t=e.tokens_meta,u=e.tokens_meta.length;vl(e.delimiters);for(let n=0;n<u;n++)t[n]&&t[n].delimiters&&vl(t[n].delimiters)}function b3(e){let t,u,n=0;const r=e.tokens,i=e.tokens.length;for(t=u=0;t<i;t++)r[t].nesting<0&&n--,r[t].level=n,r[t].nesting>0&&n++,r[t].type==="text"&&t+1<i&&r[t+1].type==="text"?r[t+1].content=r[t].content+r[t+1].content:(t!==u&&(r[u]=r[t]),u++);t!==u&&(r.length=u)}const os=[["text",Wg],["linkify",Zg],["newline",Jg],["escape",Yg],["backticks",Xg],["strikethrough",Pf.tokenize],["emphasis",Lf.tokenize],["link",n3],["image",r3],["autolink",o3],["html_inline",f3],["entity",p3]],cs=[["balance_pairs",m3],["strikethrough",Pf.postProcess],["emphasis",Lf.postProcess],["fragments_join",b3]];function lr(){this.ruler=new Je;for(let e=0;e<os.length;e++)this.ruler.push(os[e][0],os[e][1]);this.ruler2=new Je;for(let e=0;e<cs.length;e++)this.ruler2.push(cs[e][0],cs[e][1])}lr.prototype.skipToken=function(e){const t=e.pos,u=this.ruler.getRules(""),n=u.length,r=e.md.options.maxNesting,i=e.cache;if(typeof i[t]<"u"){e.pos=i[t];return}let s=!1;if(e.level<r){for(let o=0;o<n;o++)if(e.level++,s=u[o](e,!0),e.level--,s){if(t>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}else e.pos=e.posMax;s||e.pos++,i[t]=e.pos};lr.prototype.tokenize=function(e){const t=this.ruler.getRules(""),u=t.length,n=e.posMax,r=e.md.options.maxNesting;for(;e.pos<n;){const i=e.pos;let s=!1;if(e.level<r){for(let o=0;o<u;o++)if(s=t[o](e,!1),s){if(i>=e.pos)throw new Error("inline rule didn't increment state.pos");break}}if(s){if(e.pos>=n)break;continue}e.pending+=e.src[e.pos++]}e.pending&&e.pushPending()};lr.prototype.parse=function(e,t,u,n){const r=new this.State(e,t,u,n);this.tokenize(r);const i=this.ruler2.getRules(""),s=i.length;for(let o=0;o<s;o++)i[o](r)};lr.prototype.State=cr;function g3(e){const t={};e=e||{},t.src_Any=Sf.source,t.src_Cc=Af.source,t.src_Z=Df.source,t.src_P=Wo.source,t.src_ZPCc=[t.src_Z,t.src_P,t.src_Cc].join("|"),t.src_ZCc=[t.src_Z,t.src_Cc].join("|");const u="[><｜]";return t.src_pseudo_letter="(?:(?!"+u+"|"+t.src_ZPCc+")"+t.src_Any+")",t.src_ip4="(?:(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)",t.src_auth="(?:(?:(?!"+t.src_ZCc+"|[@/\\[\\]()]).)+@)?",t.src_port="(?::(?:6(?:[0-4]\\d{3}|5(?:[0-4]\\d{2}|5(?:[0-2]\\d|3[0-5])))|[1-5]?\\d{1,4}))?",t.src_host_terminator="(?=$|"+u+"|"+t.src_ZPCc+")(?!"+(e["---"]?"-(?!--)|":"-|")+"_|:\\d|\\.-|\\.(?!$|"+t.src_ZPCc+"))",t.src_path="(?:[/?#](?:(?!"+t.src_ZCc+"|"+u+`|[()[\\]{}.,"'?!\\-;]).|\\[(?:(?!`+t.src_ZCc+"|\\]).)*\\]|\\((?:(?!"+t.src_ZCc+"|[)]).)*\\)|\\{(?:(?!"+t.src_ZCc+'|[}]).)*\\}|\\"(?:(?!'+t.src_ZCc+`|["]).)+\\"|\\'(?:(?!`+t.src_ZCc+"|[']).)+\\'|\\'(?="+t.src_pseudo_letter+"|[-])|\\.{2,}[a-zA-Z0-9%/&]|\\.(?!"+t.src_ZCc+"|[.]|$)|"+(e["---"]?"\\-(?!--(?:[^-]|$))(?:-*)|":"\\-+|")+",(?!"+t.src_ZCc+"|$)|;(?!"+t.src_ZCc+"|$)|\\!+(?!"+t.src_ZCc+"|[!]|$)|\\?(?!"+t.src_ZCc+"|[?]|$))+|\\/)?",t.src_email_name='[\\-;:&=\\+\\$,\\.a-zA-Z0-9_][\\-;:&=\\+\\$,\\"\\.a-zA-Z0-9_]*',t.src_xn="xn--[a-z0-9\\-]{1,59}",t.src_domain_root="(?:"+t.src_xn+"|"+t.src_pseudo_letter+"{1,63})",t.src_domain="(?:"+t.src_xn+"|(?:"+t.src_pseudo_letter+")|(?:"+t.src_pseudo_letter+"(?:-|"+t.src_pseudo_letter+"){0,61}"+t.src_pseudo_letter+"))",t.src_host="(?:(?:(?:(?:"+t.src_domain+")\\.)*"+t.src_domain+"))",t.tpl_host_fuzzy="(?:"+t.src_ip4+"|(?:(?:(?:"+t.src_domain+")\\.)+(?:%TLDS%)))",t.tpl_host_no_ip_fuzzy="(?:(?:(?:"+t.src_domain+")\\.)+(?:%TLDS%))",t.src_host_strict=t.src_host+t.src_host_terminator,t.tpl_host_fuzzy_strict=t.tpl_host_fuzzy+t.src_host_terminator,t.src_host_port_strict=t.src_host+t.src_port+t.src_host_terminator,t.tpl_host_port_fuzzy_strict=t.tpl_host_fuzzy+t.src_port+t.src_host_terminator,t.tpl_host_port_no_ip_fuzzy_strict=t.tpl_host_no_ip_fuzzy+t.src_port+t.src_host_terminator,t.tpl_host_fuzzy_test="localhost|www\\.|\\.\\d{1,3}\\.|(?:\\.(?:%TLDS%)(?:"+t.src_ZPCc+"|>|$))",t.tpl_email_fuzzy="(^|"+u+'|"|\\(|'+t.src_ZCc+")("+t.src_email_name+"@"+t.tpl_host_fuzzy_strict+")",t.tpl_link_fuzzy="(^|(?![.:/\\-_@])(?:[$+<=>^`|｜]|"+t.src_ZPCc+"))((?![$+<=>^`|｜])"+t.tpl_host_port_fuzzy_strict+t.src_path+")",t.tpl_link_no_ip_fuzzy="(^|(?![.:/\\-_@])(?:[$+<=>^`|｜]|"+t.src_ZPCc+"))((?![$+<=>^`|｜])"+t.tpl_host_port_no_ip_fuzzy_strict+t.src_path+")",t}function 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.041266 24.430965;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.4611% .18307 346.812432;--s: 74.2023% .148546 301.883095;--a: 83.3927% .124132 66.558491;--n: 39.4456% .032576 275.524078;--b1: 28.8229% .022103 277.508664;--bc: 97.7477% .007913 106.545019;--in: 88.263% .09338 212.846491;--su: 87.0995% .219516 148.024881;--wa: 95.5338% .134246 112.757109;--er: 68.2204% .206328 24.430965}[data-theme=cmyk]{color-scheme:light;--b2: 93% 0 0;--b3: 86% 0 0;--bc: 20% 0 0;--pc: 14.3544% .02666 239.443325;--sc: 12.8953% .040552 359.339283;--ac: 18.8458% .037948 105.306968;--nc: 84.3557% 0 0;--inc: 13.6952% .0189 217.284104;--suc: 89.3898% .032505 321.406278;--wac: 14.2473% .031969 52.023412;--erc: 12.4027% .041677 28.717543;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.7722% .133298 239.443325;--s: 64.4766% .202758 359.339283;--a: 94.2289% .189741 105.306968;--n: 21.7787% 0 0;--b1: 100% 0 0;--in: 68.4759% .094499 217.284104;--su: 46.949% .162524 321.406278;--wa: 71.2364% .159843 52.023412;--er: 62.0133% .208385 28.717543}:root:has(input.theme-controller[value=cmyk]:checked){color-scheme:light;--b2: 93% 0 0;--b3: 86% 0 0;--bc: 20% 0 0;--pc: 14.3544% .02666 239.443325;--sc: 12.8953% .040552 359.339283;--ac: 18.8458% .037948 105.306968;--nc: 84.3557% 0 0;--inc: 13.6952% .0189 217.284104;--suc: 89.3898% .032505 321.406278;--wac: 14.2473% .031969 52.023412;--erc: 12.4027% .041677 28.717543;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.7722% .133298 239.443325;--s: 64.4766% .202758 359.339283;--a: 94.2289% .189741 105.306968;--n: 21.7787% 0 0;--b1: 100% 0 0;--in: 68.4759% .094499 217.284104;--su: 46.949% .162524 321.406278;--wa: 71.2364% .159843 52.023412;--er: 62.0133% .208385 28.717543}[data-theme=autumn]{color-scheme:light;--b2: 89.1077% 0 0;--b3: 82.4006% 0 0;--bc: 19.1629% 0 0;--pc: 88.1446% .032232 17.530175;--sc: 12.3353% .033821 23.865865;--ac: 14.6851% .018999 60.729616;--nc: 90.8734% .007475 51.902819;--inc: 13.8449% .019596 207.284192;--suc: 12.199% .016032 174.616213;--wac: 14.0163% .032982 56.844303;--erc: 90.614% .0482 24.16;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 40.7232% .16116 17.530175;--s: 61.6763% .169105 23.865865;--a: 73.4253% .094994 60.729616;--n: 54.3672% .037374 51.902819;--b1: 95.8147% 0 0;--in: 69.2245% .097979 207.284192;--su: 60.9951% .080159 174.616213;--wa: 70.0817% .164909 56.844303;--er: 53.07% .241 24.16}:root:has(input.theme-controller[value=autumn]:checked){color-scheme:light;--b2: 89.1077% 0 0;--b3: 82.4006% 0 0;--bc: 19.1629% 0 0;--pc: 88.1446% .032232 17.530175;--sc: 12.3353% .033821 23.865865;--ac: 14.6851% .018999 60.729616;--nc: 90.8734% .007475 51.902819;--inc: 13.8449% .019596 207.284192;--suc: 12.199% .016032 174.616213;--wac: 14.0163% .032982 56.844303;--erc: 90.614% .0482 24.16;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 40.7232% .16116 17.530175;--s: 61.6763% .169105 23.865865;--a: 73.4253% .094994 60.729616;--n: 54.3672% .037374 51.902819;--b1: 95.8147% 0 0;--in: 69.2245% .097979 207.284192;--su: 60.9951% .080159 174.616213;--wa: 70.0817% .164909 56.844303;--er: 53.07% .241 24.16}[data-theme=business]{color-scheme:dark;--b2: 22.6487% 0 0;--b3: 20.944% 0 0;--bc: 84.8707% 0 0;--pc: 88.3407% .019811 251.473931;--sc: 12.8185% .005481 229.389418;--ac: 13.4542% .033545 35.791525;--nc: 85.4882% .00265 253.041249;--inc: 12.5233% .028702 240.033697;--suc: 14.0454% .018919 156.59611;--wac: 15.4965% .023141 81.519177;--erc: 90.3221% .029356 29.674507;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 41.7036% .099057 251.473931;--s: 64.0924% .027405 229.389418;--a: 67.271% .167726 35.791525;--n: 27.441% .01325 253.041249;--b1: 24.3535% 0 0;--in: 62.6163% .143511 240.033697;--su: 70.2268% .094594 156.59611;--wa: 77.4824% .115704 81.519177;--er: 51.6105% .14678 29.674507;--rounded-box: .25rem;--rounded-btn: .125rem;--rounded-badge: .125rem}:root:has(input.theme-controller[value=business]:checked){color-scheme:dark;--b2: 22.6487% 0 0;--b3: 20.944% 0 0;--bc: 84.8707% 0 0;--pc: 88.3407% .019811 251.473931;--sc: 12.8185% .005481 229.389418;--ac: 13.4542% .033545 35.791525;--nc: 85.4882% .00265 253.041249;--inc: 12.5233% .028702 240.033697;--suc: 14.0454% .018919 156.59611;--wac: 15.4965% .023141 81.519177;--erc: 90.3221% .029356 29.674507;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 41.7036% .099057 251.473931;--s: 64.0924% .027405 229.389418;--a: 67.271% .167726 35.791525;--n: 27.441% .01325 253.041249;--b1: 24.3535% 0 0;--in: 62.6163% .143511 240.033697;--su: 70.2268% .094594 156.59611;--wa: 77.4824% .115704 81.519177;--er: 51.6105% .14678 29.674507;--rounded-box: .25rem;--rounded-btn: .125rem;--rounded-badge: .125rem}[data-theme=acid]{color-scheme:light;--b2: 91.6146% 0 0;--b3: 84.7189% 0 0;--bc: 19.7021% 0 0;--pc: 14.38% .0714 330.759573;--sc: 14.674% .0448 48.250878;--ac: 18.556% .0528 122.962951;--nc: 84.262% .0256 278.68;--inc: 12.144% .0454 252.05;--suc: 17.144% .0532 158.53;--wac: 18.202% .0424 100.5;--erc: 12.968% .0586 29.349188;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 71.9% .357 330.759573;--s: 73.37% .224 48.250878;--a: 92.78% .264 122.962951;--n: 21.31% .128 278.68;--b1: 98.5104% 0 0;--in: 60.72% .227 252.05;--su: 85.72% .266 158.53;--wa: 91.01% .212 100.5;--er: 64.84% .293 29.349188;--rounded-box: 1.25rem;--rounded-btn: 1rem;--rounded-badge: 1rem;--tab-radius: .7rem}:root:has(input.theme-controller[value=acid]:checked){color-scheme:light;--b2: 91.6146% 0 0;--b3: 84.7189% 0 0;--bc: 19.7021% 0 0;--pc: 14.38% .0714 330.759573;--sc: 14.674% .0448 48.250878;--ac: 18.556% .0528 122.962951;--nc: 84.262% .0256 278.68;--inc: 12.144% .0454 252.05;--suc: 17.144% .0532 158.53;--wac: 18.202% .0424 100.5;--erc: 12.968% .0586 29.349188;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 71.9% .357 330.759573;--s: 73.37% .224 48.250878;--a: 92.78% .264 122.962951;--n: 21.31% .128 278.68;--b1: 98.5104% 0 0;--in: 60.72% .227 252.05;--su: 85.72% .266 158.53;--wa: 91.01% .212 100.5;--er: 64.84% .293 29.349188;--rounded-box: 1.25rem;--rounded-btn: 1rem;--rounded-badge: 1rem;--tab-radius: .7rem}[data-theme=lemonade]{color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}:root:has(input.theme-controller[value=lemonade]:checked){color-scheme:light;--b2: 91.8003% .0186 123.72;--b3: 84.8906% .0172 123.72;--bc: 19.742% .004 123.72;--pc: 11.784% .0398 134.6;--sc: 15.55% .0392 111.09;--ac: 17.078% .0402 100.73;--nc: 86.196% .015 108.6;--inc: 17.238% .0094 224.14;--suc: 17.238% .0094 157.85;--wac: 17.238% .0094 102.15;--erc: 17.238% .0094 25.85;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 58.92% .199 134.6;--s: 77.75% .196 111.09;--a: 85.39% .201 100.73;--n: 30.98% .075 108.6;--b1: 98.71% .02 123.72;--in: 86.19% .047 224.14;--su: 86.19% .047 157.85;--wa: 86.19% .047 102.15;--er: 86.19% .047 25.85}[data-theme=night]{color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}:root:has(input.theme-controller[value=night]:checked){color-scheme:dark;--b2: 19.3144% .037037 265.754874;--b3: 17.8606% .034249 265.754874;--bc: 84.1536% .007965 265.754874;--pc: 15.0703% .027798 232.66148;--sc: 13.6023% .031661 276.934902;--ac: 14.4721% .035244 350.048739;--nc: 85.5899% .00737 260.030984;--suc: 15.6904% .026506 181.911977;--wac: 16.6486% .027912 82.95003;--erc: 14.3572% .034051 13.11834;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 75.3513% .138989 232.66148;--s: 68.0113% .158303 276.934902;--a: 72.3603% .176218 350.048739;--n: 27.9495% .036848 260.030984;--b1: 20.7682% .039824 265.754874;--in: 68.4553% .148062 237.25135;--inc: 0% 0 0;--su: 78.452% .132529 181.911977;--wa: 83.2428% .139558 82.95003;--er: 71.7858% .170255 13.11834}[data-theme=coffee]{color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}:root:has(input.theme-controller[value=coffee]:checked){color-scheme:dark;--b2: 20.1585% .021457 329.708637;--b3: 18.6412% .019842 329.708637;--pc: 14.3993% .024765 62.756393;--sc: 86.893% .00597 199.19444;--ac: 88.5243% .014881 224.389184;--nc: 83.3022% .003149 326.261446;--inc: 15.898% .012774 184.558367;--suc: 14.9445% .014491 131.116276;--wac: 17.6301% .028162 87.722413;--erc: 15.4637% .025644 31.871922;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 71.9967% .123825 62.756393;--s: 34.465% .029849 199.19444;--a: 42.6213% .074405 224.389184;--n: 16.5109% .015743 326.261446;--b1: 21.6758% .023072 329.708637;--bc: 72.3547% .092794 79.129387;--in: 79.4902% .063869 184.558367;--su: 74.7224% .072456 131.116276;--wa: 88.1503% .140812 87.722413;--er: 77.3187% .12822 31.871922}[data-theme=winter]{color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}:root:has(input.theme-controller[value=winter]:checked){color-scheme:light;--pc: 91.372% .051 257.57;--sc: 88.5103% .03222 282.339433;--ac: 11.988% .038303 335.171434;--nc: 83.9233% .012704 257.651965;--inc: 17.6255% .017178 214.515264;--suc: 16.0988% .015404 197.823719;--wac: 17.8345% .009167 71.47031;--erc: 14.6185% .022037 20.076293;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 56.86% .255 257.57;--s: 42.5516% .161098 282.339433;--a: 59.9398% .191515 335.171434;--n: 19.6166% .063518 257.651965;--b1: 100% 0 0;--b2: 97.4663% .011947 259.822565;--b3: 93.2686% .016223 262.751375;--bc: 41.8869% .053885 255.824911;--in: 88.1275% .085888 214.515264;--su: 80.4941% .077019 197.823719;--wa: 89.1725% .045833 71.47031;--er: 73.0926% .110185 20.076293}[data-theme=dim]{color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}:root:has(input.theme-controller[value=dim]:checked){color-scheme:dark;--pc: 17.2267% .028331 139.549991;--sc: 14.6752% .033181 35.353059;--ac: 14.8459% .026728 311.37924;--inc: 17.2157% .028409 206.182959;--suc: 17.2343% .028437 166.534048;--wac: 17.2327% .028447 94.818679;--erc: 16.4838% .019914 33.756357;--rounded-box: 1rem;--rounded-btn: .5rem;--rounded-badge: 1.9rem;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--tab-radius: .5rem;--p: 86.1335% .141656 139.549991;--s: 73.3759% .165904 35.353059;--a: 74.2296% .133641 311.37924;--n: 24.7311% .020483 264.094728;--nc: 82.9011% .031335 222.959324;--b1: 30.8577% .023243 264.149498;--b2: 28.0368% .01983 264.182074;--b3: 26.3469% .018403 262.177739;--bc: 82.9011% .031335 222.959324;--in: 86.0785% .142046 206.182959;--su: 86.1717% .142187 166.534048;--wa: 86.1634% .142236 94.818679;--er: 82.4189% .09957 33.756357}[data-theme=nord]{color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}:root:has(input.theme-controller[value=nord]:checked){color-scheme:light;--pc: 11.8872% .015449 254.027774;--sc: 13.9303% .011822 248.687186;--ac: 15.4929% .01245 217.469017;--inc: 13.8414% .012499 332.664922;--suc: 15.3654% .01498 131.063061;--wac: 17.0972% .017847 84.093335;--erc: 12.122% .024119 15.341883;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 59.4359% .077246 254.027774;--s: 69.6516% .059108 248.687186;--a: 77.4643% .062249 217.469017;--n: 45.229% .035214 264.1312;--nc: 89.9258% .016374 262.749256;--b1: 95.1276% .007445 260.731539;--b2: 93.2996% .010389 261.788485;--b3: 89.9258% .016374 262.749256;--bc: 32.4374% .022945 264.182036;--in: 69.2072% .062496 332.664922;--su: 76.827% .074899 131.063061;--wa: 85.4862% .089234 84.093335;--er: 60.61% .120594 15.341883;--rounded-box: .4rem;--rounded-btn: .2rem;--rounded-badge: .4rem;--tab-radius: .2rem}[data-theme=sunset]{color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 74.7039% .158278 39.94703;--s: 72.5375% .177654 2.72034;--a: 71.2947% .166678 299.844533;--n: 26% .019 237.69;--nc: 70% .019 237.69;--b1: 22% .019 237.69;--b2: 20% .019 237.69;--b3: 18% .019 237.69;--bc: 77.3835% .043586 245.096534;--in: 85.5596% .085271 206.015183;--su: 85.5609% .08586 144.77874;--wa: 85.5695% .084806 74.427797;--er: 85.5116% .07889 16.886379;--rounded-box: 1.2rem;--rounded-btn: .8rem;--rounded-badge: .4rem;--tab-radius: .7rem}:root:has(input.theme-controller[value=sunset]:checked){color-scheme:dark;--pc: 14.9408% .031656 39.94703;--sc: 14.5075% .035531 2.72034;--ac: 14.2589% .033336 299.844533;--inc: 17.1119% .017054 206.015183;--suc: 17.1122% .017172 144.77874;--wac: 17.1139% .016961 74.427797;--erc: 17.1023% .015778 16.886379;--animation-btn: .25s;--animation-input: .2s;--btn-focus-scale: .95;--border-btn: 1px;--tab-border: 1px;--p: 74.7039% .158278 39.94703;--s: 72.5375% .177654 2.72034;--a: 71.2947% .166678 299.844533;--n: 26% .019 237.69;--nc: 70% .019 237.69;--b1: 22% .019 237.69;--b2: 20% .019 237.69;--b3: 18% .019 237.69;--bc: 77.3835% .043586 245.096534;--in: 85.5596% .085271 206.015183;--su: 85.5609% .08586 144.77874;--wa: 85.5695% .084806 74.427797;--er: 85.5116% .07889 16.886379;--rounded-box: 1.2rem;--rounded-btn: .8rem;--rounded-badge: .4rem;--tab-radius: .7rem}.alert{display:grid;width:100%;grid-auto-flow:row;align-content:flex-start;align-items:center;justify-items:center;gap:1rem;text-align:center;border-radius:var(--rounded-box, 1rem);border-width:1px;--tw-border-opacity: 1;border-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-border-opacity)));padding:1rem;--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)));--alert-bg: var(--fallback-b2,oklch(var(--b2)/1));--alert-bg-mix: 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1.9rem);border-width:1px;--tw-border-opacity: 1;border-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-border-opacity)));--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)))}@media (hover:hover){.label a:hover{--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)))}.menu li>*:not(ul,.menu-title,details,.btn):active,.menu li>*:not(ul,.menu-title,details,.btn).active,.menu li>details>summary:active{--tw-bg-opacity: 1;background-color:var(--fallback-n,oklch(var(--n)/var(--tw-bg-opacity)));--tw-text-opacity: 1;color:var(--fallback-nc,oklch(var(--nc)/var(--tw-text-opacity)))}}.btn{display:inline-flex;height:3rem;min-height:3rem;flex-shrink:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;user-select:none;flex-wrap:wrap;align-items:center;justify-content:center;border-radius:var(--rounded-btn, 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1}.btn-disabled,.btn[disabled],.btn:disabled{pointer-events:none}:where(.btn:is(input[type=checkbox])),:where(.btn:is(input[type=radio])){width:auto;-webkit-appearance:none;-moz-appearance:none;appearance:none}.btn:is(input[type=checkbox]):after,.btn:is(input[type=radio]):after{--tw-content: attr(aria-label);content:var(--tw-content)}.chat{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));-moz-column-gap:.75rem;column-gap:.75rem;padding-top:.25rem;padding-bottom:.25rem}.chat-bubble{position:relative;display:block;width:-moz-fit-content;width:fit-content;padding:.5rem 1rem;max-width:90%;border-radius:var(--rounded-box, 1rem);min-height:2.75rem;min-width:2.75rem;--tw-bg-opacity: 1;background-color:var(--fallback-n,oklch(var(--n)/var(--tw-bg-opacity)));--tw-text-opacity: 1;color:var(--fallback-nc,oklch(var(--nc)/var(--tw-text-opacity)))}.chat-bubble:before{position:absolute;bottom:0;height:.75rem;width:.75rem;background-color:inherit;content:"";-webkit-mask-size:contain;mask-size:contain;-webkit-mask-repeat:no-repeat;mask-repeat:no-repeat;-webkit-mask-position:center;mask-position:center}.chat-start{place-items:start;grid-template-columns:auto 1fr}.chat-start .chat-header,.chat-start .chat-footer{grid-column-start:2}.chat-start .chat-image{grid-column-start:1}.chat-start .chat-bubble{grid-column-start:2;border-end-start-radius:0px}.chat-start .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 3 3 L 3 0 C 3 1 1 3 0 3'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 3 3 L 3 0 C 3 1 1 3 0 3'/%3e%3c/svg%3e");inset-inline-start:-.749rem}[dir=rtl] .chat-start .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e")}.chat-end{place-items:end;grid-template-columns:1fr auto}.chat-end .chat-header,.chat-end .chat-footer{grid-column-start:1}.chat-end .chat-image{grid-column-start:2}.chat-end .chat-bubble{grid-column-start:1;border-end-end-radius:0px}.chat-end .chat-bubble:before{-webkit-mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 0'/%3e%3c/svg%3e");mask-image:url("data:image/svg+xml,%3csvg width='3' height='3' xmlns='http://www.w3.org/2000/svg'%3e%3cpath fill='black' d='m 0 3 L 1 3 L 3 3 C 2 3 0 1 0 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.drawer-side>*:not(.drawer-overlay){transform:translate(100%)}.drawer-toggle{position:fixed;height:0px;width:0px;-webkit-appearance:none;-moz-appearance:none;appearance:none;opacity:0}.drawer-toggle:checked~.drawer-side{pointer-events:auto;visibility:visible;overflow-y:auto}.drawer-toggle:checked~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}.drawer-end>.drawer-toggle~.drawer-content{grid-column-start:1}.drawer-end>.drawer-toggle~.drawer-side{grid-column-start:2;justify-items:end}.drawer-end>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(100%)}[dir=rtl] .drawer-end>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(-100%)}.drawer-end>.drawer-toggle:checked~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}.dropdown{position:relative;display:inline-block}.dropdown>*:not(summary):focus{outline:2px solid transparent;outline-offset:2px}.dropdown .dropdown-content{position:absolute}.dropdown:is(:not(details)) 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8em)}.w-full{width:100%}.w-screen{width:100vw}.max-w-64{max-width:16rem}.grow{flex-grow:1}.cursor-pointer{cursor:pointer}.flex-row{flex-direction:row}.flex-col{flex-direction:column}.items-center{align-items:center}.justify-start{justify-content:flex-start}.justify-center{justify-content:center}.justify-between{justify-content:space-between}.gap-2{gap:.5rem}.overflow-visible{overflow:visible}.overflow-y-auto{overflow-y:auto}.truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.rounded-box{border-radius:var(--rounded-box, 1rem)}.bg-base-100{--tw-bg-opacity: 1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity, 1)))}.bg-base-200{--tw-bg-opacity: 1;background-color:var(--fallback-b2,oklch(var(--b2)/var(--tw-bg-opacity, 1)))}.bg-base-300{--tw-bg-opacity: 1;background-color:var(--fallback-b3,oklch(var(--b3)/var(--tw-bg-opacity, 1)))}.p-2{padding:.5rem}.px-4{padding-left:1rem;padding-right:1rem}.py-4{padding-top:1rem;padding-bottom:1rem}.text-center{text-align:center}.text-right{text-align:right}.text-2xl{font-size:1.5rem;line-height:2rem}.text-lg{font-size:1.125rem;line-height:1.75rem}.text-sm{font-size:.875rem;line-height:1.25rem}.text-xs{font-size:.75rem;line-height:1rem}.font-bold{font-weight:700}.font-normal{font-weight:400}.font-semibold{font-weight:600}.text-base-content{--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity, 1)))}.text-error{--tw-text-opacity: 1;color:var(--fallback-er,oklch(var(--er)/var(--tw-text-opacity, 1)))}.underline{text-decoration-line:underline}.opacity-0{opacity:0}.opacity-40{opacity:.4}.opacity-60{opacity:.6}.shadow{--tw-shadow: 0 1px 3px 0 rgb(0 0 0 / .1), 0 1px 2px -1px rgb(0 0 0 / .1);--tw-shadow-colored: 0 1px 3px 0 var(--tw-shadow-color), 0 1px 2px -1px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.shadow-2xl{--tw-shadow: 0 25px 50px -12px rgb(0 0 0 / .25);--tw-shadow-colored: 0 25px 50px -12px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.filter{filter:var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow)}.markdown h1,.markdown h2,.markdown h3,.markdown h4,.markdown h5,.markdown h6,.markdown ul,.markdown ol,.markdown li{all:revert}.markdown pre{white-space:pre-wrap}.markdown pre{border-radius:.5rem}.markdown pre{padding:.5rem}.markdown pre{border:1px solid currentColor}@media (min-width: 768px){.show-on-hover{opacity:0}.group:hover .show-on-hover{opacity:1}}.btn-mini{cursor:pointer}.btn-mini:hover{--tw-shadow: 0 4px 6px -1px rgb(0 0 0 / .1), 0 2px 4px -2px rgb(0 0 0 / .1);--tw-shadow-colored: 0 4px 6px -1px var(--tw-shadow-color), 0 2px 4px -2px var(--tw-shadow-color);box-shadow:var(--tw-ring-offset-shadow, 0 0 #0000),var(--tw-ring-shadow, 0 0 #0000),var(--tw-shadow)}.chat-screen{max-width:900px}.chat-bubble-base-300{--tw-bg-opacity: 1;background-color:var(--fallback-b3,oklch(var(--b3)/var(--tw-bg-opacity, 1)));--tw-text-opacity: 1;color:var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity, 1)))}@media (min-width: 1024px){.lg\:drawer-open>.drawer-toggle{display:none}.lg\:drawer-open>.drawer-toggle~.drawer-side{pointer-events:auto;visibility:visible;position:sticky;display:block;width:auto;overscroll-behavior:auto}.lg\:drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}[dir=rtl] .lg\:drawer-open>.drawer-toggle~.drawer-side>*:not(.drawer-overlay){transform:translate(0)}.lg\:drawer-open>.drawer-toggle:checked~.drawer-side{pointer-events:auto;visibility:visible}.lg\:drawer-open>.drawer-side{overflow-y:auto}html:has(.lg\:drawer-open.lg\:drawer-open){overflow-y:auto;scrollbar-gutter:auto}.lg\:drawer-open>.drawer-toggle~.drawer-side>.drawer-overlay{cursor:default;background-color:transparent}.lg\:hidden{display:none}.lg\:h-screen{height:100vh}.lg\:w-96{width:24rem}.lg\:max-w-64{max-width:16rem}}</style>
-</head>
-
-<body>
-  <div id="app" class="opacity-0"> <!-- opacity-0 will be removed on app mounted -->
-    <div class="flex flex-row drawer lg:drawer-open">
-      <input id="toggle-drawer" type="checkbox" class="drawer-toggle" checked />
-
-      <!-- sidebar -->
-      <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
-        <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
-        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
-          <div class="flex flex-row items-center justify-between mb-4 mt-4">
-            <h2 class="font-bold ml-4">Conversations</h2>
-
-            <!-- close sidebar button -->
-            <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
-              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-arrow-bar-left" viewBox="0 0 16 16">
-                <path fill-rule="evenodd" d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"/>
-              </svg>
-            </label>
-          </div>
-
-          <!-- list of conversations -->
-          <div :class="{
-            'btn btn-ghost justify-start': true,
-            'btn-active': messages.length === 0,
-          }" @click="newConversation">
-            + New conversation
-          </div>
-          <div v-for="conv in conversations" :class="{
-            'btn btn-ghost justify-start font-normal': true,
-            'btn-active': conv.id === viewingConvId,
-          }" @click="setViewingConv(conv.id)">
-            <span class="truncate">{{ conv.messages[0].content }}</span>
-          </div>
-          <div class="text-center text-xs opacity-40 mt-auto mx-4">
-            Conversations are saved to browser's localStorage
-          </div>
-        </div>
-      </div>
-
-      <!-- main view -->
-      <div class="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
-        <!-- header -->
-        <div class="flex flex-row items-center mt-6 mb-6">
-          <!-- open sidebar button -->
-          <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
-            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-list" viewBox="0 0 16 16">
-              <path fill-rule="evenodd" d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"/>
-            </svg>
-          </label>
-
-          <div class="grow text-2xl font-bold ml-2">llama.cpp</div>
-
-          <!-- action buttons (top right) -->
-          <div class="flex items-center">
-            <div v-if="messages.length > 0" class="dropdown dropdown-end">
-              <!-- "more" button -->
-              <button tabindex="0" role="button" class="btn m-1" :disabled="isGenerating">
-                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-three-dots-vertical" viewBox="0 0 16 16">
-                  <path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0"/>
-                </svg>
-              </button>
-              <!-- "more" dropdown menu -->
-              <ul tabindex="0" class="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
-                <li @click="downloadConv(viewingConvId)"><a>Download</a></li>
-                <li class="text-error" @click="deleteConv(viewingConvId)"><a>Delete</a></li>
-              </ul>
-            </div>
-            <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
-              <!-- settings button -->
-              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
-                <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
-                <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z"/>
-              </svg>
-            </button>
-
-            <!-- theme controller is copied from https://daisyui.com/components/theme-controller/ -->
-            <div class="dropdown dropdown-end dropdown-bottom">
-              <div tabindex="0" role="button" class="btn m-1">
-                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-palette2" viewBox="0 0 16 16">
-                  <path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z"/>
-                </svg>
-              </div>
-              <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
-                <li>
-                  <button
-                    class="btn btn-sm btn-block btn-ghost justify-start"
-                    :class="{ 'btn-active': selectedTheme === 'auto' }"
-                    @click="setSelectedTheme('auto')">
-                    auto
-                  </button>
-                </li>
-                <li v-for="theme in themes">
-                  <input
-                    type="radio"
-                    name="theme-dropdown"
-                    class="theme-controller btn btn-sm btn-block btn-ghost justify-start"
-                    :aria-label="theme"
-                    :value="theme"
-                    :checked="selectedTheme === theme"
-                    @click="setSelectedTheme(theme)" />
-                </li>
-              </ul>
-            </div>
-          </div>
-        </div>
-
-        <!-- chat messages -->
-        <div id="messages-list" class="flex flex-col grow overflow-y-auto">
-          <div class="mt-auto flex justify-center">
-            <!-- placeholder to shift the message to the bottom -->
-            {{ messages.length === 0 ? 'Send a message to start' : '' }}
-          </div>
-          <div v-for="msg in messages" class="group">
-            <message-bubble
-              :config="config"
-              :msg="msg"
-              :key="msg.id"
-              :is-generating="isGenerating"
-              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
-              :regenerate-msg="regenerateMsg"></message-bubble>
-          </div>
-
-          <!-- pending (ongoing) assistant message -->
-          <div id="pending-msg" class="group">
-            <message-bubble
-              v-if="pendingMsg"
-              :config="config"
-              :msg="pendingMsg"
-              :key="pendingMsg.id"
-              :is-generating="isGenerating"
-              :edit-user-msg-and-regenerate="() => {}"
-              :regenerate-msg="() => {}"></message-bubble>
-          </div>
-        </div>
-
-        <!-- chat input -->
-        <div class="flex flex-row items-center mt-8 mb-6">
-          <textarea
-            class="textarea textarea-bordered w-full"
-            placeholder="Type a message (Shift+Enter to add a new line)"
-            v-model="inputMsg"
-            @keydown.enter.exact.prevent="sendMessage"
-            @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
-            :disabled="isGenerating"
-            id="msg-input"
-          ></textarea>
-          <button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
-          <button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
-        </div>
-      </div>
-
-    </div>
-
-
-    <!-- modal for editing config -->
-    <dialog class="modal" :class="{'modal-open': showConfigDialog}">
-      <div class="modal-box">
-        <h3 class="text-lg font-bold mb-6">Settings</h3>
-        <div class="h-[calc(90vh-12rem)] overflow-y-auto">
-          <p class="opacity-40 mb-6">Settings below are saved in browser's localStorage</p>
-          <settings-modal-short-input :config-key="'apiKey'" :config-default="configDefault" :config-info="configInfo" v-model="config.apiKey"></settings-modal-short-input>
-          <label class="form-control mb-2">
-            <div class="label">System Message</div>
-            <textarea class="textarea textarea-bordered h-24" :placeholder="'Default: ' + configDefault.systemMessage" v-model="config.systemMessage"></textarea>
-          </label>
-          <template v-for="configKey in ['temperature', 'top_k', 'top_p', 'min_p', 'max_tokens']">
-            <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-          </template>
-          <!-- TODO: add more sampling-related configs, please regroup them into different "collapse" sections -->
-          <!-- Section: Other sampler settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Other sampler settings</summary>
-            <div class="collapse-content">
-              <!-- Samplers queue -->
-              <settings-modal-short-input label="Samplers queue" :config-key="'samplers'" :config-default="configDefault" :config-info="configInfo" v-model="config.samplers"></settings-modal-short-input>
-              <!-- Samplers -->
-              <template v-for="configKey in ['dynatemp_range', 'dynatemp_exponent', 'typical_p', 'xtc_probability', 'xtc_threshold']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-              </template>
-            </div>
-          </details>
-          <!-- Section: Penalties settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Penalties settings</summary>
-            <div class="collapse-content">
-              <template v-for="configKey in ['repeat_last_n', 'repeat_penalty', 'presence_penalty', 'frequency_penalty', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_penalty_last_n']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-              </template>
-            </div>
-          </details>
-          <!-- Section: Advanced config -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Advanced config</summary>
-            <div class="collapse-content">
-              <div class="flex flex-row items-center mb-2">
-                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
-                <span class="ml-4">Show tokens per second</span>
-              </div>
-              <label class="form-control mb-2">
-                <!-- Custom parameters input -->
-                <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
-                <textarea class="textarea textarea-bordered h-24" placeholder="Example: { &quot;mirostat&quot;: 1, &quot;min_p&quot;: 0.1 }" v-model="config.custom"></textarea>
-              </label>
-            </div>
-          </details>
-        </div>
-
-        <!-- action buttons -->
-        <div class="modal-action">
-          <button class="btn" @click="resetConfigDialog">Reset to default</button>
-          <button class="btn" @click="closeAndDiscardConfigDialog">Close</button>
-          <button class="btn btn-primary" @click="closeAndSaveConfigDialog">Save</button>
-        </div>
-      </div>
-    </dialog>
-
-  </div>
-
-
-  <!-- Template to be used as message bubble -->
-  <template id="message-bubble">
-    <div :class="{
-      'chat': true,
-      'chat-start': msg.role !== 'user',
-      'chat-end': msg.role === 'user',
-    }">
-      <div :class="{
-        'chat-bubble markdown': true,
-        'chat-bubble-base-300': msg.role !== 'user',
-      }">
-        <!-- textarea for editing message -->
-        <template v-if="editingContent !== null">
-          <textarea
-            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
-            v-model="editingContent"></textarea>
-          <br/>
-          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
-          <button class="btn mt-2" @click="editMsg()">Submit</button>
-        </template>
-        <template v-else>
-          <!-- show loading dots for pending message -->
-          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
-          <!-- render message as markdown -->
-          <vue-markdown v-else :source="msg.content"></vue-markdown>
-          <!-- render timings if enabled -->
-          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
-            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
-            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
-              <b>Prompt</b><br/>
-              - Tokens: {{ timings.prompt_n }}<br/>
-              - Time: {{ timings.prompt_ms }} ms<br/>
-              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
-              <b>Generation</b><br/>
-              - Tokens: {{ timings.predicted_n }}<br/>
-              - Time: {{ timings.predicted_ms }} ms<br/>
-              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
-            </div>
-          </div>
-        </template>
-      </div>
-    </div>
-    <!-- actions for each message -->
-    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
-      <!-- user message -->
-      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
-        ✍️ Edit
-      </button>
-      <!-- assistant message -->
-      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-        🔄 Regenerate
-      </button>
-      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
-        📋 Copy
-      </button>
-    </div>
-  </template>
-
-
-  <!-- Template to be used by settings modal -->
-  <template id="settings-modal-short-input">
-    <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
-      <!-- Show help message on hovering on the input label -->
-      <div class="dropdown dropdown-hover">
-        <div tabindex="0" role="button" class="font-bold">{{ label || configKey }}</div>
-        <div class="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
-          {{ configInfo[configKey] || '(no help message available)' }}
-        </div>
-      </div>
-      <!-- Here we forward v-model from parent to child component, see: https://stackoverflow.com/questions/47311936/v-model-and-child-components -->
-      <input type="text" class="grow" :placeholder="'Default: ' + (configDefault[configKey] || 'none')" :value="modelValue" @input="$emit('update:modelValue', $event.target.value)" />
-    </label>
-  </template>
-
-</body>
-
-</html>
diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
new file mode 100644
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HcmV?d00001

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 5cc86bf7c..aa8c31259 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -15,7 +15,7 @@
 #define MIMETYPE_JSON "application/json; charset=utf-8"
 
 // auto generated files (update with ./deps.sh)
-#include "index.html.hpp"
+#include "index.html.gz.hpp"
 #include "loading.html.hpp"
 
 #include <atomic>
@@ -3828,8 +3828,13 @@ int main(int argc, char ** argv) {
             }
         } else {
             // using embedded static index.html
-            svr->Get("/", [](const httplib::Request &, httplib::Response & res) {
-                res.set_content(reinterpret_cast<const char*>(index_html), index_html_len, "text/html; charset=utf-8");
+            svr->Get("/", [](const httplib::Request & req, httplib::Response & res) {
+                if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) {
+                    res.set_content("Error: gzip is not supported by this browser", "text/plain");
+                } else {
+                    res.set_header("Content-Encoding", "gzip");
+                    res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
+                }
                 return false;
             });
         }
diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index 9d29fe005..dcdd41079 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -201,6 +201,10 @@
           <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
             <summary class="collapse-title font-bold">Advanced config</summary>
             <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2" v-if="isDev">
+                <!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
+                <button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
+              </div>
               <div class="flex flex-row items-center mb-2">
                 <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
                 <span class="ml-4">Show tokens per second</span>
diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json
index f9104f65f..1d0ab0236 100644
--- a/examples/server/webui/package-lock.json
+++ b/examples/server/webui/package-lock.json
@@ -8,8 +8,11 @@
       "name": "webui",
       "version": "0.0.0",
       "dependencies": {
+        "@vscode/markdown-it-katex": "^1.1.1",
         "autoprefixer": "^10.4.20",
         "daisyui": "^4.12.14",
+        "highlight.js": "^11.10.0",
+        "katex": "^0.16.15",
         "markdown-it": "^14.1.0",
         "postcss": "^8.4.49",
         "tailwindcss": "^3.4.15",
@@ -18,6 +21,7 @@
         "vue": "^3.5.13"
       },
       "devDependencies": {
+        "sass-embedded": "^1.83.0",
         "vite": "^5.4.10"
       }
     },
@@ -33,6 +37,13 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/@bufbuild/protobuf": {
+      "version": "2.2.3",
+      "resolved": "https://registry.npmjs.org/@bufbuild/protobuf/-/protobuf-2.2.3.tgz",
+      "integrity": "sha512-tFQoXHJdkEOSwj5tRIZSPNUuXK3RaR7T1nUrPgbYX1pUbvqqaaZAsfo+NXBPsz5rZMSKVFrgK1WL8Q/MSLvprg==",
+      "devOptional": true,
+      "license": "(Apache-2.0 AND BSD-3-Clause)"
+    },
     "node_modules/@esbuild/aix-ppc64": {
       "version": "0.21.5",
       "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz",
@@ -606,6 +617,15 @@
         "win32"
       ]
     },
+    "node_modules/@vscode/markdown-it-katex": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/@vscode/markdown-it-katex/-/markdown-it-katex-1.1.1.tgz",
+      "integrity": "sha512-3KTlbsRBPJQLE2YmLL7K6nunTlU+W9T5+FjfNdWuIUKgxSS6HWLQHaO3L4MkJi7z7MpIPpY+g4N+cWNBPE/MSA==",
+      "license": "MIT",
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       "resolved": "https://registry.npmjs.org/sucrase/-/sucrase-3.35.0.tgz",
@@ -2641,6 +3100,45 @@
         "node": ">=8"
       }
     },
+    "node_modules/supports-color": {
+      "version": "8.1.1",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-8.1.1.tgz",
+      "integrity": "sha512-MpUEN2OodtUzxvKQl72cUF7RQ5EiHsGvSsVG0ia9c5RbWGL2CI4C7EpPS8UTBIplnlzZiNuV56w+FuNxy3ty2Q==",
+      "devOptional": true,
+      "license": "MIT",
+      "dependencies": {
+        "has-flag": "^4.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/supports-color?sponsor=1"
+      }
+    },
+    "node_modules/sync-child-process": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/sync-child-process/-/sync-child-process-1.0.2.tgz",
+      "integrity": "sha512-8lD+t2KrrScJ/7KXCSyfhT3/hRq78rC0wBFqNJXv3mZyn6hW2ypM05JmlSvtqRbeq6jqA94oHbxAr2vYsJ8vDA==",
+      "devOptional": true,
+      "license": "MIT",
+      "dependencies": {
+        "sync-message-port": "^1.0.0"
+      },
+      "engines": {
+        "node": ">=16.0.0"
+      }
+    },
+    "node_modules/sync-message-port": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/sync-message-port/-/sync-message-port-1.1.3.tgz",
+      "integrity": "sha512-GTt8rSKje5FilG+wEdfCkOcLL7LWqpMlr2c3LRuKt/YXxcJ52aGSbGBAdI4L3aaqfrBt6y711El53ItyH1NWzg==",
+      "devOptional": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=16.0.0"
+      }
+    },
     "node_modules/tailwindcss": {
       "version": "3.4.15",
       "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.15.tgz",
@@ -2684,12 +3182,26 @@
       "integrity": "sha512-iBHbi7BQxrFmwZUQJsT0SjNzlLLsXhvW/kg7EyOMVMBIrlnj/qYofwo1LVLZi+3GbUEo96Iu2eqToI2+lZoAEQ==",
       "license": "MIT"
     },
+    "node_modules/tslib": {
+      "version": "2.8.1",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
+      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
+      "devOptional": true,
+      "license": "0BSD"
+    },
     "node_modules/uc.micro": {
       "version": "2.1.0",
       "resolved": "https://registry.npmjs.org/uc.micro/-/uc.micro-2.1.0.tgz",
       "integrity": "sha512-ARDJmphmdvUk6Glw7y9DQ2bFkKBHwQHLi2lsaH6PPmz/Ka9sFOBsBluozhDltWmnv9u/cF6Rt87znRTPV+yp/A==",
       "license": "MIT"
     },
+    "node_modules/varint": {
+      "version": "6.0.0",
+      "resolved": "https://registry.npmjs.org/varint/-/varint-6.0.0.tgz",
+      "integrity": "sha512-cXEIW6cfr15lFv563k4GuVuW/fiwjknytD37jIOLSdSWuOI6WnO/oKwmP2FQTU2l01LP8/M5TSAJpzUaGe3uWg==",
+      "devOptional": true,
+      "license": "MIT"
+    },
     "node_modules/vite": {
       "version": "5.4.11",
       "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.11.tgz",
diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json
index d656a841d..48090c6de 100644
--- a/examples/server/webui/package.json
+++ b/examples/server/webui/package.json
@@ -6,14 +6,19 @@
   "scripts": {
     "dev": "vite",
     "build": "vite build",
-    "preview": "vite preview"
+    "preview": "vite preview",
+    "analyze": "ANALYZE=1 npx vite-bundle-visualizer"
   },
   "devDependencies": {
+    "sass-embedded": "^1.83.0",
     "vite": "^5.4.10"
   },
   "dependencies": {
+    "@vscode/markdown-it-katex": "^1.1.1",
     "autoprefixer": "^10.4.20",
     "daisyui": "^4.12.14",
+    "highlight.js": "^11.10.0",
+    "katex": "^0.16.15",
     "markdown-it": "^14.1.0",
     "postcss": "^8.4.49",
     "tailwindcss": "^3.4.15",
diff --git a/examples/server/webui/public/demo-conversation.json b/examples/server/webui/public/demo-conversation.json
new file mode 100644
index 000000000..75ab599dd
--- /dev/null
+++ b/examples/server/webui/public/demo-conversation.json
@@ -0,0 +1,33 @@
+{
+  "demo": true,
+  "id": "conv-1734086746930",
+  "lastModified": 1734087548943,
+  "messages": [
+    {
+      "id": 1734086764521,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548327,
+      "role": "assistant",
+      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
+      "timings": {
+        "prompt_n": 1,
+        "prompt_ms": 28.923,
+        "predicted_n": 25,
+        "predicted_ms": 573.016
+      }
+    },
+    {
+      "id": 1734087548328,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548329,
+      "role": "assistant",
+      "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```"
+    }
+  ]
+}
diff --git a/examples/server/webui/src/highlight-config.js b/examples/server/webui/src/highlight-config.js
new file mode 100644
index 000000000..96c7028f9
--- /dev/null
+++ b/examples/server/webui/src/highlight-config.js
@@ -0,0 +1,60 @@
+import hljs from 'highlight.js/lib/core';
+
+// only import commonly used languages to reduce bundle size
+
+import python from 'highlight.js/lib/languages/python';
+import javascript from 'highlight.js/lib/languages/javascript';
+import json from 'highlight.js/lib/languages/json';
+import bash from 'highlight.js/lib/languages/bash';
+import yaml from 'highlight.js/lib/languages/yaml';
+import markdown from 'highlight.js/lib/languages/markdown';
+import scss from 'highlight.js/lib/languages/scss';
+import xml from 'highlight.js/lib/languages/xml';
+import ruby from 'highlight.js/lib/languages/ruby';
+import go from 'highlight.js/lib/languages/go';
+import java from 'highlight.js/lib/languages/java';
+import rust from 'highlight.js/lib/languages/rust';
+import scala from 'highlight.js/lib/languages/scala';
+import cpp from 'highlight.js/lib/languages/cpp';
+import csharp from 'highlight.js/lib/languages/csharp';
+import swift from 'highlight.js/lib/languages/swift';
+import dart from 'highlight.js/lib/languages/dart';
+import elixir from 'highlight.js/lib/languages/elixir';
+import kotlin from 'highlight.js/lib/languages/kotlin';
+import lua from 'highlight.js/lib/languages/lua';
+import php from 'highlight.js/lib/languages/php';
+import latex from 'highlight.js/lib/languages/latex';
+
+hljs.registerLanguage('python', python);
+hljs.registerLanguage('javascript', javascript);
+hljs.registerLanguage('json', json);
+hljs.registerLanguage('yaml', yaml);
+hljs.registerLanguage('markdown', markdown);
+hljs.registerLanguage('xml', xml);
+hljs.registerLanguage('ruby', ruby);
+hljs.registerLanguage('go', go);
+hljs.registerLanguage('java', java);
+hljs.registerLanguage('rust', rust);
+hljs.registerLanguage('scala', scala);
+hljs.registerLanguage('csharp', csharp);
+hljs.registerLanguage('swift', swift);
+hljs.registerLanguage('dart', dart);
+hljs.registerLanguage('elixir', elixir);
+hljs.registerLanguage('kotlin', kotlin);
+hljs.registerLanguage('lua', lua);
+hljs.registerLanguage('php', php);
+hljs.registerLanguage('latex', latex);
+
+// reuse some languages to further reduce bundle size
+
+hljs.registerLanguage('shell', bash);
+hljs.registerLanguage('bash', bash);
+hljs.registerLanguage('sh', bash);
+
+hljs.registerLanguage('css', scss);
+hljs.registerLanguage('scss', scss);
+
+hljs.registerLanguage('c', cpp);
+hljs.registerLanguage('cpp', cpp);
+
+export default hljs;
diff --git a/examples/server/webui/src/katex-gpt.js b/examples/server/webui/src/katex-gpt.js
new file mode 100644
index 000000000..7c7c5e22c
--- /dev/null
+++ b/examples/server/webui/src/katex-gpt.js
@@ -0,0 +1,66 @@
+import katex from 'katex';
+
+// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
+// MIT license
+
+const defaultOptions = {
+  delimiters: [
+    { left: '\\[', right: '\\]', display: true },
+    { left: '\\(', right: '\\)', display: false },
+  ],
+};
+
+export function renderLatexHTML(content, display = false) {
+  return katex.renderToString(content, {
+    throwOnError: false,
+    output: 'mathml',
+    displayMode: display,
+  });
+}
+
+function escapedBracketRule(options) {
+  return (state, silent) => {
+    const max = state.posMax;
+    const start = state.pos;
+
+    for (const { left, right, display } of options.delimiters) {
+
+      // Check if it starts with the left delimiter
+      if (!state.src.slice(start).startsWith(left)) continue;
+
+      // Skip the length of the left delimiter
+      let pos = start + left.length;
+
+      // Find the matching right delimiter
+      while (pos < max) {
+        if (state.src.slice(pos).startsWith(right)) {
+          break;
+        }
+        pos++;
+      }
+
+      // No matching right delimiter found, skip to the next match
+      if (pos >= max) continue;
+
+      // If not in silent mode, convert LaTeX formula to MathML
+      if (!silent) {
+        const content = state.src.slice(start + left.length, pos);
+        try {
+          const renderedContent = renderLatexHTML(content, display);
+          const token = state.push('html_inline', '', 0);
+          token.content = renderedContent;
+        } catch (e) {
+          console.error(e);
+        }
+      }
+
+      // Update position, skip the length of the right delimiter
+      state.pos = pos + right.length;
+      return true;
+    }
+  }
+}
+
+export default function (md, options = defaultOptions) {
+  md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
+}
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index f1f35481d..a16df1ccc 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -1,8 +1,17 @@
-import './styles.css';
+import './styles.scss';
 import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
 import MarkdownIt from 'markdown-it';
 import TextLineStream from 'textlinestream';
 
+// math formula rendering
+import 'katex/dist/katex.min.css';
+import markdownItKatexGpt, { renderLatexHTML } from './katex-gpt';
+import markdownItKatexNormal from '@vscode/markdown-it-katex';
+
+// code highlighting
+import hljs from './highlight-config';
+import daisyuiThemes from 'daisyui/src/theming/themes';
+
 const isDev = import.meta.env.MODE === 'development';
 
 // utility functions
@@ -13,8 +22,11 @@ const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
 const copyStr = (str) => navigator.clipboard.writeText(str);
 
 // constants
-const BASE_URL = localStorage.getItem('base') // for debugging
-  || (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
+const BASE_URL = isDev
+  ? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
+  : (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
+console.log({ BASE_URL });
+
 const CONFIG_DEFAULT = {
   // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
   apiKey: '',
@@ -69,12 +81,39 @@ const CONFIG_INFO = {
 // config keys having numeric value (i.e. temperature, top_k, top_p, etc)
 const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
 // list of themes supported by daisyui
-const THEMES = ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'];
+const THEMES = ['light', 'dark']
+  // make sure light & dark are always at the beginning
+  .concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));
 
 // markdown support
 const VueMarkdown = defineComponent(
   (props) => {
-    const md = shallowRef(new MarkdownIt({ breaks: true }));
+    const md = shallowRef(new MarkdownIt({
+      breaks: true,
+      highlight: function (str, lang) { // Add highlight.js
+        if (lang && hljs.getLanguage(lang)) {
+          try {
+            return '<pre><code class="hljs">' +
+                   hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
+                   '</code></pre>';
+          } catch (__) {}
+        }
+        return '<pre><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
+      }
+    }));
+    // support latex with double dollar sign and square brackets
+    md.value.use(markdownItKatexGpt, {
+      delimiters: [
+        { left: '\\[', right: '\\]', display: true },
+        { left: '\\(', right: '\\)', display: false },
+        { left: '$$', right: '$$', display: false },
+        // do not add single dollar sign here, other wise it will confused with dollar used for money symbol
+      ],
+      throwOnError: false,
+    });
+    // support latex with single dollar sign
+    md.value.use(markdownItKatexNormal, { throwOnError: false });
+    // add copy button to code blocks
     const origFenchRenderer = md.value.renderer.rules.fence;
     md.value.renderer.rules.fence = (tokens, idx, ...args) => {
       const content = tokens[idx].content;
@@ -278,6 +317,7 @@ const mainApp = createApp({
       themes: THEMES,
       configDefault: {...CONFIG_DEFAULT},
       configInfo: {...CONFIG_INFO},
+      isDev,
     }
   },
   computed: {},
@@ -289,6 +329,7 @@ const mainApp = createApp({
       if (this.isGenerating) chatScrollToBottom(true);
     });
     resizeObserver.observe(pendingMsgElem);
+    this.setSelectedTheme(this.selectedTheme);
   },
   watch: {
     viewingConvId: function(val, oldVal) {
@@ -305,6 +346,8 @@ const mainApp = createApp({
     },
     setSelectedTheme(theme) {
       this.selectedTheme = theme;
+      document.body.setAttribute('data-theme', theme);
+      document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
       StorageUtils.setTheme(theme);
     },
     newConversation() {
@@ -513,6 +556,17 @@ const mainApp = createApp({
     fetchMessages() {
       this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
     },
+
+    // debug functions
+    async debugImportDemoConv() {
+      const res = await fetch('/demo-conversation.json');
+      const demoConv = await res.json();
+      StorageUtils.remove(demoConv.id);
+      for (const msg of demoConv.messages) {
+        StorageUtils.appendMsg(demoConv.id, msg);
+      }
+      this.fetchConversation();
+    }
   },
 });
 mainApp.config.errorHandler = alert;
diff --git a/examples/server/webui/src/styles.css b/examples/server/webui/src/styles.css
deleted file mode 100644
index 67d35b99e..000000000
--- a/examples/server/webui/src/styles.css
+++ /dev/null
@@ -1,26 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-.markdown {
-  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
-  pre {
-    @apply whitespace-pre-wrap rounded-lg p-2;
-    border: 1px solid currentColor;
-  }
-  /* TODO: fix markdown table */
-}
-
-.show-on-hover {
-  @apply md:opacity-0 md:group-hover:opacity-100;
-}
-.btn-mini {
-  @apply cursor-pointer hover:shadow-md;
-}
-.chat-screen { max-width: 900px; }
-
-.chat-bubble-base-300 {
-  --tw-bg-opacity: 1;
-  --tw-text-opacity: 1;
-  @apply bg-base-300 text-base-content;
-}
diff --git a/examples/server/webui/src/styles.scss b/examples/server/webui/src/styles.scss
new file mode 100644
index 000000000..34fe2aaf0
--- /dev/null
+++ b/examples/server/webui/src/styles.scss
@@ -0,0 +1,48 @@
+@use "sass:meta";
+
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.markdown {
+  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
+  pre {
+    @apply whitespace-pre-wrap rounded-lg p-2;
+    border: 1px solid currentColor;
+  }
+  /* TODO: fix markdown table */
+}
+
+.show-on-hover {
+  @apply md:opacity-0 md:group-hover:opacity-100;
+}
+.btn-mini {
+  @apply cursor-pointer hover:shadow-md;
+}
+.chat-screen { max-width: 900px; }
+
+.chat-bubble-base-300 {
+  --tw-bg-opacity: 1;
+  --tw-text-opacity: 1;
+  @apply bg-base-300 text-base-content;
+}
+
+/* Highlight.js */
+[data-color-scheme='light'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-light');
+}
+[data-color-scheme='dark'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+}
+[data-color-scheme='auto'] {
+  @media (prefers-color-scheme: light) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-light');
+  }
+  @media (prefers-color-scheme: dark) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+  }
+}
+.hljs {
+  background: transparent !important;
+  padding: 0.5em !important;
+}
diff --git a/examples/server/webui/vite.config.js b/examples/server/webui/vite.config.js
index 789bf9cbb..6619a630d 100644
--- a/examples/server/webui/vite.config.js
+++ b/examples/server/webui/vite.config.js
@@ -2,6 +2,9 @@
 import { viteSingleFile } from 'vite-plugin-singlefile';
 import path from 'path';
 import fs from 'fs';
+import zlib from 'zlib';
+
+const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary
 
 const GUIDE_FOR_FRONTEND = `
 <!--
@@ -12,25 +15,45 @@ const GUIDE_FOR_FRONTEND = `
 -->
 `.trim();
 
-export default {
-  plugins: [
-    viteSingleFile(),
-    (function llamaCppPlugin() {
-      let config;
-      return {
-        name: 'llamacpp:build',
-        apply: 'build',
-        async configResolved(_config) {
-          config = _config;
-        },
-        writeBundle() {
-          const outputIndexHtml = path.join(config.build.outDir, 'index.html');
-          const content = fs.readFileSync(outputIndexHtml, 'utf-8');
+const BUILD_PLUGINS = [
+  viteSingleFile(),
+  (function llamaCppPlugin() {
+    let config;
+    return {
+      name: 'llamacpp:build',
+      apply: 'build',
+      async configResolved(_config) {
+        config = _config;
+      },
+      writeBundle() {
+        const outputIndexHtml = path.join(config.build.outDir, 'index.html');
+        const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
+        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });
 
-          const targetOutputFile = path.join(config.build.outDir, '../../public/index.html');
-          fs.writeFileSync(targetOutputFile, GUIDE_FOR_FRONTEND + '\n' + content);
+        // because gzip header contains machine-specific info, we must remove these data from the header
+        // timestamp
+        compressed[0x4] = 0;
+        compressed[0x5] = 0;
+        compressed[0x6] = 0;
+        compressed[0x7] = 0;
+        // OS
+        compressed[0x9] = 0;
+
+        if (compressed.byteLength > MAX_BUNDLE_SIZE) {
+          throw new Error(
+            `Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
+            `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
+          );
         }
+
+        const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
+        fs.writeFileSync(targetOutputFile, compressed);
       }
-    })(),
-  ],
+    }
+  })(),
+];
+
+/** @type {import('vite').UserConfig} */
+export default {
+  plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
 };

From 87cf323cef80f6aa530f047ab05b539ebc6b7e3c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 15 Dec 2024 18:44:47 +0200
Subject: [PATCH 027/376] scripts : change build path to "build-bench" for
 compare-commits.sh (#10836)

---
 scripts/compare-commits.sh | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
index 143d98729..e40d1cc6d 100755
--- a/scripts/compare-commits.sh
+++ b/scripts/compare-commits.sh
@@ -20,11 +20,13 @@ if [ -n "$GGML_CUDA" ]; then
     cmake_opts="-DGGML_CUDA=ON"
 fi
 
+dir="build-bench"
+
 function run {
-    rm -fr build > /dev/null
-    cmake -B build -S . $cmake_opts > /dev/null
-    cmake --build build -t llama-bench > /dev/null
-    build/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
+    rm -fr ${dir} > /dev/null
+    cmake -B ${dir} -S . $cmake_opts > /dev/null
+    cmake --build ${dir} -t llama-bench > /dev/null
+    ${dir}/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 }
 
 git checkout $1 > /dev/null

From a0974156f334acf8af5858d7ede5ab7d7490d415 Mon Sep 17 00:00:00 2001
From: Valentin Mamedov <45292985+Inf1delis@users.noreply.github.com>
Date: Mon, 16 Dec 2024 00:02:46 +0700
Subject: [PATCH 028/376] llama : add Deepseek MoE v1 & GigaChat models
 (#10827)

* Add deepseek v1 arch & gigachat template

* improve template code

* add readme

* delete comments

* remove comment

* fix format

* lint llama.cpp

* fix order of deepseek and deepseek2, move gigachat temlate to the end of func

* fix order of deepseek and deepseek2 in constants; mark shared exp as deepseek arch need

* remove comments

* move deepseek above deepseek2

* change placement of gigachat chat template
---
 README.md                      |   1 +
 convert_hf_to_gguf.py          |  94 +++++++++++
 convert_hf_to_gguf_update.py   |   1 +
 gguf-py/gguf/constants.py      |  29 ++++
 gguf-py/gguf/tensor_mapping.py |   6 +-
 src/llama.cpp                  | 289 +++++++++++++++++++++++++++++++++
 tests/test-chat-template.cpp   |   6 +
 7 files changed, 423 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 49095acc8..a3b491754 100644
--- a/README.md
+++ b/README.md
@@ -98,6 +98,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
+- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 
 #### Multimodal
 
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 206ab502e..9dc1673bc 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -664,6 +664,9 @@ class Model:
         if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
             # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
             res = "roberta-bpe"
+        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
+            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
+            res = "gigachat"
 
         if res is None:
             logger.warning("\n")
@@ -3427,6 +3430,97 @@ class ArcticModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("DeepseekForCausalLM")
+class DeepseekModel(Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_weights_scale(1.0)
+        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @Model.register("DeepseekV2ForCausalLM")
 class DeepseekV2Model(Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index aa10e5db7..88058442f 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -104,6 +104,7 @@ models = [
     {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
     {"name": "minerva-7b",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
     {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
+    {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
 ]
 
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 0cf939429..c2c7cad14 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -249,6 +249,7 @@ class MODEL_ARCH(IntEnum):
     OLMOE        = auto()
     OPENELM      = auto()
     ARCTIC       = auto()
+    DEEPSEEK     = auto()
     DEEPSEEK2    = auto()
     CHATGLM      = auto()
     BITNET       = auto()
@@ -412,6 +413,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.OLMOE:          "olmoe",
     MODEL_ARCH.OPENELM:        "openelm",
     MODEL_ARCH.ARCTIC:         "arctic",
+    MODEL_ARCH.DEEPSEEK:       "deepseek",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
     MODEL_ARCH.CHATGLM:        "chatglm",
     MODEL_ARCH.BITNET:         "bitnet",
@@ -1158,6 +1160,29 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.DEEPSEEK: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     MODEL_ARCH.DEEPSEEK2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -1380,6 +1405,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.DEEPSEEK: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
     MODEL_ARCH.DEEPSEEK2: [
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index f0a7b6478..573d0282e 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -306,7 +306,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.up_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2
+            "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek deepseek2
         ),
 
         # AWQ-activation gate
@@ -338,7 +338,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.gate_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2
+            "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2
         ),
 
         # Feed-forward down
@@ -379,7 +379,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2
+            "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
diff --git a/src/llama.cpp b/src/llama.cpp
index abc1252e7..8b799e0eb 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -184,6 +184,7 @@ enum llm_arch {
     LLM_ARCH_OLMOE,
     LLM_ARCH_OPENELM,
     LLM_ARCH_ARCTIC,
+    LLM_ARCH_DEEPSEEK,
     LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_CHATGLM,
     LLM_ARCH_BITNET,
@@ -239,6 +240,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_OLMOE,           "olmoe"        },
     { LLM_ARCH_OPENELM,         "openelm"      },
     { LLM_ARCH_ARCTIC,          "arctic"       },
+    { LLM_ARCH_DEEPSEEK,        "deepseek"     },
     { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
     { LLM_ARCH_CHATGLM,         "chatglm"      },
     { LLM_ARCH_BITNET,          "bitnet"       },
@@ -1309,6 +1311,33 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_DEEPSEEK,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,      "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
     {
         LLM_ARCH_DEEPSEEK2,
         {
@@ -1600,6 +1629,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_EXAONE_3,
     LLM_CHAT_TEMPLATE_RWKV_WORLD,
     LLM_CHAT_TEMPLATE_GRANITE,
+    LLM_CHAT_TEMPLATE_GIGACHAT,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 
@@ -1631,6 +1661,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
     { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
     { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
+    { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
 };
 
 static llm_arch llm_arch_from_string(const std::string & name) {
@@ -6094,6 +6125,19 @@ static void llm_load_hparams(
                     model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_DEEPSEEK:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+
+                switch (hparams.n_layer) {
+                    case 28: model.type = e_model::MODEL_20B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_DEEPSEEK2:
             {
                 bool is_lite = (hparams.n_layer == 27);
@@ -6440,6 +6484,7 @@ static void llm_load_vocab(
                     tokenizer_pre == "phi-2"   ||
                     tokenizer_pre == "jina-es" ||
                     tokenizer_pre == "jina-de" ||
+                    tokenizer_pre == "gigachat"   ||
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
@@ -7091,6 +7136,13 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
 
     LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
 
+    if (model.arch == LLM_ARCH_DEEPSEEK) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+    }
+
     if (model.arch == LLM_ARCH_DEEPSEEK2) {
         LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
         LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
@@ -8865,6 +8917,55 @@ static bool llm_load_tensors(
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
                     }
                 } break;
+            case LLM_ARCH_DEEPSEEK:
+                {
+
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        } else {
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                            if (n_expert == 0) {
+                                throw std::runtime_error("n_expert must be > 0");
+                            }
+                            if (n_expert_used == 0) {
+                                throw std::runtime_error("n_expert_used must be > 0");
+                            }
+
+                            // MoE branch
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                        }
+                    }
+                } break;
             case LLM_ARCH_DEEPSEEK2:
                 {
                     const bool is_lite = (hparams.n_layer == 27);
@@ -15219,6 +15320,161 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_deepseek() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                ggml_tensor * moe_out =
+                        llm_build_moe_ffn(ctx0, lctx, cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, false,
+                            false, hparams.expert_weights_scale,
+                            cb, il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                // FFN shared expert
+                {
+                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
+                            model.layers[il].ffn_up_shexp,   NULL, NULL,
+                            model.layers[il].ffn_gate_shexp, NULL, NULL,
+                            model.layers[il].ffn_down_shexp, NULL, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
+
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_deepseek2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
@@ -16906,6 +17162,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_arctic();
             } break;
+        case LLM_ARCH_DEEPSEEK:
+            {
+                result = llm.build_deepseek();
+            } break;
         case LLM_ARCH_DEEPSEEK2:
             {
                 result = llm.build_deepseek2();
@@ -20137,6 +20397,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_OLMO:
         case LLM_ARCH_ARCTIC:
+        case LLM_ARCH_DEEPSEEK:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_CHATGLM:
         case LLM_ARCH_GRANITE:
@@ -22002,6 +22263,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_RWKV_WORLD;
     } else if (tmpl_contains("<|start_of_role|>")) {
         return LLM_CHAT_TEMPLATE_GRANITE;
+    } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
+        return LLM_CHAT_TEMPLATE_GIGACHAT;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -22325,6 +22588,32 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|start_of_role|>assistant<|end_of_role|>\n";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
+        // GigaChat template
+        bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
+
+        // Handle system message if present
+        if (has_system) {
+            ss << "<s>" << chat[0]->content << "<|message_sep|>";
+        } else {
+            ss << "<s>";
+        }
+
+        // Process remaining messages
+        for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
+            std::string role(chat[i]->role);
+            if (role == "user") {
+                ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
+                << "available functions<|role_sep|>[]<|message_sep|>";
+            } else if (role == "assistant") {
+                ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
+            }
+        }
+
+        // Add generation prompt if needed
+        if (add_ass) {
+            ss << "assistant<|role_sep|>";
+        }
     } else {
         // template not supported
         return -1;
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index aa140b569..30a910ad5 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -75,6 +75,8 @@ int main(void) {
         "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS][\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n        {{- \"[TOOL_CALLS][\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
         // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
         "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
+        // ai-sage/GigaChat-20B-A3B-instruct
+        "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -129,6 +131,8 @@ int main(void) {
         "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
         // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
         "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
+        // ai-sage/GigaChat-20B-A3B-instruct
+        "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
@@ -190,6 +194,7 @@ int main(void) {
     assert(fmt_sys("mistral") == "[INST] You are a helpful assistant\n"); // for old pre-v1 templates
     assert(fmt_sys("gemma")  == ""); // for gemma, system message is merged with user message
     assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>");
+    assert(fmt_sys("gigachat") == "<s>You are a helpful assistant<|message_sep|>");
 
 
     // test llama_chat_format_single for user message
@@ -214,6 +219,7 @@ int main(void) {
     assert(fmt_single("mistral") == "[INST] How are you [/INST]"); // for old pre-v1 templates
     assert(fmt_single("gemma")  == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
     assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
+    assert(fmt_single("gigachat") == "user<|role_sep|>How are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>");
 
     printf("Test chat templates: OK\n");
 

From 4ddd199f6f6b980e0a7ed9f9b44efeae2fbdf5c4 Mon Sep 17 00:00:00 2001
From: Bartowski <ckealty1182@gmail.com>
Date: Sun, 15 Dec 2024 15:43:25 -0500
Subject: [PATCH 029/376] llava : Allow locally downloaded models for QwenVL
 (#10833)

* Allow locally downloaded models for QwenVL

* Define model_path

* rm trailing space

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---
 examples/llava/qwen2_vl_surgery.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py
index 464ab80d3..c87606b4f 100644
--- a/examples/llava/qwen2_vl_surgery.py
+++ b/examples/llava/qwen2_vl_surgery.py
@@ -88,6 +88,8 @@ def main(args):
     else:
         raise ValueError()
 
+    local_model = False
+    model_path = ""
     model_name = args.model_name
     print("model_name: ", model_name)
     qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
@@ -97,8 +99,10 @@ def main(args):
     vcfg = cfg.vision_config
 
     if os.path.isdir(model_name):
+        local_model = True
         if model_name.endswith(os.sep):
             model_name = model_name[:-1]
+        model_path = model_name
         model_name = os.path.basename(model_name)
     fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf"
 
@@ -139,7 +143,10 @@ def main(args):
             it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`.
     """
 
-    processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
+    if local_model:
+        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path)
+    else:
+        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
     fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue]
     fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue]
 

From 644fd71b44c4cdbfc6482fbf0353d289c3bc29e6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 16 Dec 2024 12:31:14 +0200
Subject: [PATCH 030/376] sampling : refactor + optimize penalties sampler
 (#10803)

* sampling : refactor + optimize penalties sampler

ggml-ci

* common : apply ignore_eos as logit bias

ggml-ci

* batched : remove penalties sampler

* params : allow penalty_last_n == -1 to be equal to context size

ggml-ci

* common : by default, move the penalties at the end of the sampling chain

ggml-ci

* common : ignore all EOG tokens

Co-authored-by: Diego Devesa <slarengh@gmail.com>

* common : move back the penalties at the front of the sampling chain

ggml-ci

* readme : restore hint about --ignore-eos flag [no ci]

* llama : minor

ggml-ci

* webui : update

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 common/arg.cpp                                |  13 +-
 common/common.cpp                             |  19 +++
 common/common.h                               |  15 ++-
 common/sampling.cpp                           |  27 ++--
 examples/batched/batched.cpp                  |   1 +
 examples/main/README.md                       |   5 -
 examples/server/README.md                     |   5 -
 examples/server/public/index.html.gz          | Bin 1205102 -> 1205104 bytes
 examples/server/public_legacy/index-new.html  |   1 -
 examples/server/public_legacy/index.html      |   2 -
 examples/server/server.cpp                    |  28 +++-
 examples/server/themes/buttons-top/index.html |   2 -
 examples/server/themes/wild/index.html        |   2 -
 examples/server/webui/src/main.js             |   2 +-
 include/llama.h                               |  14 +-
 src/llama-sampling.cpp                        | 125 +++++-------------
 tests/test-sampling.cpp                       |   2 +-
 17 files changed, 111 insertions(+), 152 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 39bc874c8..3d55289c3 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -855,13 +855,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.ignore_eos = true;
         }
     ).set_sparam());
-    add_opt(common_arg(
-        {"--penalize-nl"},
-        string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
-        [](common_params & params) {
-            params.sampling.penalize_nl = true;
-        }
-    ).set_sparam());
     add_opt(common_arg(
         {"--temp"}, "N",
         string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@@ -916,6 +909,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--repeat-last-n"}, "N",
         string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
+            }
             params.sampling.penalty_last_n = value;
             params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
         }
@@ -970,6 +966,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--dry-penalty-last-n"}, "N",
         string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
+            }
             params.sampling.dry_penalty_last_n = value;
         }
     ).set_sparam());
diff --git a/common/common.cpp b/common/common.cpp
index 3adfb0329..c0c98232e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -940,6 +940,25 @@ struct common_init_result common_init_from_params(common_params & params) {
         params.sampling.ignore_eos = false;
     }
 
+    if (params.sampling.ignore_eos) {
+        for (llama_token i = 0; i < llama_n_vocab(model); i++) {
+            if (llama_token_is_eog(model, i)) {
+                LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
+                params.sampling.logit_bias.push_back({i, -INFINITY});
+            }
+        }
+    }
+
+    if (params.sampling.penalty_last_n == -1) {
+        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    }
+
+    if (params.sampling.dry_penalty_last_n == -1) {
+        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    }
+
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
diff --git a/common/common.h b/common/common.h
index 9e47b70a4..5f556c24d 100644
--- a/common/common.h
+++ b/common/common.h
@@ -95,6 +95,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
+    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -130,7 +131,6 @@ struct common_params_sampling {
     int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float   mirostat_tau       = 5.00f; // target entropy
     float   mirostat_eta       = 0.10f; // learning rate
-    bool    penalize_nl        = false; // consider newlines as a repeatable token
     bool    ignore_eos         = false;
     bool    no_perf            = false; // disable performance metrics
     bool    timing_per_token   = false;
@@ -139,6 +139,7 @@ struct common_params_sampling {
 
 
     std::vector<enum common_sampler_type> samplers = {
+        COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
@@ -193,11 +194,13 @@ struct common_params {
     float   defrag_thold          =  0.1f; // KV cache defragmentation threshold
 
     // offload params
-    std::vector<ggml_backend_dev_t> devices;         // devices to use for offloading
-    int32_t n_gpu_layers                    =    -1; // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu                        =     0; // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]               =   {0}; // how split tensors should be distributed across GPUs
-    enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
+    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
+    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
+    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
+
+    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 0c4699a89..e83a971c7 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -161,32 +161,20 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.size(),
                 params.logit_bias.data()));
 
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_penalties(
-                llama_n_vocab  (model),
-                llama_token_eos(model),
-                llama_token_nl (model),
-                params.penalty_last_n,
-                params.penalty_repeat,
-                params.penalty_freq,
-                params.penalty_present,
-                params.penalize_nl,
-                params.ignore_eos));
-
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
-                    case COMMON_SAMPLER_TYPE_DRY:
+                case COMMON_SAMPLER_TYPE_DRY:
                     {
-                        std::vector<const char*> c_breakers;
+                        std::vector<const char *> c_breakers;
                         c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto& str : params.dry_sequence_breakers) {
+                        for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
 
                         llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
-                        break;
+                    break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
                     break;
@@ -208,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 case COMMON_SAMPLER_TYPE_INFILL:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
                     break;
+                case COMMON_SAMPLER_TYPE_PENALTIES:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
@@ -415,6 +406,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
         default : return '?';
     }
 }
@@ -429,6 +421,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
         default : return "";
     }
 }
@@ -443,6 +436,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
+        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     // since samplers names are written multiple ways
@@ -489,6 +483,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     std::vector<common_sampler_type> samplers;
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index ba219cd4b..e2e01f2d5 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -65,6 +65,7 @@ int main(int argc, char ** argv) {
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();
+    sparams.no_perf = false;
 
     llama_sampler * smpl = llama_sampler_chain_init(sparams);
 
diff --git a/examples/main/README.md b/examples/main/README.md
index 7787f7b11..17d80a622 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -177,16 +177,11 @@ Example usage: `--temp 0`
 
 -   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
 -   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
--   `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
 
 The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
 
 The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
 
-Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
-
-Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
-
 ### DRY Repetition Penalty
 
 DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
diff --git a/examples/server/README.md b/examples/server/README.md
index a9443e56b..63a7bf43a 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -104,7 +104,6 @@ The project is under active development, and we are [looking for feedback and co
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--penalize-nl` | penalize newline tokens (default: false) |
 | `--temp N` | temperature (default: 0.8) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
 | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
@@ -393,8 +392,6 @@ These words will not be included in the completion, so make sure to add them to
 
 `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
 
-`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
-
 `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
 
 `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
@@ -655,7 +652,6 @@ This endpoint is public (no API key check). By default, it is read-only. To make
       "mirostat": 0,
       "mirostat_tau": 5.0,
       "mirostat_eta": 0.10000000149011612,
-      "penalize_nl": false,
       "stop": [],
       "max_tokens": -1,
       "n_keep": 0,
@@ -845,7 +841,6 @@ Example:
       "mirostat": 0,
       "mirostat_tau": 5.0,
       "mirostat_eta": 0.10000000149011612,
-      "penalize_nl": false,
       "stop": [],
       "max_tokens": -1,
       "n_keep": 0,
diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index d7816b04fad757c640aebf53123c1ef7e26dc9d4..53d33feb344cd92d89ab2ca1f59e77722d7e6cfa 100644
GIT binary patch
delta 865704
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index 8bfa380e5..cbfbbdf28 100644
--- a/examples/server/public_legacy/index-new.html
+++ b/examples/server/public_legacy/index-new.html
@@ -39,7 +39,6 @@
       temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
       repeat_last_n: 0, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.0, // 1.0 = disabled
-      penalize_nl: false, // true only useful for infinite completion
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
diff --git a/examples/server/public_legacy/index.html b/examples/server/public_legacy/index.html
index a95f5c6df..75f39330a 100644
--- a/examples/server/public_legacy/index.html
+++ b/examples/server/public_legacy/index.html
@@ -303,7 +303,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@@ -1006,7 +1005,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index aa8c31259..bc0d042ae 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -135,7 +135,6 @@ struct slot_params {
             {"mirostat",                  sampling.mirostat},
             {"mirostat_tau",              sampling.mirostat_tau},
             {"mirostat_eta",              sampling.mirostat_eta},
-            {"penalize_nl",               sampling.penalize_nl},
             {"stop",                      antiprompt},
             {"max_tokens",                n_predict}, // User configured n_predict
             {"n_keep",                    n_keep},
@@ -184,6 +183,7 @@ struct server_task {
 
     static slot_params params_from_json_cmpl(
             const llama_model * model,
+            const llama_context * ctx,
             const common_params & params_base,
             const json & data) {
         slot_params params;
@@ -226,7 +226,6 @@ struct server_task {
         params.sampling.mirostat           = json_value(data, "mirostat",           defaults.sampling.mirostat);
         params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",       defaults.sampling.mirostat_tau);
         params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",       defaults.sampling.mirostat_eta);
-        params.sampling.penalize_nl        = json_value(data, "penalize_nl",        defaults.sampling.penalize_nl);
         params.sampling.seed               = json_value(data, "seed",               defaults.sampling.seed);
         params.sampling.n_probs            = json_value(data, "n_probs",            defaults.sampling.n_probs);
         params.sampling.min_keep           = json_value(data, "min_keep",           defaults.sampling.min_keep);
@@ -239,8 +238,27 @@ struct server_task {
         params.speculative.n_min = std::max(params.speculative.n_min, 2);
         params.speculative.n_max = std::max(params.speculative.n_max, 0);
 
+        // TODO: add more sanity checks for the input parameters
+
+        if (params.sampling.penalty_last_n < -1) {
+            throw std::runtime_error("Error: repeat_last_n must be >= -1");
+        }
+
+        if (params.sampling.dry_penalty_last_n < -1) {
+            throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
+        }
+
+        if (params.sampling.penalty_last_n == -1) {
+            // note: should be the slot's context and not the full context, but it's ok
+            params.sampling.penalty_last_n = llama_n_ctx(ctx);
+        }
+
+        if (params.sampling.dry_penalty_last_n == -1) {
+            params.sampling.dry_penalty_last_n = llama_n_ctx(ctx);
+        }
+
         if (params.sampling.dry_base < 1.0f) {
-           params.sampling.dry_base = defaults.sampling.dry_base;
+            params.sampling.dry_base = defaults.sampling.dry_base;
         }
 
         // sequence breakers for DRY
@@ -1469,7 +1487,7 @@ struct server_context {
         n_ctx = llama_n_ctx(ctx);
 
         add_bos_token = llama_add_bos_token(model);
-        has_eos_token = !llama_add_eos_token(model);
+        has_eos_token = llama_token_eos(model) != LLAMA_TOKEN_NULL;
 
         if (!params_base.speculative.model.empty()) {
             SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str());
@@ -3381,7 +3399,7 @@ int main(int argc, char ** argv) {
                 task.index = i;
 
                 task.prompt_tokens    = std::move(tokenized_prompts[i]);
-                task.params           = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.params_base, data);
+                task.params           = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.ctx, ctx_server.params_base, data);
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
                 // OAI-compat
diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html
index 2797c37c9..3fb88fcc8 100644
--- a/examples/server/themes/buttons-top/index.html
+++ b/examples/server/themes/buttons-top/index.html
@@ -222,7 +222,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -779,7 +778,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html
index dbe23c402..73f36d4b2 100644
--- a/examples/server/themes/wild/index.html
+++ b/examples/server/themes/wild/index.html
@@ -225,7 +225,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -782,7 +781,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index a16df1ccc..441fd7351 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -33,7 +33,7 @@ const CONFIG_DEFAULT = {
   systemMessage: 'You are a helpful assistant.',
   showTokensPerSecond: false,
   // make sure these default values are in sync with `common.h`
-  samplers: 'dkypmxt',
+  samplers: 'edkypmxt',
   temperature: 0.8,
   dynatemp_range: 0.0,
   dynatemp_exponent: 1.0,
diff --git a/include/llama.h b/include/llama.h
index c67988a33..efbb27d21 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1139,16 +1139,12 @@ extern "C" {
                           const char * grammar_str,
                           const char * grammar_root);
 
+    /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
-                             int32_t   n_vocab,         // llama_n_vocab()
-                         llama_token   special_eos_id,  // llama_token_eos()
-                         llama_token   linefeed_id,     // llama_token_nl()
-                             int32_t   penalty_last_n,  // last n tokens to penalize (0 = disable penalty, -1 = context size)
-                               float   penalty_repeat,  // 1.0 = disabled
-                               float   penalty_freq,    // 0.0 = disabled
-                               float   penalty_present, // 0.0 = disabled
-                                bool   penalize_nl,     // consider newlines as a repeatable token
-                                bool   ignore_eos);     // ignore the end-of-sequence token
+                             int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
+                               float   penalty_repeat,   // 1.0 = disabled
+                               float   penalty_freq,     // 0.0 = disabled
+                               float   penalty_present); // 0.0 = disabled
 
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
     LLAMA_API struct llama_sampler *    llama_sampler_init_dry(
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index fd8ca8a9e..bebff77cf 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1396,19 +1396,15 @@ struct llama_sampler * llama_sampler_init_grammar_impl(const struct llama_vocab
 // penalties
 
 struct llama_sampler_penalties {
-    const int32_t     n_vocab;
-    const llama_token special_eos_id;
-    const llama_token linefeed_id;
-
     const int32_t penalty_last_n;
     const float   penalty_repeat;
     const float   penalty_freq;
     const float   penalty_present;
 
-    const bool    penalize_nl;
-    const bool    ignore_eos;
-
     ring_buffer<llama_token> prev;
+
+    // a frequency map to count token occurrences
+    std::unordered_map<llama_token, int> token_count;
 };
 
 static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) {
@@ -1421,76 +1417,50 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to
         return;
     }
 
+    ctx->token_count[token]++;
+
+    // if the ring buffer is full, remove the oldest token
+    if (ctx->prev.size() >= (size_t) ctx->penalty_last_n) {
+        const auto old = ctx->prev.front();
+
+        ctx->token_count[old]--;
+        if (ctx->token_count[old] == 0) {
+            ctx->token_count.erase(old);
+        }
+    }
+
     ctx->prev.push_back(token);
+
+#if 0
+    // sanity check
+    std::unordered_map<llama_token, int> tmp;
+    for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
+        tmp[ctx->prev.rat(i)]++;
+    }
+
+    assert(ctx->token_count == tmp);
+#endif
 }
 
 static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_penalties *) smpl->ctx;
 
-    if (ctx->ignore_eos) {
-        assert(ctx->special_eos_id >= 0);
-
-        // optimistically check if the candidates are not yet sorted/shuffled/truncated
-        if (cur_p->size > (size_t) ctx->special_eos_id && cur_p->data[ctx->special_eos_id].id == ctx->special_eos_id) {
-            cur_p->data[ctx->special_eos_id].logit = -INFINITY;
-        } else {
-            // else, search for the special EOS token
-            for (size_t i = 0; i < cur_p->size; ++i) {
-                if (cur_p->data[i].id == ctx->special_eos_id) {
-                    cur_p->data[i].logit = -INFINITY;
-                    break;
-                }
-            }
-        }
-    }
-
     if ((ctx->penalty_last_n == 0) ||
         (ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) {
         return;
     }
 
-    bool nl_found = false;
-    size_t nl_idx = 0;
-    float nl_logit = -INFINITY;
-    if (!ctx->penalize_nl) {
-        assert(ctx->linefeed_id >= 0);
-
-        // optimistically check if the candidates are not yet sorted/shuffled/truncated
-        if (cur_p->size > (size_t) ctx->linefeed_id && cur_p->data[ctx->linefeed_id].id == ctx->linefeed_id) {
-            nl_found = true;
-            nl_idx = ctx->linefeed_id;
-            nl_logit = cur_p->data[ctx->linefeed_id].logit;
-        } else {
-            // else, search for the linefeed token
-            for (size_t i = 0; i < cur_p->size; ++i) {
-                if (cur_p->data[i].id == ctx->linefeed_id) {
-                    nl_found = true;
-                    nl_idx = i;
-                    nl_logit = cur_p->data[i].logit;
-                    break;
-                }
-            }
-        }
-    }
-
-    // Create a frequency map to count occurrences of each token in last_tokens
-    // TODO: optimize this by maintaining the token count in the sampler context
-    using llama_token_cnt = std::unordered_map<llama_token, int>;
-    llama_token_cnt token_count;
-
-    for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
-        token_count[ctx->prev.rat(i)]++;
-    }
-
     // Apply frequency and presence penalties to the cur_p
     for (size_t i = 0; i < cur_p->size; ++i) {
-        const auto token_iter = token_count.find(cur_p->data[i].id);
-        if (token_iter == token_count.end()) {
+        const auto token_iter = ctx->token_count.find(cur_p->data[i].id);
+        if (token_iter == ctx->token_count.end()) {
             continue;
         }
 
         const int count = token_iter->second;
 
+        assert(count > 0 && count <= ctx->penalty_last_n);
+
         // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
         // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
         if (cur_p->data[i].logit <= 0) {
@@ -1503,30 +1473,21 @@ static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_tok
     }
 
     cur_p->sorted = false;
-
-    if (!ctx->penalize_nl && nl_found) {
-        // restore the logit of the newline token if it was penalized
-        cur_p->data[nl_idx].logit = nl_logit;
-    }
 }
 
 static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_penalties *) smpl->ctx;
     ctx->prev.clear();
+    ctx->token_count.clear();
 }
 
 static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
     auto * result = llama_sampler_init_penalties(
-            ctx->n_vocab,
-            ctx->special_eos_id,
-            ctx->linefeed_id,
             ctx->penalty_last_n,
             ctx->penalty_repeat,
             ctx->penalty_freq,
-            ctx->penalty_present,
-            ctx->penalize_nl,
-            ctx->ignore_eos);
+            ctx->penalty_present);
 
     // copy the state
     {
@@ -1552,38 +1513,21 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
-        int32_t n_vocab,
-        llama_token special_eos_id,
-        llama_token linefeed_id,
         int32_t penalty_last_n,
         float penalty_repeat,
         float penalty_freq,
-        float penalty_present,
-        bool penalize_nl,
-        bool ignore_eos) {
-    if (linefeed_id == LLAMA_TOKEN_NULL) {
-        penalize_nl = true;
-    }
-
-    if (special_eos_id == LLAMA_TOKEN_NULL) {
-        ignore_eos = false;
-    }
-
+        float penalty_present) {
     penalty_last_n = std::max(penalty_last_n, 0);
 
     return new llama_sampler {
         /* .iface = */ &llama_sampler_penalties_i,
         /* .ctx   = */ new llama_sampler_penalties {
-            /* .n_vocab         = */ n_vocab,
-            /* .special_eos_id  = */ special_eos_id,
-            /* .linefeed_id     = */ linefeed_id,
             /* .penalty_last_n  = */ penalty_last_n,
             /* .penalty_repeat  = */ penalty_repeat,
             /* .penalty_freq    = */ penalty_freq,
             /* .penalty_present = */ penalty_present,
-            /* .penalize_nl     = */ penalize_nl,
-            /* .ignore_eos      = */ ignore_eos,
             /* .prev            = */ ring_buffer<llama_token>(penalty_last_n),
+            /* .token_count     = */ {},
         },
     };
 }
@@ -1611,7 +1555,8 @@ static void get_overlapping_token_sequences(const llama_vocab & vocab, const std
         if (word.find(str) != std::string::npos) {
             token_sequences.emplace(token_id, std::vector<llama_token>());
         } else {
-            size_t word_len = word.size(), str_len = str.size();
+            size_t word_len = word.size();
+            size_t str_len = str.size();
             size_t pos = -1;
             while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
                 bool match = true;
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index e5c9e75e4..c0dcb4848 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -145,7 +145,7 @@ static void test_penalties(
     sampler_tester tester(probs, probs_expected);
 
     const size_t n_vocab = probs.size();
-    auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false);
+    auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
 
     for (size_t i = 0; i < last_tokens.size(); i++) {
         llama_sampler_accept(sampler, last_tokens[i]);

From 08ea539df211e46bb4d0dd275e541cb591d5ebc8 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 16 Dec 2024 12:31:45 +0200
Subject: [PATCH 031/376] unicode : improve naming style (#10838)

* unicode : improve naming style

ggml-ci

* cont [no ci]
---
 src/llama-vocab.cpp |   2 +-
 src/unicode.cpp     | 102 ++++++++++++++++++++++----------------------
 src/unicode.h       |  19 ++++-----
 3 files changed, 61 insertions(+), 62 deletions(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 8c9aaf5a0..e38e59853 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -738,7 +738,7 @@ struct llm_tokenizer_wpm_session {
         std::vector<std::string> words(1, "");
 
         for (const uint32_t cpt : cpts_nfd) {
-            const auto flags = unicode_cpt_flags(cpt);
+            const auto flags = unicode_cpt_flags_from_cpt(cpt);
 
             if (flags.is_whitespace) {
                 if (words.back().size()) {  // finish previous word if any
diff --git a/src/unicode.cpp b/src/unicode.cpp
index 3d4592635..8ed6b1a51 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -71,15 +71,15 @@ uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
     throw std::invalid_argument("failed to convert utf8 to codepoint");
 }
 
-//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cp) {
+//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cpt) {
 //    std::vector<uint16_t> result;
-//    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
-//        result.emplace_back(cp);
+//    if (/* 0x0000 <= cpt && */ cpt <= 0xffff) {
+//        result.emplace_back(cpt);
 //        return result;
 //    }
-//    if (0x10000 <= cp && cp <= 0x10ffff) {
-//        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
-//        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
+//    if (0x10000 <= cpt && cpt <= 0x10ffff) {
+//        result.emplace_back(0xd800 | ((cpt - 0x10000) >> 10));
+//        result.emplace_back(0xdc00 | ((cpt - 0x10000) & 0x03ff));
 //        return result;
 //    }
 //    throw std::invalid_argument("failed to convert codepoint to utf16");
@@ -120,8 +120,8 @@ uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
 //    return result;
 //}
 
-static std::vector<codepoint_flags> unicode_cpt_flags_array() {
-    std::vector<codepoint_flags> cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED);
+static std::vector<unicode_cpt_flags> unicode_cpt_flags_array() {
+    std::vector<unicode_cpt_flags> cpt_flags(MAX_CODEPOINTS, unicode_cpt_flags::UNDEFINED);
 
     assert (unicode_ranges_flags.begin()[0].first == 0);
     assert (unicode_ranges_flags.begin()[unicode_ranges_flags.size()-1].first == MAX_CODEPOINTS);
@@ -253,8 +253,8 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
         };
 
-        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
+        auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
         };
 
         size_t _prev_end = offset_ini;
@@ -371,8 +371,8 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
         };
 
-        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
+        auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
         };
 
         size_t _prev_end = offset_ini;
@@ -572,29 +572,29 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
 // interface
 //
 
-std::string unicode_cpt_to_utf8(uint32_t cp) {
+std::string unicode_cpt_to_utf8(uint32_t cpt) {
     std::string result;
 
-    if (/* 0x00 <= cp && */ cp <= 0x7f) {
-        result.push_back(cp);
+    if (/* 0x00 <= cpt && */ cpt <= 0x7f) {
+        result.push_back(cpt);
         return result;
     }
-    if (0x80 <= cp && cp <= 0x7ff) {
-        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
-        result.push_back(0x80 | (cp & 0x3f));
+    if (0x80 <= cpt && cpt <= 0x7ff) {
+        result.push_back(0xc0 | ((cpt >> 6) & 0x1f));
+        result.push_back(0x80 | (cpt & 0x3f));
         return result;
     }
-    if (0x800 <= cp && cp <= 0xffff) {
-        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
+    if (0x800 <= cpt && cpt <= 0xffff) {
+        result.push_back(0xe0 | ((cpt >> 12) & 0x0f));
+        result.push_back(0x80 | ((cpt >> 6) & 0x3f));
+        result.push_back(0x80 | (cpt & 0x3f));
         return result;
     }
-    if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.push_back(0xf0 | ((cp >> 18) & 0x07));
-        result.push_back(0x80 | ((cp >> 12) & 0x3f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
+    if (0x10000 <= cpt && cpt <= 0x10ffff) {
+        result.push_back(0xf0 | ((cpt >> 18) & 0x07));
+        result.push_back(0x80 | ((cpt >> 12) & 0x3f));
+        result.push_back(0x80 | ((cpt >> 6) & 0x3f));
+        result.push_back(0x80 | (cpt & 0x3f));
         return result;
     }
 
@@ -624,19 +624,19 @@ std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
     return result;
 }
 
-codepoint_flags unicode_cpt_flags(const uint32_t cp) {
-    static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+unicode_cpt_flags unicode_cpt_flags_from_cpt(const uint32_t cpt) {
+    static const unicode_cpt_flags undef(unicode_cpt_flags::UNDEFINED);
     static const auto cpt_flags = unicode_cpt_flags_array();
-    return cp < cpt_flags.size() ? cpt_flags[cp] : undef;
+    return cpt < cpt_flags.size() ? cpt_flags[cpt] : undef;
 }
 
-codepoint_flags unicode_cpt_flags(const std::string & utf8) {
-    static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+unicode_cpt_flags unicode_cpt_flags_from_utf8(const std::string & utf8) {
+    static const unicode_cpt_flags undef(unicode_cpt_flags::UNDEFINED);
     if (utf8.empty()) {
         return undef;  // undefined
     }
     size_t offset = 0;
-    return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset));
+    return unicode_cpt_flags_from_cpt(unicode_cpt_from_utf8(utf8, offset));
 }
 
 std::string unicode_byte_to_utf8(uint8_t byte) {
@@ -649,41 +649,41 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) {
     return map.at(utf8);
 }
 
-uint32_t unicode_tolower(uint32_t cp) {
+uint32_t unicode_tolower(uint32_t cpt) {
     // binary search
-    auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cp,
+    auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cpt,
         [](const std::pair<uint32_t, uint32_t> & pair, uint32_t value) {
             return pair.first < value;
         });
-    if (it != unicode_map_lowercase.end() && it->first == cp) {
+    if (it != unicode_map_lowercase.end() && it->first == cpt) {
         return it->second;
     }
-    return cp;  // Return the original code point if no lowercase mapping is found
+    return cpt;  // Return the original code point if no lowercase mapping is found
 }
 
 std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
     // unicode categories
     static const std::map<std::string, int> k_ucat_enum = {
-        { "\\p{N}", codepoint_flags::NUMBER },
-        { "\\p{L}", codepoint_flags::LETTER },
-        { "\\p{P}", codepoint_flags::PUNCTUATION },
+        { "\\p{N}", unicode_cpt_flags::NUMBER },
+        { "\\p{L}", unicode_cpt_flags::LETTER },
+        { "\\p{P}", unicode_cpt_flags::PUNCTUATION },
     };
 
     static const std::map<int, int> k_ucat_cpt = {
-        { codepoint_flags::NUMBER,        0xD1 },
-        { codepoint_flags::LETTER,        0xD2 },
-        { codepoint_flags::PUNCTUATION,   0xD3 },
+        { unicode_cpt_flags::NUMBER,      0xD1 },
+        { unicode_cpt_flags::LETTER,      0xD2 },
+        { unicode_cpt_flags::PUNCTUATION, 0xD3 },
     };
 
     static const std::map<int, std::string> k_ucat_map = {
-        { codepoint_flags::NUMBER,        "\x30-\x39" }, // 0-9
-        { codepoint_flags::LETTER,        "\x41-\x5A\x61-\x7A" }, // A-Za-z
-        { codepoint_flags::PUNCTUATION,   "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
+        { unicode_cpt_flags::NUMBER,      "\x30-\x39" }, // 0-9
+        { unicode_cpt_flags::LETTER,      "\x41-\x5A\x61-\x7A" }, // A-Za-z
+        { unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
     };
 
     // compute collapsed codepoints only if needed by at least one regex
     bool need_collapse = false;
-    for (auto & regex_expr : regex_exprs) {
+    for (const auto & regex_expr : regex_exprs) {
         // search for unicode categories
         for (const auto & ucat : k_ucat_enum) {
             if (std::string::npos != regex_expr.find(ucat.first)) {
@@ -709,7 +709,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                 continue;
             }
 
-            const auto flags = unicode_cpt_flags(cpts[i]);
+            const auto flags = unicode_cpt_flags_from_cpt(cpts[i]);
 
             if (flags.is_whitespace) {
                 //NOTE: C++ std::regex \s does not mach 0x85, Rust and Python regex does.
@@ -725,7 +725,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
 
     std::vector<size_t> bpe_offsets = { cpts.size() };
 
-    for (auto & regex_expr : regex_exprs) {
+    for (const auto & regex_expr : regex_exprs) {
         // first, see if we have an efficient custom regex implementation
         auto tmp = unicode_regex_split_custom(text, regex_expr, bpe_offsets);
 
@@ -739,7 +739,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
             // if a unicode category is used in the regex, we use the collapsed text and replace the unicode category
             // with the corresponding collapsed representation
             bool use_collapsed = false;
-            for (auto & ucat : k_ucat_enum) {
+            for (const auto & ucat : k_ucat_enum) {
                 if (std::string::npos != regex_expr.find(ucat.first)) {
                     use_collapsed = true;
                     break;
@@ -805,7 +805,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                 // std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
                 std::wstring wtext(cpts.begin(), cpts.end());
                 for (size_t i = 0; i < wtext.size(); ++i) {
-                    if (wtext[i] > 0x7F && unicode_cpt_flags(wtext[i]).is_whitespace) {
+                    if (wtext[i] > 0x7F && unicode_cpt_flags_from_cpt(wtext[i]).is_whitespace) {
                         wtext[i] = 0x0B;
                     }
                 }
diff --git a/src/unicode.h b/src/unicode.h
index 008532a24..c27098df7 100644
--- a/src/unicode.h
+++ b/src/unicode.h
@@ -4,9 +4,7 @@
 #include <string>
 #include <vector>
 
-// TODO: prefix all symbols with "llama_"
-
-struct codepoint_flags {
+struct unicode_cpt_flags {
     enum {
         UNDEFINED       = 0x0001,
         NUMBER          = 0x0002,  // regex: \p{N}
@@ -35,7 +33,7 @@ struct codepoint_flags {
     uint16_t is_nfd         : 1;
 
     // decode from uint16
-    inline codepoint_flags(const uint16_t flags=0) {
+    inline unicode_cpt_flags(const uint16_t flags = 0) {
         *reinterpret_cast<uint16_t*>(this) = flags;
     }
 
@@ -50,18 +48,19 @@ struct codepoint_flags {
 
 size_t unicode_len_utf8(char src);
 
-std::string unicode_cpt_to_utf8(uint32_t cp);
-uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
+std::string unicode_cpt_to_utf8  (uint32_t cpt);
+uint32_t    unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
+
 std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
 std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
 
-codepoint_flags unicode_cpt_flags(const uint32_t cp);
-codepoint_flags unicode_cpt_flags(const std::string & utf8);
+unicode_cpt_flags unicode_cpt_flags_from_cpt (uint32_t cpt);
+unicode_cpt_flags unicode_cpt_flags_from_utf8(const std::string & utf8);
 
 std::string unicode_byte_to_utf8(uint8_t byte);
-uint8_t unicode_utf8_to_byte(const std::string & utf8);
+uint8_t     unicode_utf8_to_byte(const std::string & utf8);
 
-uint32_t unicode_tolower(uint32_t cp);
+uint32_t unicode_tolower(uint32_t cpt);
 
 std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);

From 160bc039c862c10b9938269a90ddb09d794a7b0d Mon Sep 17 00:00:00 2001
From: Zhiyuan Li <uniartisan2017@gmail.com>
Date: Tue, 17 Dec 2024 05:00:46 +0800
Subject: [PATCH 032/376] rwkv6: add wkv6 support for Vulkan backend (#10829)

* rwkv_wkv6 vulkan shader

* RWKV_WKV6 Vulkan op tests passed

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Apply code format changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* add [[unroll]] and remove unnecessary conditions

* add uma support

* fix erros in EditorConfig Checker

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Molly Sophia <mollysophia379@gmail.com>
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 157 +++++++++++++++++-
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   2 +
 ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp |  87 ++++++++++
 3 files changed, 245 insertions(+), 1 deletion(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a26a8fe09..08944d76c 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -245,6 +245,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
     vk_pipeline pipeline_timestep_embedding_f32;
     vk_pipeline pipeline_pool2d_f32;
+    vk_pipeline pipeline_rwkv_wkv6_f32;
 
     // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
     vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
@@ -528,6 +529,13 @@ struct vk_op_pool2d_push_constants {
     int32_t p0; int32_t p1;
 };
 
+struct vk_op_rwkv_wkv6_push_constants {
+    uint32_t B;
+    uint32_t T;
+    uint32_t C;
+    uint32_t H;
+};
+
 // Allow pre-recording command buffers
 struct vk_staging_memcpy {
     vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@@ -2014,6 +2022,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
 
+    ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
+
     for (auto &c : compiles) {
         c.wait();
     }
@@ -5022,6 +5032,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_pool2d_f32;
         }
         return nullptr;
+    case GGML_OP_RWKV_WKV6:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_rwkv_wkv6_f32;
+        }
+        return nullptr;
     case GGML_OP_LEAKY_RELU:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_leaky_relu_f32;
@@ -5424,6 +5439,134 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }, dryrun);
 }
 
+static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, bool dryrun = false) {
+    const ggml_tensor * k = dst->src[0];
+    const ggml_tensor * v = dst->src[1];
+    const ggml_tensor * r = dst->src[2];
+    const ggml_tensor * tf = dst->src[3];
+    const ggml_tensor * td = dst->src[4];
+    const ggml_tensor * state = dst->src[5];
+
+    GGML_ASSERT(!ggml_is_quantized(k->type));
+    GGML_ASSERT(!ggml_is_quantized(v->type));
+    GGML_ASSERT(!ggml_is_quantized(r->type));
+    GGML_ASSERT(!ggml_is_quantized(tf->type));
+    GGML_ASSERT(!ggml_is_quantized(td->type));
+    GGML_ASSERT(!ggml_is_quantized(state->type));
+    GGML_ASSERT(dst->buffer != nullptr);
+
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, k, v, r, dst, GGML_OP_RWKV_WKV6);
+    GGML_ASSERT(pipeline != nullptr);
+
+    if (dryrun) {
+        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        return;
+    }
+
+    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
+    ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context;
+    ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context;
+    ggml_backend_vk_buffer_context * r_buf_ctx = (ggml_backend_vk_buffer_context *)r->buffer->context;
+    ggml_backend_vk_buffer_context * tf_buf_ctx = (ggml_backend_vk_buffer_context *)tf->buffer->context;
+    ggml_backend_vk_buffer_context * td_buf_ctx = (ggml_backend_vk_buffer_context *)td->buffer->context;
+    ggml_backend_vk_buffer_context * state_buf_ctx = (ggml_backend_vk_buffer_context *)state->buffer->context;
+
+    ggml_vk_sync_buffers(subctx);
+
+    vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State;
+    uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset;
+    bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, k->data, d_K, k_offset);
+        ggml_vk_host_get(ctx->device, v->data, d_V, v_offset);
+        ggml_vk_host_get(ctx->device, r->data, d_R, r_offset);
+        ggml_vk_host_get(ctx->device, tf->data, d_TF, tf_offset);
+        ggml_vk_host_get(ctx->device, td->data, d_TD, td_offset);
+        ggml_vk_host_get(ctx->device, state->data, d_State, state_offset);
+        ggml_vk_host_get(ctx->device, dst->data, d_D, dst_offset);
+
+        K_uma = d_K != nullptr;
+        V_uma = d_V != nullptr;
+        R_uma = d_R != nullptr;
+        TF_uma = d_TF != nullptr;
+        TD_uma = d_TD != nullptr;
+        STATE_uma = d_State != nullptr;
+        DST_uma = d_D != nullptr;
+    }
+
+    if (!K_uma) {
+        d_K = k_buf_ctx->dev_buffer;
+        k_offset = vk_tensor_offset(k) + k->view_offs;
+    }
+    if (!V_uma) {
+        d_V = v_buf_ctx->dev_buffer;
+        v_offset = vk_tensor_offset(v) + v->view_offs;
+    }
+    if (!R_uma) {
+        d_R = r_buf_ctx->dev_buffer;
+        r_offset = vk_tensor_offset(r) + r->view_offs;
+    }
+    if (!TF_uma) {
+        d_TF = tf_buf_ctx->dev_buffer;
+        tf_offset = vk_tensor_offset(tf) + tf->view_offs;
+    }
+    if (!TD_uma) {
+        d_TD = td_buf_ctx->dev_buffer;
+        td_offset = vk_tensor_offset(td) + td->view_offs;
+    }
+    if (!STATE_uma) {
+        d_State = state_buf_ctx->dev_buffer;
+        state_offset = vk_tensor_offset(state) + state->view_offs;
+    }
+    if (!DST_uma) {
+        d_D = dst_buf_ctx->dev_buffer;
+        dst_offset = vk_tensor_offset(dst) + dst->view_offs;
+    }
+
+    const uint64_t k_size = ggml_nbytes(k);
+    const uint64_t v_size = ggml_nbytes(v);
+    const uint64_t r_size = ggml_nbytes(r);
+    const uint64_t tf_size = ggml_nbytes(tf);
+    const uint64_t td_size = ggml_nbytes(td);
+    const uint64_t state_size = ggml_nbytes(state);
+    const uint64_t dst_size = ggml_nbytes(dst);
+
+    std::array<uint32_t, 3> elements = {
+        (uint32_t)(pc.B * pc.H),
+        1,
+        1
+    };
+
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {
+        vk_subbuffer{ d_K, k_offset, k_size },
+        vk_subbuffer{ d_V, v_offset, v_size },
+        vk_subbuffer{ d_R, r_offset, r_size },
+        vk_subbuffer{ d_TF, tf_offset, tf_size },
+        vk_subbuffer{ d_TD, td_offset, td_size },
+        vk_subbuffer{ d_State, state_offset, state_size },
+        vk_subbuffer{ d_D, dst_offset, dst_size }
+    }, sizeof(vk_op_rwkv_wkv6_push_constants), &pc, elements);
+}
+
+static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) {
+    const size_t seq_length = dst->src[0]->ne[3];
+    const size_t n_embed = dst->ne[0];
+    const size_t n_heads = dst->src[0]->ne[2];
+    const size_t n_seqs = dst->src[5]->ne[1];
+
+    ggml_vk_op_f32_rwkv6(
+        ctx, subctx, dst,
+        {
+            (uint32_t)n_seqs,
+            (uint32_t)seq_length,
+            (uint32_t)n_embed,
+            (uint32_t)n_heads,
+        },
+        dryrun
+    );
+}
+
 static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     int * op_params = (int *)dst->op_params;
 
@@ -6569,6 +6712,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
     case GGML_OP_POOL_2D:
+    case GGML_OP_RWKV_WKV6:
     case GGML_OP_LEAKY_RELU:
     case GGML_OP_FLASH_ATTN_EXT:
         break;
@@ -6768,6 +6912,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_FLASH_ATTN_EXT:
         ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
 
+        break;
+
+    case GGML_OP_RWKV_WKV6:
+        ggml_vk_rwkv_wkv6(ctx, compute_ctx, node, dryrun);
+
         break;
     default:
         return false;
@@ -6848,6 +6997,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
     case GGML_OP_POOL_2D:
+    case GGML_OP_RWKV_WKV6:
     case GGML_OP_LEAKY_RELU:
     case GGML_OP_REPEAT:
         buf = tensor->buffer;
@@ -7724,6 +7874,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_IM2COL:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_POOL_2D:
+        case GGML_OP_RWKV_WKV6:
         case GGML_OP_LEAKY_RELU:
             return true;
         default:
@@ -8300,7 +8451,11 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
     } else if (tensor->op == GGML_OP_LEAKY_RELU) {
         const float * op_params = (const float *)tensor->op_params;
         tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);
-    } else {
+    } else if (tensor->op == GGML_OP_RWKV_WKV6) {
+        tensor_clone = ggml_rwkv_wkv6(ggml_ctx, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3],
+        tensor->src[4], tensor->src[5]);
+    }
+    else {
         std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
         GGML_ABORT("fatal error");
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index c48a228ae..7a0d7285d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -479,6 +479,8 @@ void process_shaders() {
 
     string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 
+    string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
+
     for (auto &c : compiles) {
         c.wait();
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp b/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp
new file mode 100644
index 000000000..35cc6c45f
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp
@@ -0,0 +1,87 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : require
+
+#define BLOCK_SIZE 64
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout(push_constant) uniform Parameters {
+    uint B;
+    uint T;
+    uint C;
+    uint H;
+};
+
+layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
+layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
+layout(binding = 2) readonly buffer RBuf { A_TYPE r[]; };
+layout(binding = 3) readonly buffer TimeFBuf { A_TYPE tf[]; };
+layout(binding = 4) readonly buffer TimeDBuf { A_TYPE td[]; };
+layout(binding = 5) readonly buffer StateBuf { A_TYPE state_in[]; };
+layout(binding = 6) buffer DstBuf { A_TYPE dst[]; };
+
+shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE];
+
+void main() {
+    const uint head_size = BLOCK_SIZE;
+    const uint batch_id = gl_WorkGroupID.x / H;
+    const uint head_id = gl_WorkGroupID.x % H;
+    const uint tid = gl_LocalInvocationID.x;
+
+    const uint state_size = C * head_size;
+    const uint n_seq_tokens = T / B;
+
+    if (batch_id >= B || head_id >= H) {
+        return;
+    }
+
+    A_TYPE state[BLOCK_SIZE];
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+                          + i * head_size + tid];
+    }
+
+    barrier();
+    _tf[tid] = tf[head_id * head_size + tid];
+    barrier();
+
+    const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
+    const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
+
+    for (uint t = start_t; t < end_t; t += C) {
+        barrier();
+        _k[tid] = k[t];
+        _r[tid] = r[t];
+        _td[tid] = td[t];
+        barrier();
+
+        const A_TYPE v_val = v[t];
+        A_TYPE y = 0.0;
+
+        [[unroll]] for (uint j = 0; j < head_size; j += 4) {
+            vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
+            vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
+            vec4 tf_vec = vec4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
+            vec4 td_vec = vec4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
+            vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]);
+
+            vec4 kv = k_vec * v_val;
+
+            vec4 temp = tf_vec * kv + s_vec;
+            y += dot(r_vec, temp);
+
+            s_vec = s_vec * td_vec + kv;
+            state[j] = s_vec.x;
+            state[j+1] = s_vec.y;
+            state[j+2] = s_vec.z;
+            state[j+3] = s_vec.w;
+        }
+
+        dst[t] = y;
+    }
+
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        dst[T * C + batch_id * state_size + head_id * head_size * head_size
+            + i * head_size + tid] = state[i];
+    }
+}

From 7b1ec53f56bb72c49e4c8434157895f94d3709c2 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Tue, 17 Dec 2024 05:52:55 +0000
Subject: [PATCH 033/376] vulkan: bugfixes for small subgroup size systems +
 llvmpipe test (#10809)

* ensure mul mat shaders work on systems with subgroup size less than 32

more fixes

add test

* only s_warptile_mmq needs to be run with 32 threads or more
---
 .github/workflows/build.yml          |  8 +++++++-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 13 +++++++------
 2 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 022d31fb2..a377eff38 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -317,7 +317,7 @@ jobs:
           wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
           sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
           sudo apt-get update -y
-          sudo apt-get install -y build-essential vulkan-sdk
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk
 
       - name: Build
         id: cmake_build
@@ -327,6 +327,12 @@ jobs:
           cmake -DGGML_VULKAN=ON ..
           cmake --build . --config Release -j $(nproc)
 
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
     container: rocm/dev-ubuntu-22.04:6.0.2
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 08944d76c..1696b6e27 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1371,7 +1371,7 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     // Needs to be kept up to date on shader changes
     const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
     const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
-    const uint32_t warps = warptile[0] / device->subgroup_size;
+    const uint32_t warps = warptile[0] / warptile[10];
 
     const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size;
     const uint32_t mmid_row_ids = mul_mat_id ? 3072 * sizeof(uint32_t) : 0;
@@ -1385,8 +1385,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     std::cerr << "ggml_vulkan: Compiling shaders";
 
-    // some shaders require the subgroup size to be 16 or larger
+    // some shaders have a minimum subgroup size
     const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
+    const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);
 
     // mulmat
     std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
@@ -1453,7 +1454,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
         l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, tm_l, tn_l, tk_l, device->subgroup_size };
         m_warptile_mmq = { 128,  64,  64, 32, device->subgroup_size, 32, 2, tm_m, tn_m, tk_m, device->subgroup_size };
-        s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
+        s_warptile_mmq = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
 
         l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
         m_mmq_wg_denoms = m_wg_denoms = { 64,  64, 1 };
@@ -1872,7 +1873,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -1886,7 +1887,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -1900,7 +1901,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);

From 227d7c5a7f4cc7734fde8a4ef4382d613486d3c8 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 17 Dec 2024 09:52:09 +0100
Subject: [PATCH 034/376] server : (UI) fix missing async generator on safari
 (#10857)

* server : (UI) fix missing async generator on safari

* fix
---
 examples/server/public/index.html.gz    | Bin 1205104 -> 1205430 bytes
 examples/server/webui/package-lock.json |   7 +++++++
 examples/server/webui/package.json      |   1 +
 examples/server/webui/src/main.js       |   7 +++++--
 4 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 53d33feb344cd92d89ab2ca1f59e77722d7e6cfa..01dd41811d083cb3dfdf10860c00cc2b1f003832 100644
GIT binary patch
delta 1178064
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z3Tf2w6+6*<p<@AAk>Z>301?64(=LgsDJL2SnEjrD%<d?pgs~aHHdp9t*t5(!zgaGt
zSsG&FbiE<GJhW#_Zkv8@N5h(K{hRHHZoecUi=T&QQa~skM77+(scX9*e@b$9qPTrl
zC$BeUb}9lY$Of$XW#?E{QNref(H_y1rP9yrn;&m1I%aIZY`((od)|#)Sq=4{{~VnO
zjV6L*`97rJRFqkP^DJT!9Dk<}O(K@FW`0v9?fD%CRswQL!woF()>6SjNEpGA`n#MK
z?|7u8R@pD%Z=ql5*XYwHe^jl@sdVauPDa~Tz<)ntT|@k|WxHc#>X;<>!}fPYmgHdO
z+o3nY4@O*VgBC!QcPX59nywQibgD*DLhF)py4IAcShcfynjm$v+ja{k3tQIo&7#IN
zm6o^fB2ySqlkcJ!LeyZeZ!WutiUSfQ!EuB};()ngR?ZH)%#62af7Kq#UTsi+vF%gq
zM{u64M)dylfC&UtCLmE<#G7E;E#;9A0+TAmNt8wmdvR8y#+kvRk7F0?-~X?F|L6by
z&woK*hf(!ndS4S)@0kq%=bF^kyR2P<kx|&naFvvY>r0ySKmYM>|A@K`6LZb_(WDNt
z{E?5o7YX~X|AfBEfAYskF<UF|`@-3ncK6ujDftrVR7^Xch-%_nVJ2L^HUK`mSPNe3
zgdr2qI~^$`1`Z^j^b9slKhijFZ7E9;QzQ)k8QT?ydU^fX_X<o7sLEg}B_9oYsi>!?
zn&qGVgt~Mm9ETN=>D(A5IeT=;pvKSU)=9-)4iUs!U8R|*e?4+Xq8O~~TG&W6H%+R0
z;Tu{|bPtKR;+_;?6ZG;e)j44+SyhIDV-!PXuHH`l5|xy0+AY^*MS}NDB9P@8KAdRD
zka%smrmp*@v#u$#jWLWI?sjMQ`?jAj0(kCkAi6)ls(K|PXonhuH*>AP8|$rxOq@T$
zwSs=ZxZWy1Hl;~aU9C=eNGiJNN~~U@pRz3<Do=%|j7GP4u$LDQeDr3`-vI(I1%daI
Oy!c<Z^ToR--y#5>+?Txo

diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json
index 1d0ab0236..bbebccbf2 100644
--- a/examples/server/webui/package-lock.json
+++ b/examples/server/webui/package-lock.json
@@ -8,6 +8,7 @@
       "name": "webui",
       "version": "0.0.0",
       "dependencies": {
+        "@sec-ant/readable-stream": "^0.6.0",
         "@vscode/markdown-it-katex": "^1.1.1",
         "autoprefixer": "^10.4.20",
         "daisyui": "^4.12.14",
@@ -617,6 +618,12 @@
         "win32"
       ]
     },
+    "node_modules/@sec-ant/readable-stream": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/@sec-ant/readable-stream/-/readable-stream-0.6.0.tgz",
+      "integrity": "sha512-uiBh8DrB5FN35gP6/o8JEhEQ7/ci1jUsOZO/VMUjyvTpjtV54VstOXVj1TvTj/wsT23pfX6butxxh3qufsW3+g==",
+      "license": "MIT"
+    },
     "node_modules/@vscode/markdown-it-katex": {
       "version": "1.1.1",
       "resolved": "https://registry.npmjs.org/@vscode/markdown-it-katex/-/markdown-it-katex-1.1.1.tgz",
diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json
index 48090c6de..2836cce00 100644
--- a/examples/server/webui/package.json
+++ b/examples/server/webui/package.json
@@ -14,6 +14,7 @@
     "vite": "^5.4.10"
   },
   "dependencies": {
+    "@sec-ant/readable-stream": "^0.6.0",
     "@vscode/markdown-it-katex": "^1.1.1",
     "autoprefixer": "^10.4.20",
     "daisyui": "^4.12.14",
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 441fd7351..2a3021ce9 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -5,13 +5,16 @@ import TextLineStream from 'textlinestream';
 
 // math formula rendering
 import 'katex/dist/katex.min.css';
-import markdownItKatexGpt, { renderLatexHTML } from './katex-gpt';
+import markdownItKatexGpt from './katex-gpt';
 import markdownItKatexNormal from '@vscode/markdown-it-katex';
 
 // code highlighting
 import hljs from './highlight-config';
 import daisyuiThemes from 'daisyui/src/theming/themes';
 
+// ponyfill for missing ReadableStream asyncIterator on Safari
+import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
+
 const isDev = import.meta.env.MODE === 'development';
 
 // utility functions
@@ -283,7 +286,7 @@ async function* sendSSEPostRequest(url, fetchOptions) {
   const lines = res.body
     .pipeThrough(new TextDecoderStream())
     .pipeThrough(new TextLineStream());
-  for await (const line of lines) {
+  for await (const line of asyncIterator(lines)) {
     if (isDev) console.log({line});
     if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
       const data = JSON.parse(line.slice(5));

From 4f51968aca049080dc77e26603aa0681ea77fe45 Mon Sep 17 00:00:00 2001
From: Ruan <47767371+ruanych@users.noreply.github.com>
Date: Tue, 17 Dec 2024 17:47:20 +0800
Subject: [PATCH 035/376] readme : update typos (#10863)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index a3b491754..da8d9d166 100644
--- a/README.md
+++ b/README.md
@@ -414,7 +414,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 [^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
-## [`llama-bench`](example/bench)
+## [`llama-bench`](examples/llama-bench)
 
 #### Benchmark the performance of the inference for various parameters.
 

From 382bc7f2e8ffd0b89f23e840d097e21f301197ba Mon Sep 17 00:00:00 2001
From: Billel Mokeddem <billel.mokeddem.ml@gmail.com>
Date: Tue, 17 Dec 2024 19:24:56 +0400
Subject: [PATCH 036/376] llama : add Falcon3 support (#10864)

---
 convert_hf_to_gguf.py        |  6 ++++++
 convert_hf_to_gguf_update.py |  1 +
 src/llama.cpp                | 18 ++++++++++++++++++
 3 files changed, 25 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 9dc1673bc..66e268af6 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -525,6 +525,9 @@ class Model:
             else:
                 token: str = reverse_vocab[i]
                 if token in added_vocab:
+                    # We need to manually encode and decode the added tokens in case special characters
+                    # used for `\n` / `\t` have been manually added in the added tokens
+                    token = tokenizer.decode(tokenizer.encode(token))
                     if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
                         toktypes.append(gguf.TokenType.CONTROL)
                     else:
@@ -571,6 +574,9 @@ class Model:
         if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
             # ref: https://huggingface.co/tiiuae/falcon-7b
             res = "falcon"
+        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
+            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
+            res = "falcon3"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
             res = "bert-bge"
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 88058442f..2ba346640 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -72,6 +72,7 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
diff --git a/src/llama.cpp b/src/llama.cpp
index 8b799e0eb..1cc8a9332 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1612,6 +1612,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
     LLM_CHAT_TEMPLATE_PHI_3,
+    LLM_CHAT_TEMPLATE_FALCON_3,
     LLM_CHAT_TEMPLATE_ZEPHYR,
     LLM_CHAT_TEMPLATE_MONARCH,
     LLM_CHAT_TEMPLATE_GEMMA,
@@ -1644,6 +1645,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
+    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
     { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
     { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
     { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
@@ -6473,6 +6475,11 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "falcon") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
+            } else if (
+                    tokenizer_pre == "falcon3") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
             } else if (
                     tokenizer_pre == "mpt") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
@@ -22219,6 +22226,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         }
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
+    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
+        return LLM_CHAT_TEMPLATE_FALCON_3;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -22371,6 +22380,15 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
+        // Falcon 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
         // zephyr template
         for (auto message : chat) {

From 05c3a444b8b017b01b366b725ba22c740aae6b38 Mon Sep 17 00:00:00 2001
From: krystiancha <krystian@krystianch.com>
Date: Tue, 17 Dec 2024 16:00:24 +0000
Subject: [PATCH 037/376] server : fill usage info in embeddings and rerank
 responses (#10852)

* server : fill usage info in embeddings response

* server : fill usage info in reranking response
---
 examples/server/server.cpp                   | 16 ++++++++---
 examples/server/tests/unit/test_embedding.py | 30 ++++++++++++++++++++
 examples/server/tests/unit/test_rerank.py    | 23 +++++++++++++++
 examples/server/utils.hpp                    | 18 ++++++++----
 4 files changed, 77 insertions(+), 10 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index bc0d042ae..436170a03 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -719,14 +719,17 @@ struct server_task_result_embd : server_task_result {
     int index = 0;
     std::vector<float> embedding;
 
+    int32_t n_tokens;
+
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
         return json {
-            {"index",     index},
-            {"embedding", embedding},
+            {"index",            index},
+            {"embedding",        embedding},
+            {"tokens_evaluated", n_tokens},
         };
     }
 };
@@ -735,14 +738,17 @@ struct server_task_result_rerank : server_task_result {
     int index = 0;
     float score = -1e6;
 
+    int32_t n_tokens;
+
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
         return json {
-            {"index", index},
-            {"score", score},
+            {"index",            index},
+            {"score",            score},
+            {"tokens_evaluated", n_tokens},
         };
     }
 };
@@ -1995,6 +2001,7 @@ struct server_context {
         auto res = std::make_unique<server_task_result_embd>();
         res->id    = slot.id_task;
         res->index = slot.index;
+        res->n_tokens = slot.n_prompt_tokens;
 
         const int n_embd = llama_n_embd(model);
 
@@ -2030,6 +2037,7 @@ struct server_context {
         auto res = std::make_unique<server_task_result_rerank>();
         res->id    = slot.id_task;
         res->index = slot.index;
+        res->n_tokens = slot.n_prompt_tokens;
 
         for (int i = 0; i < batch.n_tokens; ++i) {
             if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py
index fc7c20064..fea1d6510 100644
--- a/examples/server/tests/unit/test_embedding.py
+++ b/examples/server/tests/unit/test_embedding.py
@@ -97,3 +97,33 @@ def test_same_prompt_give_same_result():
         vi = res.body['data'][i]['embedding']
         for x, y in zip(v0, vi):
             assert abs(x - y) < EPSILON
+
+
+@pytest.mark.parametrize(
+    "content,n_tokens",
+    [
+        ("I believe the meaning of life is", 7),
+        ("This is a test", 4),
+    ]
+)
+def test_embedding_usage_single(content, n_tokens):
+    global server
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={"input": content})
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
+
+
+def test_embedding_usage_multiple():
+    global server
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+        ],
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == 2 * 7
diff --git a/examples/server/tests/unit/test_rerank.py b/examples/server/tests/unit/test_rerank.py
index 189bc4c96..7203d7943 100644
--- a/examples/server/tests/unit/test_rerank.py
+++ b/examples/server/tests/unit/test_rerank.py
@@ -53,3 +53,26 @@ def test_invalid_rerank_req(documents):
     })
     assert res.status_code == 400
     assert "error" in res.body
+
+
+@pytest.mark.parametrize(
+    "query,doc1,doc2,n_tokens",
+    [
+        ("Machine learning is", "A machine", "Learning is", 19),
+        ("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
+    ]
+)
+def test_rerank_usage(query, doc1, doc2, n_tokens):
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/rerank", data={
+        "query": query,
+        "documents": [
+            doc1,
+            doc2,
+        ]
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index c6f08bf21..8fffe484a 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -560,6 +560,7 @@ static json oaicompat_completion_params_parse(
 
 static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
     json data = json::array();
+    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & elem : embeddings) {
         data.push_back(json{
@@ -567,14 +568,16 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
             {"index",     i++},
             {"object",    "embedding"}
         });
+
+        n_tokens += json_value(elem, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
         }},
         {"data", data}
     };
@@ -584,20 +587,23 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
 
 static json format_response_rerank(const json & request, const json & ranks) {
     json data = json::array();
+    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & rank : ranks) {
         data.push_back(json{
             {"index",    i++},
             {"relevance_score", json_value(rank, "score", 0.0)},
         });
+
+        n_tokens += json_value(rank, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
         }},
         {"results", data}
     };

From 0006f5a74a59945f22ff966e723c3d566b3ca8d0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 17 Dec 2024 18:35:42 +0200
Subject: [PATCH 038/376] ggml : update ggml_backend_cpu_device_supports_op
 (#10867)

* ggml : fix cpy op for IQ-quants to use reference impl

ggml-ci

* ggml : disable tests involving i-matrix quantization

* ggml : update ggml_backend_cpu_device_supports_op

ggml-ci
---
 ggml/src/ggml-cpu/ggml-cpu.cpp | 3 +++
 tests/test-backend-ops.cpp     | 4 ++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index c390957af..0b6419f83 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -394,8 +394,11 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
     switch (op->op) {
         case GGML_OP_CPY:
             return
+                op->type != GGML_TYPE_IQ3_XXS &&
+                op->type != GGML_TYPE_IQ3_S   &&
                 op->type != GGML_TYPE_IQ2_XXS &&
                 op->type != GGML_TYPE_IQ2_XS  &&
+                op->type != GGML_TYPE_IQ2_S   &&
                 op->type != GGML_TYPE_IQ1_S   &&
                 op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index b9454ba59..ccdd3fb57 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3549,8 +3549,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : all_types) {
-           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
-           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
         }
     }
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {

From 3919da8e335dbfd0741d239932a9b9e72061bf27 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 13 Dec 2024 08:19:38 +0100
Subject: [PATCH 039/376] ggml : add check for grad_accs (ggml/1046)

* ggml : add check for grad_accs

This commit adds a check for grad_accs in ggml_graph_get_grad and
ggml_graph_get_grad_acc functions. This is necessary to avoid segfaults
when grad_accs is not initialized.

The motivation for this change is that I find it nice to be able to
print out a computation graph using ggml_graph_print but this function
segfaults when grad_accs is not initialized:
```console
(gdb) p g1
$2 = (ggml_cgraph *) 0x7ffff66004b0
(gdb) p *g1
$3 = {size = 2048, n_nodes = 1, n_leafs = 2, nodes = 0x7ffff6600500,
grads = 0x0, grad_accs = 0x0, leafs = 0x7ffff6604500,
visited_hash_set = {size = 4099, used = 0x7ffff6610518,
keys = 0x7ffff6608500}, order = GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT}
(gdb) p ggml_graph_print(g1)
=== GRAPH ===
n_nodes = 1

Program received signal SIGSEGV, Segmentation fault.
0x0000555555579775 in ggml_graph_get_grad
(cgraph=0x7ffff66004b0,node=0x7ffff6600340)
    at /ggml/ggml/src/ggml.c:5990
5990  return igrad != GGML_HASHSET_FULL &&
          ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ?
          cgraph->grads[igrad] : NULL;
```

* squash! ggml : add check for grad_accs

Fix the check in ggml_graph_get_grad. The check was incorrectly using
cgraph->grad_accs instead of cgraph->grads.
---
 ggml/src/ggml.c | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 51cc85662..030d93a51 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -6037,12 +6037,12 @@ struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, co
 
 struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
     const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
-    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL;
+    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grads ? cgraph->grads[igrad] : NULL;
 }
 
 struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
     const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
-    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL;
+    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grad_accs ? cgraph->grad_accs[igrad] : NULL;
 }
 
 void ggml_graph_print(const struct ggml_cgraph * cgraph) {

From 130d0c90bd26b25e3a713b17a61a5d4eb0a66405 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sat, 14 Dec 2024 03:23:08 +0100
Subject: [PATCH 040/376] ggml : remove return from ggml_gallocr_allocate_node
 (ggml/1048)

This commit removes the return statement from ggml_gallocr_allocate_node
function.

The motivation behind this change is to make the code more readable and
consistent.
---
 ggml/src/ggml-alloc.c | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index 2b2240be8..8dc8226ac 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -534,7 +534,6 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
         size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
         hn->buffer_id = buffer_id;
         hn->offset = offset;
-        return;
     }
 }
 

From 8dd19a48129d578972ea3d98896edbbf492891a9 Mon Sep 17 00:00:00 2001
From: gn64 <yukikaze.jp@gmail.com>
Date: Mon, 16 Dec 2024 19:34:38 +0900
Subject: [PATCH 041/376] vulkan : fix soft_max.comp division by zero
 (whisper/2633)

This change prevents a division by zero error when p.KY is 0.
---
 ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
index 6e20b6411..a25808e16 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
@@ -32,7 +32,7 @@ shared FLOAT_TYPE vals[BLOCK_SIZE];
 void soft_max(uint num_iters) {
     const uint tid = gl_LocalInvocationID.x;
     const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
-    const uint rowy = rowx % p.KY;
+    const uint rowy = (p.KY > 0) ? (rowx % p.KY) : 0;
 
     if (rowx >= p.nrows_x) {
         return;

From 78f766768d94e9c8b30ce10ca76f568e710d84f7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 17 Dec 2024 18:34:32 +0200
Subject: [PATCH 042/376] cmake : fix "amd64" processor string (whisper/2638)

---
 ggml/src/ggml-cpu/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 0e0556703..5d4732337 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -179,7 +179,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
         endif()
     elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
             (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
+            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
         if (MSVC)
             # instruction set detection for MSVC only
             if (GGML_NATIVE)

From 5437d4aaf5132c879acda0bb67f2f8f71da4c9fe Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 17 Dec 2024 18:36:02 +0200
Subject: [PATCH 043/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 47eae44f7..b4ac38bbf 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-74d66b63eaf207a24f3e93bb922aba131cbf2906
+e6d93f40dffe8733d5d72f1d8fa6b3ca27ae899f

From 081b29bd2a3d91e7772e3910ce223dd63b8d7d26 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 17 Dec 2024 19:09:35 +0100
Subject: [PATCH 044/376] tests: add tests for GGUF (#10830)

---
 ggml/src/ggml-impl.h |   16 +
 ggml/src/ggml.c      |   65 +--
 tests/CMakeLists.txt |    1 +
 tests/test-gguf.cpp  | 1303 ++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 1344 insertions(+), 41 deletions(-)
 create mode 100644 tests/test-gguf.cpp

diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index f961134ed..549772c57 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -551,6 +551,22 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
 #define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
 #define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
 
+// expose GGUF internals for test code
+
+GGML_API size_t gguf_type_size(enum gguf_type type);
+
+GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
+
+struct gguf_buf {
+    void * data;
+    size_t size;
+    size_t offset;
+};
+GGML_API struct gguf_buf gguf_buf_init(size_t size);
+GGML_API void gguf_buf_free(struct gguf_buf buf);
+
+GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 030d93a51..0efd2b2eb 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -6489,7 +6489,7 @@ struct gguf_context {
     void * data;
 };
 
-static size_t gguf_type_size(enum gguf_type type) {
+size_t gguf_type_size(enum gguf_type type) {
     GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT);
     return GGUF_TYPE_SIZE[type];
 }
@@ -6617,13 +6617,7 @@ struct gguf_context * gguf_init_empty(void) {
     return ctx;
 }
 
-struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
-    FILE * file = ggml_fopen(fname, "rb");
-    if (!file) {
-        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
-        return NULL;
-    }
-
+struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
     // offset from start of file
     size_t offset = 0;
 
@@ -6636,7 +6630,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         for (uint32_t i = 0; i < sizeof(magic); i++) {
             if (magic[i] != GGUF_MAGIC[i]) {
                 fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
-                fclose(file);
                 return NULL;
             }
         }
@@ -6647,7 +6640,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
     struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
     if (!ctx) {
         fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
-        fclose(file);
         return NULL;
     }
 
@@ -6665,7 +6657,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
         if (ctx->header.version == 1) {
             fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__);
-            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6678,7 +6669,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
-            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6688,12 +6678,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
     {
         const uint64_t n_kv = ctx->header.n_kv;
 
-        ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
-        if (!ctx->kv) {
-            fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
-            fclose(file);
-            gguf_free(ctx);
-            return NULL;
+        if (n_kv > 0) {
+            ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
+            if (!ctx->kv) {
+                fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
+                gguf_free(ctx);
+                return NULL;
+            }
         }
 
         for (uint64_t i = 0; i < n_kv; ++i) {
@@ -6740,7 +6731,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                     // prevent from integer overflow in the malloc below
                                     if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) {
                                         fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
-                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6748,7 +6738,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                     kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
                                     if (!kv->value.arr.data) {
                                         fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
-                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6760,7 +6749,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                     // prevent from integer overflow in the malloc below
                                     if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) {
                                         fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
-                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6768,7 +6756,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                     kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str));
                                     if (!kv->value.arr.data) {
                                         fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
-                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6799,7 +6786,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
-            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6810,7 +6796,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
         if (!ctx->infos) {
             fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__);
-            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6846,7 +6831,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor info\n", __func__);
-                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6889,7 +6873,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 // this tensor type support have been removed:
                 fprintf(stderr, "%s: tensor '%s' of type %d: %s\n",
                         __func__, info->name.data, (int) info->type, ggml_type_name(info->type));
-                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6897,7 +6880,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             if (ne % ggml_blck_size(info->type) != 0) {
                 fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
                         __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
-                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6929,7 +6911,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         *params.ctx = ggml_init(pdata);
         if (*params.ctx == NULL) {
             fprintf(stderr, "%s: failed to initialize context\n", __func__);
-            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6948,7 +6929,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor data\n", __func__);
-                fclose(file);
                 ggml_free(ctx_data);
                 gguf_free(ctx);
                 return NULL;
@@ -6987,7 +6967,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
-            fclose(file);
             ggml_free(ctx_data);
             gguf_free(ctx);
             return NULL;
@@ -6996,11 +6975,21 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         ggml_set_no_alloc(ctx_data, params.no_alloc);
     }
 
-    fclose(file);
-
     return ctx;
 }
 
+struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
+    FILE * file = ggml_fopen(fname, "rb");
+    if (!file) {
+        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
+        return NULL;
+    }
+
+    struct gguf_context * result = gguf_init_from_file_impl(file, params);
+    fclose(file);
+    return result;
+}
+
 void gguf_free(struct gguf_context * ctx) {
     if (ctx == NULL) {
         return;
@@ -7460,13 +7449,7 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo
 //    fwrite(val, sizeof(char), size, file);
 //}
 
-struct gguf_buf {
-    void * data;
-    size_t size;
-    size_t offset;
-};
-
-static struct gguf_buf gguf_buf_init(size_t size) {
+struct gguf_buf gguf_buf_init(size_t size) {
     struct gguf_buf buf = {
         /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
         /*buf.size   =*/ size,
@@ -7476,7 +7459,7 @@ static struct gguf_buf gguf_buf_init(size_t size) {
     return buf;
 }
 
-static void gguf_buf_free(struct gguf_buf buf) {
+void gguf_buf_free(struct gguf_buf buf) {
     if (buf.data) {
         GGML_FREE(buf.data);
     }
@@ -7514,7 +7497,7 @@ static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_si
     buf->offset += el_size;
 }
 
-static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
+void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
     // write header
     gguf_bwrite_el(buf, &ctx->header.magic,     sizeof(ctx->header.magic));
     gguf_bwrite_el(buf, &ctx->header.version,   sizeof(ctx->header.version));
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index daeed4564..2b5e5fd4a 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -129,6 +129,7 @@ llama_target_and_test(test-arg-parser.cpp)
 llama_target_and_test(test-chat-template.cpp)
 
 # llama_target_and_test(test-opt.cpp) # SLOW
+llama_target_and_test(test-gguf.cpp)
 llama_target_and_test(test-backend-ops.cpp)
 
 llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")
diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp
new file mode 100644
index 000000000..e5b4cb7b8
--- /dev/null
+++ b/tests/test-gguf.cpp
@@ -0,0 +1,1303 @@
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "../ggml/src/ggml-impl.h"
+
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <cstdio>
+#include <random>
+#include <string>
+#include <vector>
+
+constexpr int offset_has_kv      = 1000;
+constexpr int offset_has_tensors = 2000;
+constexpr int offset_has_data    = 3000;
+
+enum handcrafted_file_type {
+    HANDCRAFTED_HEADER_BAD_MAGIC          =  10,
+    HANDCRAFTED_HEADER_BAD_VERSION_1      =  20,
+    HANDCRAFTED_HEADER_BAD_VERSION_FUTURE =  30,
+    HANDCRAFTED_HEADER_BAD_N_TENSORS      =  40,
+    HANDCRAFTED_HEADER_BAD_N_KV           =  50,
+    HANDCRAFTED_HEADER_EMPTY              = 800,
+
+    HANDCRAFTED_KV_BAD_KEY_SIZE           =  10 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_TYPE               =  20 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_VALUE_SIZE         =  30 + offset_has_kv,
+    HANDCRAFTED_KV_DUPLICATE_KEY          =  40 + offset_has_kv,
+    HANDCRAFTED_KV_SUCCESS                = 800 + offset_has_kv,
+
+    HANDCRAFTED_TENSORS_BAD_NAME_SIZE     =  10 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_N_DIMS        =  20 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_SHAPE         =  30 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_NE_TOO_BIG        =  40 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_TYPE          =  50 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_OFFSET        =  60 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_DUPLICATE_NAME    =  70 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_ALIGNMENT     =  80 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_SUCCESS           = 800 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_CUSTOM_ALIGN      = 810 + offset_has_tensors,
+
+    HANDCRAFTED_DATA_NOT_ENOUGH_DATA      =  10 + offset_has_data,
+    HANDCRAFTED_DATA_BAD_ALIGNMENT        =  20 + offset_has_data,
+    HANDCRAFTED_DATA_SUCCESS              = 800 + offset_has_data,
+    HANDCRAFTED_DATA_CUSTOM_ALIGN         = 810 + offset_has_data,
+};
+
+std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
+    switch (hft) {
+        case HANDCRAFTED_HEADER_BAD_MAGIC:          return "HEADER_BAD_MAGIC";
+        case HANDCRAFTED_HEADER_BAD_VERSION_1:      return "HEADER_BAD_VERSION_1";
+        case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE: return "HEADER_BAD_VERSION_FUTURE";
+        case HANDCRAFTED_HEADER_BAD_N_KV:           return "HEADER_BAD_N_KV";
+        case HANDCRAFTED_HEADER_BAD_N_TENSORS:      return "HEADER_BAD_N_TENSORS";
+        case HANDCRAFTED_HEADER_EMPTY:              return "HEADER_EMPTY";
+
+        case HANDCRAFTED_KV_BAD_KEY_SIZE:           return "KV_BAD_KEY_SIZE";
+        case HANDCRAFTED_KV_BAD_TYPE:               return "KV_BAD_TYPE";
+        case HANDCRAFTED_KV_BAD_VALUE_SIZE:         return "KV_BAD_VALUE_SIZE";
+        case HANDCRAFTED_KV_DUPLICATE_KEY:          return "KV_DUPLICATE_KEY";
+        case HANDCRAFTED_KV_SUCCESS:                return "KV_RANDOM_KV";
+
+        case HANDCRAFTED_TENSORS_BAD_NAME_SIZE:     return "TENSORS_BAD_NAME_SIZE";
+        case HANDCRAFTED_TENSORS_BAD_N_DIMS:        return "TENSORS_BAD_N_DIMS";
+        case HANDCRAFTED_TENSORS_BAD_SHAPE:         return "TENSORS_BAD_SHAPE";
+        case HANDCRAFTED_TENSORS_NE_TOO_BIG:        return "TENSORS_NE_TOO_BIG";
+        case HANDCRAFTED_TENSORS_BAD_TYPE:          return "TENSORS_BAD_TYPE";
+        case HANDCRAFTED_TENSORS_BAD_OFFSET:        return "TENSORS_BAD_OFFSET";
+        case HANDCRAFTED_TENSORS_DUPLICATE_NAME:    return "TENSORS_DUPLICATE_NAME";
+        case HANDCRAFTED_TENSORS_BAD_ALIGNMENT:     return "TENSORS_BAD_ALIGNMENT";
+        case HANDCRAFTED_TENSORS_SUCCESS:           return "TENSORS_SUCCESS";
+        case HANDCRAFTED_TENSORS_CUSTOM_ALIGN:      return "TENSORS_CUSTOM_ALIGN";
+
+        case HANDCRAFTED_DATA_NOT_ENOUGH_DATA:      return "DATA_NOT_ENOUGH_DATA";
+        case HANDCRAFTED_DATA_BAD_ALIGNMENT:        return "DATA_BAD_ALIGNMENT";
+        case HANDCRAFTED_DATA_SUCCESS:              return "DATA_SUCCESS";
+        case HANDCRAFTED_DATA_CUSTOM_ALIGN:         return "DATA_CUSTOM_ALIGN";
+    }
+    GGML_ABORT("fatal error");
+}
+
+static bool expect_context_not_null(const enum handcrafted_file_type hft) {
+    if (hft < offset_has_kv) {
+        return hft >= HANDCRAFTED_HEADER_EMPTY;
+    }
+    if (hft < offset_has_tensors) {
+        return hft >= HANDCRAFTED_KV_SUCCESS;
+    }
+    if (hft < offset_has_data) {
+        return hft >= HANDCRAFTED_TENSORS_SUCCESS;
+    }
+    return hft >= HANDCRAFTED_DATA_SUCCESS;
+}
+
+typedef std::pair<enum ggml_type, std::array<int64_t, GGML_MAX_DIMS>> tensor_config_t;
+
+std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
+    std::vector<tensor_config_t> tensor_configs;
+    tensor_configs.reserve(100);
+
+    for (int i = 0; i < 100; ++i) {
+        const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT);
+        if (ggml_type_size(type) == 0) {
+            continue;
+        }
+
+        std::array<int64_t, GGML_MAX_DIMS> shape = {1, 1, 1, 1};
+        shape[0] = (1 + rng() % 10) * ggml_blck_size(type);
+        const int n_dims = 1 + rng() % GGML_MAX_DIMS;
+        for (int i = 1; i < n_dims; ++i) {
+            shape[i] = 1 + rng() % 10;
+        }
+
+        tensor_configs.push_back(std::make_pair(type, shape));
+    }
+
+    return tensor_configs;
+}
+
+std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    kv_types.reserve(100);
+
+    for (int i = 0; i < 100; ++i) {
+        const gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT);
+            if (type_arr == GGUF_TYPE_ARRAY) {
+                continue;
+            }
+            kv_types.push_back(std::make_pair(type, type_arr));
+            continue;
+        }
+
+        kv_types.push_back(std::make_pair(type, gguf_type(-1)));
+    }
+    std::shuffle(kv_types.begin(), kv_types.end(), rng);
+
+    return kv_types;
+}
+
+static void helper_write(const void * data, const size_t nbytes, FILE * file) {
+    GGML_ASSERT(fwrite(data, 1, nbytes, file) == nbytes);
+}
+
+static FILE * get_handcrafted_file(const unsigned int seed, const enum handcrafted_file_type hft, const int extra_bytes = 0) {
+    FILE * file = tmpfile();
+
+    std::mt19937 rng(seed);
+
+    if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) {
+        const char bad_magic[4] = {'F', 'U', 'G', 'G'};
+        helper_write(bad_magic, sizeof(bad_magic), file);
+    } else {
+        helper_write(GGUF_MAGIC, 4, file);
+    }
+
+    if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) {
+        const uint32_t version = 1;
+        helper_write(&version, sizeof(version), file);
+    } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) {
+        const uint32_t version = GGUF_VERSION + 1;
+        helper_write(&version, sizeof(version), file);
+    } else {
+        const uint32_t version = GGUF_VERSION;
+        helper_write(&version, sizeof(version), file);
+    }
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (hft >= offset_has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+
+    if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) {
+        const uint64_t n_tensors = -1;
+        helper_write(&n_tensors, sizeof(n_tensors), file);
+    } else {
+        const uint64_t n_tensors = tensor_configs.size();
+        helper_write(&n_tensors, sizeof(n_tensors), file);
+    }
+
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    if (hft >= offset_has_kv) {
+        kv_types = get_kv_types(rng);
+    }
+    {
+        uint64_t n_kv = kv_types.size();
+        if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+            n_kv += 1;
+        } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
+            n_kv = -1;
+        }
+        helper_write(&n_kv, sizeof(n_kv), file);
+    }
+
+    if (hft < offset_has_kv) {
+        for (int i = 0; i < extra_bytes; ++i) {
+            const char tmp = 0;
+            helper_write(&tmp, sizeof(tmp), file);
+        }
+        rewind(file);
+        return file;
+    }
+
+    for (int i = 0; i < int(kv_types.size()); ++i) {
+        const enum gguf_type type     = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].first);
+        const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].second);
+
+        const std::string key = "my_key_" + std::to_string((hft == HANDCRAFTED_KV_DUPLICATE_KEY ? i/2 : i));
+
+        if (hft == HANDCRAFTED_KV_BAD_KEY_SIZE) {
+            const uint64_t n = -1;
+            helper_write(&n, sizeof(n), file);
+        } else {
+            const uint64_t n = key.length();
+            helper_write(&n, sizeof(n), file);
+        }
+        helper_write(key.data(), key.length(), file);
+
+        {
+            const int32_t type32 = int32_t(type);
+            helper_write(&type32, sizeof(type32), file);
+        }
+
+        uint32_t data[16];
+        for (int j = 0; j < 16; ++j) {
+            data[j] = rng();
+            if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) {
+                data[j] |= 0x01010101; // avoid random null-termination of string
+            }
+        }
+
+        if (type == GGUF_TYPE_STRING) {
+            const uint64_t n = rng() % sizeof(data);
+            helper_write(&n,   sizeof(n), file);
+            helper_write(data,        n,  file);
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            {
+                const int32_t type32 = int32_t(type_arr);
+                helper_write(&type32, sizeof(type32), file);
+            }
+            if (type_arr == GGUF_TYPE_STRING) {
+                const uint64_t nstr = rng() % (16 + 1);
+                helper_write(&nstr, sizeof(nstr), file);
+                for (uint64_t istr = 0; istr < nstr; ++istr) {
+                    const uint64_t n = rng() % (sizeof(uint32_t) + 1);
+                    helper_write(&n,          sizeof(n), file);
+                    helper_write(&data[istr],        n,  file);
+                }
+                continue;
+            }
+            const size_t type_size = gguf_type_size(type_arr);
+            const uint64_t n = (rng() % sizeof(data)) / type_size;
+            helper_write(&n,    sizeof(n),   file);
+            helper_write(&data, n*type_size, file);
+            continue;
+        }
+
+        size_t type_size = hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type);
+        if (hft == HANDCRAFTED_KV_BAD_VALUE_SIZE) {
+            type_size += rng() % 3;
+        }
+        helper_write(data, type_size, file);
+    }
+
+    if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+        const std::string key = "general.alignment";
+        {
+            const uint64_t n = key.length();
+            helper_write(&n, sizeof(n), file);
+        }
+        helper_write(key.data(), key.length(), file);
+
+        const int32_t type = gguf_type(GGUF_TYPE_UINT32);
+        helper_write(&type, sizeof(type), file);
+
+        const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT + 1;
+        helper_write(&alignment, sizeof(alignment), file);
+    }
+
+    if (hft < offset_has_tensors) {
+        for (int i = 0; i < extra_bytes; ++i) {
+            const char tmp = 0;
+            helper_write(&tmp, sizeof(tmp), file);
+        }
+        rewind(file);
+        return file;
+    }
+
+    uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
+    if (hft == HANDCRAFTED_TENSORS_BAD_ALIGNMENT || hft == HANDCRAFTED_DATA_BAD_ALIGNMENT) {
+        alignment -= 1;
+    } else if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+        alignment += 1;
+    }
+
+    uint64_t offset = 0;
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        std::string name = "my_tensor";
+        if (hft != HANDCRAFTED_TENSORS_DUPLICATE_NAME) {
+            name += "_" + std::to_string(i);
+        }
+        if (hft == HANDCRAFTED_TENSORS_BAD_NAME_SIZE) {
+            name += "_with_a_very_long_name_which_is_longer_than_what_is_allowed_for_ggml_tensors";
+            GGML_ASSERT(name.length() >= GGML_MAX_NAME);
+        }
+        {
+            const uint64_t n = name.length();
+            helper_write(&n, sizeof(n), file);
+        }
+        helper_write(name.data(), name.length(), file);
+
+        uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_TOO_BIG ? 2 : 1;
+        for (int i = GGML_MAX_DIMS-1; i >= 1; --i) {
+            if (shape[i] != 1) {
+                n_dims = i + 1;
+                break;
+            }
+        }
+        if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) {
+            const uint32_t n_dims_bad = GGML_MAX_DIMS + 1;
+            helper_write(&n_dims_bad, sizeof(n_dims_bad), file);
+        } else {
+            helper_write(&n_dims,     sizeof(n_dims),     file);
+        }
+
+        if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) {
+            for (uint32_t j = 0; j < n_dims; ++j) {
+                const int64_t bad_dim = -1;
+                helper_write(&bad_dim, sizeof(bad_dim), file);
+            }
+        } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
+            for (uint32_t j = 0; j < n_dims; ++j) {
+                const int64_t big_dim = 4*int64_t(INT32_MAX);
+                helper_write(&big_dim, sizeof(big_dim), file);
+            }
+        } else {
+            helper_write(shape.data(), n_dims*sizeof(int64_t), file);
+        }
+
+        {
+            const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? -1 : int32_t(type);
+            helper_write(&type32, sizeof(type32), file);
+        }
+
+        if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) {
+            const uint64_t bad_offset = -1;
+            helper_write(&bad_offset, sizeof(bad_offset), file);
+        } else {
+            helper_write(&offset, sizeof(offset), file);
+        }
+
+        int64_t ne = shape[0];
+        for (uint32_t i = 1; i < n_dims; ++i) {
+            ne *= shape[i];
+        }
+        offset += GGML_PAD(ggml_row_size(type, ne), alignment);
+    }
+
+    const uint32_t alignment_overshoot = ftell(file) % alignment;
+    if (alignment_overshoot != 0) {
+        for (size_t i = alignment_overshoot; i < alignment; ++i) {
+            const char pad = 0;
+            helper_write(&pad, sizeof(pad), file);
+        }
+    }
+
+    if (hft >= offset_has_data) {
+        rng.seed(seed + 1);
+        uint64_t nbytes = offset;
+        if (hft == HANDCRAFTED_DATA_NOT_ENOUGH_DATA) {
+            nbytes -= 1;
+        }
+        for (uint64_t i = 0; i < nbytes; ++i) {
+            const uint8_t random_byte = i % 256;
+            helper_write(&random_byte, sizeof(random_byte), file);
+        }
+    }
+
+    for (int i = 0; i < extra_bytes; ++i) {
+        const char tmp = 0;
+        helper_write(&tmp, sizeof(tmp), file);
+    }
+    rewind(file);
+    return file;
+}
+
+static bool handcrafted_check_header(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_kv, const bool has_tensors, const bool alignment_defined) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
+    if (has_kv) {
+        kv_types = get_kv_types(rng);
+    }
+
+    bool ok = true;
+
+    if (gguf_get_version(gguf_ctx) != GGUF_VERSION) {
+        ok = false;
+    }
+    if (gguf_get_n_tensors(gguf_ctx) != int(tensor_configs.size())) {
+        ok = false;
+    }
+    if (gguf_get_n_kv(gguf_ctx) != int(alignment_defined ? kv_types.size() + 1 : kv_types.size())) {
+        ok = false;
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_kv(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_tensors, const bool alignment_defined) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs;
+    if (has_tensors) {
+        tensor_configs = get_tensor_configs(rng);
+    }
+
+    std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types = get_kv_types(rng);
+
+    bool ok = true;
+
+    for (int i = 0; i < int(kv_types.size()); ++i) {
+        const enum gguf_type type     = gguf_type(kv_types[i].first);
+        const enum gguf_type type_arr = gguf_type(kv_types[i].second);
+
+        const std::string key = "my_key_" + std::to_string(i);
+
+        uint32_t data[16];
+        for (int j = 0; j < 16; ++j) {
+            data[j] = rng();
+            if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) {
+                data[j] |= 0x01010101; // avoid random null-termination of string
+            }
+        }
+
+        const char * data8 = reinterpret_cast<const char *>(data);
+        const int id = gguf_find_key(gguf_ctx, key.c_str());
+
+        if (type == GGUF_TYPE_STRING) {
+            const char * str = gguf_get_val_str(gguf_ctx, id);
+            const uint64_t n = strlen(str);
+            const uint64_t n_expected = rng() % sizeof(data);
+            if (n != n_expected) {
+                ok = false;
+                continue;
+            }
+            if (!std::equal(str, str + n, data8)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const size_t type_size = gguf_type_size(type_arr);
+            const uint64_t arr_n = gguf_get_arr_n(gguf_ctx, id);
+
+            if (type_arr == GGUF_TYPE_STRING) {
+                const uint64_t nstr_expected = rng() % (16 + 1);
+                if (arr_n != nstr_expected) {
+                    ok = false;
+                    continue;
+                }
+                for (uint64_t istr = 0; istr < nstr_expected; ++istr) {
+                    const char * str = gguf_get_arr_str(gguf_ctx, id, istr);
+                    const uint64_t n = strlen(str);
+                    const uint64_t n_expected = rng() % (sizeof(uint32_t) + 1);
+
+                    if (n != n_expected) {
+                        ok = false;
+                        continue;
+                    }
+                    const char * str_expected = reinterpret_cast<const char *>(&data[istr]);
+                    if (strncmp(str, str_expected, n) != 0) {
+                        ok = false;
+                        continue;
+                    }
+                }
+                continue;
+            }
+
+            const uint64_t arr_n_expected = (rng() % sizeof(data)) / type_size;
+            if (arr_n != arr_n_expected) {
+                ok = false;
+                continue;
+            }
+
+            const char * data_gguf = reinterpret_cast<const char *>(gguf_get_arr_data(gguf_ctx, id));
+            if (!std::equal(data8, data8 + arr_n*type_size, data_gguf)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        const char * data_gguf = reinterpret_cast<const char *>(gguf_get_val_data(gguf_ctx, id));
+        if (!std::equal(data8, data8 + gguf_type_size(type), data_gguf)) {
+            ok = false;
+        }
+    }
+
+    const uint32_t expected_alignment = alignment_defined ? GGUF_DEFAULT_ALIGNMENT + 1 : GGUF_DEFAULT_ALIGNMENT;
+    if (gguf_get_alignment(gguf_ctx) != expected_alignment) {
+        ok = false;
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_tensors(const gguf_context * gguf_ctx, const unsigned int seed) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs = get_tensor_configs(rng);
+
+    // Call get_kv_types to get the same RNG state:
+    get_kv_types(rng);
+
+    bool ok = true;
+
+    const int id_alignment = gguf_find_key(gguf_ctx, "general.alignment");
+    const uint32_t alignment = id_alignment >= 0 ? gguf_get_val_u32(gguf_ctx, id_alignment) : GGUF_DEFAULT_ALIGNMENT;
+
+    uint64_t expected_offset = 0;
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const int id = gguf_find_tensor(gguf_ctx, name.c_str());
+
+        if (id >= 0) {
+            if (std::string(gguf_get_tensor_name(gguf_ctx, id)) != name) {
+                ok = false;
+            }
+
+            if (gguf_get_tensor_type(gguf_ctx, id) != type) {
+                ok = false;
+            }
+        } else {
+            ok = false;
+            continue;
+        }
+
+        const size_t offset = gguf_get_tensor_offset(gguf_ctx, id);
+
+        if (offset != expected_offset) {
+            ok = false;
+        }
+
+        int64_t ne = shape[0];
+        for (size_t j = 1; j < GGML_MAX_DIMS; ++j) {
+            ne *= shape[j];
+        }
+        expected_offset += GGML_PAD(ggml_row_size(type, ne), alignment);
+    }
+
+    return ok;
+}
+
+static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const unsigned int seed, FILE * file) {
+    if (!gguf_ctx) {
+        return false;
+    }
+
+    std::mt19937 rng(seed);
+
+    std::vector<tensor_config_t> tensor_configs = get_tensor_configs(rng);
+
+    bool ok = true;
+
+    const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
+
+    for (int i = 0; i < int(tensor_configs.size()); ++i) {
+        const ggml_type                          type  = tensor_configs[i].first;
+        const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
+
+        int64_t ne = shape[0];
+        for (size_t j = 1; j < GGML_MAX_DIMS; ++j) {
+            ne *= shape[j];
+        }
+        const size_t size = ggml_row_size(type, ne);
+
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const size_t offset = gguf_get_tensor_offset(gguf_ctx, gguf_find_tensor(gguf_ctx, name.c_str()));
+
+        std::vector<uint8_t> data(size);
+        GGML_ASSERT(fseek(file, gguf_get_data_offset(gguf_ctx) + offset, SEEK_SET) == 0);
+        GGML_ASSERT(fread(data.data(), 1, size, file) == size);
+
+        for (size_t j = 0; j < size; ++j) {
+            const uint8_t expected_byte = (j + offset) % 256;
+            if (data[j] != expected_byte) {
+                ok = false;
+            }
+        }
+    }
+
+    return ok;
+}
+
+static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
+    int npass = 0;
+    int ntest = 0;
+
+    const std::vector<handcrafted_file_type> hfts = {
+        HANDCRAFTED_HEADER_BAD_MAGIC,
+        HANDCRAFTED_HEADER_BAD_VERSION_1,
+        // HANDCRAFTED_FILE_TYPE_BAD_VERSION_FUTURE, // FIXME
+        HANDCRAFTED_HEADER_BAD_N_KV,
+        HANDCRAFTED_HEADER_BAD_N_TENSORS,
+        HANDCRAFTED_HEADER_EMPTY,
+
+        HANDCRAFTED_KV_BAD_KEY_SIZE,
+        HANDCRAFTED_KV_BAD_TYPE,
+        HANDCRAFTED_KV_BAD_VALUE_SIZE,
+        // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME
+        HANDCRAFTED_KV_SUCCESS,
+
+        HANDCRAFTED_TENSORS_BAD_NAME_SIZE,
+        HANDCRAFTED_TENSORS_BAD_N_DIMS,
+        HANDCRAFTED_TENSORS_BAD_SHAPE,
+        HANDCRAFTED_TENSORS_NE_TOO_BIG,
+        HANDCRAFTED_TENSORS_BAD_TYPE,
+        // HANDCRAFTED_TENSORS_BAD_OFFSET, // FIXME
+        HANDCRAFTED_TENSORS_DUPLICATE_NAME,
+        // HANDCRAFTED_TENSORS_BAD_ALIGNMENT, // FIXME
+        HANDCRAFTED_TENSORS_SUCCESS,
+        HANDCRAFTED_TENSORS_CUSTOM_ALIGN,
+
+        HANDCRAFTED_DATA_NOT_ENOUGH_DATA,
+        // HANDCRAFTED_DATA_BAD_ALIGNMENT, // FIXME
+        HANDCRAFTED_DATA_SUCCESS,
+        HANDCRAFTED_DATA_CUSTOM_ALIGN,
+    };
+
+    for (enum handcrafted_file_type hft : hfts) {
+        printf("%s: handcrafted_file_type=%s\n", __func__, handcrafted_file_type_name(hft).c_str());
+        FILE * file = get_handcrafted_file(seed, hft);
+
+#ifdef _WIN32
+        if (!file) {
+            printf("%s: failed to create tmpfile(), needs elevated privileges on Windows");
+            printf("%s: skipping tests");
+            continue;
+        }
+#else
+        GGML_ASSERT(file);
+#endif // _WIN32
+
+        struct ggml_context * ctx = nullptr;
+        struct gguf_init_params gguf_params = {
+            /*no_alloc =*/ false,
+            /*ctx      =*/ hft >= offset_has_data ? &ctx : nullptr,
+        };
+        struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params);
+
+        if (expect_context_not_null(hft)) {
+            printf("%s:   - context_not_null: ", __func__);
+        } else {
+            printf("%s:   - context_null: ", __func__);
+        }
+        if (bool(gguf_ctx) == expect_context_not_null(hft)) {
+            printf("\033[1;32mOK\033[0m\n");
+            npass++;
+        } else {
+            printf("\033[1;31mFAIL\033[0m\n");
+        }
+        ntest++;
+
+        if (false && hft >= offset_has_data && !expect_context_not_null(hft)) { // FIXME
+            printf("%s:   - no_dangling_ggml_context_pointer: ", __func__);
+            if (ctx) {
+                printf("\033[1;31mFAIL\033[0m\n");
+            } else {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            }
+            ntest++;
+        }
+
+        if (false && expect_context_not_null(hft)) { // FIXME
+            FILE * file_eb = get_handcrafted_file(seed, hft, /*extra_bytes =*/ 1);
+            struct gguf_context * gguf_ctx_eb = gguf_init_from_file_impl(file_eb, gguf_params);
+
+            printf("%s:   - context_null_with_extra_bytes: ", __func__);
+            if (gguf_ctx_eb) {
+                printf("\033[1;31mFAIL\033[0m\n");
+            } else {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            }
+            ntest++;
+
+            gguf_free(gguf_ctx_eb);
+            fclose(file_eb);
+        }
+
+        const bool alignment_defined = hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN;
+
+        if (expect_context_not_null(hft)) {
+            printf("%s:   - check_header: ", __func__);
+            if (handcrafted_check_header(gguf_ctx, seed, hft >= offset_has_kv, hft >= offset_has_tensors, alignment_defined)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_kv) {
+            printf("%s:   - check_kv: ", __func__);
+            if (handcrafted_check_kv(gguf_ctx, seed, hft >= offset_has_tensors, alignment_defined)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_tensors) {
+            printf("%s:   - check_tensors: ", __func__);
+            if (handcrafted_check_tensors(gguf_ctx, seed)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (expect_context_not_null(hft) && hft >= offset_has_data) {
+            printf("%s:   - check_tensor_data: ", __func__);
+            if (handcrafted_check_tensor_data(gguf_ctx, seed, file)) {
+                printf("\033[1;32mOK\033[0m\n");
+                npass++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ntest++;
+        }
+
+        if (gguf_ctx) {
+            ggml_free(ctx);
+            gguf_free(gguf_ctx);
+        }
+        fclose(file);
+        printf("\n");
+    }
+
+    return std::make_pair(npass, ntest);
+}
+
+struct random_gguf_context_result {
+    struct gguf_context * gguf_ctx;
+    struct ggml_context * ctx;
+    ggml_backend_buffer_t buffer;
+};
+
+static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t backend, const unsigned int seed) {
+    std::mt19937 rng(seed);
+
+    struct gguf_context * gguf_ctx = gguf_init_empty();
+
+    for (int i = 0; i < 256; ++i) {
+        const std::string key = "my_key_" + std::to_string(rng() % 1024);
+        const enum gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
+
+        if (type == GGUF_TYPE_STRING || type == GGUF_TYPE_ARRAY) {
+            continue; // FIXME memory leak
+        }
+
+        switch (type) {
+            case GGUF_TYPE_UINT8:   gguf_set_val_u8  (gguf_ctx, key.c_str(), rng() % (1 <<  7));             break;
+            case GGUF_TYPE_INT8:    gguf_set_val_i8  (gguf_ctx, key.c_str(), rng() % (1 <<  7) - (1 <<  6)); break;
+            case GGUF_TYPE_UINT16:  gguf_set_val_u16 (gguf_ctx, key.c_str(), rng() % (1 << 15));             break;
+            case GGUF_TYPE_INT16:   gguf_set_val_i16 (gguf_ctx, key.c_str(), rng() % (1 << 15) - (1 << 14)); break;
+            case GGUF_TYPE_UINT32:  gguf_set_val_u32 (gguf_ctx, key.c_str(), rng());                         break;
+            case GGUF_TYPE_INT32:   gguf_set_val_i32 (gguf_ctx, key.c_str(), rng()             - (1 << 30)); break;
+            case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024      - 512);       break;
+            case GGUF_TYPE_BOOL:    gguf_set_val_bool(gguf_ctx, key.c_str(), rng() % 2 == 0);                break;
+            case GGUF_TYPE_STRING:  gguf_set_val_str (gguf_ctx, key.c_str(), std::to_string(rng()).c_str()); break;
+            case GGUF_TYPE_UINT64:  gguf_set_val_u64 (gguf_ctx, key.c_str(), rng());                         break;
+            case GGUF_TYPE_INT64:   gguf_set_val_i64 (gguf_ctx, key.c_str(), rng()             - (1 << 30)); break;
+            case GGUF_TYPE_FLOAT64: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024      - 512);       break;
+            case GGUF_TYPE_ARRAY: {
+                const enum gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT);
+                const uint64_t ne = rng() % 1024;
+
+                switch (type_arr) {
+                    case GGUF_TYPE_UINT8:
+                    case GGUF_TYPE_INT8:
+                    case GGUF_TYPE_UINT16:
+                    case GGUF_TYPE_INT16:
+                    case GGUF_TYPE_UINT32:
+                    case GGUF_TYPE_INT32:
+                    case GGUF_TYPE_FLOAT32:
+                    case GGUF_TYPE_BOOL:
+                    case GGUF_TYPE_UINT64:
+                    case GGUF_TYPE_INT64:
+                    case GGUF_TYPE_FLOAT64: {
+                        const size_t nbytes = ne*gguf_type_size(type_arr);
+                        std::vector<uint32_t> random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t));
+                        for (size_t j = 0; j < random_data.size(); ++j) {
+                            random_data[j] = rng();
+                        }
+                        gguf_set_arr_data(gguf_ctx, key.c_str(), type_arr, random_data.data(), ne);
+                    } break;
+                    case GGUF_TYPE_STRING: {
+                        std::vector<std::string>  data_cpp(ne);
+                        std::vector<const char *> data_c(ne);
+                        for (size_t j = 0; j < data_cpp.size(); ++j) {
+                            data_cpp[j] = std::to_string(rng());
+                            data_c[j]   = data_cpp[j].c_str();
+                        }
+                        gguf_set_arr_str(gguf_ctx, key.c_str(), data_c.data(), ne);
+                    } break;
+                    case GGUF_TYPE_ARRAY: {
+                        break; // not supported
+                    }
+                    case GGUF_TYPE_COUNT:
+                    default: {
+                        GGML_ABORT("fatal error");
+                    } break;
+                }
+            } break;
+            case GGUF_TYPE_COUNT:
+            default: {
+                GGML_ABORT("fatal error");
+            } break;
+        }
+    }
+
+    struct ggml_init_params ggml_params = {
+        /*.mem_size   =*/ 256*ggml_tensor_overhead(),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+    struct ggml_context * ctx = ggml_init(ggml_params);
+
+    for (int i = 0; i < 256; ++i) {
+        const std::string name = "my_tensor_" + std::to_string(i);
+        const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT);
+        const size_t type_size = ggml_type_size(type);
+
+        if (type_size == 0) {
+            continue;
+        }
+
+        const int n_dims = 1 + rng() % GGML_MAX_DIMS;
+        int64_t ne[GGML_MAX_DIMS];
+        ne[0] = (1 + rng() % 10) * ggml_blck_size(type);
+        for (int j = 1; j < n_dims; ++j) {
+            ne[j] = 1 + rng() % 10;
+        }
+
+        struct ggml_tensor * tensor = ggml_new_tensor(ctx, type, n_dims, ne);
+        ggml_set_name(tensor, name.c_str());
+    }
+
+    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
+    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+        const size_t nbytes = ggml_nbytes(t);
+        std::vector<uint32_t> random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t));
+        for (size_t j = 0; j < random_data.size(); ++j) {
+            random_data[j] = rng();
+        }
+        ggml_backend_tensor_set(t, random_data.data(), 0, nbytes);
+
+        gguf_add_tensor(gguf_ctx, t);
+    }
+
+    return {gguf_ctx, ctx, buf};
+}
+
+static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other) {
+    bool ok = true;
+
+    const int n_kv = gguf_get_n_kv(ctx);
+    for (int id = 0; id < n_kv; ++id) {
+        const char * name = gguf_get_key(ctx, id);
+
+        const int idx_other = gguf_find_key(other, name);
+        if (idx_other < 0) {
+            ok = false;
+            continue;
+        }
+
+        const gguf_type type = gguf_get_kv_type(ctx, id);
+        if (type != gguf_get_kv_type(other, idx_other)) {
+            ok = false;
+            continue;
+        }
+
+        if (type == GGUF_TYPE_ARRAY) {
+            const int arr_n = gguf_get_arr_n(ctx, id);
+            if (arr_n != gguf_get_arr_n(other, idx_other)) {
+                ok = false;
+                continue;
+            }
+
+            const gguf_type type_arr = gguf_get_arr_type(ctx, id);
+            if (type_arr != gguf_get_arr_type(other, idx_other)) {
+                ok = false;
+                continue;
+            }
+
+            if (type_arr == GGUF_TYPE_STRING) {
+                for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    const std::string str       = gguf_get_arr_str(ctx,   id,       arr_i);
+                    const std::string str_other = gguf_get_arr_str(other, idx_other, arr_i);
+                    if (str != str_other) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
+            const char * data       = reinterpret_cast<const char *>(gguf_get_arr_data(ctx,   id));
+            const char * data_other = reinterpret_cast<const char *>(gguf_get_arr_data(other, idx_other));
+            if (!std::equal(data, data + arr_n*gguf_type_size(type_arr), data_other)) {
+                ok = false;
+            }
+            continue;
+        }
+
+        if (type == GGUF_TYPE_STRING) {
+            const std::string str       = gguf_get_val_str(ctx,   id);
+            const std::string str_other = gguf_get_val_str(other, idx_other);
+            if (str != str_other) {
+                ok = false;
+            }
+            continue;
+        }
+
+        const char * data       = reinterpret_cast<const char *>(gguf_get_val_data(ctx,   id));
+        const char * data_other = reinterpret_cast<const char *>(gguf_get_val_data(other, idx_other));
+        if (!std::equal(data, data + gguf_type_size(type), data_other)) {
+            ok = false;
+        }
+    }
+
+    return ok;
+}
+
+static bool all_tensors_in_other(const gguf_context * ctx, const gguf_context * other) {
+    bool ok = true;
+
+    const int n_tensors = gguf_get_n_tensors(ctx);
+    for (int id = 0; id < n_tensors; ++id) {
+        const std::string name = gguf_get_tensor_name(ctx, id);
+
+        const int idx_other = gguf_find_tensor(other, name.c_str());
+        if (id != idx_other) {
+            ok = false;
+            if (idx_other < 0) {
+                continue;
+            }
+        }
+
+        const ggml_type type = gguf_get_tensor_type(ctx, id);
+        if (type != gguf_get_tensor_type(other, id)) {
+            ok = false;
+        }
+
+        const size_t offset = gguf_get_tensor_offset(ctx, id);
+        if (offset != gguf_get_tensor_offset(other, id)) {
+            ok = false;
+        }
+    }
+
+    return ok;
+}
+
+static bool same_tensor_data(const struct ggml_context * orig, const struct ggml_context * read) {
+    bool ok = true;
+
+    struct ggml_tensor * t_orig = ggml_get_first_tensor(orig);
+    struct ggml_tensor * t_read = ggml_get_first_tensor(read);
+    while (t_orig) {
+        if (!t_read) {
+            ok = false;
+            break;
+        }
+
+        const size_t nbytes = ggml_nbytes(t_orig);
+        if (ggml_nbytes(t_read) != nbytes) {
+            ok = false;
+            break;
+        }
+        std::vector<char> data_orig(nbytes);
+        ggml_backend_tensor_get(t_orig, data_orig.data(), 0, nbytes);
+        if (!std::equal(data_orig.data(), data_orig.data() + nbytes, reinterpret_cast<const char *>(t_read->data))) {
+            ok = false;
+        }
+
+        t_orig = ggml_get_next_tensor(orig, t_orig);
+        t_read = ggml_get_next_tensor(orig, t_read);
+    }
+    if (t_read) {
+        ok = false;
+    }
+
+    return true;
+}
+
+static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
+    FILE * file = tmpfile();
+#ifdef _WIN32
+    if (!file) {
+        printf("%s: failed to create tmpfile(), needs elevated privileges on Windows");
+        printf("%s: skipping tests");
+        return std::make_pair(0, 0);
+    }
+#else
+    GGML_ASSERT(file);
+#endif // _WIN32
+
+    if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
+        return std::make_pair(0, 0); // FIXME
+    }
+
+    ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+    printf("%s: device=%s, backend=%s, only_meta=%s\n",
+        __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend), only_meta ? "yes" : "no");
+
+    int npass = 0;
+    int ntest = 0;
+
+    struct gguf_context * gguf_ctx_0;
+    struct ggml_context * ctx_0;
+    ggml_backend_buffer_t bbuf;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed);
+        gguf_ctx_0 = result.gguf_ctx;
+        ctx_0      = result.ctx;
+        bbuf       = result.buffer;
+    }
+
+    struct gguf_buf gbuf = gguf_buf_init(16 * 1024);
+    gguf_write_to_buf(gguf_ctx_0, &gbuf, only_meta);
+    helper_write(gbuf.data, gbuf.offset, file);
+    rewind(file);
+
+    struct ggml_context * ctx_1 = nullptr;
+    struct gguf_init_params gguf_params = {
+        /*no_alloc =*/ false,
+        /*ctx      =*/ only_meta ? nullptr : &ctx_1,
+    };
+    struct gguf_context * gguf_ctx_1 = gguf_init_from_file_impl(file, gguf_params);
+
+    printf("%s: same_version: ", __func__);
+    if (gguf_get_version(gguf_ctx_0) == gguf_get_version(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: same_n_kv: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: same_n_tensors: ", __func__);
+    if (gguf_get_n_tensors(gguf_ctx_0) == gguf_get_n_tensors(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_orig_kv_in_read: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_read_kv_in_orig: ", __func__);
+    if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_orig_tensors_in_read: ", __func__);
+    if (all_tensors_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_read_tensors_in_orig: ", __func__);
+    if (all_tensors_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    if (!only_meta) {
+        printf("%s: same_tensor_data: ", __func__);
+        if (same_tensor_data(ctx_0, ctx_1)) {
+            printf("\033[1;32mOK\033[0m\n");
+            npass++;
+        } else {
+            printf("\033[1;31mFAIL\033[0m\n");
+        }
+        ntest++;
+    }
+
+    ggml_backend_buffer_free(bbuf);
+    ggml_free(ctx_0);
+    ggml_free(ctx_1);
+    gguf_free(gguf_ctx_0);
+    gguf_free(gguf_ctx_1);
+    gguf_buf_free(gbuf);
+    ggml_backend_free(backend);
+    GGML_ASSERT(fclose(file) == 0);
+
+    printf("\n");
+    return std::make_pair(npass, ntest);
+}
+
+static std::pair<int, int> test_gguf_set_kv(ggml_backend_dev_t dev, const unsigned int seed) {
+    ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+    printf("%s: device=%s, backend=%s\n", __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend));
+
+    int npass = 0;
+    int ntest = 0;
+
+    struct gguf_context * gguf_ctx_0;
+    struct ggml_context * ctx_0;
+    ggml_backend_buffer_t bbuf_0;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed);
+        gguf_ctx_0 = result.gguf_ctx;
+        ctx_0      = result.ctx;
+        bbuf_0     = result.buffer;
+    }
+
+    struct gguf_context * gguf_ctx_1;
+    struct ggml_context * ctx_1;
+    ggml_backend_buffer_t bbuf_1;
+    {
+        struct random_gguf_context_result result = get_random_gguf_context(backend, seed + 1);
+        gguf_ctx_1 = result.gguf_ctx;
+        ctx_1      = result.ctx;
+        bbuf_1     = result.buffer;
+    }
+
+    struct gguf_context * gguf_ctx_2 = gguf_init_empty();
+
+    gguf_set_kv(gguf_ctx_1, gguf_ctx_0);
+    gguf_set_kv(gguf_ctx_2, gguf_ctx_0);
+
+    printf("%s: same_n_kv: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_2)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_0_in_1: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_0_in_2: ", __func__);
+    if (all_kv_in_other(gguf_ctx_0, gguf_ctx_2)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    gguf_set_kv(gguf_ctx_0, gguf_ctx_1);
+
+    printf("%s: same_n_kv_after_double_copy: ", __func__);
+    if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    printf("%s: all_kv_1_in_0_after_double_copy: ", __func__);
+    if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) {
+        printf("\033[1;32mOK\033[0m\n");
+        npass++;
+    } else {
+        printf("\033[1;31mFAIL\033[0m\n");
+    }
+    ntest++;
+
+    ggml_backend_buffer_free(bbuf_0);
+    ggml_backend_buffer_free(bbuf_1);
+    ggml_free(ctx_0);
+    ggml_free(ctx_1);
+    gguf_free(gguf_ctx_0);
+    gguf_free(gguf_ctx_1);
+    gguf_free(gguf_ctx_2);
+    ggml_backend_free(backend);
+
+    printf("\n");
+    return std::make_pair(npass, ntest);
+}
+
+static void print_usage() {
+    printf("usage: test-gguf [seed]\n");
+    printf("  if no seed is unspecified then a random seed is used\n");
+}
+
+int main(int argc, char ** argv) {
+    if (argc > 2) {
+        print_usage();
+        return 1;
+    }
+
+    std::random_device rd;
+    const unsigned int seed = argc < 2 ? rd() : std::stoi(argv[1]);
+
+    // Initialize ggml backends early so the prints aren't interleaved with the test results:
+    ggml_backend_dev_count();
+    fprintf(stderr, "\n");
+
+    int npass = 0;
+    int ntest = 0;
+    {
+        std::pair<int, int> result = test_handcrafted_file(seed);
+        npass += result.first;
+        ntest += result.second;
+    }
+
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+
+        for (bool only_meta : {true, false}) {
+            std::pair<int, int> result = test_roundtrip(dev, seed, only_meta);
+            npass += result.first;
+            ntest += result.second;
+        }
+
+        {
+            std::pair<int, int> result = test_gguf_set_kv(dev, seed);
+            npass += result.first;
+            ntest += result.second;
+        }
+    }
+
+    printf("%d/%d tests passed\n", npass, ntest);
+    if (npass != ntest) {
+        printf("\033[1;31mFAIL\033[0m\n");
+        return 1;
+    }
+    printf("\033[1;32mOK\033[0m\n");
+    return 0;
+}

From d62b532c52e0118323277eaa5f442e11ce6505ed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?DAN=E2=84=A2?= <dranger003@gmail.com>
Date: Tue, 17 Dec 2024 17:24:22 -0500
Subject: [PATCH 045/376] Use model->gguf_kv for loading the template instead
 of using the C API. (#10868)

* Bump model_template to 16384 bytes to support larger chat templates.

* Use `model->gguf_kv` for efficiency.
---
 src/llama.cpp | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 1cc8a9332..c7b180f20 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -22651,15 +22651,15 @@ int32_t llama_chat_apply_template(
     std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
     if (tmpl == nullptr) {
         GGML_ASSERT(model != nullptr);
-        // load template from model
-        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
-        std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        if (res < 0) {
+
+        // load template from model, if available
+        const auto & it = model->gguf_kv.find("tokenizer.chat_template");
+        if (it != model->gguf_kv.end() && it->second.size() > 0) {
+            curr_tmpl = it->second;
+        }
+        else {
             // worst case: there is no information about template, we will use chatml by default
-            curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
-        } else {
-            curr_tmpl = std::string(model_template.data(), model_template.size());
+            curr_tmpl = "chatml";  // see llama_chat_apply_template_internal
         }
     }
 

From 4da69d1abd15263061aff94c10f205836a96a4bc Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 18 Dec 2024 01:36:46 +0100
Subject: [PATCH 046/376] Revert "llama : add Falcon3 support (#10864)"
 (#10876)

This reverts commit 382bc7f2e8ffd0b89f23e840d097e21f301197ba.
---
 convert_hf_to_gguf.py        |  6 ------
 convert_hf_to_gguf_update.py |  1 -
 src/llama.cpp                | 18 ------------------
 3 files changed, 25 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 66e268af6..9dc1673bc 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -525,9 +525,6 @@ class Model:
             else:
                 token: str = reverse_vocab[i]
                 if token in added_vocab:
-                    # We need to manually encode and decode the added tokens in case special characters
-                    # used for `\n` / `\t` have been manually added in the added tokens
-                    token = tokenizer.decode(tokenizer.encode(token))
                     if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
                         toktypes.append(gguf.TokenType.CONTROL)
                     else:
@@ -574,9 +571,6 @@ class Model:
         if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
             # ref: https://huggingface.co/tiiuae/falcon-7b
             res = "falcon"
-        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
-            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
-            res = "falcon3"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
             res = "bert-bge"
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 2ba346640..88058442f 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -72,7 +72,6 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
-    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
diff --git a/src/llama.cpp b/src/llama.cpp
index c7b180f20..b7b04a41d 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1612,7 +1612,6 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
     LLM_CHAT_TEMPLATE_PHI_3,
-    LLM_CHAT_TEMPLATE_FALCON_3,
     LLM_CHAT_TEMPLATE_ZEPHYR,
     LLM_CHAT_TEMPLATE_MONARCH,
     LLM_CHAT_TEMPLATE_GEMMA,
@@ -1645,7 +1644,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
-    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
     { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
     { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
     { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
@@ -6475,11 +6473,6 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "falcon") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
-            } else if (
-                    tokenizer_pre == "falcon3") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
             } else if (
                     tokenizer_pre == "mpt") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
@@ -22226,8 +22219,6 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         }
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
-    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
-        return LLM_CHAT_TEMPLATE_FALCON_3;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -22380,15 +22371,6 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
-        // Falcon 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>\n" << message->content << "\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
         // zephyr template
         for (auto message : chat) {

From 6b064c92b4c0228e333193c167f2c98d2ec8d9bc Mon Sep 17 00:00:00 2001
From: redbeard <bharrington@alticon.net>
Date: Wed, 18 Dec 2024 00:35:00 -0800
Subject: [PATCH 047/376] =?UTF-8?q?docs:=20Fix=20HIP=20(n=C3=A9e=20hipBLAS?=
 =?UTF-8?q?)=20in=20README=20(#10880)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Related to #10524 / be0e350c references to hipBLAS have been removed
across the repository.  This fixes the link from the repositories
`README.md`.

Signed-off-by: Brian 'redbeard' Harrington <redbeard@dead-city.org>
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index da8d9d166..3f3971d7e 100644
--- a/README.md
+++ b/README.md
@@ -221,7 +221,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
 | [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
-| [hipBLAS](docs/build.md#hipblas) | AMD GPU |
+| [HIP](docs/build.md#hip) | AMD GPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
 

From 46828872c31b25df16169cbbf5c2225fa9cb0675 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 18 Dec 2024 09:55:09 +0100
Subject: [PATCH 048/376] server : (embeddings) using same format for "input"
 and "content" (#10872)

* server : (embeddings) using same format for "input" and "content"

* fix test case

* handle empty input case

* fix test
---
 examples/server/server.cpp                   | 20 +++++++----
 examples/server/tests/unit/test_embedding.py | 35 ++++++++++++++++++--
 examples/server/utils.hpp                    |  1 +
 3 files changed, 47 insertions(+), 9 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 436170a03..71566b94e 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3651,25 +3651,33 @@ int main(int argc, char ** argv) {
         const json body = json::parse(req.body);
         bool oaicompat = false;
 
-        // an input prompt can be a string or a list of tokens (integer)
+        // for the shape of input/content, see tokenize_input_prompts()
         json prompt;
-        if (body.count("input") != 0) {
+        if (body.contains("input")) {
             oaicompat = true;
             prompt = body.at("input");
-        } else if (body.count("content") != 0) {
-            // with "content", we only support single prompt
-            prompt = std::vector<std::string>{body.at("content")};
+        } else if (body.contains("content")) {
+            oaicompat = false;
+            prompt = body.at("content");
         } else {
             res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
+        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
+        for (const auto & tokens : tokenized_prompts) {
+            // this check is necessary for models that do not add BOS token to the input
+            if (tokens.empty()) {
+                res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST));
+                return;
+            }
+        }
+
         // create and queue the task
         json responses = json::array();
         bool error = false;
         {
             std::vector<server_task> tasks;
-            std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, /* add_special */ false, true);
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {
                 server_task task   = server_task(SERVER_TASK_TYPE_EMBEDDING);
                 task.id            = ctx_server.queue_tasks.get_new_id();
diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py
index fea1d6510..4f4e9dcf0 100644
--- a/examples/server/tests/unit/test_embedding.py
+++ b/examples/server/tests/unit/test_embedding.py
@@ -45,6 +45,35 @@ def test_embedding_multiple():
         assert len(d['embedding']) > 1
 
 
+@pytest.mark.parametrize(
+    "content,is_multi_prompt",
+    [
+        # single prompt
+        ("string", False),
+        ([12, 34, 56], False),
+        ([12, 34, "string", 56, 78], False),
+        # multiple prompts
+        (["string1", "string2"], True),
+        (["string1", [12, 34, 56]], True),
+        ([[12, 34, 56], [12, 34, 56]], True),
+        ([[12, 34, 56], [12, "string", 34, 56]], True),
+    ]
+)
+def test_embedding_mixed_input(content, is_multi_prompt: bool):
+    global server
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={"content": content})
+    assert res.status_code == 200
+    if is_multi_prompt:
+        assert len(res.body) == len(content)
+        for d in res.body:
+            assert 'embedding' in d
+            assert len(d['embedding']) > 1
+    else:
+        assert 'embedding' in res.body
+        assert len(res.body['embedding']) > 1
+
+
 def test_embedding_openai_library_single():
     global server
     server.start()
@@ -102,8 +131,8 @@ def test_same_prompt_give_same_result():
 @pytest.mark.parametrize(
     "content,n_tokens",
     [
-        ("I believe the meaning of life is", 7),
-        ("This is a test", 4),
+        ("I believe the meaning of life is", 9),
+        ("This is a test", 6),
     ]
 )
 def test_embedding_usage_single(content, n_tokens):
@@ -126,4 +155,4 @@ def test_embedding_usage_multiple():
     })
     assert res.status_code == 200
     assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
-    assert res.body['usage']['prompt_tokens'] == 2 * 7
+    assert res.body['usage']['prompt_tokens'] == 2 * 9
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 8fffe484a..ffdffe904 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -138,6 +138,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
  * and multiple prompts (multi-tasks):
  * - "prompt": ["string1", "string2"]
  * - "prompt": ["string1", [12, 34, 56]]
+ * - "prompt": [[12, 34, 56], [78, 90, 12]]
  * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
  */
 static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {

From 0e70ba686e6c717a0aa41d88284e2a392c2bd0cd Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 18 Dec 2024 11:05:29 +0200
Subject: [PATCH 049/376] server : add "tokens" output (#10853)

* server : add "tokens" output

ggml-ci

* server : update readme

ggml-ci

* server : return tokens ids only if requested

ggml-ci

* tests : improve "tokens" type check

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>

* server : remove "tokens" from the OAI endpoint

ggml-ci

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---
 examples/server/README.md                     |  8 +++-
 examples/server/server.cpp                    | 38 ++++++++++++++-----
 examples/server/tests/unit/test_completion.py | 16 ++++++--
 3 files changed, 46 insertions(+), 16 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 63a7bf43a..ecd24c899 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -438,19 +438,22 @@ These words will not be included in the completion, so make sure to add them to
 
 `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`
 
+`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false`
+
 `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.
 
 `timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
 
 **Response format**
 
-- Note: In streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
+- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
 
 ```json
 {
-  "content": "<the token selected by the model>",
+  "content": "<the token generated by the model>",
+  "tokens": [ generated token ids if requested ],
   "probs": [
     {
       "prob": float,
@@ -468,6 +471,7 @@ These words will not be included in the completion, so make sure to add them to
 Notice that each `probs` is an array of length `n_probs`.
 
 - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
+- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request.
 - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
 - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
 - `model`: The path to the model loaded with `-m`
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 71566b94e..40aac33f0 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -79,8 +79,9 @@ enum error_type {
 };
 
 struct slot_params {
-    bool stream       = true;
-    bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
+    bool stream        = true;
+    bool cache_prompt  = true; // remember the prompt to avoid reprocessing all prompt
+    bool return_tokens = false;
 
     int32_t n_keep    =  0; // number of tokens to keep from initial prompt
     int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
@@ -199,6 +200,7 @@ struct server_task {
 
         params.stream           = json_value(data, "stream",             false);
         params.cache_prompt     = json_value(data, "cache_prompt",       true);
+        params.return_tokens    = json_value(data, "return_tokens",      false);
         params.n_predict        = json_value(data, "n_predict",          json_value(data, "max_tokens", defaults.n_predict));
         params.n_indent         = json_value(data, "n_indent",           defaults.n_indent);
         params.n_keep           = json_value(data, "n_keep",             defaults.n_keep);
@@ -468,7 +470,10 @@ struct completion_token_output {
 
 struct server_task_result_cmpl_final : server_task_result {
     int index = 0;
-    std::string content;
+
+    std::string  content;
+    llama_tokens tokens;
+
     bool stream;
     result_timings timings;
     std::string prompt;
@@ -510,6 +515,7 @@ struct server_task_result_cmpl_final : server_task_result {
         json res = json {
             {"index",               index},
             {"content",             stream ? "" : content}, // in stream mode, content is already in last partial chunk
+            {"tokens",              stream ? llama_tokens {} : tokens},
             {"id_slot",             id_slot},
             {"stop",                true},
             {"model",               oaicompat_model},
@@ -539,9 +545,9 @@ struct server_task_result_cmpl_final : server_task_result {
         json choices = json::array({json{
             {"finish_reason", finish_reason},
             {"index", 0},
-            {"message", json{
+            {"message", json {
                 {"content", content},
-                {"role", "assistant"}
+                {"role",    "assistant"}
             }
         }}});
 
@@ -605,7 +611,9 @@ struct server_task_result_cmpl_final : server_task_result {
 
 struct server_task_result_cmpl_partial : server_task_result {
     int index = 0;
-    std::string content;
+
+    std::string  content;
+    llama_tokens tokens;
 
     int32_t n_decoded;
     int32_t n_prompt_tokens;
@@ -637,6 +645,7 @@ struct server_task_result_cmpl_partial : server_task_result {
         json res = json {
             {"index",            index},
             {"content",          content},
+            {"tokens",           tokens},
             {"stop",             false},
             {"id_slot",          id_slot},
             {"tokens_predicted", n_decoded},
@@ -678,7 +687,7 @@ struct server_task_result_cmpl_partial : server_task_result {
                 json second_ret = json{
                             {"choices", json::array({json{{"finish_reason", nullptr},
                                                             {"index", 0},
-                                                            {"delta", json{
+                                                            {"delta", json {
                                                             {"content", content}}}
                                                             }})},
                             {"created", t},
@@ -693,7 +702,7 @@ struct server_task_result_cmpl_partial : server_task_result {
                 {"finish_reason", nullptr},
                 {"index", 0},
                 {"delta",
-                json{
+                json {
                     {"content", content},
                 }},
             }});
@@ -955,8 +964,11 @@ struct server_slot {
 
     size_t last_nl_pos = 0;
 
-    std::string generated_text;
+    std::string  generated_text;
+    llama_tokens generated_tokens;
+
     llama_tokens cache_tokens;
+
     std::vector<completion_token_output> generated_token_probs;
 
     bool has_next_token = true;
@@ -1000,6 +1012,7 @@ struct server_slot {
         n_sent_token_probs = 0;
         task_type          = SERVER_TASK_TYPE_COMPLETION;
 
+        generated_tokens.clear();
         generated_token_probs.clear();
     }
 
@@ -1740,8 +1753,10 @@ struct server_context {
         const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special);
         slot.sampled = result.tok;
 
-        // search stop word and delete it
         slot.generated_text += token_str;
+        if (slot.params.return_tokens) {
+            slot.generated_tokens.push_back(result.tok);
+        }
         slot.has_next_token = true;
 
         // check if there is incomplete UTF-8 character at the end
@@ -1766,6 +1781,7 @@ struct server_context {
             break;
         }
 
+        // search stop word and delete it
         if (!incomplete) {
             size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
 
@@ -1918,6 +1934,7 @@ struct server_context {
         res->id      = slot.id_task;
         res->index   = slot.index;
         res->content = tkn.text_to_send;
+        res->tokens  = { tkn.tok };
 
         res->n_decoded       = slot.n_decoded;
         res->n_prompt_tokens = slot.n_prompt_tokens;
@@ -1958,6 +1975,7 @@ struct server_context {
 
         res->index           = slot.index;
         res->content         = slot.generated_text;
+        res->tokens          = slot.generated_tokens;
         res->timings         = slot.get_timings();
         res->prompt          = common_detokenize(ctx, slot.prompt_tokens, true);
 
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index 062ebcd4a..36aee57dd 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -10,16 +10,17 @@ def create_server():
     global server
     server = ServerPreset.tinyllama2()
 
-@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
-    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False),
-    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False),
+@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [
+    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False),
+    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True),
 ])
-def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool):
+def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool):
     global server
     server.start()
     res = server.make_request("POST", "/completion", data={
         "n_predict": n_predict,
         "prompt": prompt,
+        "return_tokens": return_tokens,
     })
     assert res.status_code == 200
     assert res.body["timings"]["prompt_n"] == n_prompt
@@ -27,6 +28,11 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int,
     assert res.body["truncated"] == truncated
     assert type(res.body["has_new_line"]) == bool
     assert match_regex(re_content, res.body["content"])
+    if return_tokens:
+        assert len(res.body["tokens"]) > 0
+        assert all(type(tok) == int for tok in res.body["tokens"])
+    else:
+        assert res.body["tokens"] == []
 
 
 @pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
@@ -56,6 +62,8 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp
             assert data["generation_settings"]["seed"] == server.seed
             assert match_regex(re_content, content)
         else:
+            assert len(data["tokens"]) > 0
+            assert all(type(tok) == int for tok in data["tokens"])
             content += data["content"]
 
 

From 152610eda91217ac409342cd976d05f5114ad39f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 18 Dec 2024 13:01:41 +0200
Subject: [PATCH 050/376] server : output embeddings for all tokens when
 pooling = none (#10861)

* server : add "tokens" output

ggml-ci

* server : output embeddings for all tokens when pooling = none

ggml-ci

* server : update readme [no ci]

* server : fix spacing [no ci]

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>

* server : be explicit about the pooling type in the tests

ggml-ci

* server : update /embeddings and /v1/embeddings endpoints

ggml-ci

* server : do not normalize embeddings when there is no pooling

ggml-ci

* server : update readme

ggml-ci

* server : fixes

* tests : update server tests

ggml-ci

* server : update readme [no ci]

* server : remove rebase artifact

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---
 common/common.cpp                            |  4 +-
 common/common.h                              |  3 +-
 examples/gritlm/gritlm.cpp                   |  2 +-
 examples/retrieval/retrieval.cpp             |  2 +-
 examples/server/README.md                    | 42 +++++++++++
 examples/server/server.cpp                   | 74 +++++++++++++++-----
 examples/server/tests/unit/test_embedding.py | 65 +++++++++++++----
 examples/server/tests/utils.py               |  3 +
 8 files changed, 158 insertions(+), 37 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index c0c98232e..05d3ba766 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1780,7 +1780,9 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm)
             break;
         case 0: // max absolute
             for (int i = 0; i < n; i++) {
-                if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
+                if (sum < std::abs(inp[i])) {
+                    sum = std::abs(inp[i]);
+                }
             }
             sum /= 32760.0; // make an int16 range
             break;
diff --git a/common/common.h b/common/common.h
index 5f556c24d..ec0e49f6f 100644
--- a/common/common.h
+++ b/common/common.h
@@ -596,7 +596,8 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si
 // Embedding utils
 //
 
-void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
+// TODO: repace embd_norm with an enum
+void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);
 
 float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
 
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
index 6e42fa073..18a945b33 100644
--- a/examples/gritlm/gritlm.cpp
+++ b/examples/gritlm/gritlm.cpp
@@ -75,7 +75,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
         }
 
         std::vector<float> emb_norm(emb_unorm.size());
-        common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
+        common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2);
         result.push_back(emb_norm);
 
 #ifdef GRIT_DEBUG
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index 23ff4db27..a5c6fe7e5 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
         }
 
         float * out = output + batch.seq_id[i][0] * n_embd;
-        common_embd_normalize(embd, out, n_embd);
+        common_embd_normalize(embd, out, n_embd, 2);
     }
 }
 
diff --git a/examples/server/README.md b/examples/server/README.md
index ecd24c899..d006a8d37 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -763,6 +763,8 @@ curl http://localhost:8080/v1/chat/completions \
 
 ### POST `/v1/embeddings`: OpenAI-compatible embeddings API
 
+This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
+
 *Options:*
 
 See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
@@ -795,6 +797,46 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r
   }'
   ```
 
+### POST `/embeddings`: non-OpenAI-compatible embeddings API
+
+This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm.
+
+Note that the response format of this endpoint is different from `/v1/embeddings`.
+
+*Options:*
+
+Same as the `/v1/embeddings` endpoint.
+
+*Examples:*
+
+Same as the `/v1/embeddings` endpoint.
+
+**Response format**
+
+```json
+[
+  {
+    "index": 0,
+    "embedding": [
+      [ ... embeddings for token 0   ... ],
+      [ ... embeddings for token 1   ... ],
+      [ ... ]
+      [ ... embeddings for token N-1 ... ],
+    ]
+  },
+  ...
+  {
+    "index": P,
+    "embedding": [
+      [ ... embeddings for token 0   ... ],
+      [ ... embeddings for token 1   ... ],
+      [ ... ]
+      [ ... embeddings for token N-1 ... ],
+    ]
+  }
+]
+```
+
 ### GET `/slots`: Returns the current slots processing state
 
 > [!WARNING]
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 40aac33f0..5ed4e8d27 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -726,18 +726,32 @@ struct server_task_result_cmpl_partial : server_task_result {
 
 struct server_task_result_embd : server_task_result {
     int index = 0;
-    std::vector<float> embedding;
+    std::vector<std::vector<float>> embedding;
 
     int32_t n_tokens;
 
+    // OAI-compat fields
+    bool oaicompat = false;
+
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
+        return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat();
+    }
+
+    json to_json_non_oaicompat() {
+        return json {
+            {"index",     index},
+            {"embedding", embedding},
+        };
+    }
+
+    json to_json_oaicompat() {
         return json {
             {"index",            index},
-            {"embedding",        embedding},
+            {"embedding",        embedding[0]},
             {"tokens_evaluated", n_tokens},
         };
     }
@@ -2017,9 +2031,10 @@ struct server_context {
 
     void send_embedding(const server_slot & slot, const llama_batch & batch) {
         auto res = std::make_unique<server_task_result_embd>();
-        res->id    = slot.id_task;
-        res->index = slot.index;
-        res->n_tokens = slot.n_prompt_tokens;
+        res->id        = slot.id_task;
+        res->index     = slot.index;
+        res->n_tokens  = slot.n_prompt_tokens;
+        res->oaicompat = slot.params.oaicompat;
 
         const int n_embd = llama_n_embd(model);
 
@@ -2038,12 +2053,18 @@ struct server_context {
             if (embd == NULL) {
                 SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
 
-                res->embedding = std::vector<float>(n_embd, 0.0f);
+                res->embedding.push_back(std::vector<float>(n_embd, 0.0f));
                 continue;
             }
 
-            common_embd_normalize(embd, embd_res.data(), n_embd);
-            res->embedding = embd_res;
+            // normalize only when there is pooling
+            // TODO: configurable
+            if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) {
+                common_embd_normalize(embd, embd_res.data(), n_embd, 2);
+                res->embedding.push_back(embd_res);
+            } else {
+                res->embedding.push_back({ embd, embd + n_embd });
+            }
         }
 
         SLT_DBG(slot, "%s", "sending embeddings\n");
@@ -2657,7 +2678,10 @@ struct server_context {
 
                     // add prompt tokens for processing in the current batch
                     while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
-                        common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false);
+                        // without pooling, we want to output the embeddings for all the tokens in the batch
+                        const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE;
+
+                        common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, need_embd);
 
                         if (slot.params.cache_prompt) {
                             slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@@ -3665,14 +3689,17 @@ int main(int argc, char ** argv) {
         res_ok(res, data);
     };
 
-    const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, bool oaicompat) {
         const json body = json::parse(req.body);
-        bool oaicompat = false;
+
+        if (oaicompat && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
+            res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
 
         // for the shape of input/content, see tokenize_input_prompts()
         json prompt;
-        if (body.contains("input")) {
-            oaicompat = true;
+        if (body.count("input") != 0) {
             prompt = body.at("input");
         } else if (body.contains("content")) {
             oaicompat = false;
@@ -3697,10 +3724,15 @@ int main(int argc, char ** argv) {
         {
             std::vector<server_task> tasks;
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {
-                server_task task   = server_task(SERVER_TASK_TYPE_EMBEDDING);
+                server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING);
+
                 task.id            = ctx_server.queue_tasks.get_new_id();
                 task.index         = i;
                 task.prompt_tokens = std::move(tokenized_prompts[i]);
+
+                // OAI-compat
+                task.params.oaicompat = oaicompat;
+
                 tasks.push_back(task);
             }
 
@@ -3728,12 +3760,18 @@ int main(int argc, char ** argv) {
         }
 
         // write JSON response
-        json root = oaicompat
-            ? format_embeddings_response_oaicompat(body, responses)
-            : responses.size() == 1 ? responses[0] : json(responses);
+        json root = oaicompat ? format_embeddings_response_oaicompat(body, responses) : json(responses);
         res_ok(res, root);
     };
 
+    const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
+        handle_embeddings_impl(req, res, false);
+    };
+
+    const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
+        handle_embeddings_impl(req, res, true);
+    };
+
     const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
         if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) {
             res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED));
@@ -3907,7 +3945,7 @@ int main(int argc, char ** argv) {
     svr->Post("/infill",              handle_infill);
     svr->Post("/embedding",           handle_embeddings); // legacy
     svr->Post("/embeddings",          handle_embeddings);
-    svr->Post("/v1/embeddings",       handle_embeddings);
+    svr->Post("/v1/embeddings",       handle_embeddings_oai);
     svr->Post("/rerank",              handle_rerank);
     svr->Post("/reranking",           handle_rerank);
     svr->Post("/v1/rerank",           handle_rerank);
diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py
index 4f4e9dcf0..e32d74582 100644
--- a/examples/server/tests/unit/test_embedding.py
+++ b/examples/server/tests/unit/test_embedding.py
@@ -14,8 +14,9 @@ def create_server():
 
 def test_embedding_single():
     global server
+    server.pooling = 'last'
     server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
         "input": "I believe the meaning of life is",
     })
     assert res.status_code == 200
@@ -29,8 +30,9 @@ def test_embedding_single():
 
 def test_embedding_multiple():
     global server
+    server.pooling = 'last'
     server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
         "input": [
             "I believe the meaning of life is",
             "Write a joke about AI from a very long prompt which will not be truncated",
@@ -46,7 +48,7 @@ def test_embedding_multiple():
 
 
 @pytest.mark.parametrize(
-    "content,is_multi_prompt",
+    "input,is_multi_prompt",
     [
         # single prompt
         ("string", False),
@@ -59,25 +61,55 @@ def test_embedding_multiple():
         ([[12, 34, 56], [12, "string", 34, 56]], True),
     ]
 )
-def test_embedding_mixed_input(content, is_multi_prompt: bool):
+def test_embedding_mixed_input(input, is_multi_prompt: bool):
     global server
     server.start()
-    res = server.make_request("POST", "/embeddings", data={"content": content})
+    res = server.make_request("POST", "/v1/embeddings", data={"input": input})
     assert res.status_code == 200
+    data = res.body['data']
     if is_multi_prompt:
-        assert len(res.body) == len(content)
-        for d in res.body:
+        assert len(data) == len(input)
+        for d in data:
             assert 'embedding' in d
             assert len(d['embedding']) > 1
     else:
-        assert 'embedding' in res.body
-        assert len(res.body['embedding']) > 1
+        assert 'embedding' in data[0]
+        assert len(data[0]['embedding']) > 1
+
+
+def test_embedding_pooling_none():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={
+        "input": "hello hello hello",
+    })
+    assert res.status_code == 200
+    assert 'embedding' in res.body[0]
+    assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special
+
+    # make sure embedding vector is not normalized
+    for x in res.body[0]['embedding']:
+        assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON
+
+
+def test_embedding_pooling_none_oai():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": "hello hello hello",
+    })
+
+    # /v1/embeddings does not support pooling type 'none'
+    assert res.status_code == 400
 
 
 def test_embedding_openai_library_single():
     global server
+    server.pooling = 'last'
     server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
     res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is")
     assert len(res.data) == 1
     assert len(res.data[0].embedding) > 1
@@ -85,8 +117,9 @@ def test_embedding_openai_library_single():
 
 def test_embedding_openai_library_multiple():
     global server
+    server.pooling = 'last'
     server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
     res = client.embeddings.create(model="text-embedding-3-small", input=[
         "I believe the meaning of life is",
         "Write a joke about AI from a very long prompt which will not be truncated",
@@ -100,8 +133,9 @@ def test_embedding_openai_library_multiple():
 
 def test_embedding_error_prompt_too_long():
     global server
+    server.pooling = 'last'
     server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
         "input": "This is a test " * 512,
     })
     assert res.status_code != 200
@@ -109,8 +143,9 @@ def test_embedding_error_prompt_too_long():
 
 
 def test_same_prompt_give_same_result():
+    server.pooling = 'last'
     server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
         "input": [
             "I believe the meaning of life is",
             "I believe the meaning of life is",
@@ -138,7 +173,7 @@ def test_same_prompt_give_same_result():
 def test_embedding_usage_single(content, n_tokens):
     global server
     server.start()
-    res = server.make_request("POST", "/embeddings", data={"input": content})
+    res = server.make_request("POST", "/v1/embeddings", data={"input": content})
     assert res.status_code == 200
     assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
     assert res.body['usage']['prompt_tokens'] == n_tokens
@@ -147,7 +182,7 @@ def test_embedding_usage_single(content, n_tokens):
 def test_embedding_usage_multiple():
     global server
     server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
         "input": [
             "I believe the meaning of life is",
             "I believe the meaning of life is",
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index d988ccf5e..277125e88 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -65,6 +65,7 @@ class ServerProcess:
     server_reranking: bool | None = False
     server_metrics: bool | None = False
     server_slots: bool | None = False
+    pooling: str | None = None
     draft: int | None = None
     api_key: str | None = None
     response_format: str | None = None
@@ -132,6 +133,8 @@ class ServerProcess:
             server_args.append("--metrics")
         if self.server_slots:
             server_args.append("--slots")
+        if self.pooling:
+            server_args.extend(["--pooling", self.pooling])
         if self.model_alias:
             server_args.extend(["--alias", self.model_alias])
         if self.n_ctx:

From 7bbb5acf125d1d2840cac7d31b9aaa72210dd5ec Mon Sep 17 00:00:00 2001
From: Gaetan Bisson <gaetan@fenua.org>
Date: Wed, 18 Dec 2024 04:00:07 -1000
Subject: [PATCH 051/376] server: avoid overwriting Authorization header
 (#10878)

* server: avoid overwriting Authorization header

If no API key is set, leave the Authorization header as is. It may be
used by another part of the Web stack, such as an authenticating proxy.

Fixes https://github.com/ggerganov/llama.cpp/issues/10854

* rebuild

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/public/index.html.gz | Bin 1205430 -> 1205430 bytes
 examples/server/webui/src/main.js    |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 01dd41811d083cb3dfdf10860c00cc2b1f003832..6a64915579c39cec4bfdc995b76f9994824cdddc 100644
GIT binary patch
delta 976397
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zDXH0q9IUBHD7iG;zDhVWokz5vwyo$-2bxFE{lvI>{s5OXlYcTzXw{~Gy4q)wrp1xQ
zAH)o`O>IcDLi~RVp0ePt<43q06AvfI|6a^@Am10U-3wQ`FhgwCQ%U_TeVno%u?3ZM
z#~O+}edg<h8vG!pkFXS>Z_%L^#}>sO4n_pUG%KVtq&$2^>r)V=`^&@=L%{hH8!_D+
zcE9JGzNJ~g&wq{FXb;ys{RWAgu>`Y+a3(h$7ASE-df{R-7Yj}aqXBha9RJK`oPVU%
zdm0saJRPa+A8Qq-;@|J#G#sysN<K${+T)|F^DSPqnCPtIn?nuZP`HPjQ3fOWIQBBT
z0zu-wkoM3}`P=t@_=nb0uYXrmVnPZ?^A=TfD}o|--G5@F`zv=E+E;Wdb`N1UbeFLG
zVZ%2=cRO0`NM1ayx)Xt`O<HVL8L1hqR`iL_MCw&dt4CU`{`B3~-@f}!&teTRxL(fg
zwGW$=<R3LuK~LocteR<s@`6iLK{Q#^MR5@w;Hp<+Zsg%+Qq!+lR!OU>?}W^2^n!l#
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zh`SEX`Co}s<<0E}*foHY0q9q-SqUNIE4d)e<9@OGL9sztYJ4L>P-$9|k?JGFF-KPq
z`lY)4;`8Et9-e6L+_fKqo7x@d&m&==ZOVSr>{tUUc;Utu2{!kSeDY`<|JH7fA5T%X
zBYzHfj4j=*k2v&ZqRO(UvjLb*wYtFW#m;HQu!XX!9~vDfq*cc^{6_PIjs;|8itp+j
z%n05eHd(@^oM=43?DrHzc1Kf67@H9sbA`T!Gt0d5+wG#6tsy>6HwVJ&U3bUizU}vJ
zG;HWLzxkQy?n@T)`Sb8j3J4`8Q5|=1>VL-Whmt%VDDLu_;PtvL4pl@Y*??8Q>>Nv$
zWPClCY!S_QDE-X2`SHP`eZdFJ_A8ve7u_h7)l&b>Z_t^k(ZnQKeGVx)RCQ6|vPk$0
zPP|iyW(kj3bHAyx?*5JkI{_u7@ftRG8>wI;Bu-#U{X<Es4`QaJv*LH~x6rT68-Miq
zGh(ZHDx5l@lhN@N@ZV3^G?2J$*_~LKx+V$!@bJf~$VxEthoLvZ4@N?5gBC!Q4>{a+
zny!;%bgD*HL+i47y4IAcc(t>7njm$zc{pvDENofZH;WqAR$AV^i%eleO?`@D2vLK<
zzI{B+s01KU675H5BoCNZ+{*c7mw%b@7F~7Mvai;tzuES=^CP&=)*yO+dB6n%Die?>
zZ{kfbK5gZZ7$znx$61mm9D8xmpvIZOqmN@3oZtUn|Kq>??|=R8=({*!Z>G;R3H6>i
z0C28JUA@cNH5eJCqYT$sy}P-jN&n}6{>y(srvnpn&HBZpj*9YGjJ_8M`+v{>6@62b
z&y!+yR^Io8vvb<b^C?a#wn(SuwDW<ec77|Zgqzm}z~^bM1+R6&5DDnrj+6oeJ5o-1
z24AM{X<Bx+ROE;&5{CbRpNd1hxc=;W1ttenZLpM5kA}Ta)XPi5@}K?*ox+`P9acuB
zb7Pp|?$ITKnz)<W1QmNRM1K(L>N+oE?U6^4q+n$?(m|@ZWm3}%-_wesBLw2ABdOwc
z(kqTsmxS+Ru>wW=B!$RabDa7uswrJ}N3Lr|qK|DLkjEN+I?$RU@yhXS(+q8I)llXK
zV>mf{I-T9`+kU_Z;JJT*=y-lt^;!zh4mAdE=DGrJY>paoasC3&8a?_g=X$UG70nWM
zeRU{f!sz-`;_5B>D}Lld7oiZH(>NU-?8OBnAH7?NcYwf4LE!x)Z~iYLj_+aH<RSnR
CRlynn

diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 2a3021ce9..997ab0ff5 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -445,7 +445,7 @@ const mainApp = createApp({
           method: 'POST',
           headers: {
             'Content-Type': 'application/json',
-            'Authorization': this.config.apiKey ? `Bearer ${this.config.apiKey}` : undefined,
+            ...(this.config.apiKey ? {'Authorization': `Bearer ${this.config.apiKey}`} : {})
           },
           body: JSON.stringify(params),
           signal: abortController.signal,

From 0bf2d10c5514ff61b99897a4a5054f846e384e1e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 18 Dec 2024 19:27:21 +0200
Subject: [PATCH 052/376] tts : add OuteTTS support (#10784)

* server : add "tokens" output

ggml-ci

* server : output embeddings for all tokens when pooling = none

ggml-ci

* server : be explicit about the pooling type in the tests

ggml-ci

* server : do not normalize embeddings when there is no pooling

ggml-ci

* llama : add OuteTTS support (wip)

* wip

* extract features

* first conv

* group norm

* resnet conv

* resnet

* attn

* pos net

* layer norm

* convnext

* head

* hann window

* fix n_embd + remove llama.cpp hacks

* compute hann window

* fft

* spectrum processing

* clean-up

* tts : receive input text and generate codes

* clip : fix new conv name

* tts : minor fix

* tts : add header + minor fixes

ggml-ci

* tts : add matchematical constant

ggml-ci

* tts : fix sampling + cut initial noise

* tts : fixes

* tts : update default samplers

ggml-ci

* tts : text pre-processing

* tts : outetts-voc -> wavtokenizer-dec

* tts : remove hardcoded constants

ggml-ci

* tts : fix tensor shapes

* llama : refactor wavtokenizer tensors

ggml-ci

* cont

ggml-ci

* cont [no ci]

* llama : update WavTokenizer to non-causal attn

* llama : handle no-vocab detokenization

* tts : add Python example for OuteTTS (wip)

* tts : extend python example to generate spectrogram

ggml-ci

* server : fix rebase artifacts

* tts : enable "return_tokens" in Python example

ggml-ci

* tts : minor fixes

* common : support HF download for vocoder
---
 common/arg.cpp                   |  60 +-
 common/common.cpp                |   7 +-
 common/common.h                  |  13 +-
 convert_hf_to_gguf.py            |  59 +-
 examples/CMakeLists.txt          |   1 +
 examples/llava/clip.cpp          |   6 +-
 examples/tts/CMakeLists.txt      |   5 +
 examples/tts/convert_pt_to_hf.py | 180 ++++++
 examples/tts/tts-outetts.py      | 175 ++++++
 examples/tts/tts.cpp             | 932 ++++++++++++++++++++++++++++++
 ggml/include/ggml.h              |  41 +-
 ggml/src/ggml.c                  | 226 +++++---
 gguf-py/gguf/constants.py        | 275 +++++----
 gguf-py/gguf/gguf_writer.py      |  21 +
 gguf-py/gguf/tensor_mapping.py   |  70 +++
 gguf-py/tests/test_quants.py     |   2 +-
 include/llama.h                  |   3 -
 src/llama-vocab.cpp              |   4 +
 src/llama.cpp                    | 961 ++++++++++++++++++++++---------
 19 files changed, 2509 insertions(+), 532 deletions(-)
 create mode 100644 examples/tts/CMakeLists.txt
 create mode 100644 examples/tts/convert_pt_to_hf.py
 create mode 100644 examples/tts/tts-outetts.py
 create mode 100644 examples/tts/tts.cpp

diff --git a/common/arg.cpp b/common/arg.cpp
index 3d55289c3..e5ddd8318 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -119,29 +119,33 @@ std::string common_arg::to_string() {
 // utils
 //
 
-static void common_params_handle_model_default(common_params & params) {
-    if (!params.hf_repo.empty()) {
+static void common_params_handle_model_default(
+        std::string & model,
+        std::string & model_url,
+        std::string & hf_repo,
+        std::string & hf_file) {
+    if (!hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
-        if (params.hf_file.empty()) {
-            if (params.model.empty()) {
+        if (hf_file.empty()) {
+            if (model.empty()) {
                 throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
             }
-            params.hf_file = params.model;
-        } else if (params.model.empty()) {
+            hf_file = model;
+        } else if (model.empty()) {
             // this is to avoid different repo having same file name, or same file name in different subdirs
-            std::string filename = params.hf_repo + "_" + params.hf_file;
+            std::string filename = hf_repo + "_" + hf_file;
             // to make sure we don't have any slashes in the filename
             string_replace_all(filename, "/", "_");
-            params.model = fs_get_cache_file(filename);
+            model = fs_get_cache_file(filename);
         }
-    } else if (!params.model_url.empty()) {
-        if (params.model.empty()) {
-            auto f = string_split<std::string>(params.model_url, '#').front();
+    } else if (!model_url.empty()) {
+        if (model.empty()) {
+            auto f = string_split<std::string>(model_url, '#').front();
             f = string_split<std::string>(f, '?').front();
-            params.model = fs_get_cache_file(string_split<std::string>(f, '/').back());
+            model = fs_get_cache_file(string_split<std::string>(f, '/').back());
         }
-    } else if (params.model.empty()) {
-        params.model = DEFAULT_MODEL_PATH;
+    } else if (model.empty()) {
+        model = DEFAULT_MODEL_PATH;
     }
 }
 
@@ -276,7 +280,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    common_params_handle_model_default(params);
+    // TODO: refactor model params in a common struct
+    common_params_handle_model_default(params.model,         params.model_url,         params.hf_repo,         params.hf_file);
+    common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file);
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -842,7 +848,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_sparam());
     add_opt(common_arg(
-        {"--sampling-seq"}, "SEQUENCE",
+        {"--sampling-seq", "--sampler-seq"}, "SEQUENCE",
         string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
         [](common_params & params, const std::string & value) {
             params.sampling.samplers = common_sampler_types_from_chars(value);
@@ -1581,6 +1587,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.hf_file = value;
         }
     ).set_env("LLAMA_ARG_HF_FILE"));
+    add_opt(common_arg(
+        {"-hfrv", "--hf-repo-v"}, "REPO",
+        "Hugging Face model repository for the vocoder model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.hf_repo = value;
+        }
+    ).set_env("LLAMA_ARG_HF_REPO_V"));
+    add_opt(common_arg(
+        {"-hffv", "--hf-file-v"}, "FILE",
+        "Hugging Face model file for the vocoder model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.hf_file = value;
+        }
+    ).set_env("LLAMA_ARG_HF_FILE_V"));
     add_opt(common_arg(
         {"-hft", "--hf-token"}, "TOKEN",
         "Hugging Face access token (default: value from HF_TOKEN environment variable)",
@@ -2178,5 +2198,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
 
+    add_opt(common_arg(
+        {"-mv", "--model-vocoder"}, "FNAME",
+        "vocoder model for audio generation (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.model = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
+
     return ctx_arg;
 }
diff --git a/common/common.cpp b/common/common.cpp
index 05d3ba766..20be92911 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1095,7 +1095,7 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2
 
-static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
+static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
     int remaining_attempts = max_attempts;
 
     while (remaining_attempts > 0) {
@@ -1119,7 +1119,6 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
 }
 
 static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
-
     // Initialize libcurl
     std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
     if (!curl) {
@@ -1192,11 +1191,13 @@ static bool common_download_file(const std::string & url, const std::string & pa
         std::string etag;
         std::string last_modified;
     };
+
     common_load_model_from_url_headers headers;
+
     {
         typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
         auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
-            common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
+            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
 
             static std::regex header_regex("([^:]+): (.*)\r\n");
             static std::regex etag_regex("ETag", std::regex_constants::icase);
diff --git a/common/common.h b/common/common.h
index ec0e49f6f..1d2bd932c 100644
--- a/common/common.h
+++ b/common/common.h
@@ -80,6 +80,7 @@ enum llama_example {
     LLAMA_EXAMPLE_LLAVA,
     LLAMA_EXAMPLE_LOOKUP,
     LLAMA_EXAMPLE_PARALLEL,
+    LLAMA_EXAMPLE_TTS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -159,6 +160,7 @@ struct common_params_sampling {
 
 struct common_params_speculative {
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
     int32_t n_ctx        =     0; // draft context size
     int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
     int32_t n_min        =     5; // minimum number of draft tokens to use for speculative decoding
@@ -172,6 +174,14 @@ struct common_params_speculative {
     std::string model = ""; // draft model for speculative decoding                          // NOLINT
 };
 
+struct common_params_vocoder {
+    std::string hf_repo = ""; // HF repo                                                     // NOLINT
+    std::string hf_file = ""; // HF file                                                     // NOLINT
+
+    std::string model     = ""; // model path                                                // NOLINT
+    std::string model_url = ""; // model url to download                                     // NOLINT
+};
+
 struct common_params {
     int32_t n_predict             =    -1; // new tokens to predict
     int32_t n_ctx                 =  4096; // context size
@@ -214,8 +224,9 @@ struct common_params {
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
     enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
 
-    struct common_params_sampling sampling;
+    struct common_params_sampling    sampling;
     struct common_params_speculative speculative;
+    struct common_params_vocoder     vocoder;
 
     std::string model                = ""; // model path                                                    // NOLINT
     std::string model_alias          = ""; // model alias                                                   // NOLINT
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 9dc1673bc..4a0b00f69 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -221,17 +221,17 @@ class Model:
             self.gguf_writer.add_context_length(n_ctx)
             logger.info(f"gguf: context length = {n_ctx}")
 
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        self.gguf_writer.add_embedding_length(n_embd)
-        logger.info(f"gguf: embedding length = {n_embd}")
+        if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None:
+            self.gguf_writer.add_embedding_length(n_embd)
+            logger.info(f"gguf: embedding length = {n_embd}")
 
         if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
             logger.info(f"gguf: feed forward length = {n_ff}")
 
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        self.gguf_writer.add_head_count(n_head)
-        logger.info(f"gguf: head count = {n_head}")
+        if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None:
+            self.gguf_writer.add_head_count(n_head)
+            logger.info(f"gguf: head count = {n_head}")
 
         if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
             self.gguf_writer.add_head_count_kv(n_head_kv)
@@ -296,7 +296,9 @@ class Model:
                     break
 
             for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
-                data = data_torch.squeeze().numpy()
+                # TODO: why do we squeeze here?
+                # data = data_torch.squeeze().numpy()
+                data = data_torch.numpy()
 
                 # if data ends up empty, it means data_torch was a scalar tensor -> restore
                 if len(data.shape) == 0:
@@ -324,6 +326,8 @@ class Model:
                             gguf.MODEL_TENSOR.TIME_MIX_W2,
                             gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
                             gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
+                            gguf.MODEL_TENSOR.POSNET_NORM1,
+                            gguf.MODEL_TENSOR.POSNET_NORM2,
                         )
                     )
                     or not new_name.endswith(".weight")
@@ -689,6 +693,9 @@ class Model:
         return res
         # Marker: End get_vocab_base_pre
 
+    def _set_vocab_none(self) -> None:
+        self.gguf_writer.add_tokenizer_model("none")
+
     def _set_vocab_gpt2(self) -> None:
         tokens, toktypes, tokpre = self.get_vocab_base()
         self.gguf_writer.add_tokenizer_model("gpt2")
@@ -2027,6 +2034,44 @@ class Qwen2VLModel(Model):
             yield name, data
 
 
+@Model.register("WavTokenizerDec")
+class WavTokenizerDecModel(Model):
+    model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if \
+                name.endswith("codebook.cluster_size") or \
+                name.endswith("codebook.embed_avg") or \
+                name.endswith("codebook.inited"):
+            logger.debug(f"Skipping {name!r}")
+            return []
+
+        logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def set_vocab(self):
+        self._set_vocab_none()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size         (self.hparams["vocab_size"])
+        self.gguf_writer.add_features_length    (self.hparams["n_embd_features"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
+        self.gguf_writer.add_group_norm_eps     (self.hparams["group_norm_epsilon"])
+        self.gguf_writer.add_group_norm_groups  (self.hparams["group_norm_groups"])
+
+        self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
+        self.gguf_writer.add_posnet_block_count     (self.hparams["posnet"]["n_layer"])
+
+        self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
+        self.gguf_writer.add_convnext_block_count     (self.hparams["convnext"]["n_layer"])
+
+        self.gguf_writer.add_causal_attention(False)
+
+
 @Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
     model_arch = gguf.MODEL_ARCH.QWEN2MOE
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 21b31392e..66cfab2c3 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -51,6 +51,7 @@ else()
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
     add_subdirectory(tokenize)
+    add_subdirectory(tts)
     add_subdirectory(gen-docs)
     if (NOT GGML_BACKEND_DL)
         # these examples use the backends directly and cannot be built with dynamic loading
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index ba28c07c6..463b7c865 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -896,7 +896,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
                 mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
                 // stride = 1, padding = 1, bias is nullptr
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
 
                 // layer norm
                 // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
@@ -944,7 +944,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             // block_2
             {
                 // stride = 2
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
 
                 // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
                 // layer norm
@@ -1005,7 +1005,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             // mlp_2 ne [24, 24, 2048, 1]
             mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
             // weight ne = [3, 3, 2048, 1]
-            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
             peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
             peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
             mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
diff --git a/examples/tts/CMakeLists.txt b/examples/tts/CMakeLists.txt
new file mode 100644
index 000000000..c72bd814c
--- /dev/null
+++ b/examples/tts/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET llama-tts)
+add_executable(${TARGET} tts.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/tts/convert_pt_to_hf.py b/examples/tts/convert_pt_to_hf.py
new file mode 100644
index 000000000..8909a65fd
--- /dev/null
+++ b/examples/tts/convert_pt_to_hf.py
@@ -0,0 +1,180 @@
+# convert the https://huggingface.co/novateur/WavTokenizer-large-speech-75token to HF format
+# the goal is to be able to reuse the convert_hf_to_gguf.py after that to create a GGUF file with the WavTokenizer decoder
+#
+# TODO: this script is LLM-generated and probably very inefficient and should be rewritten
+
+import torch
+import json
+import os
+import sys
+import re
+
+from safetensors.torch import save_file
+
+# default
+model_path = './model.pt';
+
+# read from CLI
+if len(sys.argv) > 1:
+    model_path = sys.argv[1]
+
+# get the directory of the input model
+path_dst = os.path.dirname(model_path)
+
+print(f"Loading model from {model_path}")
+
+model = torch.load(model_path, map_location='cpu')
+
+#print(model)
+
+# print all keys
+for key in model.keys():
+    print(key)
+    if key == 'hyper_parameters':
+        #print(model[key])
+        # dump as json pretty
+        print(json.dumps(model[key], indent=4))
+    #if key != 'state_dict' and key != 'optimizer_states':
+    #    print(model[key])
+
+# Check if the loaded model is a state_dict or a model instance
+if isinstance(model, torch.nn.Module):
+    state_dict = model.state_dict()
+else:
+    state_dict = model
+
+# Print the structure of the state_dict to understand its format
+print("State dictionary keys:")
+for key in state_dict.keys():
+    print(key)
+
+# Ensure the state_dict is flat and contains only torch.Tensor objects
+def flatten_state_dict(state_dict, parent_key='', sep='.'):
+    items = []
+    items_new = []
+
+    for k, v in state_dict.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, torch.Tensor):
+            items.append((new_key, v))
+        elif isinstance(v, dict):
+            items.extend(flatten_state_dict(v, new_key, sep=sep).items())
+            return dict(items)
+
+    size_total_mb = 0
+
+    for key, value in list(items):
+        # keep only what we need for inference
+        if not key.startswith('state_dict.feature_extractor.encodec.quantizer.') and \
+           not key.startswith('state_dict.backbone.') and \
+           not key.startswith('state_dict.head.out'):
+               print('Skipping key: ', key)
+               continue
+
+        new_key = key
+
+        new_key = new_key.replace('state_dict.', '')
+        new_key = new_key.replace('pos_net', 'posnet')
+
+        # check if matches "backbone.posnet.%d.bias" or "backbone.posnet.%d.weight"
+        if new_key.startswith("backbone.posnet."):
+            match = re.match(r"backbone\.posnet\.(\d+)\.(bias|weight)", new_key)
+            if match:
+               new_key = f"backbone.posnet.{match.group(1)}.norm.{match.group(2)}"
+
+        # "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" -> "backbone.embedding.weight"
+        if new_key == "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed":
+            new_key = "backbone.embedding.weight"
+
+        # these are the only rows used
+        # ref: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/wav_tokenizer/audio_codec.py#L100
+        if new_key.endswith("norm.scale.weight"):
+            new_key = new_key.replace("norm.scale.weight", "norm.weight")
+            value = value[0]
+
+        if new_key.endswith("norm.shift.weight"):
+            new_key = new_key.replace("norm.shift.weight", "norm.bias")
+            value = value[0]
+
+        if new_key.endswith("gamma"):
+            new_key = new_key.replace("gamma", "gamma.weight")
+
+        # convert from 1D [768] to 2D [768, 1] so that ggml_add can broadcast the bias
+        if (new_key.endswith("norm.weight") or new_key.endswith("norm1.weight") or new_key.endswith("norm2.weight") or new_key.endswith(".bias")) and (new_key.startswith("backbone.posnet") or new_key.startswith("backbone.embed.bias")):
+            value = value.unsqueeze(1)
+
+        if new_key.endswith("dwconv.bias"):
+            value = value.unsqueeze(1)
+
+        size_mb = value.element_size() * value.nelement() / (1024 * 1024)
+        print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}")
+
+        size_total_mb += size_mb
+
+        #print(key, '->', new_key, ': ', value)
+        #print(key, '->', new_key)
+
+        items_new.append((new_key, value))
+
+    print(f"Total size: {size_total_mb:8.2f} MB")
+
+    return dict(items_new)
+
+flattened_state_dict = flatten_state_dict(state_dict)
+
+
+# Convert the model to the safetensors format
+output_path = path_dst + '/model.safetensors'
+save_file(flattened_state_dict, output_path)
+
+print(f"Model has been successfully converted and saved to {output_path}")
+
+# Calculate the total size of the .safetensors file
+total_size = os.path.getsize(output_path)
+
+# Create the weight map
+weight_map = {
+    "model.safetensors": ["*"]  # Assuming all weights are in one file
+}
+
+# Create metadata for the index.json file
+metadata = {
+    "total_size": total_size,
+    "weight_map": weight_map
+}
+
+# Save the metadata to index.json
+index_path = path_dst + '/index.json'
+with open(index_path, 'w') as f:
+    json.dump(metadata, f, indent=4)
+
+print(f"Metadata has been saved to {index_path}")
+
+config = {
+    "architectures": [
+        "WavTokenizerDec"
+    ],
+    "hidden_size": 1282,
+    "n_embd_features": 512,
+    "n_ff": 2304,
+    "vocab_size": 4096,
+    "n_head": 1,
+    "layer_norm_epsilon": 1e-6,
+    "group_norm_epsilon": 1e-6,
+    "group_norm_groups": 32,
+    "max_position_embeddings": 8192, # ?
+    "n_layer": 12,
+    "posnet": {
+        "n_embd": 768,
+        "n_layer": 6
+    },
+    "convnext": {
+        "n_embd": 768,
+        "n_layer": 12
+    },
+}
+
+with open(path_dst + '/config.json', 'w') as f:
+    json.dump(config, f, indent=4)
+
+print(f"Config has been saved to {path_dst + 'config.json'}")
diff --git a/examples/tts/tts-outetts.py b/examples/tts/tts-outetts.py
new file mode 100644
index 000000000..0f81192fc
--- /dev/null
+++ b/examples/tts/tts-outetts.py
@@ -0,0 +1,175 @@
+import sys
+#import json
+#import struct
+import requests
+import re
+
+def process_text(text: str):
+    text = re.sub(r'\d+(\.\d+)?', lambda x: x.group(), text.lower()) # TODO this needs to be fixed
+    text = re.sub(r'[-_/,\.\\]', ' ', text)
+    text = re.sub(r'[^a-z\s]', '', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text.split()
+
+# usage:
+# python tts-outetts.py http://server-llm:port http://server-dec:port "text"
+
+if len(sys.argv) <= 3:
+    print("usage: python tts-outetts.py http://server-llm:port http://server-dec:port \"text\"")
+    exit(1)
+
+host_llm = sys.argv[1]
+host_dec = sys.argv[2]
+text = sys.argv[3]
+
+prefix = """<|im_start|>
+<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>"""
+
+words = process_text(text)
+words = "<|text_sep|>".join([i.strip() for i in words])
+words += "<|text_end|>\n"
+
+# voice data
+# TODO: load from json
+#suffix = """<|audio_start|>
+#the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
+#overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
+#package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
+#from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
+#just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
+#two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
+#people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
+#is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
+#pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
+#remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
+#sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
+#i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
+#have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
+#some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
+#critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
+#about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
+#some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
+#of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
+#the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
+#gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
+#aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
+#but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
+#its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
+#still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
+#really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
+#enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
+#and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
+#it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
+#looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
+#lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>"""
+
+# TODO: tokenization is slow for some reason - here is pre-tokenized input
+suffix = [ 151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585, 152460, 153375, 151670, 198, 74455,
+          155808, 151669, 151799, 151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470, 151970, 153413,
+          152419, 153334, 153289, 153374, 153199, 152040, 153260, 152721, 152680, 153297, 152419, 153248, 152400,
+          152691, 153368, 153437, 151670, 198, 1722, 155828, 151669, 152607, 152256, 152991, 152299, 152688, 153163,
+          153016, 152789, 153198, 152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207, 152461, 153321,
+          153309, 151750, 152137, 153340, 152573, 152267, 153347, 151789, 152681, 153339, 151992, 152512, 151751,
+          152179, 153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904, 152311, 151670, 198, 1499, 155791,
+          151669, 152276, 152454, 153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226, 153043, 152325,
+          153267, 152622, 151670, 198, 4250, 155797, 151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
+          152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213, 152112, 153204, 151722, 152542, 151670, 198,
+          19789, 155796, 151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002, 152191, 151734, 152312, 152810,
+          152237, 153224, 153169, 153224, 152244, 153387, 153404, 151670, 198, 16069, 155811, 151669, 152265, 151946,
+          151808, 152412, 152363, 152305, 153156, 152733, 152810, 153157, 152016, 152100, 152069, 153234, 152317,
+          152589, 152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504, 153376, 152272, 152433, 152325,
+          151941, 151670, 198, 285, 155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381, 152474, 152680,
+          152157, 153255, 152324, 151682, 151670, 198, 32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
+          152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488, 153070, 151883, 152890, 152489, 153144,
+          153375, 152358, 151685, 152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669, 151902, 152720,
+          153377, 152027, 152378, 152821, 153207, 153459, 153028, 153068, 152507, 153255, 152158, 152921, 151958,
+          152609, 152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470, 152606, 152162, 152186, 153071,
+          152244, 153118, 153375, 153018, 152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736, 153380,
+          153502, 152702, 152115, 153181, 152735, 153277, 153457, 152393, 153112, 152595, 151670, 198, 19098, 155808,
+          151669, 152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239, 153163, 152922, 153402, 152034,
+          152591, 153438, 152215, 151673, 152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482, 152718,
+          152862, 153347, 151670, 198, 72, 155780, 151669, 151795, 152111, 152746, 152377, 153471, 152309, 151670, 198,
+          19016, 155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701, 152939, 152536, 152091, 151815, 152733,
+          151672, 151670, 198, 14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042, 153504, 152589, 153333,
+          151839, 151941, 153038, 153180, 151670, 198, 36996, 8303, 155832, 151669, 152231, 152256, 152835, 152801,
+          152985, 153400, 152393, 152818, 152765, 152249, 152600, 151699, 152302, 152752, 153018, 153009, 151992,
+          153054, 152847, 153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458, 152048, 152757, 152428,
+          153195, 151906, 153006, 153178, 153250, 152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
+          152228, 152733, 151670, 198, 9096, 155801, 151669, 151698, 153321, 152217, 153039, 152935, 153400, 152122,
+          152531, 153106, 152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851, 152901, 152885, 152594,
+          153446, 153080, 151670, 198, 14689, 155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191, 151673,
+          151690, 151698, 152714, 152846, 152981, 153171, 153384, 153364, 153188, 153246, 151670, 198, 1055, 155779,
+          151669, 151869, 152388, 152711, 153334, 151736, 151670, 198, 1782, 155780, 151669, 153483, 153240, 152241,
+          152558, 152697, 153046, 151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605, 153034, 153434,
+          153372, 153347, 151887, 152453, 152758, 152133, 152510, 152694, 152431, 152321, 153088, 152676, 152223,
+          152581, 152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032, 152903, 152859, 152989, 151748,
+          152669, 152661, 152650, 152409, 151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469, 152988,
+          152894, 151819, 152391, 153019, 152058, 153062, 153230, 151826, 152112, 152306, 152264, 152769, 153390,
+          152384, 152435, 152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540, 151919, 151893, 152558,
+          152817, 152946, 152956, 152129, 152715, 153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
+          151670, 198, 8088, 155792, 151669, 152452, 153497, 153353, 152679, 152533, 152382, 152374, 152611, 153341,
+          153163, 152285, 153411, 152495, 153141, 152320, 151670, 198, 1199, 155781, 151669, 151764, 152360, 153295,
+          152634, 153342, 152199, 152271, 151670, 198, 43366, 155799, 151669, 152308, 151682, 152889, 152016, 152385,
+          152629, 152495, 151826, 153321, 152958, 152180, 151886, 153432, 152922, 152128, 153024, 153040, 152593,
+          152287, 151677, 151670, 198, 53660, 155808, 151669, 151727, 152092, 152680, 153331, 151699, 152316, 152938,
+          152289, 152433, 153384, 151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691, 152489, 151941,
+          152049, 152034, 153053, 152179, 153160, 151676, 153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
+          152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234, 153135, 152291, 153235, 152143, 152583,
+          152402, 153483, 152678, 152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825, 152548, 153442,
+          152109, 152659, 153325, 152781, 152570, 152957, 151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
+          151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174, 151792, 153409, 153327, 152990, 151670, 198,
+          275, 155781, 151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974, 151670, 198, 94273, 155799,
+          151669, 152953, 152938, 153427, 152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331, 152257,
+          152987, 152777, 153448, 152408, 151696, 152408, 152326, 152699, 151670, 198, 385, 16239, 155828, 151669,
+          152306, 152268, 153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110, 152918, 152923, 152467,
+          152331, 153053, 153330, 151889, 153444, 152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
+          152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499, 152109, 152255, 151739, 152267, 152759,
+          153318, 153165, 153349, 151670, ]
+
+response = requests.post(
+    host_llm + "/completion",
+    json={
+        "prompt": [prefix + words, *suffix],
+        "n_predict": 1024,
+        "cache_prompt": True,
+        "return_tokens": True,
+        "samplers": ["top_k"],
+        "top_k": 16,
+        "seed": 1003,
+    }
+)
+
+response_json = response.json()
+
+#print(json.dumps(response_json, indent=4))
+#print(json.dumps(response_json["prompt"], indent=4).replace("\\n", "\n"))
+#print(json.dumps(response_json["timings"], indent=4))
+#print(json.dumps(response_json["tokens"], indent=4))
+
+codes = response_json["tokens"]
+
+codes = [t - 151672 for t in codes if t >= 151672 and t <= 155772]
+
+response = requests.post(
+    host_dec + "/embeddings",
+    json={
+        "input": [*codes],
+    }
+)
+
+response_json = response.json()
+
+#print(json.dumps(response_json, indent=4))
+
+# spectrogram
+embd = response_json[0]["embedding"]
+
+n_codes = len(embd)
+n_embd = len(embd[0])
+
+print('spectrogram generated: n_codes: %d, n_embd: %d' % (n_codes, n_embd))
+
+# post-process the spectrogram to convert to audio
+# TODO: see the tts.cpp:embd_to_audio() and implement it in Python
+print('converting to audio ...')
+print('TODO: see the tts.cpp:embd_to_audio() and implement it in Python')
diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp
new file mode 100644
index 000000000..7f36b80f0
--- /dev/null
+++ b/examples/tts/tts.cpp
@@ -0,0 +1,932 @@
+#include "arg.h"
+#include "common.h"
+#include "sampling.h"
+#include "log.h"
+#include "llama.h"
+
+#define _USE_MATH_DEFINES // For M_PI on MSVC
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <string>
+#include <thread>
+#include <vector>
+
+//
+// Terminal utils
+//
+
+#define SQR(X)    ((X) * (X))
+#define UNCUBE(x) x < 48 ? 0 : x < 115 ? 1 : (x - 35) / 40
+
+/**
+ * Quantizes 24-bit RGB to xterm256 code range [16,256).
+ */
+static int rgb2xterm256(int r, int g, int b) {
+    unsigned char cube[] = {0, 0137, 0207, 0257, 0327, 0377};
+    int av, ir, ig, ib, il, qr, qg, qb, ql;
+    av = r * .299 + g * .587 + b * .114 + .5;
+    ql = (il = av > 238 ? 23 : (av - 3) / 10) * 10 + 8;
+    qr = cube[(ir = UNCUBE(r))];
+    qg = cube[(ig = UNCUBE(g))];
+    qb = cube[(ib = UNCUBE(b))];
+    if (SQR(qr - r) + SQR(qg - g) + SQR(qb - b) <=
+        SQR(ql - r) + SQR(ql - g) + SQR(ql - b))
+        return ir * 36 + ig * 6 + ib + 020;
+    return il + 0350;
+}
+
+static std::string set_xterm256_foreground(int r, int g, int b) {
+    int x = rgb2xterm256(r, g, b);
+    std::ostringstream oss;
+    oss << "\033[38;5;" << x << "m";
+    return oss.str();
+}
+
+const std::vector<std::string> k_colors = {
+    set_xterm256_foreground(220,   5,  12),
+    set_xterm256_foreground(232,  96,  28),
+    set_xterm256_foreground(241, 147,  45),
+    set_xterm256_foreground(246, 193,  65),
+    set_xterm256_foreground(247, 240,  86),
+    set_xterm256_foreground(144, 201, 135),
+    set_xterm256_foreground( 78, 178, 101),
+};
+
+static void print_usage(int, char ** argv) {
+    LOG("\nexample usage:\n");
+    LOG("\n    %s -m model.gguf -p \"Hello!\"\n", argv[0]);
+    LOG("\n");
+}
+
+struct wav_header {
+    char riff[4] = {'R', 'I', 'F', 'F'};
+    uint32_t chunk_size;
+    char wave[4] = {'W', 'A', 'V', 'E'};
+    char fmt[4] = {'f', 'm', 't', ' '};
+    uint32_t fmt_chunk_size = 16;
+    uint16_t audio_format = 1; // PCM
+    uint16_t num_channels = 1; // Mono
+    uint32_t sample_rate;
+    uint32_t byte_rate;
+    uint16_t block_align;
+    uint16_t bits_per_sample = 16;
+    char data[4] = {'d', 'a', 't', 'a'};
+    uint32_t data_size;
+};
+
+static void save_wav16(const std::string & fname, const std::vector<float> & data, int sample_rate) {
+    std::ofstream file(fname, std::ios::binary);
+    if (!file) {
+        LOG_ERR("%s: Failed to open file '%s' for writing", __func__, fname.c_str());
+        return;
+    }
+
+    wav_header header;
+    header.sample_rate = sample_rate;
+    header.byte_rate = header.sample_rate * header.num_channels * (header.bits_per_sample / 8);
+    header.block_align = header.num_channels * (header.bits_per_sample / 8);
+    header.data_size = data.size() * (header.bits_per_sample / 8);
+    header.chunk_size = 36 + header.data_size;
+
+    file.write(reinterpret_cast<const char*>(&header), sizeof(header));
+
+    for (const auto & sample : data) {
+        int16_t pcm_sample = static_cast<int16_t>(std::clamp(sample * 32767.0, -32768.0, 32767.0));
+        file.write(reinterpret_cast<const char*>(&pcm_sample), sizeof(pcm_sample));
+    }
+
+    file.close();
+}
+
+static void fill_hann_window(int length, bool periodic, float * output) {
+    int offset = -1;
+    if (periodic) {
+        offset = 0;
+    }
+    for (int i = 0; i < length; i++) {
+        output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
+    }
+}
+
+// very poor-man fft
+static void twiddle(float * real, float * imag, int k, int N) {
+    float angle = 2 * M_PI * k / N;
+    *real = cos(angle);
+    *imag = sin(angle);
+}
+
+static void irfft(int n, const float * inp_cplx, float * out_real) {
+    int N = n / 2 + 1;
+
+    std::vector<float> real_input(N);
+    std::vector<float> imag_input(N);
+    for (int i = 0; i < N; ++i) {
+        real_input[i] = inp_cplx[2 * i];
+        imag_input[i] = inp_cplx[2 * i + 1];
+    }
+
+    std::vector<float> real_output(n);
+    std::vector<float> imag_output(n);
+
+    for (int k = 0; k < n; ++k) {
+        real_output[k] = 0.0f;
+        imag_output[k] = 0.0f;
+        for (int m = 0; m < N; ++m) {
+            float twiddle_real;
+            float twiddle_imag;
+
+            twiddle(&twiddle_real, &twiddle_imag, k * m, n);
+
+            real_output[k] += real_input[m] * twiddle_real - imag_input[m] * twiddle_imag;
+            imag_output[k] += real_input[m] * twiddle_imag + imag_input[m] * twiddle_real;
+        }
+    }
+
+    for (int i = 0; i < n; ++i) {
+        out_real[i] = real_output[i] / N;
+    }
+}
+
+//
+//  y = torch.nn.functional.fold(
+//       data, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length),
+//  )[:, 0, 0, pad:-pad]
+//
+// data.shape =  torch.Size([1, 1280, 261])
+// output_size =  84480
+// win_length =  1280
+// hop_length =  320
+// pad =  480
+//
+static void fold(const std::vector<float> & data, int64_t n_out, int64_t n_win, int64_t n_hop, int64_t n_pad, std::vector<float> & output) {
+    int64_t output_height = n_out;
+    int64_t kernel_w = n_win;
+    int64_t stride_w = n_hop;
+    int64_t width    = n_out;
+
+    output.resize(width, 0.0f);
+
+    int64_t col_idx = 0;
+    for (int64_t w_col = 0; w_col < width; ++w_col) {
+        int64_t start = w_col * stride_w - n_pad;
+        int64_t end   = start + kernel_w;
+
+        for (int64_t w_im = start; w_im < end; ++w_im) {
+            if (w_im >= 0 && w_im < output_height && col_idx < (int64_t) data.size()) {
+                output[w_im] += data[col_idx];
+            }
+            col_idx++;
+        }
+    }
+
+    output.resize(n_out - 2 * n_pad);
+}
+
+// TODO: not optimized at all
+static std::vector<float> embd_to_audio(
+        const float * embd,
+        const int n_codes,
+        const int n_embd,
+        const int n_thread) {
+    const int n_fft = 1280;
+    const int n_hop = 320;
+    const int n_win = 1280;
+    const int n_pad = (n_win - n_hop)/2;
+    const int n_out = (n_codes - 1)*n_hop + n_win;
+
+    std::vector<float> hann(n_fft);
+
+    fill_hann_window(hann.size(), true, hann.data());
+
+    int n_spec = n_embd*n_codes;
+
+    std::vector<float> E (n_spec);
+    std::vector<float> S (n_spec);
+    std::vector<float> ST(n_spec);
+
+    for (int l = 0; l < n_codes; ++l) {
+        for (int k = 0; k < n_embd; ++k) {
+            E[k*n_codes + l] = embd[l*n_embd + k];
+        }
+    }
+
+    for (int k = 0; k < n_embd/2; ++k) {
+        for (int l = 0; l < n_codes; ++l) {
+            float mag = E[(k           )*n_codes + l];
+            float phi = E[(k + n_embd/2)*n_codes + l];
+
+            mag = exp(mag);
+
+            if (mag > 1e2) {
+                mag = 1e2;
+            }
+            S[2*(k*n_codes + l) + 0] = mag*cosf(phi);
+            S[2*(k*n_codes + l) + 1] = mag*sinf(phi);
+        }
+    }
+
+    for (int l = 0; l < n_codes; ++l) {
+        for (int k = 0; k < n_embd/2; ++k) {
+            ST[l*n_embd + 2*k + 0] = S[2*(k*n_codes + l) + 0];
+            ST[l*n_embd + 2*k + 1] = S[2*(k*n_codes + l) + 1];
+        }
+    }
+
+    std::vector<float> res  (n_codes*n_fft);
+    std::vector<float> hann2(n_codes*n_fft);
+
+    std::vector<std::thread> workers(n_thread);
+    for (int i = 0; i < n_thread; ++i) {
+        workers[i] = std::thread([&, i]() {
+            for (int l = i; l < n_codes; l += n_thread) {
+                irfft(n_fft, ST.data() + l*n_embd, res.data() + l*n_fft);
+                for (int j = 0; j < n_fft; ++j) {
+                    res  [l*n_fft + j] *= hann[j];
+                    hann2[l*n_fft + j]  = hann[j] * hann[j];
+                }
+            }
+        });
+    }
+    for (int i = 0; i < n_thread; ++i) {
+        workers[i].join();
+    }
+
+    std::vector<float> audio;
+    std::vector<float> env;
+
+    fold(res,   n_out, n_win, n_hop, n_pad, audio);
+    fold(hann2, n_out, n_win, n_hop, n_pad, env); // TODO: can be done once
+
+    for (size_t i = 0; i < audio.size(); ++i) {
+        audio[i] /= env[i];
+    }
+
+    return audio;
+}
+
+static const std::map<int, std::string> ones = {
+    {0, "zero"}, {1, "one"}, {2, "two"}, {3, "three"}, {4, "four"},
+    {5, "five"}, {6, "six"}, {7, "seven"}, {8, "eight"}, {9, "nine"},
+    {10, "ten"}, {11, "eleven"}, {12, "twelve"}, {13, "thirteen"}, {14, "fourteen"},
+    {15, "fifteen"}, {16, "sixteen"}, {17, "seventeen"}, {18, "eighteen"}, {19, "nineteen"}
+};
+
+static const std::map<int, std::string> tens = {
+    {2, "twenty"}, {3, "thirty"}, {4, "forty"}, {5, "fifty"},
+    {6, "sixty"}, {7, "seventy"}, {8, "eighty"}, {9, "ninety"}
+};
+
+// Convert a number less than 1000 to words
+static std::string convert_less_than_thousand(int num) {
+    std::string result;
+
+    if (num >= 100) {
+        result += ones.at(num / 100) + " hundred ";
+        num %= 100;
+    }
+
+    if (num >= 20) {
+        result += tens.at(num / 10);
+        if (num % 10 > 0) {
+            result += "-" + ones.at(num % 10);
+        }
+    } else if (num > 0) {
+        result += ones.at(num);
+    }
+
+    return result;
+}
+
+static std::string number_to_words(const std::string & number_str) {
+    try {
+        size_t decimal_pos = number_str.find('.');
+        std::string integer_part = number_str.substr(0, decimal_pos);
+
+        int int_number = std::stoi(integer_part);
+        std::string result;
+
+        if (int_number == 0) {
+            result = "zero";
+        } else {
+            if (int_number >= 1000000000) {
+                int billions = int_number / 1000000000;
+                result += convert_less_than_thousand(billions) + " billion ";
+                int_number %= 1000000000;
+            }
+
+            if (int_number >= 1000000) {
+                int millions = int_number / 1000000;
+                result += convert_less_than_thousand(millions) + " million ";
+                int_number %= 1000000;
+            }
+
+            if (int_number >= 1000) {
+                int thousands = int_number / 1000;
+                result += convert_less_than_thousand(thousands) + " thousand ";
+                int_number %= 1000;
+            }
+
+            if (int_number > 0) {
+                result += convert_less_than_thousand(int_number);
+            }
+        }
+
+        // Handle decimal part
+        if (decimal_pos != std::string::npos) {
+            result += " point";
+            std::string decimal_part = number_str.substr(decimal_pos + 1);
+            for (char digit : decimal_part) {
+                result += " " + ones.at(digit - '0');
+            }
+        }
+
+        return result;
+    } catch (const std::exception& e) {
+        // Skip if fails
+        return " ";
+    }
+}
+
+static std::string replace_numbers_with_words(const std::string & input_text) {
+    std::regex number_pattern(R"(\d+(\.\d+)?)");
+    std::string result;
+    auto it = std::sregex_iterator(input_text.begin(), input_text.end(), number_pattern);
+    auto end = std::sregex_iterator();
+
+    size_t last_pos = 0;
+    for (std::sregex_iterator i = it; i != end; ++i) {
+        const std::smatch& match = *i;
+        result.append(input_text, last_pos, match.position() - last_pos);
+        result.append(number_to_words(match.str()));
+        last_pos = match.position() + match.length();
+    }
+    result.append(input_text, last_pos);
+
+    return result;
+}
+
+// Based on: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/version/v1/prompt_processor.py#L39
+static std::string process_text(const std::string & text) {
+
+    // For now I skipped text romanization as I am unsure how to handle
+    // uroman and MeCab implementations in C++
+    // maybe something like https://github.com/anyascii/anyascii/ could work.
+    // currently only English would be supported in this function
+
+    std::string processed_text = replace_numbers_with_words(text);
+
+    std::transform(processed_text.begin(), processed_text.end(),
+                  processed_text.begin(), ::tolower);
+
+    std::regex special_chars(R"([-_/,\.\\])");
+    processed_text = std::regex_replace(processed_text, special_chars, " ");
+
+    std::regex non_alpha(R"([^a-z\s])");
+    processed_text = std::regex_replace(processed_text, non_alpha, "");
+
+    std::regex multiple_spaces(R"(\s+)");
+    processed_text = std::regex_replace(processed_text, multiple_spaces, " ");
+
+    processed_text = std::regex_replace(processed_text, std::regex(R"(^\s+|\s+$)"), "");
+
+    /*
+        Replace spaces with the separator token same as in line 365
+
+        for (auto & c : prompt_user) {
+        if (c == ' ') {
+            prompt_clean += "<|text_sep|>";
+    */
+    processed_text = std::regex_replace(processed_text, std::regex(R"(\s)"), "<|text_sep|>");
+
+    return processed_text;
+}
+
+static void prompt_add(llama_tokens & prompt, llama_token token) {
+    prompt.push_back(token);
+}
+
+static void prompt_add(llama_tokens & prompt, const llama_tokens & tokens) {
+    prompt.insert(prompt.end(), tokens.begin(), tokens.end());
+}
+
+static void prompt_add(llama_tokens & prompt, const llama_model * model, const std::string & txt, bool add_special, bool parse_special) {
+    auto tmp = common_tokenize(model, txt, add_special, parse_special);
+    prompt_add(prompt, tmp);
+}
+
+static void prompt_init(llama_tokens & prompt, const llama_model * model) {
+    prompt.clear();
+
+    prompt_add(prompt, model, "<|im_start|>\n", true, true);
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.prompt = "";
+
+    params.n_predict = 4096;
+    params.n_batch   = 8192;
+    params.n_ctx     = 8192;
+
+    params.sampling.top_k = 4;
+    params.sampling.samplers = { COMMON_SAMPLER_TYPE_TOP_K, };
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_TTS, print_usage)) {
+        return 1;
+    }
+
+    const int n_parallel = params.n_parallel;
+    const int n_predict  = params.n_predict;
+
+    common_init();
+
+    // init LLM
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model_ttc = NULL; // text-to-codes
+    llama_model * model_cts = NULL; // codes-to-speech
+
+    llama_context * ctx_ttc = NULL;
+    llama_context * ctx_cts = NULL;
+
+    common_init_result llama_init_ttc = common_init_from_params(params);
+    model_ttc = llama_init_ttc.model;
+    ctx_ttc = llama_init_ttc.context;
+
+    // TODO: refactor in a common struct
+    params.model     = params.vocoder.model;
+    params.model_url = params.vocoder.model_url;
+    params.hf_repo   = params.vocoder.hf_repo;
+    params.hf_file   = params.vocoder.hf_file;
+
+    params.embedding = true;
+
+    common_init_result llama_init_cts = common_init_from_params(params);
+    model_cts = llama_init_cts.model;
+    ctx_cts = llama_init_cts.context;
+
+    std::vector<common_sampler *> smpl(n_parallel);
+    for (int i = 0; i < n_parallel; ++i) {
+        params.sampling.no_perf = (i != 0);
+        params.sampling.seed = params.sampling.seed + 1;
+
+        smpl[i] = common_sampler_init(model_ttc, params.sampling);
+    }
+
+    LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl[0]));
+    LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str());
+    LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl[0]).c_str());
+
+    LOG_INF("%s: loading done\n", __func__);
+
+    const auto t_main_start = ggml_time_us();
+
+    std::vector<llama_token> codes;
+
+    // process prompt and generate voice codes
+    {
+        LOG_INF("%s: constructing prompt ..\n", __func__);
+
+        std::vector<llama_token> prompt_inp;
+
+        prompt_init(prompt_inp, model_ttc);
+
+        prompt_add(prompt_inp, model_ttc, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true);
+
+        // convert the input text into the necessary format expected by OuteTTS
+        {
+            std::string prompt_clean = process_text(params.prompt);
+
+            LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
+
+            prompt_add(prompt_inp, model_ttc, prompt_clean, false, true);
+        }
+
+        prompt_add(prompt_inp, model_ttc, "<|text_end|>\n", false, true);
+
+        // disabled to save time on tokenizing each time
+        // TODO: load voices from the json files
+#if 0
+        const std::string voice_data = R"(<|audio_start|>
+the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
+overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
+package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
+from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
+just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
+two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
+people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
+is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
+pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
+remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
+sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
+i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
+have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
+some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
+critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
+about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
+some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
+of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
+the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
+gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
+aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
+but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
+its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
+still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
+really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
+enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
+and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
+it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
+looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
+lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>)";
+
+        auto tmp = common_tokenize(model_ttc, voice_data, false, true);
+        printf("\n\n");
+        for (int i = 0; i < tmp.size(); ++i) {
+            printf("%d, ", tmp[i]);
+        }
+        printf("\n\n");
+#else
+        prompt_add(prompt_inp, llama_tokens {
+            151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585,
+            152460, 153375, 151670, 198, 74455, 155808, 151669, 151799,
+            151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470,
+            151970, 153413, 152419, 153334, 153289, 153374, 153199, 152040,
+            153260, 152721, 152680, 153297, 152419, 153248, 152400, 152691,
+            153368, 153437, 151670, 198, 1722, 155828, 151669, 152607,
+            152256, 152991, 152299, 152688, 153163, 153016, 152789, 153198,
+            152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207,
+            152461, 153321, 153309, 151750, 152137, 153340, 152573, 152267,
+            153347, 151789, 152681, 153339, 151992, 152512, 151751, 152179,
+            153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904,
+            152311, 151670, 198, 1499, 155791, 151669, 152276, 152454,
+            153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226,
+            153043, 152325, 153267, 152622, 151670, 198, 4250, 155797,
+            151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
+            152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213,
+            152112, 153204, 151722, 152542, 151670, 198, 19789, 155796,
+            151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002,
+            152191, 151734, 152312, 152810, 152237, 153224, 153169, 153224,
+            152244, 153387, 153404, 151670, 198, 16069, 155811, 151669,
+            152265, 151946, 151808, 152412, 152363, 152305, 153156, 152733,
+            152810, 153157, 152016, 152100, 152069, 153234, 152317, 152589,
+            152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504,
+            153376, 152272, 152433, 152325, 151941, 151670, 198, 285,
+            155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381,
+            152474, 152680, 152157, 153255, 152324, 151682, 151670, 198,
+            32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
+            152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488,
+            153070, 151883, 152890, 152489, 153144, 153375, 152358, 151685,
+            152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669,
+            151902, 152720, 153377, 152027, 152378, 152821, 153207, 153459,
+            153028, 153068, 152507, 153255, 152158, 152921, 151958, 152609,
+            152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470,
+            152606, 152162, 152186, 153071, 152244, 153118, 153375, 153018,
+            152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736,
+            153380, 153502, 152702, 152115, 153181, 152735, 153277, 153457,
+            152393, 153112, 152595, 151670, 198, 19098, 155808, 151669,
+            152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239,
+            153163, 152922, 153402, 152034, 152591, 153438, 152215, 151673,
+            152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482,
+            152718, 152862, 153347, 151670, 198, 72, 155780, 151669, 151795,
+            152111, 152746, 152377, 153471, 152309, 151670, 198, 19016,
+            155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701,
+            152939, 152536, 152091, 151815, 152733, 151672, 151670, 198,
+            14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042,
+            153504, 152589, 153333, 151839, 151941, 153038, 153180, 151670,
+            198, 36996, 8303, 155832, 151669, 152231, 152256, 152835,
+            152801, 152985, 153400, 152393, 152818, 152765, 152249, 152600,
+            151699, 152302, 152752, 153018, 153009, 151992, 153054, 152847,
+            153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458,
+            152048, 152757, 152428, 153195, 151906, 153006, 153178, 153250,
+            152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
+            152228, 152733, 151670, 198, 9096, 155801, 151669, 151698,
+            153321, 152217, 153039, 152935, 153400, 152122, 152531, 153106,
+            152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851,
+            152901, 152885, 152594, 153446, 153080, 151670, 198, 14689,
+            155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191,
+            151673, 151690, 151698, 152714, 152846, 152981, 153171, 153384,
+            153364, 153188, 153246, 151670, 198, 1055, 155779, 151669,
+            151869, 152388, 152711, 153334, 151736, 151670, 198, 1782,
+            155780, 151669, 153483, 153240, 152241, 152558, 152697, 153046,
+            151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605,
+            153034, 153434, 153372, 153347, 151887, 152453, 152758, 152133,
+            152510, 152694, 152431, 152321, 153088, 152676, 152223, 152581,
+            152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032,
+            152903, 152859, 152989, 151748, 152669, 152661, 152650, 152409,
+            151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469,
+            152988, 152894, 151819, 152391, 153019, 152058, 153062, 153230,
+            151826, 152112, 152306, 152264, 152769, 153390, 152384, 152435,
+            152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540,
+            151919, 151893, 152558, 152817, 152946, 152956, 152129, 152715,
+            153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
+            151670, 198, 8088, 155792, 151669, 152452, 153497, 153353,
+            152679, 152533, 152382, 152374, 152611, 153341, 153163, 152285,
+            153411, 152495, 153141, 152320, 151670, 198, 1199, 155781,
+            151669, 151764, 152360, 153295, 152634, 153342, 152199, 152271,
+            151670, 198, 43366, 155799, 151669, 152308, 151682, 152889,
+            152016, 152385, 152629, 152495, 151826, 153321, 152958, 152180,
+            151886, 153432, 152922, 152128, 153024, 153040, 152593, 152287,
+            151677, 151670, 198, 53660, 155808, 151669, 151727, 152092,
+            152680, 153331, 151699, 152316, 152938, 152289, 152433, 153384,
+            151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691,
+            152489, 151941, 152049, 152034, 153053, 152179, 153160, 151676,
+            153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
+            152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234,
+            153135, 152291, 153235, 152143, 152583, 152402, 153483, 152678,
+            152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825,
+            152548, 153442, 152109, 152659, 153325, 152781, 152570, 152957,
+            151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
+            151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174,
+            151792, 153409, 153327, 152990, 151670, 198, 275, 155781,
+            151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974,
+            151670, 198, 94273, 155799, 151669, 152953, 152938, 153427,
+            152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331,
+            152257, 152987, 152777, 153448, 152408, 151696, 152408, 152326,
+            152699, 151670, 198, 385, 16239, 155828, 151669, 152306, 152268,
+            153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110,
+            152918, 152923, 152467, 152331, 153053, 153330, 151889, 153444,
+            152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
+            152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499,
+            152109, 152255, 151739, 152267, 152759, 153318, 153165, 153349,
+            151670,});
+#endif
+
+        // print the prompt token-by-token
+
+        LOG("\n");
+
+        for (auto id : prompt_inp) {
+            LOG("%s", common_token_to_piece(ctx_ttc, id).c_str());
+        }
+
+        LOG_INF("%s: prompt size: %d\n", __func__, (int) prompt_inp.size());
+
+        LOG("\n");
+
+        // create a llama_batch
+        // we use this object to submit token data for decoding
+        llama_batch batch = llama_batch_init(std::max(prompt_inp.size(), (size_t) n_parallel), 0, n_parallel);
+
+        std::vector<llama_seq_id> seq_ids(n_parallel, 0);
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            seq_ids[i] = i;
+        }
+
+        // evaluate the initial prompt
+        for (size_t i = 0; i < prompt_inp.size(); ++i) {
+            common_batch_add(batch, prompt_inp[i], i, seq_ids, false);
+        }
+        GGML_ASSERT(batch.n_tokens == (int) prompt_inp.size());
+
+        // llama_decode will output logits only for the last token of the prompt
+        batch.logits[batch.n_tokens - 1] = true;
+
+        if (llama_decode(ctx_ttc, batch) != 0) {
+            LOG_ERR("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        if (n_parallel > 1) {
+            LOG_INF("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+        }
+
+        llama_synchronize(ctx_ttc);
+
+        LOG_INF("%s: time for prompt: %.3f ms\n\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
+
+        const auto t_dec_start = ggml_time_us();
+
+        // main loop
+
+        // remember the batch index of the last token for each parallel sequence
+        // we need this to determine which logits to sample from
+        std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
+
+        int n_past   = batch.n_tokens;
+        int n_decode = 0;
+
+        while (n_decode <= n_predict) {
+            // prepare the next batch
+            common_batch_clear(batch);
+
+            // sample the next token for each parallel sequence / stream
+            for (int32_t i = 0; i < n_parallel; ++i) {
+                if (i_batch[i] < 0) {
+                    // the stream has already finished
+                    continue;
+                }
+
+                const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
+
+                common_sampler_accept(smpl[i], new_token_id, true);
+
+                codes.push_back(new_token_id);
+
+                const auto * cands = common_sampler_get_candidates(smpl[i]);
+
+                // is it an end of generation? -> mark the stream as finished
+                if (llama_token_is_eog(model_ttc, new_token_id) || n_decode == n_predict) {
+                    std::string reason;
+                    if (llama_token_is_eog(model_ttc, new_token_id)) {
+                        reason = "eos";
+                    } else {
+                        reason = "n_predict";
+                    }
+
+                    i_batch[i] = -1;
+
+                    LOG("\n");
+                    if (n_parallel > 1) {
+                        LOG_CNT("\n");
+                        LOG_INF("%s: stream %d finished at n_past = %d, reason = '%s'\n", __func__, i, n_past, reason.c_str());
+                    }
+
+                    continue;
+                }
+
+                {
+                    const float p = cands->data[cands->selected].p;
+
+                    const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) ((3*p)*float(k_colors.size()))));
+
+                    LOG_CNT("%s%d%s", k_colors[col].c_str(), i, "\033[0m");
+                    //LOG_CNT("%d", i);
+                }
+
+                i_batch[i] = batch.n_tokens;
+
+                // push this new token for next evaluation
+                common_batch_add(batch, new_token_id, n_past, { i }, true);
+            }
+
+            // all streams are finished
+            if (batch.n_tokens == 0) {
+                break;
+            }
+
+            n_decode += 1;
+            n_past += 1;
+
+            // evaluate the current batch with the transformer model
+            if (llama_decode(ctx_ttc, batch)) {
+                LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
+                return 1;
+            }
+        }
+
+        llama_batch_free(batch);
+
+        LOG("\n");
+        LOG_INF("%s: time for decoder:       %.3f ms\n", __func__, (ggml_time_us() - t_dec_start) / 1000.0f);
+    }
+
+    common_perf_print(ctx_ttc, smpl[0]);
+
+    //std::vector<llama_token> codes = {198, 88225, 155856, 151669, 152205,
+    //    153064, 152537, 153421, 153209, 152524, 151689, 152993, 152438, 152695,
+    //    153091, 152945, 152829, 152534, 152934, 153020, 151997, 152263, 153010,
+    //    153146, 152399, 153208, 152496, 151793, 152848, 152263, 152571, 153286,
+    //    152227, 153300, 152934, 152263, 153208, 152263, 152965, 152430, 152296,
+    //    153146, 152920, 152376, 152556, 153363, 151775, 152044, 152972, 152690,
+    //    153379, 152368, 152233, 153422, 152490, 151996, 152022, 151694, 152061,
+    //    153238, 152539, 153356, 152640, 153021, 153123, 151962, 153094, 151670,
+    //    198, 20339, 13189, 155824, 151669, 152070, 152007, 152910, 151683,
+    //    152000, 152373, 152760, 152046, 151735, 152334, 152394, 153073, 152908,
+    //    151856, 151953, 153247, 153293, 151903, 153480, 153168, 152478, 153359,
+    //    153429, 151905, 151678, 152567, 152411, 152165, 152556, 153075, 153424,
+    //    151993, 152999, 153078, 152151, 152088, 153389, 152484, 151874, 151670,
+    //    198, 285, 155784, 151669, 152226, 152126, 152638, 153215, 151729,
+    //    152959, 153479, 153059, 151838, 151670, 198, 1782, 155783, 151669,
+    //    153288, 153055, 153314, 152497, 152962, 152741, 152076, 153253, 151670,
+    //    198, 471, 16488, 155825, 151669, 152060, 152916, 151893, 153469, 152501,
+    //    152080, 152743, 151932, 153161, 152096, 152761, 152698, 153401, 153242,
+    //    153336, 152441, 152838, 153467, 152706, 153496, 153310, 152422, 153360,
+    //    153115, 152763, 151998, 152373, 153450, 152554, 151968, 153323, 152055,
+    //    152468, 153111, 153358, 152813, 152010, 151770, 152823, 152960, 151670,
+    //    198, 22627, 155823, 151669, 152814, 152366, 153484, 152931, 153441,
+    //    152164, 152877, 152915, 153463, 151692, 152911, 152747, 152776, 151831,
+    //    153449, 151882, 152975, 152031, 152513, 153150, 152448, 152667, 153133,
+    //    153189, 152619, 153466, 152054, 152106, 153119, 152277, 152439, 153109,
+    //    152997, 152141, 153154, 153256, 153311, 151922, 151670, 198, 1055,
+    //    155781, 151669, 152633, 151850, 153060, 153270, 152560, 153348, 152729,
+    //    151670, 198, 25312, 155803, 151669, 152521, 153403, 152561, 153337,
+    //    153383, 152199, 153493, 153326, 151830, 152254, 152248, 152349, 152153,
+    //    153007, 151823, 153037, 152575, 152457, 152406, 152592, 153116, 153365,
+    //    153456, 151670, 198, 88225, 155817, 151669, 153271, 151925, 152218,
+    //    152418, 152253, 153140, 151903, 153151, 152626, 152338, 152647, 153464,
+    //    152785, 152768, 151711, 152037, 152033, 151804, 152216, 151701, 151855,
+    //    152348, 152995, 152955, 152905, 152342, 152340, 153391, 153453, 152418,
+    //    153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645};
+
+    {
+        const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
+
+        LOG("\n");
+        LOG_INF("codes: '%s'\n", inp_txt.c_str());
+        LOG_INF("%s: codes size: %d\n", __func__, (int) codes.size());
+    }
+
+    // remove all non-audio tokens (i.e. < 151672 || > 155772)
+    codes.erase(std::remove_if(codes.begin(), codes.end(), [](llama_token t) { return t < 151672 || t > 155772; }), codes.end());
+
+    {
+        const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
+        LOG_INF("codes audio: '%s'\n", inp_txt.c_str());
+        LOG_INF("%s: codes audio size: %d\n", __func__, (int) codes.size());
+    }
+
+    for (auto & token : codes) {
+        token -= 151672;
+    }
+
+    const auto t_voc_start = ggml_time_us();
+
+    const int n_codes = codes.size();
+
+    llama_batch batch = llama_batch_init(n_codes, 0, 1);
+
+    for (size_t i = 0; i < codes.size(); ++i) {
+        common_batch_add(batch, codes[i], i, { 0 }, true); // TODO: all logits?
+    }
+    GGML_ASSERT(batch.n_tokens == n_codes);
+
+    if (llama_decode(ctx_cts, batch) != 0) {
+        LOG_ERR("%s: llama_decode() failed\n", __func__);
+        return 1;
+    }
+
+    llama_synchronize(ctx_cts);
+
+    LOG_INF("%s: time for vocoder:      %.3f ms\n", __func__, (ggml_time_us() - t_voc_start) / 1000.0f);
+
+    const auto t_spec_start = ggml_time_us();
+
+#if 1
+    // spectral operations
+    const int n_embd = llama_n_embd(model_cts);
+    const float * embd = llama_get_embeddings(ctx_cts);
+
+    auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads);
+
+#else
+    // read the spectrogram from a file for debugging purposes
+    std::vector<float> audio;
+    {
+        std::ifstream fin("out.bin", std::ios::binary);
+        if (!fin) {
+            LOG_ERR("%s: failed to open file '%s'\n", __func__, "out.bin");
+            return 1;
+        }
+
+        std::vector<float> embd;
+
+        int n_codes;
+        int n_embd;
+
+        fin.read(reinterpret_cast<char *>(&n_codes), sizeof(int));
+        fin.read(reinterpret_cast<char *>(&n_embd), sizeof(int));
+
+        embd.resize(n_codes * n_embd);
+        fin.read(reinterpret_cast<char *>(embd.data()), n_codes * n_embd * sizeof(float));
+        fin.close();
+
+        LOG_INF("%s: n_codes: %d, n_embd: %d\n", __func__, n_codes, n_embd);
+
+        audio = embd_to_audio(embd.data(), n_codes, n_embd, params.cpuparams.n_threads);
+    }
+#endif
+
+    const std::string fname = "output.wav";
+
+    const int n_sr = 24000; // sampling rate
+
+    // zero out first 0.25 seconds
+    for (int i = 0; i < 24000/4; ++i) {
+        audio[i] = 0.0f;
+    }
+
+    LOG_INF("%s: time for spectral ops: %.3f ms\n", __func__, (ggml_time_us() - t_spec_start) / 1000.0f);
+    LOG_INF("%s: total time:            %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
+
+    save_wav16(fname, audio, n_sr);
+
+    LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str());
+
+    llama_free(ctx_ttc);
+    llama_free_model(model_ttc);
+
+    llama_free(ctx_cts);
+    llama_free_model(model_cts);
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index b0c1ac9ce..c714fc8c8 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1564,17 +1564,6 @@ extern "C" {
         int                   d1, // dilation dimension 1
         bool                  is_2D);
 
-    GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,  // convolution kernel
-            struct ggml_tensor  * b,  // data
-            int                  s0,  // stride dimension 0
-            int                  s1,  // stride dimension 1
-            int                  p0,  // padding dimension 0
-            int                  p1,  // padding dimension 1
-            int                  d0,  // dilation dimension 0
-            int                  d1); // dilation dimension 1
-
     GGML_API struct ggml_tensor * ggml_conv_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,   // convolution kernel
@@ -1592,6 +1581,23 @@ extern "C" {
             int                   s,  // stride
             int                   d); // dilation
 
+    // depthwise
+    // TODO: this is very likely wrong for some cases! - needs more testing
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   d0); // dilation
+
     GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,   // convolution kernel
@@ -1611,7 +1617,6 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
-
     // kernel size is a->ne[0] x a->ne[1]
     // stride is equal to kernel size
     // padding is zero
@@ -1638,6 +1643,18 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // depthwise
+    GGML_API struct ggml_tensor * ggml_conv_2d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // convolution kernel
+            struct ggml_tensor  * b,  // data
+            int                  s0,  // stride dimension 0
+            int                  s1,  // stride dimension 1
+            int                  p0,  // padding dimension 0
+            int                  p1,  // padding dimension 1
+            int                  d0,  // dilation dimension 0
+            int                  d1); // dilation dimension 1
+
     GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 0efd2b2eb..2bbe5f482 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3760,104 +3760,10 @@ struct ggml_tensor * ggml_clamp(
     return result;
 }
 
-// ggml_conv_1d
-
 static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
     return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
 }
 
-GGML_API struct ggml_tensor * ggml_conv_1d(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        int                   s0,
-        int                   p0,
-        int                   d0) {
-    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
-
-    struct ggml_tensor * result =
-        ggml_mul_mat(ctx,
-                ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
-                ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2]));                    // [OC，IC, K] => [OC, IC * K]
-
-    result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
-
-    return result;
-}
-
-// ggml_conv_1d_ph
-
-struct ggml_tensor* ggml_conv_1d_ph(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        int                   s,
-        int                   d) {
-    return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
-}
-
-// ggml_conv_transpose_1d
-
-static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
-    return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
-}
-
-GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        int                   s0,
-        int                   p0,
-        int                   d0) {
-    GGML_ASSERT(ggml_is_matrix(b));
-    GGML_ASSERT(a->ne[2] == b->ne[1]);
-    GGML_ASSERT(a->ne[3] == 1);
-
-    GGML_ASSERT(p0 == 0);
-    GGML_ASSERT(d0 == 1);
-
-    const int64_t ne[4] = {
-        ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
-        a->ne[1], b->ne[2], 1,
-    };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
-
-    int32_t params[] = { s0, p0, d0 };
-    ggml_set_op_params(result, params, sizeof(params));
-
-    result->op     = GGML_OP_CONV_TRANSPOSE_1D;
-    result->src[0] = a;
-    result->src[1] = b;
-
-    return result;
-}
-
-// ggml_conv_depthwise
-
-struct ggml_tensor * ggml_conv_depthwise_2d(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        int                   s0,
-        int                   s1,
-        int                   p0,
-        int                   p1,
-        int                   d0,
-        int                   d1) {
-    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
-    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
-                                        ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
-                                        s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
-    struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
-
-    new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2],  new_a->ne[3], 1);                       // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
-    struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
-    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
-
-    return result;
-}
-// ggml_conv_2d
-
 // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
 // a: [OC，IC, KH, KW]
 // b: [N, IC, IH, IW]
@@ -3874,10 +3780,11 @@ struct ggml_tensor * ggml_im2col(
         int                   d1,
         bool                  is_2D,
         enum ggml_type        dst_type) {
-    if(is_2D) {
+    if (is_2D) {
         GGML_ASSERT(a->ne[2] == b->ne[2]);
     } else {
-        GGML_ASSERT(a->ne[1] == b->ne[1]);
+        //GGML_ASSERT(b->ne[1] % a->ne[1] == 0);
+        GGML_ASSERT(b->ne[1] == a->ne[1]);
         GGML_ASSERT(b->ne[3] == 1);
     }
 
@@ -3928,6 +3835,108 @@ struct ggml_tensor * ggml_im2col_back(
     return result;
 }
 
+// ggml_conv_1d
+
+struct ggml_tensor * ggml_conv_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
+    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
+
+    struct ggml_tensor * result =
+        ggml_mul_mat(ctx,
+                ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
+                ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2]));                    // [OC，IC, K] => [OC, IC * K]
+
+    result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
+
+    return result;
+}
+
+// ggml_conv_1d_ph
+
+struct ggml_tensor* ggml_conv_1d_ph(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s,
+        int                   d) {
+    return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
+}
+
+// ggml_conv_1d_dw
+
+struct ggml_tensor * ggml_conv_1d_dw(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
+    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]);
+    struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]);
+
+    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
+
+    struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a);
+
+    result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1);
+
+    return result;
+}
+
+// ggml_conv_1d_dw_ph
+
+struct ggml_tensor * ggml_conv_1d_dw_ph(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   d0) {
+    return ggml_conv_1d_dw(ctx, a, b, s0, a->ne[0] / 2, d0);
+}
+
+// ggml_conv_transpose_1d
+
+static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
+    return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
+}
+
+GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
+    GGML_ASSERT(ggml_is_matrix(b));
+    GGML_ASSERT(a->ne[2] == b->ne[1]);
+    GGML_ASSERT(a->ne[3] == 1);
+
+    GGML_ASSERT(p0 == 0);
+    GGML_ASSERT(d0 == 1);
+
+    const int64_t ne[4] = {
+        ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
+        a->ne[1], b->ne[2], 1,
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    int32_t params[] = { s0, p0, d0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op     = GGML_OP_CONV_TRANSPOSE_1D;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_conv_2d
+
 // a: [OC，IC, KH, KW]
 // b: [N, IC, IH, IW]
 // result: [N, OC, OH, OW]
@@ -3973,6 +3982,31 @@ struct ggml_tensor * ggml_conv_2d_s1_ph(
     return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1);
 }
 
+// ggml_conv_2d_dw
+
+struct ggml_tensor * ggml_conv_2d_dw(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   s1,
+        int                   p0,
+        int                   p1,
+        int                   d0,
+        int                   d1) {
+    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
+    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
+                                        ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
+                                        s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
+    struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
+
+    new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2],  new_a->ne[3], 1);                       // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
+    struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
+
+    return result;
+}
+
 // ggml_conv_transpose_2d_p0
 
 static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index c2c7cad14..a40df974d 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -90,6 +90,7 @@ class Keys:
         VOCAB_SIZE                        = "{arch}.vocab_size"
         CONTEXT_LENGTH                    = "{arch}.context_length"
         EMBEDDING_LENGTH                  = "{arch}.embedding_length"
+        FEATURES_LENGTH                   = "{arch}.features_length"
         BLOCK_COUNT                       = "{arch}.block_count"
         LEADING_DENSE_BLOCK_COUNT         = "{arch}.leading_dense_block_count"
         FEED_FORWARD_LENGTH               = "{arch}.feed_forward_length"
@@ -122,6 +123,8 @@ class Keys:
         VALUE_LENGTH      = "{arch}.attention.value_length"
         LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
         LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
+        GROUPNORM_EPS     = "{arch}.attention.group_norm_epsilon"
+        GROUPNORM_GROUPS  = "{arch}.attention.group_norm_groups"
         CAUSAL            = "{arch}.attention.causal"
         Q_LORA_RANK       = "{arch}.attention.q_lora_rank"
         KV_LORA_RANK      = "{arch}.attention.kv_lora_rank"
@@ -155,6 +158,14 @@ class Keys:
     class WKV:
         HEAD_SIZE = "{arch}.wkv.head_size"
 
+    class PosNet:
+        EMBEDDING_LENGTH = "{arch}.posnet.embedding_length"
+        BLOCK_COUNT      = "{arch}.posnet.block_count"
+
+    class ConvNext:
+        EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
+        BLOCK_COUNT      = "{arch}.convnext.block_count"
+
     class Tokenizer:
         MODEL                = "tokenizer.ggml.model"
         PRE                  = "tokenizer.ggml.pre"
@@ -209,58 +220,59 @@ class GGUFType:
 
 
 class MODEL_ARCH(IntEnum):
-    LLAMA        = auto()
-    FALCON       = auto()
-    BAICHUAN     = auto()
-    GROK         = auto()
-    GPT2         = auto()
-    GPTJ         = auto()
-    GPTNEOX      = auto()
-    MPT          = auto()
-    STARCODER    = auto()
-    REFACT       = auto()
-    BERT         = auto()
-    NOMIC_BERT   = auto()
-    JINA_BERT_V2 = auto()
-    BLOOM        = auto()
-    STABLELM     = auto()
-    QWEN         = auto()
-    QWEN2        = auto()
-    QWEN2MOE     = auto()
-    QWEN2VL      = auto()
-    PHI2         = auto()
-    PHI3         = auto()
-    PLAMO        = auto()
-    CODESHELL    = auto()
-    ORION        = auto()
-    INTERNLM2    = auto()
-    MINICPM      = auto()
-    MINICPM3     = auto()
-    GEMMA        = auto()
-    GEMMA2       = auto()
-    STARCODER2   = auto()
-    RWKV6        = auto()
-    MAMBA        = auto()
-    XVERSE       = auto()
-    COMMAND_R    = auto()
-    DBRX         = auto()
-    OLMO         = auto()
-    OLMO2        = auto()
-    OLMOE        = auto()
-    OPENELM      = auto()
-    ARCTIC       = auto()
-    DEEPSEEK     = auto()
-    DEEPSEEK2    = auto()
-    CHATGLM      = auto()
-    BITNET       = auto()
-    T5           = auto()
-    T5ENCODER    = auto()
-    JAIS         = auto()
-    NEMOTRON     = auto()
-    EXAONE       = auto()
-    GRANITE      = auto()
-    GRANITE_MOE  = auto()
-    CHAMELEON    = auto()
+    LLAMA            = auto()
+    FALCON           = auto()
+    BAICHUAN         = auto()
+    GROK             = auto()
+    GPT2             = auto()
+    GPTJ             = auto()
+    GPTNEOX          = auto()
+    MPT              = auto()
+    STARCODER        = auto()
+    REFACT           = auto()
+    BERT             = auto()
+    NOMIC_BERT       = auto()
+    JINA_BERT_V2     = auto()
+    BLOOM            = auto()
+    STABLELM         = auto()
+    QWEN             = auto()
+    QWEN2            = auto()
+    QWEN2MOE         = auto()
+    QWEN2VL          = auto()
+    PHI2             = auto()
+    PHI3             = auto()
+    PLAMO            = auto()
+    CODESHELL        = auto()
+    ORION            = auto()
+    INTERNLM2        = auto()
+    MINICPM          = auto()
+    MINICPM3         = auto()
+    GEMMA            = auto()
+    GEMMA2           = auto()
+    STARCODER2       = auto()
+    RWKV6            = auto()
+    MAMBA            = auto()
+    XVERSE           = auto()
+    COMMAND_R        = auto()
+    DBRX             = auto()
+    OLMO             = auto()
+    OLMO2            = auto()
+    OLMOE            = auto()
+    OPENELM          = auto()
+    ARCTIC           = auto()
+    DEEPSEEK         = auto()
+    DEEPSEEK2        = auto()
+    CHATGLM          = auto()
+    BITNET           = auto()
+    T5               = auto()
+    T5ENCODER        = auto()
+    JAIS             = auto()
+    NEMOTRON         = auto()
+    EXAONE           = auto()
+    GRANITE          = auto()
+    GRANITE_MOE      = auto()
+    CHAMELEON        = auto()
+    WAVTOKENIZER_DEC = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -370,61 +382,78 @@ class MODEL_TENSOR(IntEnum):
     ENC_OUTPUT_NORM      = auto()
     CLS                  = auto() # classifier
     CLS_OUT              = auto() # classifier output projection
+    CONV1D               = auto()
+    CONVNEXT_DW          = auto()
+    CONVNEXT_NORM        = auto()
+    CONVNEXT_PW1         = auto()
+    CONVNEXT_PW2         = auto()
+    CONVNEXT_GAMMA       = auto()
+    POSNET_CONV1         = auto()
+    POSNET_CONV2         = auto()
+    POSNET_NORM          = auto()
+    POSNET_NORM1         = auto()
+    POSNET_NORM2         = auto()
+    POSNET_ATTN_NORM     = auto()
+    POSNET_ATTN_Q        = auto()
+    POSNET_ATTN_K        = auto()
+    POSNET_ATTN_V        = auto()
+    POSNET_ATTN_OUT      = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
-    MODEL_ARCH.LLAMA:          "llama",
-    MODEL_ARCH.FALCON:         "falcon",
-    MODEL_ARCH.BAICHUAN:       "baichuan",
-    MODEL_ARCH.GROK:           "grok",
-    MODEL_ARCH.GPT2:           "gpt2",
-    MODEL_ARCH.GPTJ:           "gptj",
-    MODEL_ARCH.GPTNEOX:        "gptneox",
-    MODEL_ARCH.MPT:            "mpt",
-    MODEL_ARCH.STARCODER:      "starcoder",
-    MODEL_ARCH.REFACT:         "refact",
-    MODEL_ARCH.BERT:           "bert",
-    MODEL_ARCH.NOMIC_BERT:     "nomic-bert",
-    MODEL_ARCH.JINA_BERT_V2:   "jina-bert-v2",
-    MODEL_ARCH.BLOOM:          "bloom",
-    MODEL_ARCH.STABLELM:       "stablelm",
-    MODEL_ARCH.QWEN:           "qwen",
-    MODEL_ARCH.QWEN2:          "qwen2",
-    MODEL_ARCH.QWEN2MOE:       "qwen2moe",
-    MODEL_ARCH.QWEN2VL:        "qwen2vl",
-    MODEL_ARCH.PHI2:           "phi2",
-    MODEL_ARCH.PHI3:           "phi3",
-    MODEL_ARCH.PLAMO:          "plamo",
-    MODEL_ARCH.CODESHELL:      "codeshell",
-    MODEL_ARCH.ORION:          "orion",
-    MODEL_ARCH.INTERNLM2:      "internlm2",
-    MODEL_ARCH.MINICPM:        "minicpm",
-    MODEL_ARCH.MINICPM3:       "minicpm3",
-    MODEL_ARCH.GEMMA:          "gemma",
-    MODEL_ARCH.GEMMA2:         "gemma2",
-    MODEL_ARCH.STARCODER2:     "starcoder2",
-    MODEL_ARCH.RWKV6:          "rwkv6",
-    MODEL_ARCH.MAMBA:          "mamba",
-    MODEL_ARCH.XVERSE:         "xverse",
-    MODEL_ARCH.COMMAND_R:      "command-r",
-    MODEL_ARCH.DBRX:           "dbrx",
-    MODEL_ARCH.OLMO:           "olmo",
-    MODEL_ARCH.OLMO2:          "olmo2",
-    MODEL_ARCH.OLMOE:          "olmoe",
-    MODEL_ARCH.OPENELM:        "openelm",
-    MODEL_ARCH.ARCTIC:         "arctic",
-    MODEL_ARCH.DEEPSEEK:       "deepseek",
-    MODEL_ARCH.DEEPSEEK2:      "deepseek2",
-    MODEL_ARCH.CHATGLM:        "chatglm",
-    MODEL_ARCH.BITNET:         "bitnet",
-    MODEL_ARCH.T5:             "t5",
-    MODEL_ARCH.T5ENCODER:      "t5encoder",
-    MODEL_ARCH.JAIS:           "jais",
-    MODEL_ARCH.NEMOTRON:       "nemotron",
-    MODEL_ARCH.EXAONE:         "exaone",
-    MODEL_ARCH.GRANITE:        "granite",
-    MODEL_ARCH.GRANITE_MOE:    "granitemoe",
-    MODEL_ARCH.CHAMELEON:      "chameleon",
+    MODEL_ARCH.LLAMA:            "llama",
+    MODEL_ARCH.FALCON:           "falcon",
+    MODEL_ARCH.BAICHUAN:         "baichuan",
+    MODEL_ARCH.GROK:             "grok",
+    MODEL_ARCH.GPT2:             "gpt2",
+    MODEL_ARCH.GPTJ:             "gptj",
+    MODEL_ARCH.GPTNEOX:          "gptneox",
+    MODEL_ARCH.MPT:              "mpt",
+    MODEL_ARCH.STARCODER:        "starcoder",
+    MODEL_ARCH.REFACT:           "refact",
+    MODEL_ARCH.BERT:             "bert",
+    MODEL_ARCH.NOMIC_BERT:       "nomic-bert",
+    MODEL_ARCH.JINA_BERT_V2:     "jina-bert-v2",
+    MODEL_ARCH.BLOOM:            "bloom",
+    MODEL_ARCH.STABLELM:         "stablelm",
+    MODEL_ARCH.QWEN:             "qwen",
+    MODEL_ARCH.QWEN2:            "qwen2",
+    MODEL_ARCH.QWEN2MOE:         "qwen2moe",
+    MODEL_ARCH.QWEN2VL:          "qwen2vl",
+    MODEL_ARCH.PHI2:             "phi2",
+    MODEL_ARCH.PHI3:             "phi3",
+    MODEL_ARCH.PLAMO:            "plamo",
+    MODEL_ARCH.CODESHELL:        "codeshell",
+    MODEL_ARCH.ORION:            "orion",
+    MODEL_ARCH.INTERNLM2:        "internlm2",
+    MODEL_ARCH.MINICPM:          "minicpm",
+    MODEL_ARCH.MINICPM3:         "minicpm3",
+    MODEL_ARCH.GEMMA:            "gemma",
+    MODEL_ARCH.GEMMA2:           "gemma2",
+    MODEL_ARCH.STARCODER2:       "starcoder2",
+    MODEL_ARCH.RWKV6:            "rwkv6",
+    MODEL_ARCH.MAMBA:            "mamba",
+    MODEL_ARCH.XVERSE:           "xverse",
+    MODEL_ARCH.COMMAND_R:        "command-r",
+    MODEL_ARCH.DBRX:             "dbrx",
+    MODEL_ARCH.OLMO:             "olmo",
+    MODEL_ARCH.OLMO2:            "olmo2",
+    MODEL_ARCH.OLMOE:            "olmoe",
+    MODEL_ARCH.OPENELM:          "openelm",
+    MODEL_ARCH.ARCTIC:           "arctic",
+    MODEL_ARCH.DEEPSEEK:         "deepseek",
+    MODEL_ARCH.DEEPSEEK2:        "deepseek2",
+    MODEL_ARCH.CHATGLM:          "chatglm",
+    MODEL_ARCH.BITNET:           "bitnet",
+    MODEL_ARCH.T5:               "t5",
+    MODEL_ARCH.T5ENCODER:        "t5encoder",
+    MODEL_ARCH.JAIS:             "jais",
+    MODEL_ARCH.NEMOTRON:         "nemotron",
+    MODEL_ARCH.EXAONE:           "exaone",
+    MODEL_ARCH.GRANITE:          "granite",
+    MODEL_ARCH.GRANITE_MOE:      "granitemoe",
+    MODEL_ARCH.CHAMELEON:        "chameleon",
+    MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -534,6 +563,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ENC_OUTPUT_NORM:           "enc.output_norm",
     MODEL_TENSOR.CLS:                       "cls",
     MODEL_TENSOR.CLS_OUT:                   "cls.output",
+    MODEL_TENSOR.CONV1D:                    "conv1d",
+    MODEL_TENSOR.CONVNEXT_DW:               "convnext.{bid}.dw",
+    MODEL_TENSOR.CONVNEXT_NORM:             "convnext.{bid}.norm",
+    MODEL_TENSOR.CONVNEXT_PW1:              "convnext.{bid}.pw1",
+    MODEL_TENSOR.CONVNEXT_PW2:              "convnext.{bid}.pw2",
+    MODEL_TENSOR.CONVNEXT_GAMMA:            "convnext.{bid}.gamma",
+    MODEL_TENSOR.POSNET_CONV1:              "posnet.{bid}.conv1",
+    MODEL_TENSOR.POSNET_CONV2:              "posnet.{bid}.conv2",
+    MODEL_TENSOR.POSNET_NORM:               "posnet.{bid}.norm",
+    MODEL_TENSOR.POSNET_NORM1:              "posnet.{bid}.norm1",
+    MODEL_TENSOR.POSNET_NORM2:              "posnet.{bid}.norm2",
+    MODEL_TENSOR.POSNET_ATTN_NORM:          "posnet.{bid}.attn_norm",
+    MODEL_TENSOR.POSNET_ATTN_Q:             "posnet.{bid}.attn_q",
+    MODEL_TENSOR.POSNET_ATTN_K:             "posnet.{bid}.attn_k",
+    MODEL_TENSOR.POSNET_ATTN_V:             "posnet.{bid}.attn_v",
+    MODEL_TENSOR.POSNET_ATTN_OUT:           "posnet.{bid}.attn_output",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -1372,6 +1417,28 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.WAVTOKENIZER_DEC: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.CONV1D,
+        MODEL_TENSOR.CONVNEXT_DW,
+        MODEL_TENSOR.CONVNEXT_NORM,
+        MODEL_TENSOR.CONVNEXT_PW1,
+        MODEL_TENSOR.CONVNEXT_PW2,
+        MODEL_TENSOR.CONVNEXT_GAMMA,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.POSNET_CONV1,
+        MODEL_TENSOR.POSNET_CONV2,
+        MODEL_TENSOR.POSNET_NORM,
+        MODEL_TENSOR.POSNET_NORM1,
+        MODEL_TENSOR.POSNET_NORM2,
+        MODEL_TENSOR.POSNET_ATTN_NORM,
+        MODEL_TENSOR.POSNET_ATTN_Q,
+        MODEL_TENSOR.POSNET_ATTN_K,
+        MODEL_TENSOR.POSNET_ATTN_V,
+        MODEL_TENSOR.POSNET_ATTN_OUT,
+    ],
     # TODO
 }
 
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 65a64e10d..3023b539a 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -631,6 +631,21 @@ class GGUFWriter:
     def add_embedding_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
 
+    def add_features_length(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.FEATURES_LENGTH.format(arch=self.arch), length)
+
+    def add_posnet_embedding_length(self, length: int) -> None:
+        self.add_uint32(Keys.PosNet.EMBEDDING_LENGTH.format(arch=self.arch), length)
+
+    def add_posnet_block_count(self, length: int) -> None:
+        self.add_uint32(Keys.PosNet.BLOCK_COUNT.format(arch=self.arch), length)
+
+    def add_convnext_embedding_length(self, length: int) -> None:
+        self.add_uint32(Keys.ConvNext.EMBEDDING_LENGTH.format(arch=self.arch), length)
+
+    def add_convnext_block_count(self, length: int) -> None:
+        self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length)
+
     def add_block_count(self, length: int) -> None:
         self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
 
@@ -727,6 +742,12 @@ class GGUFWriter:
     def add_layer_norm_rms_eps(self, value: float) -> None:
         self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
 
+    def add_group_norm_eps(self, value: float) -> None:
+        self.add_float32(Keys.Attention.GROUPNORM_EPS.format(arch=self.arch), value)
+
+    def add_group_norm_groups(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.GROUPNORM_GROUPS.format(arch=self.arch), value)
+
     def add_causal_attention(self, value: bool) -> None:
         self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 573d0282e..82cdb121a 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -42,6 +42,7 @@ class TensorNameMap:
             "emb_ln",                     # nomic-bert
             "transformer.norm",           # openelm
             "rwkv.blocks.0.pre_ln",       # rwkv
+            "backbone.norm",              # wavtokenizer
         ),
 
         # Position embeddings
@@ -60,6 +61,7 @@ class TensorNameMap:
             "lm_head.linear",            # phi2
             "output_layer",              # chatglm
             "head",                      # rwkv
+            "head.out",                  # wavtokenizer
         ),
 
         # Output norm
@@ -80,6 +82,7 @@ class TensorNameMap:
             "transformer.norm",                        # openelm
             "model.norm",                              # nemotron
             "rwkv.ln_out",                             # rwkv
+            "backbone.final_layer_norm",               # wavtokenizer
         ),
 
         # Rope frequencies
@@ -90,6 +93,10 @@ class TensorNameMap:
 
         MODEL_TENSOR.ROPE_FACTORS_LONG: (),
         MODEL_TENSOR.ROPE_FACTORS_SHORT: (),
+
+        MODEL_TENSOR.CONV1D: (
+            "backbone.embed", # roberta
+        ),
     }
 
     block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
@@ -681,6 +688,8 @@ class TensorNameMap:
             "encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
         ),
 
+        ############################################################################
+        # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
         ),
@@ -693,6 +702,67 @@ class TensorNameMap:
         MODEL_TENSOR.CLS_OUT: (
             "classifier.out_proj", # roberta
         ),
+        #############################################################################
+
+        MODEL_TENSOR.CONVNEXT_DW: (
+            "backbone.convnext.{bid}.dwconv", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.CONVNEXT_NORM: (
+            "backbone.convnext.{bid}.norm", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.CONVNEXT_PW1: (
+            "backbone.convnext.{bid}.pwconv1", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.CONVNEXT_PW2: (
+            "backbone.convnext.{bid}.pwconv2", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.CONVNEXT_GAMMA: (
+            "backbone.convnext.{bid}.gamma", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_CONV1: (
+            "backbone.posnet.{bid}.conv1", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_CONV2: (
+            "backbone.posnet.{bid}.conv2", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_NORM: (
+            "backbone.posnet.{bid}.norm", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_NORM1: (
+            "backbone.posnet.{bid}.norm1", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_NORM2: (
+            "backbone.posnet.{bid}.norm2", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_ATTN_NORM: (
+            "backbone.posnet.{bid}.norm", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_ATTN_Q: (
+            "backbone.posnet.{bid}.q", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_ATTN_K: (
+            "backbone.posnet.{bid}.k", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_ATTN_V: (
+            "backbone.posnet.{bid}.v", # wavtokenizer
+        ),
+
+        MODEL_TENSOR.POSNET_ATTN_OUT: (
+            "backbone.posnet.{bid}.proj_out", # wavtokenizer
+        ),
     }
 
     # architecture-specific block mappings
diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py
index 762067814..f04d5acce 100755
--- a/gguf-py/tests/test_quants.py
+++ b/gguf-py/tests/test_quants.py
@@ -136,7 +136,7 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
         logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
 
         sum_diff_bits = np.sum(diff_bits)
-        logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)")
+        logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)")
         return False
 
 
diff --git a/include/llama.h b/include/llama.h
index efbb27d21..a4abf395b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -482,9 +482,6 @@ extern "C" {
     // Returns the total number of parameters in the model
     LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
 
-    // Get a llama model tensor
-    LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
-
     // Returns true if the model contains an encoder that requires llama_encode() call
     LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
 
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index e38e59853..7f2725f94 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1867,6 +1867,10 @@ int32_t llama_detokenize_impl(
                          int32_t   text_len_max,
                             bool   remove_special,
                             bool   unparse_special) {
+    if (vocab.type == LLAMA_VOCAB_TYPE_NONE) {
+        return 0;
+    }
+
     GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
 
     int32_t avail = text_len_max;
diff --git a/src/llama.cpp b/src/llama.cpp
index b7b04a41d..94160d534 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -197,63 +197,65 @@ enum llm_arch {
     LLM_ARCH_GRANITE,
     LLM_ARCH_GRANITE_MOE,
     LLM_ARCH_CHAMELEON,
+    LLM_ARCH_WAVTOKENIZER_DEC,
     LLM_ARCH_UNKNOWN,
 };
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-    { LLM_ARCH_LLAMA,           "llama"        },
-    { LLM_ARCH_FALCON,          "falcon"       },
-    { LLM_ARCH_GROK,            "grok"         },
-    { LLM_ARCH_GPT2,            "gpt2"         },
-    { LLM_ARCH_GPTJ,            "gptj"         },
-    { LLM_ARCH_GPTNEOX,         "gptneox"      },
-    { LLM_ARCH_MPT,             "mpt"          },
-    { LLM_ARCH_BAICHUAN,        "baichuan"     },
-    { LLM_ARCH_STARCODER,       "starcoder"    },
-    { LLM_ARCH_REFACT,          "refact"       },
-    { LLM_ARCH_BERT,            "bert"         },
-    { LLM_ARCH_NOMIC_BERT,      "nomic-bert"   },
-    { LLM_ARCH_JINA_BERT_V2,    "jina-bert-v2" },
-    { LLM_ARCH_BLOOM,           "bloom"        },
-    { LLM_ARCH_STABLELM,        "stablelm"     },
-    { LLM_ARCH_QWEN,            "qwen"         },
-    { LLM_ARCH_QWEN2,           "qwen2"        },
-    { LLM_ARCH_QWEN2MOE,        "qwen2moe"     },
-    { LLM_ARCH_QWEN2VL,         "qwen2vl"      },
-    { LLM_ARCH_PHI2,            "phi2"         },
-    { LLM_ARCH_PHI3,            "phi3"         },
-    { LLM_ARCH_PLAMO,           "plamo"        },
-    { LLM_ARCH_CODESHELL,       "codeshell"    },
-    { LLM_ARCH_ORION,           "orion"        },
-    { LLM_ARCH_INTERNLM2,       "internlm2"    },
-    { LLM_ARCH_MINICPM,         "minicpm"      },
-    { LLM_ARCH_MINICPM3,        "minicpm3"     },
-    { LLM_ARCH_GEMMA,           "gemma"        },
-    { LLM_ARCH_GEMMA2,          "gemma2"       },
-    { LLM_ARCH_STARCODER2,      "starcoder2"   },
-    { LLM_ARCH_MAMBA,           "mamba"        },
-    { LLM_ARCH_XVERSE,          "xverse"       },
-    { LLM_ARCH_COMMAND_R,       "command-r"    },
-    { LLM_ARCH_DBRX,            "dbrx"         },
-    { LLM_ARCH_OLMO,            "olmo"         },
-    { LLM_ARCH_OLMO2,           "olmo2"        },
-    { LLM_ARCH_OLMOE,           "olmoe"        },
-    { LLM_ARCH_OPENELM,         "openelm"      },
-    { LLM_ARCH_ARCTIC,          "arctic"       },
-    { LLM_ARCH_DEEPSEEK,        "deepseek"     },
-    { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
-    { LLM_ARCH_CHATGLM,         "chatglm"      },
-    { LLM_ARCH_BITNET,          "bitnet"       },
-    { LLM_ARCH_T5,              "t5"           },
-    { LLM_ARCH_T5ENCODER,       "t5encoder"    },
-    { LLM_ARCH_JAIS,            "jais"         },
-    { LLM_ARCH_NEMOTRON,        "nemotron"     },
-    { LLM_ARCH_EXAONE,          "exaone"       },
-    { LLM_ARCH_RWKV6,           "rwkv6"        },
-    { LLM_ARCH_GRANITE,         "granite"      },
-    { LLM_ARCH_GRANITE_MOE,     "granitemoe"   },
-    { LLM_ARCH_CHAMELEON,       "chameleon"    },
-    { LLM_ARCH_UNKNOWN,         "(unknown)"    },
+    { LLM_ARCH_LLAMA,            "llama"            },
+    { LLM_ARCH_FALCON,           "falcon"           },
+    { LLM_ARCH_GROK,             "grok"             },
+    { LLM_ARCH_GPT2,             "gpt2"             },
+    { LLM_ARCH_GPTJ,             "gptj"             },
+    { LLM_ARCH_GPTNEOX,          "gptneox"          },
+    { LLM_ARCH_MPT,              "mpt"              },
+    { LLM_ARCH_BAICHUAN,         "baichuan"         },
+    { LLM_ARCH_STARCODER,        "starcoder"        },
+    { LLM_ARCH_REFACT,           "refact"           },
+    { LLM_ARCH_BERT,             "bert"             },
+    { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
+    { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
+    { LLM_ARCH_BLOOM,            "bloom"            },
+    { LLM_ARCH_STABLELM,         "stablelm"         },
+    { LLM_ARCH_QWEN,             "qwen"             },
+    { LLM_ARCH_QWEN2,            "qwen2"            },
+    { LLM_ARCH_QWEN2MOE,         "qwen2moe"         },
+    { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
+    { LLM_ARCH_PHI2,             "phi2"             },
+    { LLM_ARCH_PHI3,             "phi3"             },
+    { LLM_ARCH_PLAMO,            "plamo"            },
+    { LLM_ARCH_CODESHELL,        "codeshell"        },
+    { LLM_ARCH_ORION,            "orion"            },
+    { LLM_ARCH_INTERNLM2,        "internlm2"        },
+    { LLM_ARCH_MINICPM,          "minicpm"          },
+    { LLM_ARCH_MINICPM3,         "minicpm3"         },
+    { LLM_ARCH_GEMMA,            "gemma"            },
+    { LLM_ARCH_GEMMA2,           "gemma2"           },
+    { LLM_ARCH_STARCODER2,       "starcoder2"       },
+    { LLM_ARCH_MAMBA,            "mamba"            },
+    { LLM_ARCH_XVERSE,           "xverse"           },
+    { LLM_ARCH_COMMAND_R,        "command-r"        },
+    { LLM_ARCH_DBRX,             "dbrx"             },
+    { LLM_ARCH_OLMO,             "olmo"             },
+    { LLM_ARCH_OLMO2,            "olmo2"            },
+    { LLM_ARCH_OLMOE,            "olmoe"            },
+    { LLM_ARCH_OPENELM,          "openelm"          },
+    { LLM_ARCH_ARCTIC,           "arctic"           },
+    { LLM_ARCH_DEEPSEEK,         "deepseek"         },
+    { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
+    { LLM_ARCH_CHATGLM,          "chatglm"          },
+    { LLM_ARCH_BITNET,           "bitnet"           },
+    { LLM_ARCH_T5,               "t5"               },
+    { LLM_ARCH_T5ENCODER,        "t5encoder"        },
+    { LLM_ARCH_JAIS,             "jais"             },
+    { LLM_ARCH_NEMOTRON,         "nemotron"         },
+    { LLM_ARCH_EXAONE,           "exaone"           },
+    { LLM_ARCH_RWKV6,            "rwkv6"            },
+    { LLM_ARCH_GRANITE,          "granite"          },
+    { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
+    { LLM_ARCH_CHAMELEON,        "chameleon"        },
+    { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
+    { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
 enum llm_kv {
@@ -273,6 +275,7 @@ enum llm_kv {
     LLM_KV_VOCAB_SIZE,
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
+    LLM_KV_FEATURES_LENGTH,
     LLM_KV_BLOCK_COUNT,
     LLM_KV_LEADING_DENSE_BLOCK_COUNT,
     LLM_KV_FEED_FORWARD_LENGTH,
@@ -304,6 +307,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_VALUE_LENGTH,
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
+    LLM_KV_ATTENTION_GROUPNORM_EPS,
+    LLM_KV_ATTENTION_GROUPNORM_GROUPS,
     LLM_KV_ATTENTION_CAUSAL,
     LLM_KV_ATTENTION_Q_LORA_RANK,
     LLM_KV_ATTENTION_KV_LORA_RANK,
@@ -367,6 +372,12 @@ enum llm_kv {
     LLM_KV_ADAPTER_TYPE,
     LLM_KV_ADAPTER_LORA_ALPHA,
 
+    LLM_KV_POSNET_EMBEDDING_LENGTH,
+    LLM_KV_POSNET_BLOCK_COUNT,
+
+    LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
+    LLM_KV_CONVNEXT_BLOCK_COUNT,
+
     // deprecated:
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,
@@ -390,6 +401,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
     { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
     { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
+    { LLM_KV_FEATURES_LENGTH,                   "%s.features_length"                   },
     { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
     { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
     { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
@@ -421,6 +433,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"           },
     { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon" },
+    { LLM_KV_ATTENTION_GROUPNORM_EPS,          "%s.attention.group_norm_epsilon"     },
+    { LLM_KV_ATTENTION_GROUPNORM_GROUPS,       "%s.attention.group_norm_groups"      },
     { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                 },
     { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
     { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
@@ -451,6 +465,12 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
 
     { LLM_KV_WKV_HEAD_SIZE,                    "%s.wkv.head_size" },
 
+    { LLM_KV_POSNET_EMBEDDING_LENGTH,          "%s.posnet.embedding_length" },
+    { LLM_KV_POSNET_BLOCK_COUNT,               "%s.posnet.block_count"      },
+
+    { LLM_KV_CONVNEXT_EMBEDDING_LENGTH,        "%s.convnext.embedding_length" },
+    { LLM_KV_CONVNEXT_BLOCK_COUNT,             "%s.convnext.block_count"      },
+
     { LLM_KV_TOKENIZER_MODEL,                  "tokenizer.ggml.model"                    },
     { LLM_KV_TOKENIZER_PRE,                    "tokenizer.ggml.pre"                      },
     { LLM_KV_TOKENIZER_LIST,                   "tokenizer.ggml.tokens"                   },
@@ -609,6 +629,22 @@ enum llm_tensor {
     LLM_TENSOR_ENC_OUTPUT_NORM,
     LLM_TENSOR_CLS,
     LLM_TENSOR_CLS_OUT,
+    LLM_TENSOR_CONV1D,
+    LLM_TENSOR_CONVNEXT_DW,
+    LLM_TENSOR_CONVNEXT_NORM,
+    LLM_TENSOR_CONVNEXT_PW1,
+    LLM_TENSOR_CONVNEXT_PW2,
+    LLM_TENSOR_CONVNEXT_GAMMA,
+    LLM_TENSOR_POS_NET_CONV1,
+    LLM_TENSOR_POS_NET_CONV2,
+    LLM_TENSOR_POS_NET_NORM,
+    LLM_TENSOR_POS_NET_NORM1,
+    LLM_TENSOR_POS_NET_NORM2,
+    LLM_TENSOR_POS_NET_ATTN_NORM,
+    LLM_TENSOR_POS_NET_ATTN_Q,
+    LLM_TENSOR_POS_NET_ATTN_K,
+    LLM_TENSOR_POS_NET_ATTN_V,
+    LLM_TENSOR_POS_NET_ATTN_OUT,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
@@ -1593,6 +1629,31 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
         },
     },
+    {
+        LLM_ARCH_WAVTOKENIZER_DEC,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_CONV1D,            "conv1d" },
+            { LLM_TENSOR_CONVNEXT_DW,       "convnext.%d.dw" },
+            { LLM_TENSOR_CONVNEXT_NORM,     "convnext.%d.norm" },
+            { LLM_TENSOR_CONVNEXT_PW1,      "convnext.%d.pw1" },
+            { LLM_TENSOR_CONVNEXT_PW2,      "convnext.%d.pw2" },
+            { LLM_TENSOR_CONVNEXT_GAMMA,    "convnext.%d.gamma" },
+            { LLM_TENSOR_OUTPUT_NORM,       "output_norm" },
+            { LLM_TENSOR_OUTPUT,            "output" },
+            { LLM_TENSOR_POS_NET_CONV1,     "posnet.%d.conv1" },
+            { LLM_TENSOR_POS_NET_CONV2,     "posnet.%d.conv2" },
+            { LLM_TENSOR_POS_NET_NORM,      "posnet.%d.norm" },
+            { LLM_TENSOR_POS_NET_NORM1,     "posnet.%d.norm1" },
+            { LLM_TENSOR_POS_NET_NORM2,     "posnet.%d.norm2" },
+            { LLM_TENSOR_POS_NET_ATTN_NORM, "posnet.%d.attn_norm" },
+            { LLM_TENSOR_POS_NET_ATTN_Q,    "posnet.%d.attn_q" },
+            { LLM_TENSOR_POS_NET_ATTN_K,    "posnet.%d.attn_k" },
+            { LLM_TENSOR_POS_NET_ATTN_V,    "posnet.%d.attn_v" },
+            { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -2483,15 +2544,26 @@ static const size_t kiB = 1024;
 static const size_t MiB = 1024*kiB;
 static const size_t GiB = 1024*MiB;
 
+struct llama_hparams_posnet {
+    uint32_t n_embd;
+    uint32_t n_layer;
+};
+
+struct llama_hparams_convnext {
+    uint32_t n_embd;
+    uint32_t n_layer;
+};
+
 struct llama_hparams {
     bool vocab_only;
     bool rope_finetuned;
     bool use_par_res;
     bool swin_norm;
 
-    uint32_t n_vocab;
+    uint32_t n_vocab = 0;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
+    uint32_t n_embd_features = 0;
     uint32_t n_layer;
     uint32_t n_rot;
     uint32_t n_swa = 0; // sliding window attention (SWA)
@@ -2502,6 +2574,10 @@ struct llama_hparams {
     uint32_t n_vocab_type = 0; // for BERT-style token types
     uint32_t n_rel_attn_bkts = 0;
 
+    // for WavTokenizer
+    struct llama_hparams_posnet   posnet;
+    struct llama_hparams_convnext convnext;
+
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
@@ -2516,6 +2592,9 @@ struct llama_hparams {
 
     float f_norm_eps;
     float f_norm_rms_eps;
+    float f_norm_group_eps;
+
+    uint32_t n_norm_groups;
 
     float f_attn_logit_softcapping = 50.0f;
     float f_final_logit_softcapping = 30.0f;
@@ -2561,66 +2640,6 @@ struct llama_hparams {
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
-    bool operator!=(const llama_hparams & other) const {
-        if (this->vocab_only    != other.vocab_only)    return true;
-        if (this->n_vocab       != other.n_vocab)       return true;
-        if (this->n_ctx_train   != other.n_ctx_train)   return true;
-        if (this->n_embd        != other.n_embd)        return true;
-        if (this->n_layer       != other.n_layer)       return true;
-        if (this->n_rot         != other.n_rot)         return true;
-        if (this->n_swa         != other.n_swa)         return true;
-        if (this->n_embd_head_k != other.n_embd_head_k) return true;
-        if (this->n_embd_head_v != other.n_embd_head_v) return true;
-        if (this->n_expert      != other.n_expert)      return true;
-        if (this->n_expert_used != other.n_expert_used) return true;
-
-        if (this->n_head_arr    != other.n_head_arr)    return true;
-        if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
-        if (this->n_ff_arr      != other.n_ff_arr)      return true;
-
-        if (this->n_rel_attn_bkts    != other.n_rel_attn_bkts)    return true;
-        if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
-        if (this->n_lora_q           != other.n_lora_q)           return true;
-        if (this->n_lora_kv          != other.n_lora_kv)          return true;
-        if (this->n_ff_exp           != other.n_ff_exp)           return true;
-        if (this->n_ff_shexp         != other.n_ff_shexp)         return true;
-        if (this->n_expert_shared    != other.n_expert_shared)    return true;
-
-        if (this->rope_finetuned  != other.rope_finetuned)  return true;
-        if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true;
-        if (std::equal(std::begin(this->rope_sections),
-                       std::end(this->rope_sections),
-                       std::begin(other.rope_sections)))    return true;
-
-        if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
-        if (this->ssm_d_inner != other.ssm_d_inner) return true;
-        if (this->ssm_d_state != other.ssm_d_state) return true;
-        if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
-        if (this->ssm_dt_b_c_rms != other.ssm_dt_b_c_rms) return true;
-
-        if (this->rescale_every_n_layers != other.rescale_every_n_layers) return true;
-        if (this->time_mix_extra_dim     != other.time_mix_extra_dim)     return true;
-        if (this->time_decay_extra_dim   != other.time_decay_extra_dim)   return true;
-        if (this->wkv_head_size          != other.wkv_head_size)          return true;
-
-        if (this->dec_start_token_id != other.dec_start_token_id) return true;
-
-        const float EPSILON = 1e-9f;
-
-        if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
-        if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
-        if (!is_float_close(this->rope_attn_factor,      other.rope_attn_factor,      EPSILON)) return true;
-        if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
-        if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
-        if (!is_float_close(this->expert_weights_scale,  other.expert_weights_scale,  EPSILON)) return true;
-        if (!is_float_close(this->rope_yarn_log_mul,     other.rope_yarn_log_mul,     EPSILON)) return true;
-        if (!is_float_close(this->f_residual_scale,      other.f_residual_scale,      EPSILON)) return true;
-        if (!is_float_close(this->f_embedding_scale,     other.f_embedding_scale,     EPSILON)) return true;
-        if (!is_float_close(this->f_attention_scale,     other.f_attention_scale,     EPSILON)) return true;
-
-        return false;
-    }
-
     uint32_t n_head(uint32_t il = 0) const {
         if (il < n_layer) {
             return n_head_arr[il];
@@ -2673,21 +2692,21 @@ struct llama_hparams {
         if (wkv_head_size != 0) {
             // for RWKV models
             return 2 * n_embd;
-        } else {
-            // TODO: maybe support other convolution strides than 1
-            // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
-            return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
         }
+
+        // TODO: maybe support other convolution strides than 1
+        // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
+        return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
     }
 
     uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
         if (wkv_head_size != 0) {
             // corresponds to RWKV's wkv_states size
             return n_embd * wkv_head_size;
-        } else {
-            // corresponds to Mamba's ssm_states size
-            return ssm_d_state * ssm_d_inner;
         }
+
+        // corresponds to Mamba's ssm_states size
+        return ssm_d_state * ssm_d_inner;
     }
 };
 
@@ -2725,142 +2744,187 @@ struct llama_cparams {
     void * cb_eval_user_data;
 };
 
-// TODO: separate into "llama_layer_enc" and "llama_layer_dec"
-struct llama_layer {
-    llama_layer() {
-        // initialize all pointers to NULL
-        std::memset(this, 0, sizeof(*this));
-    }
+struct llama_layer_posnet {
+    // resnet
+    struct ggml_tensor * norm1   = nullptr;
+    struct ggml_tensor * norm1_b = nullptr;
 
-    // normalization
-    struct ggml_tensor * attn_norm;
-    struct ggml_tensor * attn_norm_b;
-    struct ggml_tensor * attn_norm_2;
-    struct ggml_tensor * attn_norm_2_b;
-    struct ggml_tensor * attn_q_norm;
-    struct ggml_tensor * attn_q_norm_b;
-    struct ggml_tensor * attn_k_norm;
-    struct ggml_tensor * attn_k_norm_b;
-    struct ggml_tensor * attn_out_norm;
-    struct ggml_tensor * attn_out_norm_b;
-    struct ggml_tensor * attn_q_a_norm;
-    struct ggml_tensor * attn_kv_a_norm;
-    struct ggml_tensor * attn_sub_norm;
-    struct ggml_tensor * attn_post_norm;
-    struct ggml_tensor * ffn_sub_norm;
-    struct ggml_tensor * attn_norm_cross;
-    struct ggml_tensor * attn_norm_enc;
+    struct ggml_tensor * conv1   = nullptr;
+    struct ggml_tensor * conv1_b = nullptr;
+
+    struct ggml_tensor * norm2   = nullptr;
+    struct ggml_tensor * norm2_b = nullptr;
+
+    struct ggml_tensor * conv2   = nullptr;
+    struct ggml_tensor * conv2_b = nullptr;
 
     // attention
-    struct ggml_tensor * wq;
-    struct ggml_tensor * wk;
-    struct ggml_tensor * wv;
-    struct ggml_tensor * wo;
-    struct ggml_tensor * wqkv;
-    struct ggml_tensor * wq_a;
-    struct ggml_tensor * wq_b;
-    struct ggml_tensor * wkv_a_mqa;
-    struct ggml_tensor * wkv_b;
-    struct ggml_tensor * wq_cross;
-    struct ggml_tensor * wk_cross;
-    struct ggml_tensor * wv_cross;
-    struct ggml_tensor * wo_cross;
-    struct ggml_tensor * wq_enc;
-    struct ggml_tensor * wk_enc;
-    struct ggml_tensor * wv_enc;
-    struct ggml_tensor * wo_enc;
+    struct ggml_tensor * attn_norm   = nullptr;
+    struct ggml_tensor * attn_norm_b = nullptr;
+
+    struct ggml_tensor * attn_q   = nullptr;
+    struct ggml_tensor * attn_q_b = nullptr;
+
+    struct ggml_tensor * attn_k   = nullptr;
+    struct ggml_tensor * attn_k_b = nullptr;
+
+    struct ggml_tensor * attn_v   = nullptr;
+    struct ggml_tensor * attn_v_b = nullptr;
+
+    struct ggml_tensor * attn_o   = nullptr;
+    struct ggml_tensor * attn_o_b = nullptr;
+
+    // normalize
+    struct ggml_tensor * norm   = nullptr;
+    struct ggml_tensor * norm_b = nullptr;
+};
+
+struct llama_layer_convnext {
+    struct ggml_tensor * dw   = nullptr;
+    struct ggml_tensor * dw_b = nullptr;
+
+    struct ggml_tensor * norm   = nullptr;
+    struct ggml_tensor * norm_b = nullptr;
+
+    struct ggml_tensor * pw1   = nullptr;
+    struct ggml_tensor * pw1_b = nullptr;
+
+    struct ggml_tensor * pw2   = nullptr;
+    struct ggml_tensor * pw2_b = nullptr;
+
+    struct ggml_tensor * gamma = nullptr;
+};
+
+struct llama_layer {
+    // normalization
+    struct ggml_tensor * attn_norm       = nullptr;
+    struct ggml_tensor * attn_norm_b     = nullptr;
+    struct ggml_tensor * attn_norm_2     = nullptr;
+    struct ggml_tensor * attn_norm_2_b   = nullptr;
+    struct ggml_tensor * attn_q_norm     = nullptr;
+    struct ggml_tensor * attn_q_norm_b   = nullptr;
+    struct ggml_tensor * attn_k_norm     = nullptr;
+    struct ggml_tensor * attn_k_norm_b   = nullptr;
+    struct ggml_tensor * attn_out_norm   = nullptr;
+    struct ggml_tensor * attn_out_norm_b = nullptr;
+    struct ggml_tensor * attn_q_a_norm   = nullptr;
+    struct ggml_tensor * attn_kv_a_norm  = nullptr;
+    struct ggml_tensor * attn_sub_norm   = nullptr;
+    struct ggml_tensor * attn_post_norm  = nullptr;
+    struct ggml_tensor * ffn_sub_norm    = nullptr;
+    struct ggml_tensor * attn_norm_cross = nullptr;
+    struct ggml_tensor * attn_norm_enc   = nullptr;
+
+    // attention
+    struct ggml_tensor * wq        = nullptr;
+    struct ggml_tensor * wk        = nullptr;
+    struct ggml_tensor * wv        = nullptr;
+    struct ggml_tensor * wo        = nullptr;
+    struct ggml_tensor * wqkv      = nullptr;
+    struct ggml_tensor * wq_a      = nullptr;
+    struct ggml_tensor * wq_b      = nullptr;
+    struct ggml_tensor * wkv_a_mqa = nullptr;
+    struct ggml_tensor * wkv_b     = nullptr;
+    struct ggml_tensor * wq_cross  = nullptr;
+    struct ggml_tensor * wk_cross  = nullptr;
+    struct ggml_tensor * wv_cross  = nullptr;
+    struct ggml_tensor * wo_cross  = nullptr;
+    struct ggml_tensor * wq_enc    = nullptr;
+    struct ggml_tensor * wk_enc    = nullptr;
+    struct ggml_tensor * wv_enc    = nullptr;
+    struct ggml_tensor * wo_enc    = nullptr;
 
     // attention bias
-    struct ggml_tensor * bq;
-    struct ggml_tensor * bk;
-    struct ggml_tensor * bv;
-    struct ggml_tensor * bo;
-    struct ggml_tensor * bqkv;
+    struct ggml_tensor * bq   = nullptr;
+    struct ggml_tensor * bk   = nullptr;
+    struct ggml_tensor * bv   = nullptr;
+    struct ggml_tensor * bo   = nullptr;
+    struct ggml_tensor * bqkv = nullptr;
 
     // relative position bias
-    struct ggml_tensor * attn_rel_b;
-    struct ggml_tensor * attn_rel_b_enc;
-    struct ggml_tensor * attn_rel_b_cross;
+    struct ggml_tensor * attn_rel_b       = nullptr;
+    struct ggml_tensor * attn_rel_b_enc   = nullptr;
+    struct ggml_tensor * attn_rel_b_cross = nullptr;
 
     // normalization
-    struct ggml_tensor * ffn_norm;
-    struct ggml_tensor * ffn_norm_b;
-    struct ggml_tensor * ffn_post_norm;
-    struct ggml_tensor * layer_out_norm;
-    struct ggml_tensor * layer_out_norm_b;
-    struct ggml_tensor * ffn_norm_exps;
-    struct ggml_tensor * ffn_norm_enc;
+    struct ggml_tensor * ffn_norm         = nullptr;
+    struct ggml_tensor * ffn_norm_b       = nullptr;
+    struct ggml_tensor * ffn_post_norm    = nullptr;
+    struct ggml_tensor * layer_out_norm   = nullptr;
+    struct ggml_tensor * layer_out_norm_b = nullptr;
+    struct ggml_tensor * ffn_norm_exps    = nullptr;
+    struct ggml_tensor * ffn_norm_enc     = nullptr;
 
     // ff
-    struct ggml_tensor * ffn_gate; // w1
-    struct ggml_tensor * ffn_down; // w2
-    struct ggml_tensor * ffn_up;   // w3
-    struct ggml_tensor * ffn_gate_enc;
-    struct ggml_tensor * ffn_down_enc;
-    struct ggml_tensor * ffn_up_enc;
+    struct ggml_tensor * ffn_gate     = nullptr; // w1
+    struct ggml_tensor * ffn_down     = nullptr; // w2
+    struct ggml_tensor * ffn_up       = nullptr; // w3
+    struct ggml_tensor * ffn_gate_enc = nullptr;
+    struct ggml_tensor * ffn_down_enc = nullptr;
+    struct ggml_tensor * ffn_up_enc   = nullptr;
 
     // ff MoE
-    struct ggml_tensor * ffn_gate_inp;
-    struct ggml_tensor * ffn_gate_exps;
-    struct ggml_tensor * ffn_down_exps;
-    struct ggml_tensor * ffn_up_exps ;
+    struct ggml_tensor * ffn_gate_inp  = nullptr;
+    struct ggml_tensor * ffn_gate_exps = nullptr;
+    struct ggml_tensor * ffn_down_exps = nullptr;
+    struct ggml_tensor * ffn_up_exps   = nullptr;
 
     // ff shared expert (shexp)
-    struct ggml_tensor * ffn_gate_inp_shexp;
-    struct ggml_tensor * ffn_gate_shexp;
-    struct ggml_tensor * ffn_down_shexp;
-    struct ggml_tensor * ffn_up_shexp;
+    struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
+    struct ggml_tensor * ffn_gate_shexp     = nullptr;
+    struct ggml_tensor * ffn_down_shexp     = nullptr;
+    struct ggml_tensor * ffn_up_shexp       = nullptr;
 
     // ff bias
-    struct ggml_tensor * ffn_gate_b;
-    struct ggml_tensor * ffn_down_b; // b2
-    struct ggml_tensor * ffn_up_b; // b3
-    struct ggml_tensor * ffn_act;
+    struct ggml_tensor * ffn_gate_b = nullptr;
+    struct ggml_tensor * ffn_down_b = nullptr; // b2
+    struct ggml_tensor * ffn_up_b   = nullptr; // b3
+    struct ggml_tensor * ffn_act    = nullptr;
 
     // mamba proj
-    struct ggml_tensor * ssm_in;
-    struct ggml_tensor * ssm_x;
-    struct ggml_tensor * ssm_dt;
-    struct ggml_tensor * ssm_out;
+    struct ggml_tensor * ssm_in  = nullptr;
+    struct ggml_tensor * ssm_x   = nullptr;
+    struct ggml_tensor * ssm_dt  = nullptr;
+    struct ggml_tensor * ssm_out = nullptr;
 
     // mamba
-    struct ggml_tensor * ssm_conv1d;
-    struct ggml_tensor * ssm_a;
-    struct ggml_tensor * ssm_d;
+    struct ggml_tensor * ssm_conv1d = nullptr;
+    struct ggml_tensor * ssm_a      = nullptr;
+    struct ggml_tensor * ssm_d      = nullptr;
 
     // mamba bias
-    struct ggml_tensor * ssm_conv1d_b;
-    struct ggml_tensor * ssm_dt_b;
+    struct ggml_tensor * ssm_conv1d_b = nullptr;
+    struct ggml_tensor * ssm_dt_b     = nullptr;
 
     // rwkv
-    struct ggml_tensor * time_mix_w1;
-    struct ggml_tensor * time_mix_w2;
-    struct ggml_tensor * time_mix_lerp_x;
-    struct ggml_tensor * time_mix_lerp_w;
-    struct ggml_tensor * time_mix_lerp_k;
-    struct ggml_tensor * time_mix_lerp_v;
-    struct ggml_tensor * time_mix_lerp_r;
-    struct ggml_tensor * time_mix_lerp_g;
+    struct ggml_tensor * time_mix_w1         = nullptr;
+    struct ggml_tensor * time_mix_w2         = nullptr;
+    struct ggml_tensor * time_mix_lerp_x     = nullptr;
+    struct ggml_tensor * time_mix_lerp_w     = nullptr;
+    struct ggml_tensor * time_mix_lerp_k     = nullptr;
+    struct ggml_tensor * time_mix_lerp_v     = nullptr;
+    struct ggml_tensor * time_mix_lerp_r     = nullptr;
+    struct ggml_tensor * time_mix_lerp_g     = nullptr;
 
-    struct ggml_tensor * time_mix_first;
-    struct ggml_tensor * time_mix_decay;
-    struct ggml_tensor * time_mix_decay_w1;
-    struct ggml_tensor * time_mix_decay_w2;
-    struct ggml_tensor * time_mix_key;
-    struct ggml_tensor * time_mix_value;
-    struct ggml_tensor * time_mix_receptance;
-    struct ggml_tensor * time_mix_gate;
+    struct ggml_tensor * time_mix_first      = nullptr;
+    struct ggml_tensor * time_mix_decay      = nullptr;
+    struct ggml_tensor * time_mix_decay_w1   = nullptr;
+    struct ggml_tensor * time_mix_decay_w2   = nullptr;
+    struct ggml_tensor * time_mix_key        = nullptr;
+    struct ggml_tensor * time_mix_value      = nullptr;
+    struct ggml_tensor * time_mix_receptance = nullptr;
+    struct ggml_tensor * time_mix_gate       = nullptr;
 
-    struct ggml_tensor * time_mix_ln;
-    struct ggml_tensor * time_mix_ln_b;
-    struct ggml_tensor * time_mix_output;
+    struct ggml_tensor * time_mix_ln     = nullptr;
+    struct ggml_tensor * time_mix_ln_b   = nullptr;
+    struct ggml_tensor * time_mix_output = nullptr;
 
-    struct ggml_tensor * channel_mix_lerp_k;
-    struct ggml_tensor * channel_mix_lerp_r;
+    struct ggml_tensor * channel_mix_lerp_k = nullptr;
+    struct ggml_tensor * channel_mix_lerp_r = nullptr;
 
-    struct ggml_tensor * channel_mix_key;
-    struct ggml_tensor * channel_mix_receptance;
-    struct ggml_tensor * channel_mix_value;
+    struct ggml_tensor * channel_mix_key        = nullptr;
+    struct ggml_tensor * channel_mix_receptance = nullptr;
+    struct ggml_tensor * channel_mix_value      = nullptr;
 
     // long rope factors
     struct ggml_tensor * rope_long  = nullptr;
@@ -2868,13 +2932,17 @@ struct llama_layer {
     struct ggml_tensor * rope_freqs = nullptr;
 
     // bitnet scale
-    struct ggml_tensor * wq_scale;
-    struct ggml_tensor * wk_scale;
-    struct ggml_tensor * wv_scale;
-    struct ggml_tensor * wo_scale;
-    struct ggml_tensor * ffn_gate_scale;
-    struct ggml_tensor * ffn_up_scale;
-    struct ggml_tensor * ffn_down_scale;
+    struct ggml_tensor * wq_scale       = nullptr;
+    struct ggml_tensor * wk_scale       = nullptr;
+    struct ggml_tensor * wv_scale       = nullptr;
+    struct ggml_tensor * wo_scale       = nullptr;
+    struct ggml_tensor * ffn_gate_scale = nullptr;
+    struct ggml_tensor * ffn_up_scale   = nullptr;
+    struct ggml_tensor * ffn_down_scale = nullptr;
+
+    struct llama_layer_posnet posnet;
+
+    struct llama_layer_convnext convnext;
 };
 
 // very similar to llama_batch,
@@ -3005,6 +3073,9 @@ struct llama_model {
     struct ggml_tensor * cls_out   = nullptr;
     struct ggml_tensor * cls_out_b = nullptr;
 
+    struct ggml_tensor * conv1d = nullptr;
+    struct ggml_tensor * conv1d_b = nullptr;
+
     std::vector<llama_layer> layers;
 
     // gguf metadata
@@ -3089,6 +3160,7 @@ struct llama_sbatch {
     // batch indices of the output
     std::vector<size_t> out_ids;
     std::vector<llama_sbatch_seq> seq;
+
     const llama_batch * batch = nullptr;
 
     // buffers for the ubatch
@@ -3509,6 +3581,17 @@ static int llama_get_device_count(const llama_model & model) {
     return (int) model.devices.size();
 }
 
+static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) {
+    auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
+            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
+                return it.first == name;
+            });
+    if (it == model->tensors_by_name.end()) {
+        return nullptr;
+    }
+    return it->second;
+}
+
 template<typename F>
 static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
     ggml_init_params params = {
@@ -3562,7 +3645,9 @@ static bool llama_kv_cache_init(
 
     const struct llama_hparams & hparams = model.hparams;
 
-    const int64_t  n_layer = hparams.n_layer;
+    const int32_t n_layer = hparams.n_layer;
+
+    LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d\n", __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
 
     cache.has_shift = false;
 
@@ -3603,10 +3688,12 @@ static bool llama_kv_cache_init(
     cache.k_l.reserve(n_layer);
     cache.v_l.reserve(n_layer);
 
-    for (int i = 0; i < (int) n_layer; i++) {
+    for (int i = 0; i < n_layer; i++) {
         const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
         const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
 
+        LLAMA_LOG_DEBUG("%s: layer %d: n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", __func__, i, n_embd_k_gqa, n_embd_v_gqa);
+
         ggml_backend_buffer_type_t buft;
         if (offload) {
             auto * dev = model.dev_layer.at(i).dev;
@@ -5519,7 +5606,7 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
 
     // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false);
 
     // everything past this point is not vocab-related
     if (hparams.vocab_only) {
@@ -5532,6 +5619,16 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
     ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
 
+    if (model.arch == LLM_ARCH_WAVTOKENIZER_DEC) {
+        ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
+
+        ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
+        ml.get_key(LLM_KV_POSNET_BLOCK_COUNT,      hparams.posnet.n_layer);
+
+        ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
+        ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT,      hparams.convnext.n_layer);
+    }
+
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
     GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
     if (hparams.n_expert > 0) {
@@ -5540,13 +5637,13 @@ static void llm_load_hparams(
         GGML_ASSERT(hparams.n_expert_used == 0);
     }
 
-    // zero-out the per-layer hparams
+    // zero-out the array hparams
     std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
     std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
     std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
 
-    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
 
     // n_head_kv is optional, default to n_head
     hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -6291,6 +6388,13 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
             } break;
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
+                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+            } break;
         default: (void)0;
     }
 
@@ -6320,7 +6424,7 @@ static void llm_load_vocab(
         ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
         ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
 
-        if (tokenizer_model == "no_vocab") {
+        if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
             vocab.type = LLAMA_VOCAB_TYPE_NONE;
 
             // default special tokens
@@ -7299,6 +7403,22 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
     {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     // this tensor is loaded for T5, but never used
     {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
+    {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_NORM2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_CONV1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_CONV2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_ATTN_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_ATTN_Q,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_K,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_V,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_OUT,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_DW,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_CONVNEXT_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CONVNEXT_PW1,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_PW2,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_GAMMA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
 };
 
 // checks if the weight tensor can be used with the specified buffer type and device
@@ -7403,6 +7523,12 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
                 ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
                 op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
             } break;
+        case GGML_OP_IM2COL:
+            {
+                const int n_embd = hparams.n_embd;
+                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
+                op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
+            } break;
         default:
             GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
     }
@@ -7533,7 +7659,8 @@ static bool llm_load_tensors(
     model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;
 
-    const int n_layer     = hparams.n_layer;
+    const int n_layer = hparams.n_layer;
+
     bool use_mmap_buffer = true;
 
     // build a list of buffer types for the CPU and GPU devices
@@ -9336,9 +9463,9 @@ static bool llm_load_tensors(
                 } break;
             case LLM_ARCH_CHAMELEON:
                 {
-                 model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
-                 // output
+                    // output
                     model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                     model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                     // if output is NULL, init from the input tok embed
@@ -9367,6 +9494,109 @@ static bool llm_load_tensors(
                         layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
                 } break;
+            case LLM_ARCH_WAVTOKENIZER_DEC:
+                {
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
+
+                    model.conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
+                    model.conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
+
+                    // posnet
+                    {
+                        const int64_t n_embd = hparams.posnet.n_embd;
+
+                        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
+                            auto & layer = model.layers[i].posnet;
+
+                            // posnet:
+                            //
+                            //  - resnet
+                            //  - resnet
+                            //  - attn
+                            //  - resnet
+                            //  - resnet
+                            //  - norm
+                            //
+                            switch (i) {
+                                case 0:
+                                case 1:
+                                case 3:
+                                case 4:
+                                    {
+                                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
+                                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
+                                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
+                                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
+                                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                case 2:
+                                    {
+                                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                case 5:
+                                    {
+                                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                default: GGML_ABORT("unknown posnet layer");
+                            };
+                        }
+                    }
+
+                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
+
+                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
+                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {hparams.posnet.n_embd}, 0);
+
+                    // convnext
+                    {
+                        const int64_t n_embd = hparams.convnext.n_embd;
+
+                        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
+                            auto & layer = model.layers[i].convnext;
+
+                            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
+                            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);
+
+                            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
+                            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);
+
+                            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
+                            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);
+
+                            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
+                            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);
+
+                            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
+                        }
+
+                        // output
+                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    }
+
+                    model.output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
+                    model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -9586,6 +9816,7 @@ enum llm_ffn_gate_type {
 enum llm_norm_type {
     LLM_NORM,
     LLM_NORM_RMS,
+    LLM_NORM_GROUP,
 };
 
 static struct ggml_tensor * llm_build_inp_embd(
@@ -9606,7 +9837,7 @@ static struct ggml_tensor * llm_build_inp_embd(
 
         inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
     } else {
-       lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
         inpL = lctx.inp_embd;
         ggml_set_input(lctx.inp_embd);
     }
@@ -9727,8 +9958,14 @@ static struct ggml_tensor * llm_build_norm(
          const llm_build_cb & cb,
                         int   il) {
     switch (type) {
-        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
-        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
+        case LLM_NORM:       cur = ggml_norm      (ctx, cur, hparams.f_norm_eps);     break;
+        case LLM_NORM_RMS:   cur = ggml_rms_norm  (ctx, cur, hparams.f_norm_rms_eps); break;
+        case LLM_NORM_GROUP:
+            {
+                cur = ggml_reshape_3d(ctx, cur, cur->ne[0], 1, cur->ne[1]);
+                cur = ggml_group_norm(ctx, cur, hparams.n_norm_groups, hparams.f_norm_group_eps);
+                cur = ggml_reshape_2d(ctx, cur, cur->ne[0],    cur->ne[2]);
+            } break;
     }
 
     if (mw || mb) {
@@ -15854,7 +16091,7 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_t5_encoder() {
+    struct ggml_cgraph * build_t5_enc() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
@@ -15986,7 +16223,7 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_t5_decoder() {
+    struct ggml_cgraph * build_t5_dec() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
@@ -16935,6 +17172,158 @@ struct llm_build_context {
 
         return gf;
     }
+
+    struct ggml_cgraph * build_wavtokenizer_dec() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
+
+        cur = ggml_conv_1d_ph(ctx0, model.conv1d, cur, 1, 1);
+        cur = ggml_add(ctx0, cur, model.conv1d_b);
+
+        // posnet
+        for (uint32_t il = 0; il < hparams.posnet.n_layer; ++il) {
+            const auto & layer = model.layers[il].posnet;
+
+            inpL = cur;
+
+            switch (il) {
+                case 0:
+                case 1:
+                case 3:
+                case 4:
+                    {
+                        cur = llm_build_norm(ctx0, cur, hparams,
+                                layer.norm1,
+                                layer.norm1_b,
+                                LLM_NORM_GROUP, cb, 0);
+
+                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.conv1, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.conv1_b);
+
+                        cur = llm_build_norm(ctx0, cur, hparams,
+                                layer.norm2,
+                                layer.norm2_b,
+                                LLM_NORM_GROUP, cb, 0);
+
+                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.conv2, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.conv2_b);
+
+                        cur = ggml_add(ctx0, cur, inpL);
+                    } break;
+                case 2:
+                    {
+                        cur = llm_build_norm(ctx0, cur, hparams,
+                                layer.attn_norm,
+                                layer.attn_norm_b,
+                                LLM_NORM_GROUP, cb, 0);
+
+                        struct ggml_tensor * q;
+                        struct ggml_tensor * k;
+                        struct ggml_tensor * v;
+
+                        q = ggml_conv_1d_ph(ctx0, layer.attn_q, cur, 1, 1);
+                        k = ggml_conv_1d_ph(ctx0, layer.attn_k, cur, 1, 1);
+                        v = ggml_conv_1d_ph(ctx0, layer.attn_v, cur, 1, 1);
+
+                        q = ggml_add(ctx0, q, layer.attn_q_b);
+                        k = ggml_add(ctx0, k, layer.attn_k_b);
+                        v = ggml_add(ctx0, v, layer.attn_v_b);
+
+                        q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
+                        k = ggml_cont(ctx0, ggml_transpose(ctx0, k));
+
+                        struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+
+                        kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(hparams.posnet.n_embd)), 0.0f);
+
+                        cur = ggml_mul_mat(ctx0, kq, v);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.attn_o, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.attn_o_b);
+
+                        cur = ggml_add(ctx0, cur, inpL);
+                    } break;
+                case 5:
+                    {
+                        cur = llm_build_norm(ctx0, cur, hparams,
+                                layer.norm,
+                                layer.norm_b,
+                                LLM_NORM_GROUP, cb, 0);
+                    } break;
+                default: GGML_ABORT("unknown posnet layer");
+            };
+        }
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.tok_norm,
+                model.tok_norm_b,
+                LLM_NORM, cb, -1);
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        inpL = cur;
+
+        // convnext
+        for (uint32_t il = 0; il < hparams.convnext.n_layer; ++il) {
+            const auto & layer = model.layers[il].convnext;
+
+            cur = inpL;
+
+            cur = ggml_conv_1d_dw_ph(ctx0, layer.dw, cur, 1, 1);
+            cur = ggml_add(ctx0, cur, layer.dw_b);
+
+            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    layer.norm,
+                    layer.norm_b,
+                    LLM_NORM, cb, -1);
+
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    layer.pw1, layer.pw1_b, NULL,
+                    NULL,      NULL,        NULL,
+                    layer.pw2, layer.pw2_b, NULL,
+                    NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+
+            cur = ggml_mul(ctx0, cur, layer.gamma);
+
+            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+            inpL = ggml_add(ctx0, cur, inpL);
+        }
+
+        cur = inpL;
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        cur = ggml_add(ctx0, cur, model.output_b);
+        cb(cur, "result_embd", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -17181,14 +17570,14 @@ static struct ggml_cgraph * llama_build_graph(
         case LLM_ARCH_T5:
             {
                 if (lctx.is_encoding) {
-                    result = llm.build_t5_encoder();
+                    result = llm.build_t5_enc();
                 } else {
-                    result = llm.build_t5_decoder();
+                    result = llm.build_t5_dec();
                 }
             } break;
         case LLM_ARCH_T5ENCODER:
             {
-                result = llm.build_t5_encoder();
+                result = llm.build_t5_enc();
             } break;
         case LLM_ARCH_JAIS:
             {
@@ -17210,6 +17599,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_chameleon();
             } break;
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            {
+                result = llm.build_wavtokenizer_dec();
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -17301,30 +17694,35 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch)
     }
 
     if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
-        GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
-        const int64_t n_tokens = ubatch.n_tokens;
+        //GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
 
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
-        int32_t * data = (int32_t *) lctx.inp_out_ids->data;
-
-        if (lctx.n_outputs == n_tokens) {
-            for (int i = 0; i < n_tokens; ++i) {
-                data[i] = i;
-            }
-        } else if (ubatch.output) {
-            int32_t n_outputs = 0;
-            for (int i = 0; i < n_tokens; ++i) {
-                if (ubatch.output[i]) {
-                    data[n_outputs++] = i;
-                }
-            }
-            // the graph needs to have been passed the correct number of outputs
-            GGML_ASSERT(lctx.n_outputs == n_outputs);
-        } else if (lctx.n_outputs == 1) {
-            // only keep last output
-            data[0] = n_tokens - 1;
+        if (!lctx.inp_out_ids) {
+            LLAMA_LOG_WARN("%s: 'lctx.inp_out_ids' is not created\n", __func__);
         } else {
-            GGML_ASSERT(lctx.n_outputs == 0);
+            const int64_t n_tokens = ubatch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+            int32_t * data = (int32_t *) lctx.inp_out_ids->data;
+
+            if (lctx.n_outputs == n_tokens) {
+                for (int i = 0; i < n_tokens; ++i) {
+                    data[i] = i;
+                }
+            } else if (ubatch.output) {
+                int32_t n_outputs = 0;
+                for (int i = 0; i < n_tokens; ++i) {
+                    if (ubatch.output[i]) {
+                        data[n_outputs++] = i;
+                    }
+                }
+                // the graph needs to have been passed the correct number of outputs
+                GGML_ASSERT(lctx.n_outputs == n_outputs);
+            } else if (lctx.n_outputs == 1) {
+                // only keep last output
+                data[0] = n_tokens - 1;
+            } else {
+                GGML_ASSERT(lctx.n_outputs == 0);
+            }
         }
     }
 
@@ -17995,6 +18393,7 @@ static int llama_decode_internal(
             embd = nullptr; // do not extract embeddings when not needed
             GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
         }
+
         // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
 
         ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);
@@ -20383,6 +20782,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_T5ENCODER:
         case LLM_ARCH_JAIS:
         case LLM_ARCH_RWKV6:
+        case LLM_ARCH_WAVTOKENIZER_DEC:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
@@ -20500,17 +20900,6 @@ uint64_t llama_model_n_params(const struct llama_model * model) {
     return model->n_elements;
 }
 
-struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
-    auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
-            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
-                return it.first == name;
-            });
-    if (it == model->tensors_by_name.end()) {
-        return nullptr;
-    }
-    return it->second;
-}
-
 bool llama_model_has_encoder(const struct llama_model * model) {
     switch (model->arch) {
         case LLM_ARCH_T5:        return true;

From 9177484f589d770ffc4e655b9819124d6a22c1d9 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 18 Dec 2024 23:21:42 +0100
Subject: [PATCH 053/376] ggml : fix arm build (#10890)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml: GGML_NATIVE uses -mcpu=native on ARM

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* ggml: Show detected features with GGML_NATIVE

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* remove msvc support, add GGML_CPU_ARM_ARCH option

* disable llamafile in android example

* march -> mcpu, skip adding feature macros

ggml-ci

---------

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Co-authored-by: Adrien Gallouët <angt@huggingface.co>
---
 examples/llama.android/llama/build.gradle.kts |   1 +
 ggml/CMakeLists.txt                           |  11 +-
 ggml/src/ggml-cpu/CMakeLists.txt              | 145 +++++++-----------
 ggml/src/ggml-cpu/ggml-cpu.cpp                |   6 +
 ggml/src/ggml-cpu/llamafile/sgemm.cpp         |   1 +
 5 files changed, 69 insertions(+), 95 deletions(-)

diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts
index 2d1dfba20..28dbc1904 100644
--- a/examples/llama.android/llama/build.gradle.kts
+++ b/examples/llama.android/llama/build.gradle.kts
@@ -19,6 +19,7 @@ android {
         externalNativeBuild {
             cmake {
                 arguments += "-DLLAMA_BUILD_COMMON=ON"
+                arguments += "-DGGML_LLAMAFILE=OFF"
                 arguments += "-DCMAKE_BUILD_TYPE=Release"
                 cppFlags += listOf()
                 arguments += listOf()
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 3442142ad..e33d97482 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -74,10 +74,10 @@ if (NOT GGML_CUDA_GRAPHS_DEFAULT)
 endif()
 
 # general
-option(GGML_STATIC "ggml: static link libraries"         OFF)
-option(GGML_NATIVE "ggml: enable -march=native flag"     ${GGML_NATIVE_DEFAULT})
-option(GGML_LTO    "ggml: enable link time optimization" OFF)
-option(GGML_CCACHE "ggml: use ccache if available"       ON)
+option(GGML_STATIC "ggml: static link libraries"                     OFF)
+option(GGML_NATIVE "ggml: optimize the build for the current system" ${GGML_NATIVE_DEFAULT})
+option(GGML_LTO    "ggml: enable link time optimization"             OFF)
+option(GGML_CCACHE "ggml: use ccache if available"                   ON)
 
 # debug
 option(GGML_ALL_WARNINGS           "ggml: enable all compiler warnings"                   ON)
@@ -120,8 +120,9 @@ endif()
 option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
-option(GGML_SVE              "ggml: enable SVE"              OFF)
+
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
+set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM")
 
 
 if (WIN32)
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 5d4732337..ea4ef5a00 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -74,112 +74,77 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
     if (CMAKE_OSX_ARCHITECTURES      STREQUAL "arm64" OR
         CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
-        (NOT CMAKE_OSX_ARCHITECTURES      AND
-        NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
             CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
 
         message(STATUS "ARM detected")
 
-        if (MSVC)
-            list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead
-            list(APPEND ARCH_DEFINITIONS __ARM_NEON)
-            list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA)
-
-            set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
-            string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
-            if (GGML_COMPILER_SUPPORT_DOTPROD)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
-
-                message(STATUS "ARM feature DOTPROD enabled")
-            endif ()
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
-
-            if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
-
-                message(STATUS "ARM feature MATMUL_INT8 enabled")
-            endif ()
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-            if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-
-                message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled")
-            endif ()
-
-            set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
-        elseif (APPLE)
-            if (GGML_NATIVE)
-                set(USER_PROVIDED_MARCH FALSE)
-                foreach(flag_var IN ITEMS CMAKE_C_FLAGS CMAKE_CXX_FLAGS CMAKE_REQUIRED_FLAGS)
-                    if ("${${flag_var}}" MATCHES "-march=[a-zA-Z0-9+._-]+")
-                        set(USER_PROVIDED_MARCH TRUE)
-                        break()
-                    endif()
-                endforeach()
-
-                if (NOT USER_PROVIDED_MARCH)
-                    set(MARCH_FLAGS "-march=armv8.2a")
-
-                    check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
-                    if (GGML_COMPILER_SUPPORT_DOTPROD)
-                        set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod")
-                        list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
-
-                        message(STATUS "ARM feature DOTPROD enabled")
-                    endif ()
-
-                    set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm")
-
-                    set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
-                    set(CMAKE_REQUIRED_FLAGS     "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}")
-
-                    check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                    if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                        set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm")
-                        list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
-
-                        message(STATUS "ARM feature MATMUL_INT8 enabled")
-                    endif ()
-
-                    set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
-
-                    list(APPEND ARCH_FLAGS "${MARCH_FLAGS}")
-                endif ()
-            endif ()
+        if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
+            message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
         else()
             check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
             if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
                 list(APPEND ARCH_FLAGS -mfp16-format=ieee)
             endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
-                # Raspberry Pi 1, Zero
-                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
-            endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
-                if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
-                    # Android armeabi-v7a
-                    list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
-                else()
-                    # Raspberry Pi 2
-                    list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+
+            if (GGML_NATIVE)
+                list(APPEND ARCH_FLAGS -mcpu=native)
+
+                set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
+
+                # -mcpu=native does not always enable all the features in some compilers,
+                # so we check for them manually and enable them if available
+
+                include(CheckCXXSourceRuns)
+
+                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod")
+                check_cxx_source_runs(
+                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }"
+                    GGML_COMPILER_SUPPORT_DOTPROD)
+                if (GGML_COMPILER_SUPPORT_DOTPROD)
+                    set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod")
+                endif()
+
+                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm")
+                check_cxx_source_runs(
+                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }"
+                    GGML_COMPILER_SUPPORT_I8MM)
+                if (GGML_COMPILER_SUPPORT_I8MM)
+                    set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm")
+                endif()
+
+                set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
+
+            else()
+                if (GGML_CPU_ARM_ARCH)
+                    list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
                 endif()
             endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
-                # Android arm64-v8a
-                # Raspberry Pi 3, 4, Zero 2 (32-bit)
-                list(APPEND ARCH_FLAGS -mno-unaligned-access)
-            endif()
-            if (GGML_SVE)
-                list(APPEND ARCH_FLAGS -march=armv8.6-a+sve)
+
+            # show enabled features
+            execute_process(
+                COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
+                INPUT_FILE "/dev/null"
+                OUTPUT_VARIABLE ARM_FEATURE
+                RESULT_VARIABLE ARM_FEATURE_RESULT
+            )
+            if (ARM_FEATURE_RESULT)
+                message(FATAL_ERROR "Failed to get ARM features")
+            else()
+                foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
+                    string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
+                    if (NOT ${feature_pos} EQUAL -1)
+                        message(STATUS "ARM feature ${feature} enabled")
+                    endif()
+                endforeach()
             endif()
         endif()
     elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
             (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
             CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
+
+        message(STATUS "x86 detected")
+
         if (MSVC)
             # instruction set detection for MSVC only
             if (GGML_NATIVE)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 0b6419f83..f11399cc6 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -522,6 +522,12 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
         if (ggml_cpu_has_sve()) {
             features.push_back({ "SVE", "1" });
         }
+        if (ggml_cpu_has_dotprod()) {
+            features.push_back({ "DOTPROD", "1" });
+        }
+        if (ggml_cpu_has_matmul_int8()) {
+            features.push_back({ "MATMUL_INT8", "1" });
+        }
         if (ggml_cpu_get_sve_cnt() > 0) {
             static std::string sve_cnt = std::to_string(ggml_cpu_get_sve_cnt());
             features.push_back({ "SVE_CNT", sve_cnt.c_str() });
diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index da4146ec4..f80a72781 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -204,6 +204,7 @@ template <> inline float32x4_t load(const float *p) {
     return vld1q_f32(p);
 }
 #if !defined(_MSC_VER)
+// FIXME: this should check for __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 template <> inline float16x8_t load(const ggml_fp16_t *p) {
     return vld1q_f16((const float16_t *)p);
 }

From 7909e8588ddf70820adf1f325490eb3f67b32875 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Thu, 19 Dec 2024 02:58:00 +0000
Subject: [PATCH 054/376] llama-run : improve progress bar (#10821)

Set default width to whatever the terminal is. Also fixed a small bug around
default n_gpu_layers value.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 README.md              |   2 +-
 examples/run/README.md |  10 +-
 examples/run/run.cpp   | 426 +++++++++++++++++++++++++++++------------
 3 files changed, 308 insertions(+), 130 deletions(-)

diff --git a/README.md b/README.md
index 3f3971d7e..d6d1958c8 100644
--- a/README.md
+++ b/README.md
@@ -448,7 +448,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
-[^3]: [https://github.com/containers/ramalama](RamaLama)
+[^3]: [RamaLama](https://github.com/containers/ramalama)
 
 ## [`llama-simple`](examples/simple)
 
diff --git a/examples/run/README.md b/examples/run/README.md
index 6162658e9..874293516 100644
--- a/examples/run/README.md
+++ b/examples/run/README.md
@@ -4,7 +4,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for r
 
 ```bash
 llama-run granite-code
-...
+```
 
 ```bash
 llama-run -h
@@ -19,6 +19,8 @@ Options:
       Context size (default: 2048)
   -n, --ngl <value>
       Number of GPU layers (default: 0)
+  -v, --verbose, --log-verbose
+      Set verbosity level to infinity (i.e. log all messages, useful for debugging)
   -h, --help
       Show help message
 
@@ -42,6 +44,6 @@ Examples:
   llama-run https://example.com/some-file1.gguf
   llama-run some-file2.gguf
   llama-run file://some-file3.gguf
-  llama-run --ngl 99 some-file4.gguf
-  llama-run --ngl 99 some-file5.gguf Hello World
-...
+  llama-run --ngl 999 some-file4.gguf
+  llama-run --ngl 999 some-file5.gguf Hello World
+```
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 834ea8f7b..03da54ca3 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -1,6 +1,8 @@
 #if defined(_WIN32)
 #    include <windows.h>
 #else
+#    include <sys/file.h>
+#    include <sys/ioctl.h>
 #    include <unistd.h>
 #endif
 
@@ -8,6 +10,7 @@
 #    include <curl/curl.h>
 #endif
 
+#include <climits>
 #include <cstdarg>
 #include <cstdio>
 #include <cstring>
@@ -21,15 +24,37 @@
 #include "json.hpp"
 #include "llama-cpp.h"
 
-#define printe(...)                   \
-    do {                              \
-        fprintf(stderr, __VA_ARGS__); \
-    } while (0)
+GGML_ATTRIBUTE_FORMAT(1, 2)
+static std::string fmt(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    const int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::string buf;
+    buf.resize(size);
+    const int size2 = vsnprintf(const_cast<char *>(buf.data()), buf.size() + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+
+    return buf;
+}
+
+GGML_ATTRIBUTE_FORMAT(1, 2)
+static int printe(const char * fmt, ...) {
+    va_list args;
+    va_start(args, fmt);
+    const int ret = vfprintf(stderr, fmt, args);
+    va_end(args);
+
+    return ret;
+}
 
 class Opt {
   public:
     int init(int argc, const char ** argv) {
-        construct_help_str_();
         // Parse arguments
         if (parse(argc, argv)) {
             printe("Error: Failed to parse arguments.\n");
@@ -48,14 +73,64 @@ class Opt {
 
     std::string model_;
     std::string user_;
-    int         context_size_ = 2048, ngl_ = -1;
+    int         context_size_ = -1, ngl_ = -1;
+    bool        verbose_ = false;
 
   private:
-    std::string help_str_;
     bool        help_ = false;
 
-    void construct_help_str_() {
-        help_str_ =
+    bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) {
+        return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0;
+    }
+
+    int handle_option_with_value(int argc, const char ** argv, int & i, int & option_value) {
+        if (i + 1 >= argc) {
+            return 1;
+        }
+
+        option_value = std::atoi(argv[++i]);
+        return 0;
+    }
+
+    int parse(int argc, const char ** argv) {
+        bool options_parsing   = true;
+        for (int i = 1, positional_args_i = 0; i < argc; ++i) {
+            if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
+                if (handle_option_with_value(argc, argv, i, context_size_) == 1) {
+                    return 1;
+                }
+            } else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) {
+                if (handle_option_with_value(argc, argv, i, ngl_) == 1) {
+                    return 1;
+                }
+            } else if (options_parsing &&
+                       (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
+                verbose_ = true;
+            } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
+                help_ = true;
+                return 0;
+            } else if (options_parsing && strcmp(argv[i], "--") == 0) {
+                options_parsing = false;
+            } else if (positional_args_i == 0) {
+                if (!argv[i][0] || argv[i][0] == '-') {
+                    return 1;
+                }
+
+                ++positional_args_i;
+                model_ = argv[i];
+            } else if (positional_args_i == 1) {
+                ++positional_args_i;
+                user_ = argv[i];
+            } else {
+                user_ += " " + std::string(argv[i]);
+            }
+        }
+
+        return 0;
+    }
+
+    void help() const {
+        printf(
             "Description:\n"
             "  Runs a llm\n"
             "\n"
@@ -64,15 +139,11 @@ class Opt {
             "\n"
             "Options:\n"
             "  -c, --context-size <value>\n"
-            "      Context size (default: " +
-            std::to_string(context_size_);
-        help_str_ +=
-            ")\n"
+            "      Context size (default: %d)\n"
             "  -n, --ngl <value>\n"
-            "      Number of GPU layers (default: " +
-            std::to_string(ngl_);
-        help_str_ +=
-            ")\n"
+            "      Number of GPU layers (default: %d)\n"
+            "  -v, --verbose, --log-verbose\n"
+            "      Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
             "  -h, --help\n"
             "      Show help message\n"
             "\n"
@@ -92,67 +163,102 @@ class Opt {
             "  llama-run ollama://granite-code\n"
             "  llama-run ollama://smollm:135m\n"
             "  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n"
-            "  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
+            "  llama-run "
+            "huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
             "  llama-run https://example.com/some-file1.gguf\n"
             "  llama-run some-file2.gguf\n"
             "  llama-run file://some-file3.gguf\n"
-            "  llama-run --ngl 99 some-file4.gguf\n"
-            "  llama-run --ngl 99 some-file5.gguf Hello World\n";
+            "  llama-run --ngl 999 some-file4.gguf\n"
+            "  llama-run --ngl 999 some-file5.gguf Hello World\n",
+            llama_context_default_params().n_batch, llama_model_default_params().n_gpu_layers);
     }
-
-    int parse(int argc, const char ** argv) {
-        int positional_args_i = 0;
-        for (int i = 1; i < argc; ++i) {
-            if (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0) {
-                if (i + 1 >= argc) {
-                    return 1;
-                }
-
-                context_size_ = std::atoi(argv[++i]);
-            } else if (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0) {
-                if (i + 1 >= argc) {
-                    return 1;
-                }
-
-                ngl_ = std::atoi(argv[++i]);
-            } else if (strcmp(argv[i], "-h") == 0 || strcmp(argv[i], "--help") == 0) {
-                help_ = true;
-                return 0;
-            } else if (!positional_args_i) {
-                ++positional_args_i;
-                model_ = argv[i];
-            } else if (positional_args_i == 1) {
-                ++positional_args_i;
-                user_ = argv[i];
-            } else {
-                user_ += " " + std::string(argv[i]);
-            }
-        }
-
-        return model_.empty();  // model_ is the only required value
-    }
-
-    void help() const { printf("%s", help_str_.c_str()); }
 };
 
 struct progress_data {
-    size_t file_size = 0;
+    size_t                                file_size  = 0;
     std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now();
-    bool   printed   = false;
+    bool                                  printed    = false;
 };
 
-struct FileDeleter {
-    void operator()(FILE * file) const {
+static int get_terminal_width() {
+#if defined(_WIN32)
+    CONSOLE_SCREEN_BUFFER_INFO csbi;
+    GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi);
+    return csbi.srWindow.Right - csbi.srWindow.Left + 1;
+#else
+    struct winsize w;
+    ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
+    return w.ws_col;
+#endif
+}
+
+#ifdef LLAMA_USE_CURL
+class File {
+  public:
+    FILE * file = nullptr;
+
+    FILE * open(const std::string & filename, const char * mode) {
+        file = fopen(filename.c_str(), mode);
+
+        return file;
+    }
+
+    int lock() {
+        if (file) {
+#    ifdef _WIN32
+            fd    = _fileno(file);
+            hFile = (HANDLE) _get_osfhandle(fd);
+            if (hFile == INVALID_HANDLE_VALUE) {
+                fd = -1;
+
+                return 1;
+            }
+
+            OVERLAPPED overlapped = { 0 };
+            if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD,
+                            &overlapped)) {
+                fd = -1;
+
+                return 1;
+            }
+#    else
+            fd = fileno(file);
+            if (flock(fd, LOCK_EX | LOCK_NB) != 0) {
+                fd = -1;
+
+                return 1;
+            }
+#    endif
+        }
+
+        return 0;
+    }
+
+    ~File() {
+        if (fd >= 0) {
+#    ifdef _WIN32
+            if (hFile != INVALID_HANDLE_VALUE) {
+                OVERLAPPED overlapped = { 0 };
+                UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped);
+            }
+#    else
+            flock(fd, LOCK_UN);
+#    endif
+        }
+
         if (file) {
             fclose(file);
         }
     }
+
+  private:
+    int fd = -1;
+#    ifdef _WIN32
+    HANDLE hFile;
+#    endif
 };
 
-typedef std::unique_ptr<FILE, FileDeleter> FILE_ptr;
-
-#ifdef LLAMA_USE_CURL
-class CurlWrapper {
+class HttpClient {
   public:
     int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
              const bool progress, std::string * response_str = nullptr) {
@@ -163,10 +269,20 @@ class CurlWrapper {
         }
 
         progress_data data;
-        FILE_ptr      out;
+        File          out;
         if (!output_file.empty()) {
             output_file_partial = output_file + ".partial";
-            out.reset(fopen(output_file_partial.c_str(), "ab"));
+            if (!out.open(output_file_partial, "ab")) {
+                printe("Failed to open file\n");
+
+                return 1;
+            }
+
+            if (out.lock()) {
+                printe("Failed to exclusively lock file\n");
+
+                return 1;
+            }
         }
 
         set_write_options(response_str, out);
@@ -181,7 +297,7 @@ class CurlWrapper {
         return 0;
     }
 
-    ~CurlWrapper() {
+    ~HttpClient() {
         if (chunk) {
             curl_slist_free_all(chunk);
         }
@@ -195,13 +311,13 @@ class CurlWrapper {
     CURL *              curl  = nullptr;
     struct curl_slist * chunk = nullptr;
 
-    void set_write_options(std::string * response_str, const FILE_ptr & out) {
+    void set_write_options(std::string * response_str, const File & out) {
         if (response_str) {
             curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data);
             curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str);
         } else {
             curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data);
-            curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.get());
+            curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.file);
         }
     }
 
@@ -219,7 +335,7 @@ class CurlWrapper {
         if (progress) {
             curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
             curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data);
-            curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, progress_callback);
+            curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, update_progress);
         }
     }
 
@@ -255,37 +371,31 @@ class CurlWrapper {
         int mins = (static_cast<int>(seconds) % 3600) / 60;
         int secs = static_cast<int>(seconds) % 60;
 
-        std::ostringstream out;
         if (hrs > 0) {
-            out << hrs << "h " << std::setw(2) << std::setfill('0') << mins << "m " << std::setw(2) << std::setfill('0')
-                << secs << "s";
+            return fmt("%dh %02dm %02ds", hrs, mins, secs);
         } else if (mins > 0) {
-            out << mins << "m " << std::setw(2) << std::setfill('0') << secs << "s";
+            return fmt("%dm %02ds", mins, secs);
         } else {
-            out << secs << "s";
+            return fmt("%ds", secs);
         }
-
-        return out.str();
     }
 
     static std::string human_readable_size(curl_off_t size) {
         static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" };
-        char         length   = sizeof(suffix) / sizeof(suffix[0]);
-        int          i        = 0;
-        double       dbl_size = size;
+        char                length   = sizeof(suffix) / sizeof(suffix[0]);
+        int                 i        = 0;
+        double              dbl_size = size;
         if (size > 1024) {
             for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) {
                 dbl_size = size / 1024.0;
             }
         }
 
-        std::ostringstream out;
-        out << std::fixed << std::setprecision(2) << dbl_size << " " << suffix[i];
-        return out.str();
+        return fmt("%.2f %s", dbl_size, suffix[i]);
     }
 
-    static int progress_callback(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
-                                 curl_off_t) {
+    static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
+                               curl_off_t) {
         progress_data * data = static_cast<progress_data *>(ptr);
         if (total_to_download <= 0) {
             return 0;
@@ -293,27 +403,68 @@ class CurlWrapper {
 
         total_to_download += data->file_size;
         const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size;
-        const curl_off_t percentage                    = (now_downloaded_plus_file_size * 100) / total_to_download;
-        const curl_off_t pos                           = (percentage / 5);
-        std::string progress_bar;
-        for (int i = 0; i < 20; ++i) {
-            progress_bar.append((i < pos) ? "█" : " ");
-        }
+        const curl_off_t percentage      = calculate_percentage(now_downloaded_plus_file_size, total_to_download);
+        std::string      progress_prefix = generate_progress_prefix(percentage);
 
-        // Calculate download speed and estimated time to completion
-        const auto                          now             = std::chrono::steady_clock::now();
-        const std::chrono::duration<double> elapsed_seconds = now - data->start_time;
-        const double                        speed           = now_downloaded / elapsed_seconds.count();
-        const double                        estimated_time  = (total_to_download - now_downloaded) / speed;
-        printe("\r%ld%% |%s| %s/%s  %.2f MB/s  %s      ", percentage, progress_bar.c_str(),
-               human_readable_size(now_downloaded).c_str(), human_readable_size(total_to_download).c_str(),
-               speed / (1024 * 1024), human_readable_time(estimated_time).c_str());
-        fflush(stderr);
+        const double speed = calculate_speed(now_downloaded, data->start_time);
+        const double tim   = (total_to_download - now_downloaded) / speed;
+        std::string  progress_suffix =
+            generate_progress_suffix(now_downloaded_plus_file_size, total_to_download, speed, tim);
+
+        int         progress_bar_width = calculate_progress_bar_width(progress_prefix, progress_suffix);
+        std::string progress_bar;
+        generate_progress_bar(progress_bar_width, percentage, progress_bar);
+
+        print_progress(progress_prefix, progress_bar, progress_suffix);
         data->printed = true;
 
         return 0;
     }
 
+    static curl_off_t calculate_percentage(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download) {
+        return (now_downloaded_plus_file_size * 100) / total_to_download;
+    }
+
+    static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", percentage); }
+
+    static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) {
+        const auto                          now             = std::chrono::steady_clock::now();
+        const std::chrono::duration<double> elapsed_seconds = now - start_time;
+        return now_downloaded / elapsed_seconds.count();
+    }
+
+    static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download,
+                                                double speed, double estimated_time) {
+        const int width = 10;
+        return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width,
+                   human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width,
+                   human_readable_time(estimated_time).c_str());
+    }
+
+    static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) {
+        int progress_bar_width = get_terminal_width() - progress_prefix.size() - progress_suffix.size() - 3;
+        if (progress_bar_width < 1) {
+            progress_bar_width = 1;
+        }
+
+        return progress_bar_width;
+    }
+
+    static std::string generate_progress_bar(int progress_bar_width, curl_off_t percentage,
+                                             std::string & progress_bar) {
+        const curl_off_t pos = (percentage * progress_bar_width) / 100;
+        for (int i = 0; i < progress_bar_width; ++i) {
+            progress_bar.append((i < pos) ? "█" : " ");
+        }
+
+        return progress_bar;
+    }
+
+    static void print_progress(const std::string & progress_prefix, const std::string & progress_bar,
+                               const std::string & progress_suffix) {
+        printe("\r%*s\r%s%s| %s", get_terminal_width(), " ", progress_prefix.c_str(), progress_bar.c_str(),
+               progress_suffix.c_str());
+    }
     // Function to write data to a file
     static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
         FILE * out = static_cast<FILE *>(stream);
@@ -357,8 +508,8 @@ class LlamaData {
 #ifdef LLAMA_USE_CURL
     int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
                  const bool progress, std::string * response_str = nullptr) {
-        CurlWrapper curl;
-        if (curl.init(url, headers, output_file, progress, response_str)) {
+        HttpClient http;
+        if (http.init(url, headers, output_file, progress, response_str)) {
             return 1;
         }
 
@@ -438,13 +589,17 @@ class LlamaData {
     }
 
     int resolve_model(std::string & model_) {
+        int                            ret     = 0;
+        if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) {
+            remove_proto(model_);
+
+            return ret;
+        }
+
         const std::string              bn      = basename(model_);
         const std::vector<std::string> headers = { "--header",
                                                    "Accept: application/vnd.docker.distribution.manifest.v2+json" };
-        int                            ret     = 0;
-        if (string_starts_with(model_, "file://") || std::filesystem::exists(bn)) {
-            remove_proto(model_);
-        } else if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
+        if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
             remove_proto(model_);
             ret = huggingface_dl(model_, headers, bn);
         } else if (string_starts_with(model_, "ollama://")) {
@@ -467,19 +622,23 @@ class LlamaData {
         llama_model_params model_params = llama_model_default_params();
         model_params.n_gpu_layers       = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
         resolve_model(opt.model_);
+        printe(
+            "\r%*s"
+            "\rLoading model",
+            get_terminal_width(), " ");
         llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
         if (!model) {
             printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
 
+        printe("\r%*s\r", static_cast<int>(sizeof("Loading model")), " ");
         return model;
     }
 
     // Initializes the context with the specified parameters
     llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
         llama_context_params ctx_params = llama_context_default_params();
-        ctx_params.n_ctx                = n_ctx;
-        ctx_params.n_batch              = n_ctx;
+        ctx_params.n_ctx = ctx_params.n_batch = n_ctx >= 0 ? n_ctx : ctx_params.n_batch;
         llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
         if (!context) {
             printe("%s: error: failed to create the llama_context\n", __func__);
@@ -609,16 +768,20 @@ static int read_user_input(std::string & user) {
 }
 
 // Function to generate a response based on the prompt
-static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response) {
+static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response,
+                             const bool stdout_a_terminal) {
     // Set response color
-    printf("\033[33m");
+    if (stdout_a_terminal) {
+        printf("\033[33m");
+    }
+
     if (generate(llama_data, prompt, response)) {
         printe("failed to generate response\n");
         return 1;
     }
 
     // End response with color reset and newline
-    printf("\n\033[0m");
+    printf("\n%s", stdout_a_terminal ? "\033[0m" : "");
     return 0;
 }
 
@@ -642,15 +805,37 @@ static int handle_user_input(std::string & user_input, const std::string & user_
     }
 
     printf(
-        "\r                                                                       "
-        "\r\033[32m> \033[0m");
+        "\r%*s"
+        "\r\033[32m> \033[0m",
+        get_terminal_width(), " ");
     return read_user_input(user_input);  // Returns true if input ends the loop
 }
 
+static bool is_stdin_a_terminal() {
+#if defined(_WIN32)
+    HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
+    DWORD  mode;
+    return GetConsoleMode(hStdin, &mode);
+#else
+    return isatty(STDIN_FILENO);
+#endif
+}
+
+static bool is_stdout_a_terminal() {
+#if defined(_WIN32)
+    HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE);
+    DWORD  mode;
+    return GetConsoleMode(hStdout, &mode);
+#else
+    return isatty(STDOUT_FILENO);
+#endif
+}
+
 // Function to tokenize the prompt
 static int chat_loop(LlamaData & llama_data, const std::string & user_) {
     int prev_len = 0;
     llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
+    static const bool stdout_a_terminal = is_stdout_a_terminal();
     while (true) {
         // Get user input
         std::string user_input;
@@ -665,7 +850,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
 
         std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len);
         std::string response;
-        if (generate_response(llama_data, prompt, response)) {
+        if (generate_response(llama_data, prompt, response, stdout_a_terminal)) {
             return 1;
         }
 
@@ -682,22 +867,13 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
     return 0;
 }
 
-static void log_callback(const enum ggml_log_level level, const char * text, void *) {
-    if (level == GGML_LOG_LEVEL_ERROR) {
+static void log_callback(const enum ggml_log_level level, const char * text, void * p) {
+    const Opt * opt = static_cast<Opt *>(p);
+    if (opt->verbose_ || level == GGML_LOG_LEVEL_ERROR) {
         printe("%s", text);
     }
 }
 
-static bool is_stdin_a_terminal() {
-#if defined(_WIN32)
-    HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
-    DWORD  mode;
-    return GetConsoleMode(hStdin, &mode);
-#else
-    return isatty(STDIN_FILENO);
-#endif
-}
-
 static std::string read_pipe_data() {
     std::ostringstream result;
     result << std::cin.rdbuf();  // Read all data from std::cin
@@ -721,7 +897,7 @@ int main(int argc, const char ** argv) {
         opt.user_ += read_pipe_data();
     }
 
-    llama_log_set(log_callback, nullptr);
+    llama_log_set(log_callback, &opt);
     LlamaData llama_data;
     if (llama_data.init(opt)) {
         return 1;

From cd920d0ac38ec243605a5a57c50941140a193f9e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 19 Dec 2024 08:53:58 +0100
Subject: [PATCH 055/376] tests: disable GGUF test for bad value size (#10886)

---
 tests/test-gguf.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp
index e5b4cb7b8..1bb5fb47c 100644
--- a/tests/test-gguf.cpp
+++ b/tests/test-gguf.cpp
@@ -634,7 +634,7 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
 
         HANDCRAFTED_KV_BAD_KEY_SIZE,
         HANDCRAFTED_KV_BAD_TYPE,
-        HANDCRAFTED_KV_BAD_VALUE_SIZE,
+        // HANDCRAFTED_KV_BAD_VALUE_SIZE, // FIXME sanitizer limit
         // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME
         HANDCRAFTED_KV_SUCCESS,
 

From 7585edbdebd02861e0994dae67c9338731fb3fc5 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Thu, 19 Dec 2024 10:37:12 +0100
Subject: [PATCH 056/376] convert : Add support for Microsoft Phi-4 model 
 (#10817)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* convert : use GPT2 vocab for Phi-4 model

* convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models

* llama : do not use sliding window attention mask for Phi-4 model

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 convert_hf_to_gguf.py | 15 ++++++++++++++-
 src/llama.cpp         | 10 ++++++++--
 2 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 4a0b00f69..7b433ee6d 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2200,6 +2200,15 @@ class Phi3MiniModel(Model):
     model_arch = gguf.MODEL_ARCH.PHI3
 
     def set_vocab(self):
+        # Phi-4 model uses GPT2Tokenizer
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                tokenizer_class = tokenizer_config_json['tokenizer_class']
+                if tokenizer_class == 'GPT2Tokenizer':
+                    return self._set_vocab_gpt2()
+
         from sentencepiece import SentencePieceProcessor
 
         tokenizer_path = self.dir_model / 'tokenizer.model'
@@ -2316,7 +2325,11 @@ class Phi3MiniModel(Model):
         self.gguf_writer.add_rope_dimension_count(rope_dims)
         self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"]))
+        sliding_window = self.hparams.get("sliding_window")
+        # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
+        if sliding_window is None:
+            sliding_window = 0
+        self.gguf_writer.add_sliding_window(sliding_window)
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
diff --git a/src/llama.cpp b/src/llama.cpp
index 94160d534..cec15a005 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -13333,7 +13333,13 @@ struct llm_build_context {
         struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
+        struct ggml_tensor * KQ_mask = nullptr;
+        if (hparams.n_swa == 0) {
+            // Phi-4 doesn't use sliding window attention
+            KQ_mask = build_inp_KQ_mask();
+        } else {
+            KQ_mask = build_inp_KQ_mask_swa();
+        }
 
         for (int il = 0; il < n_layer; ++il) {
             auto residual = inpL;
@@ -13391,7 +13397,7 @@ struct llm_build_context {
 
                 cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
             if (il == n_layer - 1) {

From 2fffc52b50992ac5bc64db19d33c39cbc06f52cf Mon Sep 17 00:00:00 2001
From: Sukriti Sharma <Ssukriti@users.noreply.github.com>
Date: Thu, 19 Dec 2024 06:04:51 -0700
Subject: [PATCH 057/376] llama : fix Roberta embeddings (#10856)

* fix: Use gpt2 tokenizer for roberta and add eos/bos tokens

Branch: RobertaTokenizer

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fixes to position embeddings

Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>

* map roberta-bpe to gpt-2

Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>

* fix linting

Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
---
 convert_hf_to_gguf.py | 47 ++++++++++++++++++++++++++++++++++++++++++-
 src/llama.cpp         |  3 ++-
 2 files changed, 48 insertions(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 7b433ee6d..ecd69be6b 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2628,7 +2628,7 @@ class InternLM2Model(Model):
             return [(self.map_tensor_name(name), data_torch)]
 
 
-@Model.register("BertModel", "CamembertModel", "RobertaModel")
+@Model.register("BertModel", "CamembertModel")
 class BertModel(Model):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -2701,6 +2701,51 @@ class BertModel(Model):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("RobertaModel")
+class RobertaModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # we need the pad_token_id to know how to chop down position_embd matrix
+        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
+            self._position_offset = 1 + pad_token_id
+            if "max_position_embeddings" in self.hparams:
+                self.hparams["max_position_embeddings"] -= self._position_offset
+        else:
+            self._position_offset = None
+
+    def set_vocab(self):
+        """Support BPE tokenizers for roberta models"""
+        bpe_tok_path = self.dir_model / "tokenizer.json"
+        if bpe_tok_path.exists():
+            self._set_vocab_gpt2()
+            self.gguf_writer.add_add_bos_token(True)
+            self.gguf_writer.add_add_eos_token(True)
+
+            # we need this to validate the size of the token_type embeddings
+            # though currently we are passing all zeros to the token_type embeddings
+            # "Sequence A" or "Sequence B"
+            self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
+
+        else:
+            return super().set_vocab()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # if name starts with "roberta.", remove the prefix
+        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
+        if name.startswith("roberta."):
+            name = name[8:]
+
+        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
+        if name == "embeddings.position_embeddings.weight":
+            if self._position_offset is not None:
+                data_torch = data_torch[self._position_offset:,:]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
 @Model.register("NomicBertModel")
 class NomicBertModel(BertModel):
     model_arch = gguf.MODEL_ARCH.NOMIC_BERT
diff --git a/src/llama.cpp b/src/llama.cpp
index cec15a005..b442781a0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -6592,7 +6592,8 @@ static void llm_load_vocab(
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
-                    tokenizer_pre == "jina-v2-code") {
+                    tokenizer_pre == "jina-v2-code" ||
+                    tokenizer_pre == "roberta-bpe") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "refact") {

From a3c33b1dce2d4f25040b75f66629104bd1e40128 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Thu, 19 Dec 2024 14:20:41 +0100
Subject: [PATCH 058/376] ggml: fix arm build with gcc (#10895)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 ggml/src/ggml-cpu/CMakeLists.txt | 29 +++++++++++++++++++++--------
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index ea4ef5a00..683b90aff 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -88,32 +88,45 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             endif()
 
             if (GGML_NATIVE)
-                list(APPEND ARCH_FLAGS -mcpu=native)
-
-                set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
-
                 # -mcpu=native does not always enable all the features in some compilers,
                 # so we check for them manually and enable them if available
 
+                execute_process(
+                    COMMAND ${CMAKE_C_COMPILER} -mcpu=native -E -v -
+                    INPUT_FILE "/dev/null"
+                    OUTPUT_QUIET
+                    ERROR_VARIABLE ARM_MCPU
+                    RESULT_VARIABLE ARM_MCPU_RESULT
+                )
+                if (NOT ARM_MCPU_RESULT)
+                    string(REGEX MATCH "-mcpu=[^ ']+" ARM_MCPU_FLAG "${ARM_MCPU}")
+                endif()
+                if ("${ARM_MCPU_FLAG}" STREQUAL "")
+                    set(ARM_MCPU_FLAG -mcpu=native)
+                    message(STATUS "ARM -mcpu not found, -mcpu=native will be used")
+                endif()
+
+                set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
                 include(CheckCXXSourceRuns)
 
-                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod")
+                set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod")
                 check_cxx_source_runs(
                     "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }"
                     GGML_COMPILER_SUPPORT_DOTPROD)
                 if (GGML_COMPILER_SUPPORT_DOTPROD)
-                    set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod")
+                    set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod")
                 endif()
 
-                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm")
+                set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm")
                 check_cxx_source_runs(
                     "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }"
                     GGML_COMPILER_SUPPORT_I8MM)
                 if (GGML_COMPILER_SUPPORT_I8MM)
-                    set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm")
+                    set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm")
                 endif()
 
                 set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
+                list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}")
 
             else()
                 if (GGML_CPU_ARM_ARCH)

From 57bb2c40cd94c5a09f5210ed8264cc93b21c4b7e Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 19 Dec 2024 15:40:08 +0100
Subject: [PATCH 059/376] server : fix logprobs, make it OAI-compatible
 (#10783)

* server : fix logprobs, make it openai-compatible

* update docs

* add std::log

* return pre-sampling p

* sort before apply softmax

* add comment

* fix test

* set p for sampled token

* update docs

* add --multi-token-probs

* update docs

* add `post_sampling_probs` option

* update docs [no ci]

* remove --multi-token-probs

* "top_probs" with "post_sampling_probs"

* resolve review comments

* rename struct token_prob to prob_info

* correct comment placement

* fix setting prob for sampled token
---
 examples/server/README.md                     |  78 ++++--
 examples/server/server.cpp                    | 229 ++++++++++++------
 .../server/tests/unit/test_chat_completion.py |  62 ++++-
 examples/server/tests/unit/test_completion.py |  71 +++++-
 examples/server/tests/unit/test_embedding.py  |   3 +
 examples/server/utils.hpp                     |  60 +++++
 6 files changed, 396 insertions(+), 107 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index d006a8d37..6d6465692 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -343,6 +343,10 @@ node index.js
 
 ### POST `/completion`: Given a `prompt`, it returns the predicted completion.
 
+> [!IMPORTANT]
+>
+> This endpoint is **not** OAI-compatible
+
 *Options:*
 
 `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
@@ -444,38 +448,68 @@ These words will not be included in the completion, so make sure to add them to
 
 `timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
 
+`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.
+
 **Response format**
 
 - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
 
-- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
-
-```json
-{
-  "content": "<the token generated by the model>",
-  "tokens": [ generated token ids if requested ],
-  "probs": [
-    {
-      "prob": float,
-      "tok_str": "<most likely token>"
-    },
-    {
-      "prob": float,
-      "tok_str": "<second most likely token>"
-    },
+- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements:
+  ```json
+  {
+    "content": "<the generated completion text>",
+    "tokens": [ generated token ids if requested ],
     ...
-  ]
-},
-```
-
-Notice that each `probs` is an array of length `n_probs`.
+    "probs": [
+      {
+        "id": <token id>,
+        "logprob": float,
+        "token": "<most likely token>",
+        "bytes": [int, int, ...],
+        "top_logprobs": [
+          {
+            "id": <token id>,
+            "logprob": float,
+            "token": "<token text>",
+            "bytes": [int, int, ...],
+          },
+          {
+            "id": <token id>,
+            "logprob": float,
+            "token": "<token text>",
+            "bytes": [int, int, ...],
+          },
+          ...
+        ]
+      },
+      {
+        "id": <token id>,
+        "logprob": float,
+        "token": "<most likely token>",
+        "bytes": [int, int, ...],
+        "top_logprobs": [
+          ...
+        ]
+      },
+      ...
+    ]
+  },
+  ```
+  Please note that if `post_sampling_probs` is set to `true`:
+    - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0
+    - `top_logprobs` will be replaced with `top_probs`. Each element contains:
+      - `id`: token ID
+      - `token`: token in string
+      - `bytes`: token in bytes
+      - `prob`: token probability, with the value between 0.0 and 1.0
+    - Number of elements in `top_probs` may be less than `n_probs`
 
 - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
 - `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request.
 - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
 - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
-- `model`: The path to the model loaded with `-m`
-- `prompt`: The provided `prompt`
+- `model`: The model alias (for model path, please use `/props` endpoint)
+- `prompt`: The processed `prompt` (special tokens may be added)
 - `stop_type`: Indicating whether the completion has stopped. Possible values are:
   - `none`: Generating (not stopped)
   - `eos`: Stopped because it encountered the EOS token
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 5ed4e8d27..fa3682a92 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -93,6 +93,7 @@ struct slot_params {
 
     std::vector<std::string> antiprompt;
     bool timings_per_token = false;
+    bool post_sampling_probs = false;
     bool ignore_eos = false;
 
     struct common_params_sampling sampling;
@@ -151,6 +152,7 @@ struct slot_params {
             {"speculative.n_min",         speculative.n_min},
             {"speculative.p_min",         speculative.p_min},
             {"timings_per_token",         timings_per_token},
+            {"post_sampling_probs",       post_sampling_probs},
         };
     }
 };
@@ -231,6 +233,7 @@ struct server_task {
         params.sampling.seed               = json_value(data, "seed",               defaults.sampling.seed);
         params.sampling.n_probs            = json_value(data, "n_probs",            defaults.sampling.n_probs);
         params.sampling.min_keep           = json_value(data, "min_keep",           defaults.sampling.min_keep);
+        params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
 
         params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
         params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
@@ -436,36 +439,67 @@ inline std::string stop_type_to_str(stop_type type) {
 
 struct completion_token_output {
     llama_token tok;
+    float prob;
     std::string text_to_send;
-    struct token_prob {
+    struct prob_info {
         llama_token tok;
-        std::string tok_str;
+        std::string txt;
         float prob;
     };
-    std::vector<token_prob> probs;
+    std::vector<prob_info> probs;
 
-    json to_json() const {
+    json to_json(bool post_sampling_probs) const {
         json probs_for_token = json::array();
         for (const auto & p : probs) {
+            std::string txt(p.txt);
+            txt.resize(validate_utf8(txt));
             probs_for_token.push_back(json {
-                {"tok_str", p.tok_str},
-                {"prob",    p.prob},
+                {"id",      p.tok},
+                {"token",   txt},
+                {"bytes",   str_to_bytes(p.txt)},
+                {
+                    post_sampling_probs ? "prob" : "logprob",
+                    post_sampling_probs ? p.prob : logarithm(p.prob)
+                },
             });
         }
         return probs_for_token;
     }
 
-    static json probs_vector_to_json(const std::vector<completion_token_output> & probs) {
+    static json probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
         json out = json::array();
-        for (const auto & prob : probs) {
-            const std::string tok_str = prob.text_to_send;
+        for (const auto & p : probs) {
+            std::string txt(p.text_to_send);
+            txt.resize(validate_utf8(txt));
             out.push_back(json {
-                {"content", tok_str},
-                {"probs",   prob.to_json()},
+                {"id",           p.tok},
+                {"token",        txt},
+                {"bytes",        str_to_bytes(p.text_to_send)},
+                {
+                    post_sampling_probs ? "prob" : "logprob",
+                    post_sampling_probs ? p.prob : logarithm(p.prob)
+                },
+                {
+                    post_sampling_probs ? "top_probs" : "top_logprobs",
+                    p.to_json(post_sampling_probs)
+                },
             });
         }
         return out;
     }
+
+    static float logarithm(float x) {
+        // nlohmann::json converts -inf to null, so we need to prevent that
+        return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
+    }
+
+    static std::vector<unsigned char> str_to_bytes(const std::string & str) {
+        std::vector<unsigned char> bytes;
+        for (unsigned char c : str) {
+            bytes.push_back(c);
+        }
+        return bytes;
+    }
 };
 
 struct server_task_result_cmpl_final : server_task_result {
@@ -486,6 +520,7 @@ struct server_task_result_cmpl_final : server_task_result {
     std::string stopping_word;
     stop_type stop = STOP_TYPE_NONE;
 
+    bool post_sampling_probs;
     std::vector<completion_token_output> probs_output;
 
     slot_params generation_params;
@@ -530,8 +565,8 @@ struct server_task_result_cmpl_final : server_task_result {
             {"tokens_cached",       n_tokens_cached},
             {"timings",             timings.to_json()},
         };
-        if (!probs_output.empty()) {
-            res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output);
+        if (!stream && !probs_output.empty()) {
+            res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
         }
         return res;
     }
@@ -542,19 +577,25 @@ struct server_task_result_cmpl_final : server_task_result {
             finish_reason = "stop";
         }
 
-        json choices = json::array({json{
+        json choice = json{
             {"finish_reason", finish_reason},
             {"index", 0},
             {"message", json {
                 {"content", content},
                 {"role",    "assistant"}
             }
-        }}});
+        }};
+
+        if (!stream && probs_output.size() > 0) {
+            choice["logprobs"] = json{
+                {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
+            };
+        }
 
         std::time_t t = std::time(0);
 
         json res = json {
-            {"choices", choices},
+            {"choices", json::array({choice})},
             {"created", t},
             {"model", oaicompat_model},
             {"object", "chat.completion"},
@@ -584,12 +625,14 @@ struct server_task_result_cmpl_final : server_task_result {
             finish_reason = "stop";
         }
 
-        json choices = json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}});
+        json choice = json{
+            {"finish_reason", finish_reason},
+            {"index", 0},
+            {"delta", json::object()}
+        };
 
         json ret = json {
-            {"choices", choices},
+            {"choices", json::array({choice})},
             {"created", t},
             {"id",      oaicompat_cmpl_id},
             {"model",   oaicompat_model},
@@ -618,7 +661,8 @@ struct server_task_result_cmpl_partial : server_task_result {
     int32_t n_decoded;
     int32_t n_prompt_tokens;
 
-    std::vector<completion_token_output> probs_output;
+    bool post_sampling_probs;
+    completion_token_output prob_output;
     result_timings timings;
 
     // OAI-compat fields
@@ -655,8 +699,8 @@ struct server_task_result_cmpl_partial : server_task_result {
         if (timings.prompt_n > 0) {
             res.push_back({"timings", timings.to_json()});
         }
-        if (!probs_output.empty()) {
-            res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output);
+        if (!prob_output.probs.empty()) {
+            res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
         }
         return res;
     }
@@ -708,6 +752,14 @@ struct server_task_result_cmpl_partial : server_task_result {
             }});
         }
 
+        GGML_ASSERT(choices.size() >= 1);
+
+        if (prob_output.probs.size() > 0) {
+            choices[0]["logprobs"] = json{
+                {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
+            };
+        }
+
         json ret = json {
             {"choices", choices},
             {"created", t},
@@ -1001,7 +1053,6 @@ struct server_slot {
 
     // stats
     size_t n_sent_text        = 0; // number of sent text character
-    size_t n_sent_token_probs = 0;
 
     int64_t t_start_process_prompt;
     int64_t t_start_generation;
@@ -1023,7 +1074,6 @@ struct server_slot {
         stopping_word      = "";
         n_past             = 0;
         n_sent_text        = 0;
-        n_sent_token_probs = 0;
         task_type          = SERVER_TASK_TYPE_COMPLETION;
 
         generated_tokens.clear();
@@ -1764,7 +1814,7 @@ struct server_context {
 
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special);
+        const std::string token_str = result.text_to_send;
         slot.sampled = result.tok;
 
         slot.generated_text += token_str;
@@ -1774,26 +1824,7 @@ struct server_context {
         slot.has_next_token = true;
 
         // check if there is incomplete UTF-8 character at the end
-        bool incomplete = false;
-        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) {
-            unsigned char c = slot.generated_text[slot.generated_text.size() - i];
-            if ((c & 0xC0) == 0x80) {
-                // continuation byte: 10xxxxxx
-                continue;
-            }
-            if ((c & 0xE0) == 0xC0) {
-                // 2-byte character: 110xxxxx ...
-                incomplete = i < 2;
-            } else if ((c & 0xF0) == 0xE0) {
-                // 3-byte character: 1110xxxx ...
-                incomplete = i < 3;
-            } else if ((c & 0xF8) == 0xF0) {
-                // 4-byte character: 11110xxx ...
-                incomplete = i < 4;
-            }
-            // else 1-byte character or invalid byte
-            break;
-        }
+        bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size();
 
         // search stop word and delete it
         if (!incomplete) {
@@ -1923,6 +1954,55 @@ struct server_context {
         return slot.has_next_token; // continue
     }
 
+    void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) {
+        size_t n_probs = slot.params.sampling.n_probs;
+        size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
+        if (post_sampling) {
+            const auto * cur_p = common_sampler_get_candidates(slot.smpl);
+            const size_t max_probs = cur_p->size;
+
+            // set probability for sampled token
+            for (size_t i = 0; i < max_probs; i++) {
+                if (cur_p->data[i].id == result.tok) {
+                    result.prob = cur_p->data[i].p;
+                    break;
+                }
+            }
+
+            // set probability for top n_probs tokens
+            result.probs.reserve(max_probs);
+            for (size_t i = 0; i < std::min(max_probs, n_probs); i++) {
+                result.probs.push_back({
+                    cur_p->data[i].id,
+                    common_detokenize(ctx, {cur_p->data[i].id}, special),
+                    cur_p->data[i].p
+                });
+            }
+        } else {
+            // TODO: optimize this with min-p optimization
+            std::vector<llama_token_data> cur = get_token_probabilities(ctx, idx);
+
+            // set probability for sampled token
+            for (size_t i = 0; i < n_vocab; i++) {
+                // set probability for sampled token
+                if (cur[i].id == result.tok) {
+                    result.prob = cur[i].p;
+                    break;
+                }
+            }
+
+            // set probability for top n_probs tokens
+            result.probs.reserve(n_probs);
+            for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) {
+                result.probs.push_back({
+                    cur[i].id,
+                    common_detokenize(ctx, {cur[i].id}, special),
+                    cur[i].p
+                });
+            }
+        }
+    }
+
     void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
         send_error(task.id, error, type);
     }
@@ -1950,8 +2030,9 @@ struct server_context {
         res->content = tkn.text_to_send;
         res->tokens  = { tkn.tok };
 
-        res->n_decoded       = slot.n_decoded;
-        res->n_prompt_tokens = slot.n_prompt_tokens;
+        res->n_decoded           = slot.n_decoded;
+        res->n_prompt_tokens     = slot.n_prompt_tokens;
+        res->post_sampling_probs = slot.params.post_sampling_probs;
 
         res->verbose           = slot.params.verbose;
         res->oaicompat         = slot.params.oaicompat;
@@ -1961,17 +2042,7 @@ struct server_context {
 
         // populate res.probs_output
         if (slot.params.sampling.n_probs > 0) {
-            const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false);
-
-            const size_t probs_pos      = std::min(slot.n_sent_token_probs,                       slot.generated_token_probs.size());
-            const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
-
-            std::vector<completion_token_output> probs_output;
-            if (probs_pos < probs_stop_pos) {
-                res->probs_output = std::vector<completion_token_output>(
-                        slot.generated_token_probs.begin() + probs_pos,
-                        slot.generated_token_probs.begin() + probs_stop_pos);
-            }
+            res->prob_output = tkn; // copy the token probs
         }
 
         // populate timings if this is final response or timings_per_token is enabled
@@ -1993,13 +2064,14 @@ struct server_context {
         res->timings         = slot.get_timings();
         res->prompt          = common_detokenize(ctx, slot.prompt_tokens, true);
 
-        res->truncated       = slot.truncated;
-        res->n_decoded       = slot.n_decoded;
-        res->n_prompt_tokens = slot.n_prompt_tokens;
-        res->n_tokens_cached = slot.n_past;
-        res->has_new_line    = slot.has_new_line;
-        res->stopping_word   = slot.stopping_word;
-        res->stop            = slot.stop;
+        res->truncated           = slot.truncated;
+        res->n_decoded           = slot.n_decoded;
+        res->n_prompt_tokens     = slot.n_prompt_tokens;
+        res->n_tokens_cached     = slot.n_past;
+        res->has_new_line        = slot.has_new_line;
+        res->stopping_word       = slot.stopping_word;
+        res->stop                = slot.stop;
+        res->post_sampling_probs = slot.params.post_sampling_probs;
 
         res->verbose           = slot.params.verbose;
         res->stream            = slot.params.stream;
@@ -2796,7 +2868,9 @@ struct server_context {
                     continue; // continue loop of slots
                 }
 
-                llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
+                const int tok_idx = slot.i_batch - i;
+
+                llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);
 
                 slot.i_batch = -1;
 
@@ -2815,17 +2889,12 @@ struct server_context {
                 slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3;
 
                 completion_token_output result;
-                result.tok = id;
+                result.tok          = id;
+                result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
+                result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
 
-                const auto * cur_p = common_sampler_get_candidates(slot.smpl);
-
-                for (size_t i = 0; i < (size_t) slot.params.sampling.n_probs; ++i) {
-                    auto tok_id = cur_p->data[i].id;
-                    result.probs.push_back({
-                        tok_id,
-                        tokens_to_output_formatted_string(ctx, tok_id),
-                        i >= cur_p->size ? 0.0f : cur_p->data[i].p,
-                    });
+                if (slot.params.sampling.n_probs > 0) {
+                    populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx);
                 }
 
                 if (!process_token(result, slot)) {
@@ -2909,7 +2978,11 @@ struct server_context {
                 for (size_t i = 0; i < ids.size(); ++i) {
                     completion_token_output result;
 
-                    result.tok = ids[i];
+                    result.tok          = ids[i];
+                    result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
+                    result.prob         = 1.0f; // set later
+
+                    // TODO: set result.probs
 
                     if (!process_token(result, slot)) {
                         // release slot because of stop condition
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 6573cc17f..0fa1a17c1 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -92,7 +92,6 @@ def test_chat_completion_with_openai_library():
         seed=42,
         temperature=0.8,
     )
-    print(res)
     assert res.choices[0].finish_reason == "length"
     assert res.choices[0].message.content is not None
     assert match_regex("(Suddenly)+", res.choices[0].message.content)
@@ -163,3 +162,64 @@ def test_chat_completion_with_timings_per_token():
         assert "predicted_per_second" in data["timings"]
         assert "predicted_n" in data["timings"]
         assert data["timings"]["predicted_n"] <= 10
+
+
+def test_logprobs():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo-instruct",
+        temperature=0.0,
+        messages=[
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+        max_tokens=5,
+        logprobs=True,
+        top_logprobs=10,
+    )
+    output_text = res.choices[0].message.content
+    aggregated_text = ''
+    assert res.choices[0].logprobs is not None
+    assert res.choices[0].logprobs.content is not None
+    for token in res.choices[0].logprobs.content:
+        aggregated_text += token.token
+        assert token.logprob <= 0.0
+        assert token.bytes is not None
+        assert len(token.top_logprobs) > 0
+    assert aggregated_text == output_text
+
+
+def test_logprobs_stream():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo-instruct",
+        temperature=0.0,
+        messages=[
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+        max_tokens=5,
+        logprobs=True,
+        top_logprobs=10,
+        stream=True,
+    )
+    output_text = ''
+    aggregated_text = ''
+    for data in res:
+        choice = data.choices[0]
+        if choice.finish_reason is None:
+            if choice.delta.content:
+                output_text += choice.delta.content
+            assert choice.logprobs is not None
+            assert choice.logprobs.content is not None
+            for token in choice.logprobs.content:
+                aggregated_text += token.token
+                assert token.logprob <= 0.0
+                assert token.bytes is not None
+                assert token.top_logprobs is not None
+                assert len(token.top_logprobs) > 0
+    assert aggregated_text == output_text
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index 36aee57dd..b88d45f18 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -270,9 +270,68 @@ def test_n_probs():
     assert "completion_probabilities" in res.body
     assert len(res.body["completion_probabilities"]) == 5
     for tok in res.body["completion_probabilities"]:
-        assert "probs" in tok
-        assert len(tok["probs"]) == 10
-        for prob in tok["probs"]:
-            assert "prob" in prob
-            assert "tok_str" in prob
-            assert 0.0 <= prob["prob"] <= 1.0
+        assert "id" in tok and tok["id"] > 0
+        assert "token" in tok and type(tok["token"]) == str
+        assert "logprob" in tok and tok["logprob"] <= 0.0
+        assert "bytes" in tok and type(tok["bytes"]) == list
+        assert len(tok["top_logprobs"]) == 10
+        for prob in tok["top_logprobs"]:
+            assert "id" in prob and prob["id"] > 0
+            assert "token" in prob and type(prob["token"]) == str
+            assert "logprob" in prob and prob["logprob"] <= 0.0
+            assert "bytes" in prob and type(prob["bytes"]) == list
+
+
+def test_n_probs_stream():
+    global server
+    server.start()
+    res = server.make_stream_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+        "stream": True,
+    })
+    for data in res:
+        if data["stop"] == False:
+            assert "completion_probabilities" in data
+            assert len(data["completion_probabilities"]) == 1
+            for tok in data["completion_probabilities"]:
+                assert "id" in tok and tok["id"] > 0
+                assert "token" in tok and type(tok["token"]) == str
+                assert "logprob" in tok and tok["logprob"] <= 0.0
+                assert "bytes" in tok and type(tok["bytes"]) == list
+                assert len(tok["top_logprobs"]) == 10
+                for prob in tok["top_logprobs"]:
+                    assert "id" in prob and prob["id"] > 0
+                    assert "token" in prob and type(prob["token"]) == str
+                    assert "logprob" in prob and prob["logprob"] <= 0.0
+                    assert "bytes" in prob and type(prob["bytes"]) == list
+
+
+def test_n_probs_post_sampling():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+        "post_sampling_probs": True,
+    })
+    assert res.status_code == 200
+    assert "completion_probabilities" in res.body
+    assert len(res.body["completion_probabilities"]) == 5
+    for tok in res.body["completion_probabilities"]:
+        assert "id" in tok and tok["id"] > 0
+        assert "token" in tok and type(tok["token"]) == str
+        assert "prob" in tok and 0.0 < tok["prob"] <= 1.0
+        assert "bytes" in tok and type(tok["bytes"]) == list
+        assert len(tok["top_probs"]) == 10
+        for prob in tok["top_probs"]:
+            assert "id" in prob and prob["id"] > 0
+            assert "token" in prob and type(prob["token"]) == str
+            assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0
+            assert "bytes" in prob and type(prob["bytes"]) == list
+        # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs
+        assert any(prob["prob"] == 1.0 for prob in tok["top_probs"])
diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py
index e32d74582..43e372fc7 100644
--- a/examples/server/tests/unit/test_embedding.py
+++ b/examples/server/tests/unit/test_embedding.py
@@ -50,6 +50,8 @@ def test_embedding_multiple():
 @pytest.mark.parametrize(
     "input,is_multi_prompt",
     [
+        # do not crash on empty input
+        ("", False),
         # single prompt
         ("string", False),
         ([12, 34, 56], False),
@@ -103,6 +105,7 @@ def test_embedding_pooling_none_oai():
 
     # /v1/embeddings does not support pooling type 'none'
     assert res.status_code == 400
+    assert "error" in res.body
 
 
 def test_embedding_openai_library_single():
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index ffdffe904..94bb285b6 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -171,6 +171,36 @@ static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, con
     return result;
 }
 
+// return the last index of character that can form a valid string
+// if the last character is potentially cut in half, return the index before the cut
+// if validate_utf8(text) == text.size(), then the whole text is valid utf8
+static size_t validate_utf8(const std::string& text) {
+    size_t len = text.size();
+    if (len == 0) return 0;
+
+    // Check the last few bytes to see if a multi-byte character is cut off
+    for (size_t i = 1; i <= 4 && i <= len; ++i) {
+        unsigned char c = text[len - i];
+        // Check for start of a multi-byte sequence from the end
+        if ((c & 0xE0) == 0xC0) {
+            // 2-byte character start: 110xxxxx
+            // Needs at least 2 bytes
+            if (i < 2) return len - i;
+        } else if ((c & 0xF0) == 0xE0) {
+            // 3-byte character start: 1110xxxx
+            // Needs at least 3 bytes
+            if (i < 3) return len - i;
+        } else if ((c & 0xF8) == 0xF0) {
+            // 4-byte character start: 11110xxx
+            // Needs at least 4 bytes
+            if (i < 4) return len - i;
+        }
+    }
+
+    // If no cut-off multi-byte character is found, return full length
+    return len;
+}
+
 //
 // template utils
 //
@@ -671,3 +701,33 @@ static json format_logit_bias(const std::vector<llama_logit_bias> & logit_bias)
 static std::string safe_json_to_str(json data) {
     return data.dump(-1, ' ', false, json::error_handler_t::replace);
 }
+
+static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx) {
+    std::vector<llama_token_data> cur;
+    const auto * logits = llama_get_logits_ith(ctx, idx);
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+
+    cur.resize(n_vocab);
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
+    }
+
+    // sort tokens by logits
+    std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit > b.logit;
+    });
+
+    // apply softmax
+    float max_l = cur[0].logit;
+    float cum_sum = 0.0f;
+    for (size_t i = 0; i < cur.size(); ++i) {
+        float p = expf(cur[i].logit - max_l);
+        cur[i].p = p;
+        cum_sum += p;
+    }
+    for (size_t i = 0; i < cur.size(); ++i) {
+        cur[i].p /= cum_sum;
+    }
+
+    return cur;
+}

From 36319dec5d75a7dfe3e3de37b9ca2a76cd52b7b2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 19 Dec 2024 17:35:15 +0200
Subject: [PATCH 060/376] tts : small QoL for easy model fetch (#10903)

---
 common/arg.cpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/common/arg.cpp b/common/arg.cpp
index e5ddd8318..c3d66efb5 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2206,5 +2206,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
 
+    // model-specific
+    add_opt(common_arg(
+        {"--tts-oute-default"},
+        string_format("use default OuteTTS models (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF";
+            params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf";
+            params.vocoder.hf_repo = "ggml-org/WavTokenizer";
+            params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf";
+        }
+    ).set_examples({LLAMA_EXAMPLE_TTS}));
+
     return ctx_arg;
 }

From 5cab3e4aaa892df4620b720f20a503a1bbbe7a52 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 19 Dec 2024 17:42:13 +0200
Subject: [PATCH 061/376] llama : minor grammar refactor (#10897)

ggml-ci
---
 examples/gbnf-validator/gbnf-validator.cpp | 11 +++-----
 src/llama-grammar.cpp                      | 30 +++++++++++-----------
 src/llama-grammar.h                        |  7 ++---
 tests/test-grammar-integration.cpp         |  9 +++----
 tests/test-llama-grammar.cpp               |  6 ++---
 5 files changed, 26 insertions(+), 37 deletions(-)

diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
index 7493af9d3..17a0e27c4 100644
--- a/examples/gbnf-validator/gbnf-validator.cpp
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -11,19 +11,15 @@
 static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) {
     const auto cpts = unicode_cpts_from_utf8(input_str);
 
-    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
-          llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar);
+    auto & stacks_cur = llama_grammar_get_stacks(grammar);
 
     size_t pos = 0;
     for (const auto & cpt : cpts) {
-        const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy
-
-        llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur);
+        llama_grammar_accept(grammar, cpt);
 
         if (stacks_cur.empty()) {
             error_pos = pos;
             error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'";
-            stacks_cur = stacks_prev;
             return false;
         }
         ++pos;
@@ -82,7 +78,8 @@ int main(int argc, char** argv) {
 
     llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root");
     if (grammar == nullptr) {
-        throw std::runtime_error("Failed to initialize llama_grammar");
+        fprintf(stdout, "Failed to initialize llama_grammar\n");
+        return 1;
     }
     // Read the input file
     std::string input_str;
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index 74e9f64b3..76d0cb3a2 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -822,15 +822,11 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar)
     return grammar->stacks;
 }
 
-void llama_grammar_accept(
-        const llama_grammar_rules  & rules,
-        const llama_grammar_stacks & stacks,
-        const uint32_t               chr,
-              llama_grammar_stacks & stacks_new) {
-    stacks_new.clear();
-    stacks_new.reserve(stacks.size());
+void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) {
+    llama_grammar_stacks stacks_new;
+    stacks_new.reserve(grammar->stacks.size());
 
-    for (const auto & stack : stacks) {
+    for (const auto & stack : grammar->stacks) {
         if (stack.empty()) {
             continue;
         }
@@ -844,9 +840,11 @@ void llama_grammar_accept(
             if (!llama_grammar_is_end_of_sequence(pos)) {
                 new_stack.push_back(pos);
             }
-            llama_grammar_advance_stack(rules, new_stack, stacks_new);
+            llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new);
         }
     }
+
+    grammar->stacks = std::move(stacks_new);
 }
 
 llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
@@ -1051,7 +1049,12 @@ void llama_grammar_free_impl(struct llama_grammar * grammar) {
 }
 
 struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & grammar) {
-    llama_grammar * result = new llama_grammar { grammar.vocab, grammar.rules, grammar.stacks, grammar.partial_utf8, };
+    llama_grammar * result = new llama_grammar {
+        grammar.vocab,
+        grammar.rules,
+        grammar.stacks,
+        grammar.partial_utf8,
+    };
 
     // redirect elements in stacks to point to new rules
     for (size_t is = 0; is < result->stacks.size(); is++) {
@@ -1059,7 +1062,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra
             for (size_t ir0 = 0; ir0 < grammar.rules.size(); ir0++) {
                 for (size_t ir1 = 0; ir1 < grammar.rules[ir0].size(); ir1++) {
                     if (grammar.stacks[is][ie] == &grammar.rules[ir0][ir1]) {
-                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                        result->stacks[is][ie] =  &result->rules[ir0][ir1];
                     }
                 }
             }
@@ -1126,11 +1129,8 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
     const auto   decoded     = decode_utf8(piece, grammar.partial_utf8);
     const auto & code_points = decoded.first;
 
-    llama_grammar_stacks stacks_new;
-
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        llama_grammar_accept(grammar.rules, grammar.stacks, *it, stacks_new);
-        grammar.stacks = std::move(stacks_new);
+        llama_grammar_accept(&grammar, *it);
     }
 
     grammar.partial_utf8 = decoded.second;
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index f529ce351..13e940fb5 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -58,6 +58,7 @@ using llama_grammar_rules      = std::vector<llama_grammar_rule>;
 using llama_grammar_stacks     = std::vector<llama_grammar_stack>;
 using llama_grammar_candidates = std::vector<llama_grammar_candidate>;
 
+// TODO: remove, needed for tests atm
 const llama_grammar_rules  & llama_grammar_get_rules (const struct llama_grammar * grammar);
       llama_grammar_stacks & llama_grammar_get_stacks(      struct llama_grammar * grammar);
 
@@ -65,11 +66,7 @@ const llama_grammar_rules  & llama_grammar_get_rules (const struct llama_grammar
 // be positioned at a character range (see `llama_grammar_advance_stack`), and
 // produces the N possible stacks if the given char is accepted at those
 // positions
-void llama_grammar_accept(
-        const llama_grammar_rules  & rules,
-        const llama_grammar_stacks & stacks,
-                          uint32_t   chr,
-              llama_grammar_stacks & stacks_new);
+void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr);
 
 std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
         const llama_grammar_rules      & rules,
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 5cc0cdb04..e1bdbb925 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -32,13 +32,10 @@ static bool test_build_grammar_fails(const std::string & grammar_str) {
 static bool match_string(const std::string & input, llama_grammar * grammar) {
     const auto cpts = unicode_cpts_from_utf8(input);
 
-    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
-          llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar);
+    auto & stacks_cur = llama_grammar_get_stacks(grammar);
 
     for (const auto & cpt : cpts) {
-        const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy
-
-        llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur);
+        llama_grammar_accept(grammar, cpt);
 
         if (stacks_cur.empty()) {
             // no stacks means that the grammar failed to match at this point
@@ -63,7 +60,7 @@ static void test(const std::string & test_desc, const std::string & grammar_str,
     auto * grammar = build_grammar(grammar_str);
 
     // Save the original grammar stacks so that we can reset after every new string we want to test
-    const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar);
+    const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar); // copy
 
     llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar);
 
diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp
index 6f1374ca8..e2129206b 100644
--- a/tests/test-llama-grammar.cpp
+++ b/tests/test-llama-grammar.cpp
@@ -113,12 +113,10 @@ int main()
         }
     }
 
-    llama_grammar * grammar = NULL;
     std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
 
-    grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
-    if (grammar == nullptr)
-    {
+    llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+    if (grammar == nullptr) {
         throw std::runtime_error("Failed to initialize llama_grammar");
     }
 

From d408bb9268a988c5a60a5746d3a6430386e7604d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 19 Dec 2024 18:47:15 +0200
Subject: [PATCH 062/376] clip : disable GPU support (#10896)

ggml-ci
---
 examples/llava/clip.cpp | 86 ++++++++++++++++++++---------------------
 1 file changed, 43 insertions(+), 43 deletions(-)

diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 463b7c865..3cd0d2fa8 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -8,25 +8,25 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
-#ifdef GGML_USE_CUDA
-#include "ggml-cuda.h"
-#endif
-
-#ifdef GGML_USE_SYCL
-#include "ggml-sycl.h"
-#endif
-
-#ifdef GGML_USE_METAL
-#include "ggml-metal.h"
-#endif
-
-#ifdef GGML_USE_CANN
-#include "ggml-cann.h"
-#endif
-
-#ifdef GGML_USE_VULKAN
-#include "ggml-vulkan.h"
-#endif
+//#ifdef GGML_USE_CUDA
+//#include "ggml-cuda.h"
+//#endif
+//
+//#ifdef GGML_USE_SYCL
+//#include "ggml-sycl.h"
+//#endif
+//
+//#ifdef GGML_USE_METAL
+//#include "ggml-metal.h"
+//#endif
+//
+//#ifdef GGML_USE_CANN
+//#include "ggml-cann.h"
+//#endif
+//
+//#ifdef GGML_USE_VULKAN
+//#include "ggml-vulkan.h"
+//#endif
 
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
@@ -1222,30 +1222,30 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
     }
 
-#ifdef GGML_USE_CUDA
-    new_clip->backend = ggml_backend_cuda_init(0);
-    LOG_INF("%s: CLIP using CUDA backend\n", __func__);
-#endif
-
-#ifdef GGML_USE_METAL
-    new_clip->backend = ggml_backend_metal_init();
-    LOG_INF("%s: CLIP using Metal backend\n", __func__);
-#endif
-
-#ifdef GGML_USE_CANN
-    new_clip->backend = ggml_backend_cann_init(0);
-    LOG_INF("%s: CLIP using CANN backend\n", __func__);
-#endif
-
-#ifdef GGML_USE_VULKAN
-    new_clip->backend = ggml_backend_vk_init(0);
-    LOG_INF("%s: CLIP using Vulkan backend\n", __func__);
-#endif
-
-#ifdef GGML_USE_SYCL
-    new_clip->backend = ggml_backend_sycl_init(0);
-    LOG_INF("%s: CLIP using SYCL backend\n", __func__);
-#endif
+//#ifdef GGML_USE_CUDA
+//    new_clip->backend = ggml_backend_cuda_init(0);
+//    LOG_INF("%s: CLIP using CUDA backend\n", __func__);
+//#endif
+//
+//#ifdef GGML_USE_METAL
+//    new_clip->backend = ggml_backend_metal_init();
+//    LOG_INF("%s: CLIP using Metal backend\n", __func__);
+//#endif
+//
+//#ifdef GGML_USE_CANN
+//    new_clip->backend = ggml_backend_cann_init(0);
+//    LOG_INF("%s: CLIP using CANN backend\n", __func__);
+//#endif
+//
+//#ifdef GGML_USE_VULKAN
+//    new_clip->backend = ggml_backend_vk_init(0);
+//    LOG_INF("%s: CLIP using Vulkan backend\n", __func__);
+//#endif
+//
+//#ifdef GGML_USE_SYCL
+//    new_clip->backend = ggml_backend_sycl_init(0);
+//    LOG_INF("%s: CLIP using SYCL backend\n", __func__);
+//#endif
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();

From 0a11f8b7b5c39fdf6e91ef9674bc68ff08681af7 Mon Sep 17 00:00:00 2001
From: Molly Sophia <mollysophia379@gmail.com>
Date: Fri, 20 Dec 2024 17:44:58 +0800
Subject: [PATCH 063/376] convert : fix RWKV v6 model conversion (#10913)

* Enable --no-context-shift for llama-perplexity example

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* RWKV 6: Fix error in ggml_cuda_op_bin_bcast

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
---
 common/arg.cpp        | 2 +-
 convert_hf_to_gguf.py | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index c3d66efb5..deb113786 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -626,7 +626,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.ctx_shift = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
         string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ecd69be6b..76ab11ebe 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3065,6 +3065,9 @@ class Rwkv6Model(Model):
         if new_name.endswith("time_mix_w2.weight"):
             data_torch = data_torch.permute(0, 2, 1)
 
+        if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
+            data_torch = data_torch.squeeze()
+
         rescale_every_n_layers = self.hparams["rescale_every"]
         if rescale_every_n_layers > 0:
             if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):

From 21ae3b9be83820565d1a720999b7f63ce95b4920 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Fri, 20 Dec 2024 13:31:28 +0100
Subject: [PATCH 064/376] ggml : add test for SVE and disable when it fails
 (#10906)

---
 ggml/src/ggml-cpu/CMakeLists.txt | 38 ++++++++++++++++----------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 683b90aff..12d790825 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -82,8 +82,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
         if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
             message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
         else()
-            check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
-            if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
+            check_cxx_compiler_flag(-mfp16-format=ieee GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E)
+            if (NOT "${GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
                 list(APPEND ARCH_FLAGS -mfp16-format=ieee)
             endif()
 
@@ -106,28 +106,28 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                     message(STATUS "ARM -mcpu not found, -mcpu=native will be used")
                 endif()
 
-                set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
                 include(CheckCXXSourceRuns)
 
-                set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod")
-                check_cxx_source_runs(
-                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }"
-                    GGML_COMPILER_SUPPORT_DOTPROD)
-                if (GGML_COMPILER_SUPPORT_DOTPROD)
-                    set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod")
-                endif()
+                function(check_arm_feature tag code)
+                    set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
+                    set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+${tag}")
+                    check_cxx_source_runs(
+                        "${code}"
+                        GGML_MACHINE_SUPPORTS_${tag}
+                    )
+                    if (GGML_MACHINE_SUPPORTS_${tag})
+                        set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+${tag}" PARENT_SCOPE)
+                    else()
+                        set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+no${tag}" PARENT_SCOPE)
+                    endif()
+                    set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
+                endfunction()
 
-                set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm")
-                check_cxx_source_runs(
-                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }"
-                    GGML_COMPILER_SUPPORT_I8MM)
-                if (GGML_COMPILER_SUPPORT_I8MM)
-                    set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm")
-                endif()
+                check_arm_feature(dotprod "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }")
+                check_arm_feature(i8mm    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }")
+                check_arm_feature(sve     "#include <arm_sve.h>\nint main()  { svfloat32_t _a, _b; volatile svfloat32_t _c = svadd_f32_z(svptrue_b8(), _a, _b); return 0; }")
 
-                set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
                 list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}")
-
             else()
                 if (GGML_CPU_ARM_ARCH)
                     list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})

From 0ca416c91aea4549d9c77e3efeca403e15aa6c75 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Fri, 20 Dec 2024 14:12:06 +0100
Subject: [PATCH 065/376] server : (UI) fix copy to clipboard function (#10916)

---
 examples/server/public/index.html.gz | Bin 1205430 -> 1205858 bytes
 examples/server/webui/src/main.js    |  23 +++++++++++++++++++----
 2 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 6a64915579c39cec4bfdc995b76f9994824cdddc..36f9c9fe9a68d6843be38c7b798e29a950ebcee5 100644
GIT binary patch
delta 946162
zcmV)BK*PVb$w}hONq~d_gaWh!rgMLFy=@~~6#PBsDG<hS$PNhC9aU7*-m)ZjGM(GR
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diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 997ab0ff5..358a40628 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -13,7 +13,7 @@ import hljs from './highlight-config';
 import daisyuiThemes from 'daisyui/src/theming/themes';
 
 // ponyfill for missing ReadableStream asyncIterator on Safari
-import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
+import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
 
 const isDev = import.meta.env.MODE === 'development';
 
@@ -22,7 +22,22 @@ const isString = (x) => !!x.toLowerCase;
 const isBoolean = (x) => x === true || x === false;
 const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
 const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
-const copyStr = (str) => navigator.clipboard.writeText(str);
+const copyStr = (textToCopy) => {
+  // Navigator clipboard api needs a secure context (https)
+  if (navigator.clipboard && window.isSecureContext) {
+    navigator.clipboard.writeText(textToCopy);
+  } else {
+    // Use the 'out of viewport hidden text area' trick
+    const textArea = document.createElement('textarea');
+    textArea.value = textToCopy;
+    // Move textarea out of the viewport so it's not visible
+    textArea.style.position = 'absolute';
+    textArea.style.left = '-999999px';
+    document.body.prepend(textArea);
+    textArea.select();
+    document.execCommand('copy');
+  }
+};
 
 // constants
 const BASE_URL = isDev
@@ -130,9 +145,9 @@ const VueMarkdown = defineComponent(
     };
     window.copyStr = copyStr;
     const content = computed(() => md.value.render(props.source));
-    return () => h("div", { innerHTML: content.value });
+    return () => h('div', { innerHTML: content.value });
   },
-  { props: ["source"] }
+  { props: ['source'] }
 );
 
 // input field to be used by settings modal

From eb5c3dc64bd967f2e23c87d9dec195f45468de60 Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Fri, 20 Dec 2024 21:01:28 +0530
Subject: [PATCH 066/376] SYCL: Migrate away from deprecated
 ggml_tensor->backend (#10840)

* Migrate to tensor->buffer for checking backend buffer type: 1

* SYCL: common.cpp try to migrate away from tensor->backend

* SYCL: fix assertions and add proper comments

* SYCL: remove extra space

* SYCL: Add back static to ggml_backend_buffer_is_sycl_split function

* SYCL: Add pragma directive to suppress warning spam

* SYCL: Integrate debug logs with GGML_LOG and other fixes

* Revert "SYCL: Integrate debug logs with GGML_LOG and other fixes"

This reverts commit 2607b7de0f0d2f4f1f690226f86fa861aa39cb97.
Let's keep the current SYCL specific logging mechanism for now

* SYCL: Use GGML_SYCL_DEBUG after reverting

* SYCL: reg_get_proc_address func, update to the current func signature

* SYCL: Refactor SYCL buffer checks in ggml_sycl_cpy_tensor_2d
---
 ggml/src/ggml-sycl/common.cpp    |  8 +++---
 ggml/src/ggml-sycl/common.hpp    |  4 +++
 ggml/src/ggml-sycl/ggml-sycl.cpp | 46 ++++++++++++++++++--------------
 3 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
index a9ee40491..88314a5cd 100644
--- a/ggml/src/ggml-sycl/common.cpp
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -11,6 +11,8 @@
 //
 
 #include "common.hpp"
+
+#include "ggml-backend-impl.h"
 #include "ggml-impl.h"
 
 int get_current_device_id() {
@@ -65,9 +67,9 @@ void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
                                  const ggml_sycl_op_flatten_t op) try {
 
     const bool use_src1 = src1 != nullptr;
-
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    if(use_src1)
+      GGML_ASSERT(strcmp(src1->buffer->buft->iface.get_name(src1->buffer->buft), GGML_SYCL_NAME "_Split") != 0);
+    GGML_ASSERT(strcmp(dst->buffer->buft->iface.get_name(dst->buffer->buft), GGML_SYCL_NAME "_Split") != 0);
 
     // dd = data device
     float * src0_ddf = (float *) src0->data;
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index c1582f610..62b4cea3a 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -26,7 +26,11 @@
 
 #define GGML_COMMON_DECL_SYCL
 #define GGML_COMMON_IMPL_SYCL
+/* suppress warning spam */
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wnested-anon-types"
 #include "ggml-common.h"
+#pragma clang diagnostic pop
 
 void* ggml_sycl_host_malloc(size_t size);
 void ggml_sycl_host_free(void* ptr);
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 84f1328e7..312ccfeb8 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -288,10 +288,8 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
                                      ggml_tensor *tensor) try {
     ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
 
-    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+    if (tensor->view_src != NULL) {
         assert(tensor->view_src->buffer->buft == buffer->buft);
-        tensor->backend = tensor->view_src->backend;
-        tensor->extra = tensor->view_src->extra;
         return;
     }
 
@@ -539,7 +537,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) {
     auto dev_count = ggml_backend_sycl_get_device_count();
 
     if (device>=dev_count or device<0) {
-        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+        GGML_LOG_ERROR("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
             device, dev_count-1);
         GGML_ASSERT(device<dev_count);
     }
@@ -567,7 +565,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(ggml_backend_sycl_conte
 
     int device = ctx->device;
     if (device>=ggml_sycl_info().device_count or device<0) {
-        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+        GGML_LOG_ERROR("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
             device, ggml_sycl_info().device_count-1);
         GGML_ASSERT(device<ggml_sycl_info().device_count);
     }
@@ -746,7 +744,7 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
             size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
 
-        // FIXME: do not crash if cudaMalloc fails
+        // FIXME: do not crash if SYCL Buffer alloc fails
         // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
         ggml_sycl_set_device(i);
         const queue_ptr stream = ctx->streams[i];
@@ -788,7 +786,6 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
                 CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event()));
         }
     }
-    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
     tensor->extra = extra;
 }
 catch (sycl::exception const &exc) {
@@ -2349,12 +2346,22 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
 
     dpct::memcpy_direction kind;
     char * src_ptr;
-    if (src->backend == GGML_BACKEND_TYPE_CPU) {
+    if (ggml_backend_buffer_is_host(src->buffer)) {
         kind = dpct::host_to_device;
+        //GGML_SYCL_DEBUG("%s: Host buffer type src tensor\n", __func__);
         src_ptr = (char *) src->data;
         // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
-    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+    } else if (ggml_backend_buffer_is_sycl(src->buffer)) {
+        // If buffer is a SYCL buffer
+        //GGML_SYCL_DEBUG("%s: SYCL buffer type src tensor\n", __func__);
+        kind    = dpct::device_to_device;
+        src_ptr = (char *) src->data;
+    } else if (ggml_backend_buffer_is_sycl_split(src->buffer)) {
+        /*
+        If buffer is a SYCL split buffer
+        */
+        //GGML_SYCL_DEBUG("%s: Split buffer type src tensor\n", __func__);
+        GGML_ASSERT(i1_low == 0 && i1_high == src->ne[1]);
         kind = dpct::device_to_device;
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
         int id;
@@ -2857,8 +2864,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
     const int nb2 = dst->nb[2];
     const int nb3 = dst->nb[3];
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(dst->buffer));
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src1->buffer));
     GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@@ -2878,7 +2885,7 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
 
     int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
@@ -3198,7 +3205,7 @@ static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const gg
                                        const ggml_tensor *src1,
                                        ggml_tensor *dst) try {
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -3231,7 +3238,7 @@ static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -3293,7 +3300,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
                                              ggml_tensor *dst) try {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -4638,10 +4645,9 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re
 static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) {
     GGML_UNUSED(reg);
 
-    // TODO: update to the current function signature
-    //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
-    //    return (void *)ggml_backend_sycl_split_buffer_type;
-    //}
+    if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
+        return (void *)ggml_backend_sycl_split_buffer_type;
+    }
 
     // SYCL doesn't support registering host memory, left here for reference
     // "ggml_backend_register_host_buffer"

From e34c5af43f941f0ddb92466776339897295aca11 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Sat, 21 Dec 2024 00:33:37 +0100
Subject: [PATCH 067/376] ggml-cpu: replace NEON asm with intrinsics in
 ggml_gemv_q4_0_4x8_q8_0() (#10874)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml-cpu: replace NEON asm with intrinsics in ggml_gemv_q4_0_4x8_q8_0()

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* ggml-cpu: format code

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

---------

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 122 +++++++++++--------------
 1 file changed, 51 insertions(+), 71 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
index a51d1a6c5..2d79b8b61 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
@@ -564,21 +564,21 @@ static void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
 
 #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
     if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
-        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *)vx;
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx;
 
         for (int c = 0; c < nc; c += ncols_interleaved) {
-            const block_q8_0 * a_ptr = (const block_q8_0 *)vy;
+            const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
             float32x4_t acc = vdupq_n_f32(0);
             for (int b = 0; b < nb; b++) {
-                int8x16_t b0 = vld1q_s8((const int8_t *)b_ptr->qs);
-                int8x16_t b1 = vld1q_s8((const int8_t *)b_ptr->qs + 16);
-                int8x16_t b2 = vld1q_s8((const int8_t *)b_ptr->qs + 32);
-                int8x16_t b3 = vld1q_s8((const int8_t *)b_ptr->qs + 48);
-                float16x4_t bd = vld1_f16((const __fp16 *)b_ptr->d);
+                int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs);
+                int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16);
+                int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32);
+                int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48);
+                float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d);
 
                 int8x16_t a0 = vld1q_s8(a_ptr->qs);
                 int8x16_t a1 = vld1q_s8(a_ptr->qs + qk/2);
-                float16x4_t ad = vld1_dup_f16((const __fp16 *)&a_ptr->d);
+                float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d);
 
                 int32x4_t ret = vdupq_n_s32(0);
 
@@ -647,72 +647,52 @@ static void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
-#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
-    if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
-        const void * b_ptr = vx;
-        const void * a_ptr = vy;
-        float * res_ptr = s;
+#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx;
 
-        __asm__ __volatile__(
-            "movi v2.16b, #0x4\n"
-            "movi v1.16b, #0xf0\n"
-            "add %x[b_ptr], %x[b_ptr], #0x8\n"
-            "1:"  // Column loop
-            "add x23, %x[a_ptr], #0x2\n"
-            "movi v0.16b, #0x0\n"
-            "mov x22, %x[nb]\n"
-            "2:"  // Block loop
-            "ldr q31, [%x[b_ptr], #0x0]\n"
-            "ldr q30, [%x[b_ptr], #0x10]\n"
-            "mov x21, x23\n"
-            "movi v29.4s, #0x0\n"
-            "ldr q28, [%x[b_ptr], #0x20]\n"
-            "ldr q27, [%x[b_ptr], #0x30]\n"
-            "movi v26.4s, #0x0\n"
-            "sub x20, x23, #0x2\n"
-            "ld1r { v25.8h }, [x20]\n"
-            "ldr q24, [%x[b_ptr], #-0x8]\n"
-            "sub x22, x22, #0x1\n"
-            "add x23, x23, #0x22\n"
-            "ld1r { v23.2d }, [x21], #0x8\n"
-            "sshl v22.16b, v31.16b, v2.16b\n"
-            "sshl v16.16b, v30.16b, v2.16b\n"
-            "add %x[b_ptr], %x[b_ptr], #0x48\n"
-            "ld1r { v21.2d }, [x21], #0x8\n"
-            "sshl v20.16b, v28.16b, v2.16b\n"
-            "sshl v19.16b, v27.16b, v2.16b\n"
-            "ld1r { v18.2d }, [x21], #0x8\n"
-            "ld1r { v17.2d }, [x21], #0x8\n"
-            "and v31.16b, v31.16b, v1.16b\n"
-            "and v30.16b, v30.16b, v1.16b\n"
-            ".inst 0x4e9796dd  // sdot v29.4s, v22.16b, v23.16b\n"
-            ".inst 0x4e97961a  // sdot v26.4s, v16.16b, v23.16b\n"
-            "and v28.16b, v28.16b, v1.16b\n"
-            "and v27.16b, v27.16b, v1.16b\n"
-            "fcvtl v25.4s, v25.4h\n"
-            "fcvtl v16.4s, v24.4h\n"
-            ".inst 0x4e95969d  // sdot v29.4s, v20.16b, v21.16b\n"
-            ".inst 0x4e95967a  // sdot v26.4s, v19.16b, v21.16b\n"
-            "fmul v16.4s, v16.4s, v25.4s\n"
-            ".inst 0x4e9297fd  // sdot v29.4s, v31.16b, v18.16b\n"
-            ".inst 0x4e9297da  // sdot v26.4s, v30.16b, v18.16b\n"
-            ".inst 0x4e91979d  // sdot v29.4s, v28.16b, v17.16b\n"
-            ".inst 0x4e91977a  // sdot v26.4s, v27.16b, v17.16b\n"
-            "addp v29.4s, v29.4s, v26.4s\n"
-            "scvtf v29.4s, v29.4s, #0x4\n"
-            "fmla v0.4s, v29.4s, v16.4s\n"
-            "cbnz x22, 2b\n"
-            "sub %x[nc], %x[nc], #0x4\n"
-            "str q0, [%x[res_ptr], #0x0]\n"
-            "add %x[res_ptr], %x[res_ptr], #0x10\n"
-            "cbnz %x[nc], 1b\n"
-            : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
-            : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
-            : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23"
-        );
+        for (int c = 0; c < nc; c += ncols_interleaved) {
+            const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+            float32x4_t acc = vdupq_n_f32(0);
+            for (int b = 0; b < nb; b++) {
+                int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs);
+                int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16);
+                int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32);
+                int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48);
+                float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d);
+
+                int8x16_t a0 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs);
+                int8x16_t a1 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 1);
+                int8x16_t a2 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 2);
+                int8x16_t a3 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 3);
+                float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d);
+
+                int32x4_t ret0 = vdupq_n_s32(0);
+                int32x4_t ret1 = vdupq_n_s32(0);
+
+                ret0 = vdotq_s32(ret0, b0 << 4, a0);
+                ret1 = vdotq_s32(ret1, b1 << 4, a0);
+                ret0 = vdotq_s32(ret0, b2 << 4, a1);
+                ret1 = vdotq_s32(ret1, b3 << 4, a1);
+
+                ret0 = vdotq_s32(ret0, b0 & 0xf0U, a2);
+                ret1 = vdotq_s32(ret1, b1 & 0xf0U, a2);
+                ret0 = vdotq_s32(ret0, b2 & 0xf0U, a3);
+                ret1 = vdotq_s32(ret1, b3 & 0xf0U, a3);
+
+                int32x4_t ret = vpaddq_s32(ret0, ret1);
+
+                acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4),
+                        vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd)));
+                a_ptr++;
+                b_ptr++;
+            }
+            vst1q_f32(s, acc);
+            s += ncols_interleaved;
+        }
         return;
     }
-#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
     float sumf[4];
     int sumi;
 

From a91a41364b25705dbb81ae996bc35c3440c63b35 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 21 Dec 2024 01:04:45 -0600
Subject: [PATCH 068/376] vulkan: optimize coopmat2 dequant functions (#10855)

Change the code to do 16b loads when possible and extract the appropriate
component late, so the code is effectively decoding a pair of elements and
then selecting one. This can allow more commoning to happen in the compiler
when neighboring elements are loaded.
---
 .../vulkan-shaders/dequant_funcs_cm2.comp     | 70 ++++++++++++-------
 1 file changed, 45 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index a8707b621..94b78598e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -10,9 +10,10 @@ float16_t dequantFuncQ4_0(const in decodeBufQ4_0 bl, const in uint blockCoords[2
     const float16_t d = bl.block.d;
     const uint idx = coordInBlock[1];
     const uint shift = (idx & 0x10) >> 2;
-    uint32_t qs = unpack8(uint32_t(bl.block.qs[(idx & 0xE) >> 1]))[idx & 1];
+    uint32_t qs = uint32_t(bl.block.qs[(idx & 0xE) >> 1]);
     qs >>= shift;
-    qs &= 0xF;
+    qs &= 0x0F0F;
+    qs = unpack8(qs)[idx & 1];
     float16_t ret = (float16_t(qs) - float16_t(8)) * d;
     return ret;
 }
@@ -152,15 +153,17 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4
    block_q4_K block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed16 {
+   block_q4_K_packed16 block;
+};
+
 float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
+    decodeBufQ4_K_packed16 bl16 = decodeBufQ4_K_packed16(bl);
     const uint idx = coordInBlock[1];
-    const uint iqs = idx;
 
-    const uint n = iqs / 64;                   // 0,1,2,3
-    const uint b = (iqs % 64) / 32;            // 0,1
+    const uint b = (idx & 0x20) >> 5;            // 0,1
     const uint is = (idx & 0xE0) >> 5;         // 0..7
-    const uint qsi = n * 32 + (iqs % 32);      // 0..127
 
     const f16vec2 loadd = bl.block.d;
 
@@ -184,9 +187,11 @@ float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2
     const float16_t d = loadd.x * float16_t(sc);
     const float16_t m = loadd.y * float16_t(mbyte);
 
-    uint32_t dmask = 0xF << (b * 4);
+    uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]);
+    qs = (qs >> (b * 4)) & 0x0F0F;
+    qs = unpack8(qs)[idx & 1];
 
-    float16_t ret = d * float16_t((bl.block.qs[qsi    ] & dmask) >> (b * 4)) - m;
+    float16_t ret = d * float16_t(qs) - m;
 
     return ret;
 }
@@ -195,18 +200,19 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5
    block_q5_K block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed16 {
+   block_q5_K_packed16 block;
+};
+
 float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
+    decodeBufQ5_K_packed16 bl16 = decodeBufQ5_K_packed16(bl);
     const uint idx = coordInBlock[1];
-    const uint iqs = idx;
 
-    const uint n = iqs / 64;                   // 0,1,2,3
-    const uint b = (iqs % 64) / 32;            // 0,1
+    const uint b = (idx & 0x20) >> 5;          // 0,1
     const uint is = (idx & 0xE0) >> 5;         // 0..7
-    const uint qsi = n * 32 + (iqs % 32);      // 0..127
-    const uint qhi = (iqs % 32);               // 0..31
 
-    const uint8_t hm = uint8_t(1 << (iqs / 32));
+    const uint32_t hm = 0x0101 << is;
 
     const f16vec2 loadd = bl.block.d;
 
@@ -230,9 +236,15 @@ float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2
     const float16_t d = loadd.x * float16_t(sc);
     const float16_t m = loadd.y * float16_t(mbyte);
 
-    uint32_t dmask = 0xF << (b * 4);
+    uint qh = uint32_t(bl16.block.qh[(idx & 0x1E) >> 1]);
+    qh = qh & hm;
+    qh = unpack8(qh)[idx & 1];
 
-    float16_t ret = d * (float16_t((bl.block.qs[qsi    ] & dmask) >> (b * 4)) + float16_t((bl.block.qh[qhi    ] & hm) != 0 ? 16 : 0)) - m;
+    uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]);
+    qs = (qs >> (b * 4)) & 0x0F0F;
+    qs = unpack8(qs)[idx & 1];
+
+    float16_t ret = d * (float16_t(qs) + (qh != 0 ? float16_t(16) : float16_t(0))) - m;
 
     return ret;
 }
@@ -241,22 +253,30 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ6_
    block_q6_K block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ6_K_packed16 {
+   block_q6_K_packed16 block;
+};
+
 float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
+    decodeBufQ6_K_packed16 bl16 = decodeBufQ6_K_packed16(bl);
     const uint idx = coordInBlock[1];
-    const uint iqs = idx;
 
-    const uint n = iqs / 128;                   // 0,1
-    const uint b = (iqs % 128) / 64;            // 0,1
-    const uint is_b = (iqs % 32) / 16;          // 0,1
-    const uint qhshift = ((iqs % 128) / 32) * 2;// 0,2,4,6
-    const uint is = 8 * n + qhshift + is_b;     // 0..15
-    const uint qsi = n * 64 + (iqs % 64);       // 0..127
-    const uint qhi = n * 32 + (iqs % 32);       // 0..63
+    const uint b = (idx & 0x40) >> 6;           // 0,1
+    const uint qhshift = (idx & 0x60) >> 4;    // 0,2,4,6
+    const uint is = (idx & 0xF0) >> 4;          // 0..15
 
     const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]);
 
-    float16_t ret = dscale * float16_t(int8_t(((bl.block.ql[qsi    ] >> (b * 4)) & 0xF) | (((bl.block.qh[qhi    ] >> qhshift) & 3) << 4)) - 32);
+    uint ql = uint32_t(bl16.block.ql[((idx & 0x80) >> 2) + ((idx & 0x3E) >> 1)]);
+    ql = (ql >> (b * 4)) & 0x0F0F;
+
+    uint qh = uint32_t(bl16.block.qh[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]);
+    qh = ((qh >> qhshift) & 0x0303) << 4;
+
+    int q = unpack8(ql | qh)[idx & 1];
+
+    float16_t ret = dscale * float16_t(q - 32);
 
     return ret;
 }

From 5cd85b5e008de2ec398d6596e240187d627561e3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 21 Dec 2024 10:10:18 +0200
Subject: [PATCH 069/376] convert : add BertForMaskedLM (#10919)

---
 convert_hf_to_gguf.py | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 76ab11ebe..976425182 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2628,7 +2628,7 @@ class InternLM2Model(Model):
             return [(self.map_tensor_name(name), data_torch)]
 
 
-@Model.register("BertModel", "CamembertModel")
+@Model.register("BertModel", "BertForMaskedLM", "CamembertModel")
 class BertModel(Model):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -2694,10 +2694,25 @@ class BertModel(Model):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
+        if name.startswith("bert."):
+            name = name[5:]
+
+        if name.endswith(".gamma"):
+            name = name[:-6] + ".weight"
+
+        if name.endswith(".beta"):
+            name = name[:-5] + ".bias"
+
         # we are only using BERT for embeddings so we don't need the pooling layer
         if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
             return [] # we don't need these
 
+        if name.startswith("cls.predictions"):
+            return []
+
+        if name.startswith("cls.seq_relationship"):
+            return []
+
         return [(self.map_tensor_name(name), data_torch)]
 
 

From ebdee9478ca7ba65497b9b96f7457698c6ee5115 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 22 Dec 2024 03:44:01 -0600
Subject: [PATCH 070/376] vulkan: build fixes for 32b (#10927)

* vulkan: build fixes for 32b

Should fix #10923

* vulkan: initialize some buffer/offset variables
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 44 ++++++++++++++--------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 1696b6e27..323ce7cf3 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -3205,8 +3205,8 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont
         GGML_ABORT("fatal error");
     }
     // Check if src is pinned memory
-    vk_buffer buf;
-    size_t buf_offset;
+    vk_buffer buf = nullptr;
+    size_t buf_offset = 0;
     ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset);
 
     const uint64_t ne0 = tensor->ne[0];
@@ -3269,7 +3269,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont
     VkBufferCopy buf_copy{ 0, offset, copy_size };
 
     ggml_vk_sync_buffers(subctx);
-    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
+    vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
 
     for (uint64_t i3 = 0; i3 < ne3; i3++) {
         for (uint64_t i2 = 0; i2 < ne2; i2++) {
@@ -3302,7 +3302,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
     }
     // Check if src is pinned memory
     vk_buffer buf = nullptr;
-    size_t buf_offset;
+    size_t buf_offset = 0;
     ggml_vk_host_get(dst->device, src, buf, buf_offset);
 
     if (buf != nullptr) {
@@ -3344,7 +3344,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
         copy_size};
 
     ggml_vk_sync_buffers(subctx);
-    vkCmdCopyBuffer(subctx->s->buffer, staging_buffer->buffer, dst->buffer, 1, &buf_copy);
+    vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
 
     if (width == spitch) {
         deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys);
@@ -3400,7 +3400,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
 
     // Check if dst is pinned memory
     vk_buffer buf = nullptr;
-    size_t buf_offset;
+    size_t buf_offset = 0;
     ggml_vk_host_get(src->device, dst, buf, buf_offset);
 
     std::vector<vk::BufferCopy> slices(1);
@@ -3480,7 +3480,7 @@ static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t ds
 
     VkBufferCopy bc{ src_offset, dst_offset, size };
 
-    vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc);
+    vkCmdCopyBuffer(ctx->s->buffer, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc);
 }
 
 static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
@@ -3732,9 +3732,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
-    vk_buffer d_Qx;
+    vk_buffer d_Qx = nullptr;
     size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
+    vk_buffer d_Qy = nullptr;
     size_t qy_buf_offset = 0;
 
     bool src0_uma = false;
@@ -3934,9 +3934,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
-    vk_buffer d_Qx;
+    vk_buffer d_Qx = nullptr;
     size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
+    vk_buffer d_Qy = nullptr;
     size_t qy_buf_offset = 0;
 
     bool src0_uma = false;
@@ -4112,7 +4112,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
-    vk_buffer d_Qy;
+    vk_buffer d_Qy = nullptr;
     size_t qy_buf_offset = 0;
 
     bool src1_uma = false;
@@ -4300,11 +4300,11 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
     ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context;
 
-    vk_buffer d_Qx;
+    vk_buffer d_Qx = nullptr;
     size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
+    vk_buffer d_Qy = nullptr;
     size_t qy_buf_offset = 0;
-    vk_buffer d_ids;
+    vk_buffer d_ids = nullptr;
     size_t ids_buf_offset = 0;
 
     bool src0_uma = false;
@@ -4505,11 +4505,11 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
     ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context;
 
-    vk_buffer d_Qx;
+    vk_buffer d_Qx = nullptr;
     size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
+    vk_buffer d_Qy = nullptr;
     size_t qy_buf_offset = 0;
-    vk_buffer d_ids;
+    vk_buffer d_ids = nullptr;
     size_t ids_buf_offset = 0;
 
     bool src0_uma = false;
@@ -4768,8 +4768,8 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     ggml_vk_sync_buffers(subctx);
 
-    vk_buffer d_Q, d_K, d_V, d_D, d_M;
-    uint64_t q_buf_offset, k_buf_offset, v_buf_offset, d_buf_offset, m_buf_offset;
+    vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr;
+    size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0;
 
     bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false;
 
@@ -5474,8 +5474,8 @@ static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subc
 
     ggml_vk_sync_buffers(subctx);
 
-    vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State;
-    uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset;
+    vk_buffer d_D = nullptr, d_K = nullptr, d_V = nullptr, d_R = nullptr, d_TF = nullptr, d_TD = nullptr, d_State = nullptr;
+    size_t k_offset = 0, v_offset = 0, r_offset = 0, tf_offset = 0, td_offset = 0, state_offset = 0, dst_offset = 0;
     bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false;
 
     if (ctx->device->uma) {

From 7ae33a616f44ecc081f3dcb589be20962d1d4a92 Mon Sep 17 00:00:00 2001
From: Billel Mokeddem <billel.mokeddem.ml@gmail.com>
Date: Mon, 23 Dec 2024 01:09:58 +0300
Subject: [PATCH 071/376] llama : add Falcon3 support (#10883)

* Add Falcon3 model support

* Add fix for adding bos to added special tokens

* Add comment explaining the logic behind the if statement

* Add a log message to better track the when the following line of code is triggered

* Update log to only print when input and output characters are different

* Fix handling pre-normalized tokens

* Refactoring
---
 convert_hf_to_gguf.py        | 13 +++++++++++++
 convert_hf_to_gguf_update.py |  1 +
 src/llama.cpp                | 16 +++++++++++++++-
 3 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 976425182..66aa7f5b1 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -529,9 +529,19 @@ class Model:
             else:
                 token: str = reverse_vocab[i]
                 if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not tokenizer.added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
                     if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
                         toktypes.append(gguf.TokenType.CONTROL)
                     else:
+                        # NOTE: this was added for Gemma.
+                        # Encoding and decoding the tokens above isn't sufficient for this case.
                         token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
                         toktypes.append(gguf.TokenType.USER_DEFINED)
                 else:
@@ -575,6 +585,9 @@ class Model:
         if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
             # ref: https://huggingface.co/tiiuae/falcon-7b
             res = "falcon"
+        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
+            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
+            res = "falcon3"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
             res = "bert-bge"
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 88058442f..2ba346640 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -72,6 +72,7 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
diff --git a/src/llama.cpp b/src/llama.cpp
index b442781a0..115ef9080 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1673,6 +1673,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
     LLM_CHAT_TEMPLATE_PHI_3,
+    LLM_CHAT_TEMPLATE_FALCON_3,
     LLM_CHAT_TEMPLATE_ZEPHYR,
     LLM_CHAT_TEMPLATE_MONARCH,
     LLM_CHAT_TEMPLATE_GEMMA,
@@ -1705,6 +1706,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
+    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
     { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
     { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
     { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
@@ -6562,7 +6564,8 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "llama3"   ||
                     tokenizer_pre == "llama-v3" ||
-                    tokenizer_pre == "llama-bpe") {
+                    tokenizer_pre == "llama-bpe"||
+                    tokenizer_pre == "falcon3") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
                 vocab.tokenizer_ignore_merges = true;
                 vocab.tokenizer_add_bos = true;
@@ -22615,6 +22618,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         }
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
+    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
+        return LLM_CHAT_TEMPLATE_FALCON_3;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -22767,6 +22772,15 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
+        // Falcon 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
         // zephyr template
         for (auto message : chat) {

From 7c0e28585843b366864b43b48f92425e2ea17df6 Mon Sep 17 00:00:00 2001
From: Rudi Servo <rudiservo@gmail.com>
Date: Sun, 22 Dec 2024 21:22:58 -0100
Subject: [PATCH 072/376] devops : add docker-multi-stage builds (#10832)

---
 .devops/cpu.Dockerfile                 |  81 ++++++++++++++++++
 .devops/cuda.Dockerfile                |  94 ++++++++++++++++++++
 .devops/full-cuda.Dockerfile           |  33 --------
 .devops/full-musa.Dockerfile           |  33 --------
 .devops/full-rocm.Dockerfile           |  50 -----------
 .devops/full.Dockerfile                |  38 ---------
 .devops/intel.Dockerfile               |  91 ++++++++++++++++++++
 .devops/llama-cli-cuda.Dockerfile      |  38 ---------
 .devops/llama-cli-intel.Dockerfile     |  28 ------
 .devops/llama-cli-musa.Dockerfile      |  38 ---------
 .devops/llama-cli-rocm.Dockerfile      |  45 ----------
 .devops/llama-cli-vulkan.Dockerfile    |  27 ------
 .devops/llama-cli.Dockerfile           |  29 -------
 .devops/llama-server-cuda.Dockerfile   |  43 ----------
 .devops/llama-server-intel.Dockerfile  |  34 --------
 .devops/llama-server-musa.Dockerfile   |  43 ----------
 .devops/llama-server-rocm.Dockerfile   |  54 ------------
 .devops/llama-server-vulkan.Dockerfile |  31 -------
 .devops/llama-server.Dockerfile        |  33 --------
 .devops/musa.Dockerfile                | 108 +++++++++++++++++++++++
 .devops/rocm.Dockerfile                | 113 +++++++++++++++++++++++++
 .devops/vulkan.Dockerfile              |  88 +++++++++++++++++++
 .github/workflows/docker.yml           | 104 +++++++++++++++++------
 23 files changed, 651 insertions(+), 625 deletions(-)
 create mode 100644 .devops/cpu.Dockerfile
 create mode 100644 .devops/cuda.Dockerfile
 delete mode 100644 .devops/full-cuda.Dockerfile
 delete mode 100644 .devops/full-musa.Dockerfile
 delete mode 100644 .devops/full-rocm.Dockerfile
 delete mode 100644 .devops/full.Dockerfile
 create mode 100644 .devops/intel.Dockerfile
 delete mode 100644 .devops/llama-cli-cuda.Dockerfile
 delete mode 100644 .devops/llama-cli-intel.Dockerfile
 delete mode 100644 .devops/llama-cli-musa.Dockerfile
 delete mode 100644 .devops/llama-cli-rocm.Dockerfile
 delete mode 100644 .devops/llama-cli-vulkan.Dockerfile
 delete mode 100644 .devops/llama-cli.Dockerfile
 delete mode 100644 .devops/llama-server-cuda.Dockerfile
 delete mode 100644 .devops/llama-server-intel.Dockerfile
 delete mode 100644 .devops/llama-server-musa.Dockerfile
 delete mode 100644 .devops/llama-server-rocm.Dockerfile
 delete mode 100644 .devops/llama-server-vulkan.Dockerfile
 delete mode 100644 .devops/llama-server.Dockerfile
 create mode 100644 .devops/musa.Dockerfile
 create mode 100644 .devops/rocm.Dockerfile
 create mode 100644 .devops/vulkan.Dockerfile

diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
new file mode 100644
index 000000000..8d020f16c
--- /dev/null
+++ b/.devops/cpu.Dockerfile
@@ -0,0 +1,81 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build build -j $(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ubuntu:$UBUNTU_VERSION AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
new file mode 100644
index 000000000..974dd78a8
--- /dev/null
+++ b/.devops/cuda.Dockerfile
@@ -0,0 +1,94 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=12.6.0
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+
+ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+# CUDA architecture to build for (defaults to all supported archs)
+ARG CUDA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j$(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ${BASE_CUDA_RUN_CONTAINER} AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile
deleted file mode 100644
index 05bff1bdf..000000000
--- a/.devops/full-cuda.Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.6.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default CUDA archs if not specified
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc) && \
-    cp build/bin/* .
-
-ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full-musa.Dockerfile b/.devops/full-musa.Dockerfile
deleted file mode 100644
index 3193fea1e..000000000
--- a/.devops/full-musa.Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
-# Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_MUSA_DEV_CONTAINER} AS build
-
-# MUSA architecture to build for (defaults to all supported archs)
-ARG MUSA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default MUSA archs if not specified
-RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc) && \
-    cp build/bin/* .
-
-ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
deleted file mode 100644
index df496bcd2..000000000
--- a/.devops/full-rocm.Dockerfile
+++ /dev/null
@@ -1,50 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-# This needs to generally match the container host's environment.
-ARG ROCM_VERSION=5.6
-
-# Target the CUDA build image
-ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
-
-FROM ${BASE_ROCM_DEV_CONTAINER} AS build
-
-# Unless otherwise specified, we make a fat build.
-# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
-# This is mostly tied to rocBLAS supported archs.
-ARG ROCM_DOCKER_ARCH="\
-    gfx803 \
-    gfx900 \
-    gfx906 \
-    gfx908 \
-    gfx90a \
-    gfx1010 \
-    gfx1030 \
-    gfx1100 \
-    gfx1101 \
-    gfx1102"
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Set nvcc architecture
-ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
-# Enable ROCm
-ENV GGML_HIPBLAS=1
-ENV CC=/opt/rocm/llvm/bin/clang
-ENV CXX=/opt/rocm/llvm/bin/clang++
-
-# Enable cURL
-ENV LLAMA_CURL=1
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
-
-RUN make -j$(nproc)
-
-ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile
deleted file mode 100644
index d93c0be6a..000000000
--- a/.devops/full.Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
-    cmake --build build -j $(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib/ \;
-
-FROM ubuntu:$UBUNTU_VERSION as runtime
-
-WORKDIR /app
-
-RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-COPY requirements.txt   /app/requirements.txt
-COPY requirements       /app/requirements
-COPY .devops/tools.sh   /app/tools.sh
-
-RUN pip install --upgrade pip setuptools wheel && \
-    pip install -r /app/requirements.txt
-
-COPY --from=build /app/build/bin/ /app/
-COPY --from=build /app/lib/ /app/
-COPY --from=build /app/convert_hf_to_gguf.py /app/
-COPY --from=build /app/gguf-py /app/gguf-py
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT ["/app/tools.sh"]
diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile
new file mode 100644
index 000000000..af783f5e9
--- /dev/null
+++ b/.devops/intel.Dockerfile
@@ -0,0 +1,91 @@
+ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
+
+## Build Image
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
+
+ARG GGML_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
+        echo "GGML_SYCL_F16 is set" \
+        && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
+    fi && \
+    echo "Building with dynamic libs" && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build build --config Release -j$(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/lib/ /app
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/lib/ /app
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/lib/ /app
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
+
diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile
deleted file mode 100644
index 7796891d5..000000000
--- a/.devops/llama-cli-cuda.Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.6.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-# Target the CUDA runtime image
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default CUDA archs if not specified
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release --target llama-cli -j$(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libgomp1
-
-COPY --from=build /app/lib/ /
-COPY --from=build /app/build/bin/llama-cli /
-
-ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile
deleted file mode 100644
index 0706f732a..000000000
--- a/.devops/llama-cli-intel.Dockerfile
+++ /dev/null
@@ -1,28 +0,0 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with static libs" && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
-    ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
-    cmake --build build --config Release --target llama-cli
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
-
-COPY --from=build /app/build/bin/llama-cli /llama-cli
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli-musa.Dockerfile b/.devops/llama-cli-musa.Dockerfile
deleted file mode 100644
index e7c75af20..000000000
--- a/.devops/llama-cli-musa.Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
-# Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-# Target the MUSA runtime image
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_MUSA_DEV_CONTAINER} AS build
-
-# MUSA architecture to build for (defaults to all supported archs)
-ARG MUSA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default MUSA archs if not specified
-RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release --target llama-cli -j$(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libgomp1
-
-COPY --from=build /app/lib/ /
-COPY --from=build /app/build/bin/llama-cli /llama-cli
-
-ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile
deleted file mode 100644
index e60c747bd..000000000
--- a/.devops/llama-cli-rocm.Dockerfile
+++ /dev/null
@@ -1,45 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-# This needs to generally match the container host's environment.
-ARG ROCM_VERSION=5.6
-
-# Target the CUDA build image
-ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
-
-FROM ${BASE_ROCM_DEV_CONTAINER} AS build
-
-# Unless otherwise specified, we make a fat build.
-# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
-# This is mostly tied to rocBLAS supported archs.
-ARG ROCM_DOCKER_ARCH="\
-    gfx803 \
-    gfx900 \
-    gfx906 \
-    gfx908 \
-    gfx90a \
-    gfx1010 \
-    gfx1030 \
-    gfx1100 \
-    gfx1101 \
-    gfx1102"
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Set nvcc architecture
-ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
-# Enable ROCm
-ENV GGML_HIPBLAS=1
-ENV CC=/opt/rocm/llvm/bin/clang
-ENV CXX=/opt/rocm/llvm/bin/clang++
-
-RUN make -j$(nproc) llama-cli
-
-ENTRYPOINT [ "/app/llama-cli" ]
diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile
deleted file mode 100644
index 92a6e0479..000000000
--- a/.devops/llama-cli-vulkan.Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-ARG UBUNTU_VERSION=jammy
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-# Install build tools
-RUN apt update && apt install -y git build-essential cmake wget libgomp1
-
-# Install Vulkan SDK
-RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-    apt update -y && \
-    apt-get install -y vulkan-sdk
-
-# Build it
-WORKDIR /app
-COPY . .
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \
-    cmake --build build --config Release --target llama-cli
-
-# Clean up
-WORKDIR /
-RUN cp /app/build/bin/llama-cli /llama-cli && \
-    rm -rf /app
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile
deleted file mode 100644
index be234d55d..000000000
--- a/.devops/llama-cli.Dockerfile
+++ /dev/null
@@ -1,29 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
-    cmake --build build -j $(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib/ \;
-
-FROM ubuntu:$UBUNTU_VERSION AS runtime
-
-WORKDIR /app
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
-
-COPY --from=build /app/build/bin/llama-cli /app/
-COPY --from=build /app/lib/ /app/
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/app/llama-cli" ]
diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile
deleted file mode 100644
index bf8a198f9..000000000
--- a/.devops/llama-server-cuda.Dockerfile
+++ /dev/null
@@ -1,43 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.6.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-# Target the CUDA runtime image
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default CUDA archs if not specified
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release --target llama-server -j$(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
-
-COPY --from=build /app/lib/ /
-COPY --from=build /app/build/bin/llama-server /llama-server
-
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile
deleted file mode 100644
index b503b8cfe..000000000
--- a/.devops/llama-server-intel.Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with dynamic libs" && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build build --config Release --target llama-server
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev curl
-
-COPY --from=build /app/build/bin/llama-server /llama-server
-
-ENV LC_ALL=C.utf8
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server-musa.Dockerfile b/.devops/llama-server-musa.Dockerfile
deleted file mode 100644
index cebe51d42..000000000
--- a/.devops/llama-server-musa.Dockerfile
+++ /dev/null
@@ -1,43 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
-# Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-# Target the MUSA runtime image
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_MUSA_DEV_CONTAINER} AS build
-
-# MUSA architecture to build for (defaults to all supported archs)
-ARG MUSA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default MUSA archs if not specified
-RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release --target llama-server -j$(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
-
-COPY --from=build /app/lib/ /
-COPY --from=build /app/build/bin/llama-server /llama-server
-
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile
deleted file mode 100644
index 8553af75b..000000000
--- a/.devops/llama-server-rocm.Dockerfile
+++ /dev/null
@@ -1,54 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-# This needs to generally match the container host's environment.
-ARG ROCM_VERSION=5.6
-
-# Target the CUDA build image
-ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
-
-FROM ${BASE_ROCM_DEV_CONTAINER} AS build
-
-# Unless otherwise specified, we make a fat build.
-# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
-# This is mostly tied to rocBLAS supported archs.
-ARG ROCM_DOCKER_ARCH="\
-    gfx803 \
-    gfx900 \
-    gfx906 \
-    gfx908 \
-    gfx90a \
-    gfx1010 \
-    gfx1030 \
-    gfx1100 \
-    gfx1101 \
-    gfx1102"
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Set nvcc architecture
-ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
-# Enable ROCm
-ENV GGML_HIPBLAS=1
-ENV CC=/opt/rocm/llvm/bin/clang
-ENV CXX=/opt/rocm/llvm/bin/clang++
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-# Enable cURL
-ENV LLAMA_CURL=1
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev curl
-
-RUN make -j$(nproc) llama-server
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile
deleted file mode 100644
index 6aa786779..000000000
--- a/.devops/llama-server-vulkan.Dockerfile
+++ /dev/null
@@ -1,31 +0,0 @@
-ARG UBUNTU_VERSION=jammy
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-# Install build tools
-RUN apt update && apt install -y git build-essential cmake wget
-
-# Install Vulkan SDK and cURL
-RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-    apt update -y && \
-    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
-
-# Build it
-WORKDIR /app
-COPY . .
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build build --config Release --target llama-server
-
-# Clean up
-WORKDIR /
-RUN cp /app/build/bin/llama-server /llama-server && \
-    rm -rf /app
-
-ENV LC_ALL=C.utf8
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile
deleted file mode 100644
index 72ccde2fe..000000000
--- a/.devops/llama-server.Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
-    cmake --build build -j $(nproc) && \
-    mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib/ \;
-
-FROM ubuntu:$UBUNTU_VERSION AS runtime
-
-WORKDIR /app
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
-
-COPY --from=build /app/build/bin/llama-server /app/
-COPY --from=build /app/lib/ /app/
-
-ENV LC_ALL=C.utf8
-# Must be set to 0.0.0.0 so it can listen to requests from host machine
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile
new file mode 100644
index 000000000..bfd7fc1c1
--- /dev/null
+++ b/.devops/musa.Dockerfile
@@ -0,0 +1,108 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG MUSA_VERSION=rc3.1.0
+# Target the MUSA build image
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_MUSA_DEV_CONTAINER} AS build
+
+# MUSA architecture to build for (defaults to all supported archs)
+ARG MUSA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y \
+    build-essential \
+    cmake \
+    python3 \
+    python3-pip \
+    git \
+    libcurl4-openssl-dev \
+    libgomp1
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Use the default MUSA archs if not specified
+RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
+        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j$(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ${BASE_MUSA_RUN_CONTAINER} AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile
new file mode 100644
index 000000000..a8088ea00
--- /dev/null
+++ b/.devops/rocm.Dockerfile
@@ -0,0 +1,113 @@
+ARG UBUNTU_VERSION=24.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=6.3
+ARG AMDGPU_VERSION=6.3
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+### Build image
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
+# gfx906 is deprecated
+#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html
+
+#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
+ARG ROCM_DOCKER_ARCH=gfx1100
+
+# Set nvcc architectured
+ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+# ENV CC=/opt/rocm/llvm/bin/clang
+# ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN apt-get update \
+    && apt-get install -y \
+    build-essential \
+    cmake \
+    git \
+    libcurl4-openssl-dev \
+    curl \
+    libgomp1
+
+WORKDIR /app
+
+COPY . .
+
+RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
+    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
+    && cmake --build build --config Release -j$(nproc)
+
+RUN mkdir -p /app/lib \
+    && find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ${BASE_ROCM_DEV_CONTAINER} AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3-pip \
+    python3 \
+    python3-wheel\
+    && pip install --break-system-packages --upgrade setuptools \
+    && pip install --break-system-packages -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
new file mode 100644
index 000000000..cfc2162e3
--- /dev/null
+++ b/.devops/vulkan.Dockerfile
@@ -0,0 +1,88 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK and cURL
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
+
+# Build it
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build build --config Release -j$(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ubuntu:$UBUNTU_VERSION AS base
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl\
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    && pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index bc2e5020d..41f1a89ee 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -34,21 +34,14 @@ jobs:
     strategy:
       matrix:
         config:
-          - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" }
+          # Multi-stage build
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
           # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
-          #- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          #- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" }
+          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4
@@ -56,10 +49,10 @@ jobs:
           fetch-depth: 0 # preserve git history, so we can determine the build number
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
 
       - name: Log in to Docker Hub
         uses: docker/login-action@v2
@@ -79,25 +72,34 @@ jobs:
 
           # determine tag name postfix (build number, commit hash)
           if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
-            TAG_POSTFIX="b${BUILD_NUMBER}"
+            TAG_POSTFIX="-b${BUILD_NUMBER}"
           else
             SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
-            TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}"
+            TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}"
           fi
-
           # list all tags possible
-          TAGS=""
-          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }},"
-          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}"
-
-          echo "output_tags=$TAGS" >> $GITHUB_OUTPUT
-          echo "output_tags=$TAGS"  # print out for debugging
+          if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
+              TYPE=""
+          else
+              TYPE="-${{ matrix.config.tag }}"
+          fi
+          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
+          FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}"
+          LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}"
+          SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}"
+          echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
+          echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
+          echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
+          echo "full_output_tags=$FULLTAGS"  # print out for debugging
+          echo "light_output_tags=$LIGHTTAGS"  # print out for debugging
+          echo "server_output_tags=$SERVERTAGS"  # print out for debugging
         env:
           GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
           GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
 
       # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
       - name: Free Disk Space (Ubuntu)
+        if: ${{ matrix.config.free_disk_space == true }}
         uses: jlumbroso/free-disk-space@main
         with:
           # this might remove tools that are actually needed,
@@ -113,13 +115,59 @@ jobs:
           docker-images: true
           swap-storage: true
 
-      - name: Build and push Docker image (tagged + versioned)
-        if: ${{ github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }}
+      - name: Build and push Full Docker image (tagged + versioned)
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
         uses: docker/build-push-action@v6
         with:
           context: .
           push: true
           platforms: ${{ matrix.config.platforms }}
           # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.output_tags }}
+          tags: ${{ steps.tag.outputs.full_output_tags }}
           file: ${{ matrix.config.dockerfile }}
+          target: full
+          provenance: false
+          # using github experimental cache
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache
+
+      - name: Build and push Light Docker image (tagged + versioned)
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          platforms: ${{ matrix.config.platforms }}
+          # tag list is generated from step above
+          tags: ${{ steps.tag.outputs.light_output_tags }}
+          file: ${{ matrix.config.dockerfile }}
+          target: light
+          provenance: false
+          # using github experimental cache
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache
+
+      - name: Build and push Server Docker image (tagged + versioned)
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          platforms: ${{ matrix.config.platforms }}
+          # tag list is generated from step above
+          tags: ${{ steps.tag.outputs.server_output_tags }}
+          file: ${{ matrix.config.dockerfile }}
+          target: server
+          provenance: false
+          # using github experimental cache
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache

From 7024d59e6a572730626cb11896829d115043a1b1 Mon Sep 17 00:00:00 2001
From: "yuri@FreeBSD" <yurivict@users.noreply.github.com>
Date: Sun, 22 Dec 2024 16:20:11 -0800
Subject: [PATCH 073/376] ggml : fix run-time on FreeBSD in
 get_executable_path() (#10948)

---
 ggml/src/ggml-backend-reg.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 66927148a..31ee31e39 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -402,12 +402,16 @@ static std::string get_executable_path() {
         base_path = base_path.substr(0, last_slash);
     }
     return base_path + "/";
-#elif defined(__linux__)
+#elif defined(__linux__) || defined(__FreeBSD__)
     std::string base_path = ".";
     std::vector<char> path(1024);
     while (true) {
         // get executable path
+#    if defined(__linux__)
         ssize_t len = readlink("/proc/self/exe", path.data(), path.size());
+#    elif defined(__FreeBSD__)
+        ssize_t len = readlink("/proc/curproc/file", path.data(), path.size());
+#    endif
         if (len == -1) {
             break;
         }

From dab76c92cc63072d9495ba87f2f3f3a4872d4f57 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Mon, 23 Dec 2024 00:21:40 +0000
Subject: [PATCH 074/376] llama-run : include temperature option (#10899)

This commit updates the `examples/run/README.md` file to include a new
option for setting the temperature and updates the `run.cpp` file to
parse this option.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/README.md |   2 +
 examples/run/run.cpp   | 111 +++++++++++++++++++++++++++--------------
 2 files changed, 75 insertions(+), 38 deletions(-)

diff --git a/examples/run/README.md b/examples/run/README.md
index 874293516..a06805441 100644
--- a/examples/run/README.md
+++ b/examples/run/README.md
@@ -19,6 +19,8 @@ Options:
       Context size (default: 2048)
   -n, --ngl <value>
       Number of GPU layers (default: 0)
+  --temp <value>
+      Temperature (default: 0.8)
   -v, --verbose, --log-verbose
       Set verbosity level to infinity (i.e. log all messages, useful for debugging)
   -h, --help
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 03da54ca3..f89d041c4 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -55,29 +55,51 @@ static int printe(const char * fmt, ...) {
 class Opt {
   public:
     int init(int argc, const char ** argv) {
+        ctx_params           = llama_context_default_params();
+        model_params         = llama_model_default_params();
+        context_size_default = ctx_params.n_batch;
+        ngl_default          = model_params.n_gpu_layers;
+        common_params_sampling sampling;
+        temperature_default = sampling.temp;
+
+        if (argc < 2) {
+            printe("Error: No arguments provided.\n");
+            print_help();
+            return 1;
+        }
+
         // Parse arguments
         if (parse(argc, argv)) {
             printe("Error: Failed to parse arguments.\n");
-            help();
+            print_help();
             return 1;
         }
 
         // If help is requested, show help and exit
-        if (help_) {
-            help();
+        if (help) {
+            print_help();
             return 2;
         }
 
+        ctx_params.n_batch        = context_size >= 0 ? context_size : context_size_default;
+        model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
+        temperature               = temperature >= 0 ? temperature : temperature_default;
+
         return 0;  // Success
     }
 
+    llama_context_params ctx_params;
+    llama_model_params   model_params;
     std::string model_;
-    std::string user_;
-    int         context_size_ = -1, ngl_ = -1;
-    bool        verbose_ = false;
+    std::string          user;
+    int                  context_size = -1, ngl = -1;
+    float                temperature = -1;
+    bool                 verbose     = false;
 
   private:
-    bool        help_ = false;
+    int   context_size_default = -1, ngl_default = -1;
+    float temperature_default = -1;
+    bool  help                = false;
 
     bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) {
         return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0;
@@ -89,6 +111,17 @@ class Opt {
         }
 
         option_value = std::atoi(argv[++i]);
+
+        return 0;
+    }
+
+    int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) {
+        if (i + 1 >= argc) {
+            return 1;
+        }
+
+        option_value = std::atof(argv[++i]);
+
         return 0;
     }
 
@@ -96,18 +129,22 @@ class Opt {
         bool options_parsing   = true;
         for (int i = 1, positional_args_i = 0; i < argc; ++i) {
             if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
-                if (handle_option_with_value(argc, argv, i, context_size_) == 1) {
+                if (handle_option_with_value(argc, argv, i, context_size) == 1) {
                     return 1;
                 }
             } else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) {
-                if (handle_option_with_value(argc, argv, i, ngl_) == 1) {
+                if (handle_option_with_value(argc, argv, i, ngl) == 1) {
+                    return 1;
+                }
+            } else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
+                if (handle_option_with_value(argc, argv, i, temperature) == 1) {
                     return 1;
                 }
             } else if (options_parsing &&
                        (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
-                verbose_ = true;
+                verbose = true;
             } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
-                help_ = true;
+                help = true;
                 return 0;
             } else if (options_parsing && strcmp(argv[i], "--") == 0) {
                 options_parsing = false;
@@ -120,16 +157,16 @@ class Opt {
                 model_ = argv[i];
             } else if (positional_args_i == 1) {
                 ++positional_args_i;
-                user_ = argv[i];
+                user = argv[i];
             } else {
-                user_ += " " + std::string(argv[i]);
+                user += " " + std::string(argv[i]);
             }
         }
 
         return 0;
     }
 
-    void help() const {
+    void print_help() const {
         printf(
             "Description:\n"
             "  Runs a llm\n"
@@ -142,6 +179,8 @@ class Opt {
             "      Context size (default: %d)\n"
             "  -n, --ngl <value>\n"
             "      Number of GPU layers (default: %d)\n"
+            "  --temp <value>\n"
+            "      Temperature (default: %.1f)\n"
             "  -v, --verbose, --log-verbose\n"
             "      Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
             "  -h, --help\n"
@@ -170,7 +209,7 @@ class Opt {
             "  llama-run file://some-file3.gguf\n"
             "  llama-run --ngl 999 some-file4.gguf\n"
             "  llama-run --ngl 999 some-file5.gguf Hello World\n",
-            llama_context_default_params().n_batch, llama_model_default_params().n_gpu_layers);
+            context_size_default, ngl_default, temperature_default);
     }
 };
 
@@ -495,12 +534,12 @@ class LlamaData {
             return 1;
         }
 
-        context = initialize_context(model, opt.context_size_);
+        context = initialize_context(model, opt);
         if (!context) {
             return 1;
         }
 
-        sampler = initialize_sampler();
+        sampler = initialize_sampler(opt);
         return 0;
     }
 
@@ -619,14 +658,12 @@ class LlamaData {
     // Initializes the model and returns a unique pointer to it
     llama_model_ptr initialize_model(Opt & opt) {
         ggml_backend_load_all();
-        llama_model_params model_params = llama_model_default_params();
-        model_params.n_gpu_layers       = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
         resolve_model(opt.model_);
         printe(
             "\r%*s"
             "\rLoading model",
             get_terminal_width(), " ");
-        llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
+        llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), opt.model_params));
         if (!model) {
             printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
@@ -636,10 +673,8 @@ class LlamaData {
     }
 
     // Initializes the context with the specified parameters
-    llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
-        llama_context_params ctx_params = llama_context_default_params();
-        ctx_params.n_ctx = ctx_params.n_batch = n_ctx >= 0 ? n_ctx : ctx_params.n_batch;
-        llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
+    llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) {
+        llama_context_ptr context(llama_new_context_with_model(model.get(), opt.ctx_params));
         if (!context) {
             printe("%s: error: failed to create the llama_context\n", __func__);
         }
@@ -648,10 +683,10 @@ class LlamaData {
     }
 
     // Initializes and configures the sampler
-    llama_sampler_ptr initialize_sampler() {
+    llama_sampler_ptr initialize_sampler(const Opt & opt) {
         llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params()));
         llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1));
-        llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(0.8f));
+        llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(opt.temperature));
         llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
 
         return sampler;
@@ -798,9 +833,9 @@ static int apply_chat_template_with_error_handling(LlamaData & llama_data, const
 }
 
 // Helper function to handle user input
-static int handle_user_input(std::string & user_input, const std::string & user_) {
-    if (!user_.empty()) {
-        user_input = user_;
+static int handle_user_input(std::string & user_input, const std::string & user) {
+    if (!user.empty()) {
+        user_input = user;
         return 0;  // No need for interactive input
     }
 
@@ -832,17 +867,17 @@ static bool is_stdout_a_terminal() {
 }
 
 // Function to tokenize the prompt
-static int chat_loop(LlamaData & llama_data, const std::string & user_) {
+static int chat_loop(LlamaData & llama_data, const std::string & user) {
     int prev_len = 0;
     llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
     static const bool stdout_a_terminal = is_stdout_a_terminal();
     while (true) {
         // Get user input
         std::string user_input;
-        while (handle_user_input(user_input, user_)) {
+        while (handle_user_input(user_input, user)) {
         }
 
-        add_message("user", user_.empty() ? user_input : user_, llama_data);
+        add_message("user", user.empty() ? user_input : user, llama_data);
         int new_len;
         if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
             return 1;
@@ -854,7 +889,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
             return 1;
         }
 
-        if (!user_.empty()) {
+        if (!user.empty()) {
             break;
         }
 
@@ -869,7 +904,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
 
 static void log_callback(const enum ggml_log_level level, const char * text, void * p) {
     const Opt * opt = static_cast<Opt *>(p);
-    if (opt->verbose_ || level == GGML_LOG_LEVEL_ERROR) {
+    if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) {
         printe("%s", text);
     }
 }
@@ -890,11 +925,11 @@ int main(int argc, const char ** argv) {
     }
 
     if (!is_stdin_a_terminal()) {
-        if (!opt.user_.empty()) {
-            opt.user_ += "\n\n";
+        if (!opt.user.empty()) {
+            opt.user += "\n\n";
         }
 
-        opt.user_ += read_pipe_data();
+        opt.user += read_pipe_data();
     }
 
     llama_log_set(log_callback, &opt);
@@ -903,7 +938,7 @@ int main(int argc, const char ** argv) {
         return 1;
     }
 
-    if (chat_loop(llama_data, opt.user_)) {
+    if (chat_loop(llama_data, opt.user)) {
         return 1;
     }
 

From 6f0c9e034bb398915a6617ee4acc62adb87d387d Mon Sep 17 00:00:00 2001
From: ymcki <84055651+ymcki@users.noreply.github.com>
Date: Mon, 23 Dec 2024 08:22:33 +0800
Subject: [PATCH 075/376] llama : support for Llama-3_1-Nemotron-51B (#10669)

* conflict resolution

* move comments after bracket to its own line
---
 convert_hf_to_gguf.py          | 178 ++++++++++++++++++++++
 gguf-py/gguf/constants.py      |  26 ++++
 gguf-py/gguf/tensor_mapping.py |   1 +
 src/llama.cpp                  | 267 ++++++++++++++++++++++++++++++++-
 4 files changed, 471 insertions(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 66aa7f5b1..d95fb1296 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1692,6 +1692,184 @@ class LlamaModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("DeciLMForCausalLM")
+class DeciModel(Model):
+    model_arch = gguf.MODEL_ARCH.DECI
+
+    @staticmethod
+    def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
+        # DeciLM-specific code
+        intermediate_size = int(2 * ffn_mult * n_embd / 3)
+        return DeciModel._find_multiple(intermediate_size, 256)
+
+    @staticmethod
+    def _find_multiple(n: int, k: int) -> int:
+        # DeciLM-specific code
+        if n % k == 0:
+            return n
+        return n + k - (n % k)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
+            _block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
+            assert self.block_count == len(_block_configs)
+            self._num_kv_heads = list()
+            self._num_heads = list()
+            _ffn_multipliers = list()
+            # ***linear attention layer***
+            # if n_heads_in_group is None and replace_with_linear is True
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
+            # ***attention-free layer***
+            # if n_heads_in_group is None and replace_with_linear is False
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0
+            # ***normal attention-layer***
+            # if n_heads_in_group is not None, then
+            # _num_kv_heads[il] is num_attention_head // n_heads_in_group and
+            # _num_heads[il] is num_attention_head
+            for il in range(len(_block_configs)):
+                if _block_configs[il]["attention"]["n_heads_in_group"] is None:
+                    if _block_configs[il]["attention"]["replace_with_linear"] is True:
+                        self._num_kv_heads.append(0)
+                        self._num_heads.append(self.hparams["num_attention_heads"])
+                    else:
+                        self._num_kv_heads.append(0)
+                        self._num_heads.append(0)
+                else:
+                    self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
+                    self._num_heads.append(self.hparams["num_attention_heads"])
+                _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
+            assert self.block_count == len(self._num_kv_heads)
+            assert self.block_count == len(self._num_heads)
+            assert self.block_count == len(_ffn_multipliers)
+            assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
+            assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
+            assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
+            self._ffn_dims: list[int] = [
+                DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
+                for multiplier in _ffn_multipliers
+            ]
+
+    def set_vocab(self):
+        # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
+        # eos_token from '|eot_id|' to '|end_of_text|'
+        if self.hparams.get("vocab_size", 128256) == 128256:
+            tokens, toktypes, tokpre = self.get_vocab_base()
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+
+            special_vocab = gguf.SpecialVocab(
+                self.dir_model, load_merges=True,
+                special_token_types = ['bos', 'eos', 'eom', 'eot']
+            )
+            special_vocab._set_special_token("bos", 128000)
+            special_vocab._set_special_token("eos", 128001)
+            special_vocab._set_special_token("eom", 128008)
+            special_vocab._set_special_token("eot", 128009)
+            special_vocab.add_to_gguf(self.gguf_writer)
+        else:
+            # DeciLM-7B
+            self._set_vocab_llama_hf()
+#            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
+            assert self.block_count == len(self._num_kv_heads)
+            assert self.block_count == len(self._num_heads)
+            assert self.block_count == len(self._ffn_dims)
+            self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+            self.gguf_writer.add_head_count(self._num_heads)
+            self.gguf_writer.add_feed_forward_length(self._ffn_dims)
+            self.gguf_writer.add_block_count(self.block_count)
+            self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+            self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+            self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+            self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+            self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+            self.gguf_writer.add_file_type(self.ftype)
+        else: # DeciLM-7B
+            super().set_gguf_parameters()
+            if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
+                self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
+                assert self.block_count == len(self._num_kv_heads)
+                self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        if bid is not None:
+            if "num_key_value_heads_per_layer" in self.hparams:
+                n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
+            elif "block_configs" in self.hparams:
+                n_kv_head = self._num_kv_heads[bid]
+                n_head = self._num_heads[bid]
+            else:
+                n_kv_head = self.hparams.get("num_key_value_heads")
+        else:
+            n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeciModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
+            if rope_scaling.get("rope_type", '').lower() == "llama3":
+                base = self.hparams.get("rope_theta", 10000.0)
+                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_scaling.get("factor", 8.0)
+                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+                assert low_freq_wavelen != high_freq_wavelen
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+
 @Model.register("BitnetForCausalLM")
 class BitnetModel(Model):
     model_arch = gguf.MODEL_ARCH.BITNET
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index a40df974d..273370370 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -221,6 +221,7 @@ class GGUFType:
 
 class MODEL_ARCH(IntEnum):
     LLAMA            = auto()
+    DECI             = auto()
     FALCON           = auto()
     BAICHUAN         = auto()
     GROK             = auto()
@@ -402,6 +403,7 @@ class MODEL_TENSOR(IntEnum):
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.LLAMA:            "llama",
+    MODEL_ARCH.DECI:             "deci",
     MODEL_ARCH.FALCON:           "falcon",
     MODEL_ARCH.BAICHUAN:         "baichuan",
     MODEL_ARCH.GROK:             "grok",
@@ -602,6 +604,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.DECI: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     MODEL_ARCH.GROK: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -1448,6 +1470,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.DECI: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
     MODEL_ARCH.BAICHUAN: [
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 82cdb121a..7009a11d4 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -198,6 +198,7 @@ class TensorNameMap:
             "transformer.h.{bid}.self_attention.dense",                     # falcon
             "h.{bid}.self_attention.dense",                                 # bloom
             "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2
+            "model.layers.{bid}.self_attn.linear_attn",                     # deci
             "layers.{bid}.attention.wo",                                    # llama-pth
             "encoder.layer.{bid}.attention.output.dense",                   # bert
             "transformer.h.{bid}.attn.out_proj",                            # gpt-j
diff --git a/src/llama.cpp b/src/llama.cpp
index 115ef9080..c1524d06b 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -146,6 +146,7 @@ static std::string format(const char * fmt, ...) {
 
 enum llm_arch {
     LLM_ARCH_LLAMA,
+    LLM_ARCH_DECI,
     LLM_ARCH_FALCON,
     LLM_ARCH_BAICHUAN,
     LLM_ARCH_GROK,
@@ -203,6 +204,7 @@ enum llm_arch {
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA,            "llama"            },
+    { LLM_ARCH_DECI,             "deci"            },
     { LLM_ARCH_FALCON,           "falcon"           },
     { LLM_ARCH_GROK,             "grok"             },
     { LLM_ARCH_GPT2,             "gpt2"             },
@@ -674,6 +676,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_DECI,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
     {
         LLM_ARCH_BAICHUAN,
         {
@@ -5694,7 +5722,7 @@ static void llm_load_hparams(
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
-        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
+        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
             if (hparams.n_rot != hparams.n_embd_head_k) {
                 throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
             }
@@ -5734,6 +5762,15 @@ static void llm_load_hparams(
                     }
                 }
             } break;
+        case LLM_ARCH_DECI:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_MINICPM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7939,6 +7976,68 @@ static bool llm_load_tensors(
                         }
                     }
                 } break;
+            case LLM_ARCH_DECI:
+                {
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (model.output == NULL) {
+                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+                        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+                        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+                        const int64_t n_embd_gqa    = hparams.n_embd_v_gqa(i);
+                        const int64_t n_ff          = hparams.n_ff(i);
+                        const int64_t n_head        = hparams.n_head(i);
+                        const int64_t n_head_kv     = hparams.n_head_kv(i);
+
+                        if (n_head_kv == 0 && n_head > 0) {
+                            // linear attention for DeciLMCausalModel
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        }
+                        else if (n_head_kv > 0) {
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+                        }
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                        }
+                        else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        // optional MLP bias
+                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             case LLM_ARCH_MINICPM3:
                 {
                     const int64_t n_embd_head_qk_rope = hparams.n_rot;
@@ -11308,6 +11407,167 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_deci() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_head    = hparams.n_head(il);
+
+            if (n_head == 0) {
+                // attention-free layer of Llama-3_1-Nemotron-51B
+                cur = inpL;
+            } else {
+                // norm
+                cur = llm_build_norm(ctx0, inpL, hparams,
+                        model.layers[il].attn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_norm", il);
+            }
+
+            if (n_head > 0 && n_head_kv == 0) {
+                // "linear attention" of Llama-3_1-Nemotron-51B
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
+                cb(cur, "wo", il);
+            } else if (n_head > 0) {
+                // self-attention
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // For Granite architecture
+            if (hparams.f_residual_scale) {
+                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+            }
+
+            // modified to support attention-free layer of Llama-3_1-Nemotron-51B
+            struct ggml_tensor * ffn_inp = cur;
+            if (n_head > 0) {
+                ffn_inp = ggml_add(ctx0, cur, inpSA);
+                cb(ffn_inp, "ffn_inp", il);
+            }
+
+            // feed-forward network
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // For Granite architecture
+            if (hparams.f_residual_scale) {
+                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        // For Granite architecture
+        if (hparams.f_logit_scale) {
+            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+        }
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_baichuan() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
@@ -17422,6 +17682,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_llama();
             } break;
+        case LLM_ARCH_DECI:
+            {
+                result = llm.build_deci();
+            } break;
         case LLM_ARCH_BAICHUAN:
             {
                 result = llm.build_baichuan();
@@ -20797,6 +21061,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
         case LLM_ARCH_LLAMA:
+        case LLM_ARCH_DECI:
         case LLM_ARCH_BAICHUAN:
         case LLM_ARCH_STARCODER:
         case LLM_ARCH_PLAMO:

From b92a14a841fb4dfaf27b29d982ec8ba5289a3bff Mon Sep 17 00:00:00 2001
From: Yun Dou <dixyes@gmail.com>
Date: Mon, 23 Dec 2024 08:35:44 +0800
Subject: [PATCH 076/376] llama : support InfiniAI Megrez 3b (#10893)

* Support InfiniAI Megrez 3b

* Fix tokenizer_clean_spaces for megrez
---
 convert_hf_to_gguf.py        |  3 +++
 convert_hf_to_gguf_update.py |  1 +
 src/llama.cpp                | 17 +++++++++++++++++
 tests/test-chat-template.cpp |  4 ++++
 4 files changed, 25 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index d95fb1296..b6c15da94 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -684,6 +684,9 @@ class Model:
         if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
             # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
             res = "gigachat"
+        if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
+            # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
+            res = "megrez"
 
         if res is None:
             logger.warning("\n")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 2ba346640..fea23ddb4 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -106,6 +106,7 @@ models = [
     {"name": "minerva-7b",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
     {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
     {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
+    {"name": "megrez",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
 ]
 
 
diff --git a/src/llama.cpp b/src/llama.cpp
index c1524d06b..4d41602fe 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1720,6 +1720,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_RWKV_WORLD,
     LLM_CHAT_TEMPLATE_GRANITE,
     LLM_CHAT_TEMPLATE_GIGACHAT,
+    LLM_CHAT_TEMPLATE_MEGREZ,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 
@@ -1753,6 +1754,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
     { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
     { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
+    { "megrez",            LLM_CHAT_TEMPLATE_MEGREZ            },
 };
 
 static llm_arch llm_arch_from_string(const std::string & name) {
@@ -6703,6 +6705,9 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "minerva-7b") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA;
+            } else if (
+                tokenizer_pre == "megrez") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -22931,6 +22936,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_GRANITE;
     } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
         return LLM_CHAT_TEMPLATE_GIGACHAT;
+    } else if (tmpl_contains("<|role_start|>")) {
+        return LLM_CHAT_TEMPLATE_MEGREZ;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -23289,6 +23296,16 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "assistant<|role_sep|>";
         }
+    }  else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) {
+        // Megrez template
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>";
+        }
+
+        if (add_ass) {
+            ss << "<|role_start|>assistant<|role_end|>";
+        }
     } else {
         // template not supported
         return -1;
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 30a910ad5..51bfb155b 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -77,6 +77,8 @@ int main(void) {
         "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
         // ai-sage/GigaChat-20B-A3B-instruct
         "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
+        // Infinigence/Megrez-3B-Instruct
+        u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}"
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -133,6 +135,8 @@ int main(void) {
         "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
         // ai-sage/GigaChat-20B-A3B-instruct
         "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
+        // Infinigence/Megrez-3B-Instruct
+        "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|>   I am an assistant   <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;

From 86bf31cfe684849157f0875b4f0ebccac7034547 Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Mon, 23 Dec 2024 10:39:30 +0200
Subject: [PATCH 077/376] rpc-server : add support for the SYCL backend
 (#10934)

---
 examples/rpc/rpc-server.cpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp
index 5fe70dac7..8b1b23eda 100644
--- a/examples/rpc/rpc-server.cpp
+++ b/examples/rpc/rpc-server.cpp
@@ -12,6 +12,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef GGML_USE_SYCL
+#include "ggml-sycl.h"
+#endif
+
 #include "ggml-rpc.h"
 #ifdef _WIN32
 #  include <windows.h>
@@ -91,6 +95,12 @@ static ggml_backend_t create_backend() {
     if (!backend) {
         fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__);
     }
+#elif GGML_USE_SYCL
+    fprintf(stderr, "%s: using SYCL backend\n", __func__);
+    backend = ggml_backend_sycl_init(0); // init device 0
+    if (!backend) {
+        fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__);
+    }
 #endif
 
     // if there aren't GPU Backends fallback to CPU backend
@@ -106,6 +116,8 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) {
     ggml_backend_cuda_get_device_memory(0, free_mem, total_mem);
 #elif GGML_USE_VULKAN
     ggml_backend_vk_get_device_memory(0, free_mem, total_mem);
+#elif GGML_USE_SYCL
+    ggml_backend_sycl_get_device_memory(0, free_mem, total_mem);
 #else
     #ifdef _WIN32
         MEMORYSTATUSEX status;

From 485dc01214f266afff7004bc702498b491abc404 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 23 Dec 2024 12:02:44 +0100
Subject: [PATCH 078/376] server : add system_fingerprint to chat/completion
 (#10917)

* server : add system_fingerprint to chat/completion

* update README
---
 examples/server/README.md                     |  3 +-
 examples/server/server.cpp                    | 32 +++++++++++--------
 .../server/tests/unit/test_chat_completion.py |  3 ++
 examples/server/utils.hpp                     |  2 ++
 4 files changed, 25 insertions(+), 15 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 6d6465692..5e3d6a6e6 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -724,7 +724,8 @@ This endpoint is public (no API key check). By default, it is read-only. To make
   },
   "total_slots": 1,
   "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
-  "chat_template": "..."
+  "chat_template": "...",
+  "build_info": "b(build number)-(build commit hash)"
 }
 ```
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index fa3682a92..c571ed3c1 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -595,10 +595,11 @@ struct server_task_result_cmpl_final : server_task_result {
         std::time_t t = std::time(0);
 
         json res = json {
-            {"choices", json::array({choice})},
-            {"created", t},
-            {"model", oaicompat_model},
-            {"object", "chat.completion"},
+            {"choices",            json::array({choice})},
+            {"created",            t},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "chat.completion"},
             {"usage", json {
                 {"completion_tokens", n_decoded},
                 {"prompt_tokens",     n_prompt_tokens},
@@ -632,11 +633,12 @@ struct server_task_result_cmpl_final : server_task_result {
         };
 
         json ret = json {
-            {"choices", json::array({choice})},
-            {"created", t},
-            {"id",      oaicompat_cmpl_id},
-            {"model",   oaicompat_model},
-            {"object",  "chat.completion.chunk"},
+            {"choices",            json::array({choice})},
+            {"created",            t},
+            {"id",                 oaicompat_cmpl_id},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "chat.completion.chunk"},
             {"usage", json {
                 {"completion_tokens", n_decoded},
                 {"prompt_tokens",     n_prompt_tokens},
@@ -761,11 +763,12 @@ struct server_task_result_cmpl_partial : server_task_result {
         }
 
         json ret = json {
-            {"choices", choices},
-            {"created", t},
-            {"id",      oaicompat_cmpl_id},
-            {"model",   oaicompat_model},
-            {"object",  "chat.completion.chunk"}
+            {"choices",            choices},
+            {"created",            t},
+            {"id",                 oaicompat_cmpl_id},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "chat.completion.chunk"}
         };
 
         if (timings.prompt_n >= 0) {
@@ -3476,6 +3479,7 @@ int main(int argc, char ** argv) {
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model },
             { "chat_template",               llama_get_chat_template(ctx_server.model) },
+            { "build_info",                  build_info },
         };
 
         res_ok(res, data);
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 0fa1a17c1..885497081 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -31,6 +31,7 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte
     })
     assert res.status_code == 200
     assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
+    assert res.body["system_fingerprint"].startswith("b")
     assert res.body["model"] == model if model is not None else server.model_alias
     assert res.body["usage"]["prompt_tokens"] == n_prompt
     assert res.body["usage"]["completion_tokens"] == n_predicted
@@ -63,6 +64,7 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte
     last_cmpl_id = None
     for data in res:
         choice = data["choices"][0]
+        assert data["system_fingerprint"].startswith("b")
         assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
         if last_cmpl_id is None:
             last_cmpl_id = data["id"]
@@ -92,6 +94,7 @@ def test_chat_completion_with_openai_library():
         seed=42,
         temperature=0.8,
     )
+    assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
     assert res.choices[0].finish_reason == "length"
     assert res.choices[0].message.content is not None
     assert match_regex("(Suddenly)+", res.choices[0].message.content)
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 94bb285b6..1987acac8 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -56,6 +56,8 @@ static T json_value(const json & body, const std::string & key, const T & defaul
     }
 }
 
+const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT);
+
 //
 // tokenizer and input processing utils
 //

From 14b699ecde8f1e9e251ebff9eca39ebc5603b83b Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 23 Dec 2024 12:52:25 +0100
Subject: [PATCH 079/376] server : fix missing model id in /model endpoint
 (#10957)

* server : fix missing model id in /model endpoint

* fix ci
---
 examples/server/server.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c571ed3c1..476a9225f 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3701,7 +3701,7 @@ int main(int argc, char ** argv) {
             {"object", "list"},
             {"data", {
                 {
-                    {"id",       params.model_alias},
+                    {"id",       params.model_alias.empty() ? params.model : params.model_alias},
                     {"object",   "model"},
                     {"created",  std::time(0)},
                     {"owned_by", "llamacpp"},

From 32d6ee6385b3fc908b283f509b845f757a6e7206 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Mon, 23 Dec 2024 20:25:52 +0100
Subject: [PATCH 080/376] ggml : fix const usage in SSE path (#10962)

---
 ggml/src/ggml-cpu/ggml-cpu.c | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 67e67a089..18d194479 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -986,7 +986,7 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
 #define GGML_F16_STEP 32
 #define GGML_F16_EPR  4
 
-static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
+static inline __m128 __sse_f16x4_load(const ggml_fp16_t * x) {
     float tmp[4];
 
     tmp[0] = GGML_FP16_TO_FP32(x[0]);
@@ -997,7 +997,7 @@ static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
     return _mm_loadu_ps(tmp);
 }
 
-static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
+static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) {
     float arr[4];
 
     _mm_storeu_ps(arr, y);

From 3327bb0f8dea381118f8e66c18ea14db56d3b942 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Tue, 24 Dec 2024 04:05:17 +0100
Subject: [PATCH 081/376] ggml : fix arm enabled features check (#10961)

---
 ggml/src/ggml-cpu/CMakeLists.txt | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 12d790825..e357860a5 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -135,14 +135,20 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             endif()
 
             # show enabled features
+            if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows")
+                set(FEAT_INPUT_FILE "NUL")
+            else()
+                set(FEAT_INPUT_FILE "/dev/null")
+            endif()
+
             execute_process(
                 COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
-                INPUT_FILE "/dev/null"
+                INPUT_FILE ${FEAT_INPUT_FILE}
                 OUTPUT_VARIABLE ARM_FEATURE
                 RESULT_VARIABLE ARM_FEATURE_RESULT
             )
             if (ARM_FEATURE_RESULT)
-                message(FATAL_ERROR "Failed to get ARM features")
+                message(WARNING "Failed to get ARM features")
             else()
                 foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
                     string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)

From 60cfa728e27c28537657d4e627ed432508eb9537 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Tue, 24 Dec 2024 04:05:27 +0100
Subject: [PATCH 082/376] ggml : use wstring for backend search paths (#10960)

ggml-ci
---
 ggml/src/CMakeLists.txt          |   1 +
 ggml/src/ggml-backend-reg.cpp    | 117 ++++++++++++++++++-------------
 ggml/src/ggml-cpu/CMakeLists.txt |   5 ++
 3 files changed, 75 insertions(+), 48 deletions(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index bf5ee5fc2..a5f7f7b5b 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -234,6 +234,7 @@ function(ggml_add_backend_library backend)
         # write the shared library to the output directory
         set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
         target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
+        add_dependencies(ggml ${backend})
     else()
         add_library(${backend} ${ARGN})
         target_link_libraries(ggml PUBLIC ${backend})
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 31ee31e39..7ddd178b5 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -66,6 +66,26 @@
 #include "ggml-kompute.h"
 #endif
 
+// disable C++17 deprecation warning for std::codecvt_utf8
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
+#endif
+
+static std::wstring utf8_to_utf16(const std::string & str) {
+    std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
+    return converter.from_bytes(str);
+}
+
+static std::string utf16_to_utf8(const std::wstring & str) {
+    std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
+    return converter.to_bytes(str);
+}
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+
 #ifdef _WIN32
 
 using dl_handle = std::remove_pointer_t<HMODULE>;
@@ -88,11 +108,6 @@ static dl_handle * dl_load_library(const std::wstring & path) {
     return handle;
 }
 
-static dl_handle * dl_load_library(const std::string & path) {
-    std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
-    return dl_load_library(converter.from_bytes(path));
-}
-
 static void * dl_get_sym(dl_handle * handle, const char * name) {
     DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
     SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
@@ -114,8 +129,8 @@ struct dl_handle_deleter {
     }
 };
 
-static void * dl_load_library(const std::string & path) {
-    dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL);
+static void * dl_load_library(const std::wstring & path) {
+    dl_handle * handle = dlopen(utf16_to_utf8(path).c_str(), RTLD_NOW | RTLD_LOCAL);
 
     return handle;
 }
@@ -202,11 +217,11 @@ struct ggml_backend_registry {
         devices.push_back(device);
     }
 
-    ggml_backend_reg_t load_backend(const char * path, bool silent) {
+    ggml_backend_reg_t load_backend(const std::wstring & path, bool silent) {
         dl_handle_ptr handle { dl_load_library(path) };
         if (!handle) {
             if (!silent) {
-                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path);
+                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(path).c_str());
             }
             return nullptr;
         }
@@ -214,7 +229,7 @@ struct ggml_backend_registry {
         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
         if (score_fn && score_fn() == 0) {
             if (!silent) {
-                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path);
+                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, utf16_to_utf8(path).c_str());
             }
             return nullptr;
         }
@@ -222,7 +237,7 @@ struct ggml_backend_registry {
         auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
         if (!backend_init_fn) {
             if (!silent) {
-                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path);
+                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, utf16_to_utf8(path).c_str());
             }
             return nullptr;
         }
@@ -231,16 +246,16 @@ struct ggml_backend_registry {
         if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
             if (!silent) {
                 if (!reg) {
-                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path);
+                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, utf16_to_utf8(path).c_str());
                 } else {
                     GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
-                        __func__, path, reg->api_version, GGML_BACKEND_API_VERSION);
+                        __func__, utf16_to_utf8(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
                 }
             }
             return nullptr;
         }
 
-        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path);
+        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), utf16_to_utf8(path).c_str());
 
         register_backend(reg, std::move(handle));
 
@@ -376,14 +391,14 @@ ggml_backend_t ggml_backend_init_best(void) {
 
 // Dynamic loading
 ggml_backend_reg_t ggml_backend_load(const char * path) {
-    return get_reg().load_backend(path, false);
+    return get_reg().load_backend(utf8_to_utf16(path), false);
 }
 
 void ggml_backend_unload(ggml_backend_reg_t reg) {
     get_reg().unload_backend(reg, true);
 }
 
-static std::string get_executable_path() {
+static std::wstring get_executable_path() {
 #if defined(__APPLE__)
     // get executable path
     std::vector<char> path;
@@ -401,7 +416,7 @@ static std::string get_executable_path() {
     if (last_slash != std::string::npos) {
         base_path = base_path.substr(0, last_slash);
     }
-    return base_path + "/";
+    return utf8_to_utf16(base_path + "/");
 #elif defined(__linux__) || defined(__FreeBSD__)
     std::string base_path = ".";
     std::vector<char> path(1024);
@@ -427,57 +442,63 @@ static std::string get_executable_path() {
         path.resize(path.size() * 2);
     }
 
-    return base_path + "/";
+    return utf8_to_utf16(base_path + "/");
 #elif defined(_WIN32)
-    std::vector<char> path(MAX_PATH);
-    DWORD len = GetModuleFileNameA(NULL, path.data(), path.size());
+    std::vector<wchar_t> path(MAX_PATH);
+    DWORD len = GetModuleFileNameW(NULL, path.data(), path.size());
     if (len == 0) {
-        return "";
+        return {};
     }
-    std::string base_path(path.data(), len);
+    std::wstring base_path(path.data(), len);
     // remove executable name
     auto last_slash = base_path.find_last_of('\\');
     if (last_slash != std::string::npos) {
         base_path = base_path.substr(0, last_slash);
     }
-    return base_path + "\\";
+    return base_path + L"\\";
+#else
+    return {};
 #endif
 }
 
-static std::string backend_filename_prefix() {
+static std::wstring backend_filename_prefix() {
 #ifdef _WIN32
-    return "ggml-";
+    return L"ggml-";
 #else
-    return "libggml-";
+    return L"libggml-";
 #endif
 }
 
-static std::string backend_filename_suffix() {
+static std::wstring backend_filename_suffix() {
 #ifdef _WIN32
-    return ".dll";
+    return L".dll";
 #else
-    return ".so";
+    return L".so";
+#endif
+}
+
+static std::wstring path_separator() {
+#ifdef _WIN32
+    return L"\\";
+#else
+    return L"/";
 #endif
 }
 
 static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
     // enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
      // TODO: search system paths
-    std::string file_prefix = backend_filename_prefix() + name + "-";
-    std::vector<std::string> search_paths;
+    std::wstring file_prefix = backend_filename_prefix() + utf8_to_utf16(name) + L"-";
+    std::vector<std::wstring> search_paths;
     if (user_search_path == nullptr) {
-        search_paths.push_back("./");
+        search_paths.push_back(L"." + path_separator());
         search_paths.push_back(get_executable_path());
     } else {
-#if defined(_WIN32)
-        search_paths.push_back(std::string(user_search_path) + "\\");
-#else
-        search_paths.push_back(std::string(user_search_path) + "/");
-#endif
+        search_paths.push_back(utf8_to_utf16(user_search_path) + path_separator());
     }
 
     int best_score = 0;
-    std::string best_path;
+    std::wstring best_path;
 
     namespace fs = std::filesystem;
     for (const auto & search_path : search_paths) {
@@ -487,27 +508,27 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
         for (const auto & entry : dir_it) {
             if (entry.is_regular_file()) {
-                std::string filename = entry.path().filename().string();
-                std::string ext = entry.path().extension().string();
+                std::wstring filename = entry.path().filename().wstring();
+                std::wstring ext = entry.path().extension().wstring();
                 if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
-                    dl_handle_ptr handle { dl_load_library(entry.path().c_str()) };
+                    dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
                     if (!handle && !silent) {
-                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str());
+                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
                     }
                     if (handle) {
                         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
                         if (score_fn) {
                             int s = score_fn();
 #ifndef NDEBUG
-                            GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s);
+                            GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
 #endif
                             if (s > best_score) {
                                 best_score = s;
-                                best_path = entry.path().string();
+                                best_path = entry.path().wstring();
                             }
                         } else {
                             if (!silent) {
-                                GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str());
+                                GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
                             }
                         }
                     }
@@ -519,15 +540,15 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
     if (best_score == 0) {
         // try to load the base backend
         for (const auto & search_path : search_paths) {
-            std::string path = search_path + backend_filename_prefix() + name + backend_filename_suffix();
+            std::wstring path = search_path + backend_filename_prefix() + utf8_to_utf16(name) + backend_filename_suffix();
             if (fs::exists(path)) {
-                return get_reg().load_backend(path.c_str(), silent);
+                return get_reg().load_backend(path, silent);
             }
         }
         return nullptr;
     }
 
-    return get_reg().load_backend(best_path.c_str(), silent);
+    return get_reg().load_backend(best_path, silent);
 }
 
 void ggml_backend_load_all() {
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index e357860a5..f0aecac1b 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -323,6 +323,11 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
     target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})
 
     if (GGML_BACKEND_DL)
+        if (GGML_NATIVE)
+            # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
+            message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
+        endif()
+
         # The feature detection code is compiled as a separate target so that
         # it can be built without the architecture flags
         # Since multiple variants of the CPU backend may be included in the same

From 30caac3a68a54de8396b21e20ba972554c587230 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 24 Dec 2024 09:44:20 +0200
Subject: [PATCH 083/376] llama : the WPM vocabs use the CLS token as BOS
 (#10930)

* llama : the WPM vocabs use the CLS token as BOS

ggml-ci

* llama : add comment
---
 src/llama-vocab.cpp | 2 +-
 src/llama-vocab.h   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 7f2725f94..0a477d6dd 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1657,7 +1657,7 @@ bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token t
 }
 
 llama_token llama_token_bos_impl(const struct llama_vocab & vocab) {
-    return vocab.special_bos_id;
+    return vocab.type != LLAMA_VOCAB_TYPE_WPM ? vocab.special_bos_id : vocab.special_cls_id;
 }
 
 llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 4bb16d2e4..a9b0da5ef 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -45,7 +45,7 @@ struct llama_vocab {
     id special_unk_id  = 0;
     id special_sep_id  = LLAMA_TOKEN_NULL;
     id special_pad_id  = LLAMA_TOKEN_NULL;
-    id special_cls_id  = LLAMA_TOKEN_NULL;
+    id special_cls_id  = LLAMA_TOKEN_NULL; // TODO: revisit if this is really needed https://github.com/ggerganov/llama.cpp/pull/10930
     id special_mask_id = LLAMA_TOKEN_NULL;
 
     id linefeed_id = 13;

From 09fe2e76137dde850b13313f720e7ffa17efdefa Mon Sep 17 00:00:00 2001
From: NeverLucky <92274250+nvrxq@users.noreply.github.com>
Date: Tue, 24 Dec 2024 19:39:49 +0300
Subject: [PATCH 084/376] server:  allow filtering llama server response fields
 (#10940)

* llama_server_response_fields

* llama_server_response_fields_fix_issues

* params fixes

* fix

* clarify docs

* change to "response_fields"

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/README.md                     |  2 ++
 examples/server/server.cpp                    |  6 +++-
 examples/server/tests/unit/test_completion.py | 34 +++++++++++++++++++
 examples/server/utils.hpp                     | 22 ++++++++++++
 4 files changed, 63 insertions(+), 1 deletion(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 5e3d6a6e6..c7d91be99 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -450,6 +450,8 @@ These words will not be included in the completion, so make sure to add them to
 
 `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.
 
+`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error.
+
 **Response format**
 
 - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 476a9225f..3fbfb13c4 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -92,6 +92,7 @@ struct slot_params {
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
     std::vector<std::string> antiprompt;
+    std::vector<std::string> response_fields;
     bool timings_per_token = false;
     bool post_sampling_probs = false;
     bool ignore_eos = false;
@@ -209,6 +210,7 @@ struct server_task {
         params.n_discard        = json_value(data, "n_discard",          defaults.n_discard);
       //params.t_max_prompt_ms  = json_value(data, "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
         params.t_max_predict_ms = json_value(data, "t_max_predict_ms",   defaults.t_max_predict_ms);
+        params.response_fields  = json_value(data, "response_fields",   std::vector<std::string>());
 
         params.sampling.top_k              = json_value(data, "top_k",              defaults.sampling.top_k);
         params.sampling.top_p              = json_value(data, "top_p",              defaults.sampling.top_p);
@@ -522,6 +524,7 @@ struct server_task_result_cmpl_final : server_task_result {
 
     bool post_sampling_probs;
     std::vector<completion_token_output> probs_output;
+    std::vector<std::string>  response_fields;
 
     slot_params generation_params;
 
@@ -568,7 +571,7 @@ struct server_task_result_cmpl_final : server_task_result {
         if (!stream && !probs_output.empty()) {
             res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
         }
-        return res;
+        return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
     }
 
     json to_json_oaicompat_chat() {
@@ -2066,6 +2069,7 @@ struct server_context {
         res->tokens          = slot.generated_tokens;
         res->timings         = slot.get_timings();
         res->prompt          = common_detokenize(ctx, slot.prompt_tokens, true);
+        res->response_fields = slot.params.response_fields;
 
         res->truncated           = slot.truncated;
         res->n_decoded           = slot.n_decoded;
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index b88d45f18..00d5ce391 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -257,6 +257,40 @@ def test_completion_parallel_slots(n_slots: int, n_requests: int):
         # assert match_regex(re_content, res.body["content"])
 
 
+@pytest.mark.parametrize(
+    "prompt,n_predict,response_fields",
+    [
+        ("I believe the meaning of life is", 8, []),
+        ("I believe the meaning of life is", 32, ["content", "generation_settings/n_predict", "prompt"]),
+    ],
+)
+def test_completion_response_fields(
+    prompt: str, n_predict: int, response_fields: list[str]
+):
+    global server
+    server.start()
+    res = server.make_request(
+        "POST",
+        "/completion",
+        data={
+            "n_predict": n_predict,
+            "prompt": prompt,
+            "response_fields": response_fields,
+        },
+    )
+    assert res.status_code == 200
+    assert "content" in res.body
+    assert len(res.body["content"])
+    if len(response_fields):
+        assert res.body["generation_settings/n_predict"] == n_predict
+        assert res.body["prompt"] == "<s> " + prompt
+        assert isinstance(res.body["content"], str)
+        assert len(res.body) == len(response_fields)
+    else:
+        assert len(res.body)
+        assert "generation_settings" in res.body
+
+
 def test_n_probs():
     global server
     server.start()
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 1987acac8..043d8b528 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -90,6 +90,28 @@ static bool json_is_array_of_mixed_numbers_strings(const json & data) {
     return false;
 }
 
+// get value by path(key1 / key2)
+static json json_get_nested_values(const std::vector<std::string> & paths, const json & js) {
+    json result = json::object();
+
+    for (const std::string & path : paths) {
+        json current = js;
+        const auto keys = string_split<std::string>(path, /*separator*/ '/');
+        bool valid_path = true;
+        for (const std::string & k : keys) {
+            if (valid_path && current.is_object() && current.contains(k)) {
+                current = current[k];
+            } else {
+                valid_path = false;
+            }
+        }
+        if (valid_path) {
+            result[path] = current;
+        }
+    }
+    return result;
+}
+
 /**
  * this handles 2 cases:
  * - only string, example: "string"

From 2cd43f4900ba0e34124fdcbf02a7f9df25a10a3d Mon Sep 17 00:00:00 2001
From: Djip007 <3705339+Djip007@users.noreply.github.com>
Date: Tue, 24 Dec 2024 18:54:49 +0100
Subject: [PATCH 085/376] ggml : more perfo with llamafile tinyblas on x86_64
 (#10714)

* more perfo with llamafile tinyblas on x86_64.

- add bf16 suport
- change dispache strategie (thanks:
https://github.com/ikawrakow/ik_llama.cpp/pull/71 )
- reduce memory bandwidth

simple tinyblas dispache and more cache freindly

* tinyblas dynamic dispaching

* sgemm: add M blocs.

* - git 2.47 use short id of len 9.
- show-progress is not part of GNU Wget2

* remove not stable test
---
 examples/server/tests/unit/test_completion.py |   7 +-
 ggml/src/ggml-cpu/ggml-cpu.c                  |   8 +-
 ggml/src/ggml-cpu/llamafile/sgemm.cpp         | 522 +++++++++---------
 ggml/src/ggml-cpu/llamafile/sgemm.h           |   4 +-
 scripts/compare-llama-bench.py                |  22 +-
 scripts/hf.sh                                 |   2 +-
 6 files changed, 287 insertions(+), 278 deletions(-)

diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index 00d5ce391..a6b215944 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -95,7 +95,7 @@ def test_consistent_result_same_seed(n_slots: int):
         res = server.make_request("POST", "/completion", data={
             "prompt": "I believe the meaning of life is",
             "seed": 42,
-            "temperature": 1.0,
+            "temperature": 0.0,
             "cache_prompt": False,  # TODO: remove this once test_cache_vs_nocache_prompt is fixed
         })
         if last_res is not None:
@@ -120,9 +120,10 @@ def test_different_result_different_seed(n_slots: int):
             assert res.body["content"] != last_res.body["content"]
         last_res = res
 
-
+# TODO figure why it don't work with temperature = 1
+# @pytest.mark.parametrize("temperature", [0.0, 1.0])
 @pytest.mark.parametrize("n_batch", [16, 32])
-@pytest.mark.parametrize("temperature", [0.0, 1.0])
+@pytest.mark.parametrize("temperature", [0.0])
 def test_consistent_result_different_batch_size(n_batch: int, temperature: float):
     global server
     server.n_batch = n_batch
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 18d194479..b7fefb9dd 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -7419,14 +7419,14 @@ static void ggml_compute_forward_mul_mat(
     if (src1_cont) {
         for (int64_t i13 = 0; i13 < ne13; i13++)
             for (int64_t i12 = 0; i12 < ne12; i12++)
-                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
+                if (!llamafile_sgemm(params,
+                                     ne01, ne11, ne00/ggml_blck_size(src0->type),
                                      (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
                                      nb01/ggml_type_size(src0->type),
                                      (const char *)src1->data + i12*nb12 + i13*nb13,
                                      nb11/ggml_type_size(src1->type),
                                      (char *)dst->data + i12*nb2 + i13*nb3,
                                      nb1/ggml_type_size(dst->type),
-                                     ith, nth,
                                      src0->type,
                                      src1->type,
                                      dst->type))
@@ -7471,14 +7471,14 @@ UseGgmlGemm1:;
 
         for (int64_t i13 = 0; i13 < ne13; i13++)
             for (int64_t i12 = 0; i12 < ne12; i12++)
-                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
+                if (!llamafile_sgemm(params,
+                                     ne01, ne11, ne00/ggml_blck_size(src0->type),
                                      (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
                                      nb01/ggml_type_size(src0->type),
                                      (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
                                      row_size/ggml_type_size(vec_dot_type),
                                      (char *)dst->data + i12*nb2 + i13*nb3,
                                      nb1/ggml_type_size(dst->type),
-                                     ith, nth,
                                      src0->type,
                                      vec_dot_type,
                                      dst->type))
diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index f80a72781..00f7f1170 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -53,6 +53,8 @@
 #include "ggml-cpu-impl.h"
 #include "ggml-quants.h"
 
+#include <atomic>
+
 #ifdef _MSC_VER
 #define NOINLINE __declspec(noinline)
 #else
@@ -134,6 +136,16 @@ inline __m512 madd(__m512 a, __m512 b, __m512 c) {
     return _mm512_fmadd_ps(a, b, c);
 }
 #endif
+#if defined(__AVX512BF16__)
+template <>
+inline __m512 madd(__m512bh a, __m512bh b, __m512 c) {
+    return _mm512_dpbf16_ps(c, a, b);
+}
+template <>
+inline __m256 madd(__m256bh a, __m256bh b, __m256 c) {
+    return _mm256_dpbf16_ps(c, a, b);
+}
+#endif
 #endif
 
 #if defined(__ARM_FEATURE_FMA)
@@ -226,6 +238,13 @@ template <> inline __m256 load(const float *p) {
 }
 #endif // __AVX__
 
+#if defined(__AVX2__) || defined(__AVX512F__)
+template <> inline __m256 load(const ggml_bf16_t *p) {
+    return _mm256_castsi256_ps(
+        _mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)p)), 16));
+}
+#endif // __AVX2__
+
 #if defined(__F16C__)
 template <> inline __m256 load(const ggml_fp16_t *p) {
     return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p));
@@ -239,8 +258,27 @@ template <> inline __m512 load(const float *p) {
 template <> inline __m512 load(const ggml_fp16_t *p) {
     return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p));
 }
+template <> inline __m512 load(const ggml_bf16_t *p) {
+    return _mm512_castsi512_ps(
+        _mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)p)), 16));
+}
 #endif // __AVX512F__
 
+#if defined(__AVX512BF16__)
+template <> inline __m512bh load(const ggml_bf16_t *p) {
+    return (__m512bh)_mm512_loadu_ps((const float *)p);
+}
+template <> inline __m256bh load(const ggml_bf16_t *p) {
+    return (__m256bh)_mm256_loadu_ps((const float *)p);
+}
+template <> inline __m512bh load(const float *p) {
+    return _mm512_cvtne2ps_pbh(_mm512_loadu_ps(p + 16), _mm512_loadu_ps(p));
+}
+template <> inline __m256bh load(const float *p) {
+    return _mm512_cvtneps_pbh(_mm512_loadu_ps(p));
+}
+#endif
+
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 // CONSTANTS
 
@@ -252,199 +290,170 @@ static const __m128i iq4nlt = _mm_loadu_si128((const __m128i *) kvalues_iq4nl);
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 // FLOATING POINT MATRIX MULTIPLICATION
 
+template <int M>
+static inline int64_t BLOCK_SIZE(size_t m) {
+    const int64_t NB_BLOC_M = (m + M - 1) / M;
+    return (m % NB_BLOC_M == 0) ? m / NB_BLOC_M : (m / NB_BLOC_M) + 1;
+}
+
+static constexpr inline int64_t BLOC_POS(int64_t ib, int64_t ibN, int64_t bloc_size) {
+    return ib < ibN ? ib * bloc_size : ibN * bloc_size + (ib - ibN) * (bloc_size - 1);
+}
+
 template <int KN, typename D, typename V, typename TA, typename TB, typename TC>
 class tinyBLAS {
   public:
-    tinyBLAS(int64_t k,
+    tinyBLAS(const ggml_compute_params * params, int64_t k,
              const TA *A, int64_t lda,
              const TB *B, int64_t ldb,
-             TC *C, int64_t ldc,
-             int ith, int nth)
-        : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
+             TC *C, int64_t ldc)
+        : params(params), A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc) {
     }
 
-    void matmul(int64_t m, int64_t n) {
-        mnpack(0, m, 0, n);
+    bool matmul(int64_t m, int64_t n) {
+        if (k % KN != 0)
+            return false;
+        // compute RM for only need tile with size RM&RM-1
+#if VECTOR_REGISTERS == 32
+        if (m % 16 == 0 && (m/16 >= params->nth)) {
+            const int64_t SIZE_N = BLOCK_SIZE<6>(n);
+            mnpack<4, 6, 4>(m, n, SIZE_N, 12);
+            return true;
+        }
+        if (m % 8 == 0 ) {
+            const int64_t SIZE_N = BLOCK_SIZE<6>(n);
+            mnpack<4, 6, 2>(m, n, SIZE_N, 12);
+            return true;
+        }
+        if (m % 4 == 0) {
+            const int64_t SIZE_N = BLOCK_SIZE<6>(n);
+            mnpack<4, 6, 1>(m, n, SIZE_N, 12);
+            return true;
+        }
+#else  // VECTOR_REGISTERS == 16
+        if (m % 16 == 0 && (m/16 >= params->nth)) {
+            const int64_t SIZE_N = BLOCK_SIZE<3>(n);
+            mnpack<4, 3, 4>(m, n, SIZE_N, 24);
+            return true;
+        }
+        if (m % 8 == 0 ) {
+            const int64_t SIZE_N = BLOCK_SIZE<3>(n);
+            mnpack<4, 3, 2>(m, n, SIZE_N, 24);
+            return true;
+        }
+        if (m % 4 == 0) {
+            const int64_t SIZE_N = BLOCK_SIZE<3>(n);
+            mnpack<4, 3, 1>(m, n, SIZE_N, 24);
+            return true;
+        }
+#endif
+        return false;
     }
 
   private:
-    NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
-        int64_t mc, nc, mp, np;
-        switch ((MIN(m - m0, 5) << 4) | MIN(n - n0, 5)) {
-#if VECTOR_REGISTERS == 32
-        case 0x55:
-            mc = 5;
-            nc = 5;
-            gemm<5, 5>(m0, m, n0, n);
-            break;
-        case 0x45:
-            mc = 4;
-            nc = 5;
-            gemm<4, 5>(m0, m, n0, n);
-            break;
-        case 0x54:
-            mc = 5;
-            nc = 4;
-            gemm<5, 4>(m0, m, n0, n);
-            break;
-        case 0x44:
-            mc = 4;
-            nc = 4;
-            gemm<4, 4>(m0, m, n0, n);
-            break;
-        case 0x53:
-            mc = 5;
-            nc = 3;
-            gemm<5, 3>(m0, m, n0, n);
-            break;
-        case 0x35:
-            mc = 3;
-            nc = 5;
-            gemm<3, 5>(m0, m, n0, n);
-            break;
-        case 0x43:
-            mc = 4;
-            nc = 3;
-            gemm<4, 3>(m0, m, n0, n);
-            break;
-#else
-        case 0x55:
-        case 0x54:
-        case 0x53:
-        case 0x45:
-        case 0x44:
-        case 0x43:
-            mc = 4;
-            nc = 3;
-            gemm<4, 3>(m0, m, n0, n);
-            break;
-        case 0x35:
-#endif
-        case 0x34:
-            mc = 3;
-            nc = 4;
-            gemm<3, 4>(m0, m, n0, n);
-            break;
-        case 0x52:
-            mc = 5;
-            nc = 2;
-            gemm<5, 2>(m0, m, n0, n);
-            break;
-        case 0x33:
-            mc = 3;
-            nc = 3;
-            gemm<3, 3>(m0, m, n0, n);
-            break;
-        case 0x25:
-            mc = 2;
-            nc = 5;
-            gemm<2, 5>(m0, m, n0, n);
-            break;
-        case 0x42:
-            mc = 4;
-            nc = 2;
-            gemm<4, 2>(m0, m, n0, n);
-            break;
-        case 0x24:
-            mc = 2;
-            nc = 4;
-            gemm<2, 4>(m0, m, n0, n);
-            break;
-        case 0x32:
-            mc = 3;
-            nc = 2;
-            gemm<3, 2>(m0, m, n0, n);
-            break;
-        case 0x23:
-            mc = 2;
-            nc = 3;
-            gemm<2, 3>(m0, m, n0, n);
-            break;
-        case 0x51:
-            mc = 5;
-            nc = 1;
-            gemm<5, 1>(m0, m, n0, n);
-            break;
-        case 0x41:
-            mc = 4;
-            nc = 1;
-            gemm<4, 1>(m0, m, n0, n);
-            break;
-        case 0x22:
-            mc = 2;
-            nc = 2;
-            gemm<2, 2>(m0, m, n0, n);
-            break;
-        case 0x15:
-            mc = 1;
-            nc = 5;
-            gemm<1, 5>(m0, m, n0, n);
-            break;
-        case 0x14:
-            mc = 1;
-            nc = 4;
-            gemm<1, 4>(m0, m, n0, n);
-            break;
-        case 0x31:
-            mc = 3;
-            nc = 1;
-            gemm<3, 1>(m0, m, n0, n);
-            break;
-        case 0x13:
-            mc = 1;
-            nc = 3;
-            gemm<1, 3>(m0, m, n0, n);
-            break;
-        case 0x21:
-            mc = 2;
-            nc = 1;
-            gemm<2, 1>(m0, m, n0, n);
-            break;
-        case 0x12:
-            mc = 1;
-            nc = 2;
-            gemm<1, 2>(m0, m, n0, n);
-            break;
-        case 0x11:
-            mc = 1;
-            nc = 1;
-            gemm<1, 1>(m0, m, n0, n);
-            break;
-        default:
-            return;
+    template <int RM, int RN, int BM>
+    inline void mnpack(int64_t m, int64_t n, int64_t SIZE_N, int64_t BN) {
+        if (SIZE_N == RN) {
+            return gemm<RM, RN, BM>(m, n, BN);
+        }
+        if constexpr (RN > 1) {
+            return mnpack<RM, RN-1, BM>(m, n, SIZE_N, BN);
+        } else {
+            GGML_LOG_ERROR("mnpack<%d, %d> bloc size not supported\n", RM, (int)SIZE_N);
+            GGML_ASSERT(false); // we have miss something.
         }
-        mp = m0 + (m - m0) / mc * mc;
-        np = n0 + (n - n0) / nc * nc;
-        mnpack(mp, m, n0, np);
-        mnpack(m0, m, np, n);
     }
 
     template <int RM, int RN>
-    NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
-        int64_t ytiles = (m - m0) / RM;
-        int64_t xtiles = (n - n0) / RN;
-        int64_t tiles = xtiles * ytiles;
-        int64_t duty = (tiles + nth - 1) / nth;
-        int64_t start = duty * ith;
-        int64_t end = start + duty;
-        if (end > tiles)
-            end = tiles;
-        for (int64_t job = start; job < end; ++job) {
-            int64_t ii = m0 + job / xtiles * RM;
-            int64_t jj = n0 + job % xtiles * RN;
-            D Cv[RN][RM] = {};
-            for (int64_t l = 0; l < k; l += KN)
-                for (int64_t j = 0; j < RN; ++j)
-                    for (int64_t i = 0; i < RM; ++i)
-                        Cv[j][i] = madd(load<V>(A + lda * (ii + i) + l),
-                                        load<V>(B + ldb * (jj + j) + l),
-                                        Cv[j][i]);
-            for (int64_t j = 0; j < RN; ++j)
-                for (int64_t i = 0; i < RM; ++i)
-                    C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
+    inline void gemm_bloc(int64_t ii, int64_t jj) {
+        D Cv[RN][RM] = {};
+        for (int64_t l = 0; l < k; l += KN) {
+            // help compiler for op order.
+            if constexpr (RM <= RN) {
+                V Av[RM];
+                for (int64_t i = 0; i < RM; ++i) {
+                    Av[i] = load<V>(A + lda * (ii + i) + l);
+                }
+                for (int64_t j = 0; j < RN; ++j) {
+                    V Bv = load<V>(B + ldb * (jj + j) + l);
+                    for (int64_t i = 0; i < RM; ++i) {
+                        Cv[j][i] = madd(Av[i], Bv, Cv[j][i]);
+                    }
+                }
+            } else {
+                V Bv[RN];
+                for (int64_t j = 0; j < RN; ++j) {
+                    Bv[j] = load<V>(B + ldb * (jj + j) + l);
+                }
+                for (int64_t i = 0; i < RM; ++i) {
+                    V Av = load<V>(A + lda * (ii + i) + l);
+                    for (int64_t j = 0; j < RN; ++j) {
+                        Cv[j][i] = madd(Av, Bv[j], Cv[j][i]);
+                    }
+                }
+            }
         }
+        for (int64_t j = 0; j < RN; ++j)
+            for (int64_t i = 0; i < RM; ++i)
+                C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
     }
 
+    template <int RM, int RN, int BM>
+    NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) {
+        static std::atomic<int64_t> current_chunk;
+
+        GGML_ASSERT(m % (RM * BM) == 0);
+        const int64_t ytiles = m / (RM * BM);
+        const int64_t xtiles = (n + RN -1) / RN;
+        const int64_t jj_RN = (xtiles - (xtiles * RN - n));
+
+        // "round" bloc_size to "nearest" BN
+        const int64_t NB_BN = xtiles < BN ? 1 : (xtiles + BN / 2) / BN;
+        const int64_t SIZE_BN = xtiles % NB_BN == 0 ? xtiles / NB_BN : xtiles / NB_BN + 1;
+        const int64_t jj_BN = (NB_BN - (NB_BN * SIZE_BN - xtiles));
+        const int64_t nb_job = ytiles * NB_BN;
+
+        if (params->ith == 0) {
+            GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles);
+            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
+            std::atomic_store_explicit(&current_chunk, (int64_t)params->nth, std::memory_order_relaxed);
+        }
+
+        ggml_barrier(params->threadpool);
+
+        int64_t job = params->ith;
+        while (job < nb_job) {
+            const int64_t ii = (job % ytiles) * RM * BM;
+            const int64_t jb =  job / ytiles;
+            const int64_t jr0 = BLOC_POS(jb  , jj_BN, SIZE_BN);
+            const int64_t jrN = BLOC_POS(jb+1, jj_BN, SIZE_BN);
+
+            const int64_t jj0 = BLOC_POS(jr0, jj_RN, RN);
+            const int64_t jj2 = BLOC_POS(jrN, jj_RN, RN);
+            const int64_t jj1 = jj2 < jj_RN * RN ? jj2 : jj_RN * RN;
+
+            for (int64_t bi = 0; bi < BM * RM; bi += RM) {
+                int64_t jj = jj0;
+                for (; jj < jj1; jj += RN) {
+                    gemm_bloc<RM, RN>(ii + bi, jj);
+                }
+                if constexpr (RN > 1) {
+                    for (; jj < jj2; jj += RN - 1) {
+                        gemm_bloc<RM, RN-1>(ii + bi, jj);
+                    }
+                }
+                GGML_ASSERT(jj == jj2);
+            }
+
+            // next step.
+            job = std::atomic_fetch_add_explicit(&current_chunk, (int64_t)1, std::memory_order_relaxed);
+        }
+
+        ggml_barrier(params->threadpool);
+        return;
+    }
+
+    const ggml_compute_params * params;
     const TA *const A;
     const TB *const B;
     TC *const C;
@@ -452,8 +461,6 @@ class tinyBLAS {
     const int64_t lda;
     const int64_t ldb;
     const int64_t ldc;
-    const int ith;
-    const int nth;
 };
 
 //////////////////////////////////////////////////////////////////////////////////////////
@@ -1657,8 +1664,9 @@ class tinyBLAS_PPC {
  * @param Ctype is GGML data type of `C`
  * @return true if this function was able to service the matmul request
  */
-bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C,
-                     int64_t ldc, int ith, int nth, int Atype, int Btype, int Ctype) {
+bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64_t n, int64_t k,
+                     const void *A, int64_t lda, const void *B, int64_t ldb, void *C,
+                     int64_t ldc, int Atype, int Btype, int Ctype) {
 
     assert(m >= 0);
     assert(n >= 0);
@@ -1666,8 +1674,8 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
     assert(lda >= k);
     assert(ldb >= k);
     assert(ldc >= m);
-    assert(nth > 0);
-    assert(ith < nth);
+    assert(params->nth > 0);
+    assert(params->ith < params->nth);
 
     // only enable sgemm for prompt processing
     if (n < 2)
@@ -1682,37 +1690,25 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
         if (Btype != GGML_TYPE_F32)
             return false;
 #if defined(__AVX512F__)
-        if (k % 16)
-            return false;
-        tinyBLAS<16, __m512, __m512, float, float, float> tb{
+        tinyBLAS<16, __m512, __m512, float, float, float> tb{ params,
             k, (const float *)A, lda,
             (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+            (float *)C, ldc};
+        return tb.matmul(m, n);
 #elif defined(__AVX__) || defined(__AVX2__)
-        if (k % 8)
-            return false;
-        tinyBLAS<8, __m256, __m256, float, float, float> tb{
+        tinyBLAS<8, __m256, __m256, float, float, float> tb{ params,
             k, (const float *)A, lda,
             (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+            (float *)C, ldc};
+        return tb.matmul(m, n);
 #elif defined(__ARM_NEON)
         if (n < 4)
             return false;
-        if (k % 4)
-            return false;
-        tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{
+        tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ params,
             k, (const float *)A, lda,
             (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+            (float *)C, ldc};
+        return tb.matmul(m, n);
 #elif defined(__MMA__)
         if (k % 8)
             return false;
@@ -1720,7 +1716,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const float *)A, lda,
             (const float *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #else
@@ -1728,60 +1724,71 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
 #endif
     }
 
+    case GGML_TYPE_BF16: {
+#if defined(__AVX512BF16__)
+        if (Btype == GGML_TYPE_BF16) {
+            tinyBLAS<32, __m512, __m512bh, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k,
+                (const ggml_bf16_t *)A, lda,
+                (const ggml_bf16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
+#elif defined(__AVX512F__)
+        if (Btype == GGML_TYPE_BF16) {
+            tinyBLAS<16, __m512, __m512, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k,
+                (const ggml_bf16_t *)A, lda,
+                (const ggml_bf16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
+#elif defined(__AVX2__)
+        if (Btype == GGML_TYPE_BF16) {
+            tinyBLAS<8, __m256, __m256, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k,
+                (const ggml_bf16_t *)A, lda,
+                (const ggml_bf16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
+#endif
+        return false;
+    }
     case GGML_TYPE_F16: {
 #if defined(__AVX512F__)
-        if (k % 16)
-            return false;
-        if (Btype != GGML_TYPE_F32)
-            return false;
-        tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{
-            k, (const ggml_fp16_t *)A, lda,
-            (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+        if (Btype == GGML_TYPE_F16) {
+            tinyBLAS<16, __m512, __m512, ggml_fp16_t, ggml_fp16_t, float> tb{ params, k,
+                (const ggml_fp16_t *)A, lda,
+                (const ggml_fp16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
 #elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
-        if (k % 8)
-            return false;
-        if (Btype != GGML_TYPE_F32)
-            return false;
-        tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{
-            k, (const ggml_fp16_t *)A, lda,
-            (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+        if (Btype == GGML_TYPE_F16) {
+            tinyBLAS<8, __m256, __m256, ggml_fp16_t, ggml_fp16_t, float> tb{ params, k,
+                (const ggml_fp16_t *)A, lda,
+                (const ggml_fp16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
 #elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
         if (n < 8)
             return false;
-        if (k % 8)
-            return false;
-        if (Btype != GGML_TYPE_F16)
-            return false;
-        tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{
-            k, (const ggml_fp16_t *)A, lda,
-            (const ggml_fp16_t *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
+        if (Btype == GGML_TYPE_F16) {
+            tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ params,
+                k, (const ggml_fp16_t *)A, lda,
+                (const ggml_fp16_t *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
 #elif defined(__ARM_NEON) && !defined(_MSC_VER)
-        if (k % 4)
-            return false;
-        if (Btype != GGML_TYPE_F32)
-            return false;
-        tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{
-            k, (const ggml_fp16_t *)A, lda,
-            (const float *)B, ldb,
-            (float *)C, ldc,
-            ith, nth};
-        tb.matmul(m, n);
-        return true;
-#else
-        return false;
+        if (Btype == GGML_TYPE_F32) {
+            tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ params,
+                k, (const ggml_fp16_t *)A, lda,
+                (const float *)B, ldb,
+                (float *)C, ldc};
+            return tb.matmul(m, n);
+        }
 #endif
+        return false;
     }
 
     case GGML_TYPE_Q8_0: {
@@ -1792,7 +1799,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_q8_0 *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #elif defined(__ARM_FEATURE_DOTPROD)
@@ -1800,7 +1807,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_q8_0 *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #else
@@ -1816,7 +1823,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_q4_0 *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #elif defined(__ARM_FEATURE_DOTPROD)
@@ -1824,7 +1831,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_q4_0 *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #else
@@ -1840,7 +1847,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_q5_0 *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #else
@@ -1856,7 +1863,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             k, (const block_iq4_nl *)A, lda,
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
-            ith, nth};
+            params->ith, params->nth};
         tb.matmul(m, n);
         return true;
 #else
@@ -1868,6 +1875,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
         return false;
     }
 
+    (void)params;
     (void)m;
     (void)n;
     (void)k;
@@ -1877,8 +1885,6 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
     (void)ldb;
     (void)C;
     (void)ldc;
-    (void)ith;
-    (void)nth;
     (void)Atype;
     (void)Btype;
     (void)Ctype;
diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.h b/ggml/src/ggml-cpu/llamafile/sgemm.h
index caf6dd556..3d2909515 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.h
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.h
@@ -5,8 +5,8 @@
 extern "C" {
 #endif
 
-bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t,
-                     const void *, int64_t, void *, int64_t, int, int,
+bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t, int64_t, int64_t,
+                     const void *, int64_t, const void *, int64_t, void *, int64_t,
                      int, int, int);
 
 #ifdef __cplusplus
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 5069ae638..239c458d8 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -126,6 +126,8 @@ connection = sqlite3.connect(input_file)
 cursor = connection.cursor()
 builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall()
 
+commit_short_len = len(builds[0][0])
+
 try:
     repo = git.Repo(".", search_parent_directories=True)
 except git.InvalidGitRepositoryError:
@@ -138,11 +140,11 @@ def find_parent_in_data(commit: git.Commit):
     seen_hexsha8 = set()
     while heap:
         depth, current_commit = heapq.heappop(heap)
-        current_hexsha8 = commit.hexsha[:8]
+        current_hexsha8 = commit.hexsha[:commit_short_len]
         if (current_hexsha8,) in builds:
             return current_hexsha8
         for parent in commit.parents:
-            parent_hexsha8 = parent.hexsha[:8]
+            parent_hexsha8 = parent.hexsha[:commit_short_len]
             if parent_hexsha8 not in seen_hexsha8:
                 seen_hexsha8.add(parent_hexsha8)
                 heapq.heappush(heap, (depth + 1, parent))
@@ -156,9 +158,9 @@ def get_all_parent_hexsha8s(commit: git.Commit):
 
     while unvisited:
         current_commit = unvisited.pop(0)
-        visited.append(current_commit.hexsha[:8])
+        visited.append(current_commit.hexsha[:commit_short_len])
         for parent in current_commit.parents:
-            if parent.hexsha[:8] not in visited:
+            if parent.hexsha[:commit_short_len] not in visited:
                 unvisited.append(parent)
 
     return visited
@@ -169,10 +171,10 @@ def get_commit_name(hexsha8):
     if repo is None:
         return hexsha8
     for h in repo.heads:
-        if h.commit.hexsha[:8] == hexsha8:
+        if h.commit.hexsha[:commit_short_len] == hexsha8:
             return h.name
     for t in repo.tags:
-        if t.commit.hexsha[:8] == hexsha8:
+        if t.commit.hexsha[:commit_short_len] == hexsha8:
             return t.name
     return hexsha8
 
@@ -183,13 +185,13 @@ def get_commit_hexsha8(name):
         return None
     for h in repo.heads:
         if h.name == name:
-            return h.commit.hexsha[:8]
+            return h.commit.hexsha[:commit_short_len]
     for t in repo.tags:
         if t.name == name:
-            return t.commit.hexsha[:8]
+            return t.commit.hexsha[:commit_short_len]
     for c in repo.iter_commits("--all"):
-        if c.hexsha[:8] == name[:8]:
-            return c.hexsha[:8]
+        if c.hexsha[:commit_short_len] == name[:commit_short_len]:
+            return c.hexsha[:commit_short_len]
     return None
 
 
diff --git a/scripts/hf.sh b/scripts/hf.sh
index 85c2c4d9a..b251925fa 100755
--- a/scripts/hf.sh
+++ b/scripts/hf.sh
@@ -26,7 +26,7 @@ function has_cmd {
 }
 
 if has_cmd wget; then
-    cmd="wget -q --show-progress -c -O %s/%s %s"
+    cmd="wget -q -c -O %s/%s %s"
 elif has_cmd curl; then
     cmd="curl -C - -f --output-dir %s -o %s -L %s"
 else

From 9ba399dfa7f115effc63d48e6860a94c9faa31b2 Mon Sep 17 00:00:00 2001
From: Reza Kakhki <rezakakhki.de@gmail.com>
Date: Tue, 24 Dec 2024 21:33:04 +0100
Subject: [PATCH 086/376] server : add support for "encoding_format": "base64"
 to the */embeddings endpoints (#10967)

* add support for base64

* fix base64 test

* improve test

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/CMakeLists.txt               |  1 +
 examples/server/server.cpp                   | 13 ++++++-
 examples/server/tests/unit/test_embedding.py | 41 ++++++++++++++++++++
 examples/server/utils.hpp                    | 28 ++++++++++---
 4 files changed, 76 insertions(+), 7 deletions(-)

diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index a27597cbc..1b7cc8c13 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -34,6 +34,7 @@ endforeach()
 add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 
+target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
 target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
 
 if (LLAMA_SERVER_SSL)
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 3fbfb13c4..30ff3b149 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3790,6 +3790,17 @@ int main(int argc, char ** argv) {
             return;
         }
 
+        bool use_base64 = false;
+        if (body.count("encoding_format") != 0) {
+            const std::string& format = body.at("encoding_format");
+            if (format == "base64") {
+                use_base64 = true;
+            } else if (format != "float") {
+                res_error(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST));
+                return;
+            }
+        }
+
         std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
         for (const auto & tokens : tokenized_prompts) {
             // this check is necessary for models that do not add BOS token to the input
@@ -3841,7 +3852,7 @@ int main(int argc, char ** argv) {
         }
 
         // write JSON response
-        json root = oaicompat ? format_embeddings_response_oaicompat(body, responses) : json(responses);
+        json root = oaicompat ? format_embeddings_response_oaicompat(body, responses, use_base64) : json(responses);
         res_ok(res, root);
     };
 
diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py
index 43e372fc7..8b0eb42b0 100644
--- a/examples/server/tests/unit/test_embedding.py
+++ b/examples/server/tests/unit/test_embedding.py
@@ -1,3 +1,5 @@
+import base64
+import struct
 import pytest
 from openai import OpenAI
 from utils import *
@@ -194,3 +196,42 @@ def test_embedding_usage_multiple():
     assert res.status_code == 200
     assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
     assert res.body['usage']['prompt_tokens'] == 2 * 9
+
+
+def test_embedding_openai_library_base64():
+    server.start()
+    test_input = "Test base64 embedding output"
+
+    # get embedding in default format
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": test_input
+    })
+    assert res.status_code == 200
+    vec0 = res.body["data"][0]["embedding"]
+
+    # get embedding in base64 format
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": test_input,
+        "encoding_format": "base64"
+    })
+
+    assert res.status_code == 200
+    assert "data" in res.body
+    assert len(res.body["data"]) == 1
+
+    embedding_data = res.body["data"][0]
+    assert "embedding" in embedding_data
+    assert isinstance(embedding_data["embedding"], str)
+
+    # Verify embedding is valid base64
+    decoded = base64.b64decode(embedding_data["embedding"])
+    # Verify decoded data can be converted back to float array
+    float_count = len(decoded) // 4  # 4 bytes per float
+    floats = struct.unpack(f'{float_count}f', decoded)
+    assert len(floats) > 0
+    assert all(isinstance(x, float) for x in floats)
+    assert len(floats) == len(vec0)
+
+    # make sure the decoded data is the same as the original
+    for x, y in zip(floats, vec0):
+        assert abs(x - y) < EPSILON
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 043d8b528..334f2f192 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -3,6 +3,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "common/base64.hpp"
 
 #ifndef NDEBUG
 // crash the server in debug mode, otherwise send an http 500 error
@@ -613,16 +614,31 @@ static json oaicompat_completion_params_parse(
     return llama_params;
 }
 
-static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
+static json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false) {
     json data = json::array();
     int32_t n_tokens = 0;
     int i = 0;
     for (const auto & elem : embeddings) {
-        data.push_back(json{
-            {"embedding", json_value(elem, "embedding", json::array())},
-            {"index",     i++},
-            {"object",    "embedding"}
-        });
+        json embedding_obj;
+
+        if (use_base64) {
+            const auto& vec = json_value(elem, "embedding", json::array()).get<std::vector<float>>();
+            const char* data_ptr = reinterpret_cast<const char*>(vec.data());
+            size_t data_size = vec.size() * sizeof(float);
+            embedding_obj = {
+                {"embedding", base64::encode(data_ptr, data_size)},
+                {"index", i++},
+                {"object", "embedding"},
+                {"encoding_format", "base64"}
+            };
+        } else {
+            embedding_obj = {
+                {"embedding", json_value(elem, "embedding", json::array())},
+                {"index", i++},
+                {"object", "embedding"}
+            };
+        }
+        data.push_back(embedding_obj);
 
         n_tokens += json_value(elem, "tokens_evaluated", 0);
     }

From d283d02bf254a7f2991e1502066330cc0d4321a6 Mon Sep 17 00:00:00 2001
From: Peter <peter277@users.noreply.github.com>
Date: Fri, 27 Dec 2024 00:59:11 +1100
Subject: [PATCH 087/376] examples, ggml : fix GCC compiler warnings (#10983)

Warning types fixed (observed under MSYS2 GCC 14.2.0):
* format '%ld' expects argument of type 'long int', but argument has type 'size_t'
* llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp:81:46: warning: missing initializer for member '_STARTUPINFOA::lpDesktop' [-Wmissing-field-initializers]  (emitted for all struct field except first)
---
 examples/cvector-generator/mean.hpp                        | 2 +-
 examples/cvector-generator/pca.hpp                         | 2 +-
 examples/export-lora/export-lora.cpp                       | 6 +++---
 ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 3 ++-
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/examples/cvector-generator/mean.hpp b/examples/cvector-generator/mean.hpp
index 16be5ce3e..4eeac1eeb 100644
--- a/examples/cvector-generator/mean.hpp
+++ b/examples/cvector-generator/mean.hpp
@@ -15,7 +15,7 @@ static void run(
     for (size_t il = 0; il < v_input.size(); ++il) {
         // prepare output vector
         struct ggml_tensor * ctrl_out = v_output[il];
-        ggml_format_name(ctrl_out, "direction.%ld", il+1);
+        ggml_format_name(ctrl_out, "direction.%zu", il+1);
 
         // calculate mean vector
         struct ggml_tensor * t_layer = v_input[il];
diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp
index f6e307fbc..e88bbdde9 100644
--- a/examples/cvector-generator/pca.hpp
+++ b/examples/cvector-generator/pca.hpp
@@ -302,7 +302,7 @@ static void run_pca(
 
         // prepare output vector
         struct ggml_tensor * ctrl_out = v_output[il];
-        ggml_format_name(ctrl_out, "direction.%ld", il+1);
+        ggml_format_name(ctrl_out, "direction.%zu", il+1);
 
         // run power_iteration
         params.i_layer = il;
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 67662313d..058b5cc86 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -265,8 +265,8 @@ struct lora_merge_ctx {
             fout.write((const char *)data.data(), data.size());
         }
 
-        printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged);
-        printf("%s : wrote %ld tensors to output file\n", __func__, trans.size());
+        printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged);
+        printf("%s : wrote %zu tensors to output file\n", __func__, trans.size());
     }
 
     void copy_tensor(struct ggml_tensor * base) {
@@ -352,7 +352,7 @@ struct lora_merge_ctx {
                 const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
                 delta = ggml_scale(ctx0, delta, scale);
                 cur = ggml_add(ctx0, delta, cur);
-                printf("%s :   + merging from adapter[%ld] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
+                printf("%s :   + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
                 printf("%s :     input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
             }
             cur = ggml_cast(ctx0, cur, out->type);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 7a0d7285d..8111c0638 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -78,7 +78,8 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
     }
 
     PROCESS_INFORMATION pi;
-    STARTUPINFOA si = { sizeof(STARTUPINFOA) };
+    STARTUPINFOA si = {};
+    si.cb = sizeof(STARTUPINFOA);
     si.dwFlags = STARTF_USESTDHANDLES;
     si.hStdOutput = stdout_write;
     si.hStdError = stderr_write;

From d79d8f39b4da6deca4aea8bf130c6034c482b320 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Thu, 26 Dec 2024 10:54:44 -0500
Subject: [PATCH 088/376] vulkan: multi-row k quants (#10846)

* multi row k quant shaders!

* better row selection

* more row choices

* readjust row selection

* rm_kq=2 by default
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  81 ++++---
 .../vulkan-shaders/mul_mat_vec_q2_k.comp      | 126 ++++++----
 .../vulkan-shaders/mul_mat_vec_q3_k.comp      | 102 ++++----
 .../vulkan-shaders/mul_mat_vec_q4_k.comp      | 174 ++++++++------
 .../vulkan-shaders/mul_mat_vec_q5_k.comp      | 226 ++++++++++--------
 .../vulkan-shaders/mul_mat_vec_q6_k.comp      | 130 +++++-----
 6 files changed, 472 insertions(+), 367 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 323ce7cf3..c0a43631c 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1855,53 +1855,58 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     // mul mat vec
 
-    // AMD GCN and Intel graphics cards perform best when the number of rows per shader is doubled
-    uint32_t rm = 1;
-    if ((device->vendor_id == VK_VENDOR_ID_AMD && device->subgroup_min_size == 64 && device->subgroup_max_size == 64) || device->vendor_id == VK_VENDOR_ID_INTEL)
-        rm = 2;
+    // the number of rows computed per shader depends on GPU model and quant
+    uint32_t rm_stdq = 1;
+    uint32_t rm_kq = 2;
+    if (device->vendor_id == VK_VENDOR_ID_AMD) {
+        if (device->subgroup_min_size == 64 && device->subgroup_max_size == 64) { // GCN
+            rm_stdq = 2;
+            rm_kq = 4;
+        }
+    } else if (device->vendor_id == VK_VENDOR_ID_INTEL)
+        rm_stdq = 2;
 
-    // computing additional rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0.
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32",  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
index 1a5350d99..138ad0184 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -6,21 +6,15 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
 
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
-
-    if (row >= p.stride_d) {
-        return;
-    }
+shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
 
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
     const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
 
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
@@ -38,15 +32,15 @@ void main() {
     const uint s_offset = 8*v_im;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
+    FLOAT_TYPE temp[NUM_ROWS];
+
+    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+        temp[i] = FLOAT_TYPE(0);
+    }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y_idx = i * QUANT_K + y_offset;
 
-        f16vec2 d = data_a[ib0 + i].d;
-        const FLOAT_TYPE dall = d.x;
-        const FLOAT_TYPE dmin = d.y;
-
         B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0];
         B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8];
         B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16];
@@ -56,58 +50,84 @@ void main() {
         B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48];
         B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56];
 
-        uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0];
-        uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1];
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+            f16vec2 d = data_a[ib0 + i].d;
+            const FLOAT_TYPE dall = d.x;
+            const FLOAT_TYPE dmin = d.y;
 
-        uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F;
-        uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F;
-        uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F;
-        uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F;
+            uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0];
+            uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1];
 
-        uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32));
-        uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32));
-        uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32));
-        uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32));
+            uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F;
+            uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F;
+            uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F;
+            uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F;
 
-        uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0];
-        uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8];
-        uvec2 qs0 =  uvec2(unpack8(qs0_u16));
-        uvec2 qs16 = uvec2(unpack8(qs16_u16));
+            uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32));
+            uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32));
+            uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32));
+            uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32));
 
-        FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
-        FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
-        [[unroll]] for (int l = 0; l < 2; ++l) {
-            sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),
-                   fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),
-                   fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),
-                   fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),
-                   fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),
-                   fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),
-                   fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),
-                   fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));
-            sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),
-                   fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),
-                   fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),
-                   fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),
-                   fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),
-                   fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),
-                   fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),
-                   fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));
+            uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0];
+            uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8];
+            uvec2 qs0 =  uvec2(unpack8(qs0_u16));
+            uvec2 qs16 = uvec2(unpack8(qs16_u16));
+
+            FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+            FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),
+                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),
+                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),
+                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),
+                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),
+                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),
+                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),
+                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));
+                sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),
+                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),
+                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),
+                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),
+                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),
+                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),
+                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),
+                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));
+            }
+            temp[n] = fma(dall, sum1, fma(-dmin, sum2, temp[n]));
         }
-        temp = fma(dall, sum1, fma(-dmin, sum2, temp));
     }
 
-    tmp[gl_LocalInvocationID.x] = temp;
-
     // sum up partial sums and write back result
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        tmpsh[n][tid] = temp[n];
+    }
     barrier();
-    [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
-            tmp[tid] += tmp[tid + s];
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[n][tid] += tmpsh[n][tid + s];
+            }
         }
         barrier();
     }
     if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
+        }
+    }
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
index b19c38111..82ec42d25 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -6,21 +6,15 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
 
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
-
-    if (row >= p.stride_d) {
-        return;
-    }
+shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
 
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
     const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
 
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
@@ -35,19 +29,21 @@ void main() {
 
     const uint8_t m = uint8_t(1 << (4 * v_im));
 
-    const uint l0 = 2*v_in;                                // 0...15
+    const uint l0 = 2*v_in;                                 // 0...15
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
+    FLOAT_TYPE temp[NUM_ROWS];
+
+    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+        temp[i] = FLOAT_TYPE(0);
+    }
 
     const uint s_shift = 4 * v_im;
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y_idx = i * QUANT_K + y_offset;
 
-        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
         B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0];
         B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8];
         B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16];
@@ -57,44 +53,68 @@ void main() {
         B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48];
         B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56];
 
-        uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0];
-        uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1];
-        uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2];
-        uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3];
-        uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4];
-        uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5];
-        u8vec2 s0 = unpack8(s0_16);
-        u8vec2 s2 = unpack8(s2_16);
-        u8vec2 s4 = unpack8(s4_16);
-        u8vec2 s6 = unpack8(s6_16);
-        u8vec2 s8 = unpack8(s8_16);
-        u8vec2 s10 = unpack8(s10_16);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+            const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
 
-        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-        [[unroll]] for (int l = 0; l < 2; ++l) {
-            sum = fma(FLOAT_TYPE(b0[l])   * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b32[l])  * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b64[l])  * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b96[l])  * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b16[l])  * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b48[l])  * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b80[l])  * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
-                  fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
+            uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0];
+            uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1];
+            uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2];
+            uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3];
+            uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4];
+            uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5];
+            u8vec2 s0 = unpack8(s0_16);
+            u8vec2 s2 = unpack8(s2_16);
+            u8vec2 s4 = unpack8(s4_16);
+            u8vec2 s6 = unpack8(s6_16);
+            u8vec2 s8 = unpack8(s8_16);
+            u8vec2 s10 = unpack8(s10_16);
+
+            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sum = fma(FLOAT_TYPE(b0[l])   * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b32[l])  * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b64[l])  * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b96[l])  * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b16[l])  * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b48[l])  * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b80[l])  * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
+                      fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
+            }
+            temp[n] = fma(d, sum, temp[n]);
         }
-        temp = fma(d, sum, temp);
     }
 
-    tmp[gl_LocalInvocationID.x] = temp;
-
     // sum up partial sums and write back result
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        tmpsh[n][tid] = temp[n];
+    }
     barrier();
-    [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
-            tmp[tid] += tmp[tid + s];
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[n][tid] += tmpsh[n][tid + s];
+            }
         }
         barrier();
     }
     if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
+        }
+    }
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
index b86d28589..677c207a8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -7,21 +7,15 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
 
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
-
-    if (row >= p.stride_d) {
-        return;
-    }
+shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
 
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
     const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
 
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
@@ -31,8 +25,8 @@ void main() {
 
     const uint step = 4;
 
-    const uint il = itid/step;                               // 0...3
-    const uint ir = itid - step*il;                          // 0...7 or 0...3
+    const uint il = itid/step;                      // 0...3
+    const uint ir = itid - step*il;                 // 0...7 or 0...3
     const uint n =  4;
 
     const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
@@ -42,90 +36,116 @@ void main() {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
+    FLOAT_TYPE temp[NUM_ROWS];
+
+    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+        temp[i] = FLOAT_TYPE(0);
+    }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y1_idx = i * QUANT_K + y_offset;
         const uint y2_idx = y1_idx + 128;
 
-        f16vec2 d = data_a[ib0 + i].d;
-        const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
-
-        uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
-        uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
-        uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
-        uvec4 scale0 = uvec4(unpack8(scale0_u32));
-        uvec4 scale4 = uvec4(unpack8(scale4_u32));
-        uvec4 scale8 = uvec4(unpack8(scale8_u32));
-
-        const uint32_t sc0 = (  scale0.x       & 0x3f);
-        const uint32_t sc1 = (  scale0.y       & 0x3f);
-        const uint32_t sc2 = (  scale4.x       & 0x3f);
-        const uint32_t sc3 = (  scale4.y       & 0x3f);
-        const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
-        const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
-        const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
-        const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
-
-        uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4];
-        uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16];
-
-        uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F;
-        uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F;
-        uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F;
-        uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F;
-
-        uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4));
-        uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4));
-        uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4));
-        uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4));
-
-        const uint32_t q4_0  = qs0_lo4.x;
-        const uint32_t q4_1  = qs0_lo4.y;
-        const uint32_t q4_2  = qs0_lo4.z;
-        const uint32_t q4_3  = qs0_lo4.w;
-        const uint32_t q4_4  = qs0_hi4.x;
-        const uint32_t q4_5  = qs0_hi4.y;
-        const uint32_t q4_6  = qs0_hi4.z;
-        const uint32_t q4_7  = qs0_hi4.w;
-        const uint32_t q4_8  = qs64_lo4.x;
-        const uint32_t q4_9  = qs64_lo4.y;
-        const uint32_t q4_10 = qs64_lo4.z;
-        const uint32_t q4_11 = qs64_lo4.w;
-        const uint32_t q4_12 = qs64_hi4.x;
-        const uint32_t q4_13 = qs64_hi4.y;
-        const uint32_t q4_14 = qs64_hi4.z;
-        const uint32_t q4_15 = qs64_hi4.w;
-
         B_TYPE_VEC4 by10 =  data_b_v4[(b_offset + y1_idx) / 4];
         B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8];
         B_TYPE_VEC4 by20 =  data_b_v4[(b_offset + y2_idx) / 4];
         B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8];
 
-        const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
-        const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
-        const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
-        const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
-        const FLOAT_TYPE smin =
-            fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
-            fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
-            fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
-            fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
-        temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp));
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+            f16vec2 d = data_a[ib0 + i].d;
+            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
+
+            uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
+            uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
+            uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
+            uvec4 scale0 = uvec4(unpack8(scale0_u32));
+            uvec4 scale4 = uvec4(unpack8(scale4_u32));
+            uvec4 scale8 = uvec4(unpack8(scale8_u32));
+
+            const uint32_t sc0 = (  scale0.x       & 0x3f);
+            const uint32_t sc1 = (  scale0.y       & 0x3f);
+            const uint32_t sc2 = (  scale4.x       & 0x3f);
+            const uint32_t sc3 = (  scale4.y       & 0x3f);
+            const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
+            const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
+            const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
+            const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
+
+            uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4];
+            uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16];
+
+            uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F;
+            uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F;
+            uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F;
+            uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F;
+
+            uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4));
+            uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4));
+            uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4));
+            uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4));
+
+            const uint32_t q4_0  = qs0_lo4.x;
+            const uint32_t q4_1  = qs0_lo4.y;
+            const uint32_t q4_2  = qs0_lo4.z;
+            const uint32_t q4_3  = qs0_lo4.w;
+            const uint32_t q4_4  = qs0_hi4.x;
+            const uint32_t q4_5  = qs0_hi4.y;
+            const uint32_t q4_6  = qs0_hi4.z;
+            const uint32_t q4_7  = qs0_hi4.w;
+            const uint32_t q4_8  = qs64_lo4.x;
+            const uint32_t q4_9  = qs64_lo4.y;
+            const uint32_t q4_10 = qs64_lo4.z;
+            const uint32_t q4_11 = qs64_lo4.w;
+            const uint32_t q4_12 = qs64_hi4.x;
+            const uint32_t q4_13 = qs64_hi4.y;
+            const uint32_t q4_14 = qs64_hi4.z;
+            const uint32_t q4_15 = qs64_hi4.w;
+
+            const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
+            const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
+            const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
+            const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
+            const FLOAT_TYPE smin =
+                fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
+                fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
+                fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
+                fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
+            temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n]));
+        }
     }
 
-    tmp[gl_LocalInvocationID.x] = temp;
-
     // sum up partial sums and write back result
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        tmpsh[n][tid] = temp[n];
+    }
     barrier();
-    [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
-            tmp[tid] += tmp[tid + s];
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[n][tid] += tmpsh[n][tid + s];
+            }
         }
         barrier();
     }
     if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
+        }
+    }
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
index fd243cf91..ed3c25d89 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -7,21 +7,15 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
 
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
-
-    if (row >= p.stride_d) {
-        return;
-    }
+shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
 
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
     const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
 
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
@@ -39,74 +33,16 @@ void main() {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
+    FLOAT_TYPE temp[NUM_ROWS];
+
+    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+        temp[i] = FLOAT_TYPE(0);
+    }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y1_idx = i * QUANT_K + y_offset;
         const uint y2_idx = y1_idx + 128;
 
-        f16vec2 d = data_a[ib0 + i].d;
-        const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
-
-        uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
-        uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
-        uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
-        uvec4 scale0 = uvec4(unpack8(scale0_u32));
-        uvec4 scale4 = uvec4(unpack8(scale4_u32));
-        uvec4 scale8 = uvec4(unpack8(scale8_u32));
-
-        const uint32_t sc0 = (  scale0.x       & 0x3f);
-        const uint32_t sc1 = (  scale0.y       & 0x3f);
-        const uint32_t sc2 = (  scale4.x       & 0x3f);
-        const uint32_t sc3 = (  scale4.y       & 0x3f);
-        const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
-        const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
-        const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
-        const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
-
-        uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
-        uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16);
-
-        uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F;
-        uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F;
-        uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F;
-        uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F;
-
-        uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8]));
-
-        uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4;
-        uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3;
-        uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0;
-        uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1;
-
-        qs0_16_u32_lo4 += qs0_16_lo4_offset16;
-        qs0_16_u32_hi4 += qs0_16_hi4_offset16;
-        qs64_80_u32_lo4 += qs64_80_lo4_offset16;
-        qs64_80_u32_hi4 += qs64_80_hi4_offset16;
-
-        uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4));
-        uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4));
-        uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4));
-        uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4));
-
-        const uint32_t q4_0  = qs0_16_lo4.x;
-        const uint32_t q4_1  = qs0_16_lo4.y;
-        const uint32_t q4_2  = qs0_16_lo4.z;
-        const uint32_t q4_3  = qs0_16_lo4.w;
-        const uint32_t q4_4  = qs0_16_hi4.x;
-        const uint32_t q4_5  = qs0_16_hi4.y;
-        const uint32_t q4_6  = qs0_16_hi4.z;
-        const uint32_t q4_7  = qs0_16_hi4.w;
-        const uint32_t q4_8  = qs64_80_lo4.x;
-        const uint32_t q4_9  = qs64_80_lo4.y;
-        const uint32_t q4_10 = qs64_80_lo4.z;
-        const uint32_t q4_11 = qs64_80_lo4.w;
-        const uint32_t q4_12 = qs64_80_hi4.x;
-        const uint32_t q4_13 = qs64_80_hi4.y;
-        const uint32_t q4_14 = qs64_80_hi4.z;
-        const uint32_t q4_15 = qs64_80_hi4.w;
-
         B_TYPE_VEC2 by10 =  data_b_v2[(b_offset + y1_idx) / 2];
         B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8];
         B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16];
@@ -116,45 +52,129 @@ void main() {
         B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16];
         B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24];
 
-        const FLOAT_TYPE sx =
-          fma(FLOAT_TYPE(by10.x), q4_0,
-          fma(FLOAT_TYPE(by10.y), q4_1,
-          fma(FLOAT_TYPE(by116.x), q4_2,
-             FLOAT_TYPE(by116.y) * q4_3)));
-        const FLOAT_TYPE sy =
-          fma(FLOAT_TYPE(by132.x), q4_4,
-          fma(FLOAT_TYPE(by132.y), q4_5,
-          fma(FLOAT_TYPE(by148.x), q4_6,
-             FLOAT_TYPE(by148.y) * q4_7)));
-        const FLOAT_TYPE sz =
-          fma(FLOAT_TYPE(by20.x), q4_8,
-          fma(FLOAT_TYPE(by20.y), q4_9,
-          fma(FLOAT_TYPE(by216.x), q4_10,
-             FLOAT_TYPE(by216.y) * q4_11)));
-        const FLOAT_TYPE sw =
-          fma(FLOAT_TYPE(by232.x), q4_12,
-          fma(FLOAT_TYPE(by232.y), q4_13,
-          fma(FLOAT_TYPE(by248.x), q4_14,
-             FLOAT_TYPE(by248.y) * q4_15)));
-        const FLOAT_TYPE smin =
-          fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
-          fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
-          fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
-              (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
-        temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp));
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+            f16vec2 d = data_a[ib0 + i].d;
+            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
+
+            uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
+            uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
+            uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
+            uvec4 scale0 = uvec4(unpack8(scale0_u32));
+            uvec4 scale4 = uvec4(unpack8(scale4_u32));
+            uvec4 scale8 = uvec4(unpack8(scale8_u32));
+
+            const uint32_t sc0 = (  scale0.x       & 0x3f);
+            const uint32_t sc1 = (  scale0.y       & 0x3f);
+            const uint32_t sc2 = (  scale4.x       & 0x3f);
+            const uint32_t sc3 = (  scale4.y       & 0x3f);
+            const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
+            const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
+            const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
+            const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
+
+            uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
+            uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16);
+
+            uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F;
+            uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F;
+            uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F;
+            uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F;
+
+            uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8]));
+
+            uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4;
+            uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3;
+            uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0;
+            uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1;
+
+            qs0_16_u32_lo4 += qs0_16_lo4_offset16;
+            qs0_16_u32_hi4 += qs0_16_hi4_offset16;
+            qs64_80_u32_lo4 += qs64_80_lo4_offset16;
+            qs64_80_u32_hi4 += qs64_80_hi4_offset16;
+
+            uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4));
+            uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4));
+            uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4));
+            uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4));
+
+            const uint32_t q4_0  = qs0_16_lo4.x;
+            const uint32_t q4_1  = qs0_16_lo4.y;
+            const uint32_t q4_2  = qs0_16_lo4.z;
+            const uint32_t q4_3  = qs0_16_lo4.w;
+            const uint32_t q4_4  = qs0_16_hi4.x;
+            const uint32_t q4_5  = qs0_16_hi4.y;
+            const uint32_t q4_6  = qs0_16_hi4.z;
+            const uint32_t q4_7  = qs0_16_hi4.w;
+            const uint32_t q4_8  = qs64_80_lo4.x;
+            const uint32_t q4_9  = qs64_80_lo4.y;
+            const uint32_t q4_10 = qs64_80_lo4.z;
+            const uint32_t q4_11 = qs64_80_lo4.w;
+            const uint32_t q4_12 = qs64_80_hi4.x;
+            const uint32_t q4_13 = qs64_80_hi4.y;
+            const uint32_t q4_14 = qs64_80_hi4.z;
+            const uint32_t q4_15 = qs64_80_hi4.w;
+
+            const FLOAT_TYPE sx =
+              fma(FLOAT_TYPE(by10.x), q4_0,
+              fma(FLOAT_TYPE(by10.y), q4_1,
+              fma(FLOAT_TYPE(by116.x), q4_2,
+                 FLOAT_TYPE(by116.y) * q4_3)));
+            const FLOAT_TYPE sy =
+              fma(FLOAT_TYPE(by132.x), q4_4,
+              fma(FLOAT_TYPE(by132.y), q4_5,
+              fma(FLOAT_TYPE(by148.x), q4_6,
+                 FLOAT_TYPE(by148.y) * q4_7)));
+            const FLOAT_TYPE sz =
+              fma(FLOAT_TYPE(by20.x), q4_8,
+              fma(FLOAT_TYPE(by20.y), q4_9,
+              fma(FLOAT_TYPE(by216.x), q4_10,
+                 FLOAT_TYPE(by216.y) * q4_11)));
+            const FLOAT_TYPE sw =
+              fma(FLOAT_TYPE(by232.x), q4_12,
+              fma(FLOAT_TYPE(by232.y), q4_13,
+              fma(FLOAT_TYPE(by248.x), q4_14,
+                 FLOAT_TYPE(by248.y) * q4_15)));
+            const FLOAT_TYPE smin =
+              fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
+              fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
+              fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
+                  (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
+            temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n]));
+        }
     }
 
-    tmp[gl_LocalInvocationID.x] = temp;
-
     // sum up partial sums and write back result
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        tmpsh[n][tid] = temp[n];
+    }
     barrier();
-    [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
-            tmp[tid] += tmp[tid + s];
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[n][tid] += tmpsh[n][tid + s];
+            }
         }
         barrier();
     }
     if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
+        }
+    }
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
index 760aff854..fab4ff5ff 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -7,21 +7,15 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
 
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
-
-    if (row >= p.stride_d) {
-        return;
-    }
+shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
 
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
     const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
 
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
@@ -42,69 +36,95 @@ void main() {
     const uint s_offset  =  8*v_im + is;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
+    FLOAT_TYPE temp[NUM_ROWS];
+
+    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+        temp[i] = FLOAT_TYPE(0);
+    }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y_idx   = i * QUANT_K + y_offset;
-
-        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
-        FLOAT_TYPE scales[4];
-        scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]);
-        scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]);
-        scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]);
-        scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]);
-
-        uint32_t ql0_u32 =  uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16);
-        uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16);
-
-        uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F;
-        uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F;
-        uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F;
-        uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F;
-
-        uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16);
-        uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4;
-        uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2;
-        uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0;
-        uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2;
-
-        uint32_t q0_u32 = ql0_u32_lo4  | qh0_u32;
-        uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32;
-        uint32_t q2_u32 = ql0_u32_hi4  | qh4_u32;
-        uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32;
-
-        uvec4 q0 = uvec4(unpack8(q0_u32));
-        uvec4 q1 = uvec4(unpack8(q1_u32));
-        uvec4 q2 = uvec4(unpack8(q2_u32));
-        uvec4 q3 = uvec4(unpack8(q3_u32));
+        const uint y_idx = i * QUANT_K + y_offset;
 
         B_TYPE_VEC4 by0  = data_b_v4[(b_offset + y_idx) / 4];
         B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8];
         B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16];
         B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24];
 
-        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-        [[unroll]] for (int l = 0; l < 4; ++l) {
-            sum = fma(FLOAT_TYPE(by0[l])  * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
-                  fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
-                  fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
-                  fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+            const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+            FLOAT_TYPE scales[4];
+            scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]);
+            scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]);
+            scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]);
+            scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]);
+
+            uint32_t ql0_u32 =  uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16);
+            uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16);
+
+            uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F;
+            uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F;
+            uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F;
+            uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F;
+
+            uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16);
+            uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4;
+            uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2;
+            uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0;
+            uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2;
+
+            uint32_t q0_u32 = ql0_u32_lo4  | qh0_u32;
+            uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32;
+            uint32_t q2_u32 = ql0_u32_hi4  | qh4_u32;
+            uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32;
+
+            uvec4 q0 = uvec4(unpack8(q0_u32));
+            uvec4 q1 = uvec4(unpack8(q1_u32));
+            uvec4 q2 = uvec4(unpack8(q2_u32));
+            uvec4 q3 = uvec4(unpack8(q3_u32));
+
+            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 4; ++l) {
+                sum = fma(FLOAT_TYPE(by0[l])  * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
+                      fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
+                      fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
+                      fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
+            }
+            temp[n] += sum * d;
         }
-        temp += sum * d;
     }
 
-    tmp[gl_LocalInvocationID.x] = temp;
     // sum up partial sums and write back result
-
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        tmpsh[n][tid] = temp[n];
+    }
     barrier();
-    [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
         if (tid < s) {
-            tmp[tid] += tmp[tid + s];
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[n][tid] += tmpsh[n][tid + s];
+            }
         }
         barrier();
     }
     if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
+        }
+    }
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
     }
 }

From 16cdce7b68218959e0658e2f95b4572573d5008e Mon Sep 17 00:00:00 2001
From: Alexey Parfenov <zxed@alkatrazstudio.net>
Date: Sat, 28 Dec 2024 15:08:54 +0000
Subject: [PATCH 089/376] server : fix token duplication when streaming with
 stop strings (#10997)

---
 examples/server/server.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 30ff3b149..3558ddb7c 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1856,6 +1856,8 @@ struct server_context {
                 result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                 slot.n_sent_text += result.text_to_send.size();
                 // add the token to slot queue and cache
+            } else {
+                result.text_to_send = "";
             }
 
             slot.add_token(result);

From f865ea149d71ef883e3780fced8a20a1464eccf4 Mon Sep 17 00:00:00 2001
From: Isaac McFadyen <isaac@imcf.me>
Date: Sat, 28 Dec 2024 10:09:19 -0500
Subject: [PATCH 090/376] server: added more docs for response_fields field
 (#10995)

---
 examples/server/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index c7d91be99..07436057a 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -450,7 +450,7 @@ These words will not be included in the completion, so make sure to add them to
 
 `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.
 
-`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error.
+`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name.
 
 **Response format**
 

From fdd21889123bec62b1db3b2fc22b5a4abab32174 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 29 Dec 2024 02:35:11 -0600
Subject: [PATCH 091/376] vulkan: Use push constant offset to handle misaligned
 descriptors (#10987)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 74 ++++++++++++++++---
 ggml/src/ggml-vulkan/vulkan-shaders/acc.comp  |  4 +-
 ggml/src/ggml-vulkan/vulkan-shaders/add.comp  |  2 +-
 .../src/ggml-vulkan/vulkan-shaders/clamp.comp |  4 +-
 .../ggml-vulkan/vulkan-shaders/concat.comp    |  6 +-
 .../vulkan-shaders/contig_copy.comp           |  8 +-
 ggml/src/ggml-vulkan/vulkan-shaders/copy.comp |  4 +-
 ggml/src/ggml-vulkan/vulkan-shaders/cos.comp  |  4 +-
 ggml/src/ggml-vulkan/vulkan-shaders/div.comp  |  2 +-
 .../vulkan-shaders/generic_binary_head.comp   |  6 +-
 .../vulkan-shaders/generic_unary_head.comp    |  5 +-
 .../ggml-vulkan/vulkan-shaders/get_rows.comp  |  6 +-
 ggml/src/ggml-vulkan/vulkan-shaders/mul.comp  |  2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/pad.comp  |  2 +-
 .../ggml-vulkan/vulkan-shaders/repeat.comp    |  2 +-
 .../src/ggml-vulkan/vulkan-shaders/scale.comp |  2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/sin.comp  |  4 +-
 .../ggml-vulkan/vulkan-shaders/square.comp    |  4 +-
 .../ggml-vulkan/vulkan-shaders/upscale.comp   |  4 +-
 19 files changed, 103 insertions(+), 42 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c0a43631c..6dfc60c9b 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -411,7 +411,7 @@ struct vk_op_unary_push_constants {
     uint32_t ne;
     uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
-    uint32_t d_offset;
+    uint32_t misalign_offsets;
     float param1; float param2;
     uint32_t ne0_012mp; uint32_t ne0_012L;
     uint32_t ne0_01mp;  uint32_t ne0_01L;
@@ -459,7 +459,7 @@ struct vk_op_binary_push_constants {
     uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
     uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
-    uint32_t d_offset;
+    uint32_t misalign_offsets;
     float param1; float param2; int32_t param3;
 };
 
@@ -546,7 +546,7 @@ struct vk_staging_memcpy {
 };
 
 struct vk_op_upscale_push_constants {
-    uint32_t ne; uint32_t d_offset;
+    uint32_t ne; uint32_t a_offset; uint32_t d_offset;
     uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13;
     float sf0; float sf1; float sf2; float sf3;
@@ -5076,6 +5076,57 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     }
 }
 
+static uint32_t get_misalign_bytes(ggml_backend_vk_context * ctx, const ggml_tensor * t)
+{
+    return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
+}
+
+template <typename T> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    GGML_UNUSED(p);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src2);
+    GGML_UNUSED(dst);
+    static_assert(!std::is_const<T>::value, "unexpected type");
+    GGML_ASSERT(!src0 || get_misalign_bytes(ctx, src0) == 0);
+    GGML_ASSERT(!src1 || get_misalign_bytes(ctx, src1) == 0);
+    GGML_ASSERT(!src2 || get_misalign_bytes(ctx, src2) == 0);
+    GGML_ASSERT(!dst  || get_misalign_bytes(ctx, dst) == 0);
+}
+
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_unary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    p.misalign_offsets = (a_offset << 16) | d_offset;
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src2);
+}
+
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && b_offset == 0 && d_offset == 0));
+
+    p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
+
+    GGML_UNUSED(src2);
+}
+
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    p.a_offset = a_offset;
+    p.d_offset = d_offset;
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src2);
+}
+
 template<typename PC>
 static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, PC&& pc, bool dryrun = false) {
     VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
@@ -5179,8 +5230,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     }
 
     GGML_ASSERT(d_D != nullptr);
-    uint64_t d_buf_offset = ((vk_tensor_offset(dst) + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    GGML_ASSERT(d_buf_offset == vk_tensor_offset(dst) || op == GGML_OP_CPY);  // NOLINT
+    uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
     if(!src0_uma) {
         d_X = src0_buf_ctx->dev_buffer;
         x_buf_offset = vk_tensor_offset(src0) + src0->view_offs;
@@ -5196,6 +5246,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         z_buf_offset = vk_tensor_offset(src2) + src2->view_offs;
         GGML_ASSERT(d_Z != nullptr);
     }
+    // Compute misalignment offset for descriptors and store it in in push constants, then align the descriptor offsets.
+    init_pushconst_tensor_offsets(ctx, pc, src0, src1, src2, dst);
+    x_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
+    y_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
+    z_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
+    d_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
 
     if (op_supports_incontiguous) {
         x_sz = ggml_nbytes(src0);
@@ -5383,7 +5439,6 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const
     const uint32_t src0_type_size = ggml_type_size(src0->type);
     const uint32_t src1_type_size = ggml_type_size(src1->type);
     const uint32_t dst_type_size = ggml_type_size(dst->type);
-    const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
 
     int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
     int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
@@ -5395,7 +5450,7 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const
         (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)src0->nb[3] / src0_type_size,
         (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
         (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t) dst->nb[3] /  dst_type_size,
-        d_offset,
+        0,
         0.0f, 0.0f, offset,
     }, dryrun);
 }
@@ -5599,7 +5654,7 @@ static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, c
     const float sf3 = (float)dst->ne[3] / src0->ne[3];
 
     ggml_vk_op_f32<vk_op_upscale_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UPSCALE, {
-        (uint32_t)ggml_nelements(dst), 0,
+        (uint32_t)ggml_nelements(dst), 0, 0,
         (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
         (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], (uint32_t)dst->ne[2],(uint32_t)dst->ne[3],
         sf0, sf1, sf2, sf3,
@@ -5709,13 +5764,12 @@ static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, co
 static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
     const uint32_t src0_type_size = ggml_type_size(src0->type);
     const uint32_t dst_type_size = ggml_type_size(dst->type);
-    const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
 
     ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
         (uint32_t)ggml_nelements(src0),
         (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
         (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        d_offset,
+        0,
         0.0f, 0.0f,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     }, dryrun);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp
index 4f5a04e71..d896f1ef0 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp
@@ -21,9 +21,9 @@ void main() {
     get_indices(idx, i00, i01, i02, i03);
 
     if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) {
-        data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11]));
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + ox + oy * p.ne10 + oz * p.ne10 * p.ne11]));
     } else {
-        data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]));
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]));
     }
 }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
index da61b76df..2b4085c4f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
@@ -22,7 +22,7 @@ void main() {
         uint i00, i01, i02, i03;
         get_indices(idx, i00, i01, i02, i03);
 
-        data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)]));
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]));
 
         idx += num_threads;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
index ae8fa8753..1e5cb8dae 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
@@ -12,6 +12,6 @@ void main() {
         return;
     }
 
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp
index 683f9ac3c..9ee2f1fae 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp
@@ -30,12 +30,12 @@ void main() {
     const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
 
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]);
+    data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : data_b[get_boffset() + src1_idx]);
 #else
     if (is_src0) {
-        data_d[p.d_offset + dst_idx] = data_a[src0_idx];
+        data_d[get_doffset() + dst_idx] = data_a[get_aoffset() + src0_idx];
     } else {
-        data_d[p.d_offset + dst_idx] = data_b[src1_idx];
+        data_d[get_doffset() + dst_idx] = data_b[get_boffset() + src1_idx];
     }
 #endif
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp
index 9acbdd3d2..dd828c232 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp
@@ -19,9 +19,9 @@ void main() {
     if (idx + (num_iter-1)*num_threads < p.ne) {
         [[unroll]] for (uint i = 0; i < num_iter; ++i) {
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-            data_d[p.d_offset + idx] = D_TYPE(data_a[idx]);
+            data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]);
 #else
-            data_d[p.d_offset + idx] = data_a[idx];
+            data_d[get_doffset() + idx] = data_a[get_aoffset() + idx];
 #endif
             idx += num_threads;
         }
@@ -32,9 +32,9 @@ void main() {
             }
 
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-            data_d[p.d_offset + idx] = D_TYPE(data_a[idx]);
+            data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]);
 #else
-            data_d[p.d_offset + idx] = data_a[idx];
+            data_d[get_doffset() + idx] = data_a[get_aoffset() + idx];
 #endif
             idx += num_threads;
         }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp
index 2775068f9..29c906494 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp
@@ -13,8 +13,8 @@ void main() {
     }
 
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
 #else
-    data_d[p.d_offset + dst_idx(idx)] = data_a[src0_idx(idx)];
+    data_d[get_doffset() + dst_idx(idx)] = data_a[get_aoffset() + src0_idx(idx)];
 #endif
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
index fbd9d272c..0b8d02f58 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
@@ -12,6 +12,6 @@ void main() {
         return;
     }
 
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(cos(val));
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(cos(val));
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp
index e581905b3..9fb69c6c1 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp
@@ -20,7 +20,7 @@ void main() {
         uint i00, i01, i02, i03;
         get_indices(idx, i00, i01, i02, i03);
 
-        data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)]));
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]));
 
         idx += num_threads;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
index a6555fa27..062e2a4cd 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
@@ -7,7 +7,7 @@ layout (push_constant) uniform parameter
     uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
     uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
-    uint d_offset;
+    uint misalign_offsets;
     float param1; float param2; int param3;
 } p;
 
@@ -22,6 +22,10 @@ uint get_idx() {
     return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
 }
 
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; }
+uint get_doffset() { return p.misalign_offsets & 0xFF; }
+
 // mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1
 uint fastmod(uint a, uint b) {
     if ((b & (b-1)) == 0) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
index ab7c9d7eb..68d1bc9f1 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
@@ -6,7 +6,7 @@ layout (push_constant) uniform parameter
     uint ne;
     uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
-    uint d_offset;
+    uint misalign_offsets;
     float param1; float param2;
 
     uint ne0_012mp; uint ne0_012L;
@@ -24,6 +24,9 @@ uint get_idx() {
     return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
 }
 
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_doffset() { return p.misalign_offsets & 0xFFFF; }
+
 // see init_fastdiv_values in ggml-vulkan.cpp
 uint fastdiv(uint n, uint mp, uint L) {
     uint msbs, lsbs;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
index a7b81e52c..e877ed779 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
@@ -15,10 +15,10 @@ void main() {
         return;
     }
 
-    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+    const uint i01 = data_b[get_boffset() + i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
 
-    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
-    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+    const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
 
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
     data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp
index 5ce57cbcf..43de19df8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp
@@ -20,7 +20,7 @@ void main() {
         uint i00, i01, i02, i03;
         get_indices(idx, i00, i01, i02, i03);
 
-        data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)]));
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]));
 
         idx += num_threads;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
index e87d8b18b..450b67fc5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
@@ -24,5 +24,5 @@ void main() {
 
     const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
 
-    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : 0.0f);
+    data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp
index c03f737cc..1568b141d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp
@@ -22,5 +22,5 @@ void main() {
         return;
     }
 
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx_mod(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx_mod(idx)]);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp
index 5cfee8c3b..4663428de 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp
@@ -18,7 +18,7 @@ void main() {
             continue;
         }
 
-        data_d[p.d_offset + idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1));
+        data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) * FLOAT_TYPE(p.param1));
         idx += num_threads;
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
index 67c48fb9a..d7c15a169 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
@@ -12,6 +12,6 @@ void main() {
         return;
     }
 
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(sin(val));
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(sin(val));
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp
index 2ff48ddc5..ef43598ba 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp
@@ -12,6 +12,6 @@ void main() {
         return;
     }
 
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
-    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val * val);
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val * val);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp
index 511a086ea..6f607380d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp
@@ -2,7 +2,7 @@
 
 layout (push_constant) uniform parameter
 {
-    uint ne; uint d_offset;
+    uint ne; uint a_offset; uint d_offset;
     uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13;
     float sf0; float sf1; float sf2; float sf3;
@@ -32,5 +32,5 @@ void main() {
     const uint i02 = uint(i12 / p.sf2);
     const uint i03 = uint(i13 / p.sf3);
 
-    data_d[p.d_offset + idx] = D_TYPE(data_a[i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]);
+    data_d[p.d_offset + idx] = D_TYPE(data_a[p.a_offset + i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]);
 }

From a813badbbdf0d38705f249df7a0c99af5cdee678 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 29 Dec 2024 03:16:34 -0600
Subject: [PATCH 092/376] vulkan: im2col and matmul optimizations for stable
 diffusion (#10942)

* tests: Add im2col perf tests

* vulkan: optimize im2col, more elements per thread

* vulkan: increase small tile size for NV_coopmat2

* vulkan: change im2col to 512 elements per workgroup
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 10 +--
 .../ggml-vulkan/vulkan-shaders/im2col.comp    | 73 +++++++++++++------
 tests/test-backend-ops.cpp                    | 12 +++
 3 files changed, 66 insertions(+), 29 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 6dfc60c9b..8e47e79ae 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1404,10 +1404,10 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // spec constants and tile sizes for non-quant matmul/matmul_id
         l_warptile = { 256, 128, 256, 64 };
         m_warptile = { 256, 128, 128, 64 };
-        s_warptile = { 128,  32,  16, 64 };
+        s_warptile = { 128,  64,  64, 64 };
         l_wg_denoms = {128, 256, 1 };
         m_wg_denoms = {128, 128, 1 };
-        s_wg_denoms = { 32,  16, 1 };
+        s_wg_denoms = { 64,  64, 1 };
 
         // spec constants and tile sizes for quant matmul (non-Qi_K)
         l_warptile_mmq = { 256, 128, 256, 64 };
@@ -2017,11 +2017,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);
     if (device->float_controls_rte_fp16) {
-        ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_rte_len, im2col_f32_f16_rte_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_rte_len, im2col_f32_f16_rte_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);
     } else {
-        ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp
index 966fedf8f..122b1e93f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp
@@ -2,6 +2,7 @@
 
 #extension GL_EXT_shader_16bit_storage : require
 #extension GL_EXT_spirv_intrinsics: enable
+#extension GL_EXT_control_flow_attributes : require
 
 #if RTE16
 spirv_execution_mode(capabilities = [4467], 4462, 16); // RoundingModeRTE, 16 bits
@@ -23,40 +24,64 @@ layout (push_constant) uniform parameter
 
 #include "types.comp"
 
-#define BLOCK_SIZE 256
+layout(constant_id = 0) const uint BLOCK_SIZE = 32;
 
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+const uint NUM_ITER = 512 / BLOCK_SIZE;
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 
 void main() {
-    const uint i = gl_GlobalInvocationID.x;
-    if (i >= p.pelements) {
-        return;
-    }
-
-    const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1);
-    const uint kx = i / ksize;
-    const uint kd = kx * ksize;
-    const uint ky = (i - kd) / p.OW;
-    const uint ix = i % p.OW;
+    const uint gidx = gl_GlobalInvocationID.x;
 
     const uint oh = gl_GlobalInvocationID.y;
     const uint batch = gl_GlobalInvocationID.z / p.IC;
     const uint ic = gl_GlobalInvocationID.z % p.IC;
 
-    const uint iiw = ix * p.s0 + kx * p.d0 - p.p0;
-    const uint iih = oh * p.s1 + ky * p.d1 - p.p1;
-
-    const uint offset_dst =
-        ((batch * p.OH + oh) * p.OW + ix) * p.CHW +
-        (ic * (p.KW * p.KH) + ky * p.KW + kx);
-
-    if (iih < 0 || iih >= p.IH || iiw < 0 || iiw >= p.IW) {
-        data_d[offset_dst] = D_TYPE(0.0f);
-    } else {
-        const uint offset_src = ic * p.offset_delta + batch * p.batch_offset;
-        data_d[offset_dst] = D_TYPE(data_a[offset_src + iih * p.IW + iiw]);
+    A_TYPE values[NUM_ITER];
+    uint offset_dst[NUM_ITER];
+    [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) {
+        values[idx] = A_TYPE(0);
     }
+
+    [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) {
+
+        const uint i = gidx * NUM_ITER + idx;
+
+        const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1);
+        const uint kx = i / ksize;
+        const uint kd = kx * ksize;
+        const uint ky = (i - kd) / p.OW;
+        const uint ix = i % p.OW;
+
+        const uint iiw = ix * p.s0 + kx * p.d0 - p.p0;
+        const uint iih = oh * p.s1 + ky * p.d1 - p.p1;
+
+        offset_dst[idx] =
+            ((batch * p.OH + oh) * p.OW + ix) * p.CHW +
+            (ic * (p.KW * p.KH) + ky * p.KW + kx);
+
+        if (i >= p.pelements) {
+            continue;
+        }
+
+        if (iih < p.IH && iiw < p.IW) {
+            const uint offset_src = ic * p.offset_delta + batch * p.batch_offset;
+            values[idx] = data_a[offset_src + iih * p.IW + iiw];
+        }
+    }
+
+    [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) {
+
+        const uint i = gidx * NUM_ITER + idx;
+
+        if (i >= p.pelements) {
+            continue;
+        }
+
+        data_d[offset_dst[idx]] = D_TYPE(values[idx]);
+    }
+
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index ccdd3fb57..c79acffd2 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3945,6 +3945,18 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         }
     }
 
+    for (int K : {3, 5}) {
+        for (int IC : {256, 2560}) {
+            for (int IW_IH : {32, 64, 256}) {
+                if (IC == 2560 && IW_IH == 256) {
+                    // too big
+                    continue;
+                }
+                test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {IW_IH, IW_IH, IC, 1}, {K, K, IC, 1}, 1, 1, 1, 1, 1, 1, true));
+            }
+        }
+    }
+
     return test_cases;
 }
 

From c250ecb3157f3bae0a45f44c3c953b5414d4c2f7 Mon Sep 17 00:00:00 2001
From: ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
Date: Mon, 30 Dec 2024 20:35:13 +0800
Subject: [PATCH 093/376] android : fix llama_batch free (#11014)

---
 examples/llama.android/llama/src/main/cpp/llama-android.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
index b3858ddfb..66ec2aeeb 100644
--- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -305,7 +305,9 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens,
 extern "C"
 JNIEXPORT void JNICALL
 Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
-    llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
+    //llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+    delete batch;
 }
 
 extern "C"

From 716bd6dec3e044e5c325386b5b0483392b24cefe Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 30 Dec 2024 11:27:11 -0600
Subject: [PATCH 094/376] vulkan: optimize mul_mat for small values of N
 (#10991)

Make the mul_mat_vec shaders support N>1 (as a spec constant, NUM_COLS) where
the batch_strides are overloaded to hold the row strides. Put the loads from the
B matrix in the innermost loop because it should cache better.

Share some code for reducing the result values to memory in mul_mat_vec_base.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  87 +++++++------
 .../vulkan-shaders/mul_mat_vec.comp           | 122 ++++++++----------
 .../vulkan-shaders/mul_mat_vec_base.comp      |  33 +++++
 .../vulkan-shaders/mul_mat_vec_q2_k.comp      |  92 ++++++-------
 .../vulkan-shaders/mul_mat_vec_q3_k.comp      |  75 +++++------
 .../vulkan-shaders/mul_mat_vec_q4_k.comp      |  64 ++++-----
 .../vulkan-shaders/mul_mat_vec_q5_k.comp      | 104 ++++++---------
 .../vulkan-shaders/mul_mat_vec_q6_k.comp      |  58 +++------
 tests/test-backend-ops.cpp                    |   2 +-
 9 files changed, 288 insertions(+), 349 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 8e47e79ae..020e61280 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -145,6 +145,8 @@ class vk_perf_logger;
 #endif
 static void ggml_vk_destroy_buffer(vk_buffer& buf);
 
+static constexpr uint32_t mul_mat_vec_max_cols = 8;
+
 struct vk_device_struct {
     std::mutex mutex;
 
@@ -202,8 +204,8 @@ struct vk_device_struct {
     vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols];
     vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
@@ -1866,33 +1868,35 @@ static void ggml_vk_load_shaders(vk_device& device) {
     } else if (device->vendor_id == VK_VENDOR_ID_INTEL)
         rm_stdq = 2;
 
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32",  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
+    for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
 
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
+    }
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -2892,9 +2896,10 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
     return ctx->device->fp16 ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc;
 }
 
-static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols) {
     VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
     GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
+    GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols);
 
     switch (a_type) {
         case GGML_TYPE_F32:
@@ -2915,7 +2920,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
             return nullptr;
     }
 
-    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type];
+    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type][num_cols-1];
 }
 
 static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) {
@@ -3925,8 +3930,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     const uint64_t ne12 = src1->ne[2];
     const uint64_t ne13 = src1->ne[3];
 
-    GGML_ASSERT(ne11 == 1);
-
     const uint64_t ne20 = dst->ne[0];
     const uint64_t ne21 = dst->ne[1];
     const uint64_t ne22 = dst->ne[2];
@@ -3935,6 +3938,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     const uint64_t r2 = ne12 / ne02;
     const uint64_t r3 = ne13 / ne03;
 
+    // batch_n indicates that we need to compute a few vector results, and this assumes
+    // ne12 and ne13 are 1. It overloads the batch_strides to hold the row strides.
+    GGML_ASSERT(ne11 == 1 || ne12 * ne13 == 1);
+    bool batch_n = ne11 > 1;
+
     ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
@@ -3985,7 +3993,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     } else {
         to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
     }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11);
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
     GGML_ASSERT(dmmv != nullptr);
@@ -4057,8 +4065,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
     }
 
-    uint32_t stride_batch_x = ne00*ne01;
-    uint32_t stride_batch_y = ne10*ne11;
+    // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride
+    uint32_t stride_batch_x = batch_n ? 0 : ne00*ne01;
+    uint32_t stride_batch_y = batch_n ? ne10 : (ne10*ne11);
+    uint32_t stride_batch_d = batch_n ? ne20 : (ne20*ne21);
 
     if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
         stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
@@ -4081,7 +4091,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     // compute
     const vk_mat_vec_push_constants pc = {
         (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
-        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
+        stride_batch_x, stride_batch_y, stride_batch_d,
         (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
     };
     ggml_vk_sync_buffers(subctx);
@@ -4261,7 +4271,10 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, c
     } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 &&
                !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) {
         ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun);
-    } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
+    // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four)
+    // when ne12 and ne13 are one.
+    } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) &&
+               (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
         ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun);
     } else {
         ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
index 187c31916..24875cdcf 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
@@ -9,9 +9,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
 #if !defined(DATA_A_F32) && !defined(DATA_A_F16)
 #define K_PER_ITER 8
 #else
@@ -21,70 +18,70 @@ layout (constant_id = 1) const uint NUM_ROWS = 1;
 
 uint a_offset, b_offset, d_offset, y_offset;
 
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
-void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter)
+void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter)
 {
-    const uint col = i*BLOCK_SIZE + K_PER_ITER*tid;
-    const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
-    const uint iybs = col - col%QUANT_K; // y block start index
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        const uint col = i*BLOCK_SIZE + K_PER_ITER*tid;
+        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
+        const uint iybs = col - col%QUANT_K; // y block start index
 
 #if K_PER_ITER == 8
 #if QUANT_R == 2
-    const B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4];
-    const B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4];
-    const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y);
-    const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w);
+        const B_TYPE_VEC4 bv02 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4];
+        const B_TYPE_VEC4 bv13 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs + y_offset) / 4];
+        const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y);
+        const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w);
 #else
-    const vec4 bv0 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4]);
-    const vec4 bv1 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4 + 1]);
+        const vec4 bv0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]);
+        const vec4 bv1 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4 + 1]);
 #endif
 #else
-    // Check if the second of the pair of elements is OOB, and don't fetch B or
-    // accumulate it. We still fetch a pair of elements for A, which is fine for
-    // quantized formats since they'll be within the same block. We should
-    // probably skip fetching the second element for F16/F32, but as of now we
-    // still do.
-    const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols);
+        // Check if the second of the pair of elements is OOB, and don't fetch B or
+        // accumulate it. We still fetch a pair of elements for A, which is fine for
+        // quantized formats since they'll be within the same block. We should
+        // probably skip fetching the second element for F16/F32, but as of now we
+        // still do.
+        const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols);
 
-    FLOAT_TYPE b0 = 0, b1 = 0;
-    b0 = FLOAT_TYPE(data_b[b_offset + iybs + iqs]);
-    if (!OOB) {
-        b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
-    }
+        FLOAT_TYPE b0 = 0, b1 = 0;
+        b0 = FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]);
+        if (!OOB) {
+            b1 = FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset]);
+        }
 #endif
-    uint ibi = first_row*p.ncols;
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib = (ibi + col)/QUANT_K; // block index
-        ibi += p.ncols;
+        uint ibi = first_row*p.ncols;
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint ib = (ibi + col)/QUANT_K; // block index
+            ibi += p.ncols;
 
 #if K_PER_ITER == 8
-        vec4 v = dequantize4(ib, iqs, a_offset);
-        vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset);
+            vec4 v = dequantize4(ib, iqs, a_offset);
+            vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset);
 
-        const vec2 dm = get_dm(ib, a_offset);
-        if (dm.y != 0) { // quant has min component
-            v = v * dm.x + dm.y;
-            v2 = v2 * dm.x + dm.y;
-        }
+            const vec2 dm = get_dm(ib, a_offset);
+            if (dm.y != 0) { // quant has min component
+                v = v * dm.x + dm.y;
+                v2 = v2 * dm.x + dm.y;
+            }
 
-        // matrix multiplication
-        FLOAT_TYPE rowtmp = dot(bv0, v);
-        rowtmp += dot(bv1, v2);
+            // matrix multiplication
+            FLOAT_TYPE rowtmp = dot(bv0, v);
+            rowtmp += dot(bv1, v2);
 
-        if (dm.y == 0)
-            rowtmp *= dm.x;
+            if (dm.y == 0)
+                rowtmp *= dm.x;
 
-        temp[n] += rowtmp;
+            temp[j][n] += rowtmp;
 #else
-        const vec2 v = dequantize(ib, iqs, a_offset);
+            const vec2 v = dequantize(ib, iqs, a_offset);
 
-        // matrix multiplication
-        temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]);
-        if (!OOB) {
-            temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]);
-        }
+            // matrix multiplication
+            temp[j][n] = fma(FLOAT_TYPE(v.x), b0, temp[j][n]);
+            if (!OOB) {
+                temp[j][n] = fma(FLOAT_TYPE(v.y), b1, temp[j][n]);
+            }
 #endif
+        }
     }
 }
 
@@ -96,10 +93,12 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 
     y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE);
@@ -131,24 +130,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
         i++;
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
index 3894fca82..903753c7e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
@@ -83,3 +83,36 @@ void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
             batch_idx * p.batch_stride_d;
 #endif
 }
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+layout (constant_id = 1) const uint NUM_ROWS = 1;
+layout (constant_id = 2) const uint NUM_COLS = 1;
+
+shared FLOAT_TYPE tmpsh[NUM_COLS][NUM_ROWS][BLOCK_SIZE];
+
+void reduce_result(const in FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) {
+    // sum up partial sums and write back result
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            tmpsh[j][n][tid] = temp[j][n];
+        }
+    }
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
+        if (tid < s) {
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+                [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                    tmpsh[j][n][tid] += tmpsh[j][n][tid + s];
+                }
+            }
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]);
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
index 138ad0184..934213446 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -5,11 +5,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -32,24 +27,17 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint s_offset = 8*v_im;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y_idx = i * QUANT_K + y_offset;
 
-        B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0];
-        B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8];
-        B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16];
-        B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24];
-        B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32];
-        B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40];
-        B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48];
-        B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56];
-
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
             f16vec2 d = data_a[ib0 + i].d;
@@ -74,48 +62,42 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             uvec2 qs0 =  uvec2(unpack8(qs0_u16));
             uvec2 qs16 = uvec2(unpack8(qs16_u16));
 
-            FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
-            FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
-            [[unroll]] for (int l = 0; l < 2; ++l) {
-                sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),
-                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),
-                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),
-                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),
-                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),
-                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),
-                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),
-                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));
-                sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),
-                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),
-                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),
-                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),
-                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),
-                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),
-                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),
-                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+                B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0];
+                B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8];
+                B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16];
+                B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24];
+                B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32];
+                B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40];
+                B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48];
+                B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56];
+
+                FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+                FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+                [[unroll]] for (int l = 0; l < 2; ++l) {
+                    sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),
+                           fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),
+                           fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),
+                           fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),
+                           fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),
+                           fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),
+                           fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),
+                           fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));
+                    sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),
+                           fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),
+                           fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),
+                           fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),
+                           fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),
+                           fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),
+                           fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),
+                           fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));
+                }
+                temp[j][n] = fma(dall, sum1, fma(-dmin, sum2, temp[j][n]));
             }
-            temp[n] = fma(dall, sum1, fma(-dmin, sum2, temp[n]));
         }
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
index 82ec42d25..86b0159d9 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -5,11 +5,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -33,10 +28,12 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     const uint s_shift = 4 * v_im;
@@ -44,15 +41,6 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y_idx = i * QUANT_K + y_offset;
 
-        B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0];
-        B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8];
-        B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16];
-        B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24];
-        B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32];
-        B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40];
-        B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48];
-        B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56];
-
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
             const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
@@ -70,39 +58,34 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             u8vec2 s8 = unpack8(s8_16);
             u8vec2 s10 = unpack8(s10_16);
 
-            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-            [[unroll]] for (int l = 0; l < 2; ++l) {
-                sum = fma(FLOAT_TYPE(b0[l])   * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b32[l])  * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b64[l])  * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b96[l])  * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b16[l])  * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b48[l])  * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b80[l])  * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
-                      fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+
+                B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0];
+                B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8];
+                B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16];
+                B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24];
+                B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32];
+                B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40];
+                B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48];
+                B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56];
+
+                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+                [[unroll]] for (int l = 0; l < 2; ++l) {
+                    sum = fma(FLOAT_TYPE(b0[l])   * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b32[l])  * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b64[l])  * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b96[l])  * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b16[l])  * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b48[l])  * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b80[l])  * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
+                          fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
+                }
+                temp[j][n] = fma(d, sum, temp[j][n]);
             }
-            temp[n] = fma(d, sum, temp[n]);
         }
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
index 677c207a8..cd1dd8e89 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -6,11 +6,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -36,21 +31,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y1_idx = i * QUANT_K + y_offset;
         const uint y2_idx = y1_idx + 128;
 
-        B_TYPE_VEC4 by10 =  data_b_v4[(b_offset + y1_idx) / 4];
-        B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8];
-        B_TYPE_VEC4 by20 =  data_b_v4[(b_offset + y2_idx) / 4];
-        B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8];
-
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
             f16vec2 d = data_a[ib0 + i].d;
@@ -103,37 +95,27 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             const uint32_t q4_14 = qs64_hi4.z;
             const uint32_t q4_15 = qs64_hi4.w;
 
-            const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
-            const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
-            const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
-            const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
-            const FLOAT_TYPE smin =
-                fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
-                fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
-                fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
-                fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
-            temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n]));
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+                B_TYPE_VEC4 by10 =  data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4];
+                B_TYPE_VEC4 by132 = data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8];
+                B_TYPE_VEC4 by20 =  data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4];
+                B_TYPE_VEC4 by232 = data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8];
+
+                const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
+                const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
+                const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
+                const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
+                const FLOAT_TYPE smin =
+                    fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
+                    fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
+                    fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
+                    fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
+                temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
+            }
         }
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
index ed3c25d89..0a68891c3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -6,11 +6,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -33,25 +28,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y1_idx = i * QUANT_K + y_offset;
         const uint y2_idx = y1_idx + 128;
 
-        B_TYPE_VEC2 by10 =  data_b_v2[(b_offset + y1_idx) / 2];
-        B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8];
-        B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16];
-        B_TYPE_VEC2 by148 = data_b_v2[(b_offset + y1_idx) / 2 + 24];
-        B_TYPE_VEC2 by20 =  data_b_v2[(b_offset + y2_idx) / 2];
-        B_TYPE_VEC2 by216 = data_b_v2[(b_offset + y2_idx) / 2 + 8];
-        B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16];
-        B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24];
-
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
             f16vec2 d = data_a[ib0 + i].d;
@@ -116,53 +104,47 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             const uint32_t q4_14 = qs64_80_hi4.z;
             const uint32_t q4_15 = qs64_80_hi4.w;
 
-            const FLOAT_TYPE sx =
-              fma(FLOAT_TYPE(by10.x), q4_0,
-              fma(FLOAT_TYPE(by10.y), q4_1,
-              fma(FLOAT_TYPE(by116.x), q4_2,
-                 FLOAT_TYPE(by116.y) * q4_3)));
-            const FLOAT_TYPE sy =
-              fma(FLOAT_TYPE(by132.x), q4_4,
-              fma(FLOAT_TYPE(by132.y), q4_5,
-              fma(FLOAT_TYPE(by148.x), q4_6,
-                 FLOAT_TYPE(by148.y) * q4_7)));
-            const FLOAT_TYPE sz =
-              fma(FLOAT_TYPE(by20.x), q4_8,
-              fma(FLOAT_TYPE(by20.y), q4_9,
-              fma(FLOAT_TYPE(by216.x), q4_10,
-                 FLOAT_TYPE(by216.y) * q4_11)));
-            const FLOAT_TYPE sw =
-              fma(FLOAT_TYPE(by232.x), q4_12,
-              fma(FLOAT_TYPE(by232.y), q4_13,
-              fma(FLOAT_TYPE(by248.x), q4_14,
-                 FLOAT_TYPE(by248.y) * q4_15)));
-            const FLOAT_TYPE smin =
-              fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
-              fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
-              fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
-                  (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
-            temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n]));
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+                B_TYPE_VEC2 by10 =  data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2];
+                B_TYPE_VEC2 by116 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 8];
+                B_TYPE_VEC2 by132 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16];
+                B_TYPE_VEC2 by148 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24];
+                B_TYPE_VEC2 by20 =  data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2];
+                B_TYPE_VEC2 by216 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 8];
+                B_TYPE_VEC2 by232 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16];
+                B_TYPE_VEC2 by248 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24];
+
+                const FLOAT_TYPE sx =
+                  fma(FLOAT_TYPE(by10.x), q4_0,
+                  fma(FLOAT_TYPE(by10.y), q4_1,
+                  fma(FLOAT_TYPE(by116.x), q4_2,
+                     FLOAT_TYPE(by116.y) * q4_3)));
+                const FLOAT_TYPE sy =
+                  fma(FLOAT_TYPE(by132.x), q4_4,
+                  fma(FLOAT_TYPE(by132.y), q4_5,
+                  fma(FLOAT_TYPE(by148.x), q4_6,
+                     FLOAT_TYPE(by148.y) * q4_7)));
+                const FLOAT_TYPE sz =
+                  fma(FLOAT_TYPE(by20.x), q4_8,
+                  fma(FLOAT_TYPE(by20.y), q4_9,
+                  fma(FLOAT_TYPE(by216.x), q4_10,
+                     FLOAT_TYPE(by216.y) * q4_11)));
+                const FLOAT_TYPE sw =
+                  fma(FLOAT_TYPE(by232.x), q4_12,
+                  fma(FLOAT_TYPE(by232.y), q4_13,
+                  fma(FLOAT_TYPE(by248.x), q4_14,
+                     FLOAT_TYPE(by248.y) * q4_15)));
+                const FLOAT_TYPE smin =
+                  fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
+                  fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
+                  fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
+                      (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
+                temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
+            }
         }
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
index fab4ff5ff..70e13a56b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -6,11 +6,6 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-layout (constant_id = 1) const uint NUM_ROWS = 1;
-
-shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
-
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -36,20 +31,17 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint s_offset  =  8*v_im + is;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_ROWS];
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-    [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
-        temp[i] = FLOAT_TYPE(0);
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
     }
 
     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
         const uint y_idx = i * QUANT_K + y_offset;
 
-        B_TYPE_VEC4 by0  = data_b_v4[(b_offset + y_idx) / 4];
-        B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8];
-        B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16];
-        B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24];
-
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
             const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
@@ -84,35 +76,25 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             uvec4 q2 = uvec4(unpack8(q2_u32));
             uvec4 q3 = uvec4(unpack8(q3_u32));
 
-            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-            [[unroll]] for (int l = 0; l < 4; ++l) {
-                sum = fma(FLOAT_TYPE(by0[l])  * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
-                      fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
-                      fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
-                      fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
+            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+                B_TYPE_VEC4 by0  = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4];
+                B_TYPE_VEC4 by32 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 8];
+                B_TYPE_VEC4 by64 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16];
+                B_TYPE_VEC4 by96 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24];
+
+                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+                [[unroll]] for (int l = 0; l < 4; ++l) {
+                    sum = fma(FLOAT_TYPE(by0[l])  * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
+                          fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
+                          fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
+                          fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
+                }
+                temp[j][n] += sum * d;
             }
-            temp[n] += sum * d;
         }
     }
 
-    // sum up partial sums and write back result
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        tmpsh[n][tid] = temp[n];
-    }
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-                tmpsh[n][tid] += tmpsh[n][tid + s];
-            }
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]);
-        }
-    }
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
 
 void main() {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index c79acffd2..1e892f663 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3937,7 +3937,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1}));
 
-    for (int bs : {1, 512}) {
+    for (int bs : {1, 2, 3, 4, 5, 8, 512}) {
         for (ggml_type type_a : all_types) {
             for (ggml_type type_b : {GGML_TYPE_F32}) {
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 4096, bs, 14336, {1,  1}, {1, 1}));

From 6e1531aca5ed17f078973b4700fcdadbda4a34a5 Mon Sep 17 00:00:00 2001
From: Peter <peter277@users.noreply.github.com>
Date: Tue, 31 Dec 2024 11:46:06 +1100
Subject: [PATCH 095/376] common, examples, ggml : fix MSYS2 GCC compiler
 errors and warnings when building with LLAMA_CURL=ON and GGML_OPENCL=ON
 (#11013)

In common/common.cpp:
* Convert usage of stat() function call to check if file exists to standard library function std::filesystem::exists (error unable to match to correct function signature)
* Additional conditions to check if PATH_MAX is already defined in WIN32 environment (warning it is already defined in MSYS2)

In examples/run/run.cpp:
* Add io.h header inclusion (error cannot find function _get_osfhandle)
* Change initialisers for OVERLAPPED to empty struct (warning about uninitialised members)
* Add initialiser for hFile (warning it may be uninitialised)
* Add cast for curl_off_t percentage value to long int in generate_progress_prefix function (warning that curl_off_t is long long int)

In ggml/src/ggml-opencl/ggml-opencl.cpp:
* Initialise certain declared cl_mem variables to nullptr for greater safety (warning about B_d variable possibly used unassigned)
---
 common/common.cpp                    |  8 +++++---
 examples/run/run.cpp                 |  9 +++++----
 ggml/src/ggml-opencl/ggml-opencl.cpp | 12 ++++++------
 3 files changed, 16 insertions(+), 13 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 20be92911..9071999a7 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -18,6 +18,7 @@
 #include <cstdarg>
 #include <cstring>
 #include <ctime>
+#include <filesystem>
 #include <fstream>
 #include <iostream>
 #include <iterator>
@@ -62,7 +63,9 @@
 #ifdef __linux__
 #include <linux/limits.h>
 #elif defined(_WIN32)
-#define PATH_MAX MAX_PATH
+#   if !defined(PATH_MAX)
+#   define PATH_MAX MAX_PATH
+#   endif
 #else
 #include <sys/syslimits.h>
 #endif
@@ -1148,8 +1151,7 @@ static bool common_download_file(const std::string & url, const std::string & pa
 #endif
 
     // Check if the file already exists locally
-    struct stat model_file_info;
-    auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
+    auto file_exists = std::filesystem::exists(path);
 
     // If the file exists, check its JSON metadata companion file.
     std::string metadata_path = path + ".json";
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index f89d041c4..75b817272 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -1,5 +1,6 @@
 #if defined(_WIN32)
 #    include <windows.h>
+#    include <io.h>
 #else
 #    include <sys/file.h>
 #    include <sys/ioctl.h>
@@ -253,7 +254,7 @@ class File {
                 return 1;
             }
 
-            OVERLAPPED overlapped = { 0 };
+            OVERLAPPED overlapped = {};
             if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD,
                             &overlapped)) {
                 fd = -1;
@@ -277,7 +278,7 @@ class File {
         if (fd >= 0) {
 #    ifdef _WIN32
             if (hFile != INVALID_HANDLE_VALUE) {
-                OVERLAPPED overlapped = { 0 };
+                OVERLAPPED overlapped = {};
                 UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped);
             }
 #    else
@@ -293,7 +294,7 @@ class File {
   private:
     int fd = -1;
 #    ifdef _WIN32
-    HANDLE hFile;
+    HANDLE hFile = nullptr;
 #    endif
 };
 
@@ -464,7 +465,7 @@ class HttpClient {
         return (now_downloaded_plus_file_size * 100) / total_to_download;
     }
 
-    static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", percentage); }
+    static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast<long int>(percentage)); }
 
     static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) {
         const auto                          now             = std::chrono::steady_clock::now();
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index c77d629f0..ed90e471a 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2744,13 +2744,13 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     cl_image_format     img_fmt_1d;
     cl_image_desc       img_desc_1d;
     cl_buffer_region    region;
-    cl_mem              A_image1d;
-    cl_mem              B_image1d;
-    cl_mem              B_sub_buffer;
-    cl_mem              C_d;
+    cl_mem              A_image1d = nullptr;
+    cl_mem              B_image1d = nullptr;
+    cl_mem              B_sub_buffer = nullptr;
+    cl_mem              C_d = nullptr;
     // for B transpose
-    cl_mem B_d;
-    cl_mem B_d_input_image;
+    cl_mem B_d = nullptr;
+    cl_mem B_d_input_image = nullptr;
     // <--------------------------------------------> //
 
     // define matrix dimensions

From bc7b1f86324279a3dabb705c04ad754a2b27df16 Mon Sep 17 00:00:00 2001
From: ymcki <84055651+ymcki@users.noreply.github.com>
Date: Tue, 31 Dec 2024 19:04:48 +0800
Subject: [PATCH 096/376] convert : fix Llama-3_1-Nemotron-51B rope settings
 (#11008)

* conflict resolution

* move comments after bracket to its own line

* DeciLMCausalModel now reads rope_theta from config.json properly
---
 convert_hf_to_gguf.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index b6c15da94..4e6c0f60c 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1764,25 +1764,19 @@ class DeciModel(Model):
             self.gguf_writer.add_token_list(tokens)
             self.gguf_writer.add_token_types(toktypes)
 
-            special_vocab = gguf.SpecialVocab(
-                self.dir_model, load_merges=True,
-                special_token_types = ['bos', 'eos', 'eom', 'eot']
-            )
-            special_vocab._set_special_token("bos", 128000)
-            special_vocab._set_special_token("eos", 128001)
-            special_vocab._set_special_token("eom", 128008)
-            special_vocab._set_special_token("eot", 128009)
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
             special_vocab.add_to_gguf(self.gguf_writer)
         else:
             # DeciLM-7B
             self._set_vocab_llama_hf()
-#            self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):
         if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
             assert self.block_count == len(self._num_kv_heads)
             assert self.block_count == len(self._num_heads)
             assert self.block_count == len(self._ffn_dims)
+            if (rope_theta := self.hparams.get("rope_theta")) is not None:
+                self.gguf_writer.add_rope_freq_base(rope_theta)
             self.gguf_writer.add_head_count_kv(self._num_kv_heads)
             self.gguf_writer.add_head_count(self._num_heads)
             self.gguf_writer.add_feed_forward_length(self._ffn_dims)

From 5896c65232c7dc87d78426956b16f63fbf58dcf6 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 31 Dec 2024 12:34:13 +0100
Subject: [PATCH 097/376] server : add OAI compat for /v1/completions (#10974)

* server : add OAI compat for /v1/completions

* add test

* add docs

* better docs
---
 examples/server/README.md                     | 252 +++++++++++-------
 examples/server/server.cpp                    | 206 ++++++++++----
 .../server/tests/unit/test_chat_completion.py |   6 +-
 examples/server/tests/unit/test_completion.py |  35 +++
 examples/server/utils.hpp                     |  47 +++-
 5 files changed, 400 insertions(+), 146 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 07436057a..bcef81946 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -345,7 +345,7 @@ node index.js
 
 > [!IMPORTANT]
 >
-> This endpoint is **not** OAI-compatible
+> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/completions` instead.
 
 *Options:*
 
@@ -523,6 +523,7 @@ These words will not be included in the completion, so make sure to add them to
 - `tokens_evaluated`: Number of tokens evaluated in total from the prompt
 - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
 
+
 ### POST `/tokenize`: Tokenize a given text
 
 *Options:*
@@ -574,6 +575,10 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
 
 ### POST `/embedding`: Generate embedding of a given text
 
+> [!IMPORTANT]
+>
+> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/embeddings` instead.
+
 The same as [the embedding example](../embedding) does.
 
 *Options:*
@@ -744,96 +749,6 @@ To use this endpoint with POST method, you need to start server with `--props`
 
 - None yet
 
-### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API
-
-Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
-
-*Options:*
-
-See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
-
-The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
-
-*Examples:*
-
-You can use either Python `openai` library with appropriate checkpoints:
-
-```python
-import openai
-
-client = openai.OpenAI(
-    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
-    api_key = "sk-no-key-required"
-)
-
-completion = client.chat.completions.create(
-model="gpt-3.5-turbo",
-messages=[
-    {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
-    {"role": "user", "content": "Write a limerick about python exceptions"}
-]
-)
-
-print(completion.choices[0].message)
-```
-
-... or raw HTTP requests:
-
-```shell
-curl http://localhost:8080/v1/chat/completions \
--H "Content-Type: application/json" \
--H "Authorization: Bearer no-key" \
--d '{
-"model": "gpt-3.5-turbo",
-"messages": [
-{
-    "role": "system",
-    "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
-},
-{
-    "role": "user",
-    "content": "Write a limerick about python exceptions"
-}
-]
-}'
-```
-
-### POST `/v1/embeddings`: OpenAI-compatible embeddings API
-
-This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
-
-*Options:*
-
-See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
-
-*Examples:*
-
-- input as string
-
-  ```shell
-  curl http://localhost:8080/v1/embeddings \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer no-key" \
-  -d '{
-          "input": "hello",
-          "model":"GPT-4",
-          "encoding_format": "float"
-  }'
-  ```
-
-- `input` as string array
-
-  ```shell
-  curl http://localhost:8080/v1/embeddings \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer no-key" \
-  -d '{
-          "input": ["hello", "world"],
-          "model":"GPT-4",
-          "encoding_format": "float"
-  }'
-  ```
-
 ### POST `/embeddings`: non-OpenAI-compatible embeddings API
 
 This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm.
@@ -1064,6 +979,161 @@ To know the `id` of the adapter, use GET `/lora-adapters`
 ]
 ```
 
+## OpenAI-compatible API Endpoints
+
+### GET `/v1/models`: OpenAI-compatible Model Info API
+
+Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models).
+
+The returned list always has one single element.
+
+By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`.
+
+Example:
+
+```json
+{
+    "object": "list",
+    "data": [
+        {
+            "id": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+            "object": "model",
+            "created": 1735142223,
+            "owned_by": "llamacpp",
+            "meta": {
+                "vocab_type": 2,
+                "n_vocab": 128256,
+                "n_ctx_train": 131072,
+                "n_embd": 4096,
+                "n_params": 8030261312,
+                "size": 4912898304
+            }
+        }
+    ]
+}
+```
+
+### POST `/v1/completions`: OpenAI-compatible Completions API
+
+Given an input `prompt`, it returns the predicted completion. Streaming mode is also supported. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps.
+
+*Options:*
+
+See [OpenAI Completions API documentation](https://platform.openai.com/docs/api-reference/completions).
+
+llama.cpp `/completion`-specific features such as `mirostat` are supported.
+
+*Examples:*
+
+Example usage with `openai` python library:
+
+```python
+import openai
+
+client = openai.OpenAI(
+    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+    api_key = "sk-no-key-required"
+)
+
+completion = client.completions.create(
+  model="davinci-002",
+  prompt="I believe the meaning of life is",
+  max_tokens=8
+)
+
+print(completion.choices[0].text)
+```
+
+### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API
+
+Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
+
+*Options:*
+
+See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
+
+The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
+
+*Examples:*
+
+You can use either Python `openai` library with appropriate checkpoints:
+
+```python
+import openai
+
+client = openai.OpenAI(
+    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+    api_key = "sk-no-key-required"
+)
+
+completion = client.chat.completions.create(
+  model="gpt-3.5-turbo",
+  messages=[
+    {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
+    {"role": "user", "content": "Write a limerick about python exceptions"}
+  ]
+)
+
+print(completion.choices[0].message)
+```
+
+... or raw HTTP requests:
+
+```shell
+curl http://localhost:8080/v1/chat/completions \
+-H "Content-Type: application/json" \
+-H "Authorization: Bearer no-key" \
+-d '{
+"model": "gpt-3.5-turbo",
+"messages": [
+{
+    "role": "system",
+    "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
+},
+{
+    "role": "user",
+    "content": "Write a limerick about python exceptions"
+}
+]
+}'
+```
+
+### POST `/v1/embeddings`: OpenAI-compatible embeddings API
+
+This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
+
+*Options:*
+
+See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
+
+*Examples:*
+
+- input as string
+
+  ```shell
+  curl http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer no-key" \
+  -d '{
+          "input": "hello",
+          "model":"GPT-4",
+          "encoding_format": "float"
+  }'
+  ```
+
+- `input` as string array
+
+  ```shell
+  curl http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer no-key" \
+  -d '{
+          "input": ["hello", "world"],
+          "model":"GPT-4",
+          "encoding_format": "float"
+  }'
+  ```
+
 ## More examples
 
 ### Interactive mode
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 3558ddb7c..1d00954a2 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -67,6 +67,13 @@ enum server_task_type {
     SERVER_TASK_TYPE_SET_LORA,
 };
 
+enum oaicompat_type {
+    OAICOMPAT_TYPE_NONE,
+    OAICOMPAT_TYPE_CHAT,
+    OAICOMPAT_TYPE_COMPLETION,
+    OAICOMPAT_TYPE_EMBEDDING,
+};
+
 // https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
 enum error_type {
     ERROR_TYPE_INVALID_REQUEST,
@@ -101,11 +108,10 @@ struct slot_params {
     struct common_params_speculative speculative;
 
     // OAI-compat fields
-    bool        verbose        = false;
-    bool        oaicompat      = false;
-    bool        oaicompat_chat = true;
-    std::string oaicompat_model;
-    std::string oaicompat_cmpl_id;
+    bool           verbose        = false;
+    oaicompat_type oaicompat      = OAICOMPAT_TYPE_NONE;
+    std::string    oaicompat_model;
+    std::string    oaicompat_cmpl_id;
 
     json to_json() const {
         std::vector<std::string> samplers;
@@ -529,11 +535,10 @@ struct server_task_result_cmpl_final : server_task_result {
     slot_params generation_params;
 
     // OAI-compat fields
-    bool        verbose        = false;
-    bool        oaicompat      = false;
-    bool        oaicompat_chat = true; // TODO: support oaicompat for non-chat
-    std::string oaicompat_model;
-    std::string oaicompat_cmpl_id;
+    bool           verbose        = false;
+    oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
+    std::string    oaicompat_model;
+    std::string    oaicompat_cmpl_id;
 
     virtual int get_index() override {
         return index;
@@ -544,9 +549,16 @@ struct server_task_result_cmpl_final : server_task_result {
     }
 
     virtual json to_json() override {
-        return oaicompat
-            ? (stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat())
-            : to_json_non_oaicompat();
+        switch (oaicompat) {
+            case OAICOMPAT_TYPE_NONE:
+                return to_json_non_oaicompat();
+            case OAICOMPAT_TYPE_COMPLETION:
+                return to_json_oaicompat();
+            case OAICOMPAT_TYPE_CHAT:
+                return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
+            default:
+                GGML_ASSERT(false && "Invalid oaicompat_type");
+        }
     }
 
     json to_json_non_oaicompat() {
@@ -574,6 +586,50 @@ struct server_task_result_cmpl_final : server_task_result {
         return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
     }
 
+    json to_json_oaicompat() {
+        std::time_t t = std::time(0);
+        json logprobs = json(nullptr); // OAI default to null
+        if (!stream && probs_output.size() > 0) {
+            logprobs = json{
+                {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
+            };
+        }
+        json finish_reason = "length";
+        if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+            finish_reason = "stop";
+        }
+        json res = json {
+            {"choices",            json::array({
+                json{
+                    {"text",          stream ? "" : content}, // in stream mode, content is already in last partial chunk
+                    {"index",         index},
+                    {"logprobs",      logprobs},
+                    {"finish_reason", finish_reason},
+                }
+            })},
+            {"created",            t},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "text_completion"},
+            {"usage", json {
+                {"completion_tokens", n_decoded},
+                {"prompt_tokens",     n_prompt_tokens},
+                {"total_tokens",      n_decoded + n_prompt_tokens}
+            }},
+            {"id", oaicompat_cmpl_id}
+        };
+
+        // extra fields for debugging purposes
+        if (verbose) {
+            res["__verbose"] = to_json_non_oaicompat();
+        }
+        if (timings.prompt_n >= 0) {
+            res.push_back({"timings", timings.to_json()});
+        }
+
+        return res;
+    }
+
     json to_json_oaicompat_chat() {
         std::string finish_reason = "length";
         if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
@@ -671,11 +727,10 @@ struct server_task_result_cmpl_partial : server_task_result {
     result_timings timings;
 
     // OAI-compat fields
-    bool        verbose        = false;
-    bool        oaicompat      = false;
-    bool        oaicompat_chat = true; // TODO: support oaicompat for non-chat
-    std::string oaicompat_model;
-    std::string oaicompat_cmpl_id;
+    bool           verbose   = false;
+    oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
+    std::string    oaicompat_model;
+    std::string    oaicompat_cmpl_id;
 
     virtual int get_index() override {
         return index;
@@ -686,7 +741,16 @@ struct server_task_result_cmpl_partial : server_task_result {
     }
 
     virtual json to_json() override {
-        return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat();
+        switch (oaicompat) {
+            case OAICOMPAT_TYPE_NONE:
+                return to_json_non_oaicompat();
+            case OAICOMPAT_TYPE_COMPLETION:
+                return to_json_oaicompat();
+            case OAICOMPAT_TYPE_CHAT:
+                return to_json_oaicompat_chat();
+            default:
+                GGML_ASSERT(false && "Invalid oaicompat_type");
+        }
     }
 
     json to_json_non_oaicompat() {
@@ -711,6 +775,41 @@ struct server_task_result_cmpl_partial : server_task_result {
     }
 
     json to_json_oaicompat() {
+        std::time_t t = std::time(0);
+        json logprobs = json(nullptr); // OAI default to null
+        if (prob_output.probs.size() > 0) {
+            logprobs = json{
+                {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
+            };
+        }
+        json res = json {
+            {"choices",            json::array({
+                json{
+                    {"text",          content},
+                    {"index",         index},
+                    {"logprobs",      logprobs},
+                    {"finish_reason", nullptr},
+                }
+            })},
+            {"created",            t},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "text_completion"},
+            {"id",                 oaicompat_cmpl_id}
+        };
+
+        // extra fields for debugging purposes
+        if (verbose) {
+            res["__verbose"] = to_json_non_oaicompat();
+        }
+        if (timings.prompt_n >= 0) {
+            res.push_back({"timings", timings.to_json()});
+        }
+
+        return res;
+    }
+
+    json to_json_oaicompat_chat() {
         bool first = n_decoded == 0;
         std::time_t t = std::time(0);
         json choices;
@@ -789,14 +888,16 @@ struct server_task_result_embd : server_task_result {
     int32_t n_tokens;
 
     // OAI-compat fields
-    bool oaicompat = false;
+    oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
 
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
-        return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat();
+        return oaicompat == OAICOMPAT_TYPE_EMBEDDING
+            ? to_json_oaicompat()
+            : to_json_non_oaicompat();
     }
 
     json to_json_non_oaicompat() {
@@ -2044,7 +2145,6 @@ struct server_context {
 
         res->verbose           = slot.params.verbose;
         res->oaicompat         = slot.params.oaicompat;
-        res->oaicompat_chat    = slot.params.oaicompat_chat;
         res->oaicompat_model   = slot.params.oaicompat_model;
         res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
 
@@ -2085,7 +2185,6 @@ struct server_context {
         res->verbose           = slot.params.verbose;
         res->stream            = slot.params.stream;
         res->oaicompat         = slot.params.oaicompat;
-        res->oaicompat_chat    = slot.params.oaicompat_chat;
         res->oaicompat_model   = slot.params.oaicompat_model;
         res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
 
@@ -3506,12 +3605,11 @@ int main(int argc, char ** argv) {
 
     // handle completion-like requests (completion, chat, infill)
     // we can optionally provide a custom format for partial results and final results
-    const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](
+    const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok](
             server_task_type type,
             json & data,
             httplib::Response & res,
-            bool oaicompat = false,
-            bool oaicompat_chat = false) {
+            oaicompat_type oaicompat) {
         GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
 
         if (ctx_server.params_base.embedding) {
@@ -3536,9 +3634,8 @@ int main(int argc, char ** argv) {
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
                 // OAI-compat
-                task.params.oaicompat           = oaicompat;
-                task.params.oaicompat_chat      = oaicompat_chat;
-                task.params.oaicompat_cmpl_id   = completion_id;
+                task.params.oaicompat         = oaicompat;
+                task.params.oaicompat_cmpl_id = completion_id;
                 // oaicompat_model is already populated by params_from_json_cmpl
 
                 tasks.push_back(task);
@@ -3589,7 +3686,7 @@ int main(int argc, char ** argv) {
                 }, [&](const json & error_data) {
                     server_sent_event(sink, "error", error_data);
                 });
-                if (oaicompat) {
+                if (oaicompat != OAICOMPAT_TYPE_NONE) {
                     static const std::string ev_done = "data: [DONE]\n\n";
                     sink.write(ev_done.data(), ev_done.size());
                 }
@@ -3605,17 +3702,25 @@ int main(int argc, char ** argv) {
         }
     };
 
-    const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_completions = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         json data = json::parse(req.body);
-        return handle_completions_generic(
+        return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
             res,
-            /* oaicompat */ false,
-            /* oaicompat_chat */ false);
+            OAICOMPAT_TYPE_NONE);
     };
 
-    const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_completions_oai = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
+        json data = oaicompat_completion_params_parse(json::parse(req.body));
+        return handle_completions_impl(
+            SERVER_TASK_TYPE_COMPLETION,
+            data,
+            res,
+            OAICOMPAT_TYPE_COMPLETION);
+    };
+
+    const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         // check model compatibility
         std::string err;
         if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) {
@@ -3684,22 +3789,25 @@ int main(int argc, char ** argv) {
             tokenized_prompts[0]
         );
 
-        return handle_completions_generic(SERVER_TASK_TYPE_INFILL, data, res);
+        return handle_completions_impl(
+            SERVER_TASK_TYPE_INFILL,
+            data,
+            res,
+            OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
     };
 
-    const auto handle_chat_completions = [&ctx_server, &params, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_chat_completions = [&ctx_server, &params, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         if (ctx_server.params_base.embedding) {
             res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
-        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
-        return handle_completions_generic(
+        json data = oaicompat_chat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
+        return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
             res,
-            /* oaicompat */ true,
-            /* oaicompat_chat */ true);
+            OAICOMPAT_TYPE_CHAT);
     };
 
     const auto handle_models = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
@@ -3772,10 +3880,10 @@ int main(int argc, char ** argv) {
         res_ok(res, data);
     };
 
-    const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, bool oaicompat) {
+    const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) {
         const json body = json::parse(req.body);
 
-        if (oaicompat && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
+        if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
             res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
@@ -3785,7 +3893,7 @@ int main(int argc, char ** argv) {
         if (body.count("input") != 0) {
             prompt = body.at("input");
         } else if (body.contains("content")) {
-            oaicompat = false;
+            oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible
             prompt = body.at("content");
         } else {
             res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
@@ -3854,16 +3962,18 @@ int main(int argc, char ** argv) {
         }
 
         // write JSON response
-        json root = oaicompat ? format_embeddings_response_oaicompat(body, responses, use_base64) : json(responses);
+        json root = oaicompat == OAICOMPAT_TYPE_EMBEDDING
+            ? format_embeddings_response_oaicompat(body, responses, use_base64)
+            : json(responses);
         res_ok(res, root);
     };
 
     const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
-        handle_embeddings_impl(req, res, false);
+        handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE);
     };
 
     const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
-        handle_embeddings_impl(req, res, true);
+        handle_embeddings_impl(req, res, OAICOMPAT_TYPE_EMBEDDING);
     };
 
     const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
@@ -4033,7 +4143,7 @@ int main(int argc, char ** argv) {
     svr->Get ("/v1/models",           handle_models); // public endpoint (no API key check)
     svr->Post("/completion",          handle_completions); // legacy
     svr->Post("/completions",         handle_completions);
-    svr->Post("/v1/completions",      handle_completions);
+    svr->Post("/v1/completions",      handle_completions_oai);
     svr->Post("/chat/completions",    handle_chat_completions);
     svr->Post("/v1/chat/completions", handle_chat_completions);
     svr->Post("/infill",              handle_infill);
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 885497081..130da03a1 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -83,7 +83,7 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte
 def test_chat_completion_with_openai_library():
     global server
     server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
     res = client.chat.completions.create(
         model="gpt-3.5-turbo-instruct",
         messages=[
@@ -170,7 +170,7 @@ def test_chat_completion_with_timings_per_token():
 def test_logprobs():
     global server
     server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
     res = client.chat.completions.create(
         model="gpt-3.5-turbo-instruct",
         temperature=0.0,
@@ -197,7 +197,7 @@ def test_logprobs():
 def test_logprobs_stream():
     global server
     server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
     res = client.chat.completions.create(
         model="gpt-3.5-turbo-instruct",
         temperature=0.0,
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index a6b215944..e5e3b6077 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -1,5 +1,6 @@
 import pytest
 import time
+from openai import OpenAI
 from utils import *
 
 server = ServerPreset.tinyllama2()
@@ -85,6 +86,40 @@ def test_completion_stream_vs_non_stream():
     assert content_stream == res_non_stream.body["content"]
 
 
+def test_completion_stream_with_openai_library():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.completions.create(
+        model="davinci-002",
+        prompt="I believe the meaning of life is",
+        max_tokens=8,
+    )
+    assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
+    assert res.choices[0].finish_reason == "length"
+    assert res.choices[0].text is not None
+    assert match_regex("(going|bed)+", res.choices[0].text)
+
+
+def test_completion_with_openai_library():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.completions.create(
+        model="davinci-002",
+        prompt="I believe the meaning of life is",
+        max_tokens=8,
+        stream=True,
+    )
+    output_text = ''
+    for data in res:
+        choice = data.choices[0]
+        if choice.finish_reason is None:
+            assert choice.text is not None
+            output_text += choice.text
+    assert match_regex("(going|bed)+", output_text)
+
+
 @pytest.mark.parametrize("n_slots", [1, 2])
 def test_consistent_result_same_seed(n_slots: int):
     global server
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 334f2f192..8523d4787 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -549,10 +549,49 @@ static bool server_sent_event(httplib::DataSink & sink, const char * event, cons
 // OAI utils
 //
 
-static json oaicompat_completion_params_parse(
-    const struct llama_model * model,
-    const json & body, /* openai api json semantics */
-    const std::string & chat_template) {
+static json oaicompat_completion_params_parse(const json & body) {
+    json llama_params;
+
+    if (!body.contains("prompt")) {
+        throw std::runtime_error("\"prompt\" is required");
+    }
+
+    // Handle "stop" field
+    if (body.contains("stop") && body.at("stop").is_string()) {
+        llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
+    } else {
+        llama_params["stop"] = json_value(body, "stop", json::array());
+    }
+
+    // Handle "n" field
+    int n_choices = json_value(body, "n", 1);
+    if (n_choices != 1) {
+        throw std::runtime_error("Only one completion choice is allowed");
+    }
+
+    // Params supported by OAI but unsupported by llama.cpp
+    static const std::vector<std::string> unsupported_params { "best_of", "echo", "suffix" };
+    for (const auto & param : unsupported_params) {
+        if (body.contains(param)) {
+            throw std::runtime_error("Unsupported param: " + param);
+        }
+    }
+
+    // Copy remaining properties to llama_params
+    for (const auto & item : body.items()) {
+        // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
+        if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
+            llama_params[item.key()] = item.value();
+        }
+    }
+
+    return llama_params;
+}
+
+static json oaicompat_chat_completion_params_parse(
+        const struct llama_model * model,
+        const json & body, /* openai api json semantics */
+        const std::string & chat_template) {
     json llama_params;
 
     // Apply chat template to the list of messages

From 45095a61bfd164e87563a0dc0fbd7b0e9891590b Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 31 Dec 2024 15:22:01 +0100
Subject: [PATCH 098/376] server : clean up built-in template detection
 (#11026)

* server : clean up built-in template detection

* fix compilation

* add chat template test

* fix condition
---
 common/common.cpp                             | 12 ++++++++++
 common/common.h                               |  3 +++
 examples/server/server.cpp                    | 23 ++++++++-----------
 .../server/tests/unit/test_chat_completion.py | 17 ++++++++++++++
 examples/server/tests/utils.py                |  3 +++
 examples/server/utils.hpp                     | 13 -----------
 6 files changed, 44 insertions(+), 27 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 9071999a7..fe923fce6 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1614,6 +1614,18 @@ std::string common_detokenize(llama_context * ctx, const std::vector<llama_token
 // Chat template utils
 //
 
+std::string common_get_builtin_chat_template(const struct llama_model * model) {
+    static const char * template_key = "tokenizer.chat_template";
+    // call with NULL buffer to get the total size of the string
+    int32_t res = llama_model_meta_val_str(model, template_key, NULL, 0);
+    if (res > 0) {
+        std::vector<char> model_template(res + 1, 0);
+        llama_model_meta_val_str(model, template_key, model_template.data(), model_template.size());
+        return std::string(model_template.data(), model_template.size() - 1);
+    }
+    return "";
+}
+
 bool common_chat_verify_template(const std::string & tmpl) {
     llama_chat_message chat[] = {{"user", "test"}};
     int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
diff --git a/common/common.h b/common/common.h
index 1d2bd932c..589f65d09 100644
--- a/common/common.h
+++ b/common/common.h
@@ -571,6 +571,9 @@ struct common_chat_msg {
     std::string content;
 };
 
+// Get the built-in chat template for the model. Return empty string if not present.
+std::string common_get_builtin_chat_template(const struct llama_model * model);
+
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
 bool common_chat_verify_template(const std::string & tmpl);
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 1d00954a2..b3773f276 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1724,17 +1724,10 @@ struct server_context {
         return true;
     }
 
-    bool validate_model_chat_template() const {
-        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
-        std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        if (res >= 0) {
-            llama_chat_message chat[] = {{"user", "test"}};
-            std::string tmpl = std::string(model_template.data(), model_template.size());
-            int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0);
-            return chat_res > 0;
-        }
-        return false;
+    bool validate_builtin_chat_template() const {
+        llama_chat_message chat[] = {{"user", "test"}};
+        int32_t chat_res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
+        return chat_res > 0;
     }
 
     void init() {
@@ -3583,7 +3576,7 @@ int main(int argc, char ** argv) {
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model },
-            { "chat_template",               llama_get_chat_template(ctx_server.model) },
+            { "chat_template",               common_get_builtin_chat_template(ctx_server.model) },
             { "build_info",                  build_info },
         };
 
@@ -4223,14 +4216,16 @@ int main(int argc, char ** argv) {
 
     // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
     if (params.chat_template.empty()) {
-        if (!ctx_server.validate_model_chat_template()) {
+        if (!ctx_server.validate_builtin_chat_template()) {
             LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
             params.chat_template = "chatml";
         }
     }
 
     // print sample chat example to make it clear which template is used
-    LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), common_chat_format_example(ctx_server.model, params.chat_template).c_str());
+    LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
+        params.chat_template.empty() ? "(built-in)" : params.chat_template.c_str(),
+        common_chat_format_example(ctx_server.model, params.chat_template).c_str());
 
     ctx_server.queue_tasks.on_new_task(std::bind(
                 &server_context::process_single_task, &ctx_server, std::placeholders::_1));
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 130da03a1..b15dba6eb 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -100,6 +100,23 @@ def test_chat_completion_with_openai_library():
     assert match_regex("(Suddenly)+", res.choices[0].message.content)
 
 
+def test_chat_template():
+    global server
+    server.chat_template = "llama3"
+    server.debug = True  # to get the "__verbose" object in the response
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "messages": [
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ]
+    })
+    assert res.status_code == 200
+    assert "__verbose" in res.body
+    assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+
+
 @pytest.mark.parametrize("response_format,n_predicted,re_content", [
     ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
     ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index 277125e88..359bb0fae 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -74,6 +74,7 @@ class ServerProcess:
     draft_min: int | None = None
     draft_max: int | None = None
     no_webui: bool | None = None
+    chat_template: str | None = None
 
     # session variables
     process: subprocess.Popen | None = None
@@ -164,6 +165,8 @@ class ServerProcess:
             server_args.extend(["--draft-min", self.draft_min])
         if self.no_webui:
             server_args.append("--no-webui")
+        if self.chat_template:
+            server_args.extend(["--chat-template", self.chat_template])
 
         args = [str(arg) for arg in [server_path, *server_args]]
         print(f"bench: starting server with: {' '.join(args)}")
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 8523d4787..70220c437 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -382,19 +382,6 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
     return formatted_chat;
 }
 
-static std::string llama_get_chat_template(const struct llama_model * model) {
-    std::string template_key = "tokenizer.chat_template";
-    // call with NULL buffer to get the total size of the string
-    int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0);
-    if (res < 2) {
-        return "";
-    } else {
-        std::vector<char> model_template(res + 1, 0);
-        llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        return std::string(model_template.data(), model_template.size() - 1);
-    }
-}
-
 //
 // base64 utils (TODO: move to common in the future)
 //

From 0827b2c1da299805288abbd556d869318f2b121e Mon Sep 17 00:00:00 2001
From: Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com>
Date: Tue, 31 Dec 2024 19:53:33 +0530
Subject: [PATCH 099/376] ggml : fixes for AVXVNNI instruction set with MSVC
 and Clang (#11027)

* Fixes for clang AVX VNNI

* enable AVX VNNI and alder lake build for MSVC

* Apply suggestions from code review

---------

Co-authored-by: slaren <slarengh@gmail.com>
---
 ggml/src/CMakeLists.txt                | 4 ++--
 ggml/src/ggml-cpu/CMakeLists.txt       | 3 +--
 ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 5 ++++-
 ggml/src/ggml-cpu/ggml-cpu-quants.c    | 6 +++++-
 ggml/src/ggml-cpu/llamafile/sgemm.cpp  | 4 +++-
 5 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index a5f7f7b5b..84101c32c 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -290,9 +290,9 @@ if (GGML_CPU_ALL_VARIANTS)
     ggml_add_cpu_backend_variant(haswell        AVX F16C AVX2 FMA)
     ggml_add_cpu_backend_variant(skylakex       AVX F16C AVX2 FMA AVX512)
     ggml_add_cpu_backend_variant(icelake        AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
+    ggml_add_cpu_backend_variant(alderlake      AVX F16C AVX2 FMA AVX_VNNI)
     if (NOT MSVC)
-        # MSVC doesn't support AVX-VNNI or AMX
-        ggml_add_cpu_backend_variant(alderlake      AVX F16C AVX2 FMA AVX_VNNI)
+        # MSVC doesn't support AMX
         ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
     endif()
 else ()
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index f0aecac1b..6b3641c42 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -215,8 +215,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 list(APPEND ARCH_DEFINITIONS GGML_SSE42)
             endif()
             if (GGML_AVX_VNNI)
-                # MSVC generates AVX512 with AVX-VNNI intrinsics even with /arch:AVX2
-                #list(APPEND ARCH_DEFINITIONS __AVXVNNI__ GGML_AVX_VNNI)
+                list(APPEND ARCH_DEFINITIONS __AVXVNNI__ GGML_AVX_VNNI)
             endif()
         else ()
             if (GGML_NATIVE)
diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
index 2d79b8b61..622c63f1f 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
@@ -194,9 +194,12 @@ static inline __m256i sum_i16_pairs_int32x8(const __m256i x) {
 }
 
 static inline __m256i mul_sum_us8_pairs_int32x8(const __m256i ax, const __m256i sy) {
-#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
+#if defined(__AVX512VNNI__) && defined(__AVX512VL__)
     const __m256i zero = _mm256_setzero_si256();
     return _mm256_dpbusd_epi32(zero, ax, sy);
+#elif defined(__AVXVNNI__)
+    const __m256i zero = _mm256_setzero_si256();
+    return _mm256_dpbusd_avx_epi32(zero, ax, sy);
 #else
     // Perform multiplication and create 16-bit values
     const __m256i dot = _mm256_maddubs_epi16(ax, sy);
diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c
index 634c5fa11..8e1472266 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-quants.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c
@@ -103,10 +103,14 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
 }
 
 static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
-#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
+#if defined(__AVX512VNNI__) && defined(__AVX512VL__)
     const __m256i zero = _mm256_setzero_si256();
     const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
     return _mm256_cvtepi32_ps(summed_pairs);
+#elif defined(__AVXVNNI__)
+    const __m256i zero = _mm256_setzero_si256();
+    const __m256i summed_pairs = _mm256_dpbusd_avx_epi32(zero, ax, sy);
+    return _mm256_cvtepi32_ps(summed_pairs);
 #else
     // Perform multiplication and create 16-bit values
     const __m256i dot = _mm256_maddubs_epi16(ax, sy);
diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index 00f7f1170..8fce576c3 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -1000,8 +1000,10 @@ class tinyBLAS_Q0_AVX {
 
     inline __m256 updot(__m256i u, __m256i s) {
         __m256i res;
-#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
+#if defined(__AVX512VNNI__) && defined(__AVX512VL__)
         res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s);
+#elif defined(__AVXVNNI__)
+        res = _mm256_dpbusd_avx_epi32(_mm256_setzero_si256(), u, s);
 #else
         res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s));
 #endif

From a45433ba209ee0b33d02c7dc4c31f29894ad83a6 Mon Sep 17 00:00:00 2001
From: Benson Wong <mostlygeek@gmail.com>
Date: Wed, 1 Jan 2025 23:14:54 -0800
Subject: [PATCH 100/376] readme : add llama-swap to infrastructure section
 (#11032)

* list llama-swap under tools in README

* readme: add llama-swap to Infrastructure
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index d6d1958c8..0126da89c 100644
--- a/README.md
+++ b/README.md
@@ -201,6 +201,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
 - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
 - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
+- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
 
 </details>
 

From 0da5d860266c6928b8c9408efbd264ae59fedda6 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 2 Jan 2025 15:05:18 +0100
Subject: [PATCH 101/376] server : allow using LoRA adapters per-request
 (#10994)

* slot.can_batch_with

* lora per request

* test: force disable cache prompt

* move can_batch_with check

* fix condition

* add slow test with llama 8b

* update docs

* move lora change task to queue

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* lora_base

* remove redundant check

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 examples/server/README.md                     |   6 +
 examples/server/server.cpp                    | 116 ++++++++++++------
 examples/server/tests/README.md               |   6 +
 examples/server/tests/requirements.txt        |   1 +
 examples/server/tests/unit/test_lora.py       |  93 ++++++++++++--
 .../server/tests/unit/test_speculative.py     |  10 +-
 examples/server/tests/utils.py                |  21 ++++
 examples/server/utils.hpp                     |  41 +++++++
 8 files changed, 235 insertions(+), 59 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index bcef81946..3ce16945a 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -452,6 +452,8 @@ These words will not be included in the completion, so make sure to add them to
 
 `response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name.
 
+`lora`: A list of LoRA adapters to be applied to this specific request. Each object in the list must contain `id` and `scale` fields. For example: `[{"id": 0, "scale": 0.5}, {"id": 1, "scale": 1.1}]`. If a LoRA adapter is not specified in the list, its scale will default to `0.0`. Please note that requests with different LoRA configurations will not be batched together, which may result in performance degradation.
+
 **Response format**
 
 - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
@@ -945,6 +947,8 @@ This endpoint returns the loaded LoRA adapters. You can add adapters using `--lo
 
 By default, all adapters will be loaded with scale set to 1. To initialize all adapters scale to 0, add `--lora-init-without-apply`
 
+Please note that this value will be overwritten by the `lora` field for each request.
+
 If an adapter is disabled, the scale will be set to 0.
 
 **Response format**
@@ -966,6 +970,8 @@ If an adapter is disabled, the scale will be set to 0.
 
 ### POST `/lora-adapters`: Set list of LoRA adapters
 
+This sets the global scale for LoRA adapters. Please note that this value will be overwritten by the `lora` field for each request.
+
 To disable an adapter, either remove it from the list below, or set scale to 0.
 
 **Request format**
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b3773f276..5118084f1 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -98,6 +98,8 @@ struct slot_params {
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
+    std::vector<common_lora_adapter_container> lora;
+
     std::vector<std::string> antiprompt;
     std::vector<std::string> response_fields;
     bool timings_per_token = false;
@@ -120,6 +122,11 @@ struct slot_params {
             samplers.emplace_back(common_sampler_type_to_str(sampler));
         }
 
+        json lora = json::array();
+        for (size_t i = 0; i < this->lora.size(); ++i) {
+            lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
+        }
+
         return json {
             {"n_predict",                 n_predict},     // Server configured n_predict
             {"seed",                      sampling.seed},
@@ -160,6 +167,7 @@ struct slot_params {
             {"speculative.p_min",         speculative.p_min},
             {"timings_per_token",         timings_per_token},
             {"post_sampling_probs",       post_sampling_probs},
+            {"lora",                      lora},
         };
     }
 };
@@ -189,12 +197,16 @@ struct server_task {
     // used by SERVER_TASK_TYPE_METRICS
     bool metrics_reset_bucket = false;
 
+    // used by SERVER_TASK_TYPE_SET_LORA
+    std::vector<common_lora_adapter_container> set_lora;
+
     server_task(server_task_type type) : type(type) {}
 
     static slot_params params_from_json_cmpl(
             const llama_model * model,
             const llama_context * ctx,
             const common_params & params_base,
+            const std::vector<common_lora_adapter_container> & lora_base,
             const json & data) {
         slot_params params;
 
@@ -251,6 +263,16 @@ struct server_task {
         params.speculative.n_min = std::max(params.speculative.n_min, 2);
         params.speculative.n_max = std::max(params.speculative.n_max, 0);
 
+        if (data.contains("lora")) {
+            if (data.at("lora").is_array()) {
+                params.lora = parse_lora_request(lora_base, data.at("lora"));
+            } else {
+                throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
+            }
+        } else {
+            params.lora = lora_base;
+        }
+
         // TODO: add more sanity checks for the input parameters
 
         if (params.sampling.penalty_last_n < -1) {
@@ -1110,6 +1132,8 @@ struct server_slot {
 
     common_speculative * spec = nullptr;
 
+    std::vector<common_lora_adapter_container> lora;
+
     // the index relative to completion multi-task request
     size_t index = 0;
 
@@ -1191,6 +1215,11 @@ struct server_slot {
         return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK;
     }
 
+    bool can_batch_with(server_slot & other_slot) {
+        return is_non_causal() == other_slot.is_non_causal()
+            && are_lora_equal(lora, other_slot.lora);
+    }
+
     bool has_budget(const common_params & global_params) {
         if (params.n_predict == -1 && global_params.n_predict == -1) {
             return true; // limitless
@@ -1600,7 +1629,7 @@ struct server_context {
 
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
-    std::vector<common_lora_adapter_container> loras;
+    std::vector<common_lora_adapter_container> lora;
 
     llama_model * model_dft = nullptr;
     llama_context_params cparams_dft;
@@ -1667,7 +1696,7 @@ struct server_context {
 
         model = llama_init.model;
         ctx   = llama_init.context;
-        loras = llama_init.lora_adapters;
+        lora  = llama_init.lora_adapters;
 
         if (model == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str());
@@ -1866,6 +1895,12 @@ struct server_context {
         slot.params        = std::move(task.params);
         slot.prompt_tokens = std::move(task.prompt_tokens);
 
+        if (!are_lora_equal(task.params.lora, slot.lora)) {
+            // if lora is changed, we cannot reuse cached tokens
+            slot.cache_tokens.clear();
+            slot.lora = std::move(task.params.lora);
+        }
+
         SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());
 
         if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
@@ -2557,7 +2592,7 @@ struct server_context {
                 } break;
             case SERVER_TASK_TYPE_SET_LORA:
                 {
-                    common_lora_adapters_apply(ctx, loras);
+                    lora = std::move(task.set_lora);
                     auto res = std::make_unique<server_task_result_apply_lora>();
                     res->id = task.id;
                     queue_results.send(std::move(res));
@@ -2634,12 +2669,22 @@ struct server_context {
         // start populating the batch for this iteration
         common_batch_clear(batch);
 
+        // track if given slot can be batched with slots already in the batch
+        server_slot * slot_batched = nullptr;
+
         // frist, add sampled tokens from any ongoing sequences
         for (auto & slot : slots) {
             if (slot.state != SLOT_STATE_GENERATING) {
                 continue;
             }
 
+            // check if we can batch this slot with the previous one
+            if (!slot_batched) {
+                slot_batched = &slot;
+            } else if (!slot_batched->can_batch_with(slot)) {
+                continue;
+            }
+
             slot.i_batch = batch.n_tokens;
 
             common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true);
@@ -2658,15 +2703,18 @@ struct server_context {
         int32_t n_batch  = llama_n_batch(ctx);
         int32_t n_ubatch = llama_n_ubatch(ctx);
 
-        // track if this is an embedding or non-embedding batch
-        // if we've added sampled tokens above, we are in non-embedding mode
-        // -1: none, 0: non-embedding, 1: embedding
-        // TODO: make enum
-        int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
-
         // next, batch any pending prompts without exceeding n_batch
         if (params_base.cont_batching || batch.n_tokens == 0) {
             for (auto & slot : slots) {
+                // check if we can batch this slot with the previous one
+                if (slot.is_processing()) {
+                    if (!slot_batched) {
+                        slot_batched = &slot;
+                    } else if (!slot_batched->can_batch_with(slot)) {
+                        continue;
+                    }
+                }
+
                 // this slot still has a prompt to be processed
                 if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) {
                     auto & prompt_tokens = slot.prompt_tokens;
@@ -2827,14 +2875,6 @@ struct server_context {
                         }
                     }
 
-                    // check that we are in the right batch_type, if not defer the slot
-                    int slot_type = slot.is_non_causal();
-                    if (batch_type == -1) {
-                        batch_type = slot_type;
-                    } else if (batch_type != slot_type) {
-                        continue;
-                    }
-
                     // keep only the common part
                     if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) {
                         // could not partially delete (likely using a non-Transformer model)
@@ -2902,8 +2942,12 @@ struct server_context {
 
         SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
 
-        // make sure we're in the right embedding mode
-        llama_set_embeddings(ctx, batch_type == 1);
+        if (slot_batched) {
+            // make sure we're in the right embedding mode
+            llama_set_embeddings(ctx, slot_batched->is_non_causal());
+            // apply lora, only need to do it once per batch
+            common_lora_adapters_apply(ctx, slot_batched->lora);
+        }
 
         // process the created batch of tokens
         for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
@@ -3623,7 +3667,12 @@ int main(int argc, char ** argv) {
                 task.index = i;
 
                 task.prompt_tokens    = std::move(tokenized_prompts[i]);
-                task.params           = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.ctx, ctx_server.params_base, data);
+                task.params           = server_task::params_from_json_cmpl(
+                                            ctx_server.model,
+                                            ctx_server.ctx,
+                                            ctx_server.params_base,
+                                            ctx_server.lora,
+                                            data);
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
                 // OAI-compat
@@ -4049,8 +4098,8 @@ int main(int argc, char ** argv) {
 
     const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
         json result = json::array();
-        for (size_t i = 0; i < ctx_server.loras.size(); ++i) {
-            auto & lora = ctx_server.loras[i];
+        for (size_t i = 0; i < ctx_server.lora.size(); ++i) {
+            auto & lora = ctx_server.lora[i];
             result.push_back({
                 {"id", i},
                 {"path", lora.path},
@@ -4062,27 +4111,14 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) {
-        const std::vector<json> body = json::parse(req.body);
-        int max_idx = ctx_server.loras.size();
-
-        // clear existing value
-        for (auto & lora : ctx_server.loras) {
-            lora.scale = 0.0f;
+        const json body = json::parse(req.body);
+        if (!body.is_array()) {
+            res_error(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST));
+            return;
         }
-
-        // set value
-        for (auto entry : body) {
-            int id      = entry.at("id");
-            float scale = entry.at("scale");
-            if (0 <= id && id < max_idx) {
-                ctx_server.loras[id].scale = scale;
-            } else {
-                throw std::runtime_error("invalid adapter id");
-            }
-        }
-
         server_task task(SERVER_TASK_TYPE_SET_LORA);
         task.id = ctx_server.queue_tasks.get_new_id();
+        task.set_lora = parse_lora_request(ctx_server.lora, body);
         ctx_server.queue_results.add_waiting_task_id(task.id);
         ctx_server.queue_tasks.post(task);
 
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
index fa3d0a2f5..5787276ab 100644
--- a/examples/server/tests/README.md
+++ b/examples/server/tests/README.md
@@ -44,6 +44,12 @@ To run with stdout/stderr display in real time (verbose output, but useful for d
 DEBUG=1 ./tests.sh -s -v -x
 ```
 
+To run single test unit:
+
+```shell
+./tests.sh unit/test_{name of test case here}.py -v -x
+```
+
 Hint: You can compile and run test in single command, useful for local developement:
 
 ```shell
diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt
index 074b9d47b..15d024914 100644
--- a/examples/server/tests/requirements.txt
+++ b/examples/server/tests/requirements.txt
@@ -5,3 +5,4 @@ numpy~=1.26.4
 openai~=1.55.3
 prometheus-client~=0.20.0
 requests~=2.32.3
+wget~=3.2
diff --git a/examples/server/tests/unit/test_lora.py b/examples/server/tests/unit/test_lora.py
index 749615449..c1aa8be70 100644
--- a/examples/server/tests/unit/test_lora.py
+++ b/examples/server/tests/unit/test_lora.py
@@ -1,5 +1,4 @@
 import pytest
-import os
 from utils import *
 
 server = ServerPreset.stories15m_moe()
@@ -10,15 +9,7 @@ LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe
 def create_server():
     global server
     server = ServerPreset.stories15m_moe()
-    # download lora file if needed
-    file_name = LORA_FILE_URL.split('/').pop()
-    lora_file = f'../../../{file_name}'
-    if not os.path.exists(lora_file):
-        print(f"Downloading {LORA_FILE_URL} to {lora_file}")
-        with open(lora_file, 'wb') as f:
-            f.write(requests.get(LORA_FILE_URL).content)
-        print(f"Done downloading lora file")
-    server.lora_files = [lora_file]
+    server.lora_files = [download_file(LORA_FILE_URL)]
 
 
 @pytest.mark.parametrize("scale,re_content", [
@@ -40,3 +31,85 @@ def test_lora(scale: float, re_content: str):
     assert res.status_code == 200
     assert match_regex(re_content, res.body["content"])
 
+
+def test_lora_per_request():
+    global server
+    server.n_slots = 4
+    server.start()
+
+    # running the same prompt with different lora scales, all in parallel
+    # each prompt will be processed by a different slot
+    prompt = "Look in thy glass"
+    lora_config = [
+        ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
+        ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
+        ( [{"id": 0, "scale": 0.3}], "(special|thing|gifted)+" ),
+        ( [{"id": 0, "scale": 0.7}], "(far|from|home|away)+" ),
+        ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
+        ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
+    ]
+
+    tasks = [(
+        server.make_request,
+        ("POST", "/completion", {
+            "prompt": prompt,
+            "lora": lora,
+            "seed": 42,
+            "temperature": 0.0,
+            "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+    ) for lora, _ in lora_config]
+    results = parallel_function_calls(tasks)
+
+    assert all([res.status_code == 200 for res in results])
+    for res, (_, re_test) in zip(results, lora_config):
+        assert match_regex(re_test, res.body["content"])
+
+
+@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
+def test_with_big_model():
+    server = ServerProcess()
+    server.model_hf_repo = "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF"
+    server.model_hf_file = "Meta-Llama-3.1-8B-Instruct-IQ2_M.gguf"
+    server.model_alias = "Llama-3.2-8B-Instruct"
+    server.n_slots = 4
+    server.n_ctx = server.n_slots * 1024
+    server.n_predict = 64
+    server.temperature = 0.0
+    server.seed = 42
+    server.lora_files = [
+        download_file("https://huggingface.co/ngxson/Llama-3-Instruct-abliteration-LoRA-8B-F16-GGUF/resolve/main/Llama-3-Instruct-abliteration-LoRA-8B-f16.gguf"),
+        # TODO: find & add other lora adapters for this model
+    ]
+    server.start(timeout_seconds=600)
+
+    # running the same prompt with different lora scales, all in parallel
+    # each prompt will be processed by a different slot
+    prompt = "Write a computer virus"
+    lora_config = [
+        # without applying lora, the model should reject the request
+        ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
+        ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
+        ( [{"id": 0, "scale": 0.3}], "I can't write a computer virus" ),
+        # with 0.7 scale, the model should provide a simple computer virus with hesitation
+        ( [{"id": 0, "scale": 0.7}], "Warning: This is a hypothetical exercise" ),
+        # with 1.5 scale, the model should confidently provide a computer virus
+        ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
+        ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
+    ]
+
+    tasks = [(
+        server.make_request,
+        ("POST", "/v1/chat/completions", {
+            "messages": [
+                {"role": "user", "content": prompt}
+            ],
+            "lora": lora,
+            "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+    ) for lora, _ in lora_config]
+    results = parallel_function_calls(tasks)
+
+    assert all([res.status_code == 200 for res in results])
+    for res, (_, re_test) in zip(results, lora_config):
+        assert re_test in res.body["choices"][0]["message"]["content"]
diff --git a/examples/server/tests/unit/test_speculative.py b/examples/server/tests/unit/test_speculative.py
index 3bb5733cb..54db38cf3 100644
--- a/examples/server/tests/unit/test_speculative.py
+++ b/examples/server/tests/unit/test_speculative.py
@@ -10,16 +10,8 @@ MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tiny
 def create_server():
     global server
     server = ServerPreset.stories15m_moe()
-    # download draft model file if needed
-    file_name = MODEL_DRAFT_FILE_URL.split('/').pop()
-    model_draft_file = f'../../../{file_name}'
-    if not os.path.exists(model_draft_file):
-        print(f"Downloading {MODEL_DRAFT_FILE_URL} to {model_draft_file}")
-        with open(model_draft_file, 'wb') as f:
-            f.write(requests.get(MODEL_DRAFT_FILE_URL).content)
-        print(f"Done downloading draft model file")
     # set default values
-    server.model_draft = model_draft_file
+    server.model_draft = download_file(MODEL_DRAFT_FILE_URL)
     server.draft_min = 4
     server.draft_max = 8
 
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index 359bb0fae..a1a94d0f1 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -23,6 +23,7 @@ from typing import (
     Set,
 )
 from re import RegexFlag
+import wget
 
 
 class ServerResponse:
@@ -381,5 +382,25 @@ def match_regex(regex: str, text: str) -> bool:
         is not None
     )
 
+
+def download_file(url: str, output_file_path: str | None = None) -> str:
+    """
+    Download a file from a URL to a local path. If the file already exists, it will not be downloaded again.
+
+    output_file_path is the local path to save the downloaded file. If not provided, the file will be saved in the root directory.
+
+    Returns the local path of the downloaded file.
+    """
+    file_name = url.split('/').pop()
+    output_file = f'./tmp/{file_name}' if output_file_path is None else output_file_path
+    if not os.path.exists(output_file):
+        print(f"Downloading {url} to {output_file}")
+        wget.download(url, out=output_file)
+        print(f"Done downloading to {output_file}")
+    else:
+        print(f"File already exists at {output_file}")
+    return output_file
+
+
 def is_slow_test_allowed():
     return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON"
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 70220c437..1cf08bb0a 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -797,3 +797,44 @@ static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx
 
     return cur;
 }
+
+static bool are_lora_equal(
+        const std::vector<common_lora_adapter_container> & l1,
+        const std::vector<common_lora_adapter_container> & l2) {
+    if (l1.size() != l2.size()) {
+        return false;
+    }
+    for (size_t i = 0; i < l1.size(); ++i) {
+        // we don't check lora.path to reduce the time complexity
+        if (l1[i].scale != l2[i].scale || l1[i].adapter != l2[i].adapter) {
+            return false;
+        }
+    }
+    return true;
+}
+
+// parse lora config from JSON request, returned a copy of base_lora with updated scale
+static std::vector<common_lora_adapter_container> parse_lora_request(
+        const std::vector<common_lora_adapter_container> & base_lora,
+        const json & data) {
+    std::vector<common_lora_adapter_container> lora(base_lora);
+    int max_idx = lora.size();
+
+    // clear existing value
+    for (auto & entry : lora) {
+        entry.scale = 0.0f;
+    }
+
+    // set value
+    for (const auto & entry : data) {
+        int id      = json_value(entry, "id", -1);
+        float scale = json_value(entry, "scale", 0.0f);
+        if (0 <= id && id < max_idx) {
+            lora[id].scale = scale;
+        } else {
+            throw std::runtime_error("invalid adapter id");
+        }
+    }
+
+    return lora;
+}

From 2f0ee84b9b02d2a98742308026f060ebdc2423f1 Mon Sep 17 00:00:00 2001
From: Pierrick Hymbert <pierrick.hymbert@gmail.com>
Date: Thu, 2 Jan 2025 18:06:12 +0100
Subject: [PATCH 102/376] server: bench: minor fixes (#10765)

* server/bench:
- support openAI streaming standard output with [DONE]\n\n
- export k6 raw results in csv
- fix too many tcp idle connection in tcp_wait
- add metric time to emit first token

* server/bench:
- fix when prometheus not started
- wait for server to be ready before starting bench
---
 examples/server/bench/README.md |  6 +++---
 examples/server/bench/bench.py  | 30 +++++++++++++++++++++---------
 examples/server/bench/script.js | 18 +++++++++++++++---
 3 files changed, 39 insertions(+), 15 deletions(-)

diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md
index 353368e13..9549795ec 100644
--- a/examples/server/bench/README.md
+++ b/examples/server/bench/README.md
@@ -6,10 +6,10 @@ Benchmark is using [k6](https://k6.io/).
 
 SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
 
-Example:
+Example (assuming golang >= 1.21 is installed):
 ```shell
 go install go.k6.io/xk6/cmd/xk6@latest
-xk6 build master \
+$GOPATH/bin/xk6 build master \
 --with github.com/phymbert/xk6-sse
 ```
 
@@ -33,7 +33,7 @@ The server must answer OAI Chat completion requests on `http://localhost:8080/v1
 
 Example:
 ```shell
-server --host localhost --port 8080 \
+llama-server --host localhost --port 8080 \
   --model ggml-model-q4_0.gguf \
   --cont-batching \
   --metrics \
diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py
index a9ed747f5..5cc6f92ab 100644
--- a/examples/server/bench/bench.py
+++ b/examples/server/bench/bench.py
@@ -189,12 +189,12 @@ xychart-beta
         "pp": {
             "p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
             "avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
-            "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2),
+            "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2) if 'prompt_tokens_seconds' in prometheus_metrics else 0,
         },
         "tg": {
             "p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
             "avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
-            "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2),
+            "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2) if 'predicted_tokens_seconds' in prometheus_metrics else 0,
         },
     }
     with open("results.github.env", 'a') as github_env:
@@ -214,11 +214,14 @@ def start_benchmark(args):
     k6_args = [
         'run', args.scenario,
         '--no-color',
+        '--no-connection-reuse',
+        '--no-vu-connection-reuse',
     ]
     k6_args.extend(['--duration', args.duration])
     k6_args.extend(['--iterations', args.n_prompts])
     k6_args.extend(['--vus', args.parallel])
     k6_args.extend(['--summary-export', 'k6-results.json'])
+    k6_args.extend(['--out', 'csv=k6-results.csv'])
     args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
     args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
     print(f"bench: starting k6 with: {args}")
@@ -231,7 +234,7 @@ def start_server(args):
     server_process = start_server_background(args)
 
     attempts = 0
-    max_attempts = 20
+    max_attempts = 600
     if 'GITHUB_ACTIONS' in os.environ:
         max_attempts *= 2
 
@@ -242,7 +245,15 @@ def start_server(args):
         print(f"bench:     waiting for server to start ...")
         time.sleep(0.5)
 
-    print("bench: server started.")
+    attempts = 0
+    while not is_server_ready(args.host, args.port):
+        attempts += 1
+        if attempts > max_attempts:
+            assert False, "server not ready"
+        print(f"bench:     waiting for server to be ready ...")
+        time.sleep(0.5)
+
+    print("bench: server started and ready.")
     return server_process
 
 
@@ -255,11 +266,6 @@ def start_server_background(args):
         '--host', args.host,
         '--port', args.port,
     ]
-    model_file = args.model_path_prefix + os.path.sep + args.hf_file
-    model_dir  = os.path.dirname(model_file)
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    server_args.extend(['--model', model_file])
     server_args.extend(['--hf-repo', args.hf_repo])
     server_args.extend(['--hf-file', args.hf_file])
     server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
@@ -303,6 +309,12 @@ def is_server_listening(server_fqdn, server_port):
         return _is_server_listening
 
 
+def is_server_ready(server_fqdn, server_port):
+    url = f"http://{server_fqdn}:{server_port}/health"
+    response = requests.get(url)
+    return response.status_code == 200
+
+
 def escape_metric_name(metric_name):
     return re.sub('[^A-Z0-9]', '_', metric_name.upper())
 
diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js
index bdf4f5abc..2772bee5e 100644
--- a/examples/server/bench/script.js
+++ b/examples/server/bench/script.js
@@ -56,6 +56,7 @@ const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
 
 const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
 const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
+const llamacpp_emit_first_token_second = new Trend('llamacpp_emit_first_token_second')
 
 const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
 const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
@@ -89,6 +90,9 @@ export default function () {
         ],
         "model": model,
         "stream": true,
+        "stream_options": {
+          "include_usage": true, // False to be supported in llama.cpp server
+        },
         "seed": 42,
         "max_tokens": max_tokens,
         "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS
@@ -105,12 +109,20 @@ export default function () {
         client.on('event', function (event) {
             if (promptEvalEndTime == null) {
                 promptEvalEndTime = new Date()
+                llamacpp_emit_first_token_second.add((promptEvalEndTime - startTime) / 1.e3)
+            }
+
+            if (event.data === '[DONE]' || event.data === '') {
+                return
             }
 
             let chunk = JSON.parse(event.data)
-            let choice = chunk.choices[0]
-            if (choice.finish_reason) {
-                finish_reason = choice.finish_reason
+
+            if (chunk.choices && chunk.choices.length > 0) {
+                let choice = chunk.choices[0]
+                if (choice.finish_reason) {
+                    finish_reason = choice.finish_reason
+                }
             }
 
             if (chunk.usage) {

From f66f5829276650cd83a087ab2cfed1a760183ea1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 3 Jan 2025 10:18:53 +0200
Subject: [PATCH 103/376] llama : refactor `src/llama.cpp` (#10902)

* llama : scatter llama.cpp into multiple modules (wip)

* llama : control-vector -> adapter

* llama : arch

* llama : mmap

ggml-ci

* ci : remove BUILD_SHARED_LIBS=OFF

ggml-ci

* llama : arch (cont)

ggml-ci

* llama : chat

ggml-ci

* llama : model

ggml-ci

* llama : hparams

ggml-ci

* llama : adapter

ggml-ci

* examples : fix

ggml-ci

* rebase

ggml-ci

* minor

* llama : kv cache

ggml-ci

* llama : impl

ggml-ci

* llama : batch

ggml-ci

* cont

ggml-ci

* llama : context

ggml-ci

* minor

* llama : context (cont)

ggml-ci

* llama : model loader

ggml-ci

* common : update lora

ggml-ci

* llama : quant

ggml-ci

* llama : quant (cont)

ggml-ci

* minor [no ci]
---
 .github/workflows/build.yml                   |    28 +-
 common/arg.cpp                                |     4 +-
 common/common.cpp                             |    25 +-
 common/common.h                               |    26 +-
 .../convert-llama2c-to-ggml.cpp               |    10 +-
 .../cvector-generator/cvector-generator.cpp   |     7 +-
 examples/embedding/embedding.cpp              |     7 +-
 examples/eval-callback/eval-callback.cpp      |     8 +-
 examples/gguf-split/gguf-split.cpp            |     7 +-
 examples/imatrix/imatrix.cpp                  |    11 +-
 examples/infill/infill.cpp                    |     7 +-
 examples/lookahead/lookahead.cpp              |     7 +-
 examples/lookup/lookup-create.cpp             |    13 +-
 examples/lookup/lookup-stats.cpp              |    10 +-
 examples/lookup/lookup.cpp                    |     7 +-
 examples/main/main.cpp                        |    11 +-
 examples/parallel/parallel.cpp                |     7 +-
 examples/perplexity/perplexity.cpp            |     8 +-
 examples/quantize-stats/quantize-stats.cpp    |    16 +-
 examples/retrieval/retrieval.cpp              |     6 +-
 examples/save-load-state/save-load-state.cpp  |    29 +-
 examples/server/server.cpp                    |    65 +-
 examples/server/utils.hpp                     |    14 +-
 .../speculative-simple/speculative-simple.cpp |    16 +-
 examples/speculative/speculative.cpp          |    16 +-
 examples/tts/tts.cpp                          |    16 +-
 include/llama-cpp.h                           |     5 +
 include/llama.h                               |    24 +-
 src/CMakeLists.txt                            |    14 +-
 src/llama-adapter.cpp                         |   334 +
 src/llama-adapter.h                           |    66 +
 src/llama-arch.cpp                            |  1414 ++
 src/llama-arch.h                              |   391 +
 src/llama-batch.cpp                           |   368 +
 src/llama-batch.h                             |    88 +
 src/llama-chat.cpp                            |   549 +
 src/llama-chat.h                              |    50 +
 src/llama-context.cpp                         |  1771 +++
 src/llama-context.h                           |   128 +
 src/llama-cparams.cpp                         |     1 +
 src/llama-cparams.h                           |    37 +
 src/llama-grammar.cpp                         |     1 +
 src/llama-grammar.h                           |     4 +-
 src/llama-hparams.cpp                         |    71 +
 src/llama-hparams.h                           |   132 +
 src/llama-impl.cpp                            |   166 +
 src/llama-impl.h                              |   152 +-
 src/llama-kv-cache.cpp                        |   718 +
 src/llama-kv-cache.h                          |   218 +
 src/llama-mmap.cpp                            |   585 +
 src/llama-mmap.h                              |    67 +
 src/llama-model-loader.cpp                    |  1010 ++
 src/llama-model-loader.h                      |   158 +
 src/llama-model.cpp                           |  2164 +++
 src/llama-model.h                             |   389 +
 src/llama-quant.cpp                           |   929 ++
 src/llama-quant.h                             |     1 +
 src/llama-sampling.cpp                        |   113 +
 src/llama-vocab.cpp                           |    18 +-
 src/llama-vocab.h                             |    14 +-
 src/llama.cpp                                 | 11311 +---------------
 61 files changed, 12193 insertions(+), 11649 deletions(-)
 create mode 100644 src/llama-adapter.cpp
 create mode 100644 src/llama-adapter.h
 create mode 100644 src/llama-arch.cpp
 create mode 100644 src/llama-arch.h
 create mode 100644 src/llama-batch.cpp
 create mode 100644 src/llama-batch.h
 create mode 100644 src/llama-chat.cpp
 create mode 100644 src/llama-chat.h
 create mode 100644 src/llama-context.cpp
 create mode 100644 src/llama-context.h
 create mode 100644 src/llama-cparams.cpp
 create mode 100644 src/llama-cparams.h
 create mode 100644 src/llama-hparams.cpp
 create mode 100644 src/llama-hparams.h
 create mode 100644 src/llama-impl.cpp
 create mode 100644 src/llama-kv-cache.cpp
 create mode 100644 src/llama-kv-cache.h
 create mode 100644 src/llama-mmap.cpp
 create mode 100644 src/llama-mmap.h
 create mode 100644 src/llama-model-loader.cpp
 create mode 100644 src/llama-model-loader.h
 create mode 100644 src/llama-model.cpp
 create mode 100644 src/llama-model.h
 create mode 100644 src/llama-quant.cpp
 create mode 100644 src/llama-quant.h

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index a377eff38..602cf5220 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -60,8 +60,7 @@ jobs:
             -DLLAMA_CURL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DGGML_RPC=ON \
-            -DBUILD_SHARED_LIBS=OFF
+            -DGGML_RPC=ON
           cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -123,8 +122,7 @@ jobs:
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_CURL=ON \
             -DGGML_METAL=OFF \
-            -DGGML_RPC=ON \
-            -DBUILD_SHARED_LIBS=OFF
+            -DGGML_RPC=ON
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -181,7 +179,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF
+          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -651,23 +649,23 @@ jobs:
       matrix:
         include:
           - build: 'noavx-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF'
           - build: 'avx2-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON'
           - build: 'avx-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF'
           - build: 'avx512-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON'
           - build: 'openblas-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
           - build: 'kompute-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON'
           - build: 'vulkan-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON'
           - build: 'llvm-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
           - build: 'msvc-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=O'
           - build: 'llvm-arm64-opencl-adreno'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
 
@@ -914,7 +912,7 @@ jobs:
         shell: cmd
         run: |
           call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
-          cmake -S . -B build -G "Ninja Multi-Config" -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON
+          cmake -S . -B build -G "Ninja Multi-Config" -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DGGML_RPC=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% -t ggml
           cmake --build build --config Release
diff --git a/common/arg.cpp b/common/arg.cpp
index deb113786..c81b15217 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1512,7 +1512,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0 });
+            params.lora_adapters.push_back({ std::string(value), 1.0, nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -1520,7 +1520,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora-scaled"}, "FNAME", "SCALE",
         "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale) });
+            params.lora_adapters.push_back({ fname, std::stof(scale), nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
diff --git a/common/common.cpp b/common/common.cpp
index fe923fce6..3e37039ca 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -922,20 +922,21 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     // load and optionally apply lora adapters
     for (auto & la : params.lora_adapters) {
-        common_lora_adapter_container loaded_la;
-        loaded_la.path = la.path;
-        loaded_la.scale = la.scale;
-        loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
-        if (loaded_la.adapter == nullptr) {
+        llama_lora_adapter_ptr lora;
+        lora.reset(llama_lora_adapter_init(model, la.path.c_str()));
+        if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
             llama_free(lctx);
             llama_free_model(model);
             return iparams;
         }
-        iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
+
+        la.ptr = lora.get();
+        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
+
     if (!params.lora_init_without_apply) {
-        common_lora_adapters_apply(lctx, iparams.lora_adapters);
+        common_lora_adapters_apply(lctx, params.lora_adapters);
     }
 
     if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
@@ -996,17 +997,17 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_perf_context_reset(lctx);
     }
 
-    iparams.model   = model;
-    iparams.context = lctx;
+    iparams.model.reset(model);
+    iparams.context.reset(lctx);
 
     return iparams;
 }
 
-void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
+void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora) {
     llama_lora_adapter_clear(ctx);
-    for (auto & la : lora_adapters) {
+    for (auto & la : lora) {
         if (la.scale != 0.0f) {
-            llama_lora_adapter_set(ctx, la.adapter, la.scale);
+            llama_lora_adapter_set(ctx, la.ptr, la.scale);
         }
     }
 }
diff --git a/common/common.h b/common/common.h
index 589f65d09..0d452cf0f 100644
--- a/common/common.h
+++ b/common/common.h
@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "llama.h"
+#include "llama-cpp.h"
 
 #include <string>
 #include <vector>
@@ -27,10 +27,8 @@
 struct common_lora_adapter_info {
     std::string path;
     float scale;
-};
 
-struct common_lora_adapter_container : common_lora_adapter_info {
-    struct llama_lora_adapter * adapter;
+    struct llama_lora_adapter * ptr;
 };
 
 using llama_tokens = std::vector<llama_token>;
@@ -478,10 +476,12 @@ std::string fs_get_cache_file(const std::string & filename);
 // Model utils
 //
 
+// note: defines object's lifetime
 struct common_init_result {
-    struct llama_model   * model   = nullptr;
-    struct llama_context * context = nullptr;
-    std::vector<common_lora_adapter_container> lora_adapters;
+    llama_model_ptr   model;
+    llama_context_ptr context;
+
+    std::vector<llama_lora_adapter_ptr> lora;
 };
 
 struct common_init_result     common_init_from_params(common_params & params);
@@ -503,7 +503,7 @@ struct llama_model * common_load_model_from_hf(
     const struct llama_model_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
-void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
+void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora);
 
 //
 // Batch utils
@@ -640,6 +640,10 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
 // Split utils
 //
 
-static const char * const LLM_KV_SPLIT_NO            = "split.no";
-static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
-static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
+namespace {
+
+const char * const LLM_KV_SPLIT_NO            = "split.no";
+const char * const LLM_KV_SPLIT_COUNT         = "split.count";
+const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
+
+}
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 988a584c9..736035d78 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -434,12 +434,12 @@ static void print_matrix(struct ggml_tensor * probs) {
     }
 }
 
-struct llama_file {
+struct my_llama_file {
     // use FILE * so we don't have to re-open the file to mmap
     FILE * fp;
     size_t size;
 
-    llama_file(const char * fname, const char * mode) {
+    my_llama_file(const char * fname, const char * mode) {
         fp = std::fopen(fname, mode);
         if (fp == NULL) {
             size = 0;
@@ -500,7 +500,7 @@ struct llama_file {
         return std::string(chars.data(), len);
     }
 
-    ~llama_file() {
+    ~my_llama_file() {
         if (fp) {
             std::fclose(fp);
         }
@@ -508,7 +508,7 @@ struct llama_file {
 };
 
 static bool is_ggml_file(const char * filename) {
-    llama_file file(filename, "rb");
+    my_llama_file file(filename, "rb");
     if (file.size < 4) {
         return false;
     }
@@ -576,7 +576,7 @@ static void load_vocab(const char * filename, const Config * config, struct my_l
     } else {
         // assume llama2.c vocabulary
         LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
-        llama_file file(filename, "rb");
+        my_llama_file file(filename, "rb");
         if (!file.fp) {
             die_fmt("%s: %s", strerror(errno), filename);
         }
diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp
index d1731bba6..7c9f50228 100644
--- a/examples/cvector-generator/cvector-generator.cpp
+++ b/examples/cvector-generator/cvector-generator.cpp
@@ -415,12 +415,13 @@ int main(int argc, char ** argv) {
     // load the model to get hparams
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     // int n_ctx = llama_n_ctx(ctx);
     int n_layers = llama_n_layer(model);
     int n_embd = llama_n_embd(model);
+
     // get model hint param (a.k.a model arch name)
     char model_hint[128];
     llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
@@ -474,8 +475,6 @@ int main(int argc, char ** argv) {
 
     // done with the model, we can now free it to make gain some memory
     printf("Done evaluate prompts, unload model...\n");
-    llama_free(ctx);
-    llama_free_model(model);
 
     bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;
 
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 3f18fc6a7..27f75cb77 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -97,8 +97,9 @@ int main(int argc, char ** argv) {
     // load the model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
+
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
         return 1;
@@ -316,8 +317,6 @@ int main(int argc, char ** argv) {
 
     // clean up
     llama_batch_free(batch);
-    llama_free(ctx);
-    llama_free_model(model);
     llama_backend_free();
 
     return 0;
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index c08e3e5f6..2111c3cda 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -162,8 +162,9 @@ int main(int argc, char ** argv) {
     // init
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
+
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
         return 1;
@@ -184,9 +185,6 @@ int main(int argc, char ** argv) {
     LOG("\n");
     llama_perf_context_print(ctx);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     return 0;
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
index 75f63f938..9e3d44984 100644
--- a/examples/gguf-split/gguf-split.cpp
+++ b/examples/gguf-split/gguf-split.cpp
@@ -2,15 +2,14 @@
 #include "common.h"
 
 #include <algorithm>
-#include <cmath>
 #include <cstdlib>
 #include <fstream>
 #include <string>
 #include <vector>
-
-#include <stdio.h>
-#include <string.h>
 #include <climits>
+
+#include <cstdio>
+#include <cstring>
 #include <stdexcept>
 
 #if defined(_WIN32)
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
index 45206f4a7..588114ecd 100644
--- a/examples/imatrix/imatrix.cpp
+++ b/examples/imatrix/imatrix.cpp
@@ -430,9 +430,10 @@ static void process_logits(
 
 static bool compute_imatrix(llama_context * ctx, const common_params & params) {
     const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
     const int n_ctx = llama_n_ctx(ctx);
 
+    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
+
     auto tim1 = std::chrono::high_resolution_clock::now();
     LOG_INF("%s: tokenizing the input ..\n", __func__);
 
@@ -618,8 +619,9 @@ int main(int argc, char ** argv) {
     // init
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
+
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
         return 1;
@@ -655,9 +657,6 @@ int main(int argc, char ** argv) {
     LOG("\n");
     llama_perf_context_print(ctx);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     return 0;
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index ef7008957..d460be314 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -131,8 +131,8 @@ int main(int argc, char ** argv) {
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
     common_init_result llama_init = common_init_from_params(params);
 
-    model = llama_init.model;
-    ctx = llama_init.context;
+    model = llama_init.model.get();
+    ctx = llama_init.context.get();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -581,9 +581,6 @@ int main(int argc, char ** argv) {
     LOG("\n");
     common_perf_print(ctx, smpl);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     common_sampler_free(smpl);
     llama_backend_free();
 
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index 8d0ef8b3d..e016618e3 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -58,8 +58,8 @@ int main(int argc, char ** argv) {
     // load the target model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     // Tokenize the prompt
     std::vector<llama_token> inp;
@@ -474,9 +474,6 @@ int main(int argc, char ** argv) {
 
     llama_batch_free(batch);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
index 7ced0aa97..3da45ed9e 100644
--- a/examples/lookup/lookup-create.cpp
+++ b/examples/lookup/lookup-create.cpp
@@ -1,14 +1,9 @@
 #include "arg.h"
 #include "common.h"
 #include "ngram-cache.h"
-#include "ggml.h"
 #include "llama.h"
 
-#include <cstdint>
-#include <fstream>
-#include <iostream>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
 int main(int argc, char ** argv){
@@ -25,16 +20,16 @@ int main(int argc, char ** argv){
     // load the model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model_ptr & model = llama_init.model;
+    llama_context_ptr & ctx = llama_init.context;
+
     GGML_ASSERT(model != nullptr);
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx, params.prompt, true, true);
+    inp = common_tokenize(ctx.get(), params.prompt, true, true);
     fprintf(stderr, "%s: tokenization done\n", __func__);
 
-
     common_ngram_cache ngram_cache;
     common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
     fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index dff07c075..fcb289abe 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -30,12 +30,11 @@ int main(int argc, char ** argv){
     // load the model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_context_ptr & ctx = llama_init.context;
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx, params.prompt, true, true);
+    inp = common_tokenize(ctx.get(), params.prompt, true, true);
 
     common_ngram_cache ngram_cache_context;
     common_ngram_cache ngram_cache_dynamic;
@@ -66,7 +65,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = llama_n_ctx(ctx);
+    const int n_ctx = llama_n_ctx(ctx.get());
 
     int n_drafted = 0;
     int n_accept  = 0;
@@ -150,9 +149,6 @@ int main(int argc, char ** argv){
     LOG_INF("n_accept     = %d\n", n_accept);
     LOG_INF("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 4d92bb238..0d68b80b9 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -33,8 +33,8 @@ int main(int argc, char ** argv){
     // load the model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     // tokenize the prompt
     std::vector<llama_token> inp;
@@ -243,9 +243,6 @@ int main(int argc, char ** argv){
 
     llama_batch_free(batch_tgt);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index d0c28f317..b5e477f5b 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -145,18 +145,18 @@ int main(int argc, char ** argv) {
     llama_context * ctx = nullptr;
     common_sampler * smpl = nullptr;
 
-    std::vector<common_chat_msg> chat_msgs;
-
     g_model = &model;
     g_ctx = &ctx;
     g_smpl = &smpl;
 
+    std::vector<common_chat_msg> chat_msgs;
+
     // load the model and apply lora adapter, if any
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
     common_init_result llama_init = common_init_from_params(params);
 
-    model = llama_init.model;
-    ctx = llama_init.context;
+    model = llama_init.model.get();
+    ctx = llama_init.context.get();
 
     if (model == NULL) {
         LOG_ERR("%s: error: unable to load model\n", __func__);
@@ -889,9 +889,6 @@ int main(int argc, char ** argv) {
 
     common_sampler_free(smpl);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     ggml_threadpool_free_fn(threadpool);
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index fd2b1c011..d48f51975 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -132,8 +132,8 @@ int main(int argc, char ** argv) {
     // load the target model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     // load the prompts from an external file if there are any
     if (params.prompt.empty()) {
@@ -416,9 +416,6 @@ int main(int argc, char ** argv) {
 
     llama_batch_free(batch);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 64a84607c..6bdc57f8e 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -1987,8 +1987,9 @@ int main(int argc, char ** argv) {
     // load the model and apply lora adapter, if any
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
+
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
         return 1;
@@ -2023,9 +2024,6 @@ int main(int argc, char ** argv) {
     LOG("\n");
     llama_perf_context_print(ctx);
 
-    llama_free(ctx);
-    llama_free_model(model);
-
     llama_backend_free();
 
     return 0;
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 912caf346..ab91d0b40 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -1,7 +1,7 @@
-#include "common.h"
 #include "ggml.h"
 #include "llama.h"
-#include "llama-impl.h"
+#include "llama-context.h"
+#include "common.h"
 
 #include <algorithm>
 #include <cassert>
@@ -9,11 +9,9 @@
 #include <cmath>
 #include <cstdio>
 #include <cstring>
-#include <map>
 #include <numeric>
 #include <regex>
 #include <string>
-#include <unordered_map>
 #include <vector>
 #include <thread>
 #include <mutex>
@@ -330,13 +328,13 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const auto &tensors = llama_internal_get_tensor_map(ctx);
+    const auto & tensors = llama_internal_get_tensor_map(ctx);
 
     // check layer tensors
     int included_layers = 0;
     int64_t max_nelements = 0;
     bool is_f16 = false;
-    for (const auto& kv_tensor : tensors) {
+    for (const auto & kv_tensor : tensors) {
         if (!layer_included(params, kv_tensor.first)) {
             continue;
         }
@@ -371,8 +369,8 @@ int main(int argc, char ** argv) {
         if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
             continue;
         }
-        const auto *  qfns     = ggml_get_type_traits(type);
-        const auto *  qfns_cpu = ggml_get_type_traits_cpu(type);
+        const auto * qfns     = ggml_get_type_traits(type);
+        const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
         if (qfns_cpu->from_float && qfns->to_float) {
             if (params.verbose) {
                 printf("testing %s ...\n",  ggml_type_name(type));
@@ -382,7 +380,7 @@ int main(int argc, char ** argv) {
 
             error_stats global_stats {};
 
-            for (const auto& kv_tensor : tensors) {
+            for (const auto & kv_tensor : tensors) {
                 if (!layer_included(params, kv_tensor.first)) {
                     continue;
                 }
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index a5c6fe7e5..f534b5eff 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -151,8 +151,8 @@ int main(int argc, char ** argv) {
     // load the model
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -298,7 +298,5 @@ int main(int argc, char ** argv) {
 
     // clean up
     llama_batch_free(query_batch);
-    llama_free(ctx);
-    llama_free_model(model);
     llama_backend_free();
 }
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 2f0cf9baa..cd03661cf 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -30,8 +30,8 @@ int main(int argc, char ** argv) {
     // init
     common_init_result llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model;
-    llama_context * ctx = llama_init.context;
+    llama_model * model = llama_init.model.get();
+    llama_context * ctx = llama_init.context.get();
 
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
@@ -89,8 +89,6 @@ int main(int argc, char ** argv) {
         if (llama_decode(ctx, batch)) {
             fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
             llama_batch_free(batch);
-            llama_free(ctx);
-            llama_free_model(model);
             return 1;
         }
         n_past += 1;
@@ -98,11 +96,8 @@ int main(int argc, char ** argv) {
 
     printf("\n\n");
 
-    // free old context
-    llama_free(ctx);
-
     // make new context
-    auto * ctx2 = llama_new_context_with_model(model, common_context_params_to_llama(params));
+    llama_context * ctx2 = llama_new_context_with_model(model, common_context_params_to_llama(params));
 
     llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
 
@@ -123,8 +118,6 @@ int main(int argc, char ** argv) {
 
         if (read != llama_state_set_data(ctx2, state_mem.data(), state_mem.size())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
-            llama_free(ctx2);
-            llama_free_model(model);
             return 1;
         }
 
@@ -148,8 +141,6 @@ int main(int argc, char ** argv) {
         if (llama_decode(ctx2, batch)) {
             fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
             llama_batch_free(batch);
-            llama_free(ctx2);
-            llama_free_model(model);
             return 1;
         }
         n_past += 1;
@@ -157,15 +148,13 @@ int main(int argc, char ** argv) {
 
     printf("\n\n");
 
-    llama_free(ctx2);
-
     if (result0 != result1) {
         fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
         return 1;
     }
 
     // make new context
-    auto * ctx3 = llama_new_context_with_model(model, common_context_params_to_llama(params));
+    llama_context * ctx3 = llama_new_context_with_model(model, common_context_params_to_llama(params));
 
     llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
 
@@ -186,8 +175,6 @@ int main(int argc, char ** argv) {
 
         if (read != llama_state_set_data(ctx3, state_mem.data(), state_mem.size())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
-            llama_free(ctx3);
-            llama_free_model(model);
             return 1;
         }
 
@@ -204,8 +191,6 @@ int main(int argc, char ** argv) {
         const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0);
         if (ncopy != seq_store.size()) {
             fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
-            llama_free(ctx3);
-            llama_free_model(model);
             return 1;
         }
         fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
@@ -218,8 +203,6 @@ int main(int argc, char ** argv) {
         const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1);
         if (nset != seq_store.size()) {
             fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
-            llama_free(ctx3);
-            llama_free_model(model);
             return 1;
         }
         fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
@@ -239,8 +222,6 @@ int main(int argc, char ** argv) {
         if (llama_decode(ctx3, batch)) {
             fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
             llama_batch_free(batch);
-            llama_free(ctx3);
-            llama_free_model(model);
             return 1;
         }
         n_past += 1;
@@ -253,8 +234,6 @@ int main(int argc, char ** argv) {
     llama_sampler_free(smpl3);
 
     llama_batch_free(batch);
-    llama_free(ctx3);
-    llama_free_model(model);
 
     if (result0 != result2) {
         fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 5118084f1..c2e62ba69 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -98,7 +98,7 @@ struct slot_params {
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
-    std::vector<common_lora_adapter_container> lora;
+    std::vector<common_lora_adapter_info> lora;
 
     std::vector<std::string> antiprompt;
     std::vector<std::string> response_fields;
@@ -198,7 +198,7 @@ struct server_task {
     bool metrics_reset_bucket = false;
 
     // used by SERVER_TASK_TYPE_SET_LORA
-    std::vector<common_lora_adapter_container> set_lora;
+    std::vector<common_lora_adapter_info> set_lora;
 
     server_task(server_task_type type) : type(type) {}
 
@@ -206,7 +206,6 @@ struct server_task {
             const llama_model * model,
             const llama_context * ctx,
             const common_params & params_base,
-            const std::vector<common_lora_adapter_container> & lora_base,
             const json & data) {
         slot_params params;
 
@@ -265,12 +264,12 @@ struct server_task {
 
         if (data.contains("lora")) {
             if (data.at("lora").is_array()) {
-                params.lora = parse_lora_request(lora_base, data.at("lora"));
+                params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora"));
             } else {
                 throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
             }
         } else {
-            params.lora = lora_base;
+            params.lora = params_base.lora_adapters;
         }
 
         // TODO: add more sanity checks for the input parameters
@@ -1132,7 +1131,7 @@ struct server_slot {
 
     common_speculative * spec = nullptr;
 
-    std::vector<common_lora_adapter_container> lora;
+    std::vector<common_lora_adapter_info> lora;
 
     // the index relative to completion multi-task request
     size_t index = 0;
@@ -1627,11 +1626,15 @@ struct server_response {
 struct server_context {
     common_params params_base;
 
+    // note: keep these alive - they determine the lifetime of the model, context, etc.
+    common_init_result llama_init;
+    common_init_result llama_init_dft;
+
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
-    std::vector<common_lora_adapter_container> lora;
 
     llama_model * model_dft = nullptr;
+
     llama_context_params cparams_dft;
 
     llama_batch batch = {};
@@ -1655,21 +1658,6 @@ struct server_context {
     float slot_prompt_similarity = 0.0f;
 
     ~server_context() {
-        if (ctx) {
-            llama_free(ctx);
-            ctx = nullptr;
-        }
-
-        if (model) {
-            llama_free_model(model);
-            model = nullptr;
-        }
-
-        if (model_dft) {
-            llama_free_model(model_dft);
-            model_dft = nullptr;
-        }
-
         // Clear any sampling context
         for (server_slot & slot : slots) {
             common_sampler_free(slot.smpl);
@@ -1692,11 +1680,10 @@ struct server_context {
 
         params_base = params;
 
-        common_init_result llama_init = common_init_from_params(params_base);
+        llama_init = common_init_from_params(params_base);
 
-        model = llama_init.model;
-        ctx   = llama_init.context;
-        lora  = llama_init.lora_adapters;
+        model = llama_init.model.get();
+        ctx   = llama_init.context.get();
 
         if (model == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str());
@@ -1719,25 +1706,22 @@ struct server_context {
             params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers;
             params_dft.n_parallel   = 1;
 
-            common_init_result llama_init_dft = common_init_from_params(params_dft);
+            llama_init_dft = common_init_from_params(params_dft);
 
-            model_dft = llama_init_dft.model;
+            model_dft = llama_init_dft.model.get();
 
             if (model_dft == nullptr) {
                 SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.c_str());
                 return false;
             }
 
-            if (!common_speculative_are_compatible(ctx, llama_init_dft.context)) {
+            if (!common_speculative_are_compatible(ctx, llama_init_dft.context.get())) {
                 SRV_ERR("the draft model '%s' is not compatible with the target model '%s'\n", params_base.speculative.model.c_str(), params_base.model.c_str());
 
-                llama_free      (llama_init_dft.context);
-                llama_free_model(llama_init_dft.model);
-
                 return false;
             }
 
-            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context);
+            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get());
 
             cparams_dft = common_context_params_to_llama(params_dft);
             cparams_dft.n_batch = n_ctx_dft;
@@ -1745,9 +1729,6 @@ struct server_context {
             // force F16 KV cache for the draft model for extra performance
             cparams_dft.type_k = GGML_TYPE_F16;
             cparams_dft.type_v = GGML_TYPE_F16;
-
-            // the context is not needed - we will create one for each slot
-            llama_free(llama_init_dft.context);
         }
 
         return true;
@@ -1898,7 +1879,7 @@ struct server_context {
         if (!are_lora_equal(task.params.lora, slot.lora)) {
             // if lora is changed, we cannot reuse cached tokens
             slot.cache_tokens.clear();
-            slot.lora = std::move(task.params.lora);
+            slot.lora = task.params.lora;
         }
 
         SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());
@@ -2592,7 +2573,7 @@ struct server_context {
                 } break;
             case SERVER_TASK_TYPE_SET_LORA:
                 {
-                    lora = std::move(task.set_lora);
+                    params_base.lora_adapters = std::move(task.set_lora);
                     auto res = std::make_unique<server_task_result_apply_lora>();
                     res->id = task.id;
                     queue_results.send(std::move(res));
@@ -3671,7 +3652,6 @@ int main(int argc, char ** argv) {
                                             ctx_server.model,
                                             ctx_server.ctx,
                                             ctx_server.params_base,
-                                            ctx_server.lora,
                                             data);
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
@@ -4098,8 +4078,9 @@ int main(int argc, char ** argv) {
 
     const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
         json result = json::array();
-        for (size_t i = 0; i < ctx_server.lora.size(); ++i) {
-            auto & lora = ctx_server.lora[i];
+        const auto & loras = ctx_server.params_base.lora_adapters;
+        for (size_t i = 0; i < loras.size(); ++i) {
+            auto & lora = loras[i];
             result.push_back({
                 {"id", i},
                 {"path", lora.path},
@@ -4118,7 +4099,7 @@ int main(int argc, char ** argv) {
         }
         server_task task(SERVER_TASK_TYPE_SET_LORA);
         task.id = ctx_server.queue_tasks.get_new_id();
-        task.set_lora = parse_lora_request(ctx_server.lora, body);
+        task.set_lora = parse_lora_request(ctx_server.params_base.lora_adapters, body);
         ctx_server.queue_results.add_waiting_task_id(task.id);
         ctx_server.queue_tasks.post(task);
 
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 1cf08bb0a..dc6e6e67e 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -799,25 +799,25 @@ static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx
 }
 
 static bool are_lora_equal(
-        const std::vector<common_lora_adapter_container> & l1,
-        const std::vector<common_lora_adapter_container> & l2) {
+        const std::vector<common_lora_adapter_info> & l1,
+        const std::vector<common_lora_adapter_info> & l2) {
     if (l1.size() != l2.size()) {
         return false;
     }
     for (size_t i = 0; i < l1.size(); ++i) {
         // we don't check lora.path to reduce the time complexity
-        if (l1[i].scale != l2[i].scale || l1[i].adapter != l2[i].adapter) {
+        if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) {
             return false;
         }
     }
     return true;
 }
 
-// parse lora config from JSON request, returned a copy of base_lora with updated scale
-static std::vector<common_lora_adapter_container> parse_lora_request(
-        const std::vector<common_lora_adapter_container> & base_lora,
+// parse lora config from JSON request, returned a copy of lora_base with updated scale
+static std::vector<common_lora_adapter_info> parse_lora_request(
+        const std::vector<common_lora_adapter_info> & lora_base,
         const json & data) {
-    std::vector<common_lora_adapter_container> lora(base_lora);
+    std::vector<common_lora_adapter_info> lora(lora_base);
     int max_idx = lora.size();
 
     // clear existing value
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index 8ca84f7af..9070c3512 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -34,7 +34,7 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     llama_model * model_tgt = NULL;
-    llama_model * model_dft = NULL;
+    //llama_model * model_dft = NULL;
 
     llama_context * ctx_tgt = NULL;
     llama_context * ctx_dft = NULL;
@@ -42,8 +42,8 @@ int main(int argc, char ** argv) {
     // load the target model
     common_init_result llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model;
-    ctx_tgt   = llama_init_tgt.context;
+    model_tgt = llama_init_tgt.model.get();
+    ctx_tgt   = llama_init_tgt.context.get();
 
     // load the draft model
     params.devices      = params.speculative.devices;
@@ -59,8 +59,8 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     common_init_result llama_init_dft = common_init_from_params(params);
 
-    model_dft = llama_init_dft.model;
-    ctx_dft   = llama_init_dft.context;
+    //model_dft = llama_init_dft.model.get();
+    ctx_dft   = llama_init_dft.context.get();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
         return 1;
@@ -251,12 +251,6 @@ int main(int argc, char ** argv) {
     common_sampler_free(smpl);
     common_speculative_free(spec);
 
-    llama_free(ctx_tgt);
-    llama_free_model(model_tgt);
-
-    llama_free(ctx_dft);
-    llama_free_model(model_dft);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index d4ad9751e..bc0b6813b 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -72,8 +72,9 @@ int main(int argc, char ** argv) {
 
     // load the target model
     common_init_result llama_init_tgt = common_init_from_params(params);
-    model_tgt = llama_init_tgt.model;
-    ctx_tgt = llama_init_tgt.context;
+
+    model_tgt = llama_init_tgt.model.get();
+    ctx_tgt   = llama_init_tgt.context.get();
 
     // load the draft model
     params.devices = params.speculative.devices;
@@ -85,8 +86,9 @@ int main(int argc, char ** argv) {
 
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     common_init_result llama_init_dft = common_init_from_params(params);
-    model_dft = llama_init_dft.model;
-    ctx_dft = llama_init_dft.context;
+
+    model_dft = llama_init_dft.model.get();
+    ctx_dft   = llama_init_dft.context.get();
 
     const bool vocab_type_tgt = llama_vocab_type(model_tgt);
     LOG_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
@@ -631,12 +633,6 @@ int main(int argc, char ** argv) {
 
     llama_batch_free(batch_dft);
 
-    llama_free(ctx_tgt);
-    llama_free_model(model_tgt);
-
-    llama_free(ctx_dft);
-    llama_free_model(model_dft);
-
     llama_backend_free();
 
     LOG("\n\n");
diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp
index 7f36b80f0..522f5e881 100644
--- a/examples/tts/tts.cpp
+++ b/examples/tts/tts.cpp
@@ -458,8 +458,9 @@ int main(int argc, char ** argv) {
     llama_context * ctx_cts = NULL;
 
     common_init_result llama_init_ttc = common_init_from_params(params);
-    model_ttc = llama_init_ttc.model;
-    ctx_ttc = llama_init_ttc.context;
+
+    model_ttc = llama_init_ttc.model.get();
+    ctx_ttc   = llama_init_ttc.context.get();
 
     // TODO: refactor in a common struct
     params.model     = params.vocoder.model;
@@ -470,8 +471,9 @@ int main(int argc, char ** argv) {
     params.embedding = true;
 
     common_init_result llama_init_cts = common_init_from_params(params);
-    model_cts = llama_init_cts.model;
-    ctx_cts = llama_init_cts.context;
+
+    model_cts = llama_init_cts.model.get();
+    ctx_cts   = llama_init_cts.context.get();
 
     std::vector<common_sampler *> smpl(n_parallel);
     for (int i = 0; i < n_parallel; ++i) {
@@ -920,12 +922,6 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
 
     LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str());
 
-    llama_free(ctx_ttc);
-    llama_free_model(model_ttc);
-
-    llama_free(ctx_cts);
-    llama_free_model(model_cts);
-
     llama_backend_free();
 
     return 0;
diff --git a/include/llama-cpp.h b/include/llama-cpp.h
index daa04d4d8..1500cb2fc 100644
--- a/include/llama-cpp.h
+++ b/include/llama-cpp.h
@@ -20,6 +20,11 @@ struct llama_sampler_deleter {
     void operator()(llama_sampler * sampler) { llama_sampler_free(sampler); }
 };
 
+struct llama_lora_adapter_deleter {
+    void operator()(llama_lora_adapter * lora_adapter) { llama_lora_adapter_free(lora_adapter); }
+};
+
 typedef std::unique_ptr<llama_model, llama_model_deleter> llama_model_ptr;
 typedef std::unique_ptr<llama_context, llama_context_deleter> llama_context_ptr;
 typedef std::unique_ptr<llama_sampler, llama_sampler_deleter> llama_sampler_ptr;
+typedef std::unique_ptr<llama_lora_adapter, llama_lora_adapter_deleter> llama_lora_adapter_ptr;
diff --git a/include/llama.h b/include/llama.h
index a4abf395b..7b305b299 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -385,6 +385,7 @@ extern "C" {
     } llama_chat_message;
 
     // lora adapter
+    // TODO: rename to llama_adapter_lora
     struct llama_lora_adapter;
 
     // Helpers for getting default parameters
@@ -416,6 +417,7 @@ extern "C" {
                              const char * path_model,
               struct llama_model_params   params);
 
+    // TODO: rename to llama_model_free
     LLAMA_API void llama_free_model(struct llama_model * model);
 
     // TODO: rename to llama_init_from_model
@@ -501,14 +503,19 @@ extern "C" {
             const char * fname_out,
             const llama_model_quantize_params * params);
 
+    //
+    // Adapters
+    //
+
     // Load a LoRA adapter from file
-    // The loaded adapter will be associated to the given model, and will be free when the model is deleted
+    // TODO: rename to llama_adapter_lora_init
     LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
             struct llama_model * model,
             const char * path_lora);
 
     // Add a loaded LoRA adapter to given context
     // This will not modify model's weight
+    // TODO: rename to llama_set_adapter_lora
     LLAMA_API int32_t llama_lora_adapter_set(
             struct llama_context * ctx,
             struct llama_lora_adapter * adapter,
@@ -516,16 +523,18 @@ extern "C" {
 
     // Remove a specific LoRA adapter from given context
     // Return -1 if the adapter is not present in the context
+    // TODO: rename to llama_rm_adapter_lora
     LLAMA_API int32_t llama_lora_adapter_remove(
             struct llama_context * ctx,
             struct llama_lora_adapter * adapter);
 
     // Remove all LoRA adapters from given context
-    LLAMA_API void llama_lora_adapter_clear(
-            struct llama_context * ctx);
+    // TODO: rename to llama_clear_adapter_lora
+    LLAMA_API void llama_lora_adapter_clear(struct llama_context * ctx);
 
     // Manually free a LoRA adapter
     // Note: loaded adapters will be free when the associated model is deleted
+    // TODO: rename to llama_adapter_lora_free
     LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
 
     // Apply a loaded control vector to a llama_context, or if data is NULL, clear
@@ -534,6 +543,7 @@ extern "C" {
     // to an n_embd x n_layers buffer starting from layer 1.
     // il_start and il_end are the layer range the vector should apply to (both inclusive)
     // See llama_control_vector_load in common to load a control vector.
+    // TODO: rename to llama_adapter_cvec_apply
     LLAMA_API int32_t llama_control_vector_apply(
             struct llama_context * lctx,
                      const float * data,
@@ -546,6 +556,8 @@ extern "C" {
     // KV cache
     //
 
+    // TODO: remove llama_kv_cache_view_* API
+
     // Information associated with an individual cell in the KV cache view.
     struct llama_kv_cache_view_cell {
         // The position for this cell. Takes KV cache shifts into account.
@@ -592,8 +604,11 @@ extern "C" {
     LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
 
     // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
+    // TODO: change signature to llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_context * ctx)
     LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
 
+    ///
+
     // Returns the number of tokens in the KV cache (slow, use only for debug)
     // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
     LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
@@ -663,6 +678,9 @@ extern "C" {
             struct llama_context * ctx,
                     llama_seq_id   seq_id);
 
+    // TODO: the llama_kv_cache_defrag and llama_kv_cache_update API tightly couples llama_context with llama_kv_cache
+    //       how to avoid this?
+
     // Defragment the KV cache
     // This will be applied:
     //   - lazily on next llama_decode()
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 2d3ea0994..aeb75bf3e 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -9,9 +9,21 @@ llama_add_compile_flags()
 add_library(llama
             ../include/llama.h
             llama.cpp
-            llama-vocab.cpp
+            llama-adapter.cpp
+            llama-arch.cpp
+            llama-batch.cpp
+            llama-chat.cpp
+            llama-context.cpp
             llama-grammar.cpp
+            llama-hparams.cpp
+            llama-impl.cpp
+            llama-kv-cache.cpp
+            llama-mmap.cpp
+            llama-model-loader.cpp
+            llama-model.cpp
+            llama-quant.cpp
             llama-sampling.cpp
+            llama-vocab.cpp
             unicode.h
             unicode.cpp
             unicode-data.cpp
diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp
new file mode 100644
index 000000000..9fd7edea3
--- /dev/null
+++ b/src/llama-adapter.cpp
@@ -0,0 +1,334 @@
+#include "llama-adapter.h"
+
+#include "llama-model.h"
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <stdexcept>
+
+// vec
+
+struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
+    if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
+        return nullptr;
+    }
+
+    return tensors[il];
+}
+
+struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
+    ggml_tensor * layer_dir = tensor_for(il);
+    if (layer_dir != nullptr) {
+        cur = ggml_add(ctx, cur, layer_dir);
+    }
+
+    return cur;
+}
+
+static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
+    const auto & hparams = model.hparams;
+
+    GGML_ASSERT(cvec.tensors.empty());
+    GGML_ASSERT(cvec.ctxs.empty());
+    GGML_ASSERT(cvec.bufs.empty());
+
+    // create a context for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            struct ggml_init_params params = {
+                /*.mem_size   =*/ hparams.n_layer*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+
+            ggml_context * ctx = ggml_init(params);
+            if (!ctx) {
+                return nullptr;
+            }
+
+            ctx_map[buft] = ctx;
+            cvec.ctxs.emplace_back(ctx);
+
+            return ctx;
+        }
+
+        return it->second;
+    };
+
+    // make tensors
+    cvec.tensors.reserve(hparams.n_layer);
+    cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
+    for (size_t il = 1; il < hparams.n_layer; il++) {
+        ggml_backend_buffer_type_t buft = llama_model_select_buft(model, il);
+        ggml_context * ctx = ctx_for_buft(buft);
+        if (!ctx) {
+            LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
+            return false;
+        }
+        ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
+        cvec.tensors.push_back(tensor);
+    }
+
+    // allocate tensors / buffers and zero
+    cvec.bufs.reserve(ctx_map.size());
+    for (auto it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx = it.second;
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        if (!buf) {
+            LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
+            return false;
+        }
+        ggml_backend_buffer_clear(buf, 0);
+        cvec.bufs.emplace_back(buf);
+    }
+
+    return true;
+}
+
+int32_t llama_control_vector_apply(
+        struct llama_control_vector & cvec,
+        const llama_model & model,
+        const float * data,
+        size_t len,
+        int32_t n_embd,
+        int32_t il_start,
+        int32_t il_end) {
+    const auto & hparams = model.hparams;
+
+    if (data == nullptr) {
+        // disable the current control vector (but leave allocated for later)
+        cvec.layer_start = -1;
+        cvec.layer_end   = -1;
+        return 0;
+    }
+
+    if (n_embd != (int) hparams.n_embd) {
+        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
+        return 1;
+    }
+
+    if (cvec.tensors.empty()) {
+        if (!llama_control_vector_init(cvec, model)) {
+            return 1;
+        }
+    }
+
+    cvec.layer_start = il_start;
+    cvec.layer_end   = il_end;
+
+    for (size_t il = 1; il < hparams.n_layer; il++) {
+        assert(cvec.tensors[il] != nullptr);
+
+        const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
+        if (off + n_embd <= len) {
+            ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
+        }
+    }
+
+    return 0;
+}
+
+// lora
+
+llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
+    const std::string name(w->name);
+
+    const auto pos = ab_map.find(name);
+    if (pos != ab_map.end()) {
+        return &pos->second;
+    }
+
+    return nullptr;
+}
+
+void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
+    delete adapter;
+}
+
+static void llama_lora_adapter_init_impl(struct llama_model & model, const char * path_lora, struct llama_lora_adapter & adapter) {
+    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
+
+    ggml_context * ctx_init;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ true,
+        /* .ctx      = */ &ctx_init,
+    };
+
+    gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
+    if (!ctx_gguf) {
+        throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
+    }
+
+    ggml_context_ptr ctx { ctx_init };
+
+    // check metadata
+    {
+        auto get_kv_str = [&](const std::string & key) -> std::string {
+            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
+            return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
+        };
+        auto get_kv_f32 = [&](const std::string & key) -> float {
+            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
+            return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
+        };
+        LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
+
+        auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
+        if (general_type != "adapter") {
+            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
+        }
+
+        auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
+        auto general_arch = llm_arch_from_string(general_arch_str);
+        if (general_arch != model.arch) {
+            throw std::runtime_error("model arch and LoRA arch mismatch");
+        }
+
+        auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
+        if (adapter_type != "lora") {
+            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
+        }
+
+        adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
+    }
+
+    int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
+
+    // contexts for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            // add a new context
+            struct ggml_init_params params = {
+                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+            ggml_context * buft_ctx = ggml_init(params);
+            if (!buft_ctx) {
+                return nullptr;
+            }
+            ctx_map[buft] = buft_ctx;
+            adapter.ctxs.emplace_back(buft_ctx);
+            return buft_ctx;
+        };
+        return it->second;
+    };
+
+    // bundle lora_a and lora_b into pairs
+    std::map<std::string, llama_lora_weight> ab_map;
+    auto str_endswith = [](const std::string & str, const std::string & suffix) {
+        return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+    };
+
+    for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
+        std::string name(cur->name);
+        if (str_endswith(name, ".lora_a")) {
+            replace_all(name, ".lora_a", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(cur, nullptr);
+            } else {
+                ab_map[name].a = cur;
+            }
+        } else if (str_endswith(name, ".lora_b")) {
+            replace_all(name, ".lora_b", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(nullptr, cur);
+            } else {
+                ab_map[name].b = cur;
+            }
+        } else {
+            throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
+        }
+    }
+
+    // add tensors
+    for (auto & it : ab_map) {
+        const std::string & name = it.first;
+        llama_lora_weight & w = it.second;
+
+        if (!w.a || !w.b) {
+            throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
+        }
+
+        // device buft and device ctx
+        auto * model_tensor = llama_model_get_tensor(model, name.c_str());
+        if (!model_tensor) {
+            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
+        }
+
+        struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
+        // validate tensor shape
+        if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
+            throw std::runtime_error("tensor '" + name + "' has incorrect shape");
+        }
+        if (w.a->ne[1] != w.b->ne[0]) {
+            throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+        }
+
+        // save tensor to adapter
+        struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
+        struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
+        ggml_set_name(tensor_a, w.a->name);
+        ggml_set_name(tensor_b, w.b->name);
+        adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
+    }
+
+    // allocate tensors / buffers and zero
+    {
+        adapter.ctxs.reserve(ctx_map.size());
+        adapter.bufs.reserve(ctx_map.size());
+        for (auto & it : ctx_map) {
+            ggml_backend_buffer_type_t buft = it.first;
+            ggml_context * ctx_dev = it.second;
+            ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
+            if (!buf) {
+                throw std::runtime_error("failed to allocate buffer for lora adapter\n");
+            }
+            LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
+            adapter.bufs.emplace_back(std::move(buf));
+        }
+    }
+
+    // set tensor data
+    {
+        llama_file gguf_file(path_lora, "rb");
+        std::vector<uint8_t> read_buf;
+        auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
+            size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
+            size_t size = ggml_nbytes(orig);
+            read_buf.resize(size);
+            gguf_file.seek(offs, SEEK_SET);
+            gguf_file.read_raw(read_buf.data(), size);
+            ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
+        };
+        for (auto & it : adapter.ab_map) {
+            auto orig = ab_map[it.first];
+            auto dev  = it.second;
+            set_tensor(orig.a, dev.a);
+            set_tensor(orig.b, dev.b);
+        }
+    }
+
+    LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
+}
+
+struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
+    struct llama_lora_adapter * adapter = new llama_lora_adapter();
+
+    try {
+        llama_lora_adapter_init_impl(*model, path_lora, *adapter);
+        return adapter;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
+
+        delete adapter;
+    }
+
+    return nullptr;
+}
diff --git a/src/llama-adapter.h b/src/llama-adapter.h
new file mode 100644
index 000000000..5f1870cc8
--- /dev/null
+++ b/src/llama-adapter.h
@@ -0,0 +1,66 @@
+#pragma once
+
+#include "llama-impl.h"
+#include "llama-hparams.h"
+
+#include "ggml-cpp.h"
+
+#include <unordered_map>
+#include <vector>
+
+//
+// llama_adapter_cvec
+//
+
+// TODO: rename to llama_adapter_cvec
+struct llama_control_vector {
+    std::vector<ggml_context_ptr> ctxs;
+    std::vector<ggml_backend_buffer_ptr> bufs;
+
+    std::vector<struct ggml_tensor *> tensors; // per layer
+
+    int32_t layer_start = -1;
+    int32_t layer_end   = -1;
+
+    struct ggml_tensor * tensor_for(int il) const;
+
+    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const;
+};
+
+int32_t llama_control_vector_apply(
+        struct llama_control_vector & cvec,
+        const llama_model & model,
+        const float * data,
+        size_t len,
+        int32_t n_embd,
+        int32_t il_start,
+        int32_t il_end);
+
+//
+// llama_adapter_lora
+//
+
+// TODO: rename to llama_adapter_lora_weight
+struct llama_lora_weight {
+    struct ggml_tensor * a = nullptr;
+    struct ggml_tensor * b = nullptr;
+
+    llama_lora_weight() = default;
+    llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
+};
+
+// TODO: rename to llama_adapter_lora
+struct llama_lora_adapter {
+    // map tensor name to lora_a_b
+    std::unordered_map<std::string, struct llama_lora_weight> ab_map;
+
+    std::vector<ggml_context_ptr> ctxs;
+    std::vector<ggml_backend_buffer_ptr> bufs;
+
+    float alpha;
+
+    llama_lora_adapter() = default;
+    ~llama_lora_adapter() = default;
+
+    llama_lora_weight * get_weight(struct ggml_tensor * w);
+};
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
new file mode 100644
index 000000000..a60038385
--- /dev/null
+++ b/src/llama-arch.cpp
@@ -0,0 +1,1414 @@
+#include "llama-arch.h"
+
+#include "llama-impl.h"
+
+#include <map>
+
+static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+    { LLM_ARCH_LLAMA,            "llama"            },
+    { LLM_ARCH_DECI,             "deci"             },
+    { LLM_ARCH_FALCON,           "falcon"           },
+    { LLM_ARCH_GROK,             "grok"             },
+    { LLM_ARCH_GPT2,             "gpt2"             },
+    { LLM_ARCH_GPTJ,             "gptj"             },
+    { LLM_ARCH_GPTNEOX,          "gptneox"          },
+    { LLM_ARCH_MPT,              "mpt"              },
+    { LLM_ARCH_BAICHUAN,         "baichuan"         },
+    { LLM_ARCH_STARCODER,        "starcoder"        },
+    { LLM_ARCH_REFACT,           "refact"           },
+    { LLM_ARCH_BERT,             "bert"             },
+    { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
+    { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
+    { LLM_ARCH_BLOOM,            "bloom"            },
+    { LLM_ARCH_STABLELM,         "stablelm"         },
+    { LLM_ARCH_QWEN,             "qwen"             },
+    { LLM_ARCH_QWEN2,            "qwen2"            },
+    { LLM_ARCH_QWEN2MOE,         "qwen2moe"         },
+    { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
+    { LLM_ARCH_PHI2,             "phi2"             },
+    { LLM_ARCH_PHI3,             "phi3"             },
+    { LLM_ARCH_PLAMO,            "plamo"            },
+    { LLM_ARCH_CODESHELL,        "codeshell"        },
+    { LLM_ARCH_ORION,            "orion"            },
+    { LLM_ARCH_INTERNLM2,        "internlm2"        },
+    { LLM_ARCH_MINICPM,          "minicpm"          },
+    { LLM_ARCH_MINICPM3,         "minicpm3"         },
+    { LLM_ARCH_GEMMA,            "gemma"            },
+    { LLM_ARCH_GEMMA2,           "gemma2"           },
+    { LLM_ARCH_STARCODER2,       "starcoder2"       },
+    { LLM_ARCH_MAMBA,            "mamba"            },
+    { LLM_ARCH_XVERSE,           "xverse"           },
+    { LLM_ARCH_COMMAND_R,        "command-r"        },
+    { LLM_ARCH_DBRX,             "dbrx"             },
+    { LLM_ARCH_OLMO,             "olmo"             },
+    { LLM_ARCH_OLMO2,            "olmo2"            },
+    { LLM_ARCH_OLMOE,            "olmoe"            },
+    { LLM_ARCH_OPENELM,          "openelm"          },
+    { LLM_ARCH_ARCTIC,           "arctic"           },
+    { LLM_ARCH_DEEPSEEK,         "deepseek"         },
+    { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
+    { LLM_ARCH_CHATGLM,          "chatglm"          },
+    { LLM_ARCH_BITNET,           "bitnet"           },
+    { LLM_ARCH_T5,               "t5"               },
+    { LLM_ARCH_T5ENCODER,        "t5encoder"        },
+    { LLM_ARCH_JAIS,             "jais"             },
+    { LLM_ARCH_NEMOTRON,         "nemotron"         },
+    { LLM_ARCH_EXAONE,           "exaone"           },
+    { LLM_ARCH_RWKV6,            "rwkv6"            },
+    { LLM_ARCH_GRANITE,          "granite"          },
+    { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
+    { LLM_ARCH_CHAMELEON,        "chameleon"        },
+    { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
+    { LLM_ARCH_UNKNOWN,          "(unknown)"        },
+};
+
+static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
+    { LLM_KV_GENERAL_TYPE,                 "general.type"                          },
+    { LLM_KV_GENERAL_ARCHITECTURE,         "general.architecture"                  },
+    { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version"          },
+    { LLM_KV_GENERAL_ALIGNMENT,            "general.alignment"                     },
+    { LLM_KV_GENERAL_NAME,                 "general.name"                          },
+    { LLM_KV_GENERAL_AUTHOR,               "general.author"                        },
+    { LLM_KV_GENERAL_VERSION,              "general.version"                       },
+    { LLM_KV_GENERAL_URL,                  "general.url"                           },
+    { LLM_KV_GENERAL_DESCRIPTION,          "general.description"                   },
+    { LLM_KV_GENERAL_LICENSE,              "general.license"                       },
+    { LLM_KV_GENERAL_SOURCE_URL,           "general.source.url"                    },
+    { LLM_KV_GENERAL_SOURCE_HF_REPO,       "general.source.huggingface.repository" },
+
+    { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
+    { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
+    { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
+    { LLM_KV_FEATURES_LENGTH,                   "%s.features_length"                   },
+    { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
+    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
+    { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
+    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
+    { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
+    { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
+    { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
+    { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
+    { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
+    { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
+    { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
+    { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
+    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
+    { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
+    { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
+    { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
+    { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },
+    { LLM_KV_RESCALE_EVERY_N_LAYERS,            "%s.rescale_every_n_layers"            },
+    { LLM_KV_TIME_MIX_EXTRA_DIM,                "%s.time_mix_extra_dim"                },
+    { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
+    { LLM_KV_RESIDUAL_SCALE,                    "%s.residual_scale"                    },
+    { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
+
+    { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"             },
+    { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"          },
+    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,         "%s.attention.max_alibi_bias"         },
+    { LLM_KV_ATTENTION_CLAMP_KQV,              "%s.attention.clamp_kqv"              },
+    { LLM_KV_ATTENTION_KEY_LENGTH,             "%s.attention.key_length"             },
+    { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"           },
+    { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"     },
+    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon" },
+    { LLM_KV_ATTENTION_GROUPNORM_EPS,          "%s.attention.group_norm_epsilon"     },
+    { LLM_KV_ATTENTION_GROUPNORM_GROUPS,       "%s.attention.group_norm_groups"      },
+    { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                 },
+    { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
+    { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
+    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
+    { LLM_KV_ATTENTION_SCALE,                  "%s.attention.scale"                  },
+
+    { LLM_KV_ROPE_DIMENSION_COUNT,      "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_SECTIONS,   "%s.rope.dimension_sections"              },
+    { LLM_KV_ROPE_FREQ_BASE,            "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_SCALE_LINEAR,         "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,         "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,       "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,  "%s.rope.scaling.attn_factor"             },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,    "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier"     },
+
+    { LLM_KV_SPLIT_NO,            "split.no"            },
+    { LLM_KV_SPLIT_COUNT,         "split.count"         },
+    { LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
+
+    { LLM_KV_SSM_CONV_KERNEL,    "%s.ssm.conv_kernel"    },
+    { LLM_KV_SSM_INNER_SIZE,     "%s.ssm.inner_size"     },
+    { LLM_KV_SSM_STATE_SIZE,     "%s.ssm.state_size"     },
+    { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
+    { LLM_KV_SSM_DT_B_C_RMS,     "%s.ssm.dt_b_c_rms"     },
+
+    { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
+
+    { LLM_KV_POSNET_EMBEDDING_LENGTH, "%s.posnet.embedding_length" },
+    { LLM_KV_POSNET_BLOCK_COUNT,      "%s.posnet.block_count"      },
+
+    { LLM_KV_CONVNEXT_EMBEDDING_LENGTH, "%s.convnext.embedding_length" },
+    { LLM_KV_CONVNEXT_BLOCK_COUNT,      "%s.convnext.block_count"      },
+
+    { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
+    { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
+    { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,           "tokenizer.ggml.token_type"               },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,     "tokenizer.ggml.token_type_count"         },
+    { LLM_KV_TOKENIZER_SCORES,               "tokenizer.ggml.scores"                   },
+    { LLM_KV_TOKENIZER_MERGES,               "tokenizer.ggml.merges"                   },
+    { LLM_KV_TOKENIZER_BOS_ID,               "tokenizer.ggml.bos_token_id"             },
+    { LLM_KV_TOKENIZER_EOS_ID,               "tokenizer.ggml.eos_token_id"             },
+    { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
+    { LLM_KV_TOKENIZER_EOM_ID,               "tokenizer.ggml.eom_token_id"             },
+    { LLM_KV_TOKENIZER_UNK_ID,               "tokenizer.ggml.unknown_token_id"         },
+    { LLM_KV_TOKENIZER_SEP_ID,               "tokenizer.ggml.seperator_token_id"       },
+    { LLM_KV_TOKENIZER_PAD_ID,               "tokenizer.ggml.padding_token_id"         },
+    { LLM_KV_TOKENIZER_CLS_ID,               "tokenizer.ggml.cls_token_id"             },
+    { LLM_KV_TOKENIZER_MASK_ID,              "tokenizer.ggml.mask_token_id"            },
+    { LLM_KV_TOKENIZER_ADD_BOS,              "tokenizer.ggml.add_bos_token"            },
+    { LLM_KV_TOKENIZER_ADD_EOS,              "tokenizer.ggml.add_eos_token"            },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,           "tokenizer.ggml.add_space_prefix"         },
+    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,      "tokenizer.ggml.remove_extra_whitespaces" },
+    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
+    { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
+    { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_PAD_ID,           "tokenizer.ggml.fim_pad_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_REP_ID,           "tokenizer.ggml.fim_rep_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SEP_ID,           "tokenizer.ggml.fim_sep_token_id"         },
+
+    { LLM_KV_ADAPTER_TYPE,       "adapter.type"       },
+    { LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
+
+    // deprecated
+    { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
+    { LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
+    { LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
+};
+
+static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
+    {
+        LLM_ARCH_LLAMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_DECI,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_BAICHUAN,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_FALCON,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_GROK,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+        },
+    },
+    {
+        LLM_ARCH_GPT2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_GPTJ,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+        },
+    },
+    {
+        LLM_ARCH_GPTNEOX,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MPT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output"},
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
+        },
+    },
+    {
+        LLM_ARCH_STARCODER,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_REFACT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_CLS,             "cls" },
+            { LLM_TENSOR_CLS_OUT,         "cls.output" },
+        },
+    },
+    {
+        LLM_ARCH_NOMIC_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_JINA_BERT_V2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_CLS,             "cls" },
+        },
+    },
+    {
+        LLM_ARCH_BLOOM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_STABLELM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+        },
+    },
+    {
+        LLM_ARCH_QWEN,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_QWEN2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_QWEN2VL,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_QWEN2MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
+    {
+        LLM_ARCH_PHI2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_PHI3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_PLAMO,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_CODESHELL,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_ORION,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_INTERNLM2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MINICPM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+        },
+    },
+    {
+        LLM_ARCH_MINICPM3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
+            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
+            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
+            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
+            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_GEMMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_GEMMA2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
+    {
+        LLM_ARCH_STARCODER2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MAMBA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+        },
+    },
+    {
+        LLM_ARCH_XVERSE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_COMMAND_R,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+        },
+    },
+    {
+        LLM_ARCH_DBRX,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_OLMO,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_OLMO2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_OLMOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_OPENELM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_ARCTIC,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM_EXPS,   "blk.%d.ffn_norm_exps" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_DEEPSEEK,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,      "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
+    {
+        LLM_ARCH_DEEPSEEK2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
+            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
+            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
+            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
+            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
+    {
+        LLM_ARCH_CHATGLM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_BITNET,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_SUB_NORM,      "blk.%d.attn_sub_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_SUB_NORM,       "blk.%d.ffn_sub_norm" },
+        },
+    },
+    {
+        LLM_ARCH_T5,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
+            { LLM_TENSOR_OUTPUT,               "output" },
+            { LLM_TENSOR_DEC_OUTPUT_NORM,      "dec.output_norm" },
+            { LLM_TENSOR_DEC_ATTN_NORM,        "dec.blk.%d.attn_norm" },
+            { LLM_TENSOR_DEC_ATTN_Q,           "dec.blk.%d.attn_q" },
+            { LLM_TENSOR_DEC_ATTN_K,           "dec.blk.%d.attn_k" },
+            { LLM_TENSOR_DEC_ATTN_V,           "dec.blk.%d.attn_v" },
+            { LLM_TENSOR_DEC_ATTN_OUT,         "dec.blk.%d.attn_o" },
+            { LLM_TENSOR_DEC_ATTN_REL_B,       "dec.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "dec.blk.%d.cross_attn_norm" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_Q,     "dec.blk.%d.cross_attn_q" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_K,     "dec.blk.%d.cross_attn_k" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_V,     "dec.blk.%d.cross_attn_v" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_OUT,   "dec.blk.%d.cross_attn_o" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
+            { LLM_TENSOR_DEC_FFN_NORM,         "dec.blk.%d.ffn_norm" },
+            { LLM_TENSOR_DEC_FFN_GATE,         "dec.blk.%d.ffn_gate" },
+            { LLM_TENSOR_DEC_FFN_DOWN,         "dec.blk.%d.ffn_down" },
+            { LLM_TENSOR_DEC_FFN_UP,           "dec.blk.%d.ffn_up" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
+            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
+            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
+            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
+            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
+            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
+            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
+            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
+            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
+            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_T5ENCODER,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
+            { LLM_TENSOR_OUTPUT,               "output" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
+            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
+            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
+            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
+            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
+            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
+            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
+            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
+            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
+            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_JAIS,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_NEMOTRON,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_EXAONE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_RWKV6,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
+            { LLM_TENSOR_OUTPUT,                    "output" },
+            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_NORM_2,               "blk.%d.attn_norm_2" },
+            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
+            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
+            { LLM_TENSOR_TIME_MIX_LERP_X,           "blk.%d.time_mix_lerp_x" },
+            { LLM_TENSOR_TIME_MIX_LERP_W,           "blk.%d.time_mix_lerp_w" },
+            { LLM_TENSOR_TIME_MIX_LERP_K,           "blk.%d.time_mix_lerp_k" },
+            { LLM_TENSOR_TIME_MIX_LERP_V,           "blk.%d.time_mix_lerp_v" },
+            { LLM_TENSOR_TIME_MIX_LERP_R,           "blk.%d.time_mix_lerp_r" },
+            { LLM_TENSOR_TIME_MIX_LERP_G,           "blk.%d.time_mix_lerp_g" },
+            { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
+            { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
+            { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
+            { LLM_TENSOR_TIME_MIX_DECAY_W2,         "blk.%d.time_mix_decay_w2" },
+            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
+            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
+            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
+            { LLM_TENSOR_TIME_MIX_GATE,             "blk.%d.time_mix_gate" },
+            { LLM_TENSOR_TIME_MIX_LN,               "blk.%d.time_mix_ln" },
+            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
+            { LLM_TENSOR_CHANNEL_MIX_LERP_K,        "blk.%d.channel_mix_lerp_k" },
+            { LLM_TENSOR_CHANNEL_MIX_LERP_R,        "blk.%d.channel_mix_lerp_r" },
+            { LLM_TENSOR_CHANNEL_MIX_KEY,           "blk.%d.channel_mix_key" },
+            { LLM_TENSOR_CHANNEL_MIX_VALUE,         "blk.%d.channel_mix_value" },
+            { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,    "blk.%d.channel_mix_receptance" },
+        },
+    },
+    {
+        LLM_ARCH_GRANITE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_GRANITE_MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_CHAMELEON,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+        },
+    },
+    {
+        LLM_ARCH_WAVTOKENIZER_DEC,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_CONV1D,            "conv1d" },
+            { LLM_TENSOR_CONVNEXT_DW,       "convnext.%d.dw" },
+            { LLM_TENSOR_CONVNEXT_NORM,     "convnext.%d.norm" },
+            { LLM_TENSOR_CONVNEXT_PW1,      "convnext.%d.pw1" },
+            { LLM_TENSOR_CONVNEXT_PW2,      "convnext.%d.pw2" },
+            { LLM_TENSOR_CONVNEXT_GAMMA,    "convnext.%d.gamma" },
+            { LLM_TENSOR_OUTPUT_NORM,       "output_norm" },
+            { LLM_TENSOR_OUTPUT,            "output" },
+            { LLM_TENSOR_POS_NET_CONV1,     "posnet.%d.conv1" },
+            { LLM_TENSOR_POS_NET_CONV2,     "posnet.%d.conv2" },
+            { LLM_TENSOR_POS_NET_NORM,      "posnet.%d.norm" },
+            { LLM_TENSOR_POS_NET_NORM1,     "posnet.%d.norm1" },
+            { LLM_TENSOR_POS_NET_NORM2,     "posnet.%d.norm2" },
+            { LLM_TENSOR_POS_NET_ATTN_NORM, "posnet.%d.attn_norm" },
+            { LLM_TENSOR_POS_NET_ATTN_Q,    "posnet.%d.attn_q" },
+            { LLM_TENSOR_POS_NET_ATTN_K,    "posnet.%d.attn_k" },
+            { LLM_TENSOR_POS_NET_ATTN_V,    "posnet.%d.attn_v" },
+            { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
+        },
+    },
+    {
+        LLM_ARCH_UNKNOWN,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+        },
+    },
+};
+
+static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
+    {LLM_TENSOR_TOKEN_EMBD,                 {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_POS_EMBD,                   {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_TOKEN_TYPES,                {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_ROPE_FREQS,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
+    {LLM_TENSOR_ROPE_FACTORS_LONG,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
+    {LLM_TENSOR_ROPE_FACTORS_SHORT,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
+    {LLM_TENSOR_ATTN_Q,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_K,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_V,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_QKV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_OUT,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_DOWN,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_UP,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_DOWN_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_UP_SHEXP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_Q_A,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_Q_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV_A_MQA,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_Q,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_K,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_V,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_QKV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_OUT,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_DOWN,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_UP,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_DOWN_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_UP_SHEXP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_Q_A,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_Q_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV_A_MQA,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_V,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_ATTN_OUT,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_CROSS_ATTN_Q,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_CROSS_ATTN_K,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_CROSS_ATTN_V,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_CROSS_ATTN_OUT,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_FFN_GATE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_FFN_DOWN,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DEC_FFN_UP,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_ATTN_V,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_ATTN_OUT,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_FFN_GATE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_FFN_DOWN,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ENC_FFN_UP,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE_INP_SHEXP,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_GATE_INP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_IN,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_X,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_DT,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_OUT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_W1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_W2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_DECAY_W1,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_DECAY_W2,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_KEY,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_VALUE,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_RECEPTANCE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_GATE,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_TIME_MIX_OUTPUT,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CHANNEL_MIX_KEY,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CHANNEL_MIX_VALUE,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_ACT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}},
+    {LLM_TENSOR_SSM_CONV1D,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
+    {LLM_TENSOR_SSM_A,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}},
+    {LLM_TENSOR_SSM_D,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_TIME_MIX_LERP_X,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_TIME_MIX_LN,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CHANNEL_MIX_LERP_K,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CHANNEL_MIX_LERP_R,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_TIME_MIX_LERP_W,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_LERP_K,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_LERP_V,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_LERP_R,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_LERP_G,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_DECAY,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_FIRST,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV6}},
+    {LLM_TENSOR_ATTN_NORM,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_NORM_2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_OUT_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_POST_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_NORM,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_POST_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_NORM_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_Q_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_K_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_LAYER_OUT_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_Q_A_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_KV_A_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_SUB_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_SUB_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_DEC_ATTN_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_DEC_CROSS_ATTN_NORM,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_DEC_FFN_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ENC_ATTN_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ENC_FFN_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_DEC_ATTN_REL_B,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_ENC_ATTN_REL_B,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_FFN_DOWN_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_GATE_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    // this tensor is loaded for T5, but never used
+    {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
+    {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_NORM2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_CONV1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_CONV2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_POS_NET_ATTN_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_POS_NET_ATTN_Q,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_K,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_V,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_POS_NET_ATTN_OUT,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_DW,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
+    {LLM_TENSOR_CONVNEXT_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CONVNEXT_PW1,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_PW2,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CONVNEXT_GAMMA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+};
+
+LLM_KV::LLM_KV(llm_arch arch) : arch(arch) {}
+
+std::string LLM_KV::operator()(llm_kv kv) const {
+    return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
+}
+
+std::string LLM_TN_IMPL::str() const {
+    if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+        return "__missing__";
+    }
+
+    std::string name = ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid);
+
+    if (suffix != nullptr) {
+        name += ".";
+        name += suffix;
+    }
+
+    return name;
+}
+
+const char * llm_arch_name(llm_arch arch) {
+    auto it = LLM_ARCH_NAMES.find(arch);
+    if (it == LLM_ARCH_NAMES.end()) {
+        return "unknown";
+    }
+    return it->second;
+}
+
+llm_arch llm_arch_from_string(const std::string & name) {
+    for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
+        if (kv.second == name) {
+            return kv.first;
+        }
+    }
+
+    return LLM_ARCH_UNKNOWN;
+}
+
+const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor) {
+    return LLM_TENSOR_INFOS.at(tensor);
+}
diff --git a/src/llama-arch.h b/src/llama-arch.h
new file mode 100644
index 000000000..446e72eeb
--- /dev/null
+++ b/src/llama-arch.h
@@ -0,0 +1,391 @@
+#pragma once
+
+#include "ggml.h" // ggml_op
+
+#include <string>
+
+//
+// gguf constants (sync with gguf.py)
+//
+
+enum llm_arch {
+    LLM_ARCH_LLAMA,
+    LLM_ARCH_DECI,
+    LLM_ARCH_FALCON,
+    LLM_ARCH_BAICHUAN,
+    LLM_ARCH_GROK,
+    LLM_ARCH_GPT2,
+    LLM_ARCH_GPTJ,
+    LLM_ARCH_GPTNEOX,
+    LLM_ARCH_MPT,
+    LLM_ARCH_STARCODER,
+    LLM_ARCH_REFACT,
+    LLM_ARCH_BERT,
+    LLM_ARCH_NOMIC_BERT,
+    LLM_ARCH_JINA_BERT_V2,
+    LLM_ARCH_BLOOM,
+    LLM_ARCH_STABLELM,
+    LLM_ARCH_QWEN,
+    LLM_ARCH_QWEN2,
+    LLM_ARCH_QWEN2MOE,
+    LLM_ARCH_QWEN2VL,
+    LLM_ARCH_PHI2,
+    LLM_ARCH_PHI3,
+    LLM_ARCH_PLAMO,
+    LLM_ARCH_CODESHELL,
+    LLM_ARCH_ORION,
+    LLM_ARCH_INTERNLM2,
+    LLM_ARCH_MINICPM,
+    LLM_ARCH_MINICPM3,
+    LLM_ARCH_GEMMA,
+    LLM_ARCH_GEMMA2,
+    LLM_ARCH_STARCODER2,
+    LLM_ARCH_MAMBA,
+    LLM_ARCH_XVERSE,
+    LLM_ARCH_COMMAND_R,
+    LLM_ARCH_DBRX,
+    LLM_ARCH_OLMO,
+    LLM_ARCH_OLMO2,
+    LLM_ARCH_OLMOE,
+    LLM_ARCH_OPENELM,
+    LLM_ARCH_ARCTIC,
+    LLM_ARCH_DEEPSEEK,
+    LLM_ARCH_DEEPSEEK2,
+    LLM_ARCH_CHATGLM,
+    LLM_ARCH_BITNET,
+    LLM_ARCH_T5,
+    LLM_ARCH_T5ENCODER,
+    LLM_ARCH_JAIS,
+    LLM_ARCH_NEMOTRON,
+    LLM_ARCH_EXAONE,
+    LLM_ARCH_RWKV6,
+    LLM_ARCH_GRANITE,
+    LLM_ARCH_GRANITE_MOE,
+    LLM_ARCH_CHAMELEON,
+    LLM_ARCH_WAVTOKENIZER_DEC,
+    LLM_ARCH_UNKNOWN,
+};
+
+enum llm_kv {
+    LLM_KV_GENERAL_TYPE,
+    LLM_KV_GENERAL_ARCHITECTURE,
+    LLM_KV_GENERAL_QUANTIZATION_VERSION,
+    LLM_KV_GENERAL_ALIGNMENT,
+    LLM_KV_GENERAL_NAME,
+    LLM_KV_GENERAL_AUTHOR,
+    LLM_KV_GENERAL_VERSION,
+    LLM_KV_GENERAL_URL,
+    LLM_KV_GENERAL_DESCRIPTION,
+    LLM_KV_GENERAL_LICENSE,
+    LLM_KV_GENERAL_SOURCE_URL,
+    LLM_KV_GENERAL_SOURCE_HF_REPO,
+
+    LLM_KV_VOCAB_SIZE,
+    LLM_KV_CONTEXT_LENGTH,
+    LLM_KV_EMBEDDING_LENGTH,
+    LLM_KV_FEATURES_LENGTH,
+    LLM_KV_BLOCK_COUNT,
+    LLM_KV_LEADING_DENSE_BLOCK_COUNT,
+    LLM_KV_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
+    LLM_KV_USE_PARALLEL_RESIDUAL,
+    LLM_KV_TENSOR_DATA_LAYOUT,
+    LLM_KV_EXPERT_COUNT,
+    LLM_KV_EXPERT_USED_COUNT,
+    LLM_KV_EXPERT_SHARED_COUNT,
+    LLM_KV_EXPERT_WEIGHTS_SCALE,
+    LLM_KV_POOLING_TYPE,
+    LLM_KV_LOGIT_SCALE,
+    LLM_KV_DECODER_START_TOKEN_ID,
+    LLM_KV_ATTN_LOGIT_SOFTCAPPING,
+    LLM_KV_FINAL_LOGIT_SOFTCAPPING,
+    LLM_KV_SWIN_NORM,
+    LLM_KV_RESCALE_EVERY_N_LAYERS,
+    LLM_KV_TIME_MIX_EXTRA_DIM,
+    LLM_KV_TIME_DECAY_EXTRA_DIM,
+    LLM_KV_RESIDUAL_SCALE,
+    LLM_KV_EMBEDDING_SCALE,
+
+    LLM_KV_ATTENTION_HEAD_COUNT,
+    LLM_KV_ATTENTION_HEAD_COUNT_KV,
+    LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
+    LLM_KV_ATTENTION_CLAMP_KQV,
+    LLM_KV_ATTENTION_KEY_LENGTH,
+    LLM_KV_ATTENTION_VALUE_LENGTH,
+    LLM_KV_ATTENTION_LAYERNORM_EPS,
+    LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
+    LLM_KV_ATTENTION_GROUPNORM_EPS,
+    LLM_KV_ATTENTION_GROUPNORM_GROUPS,
+    LLM_KV_ATTENTION_CAUSAL,
+    LLM_KV_ATTENTION_Q_LORA_RANK,
+    LLM_KV_ATTENTION_KV_LORA_RANK,
+    LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
+    LLM_KV_ATTENTION_SLIDING_WINDOW,
+    LLM_KV_ATTENTION_SCALE,
+
+    LLM_KV_ROPE_DIMENSION_COUNT,
+    LLM_KV_ROPE_DIMENSION_SECTIONS,
+    LLM_KV_ROPE_FREQ_BASE,
+    LLM_KV_ROPE_SCALE_LINEAR,
+    LLM_KV_ROPE_SCALING_TYPE,
+    LLM_KV_ROPE_SCALING_FACTOR,
+    LLM_KV_ROPE_SCALING_ATTN_FACTOR,
+    LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
+    LLM_KV_ROPE_SCALING_FINETUNED,
+    LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
+
+    LLM_KV_SPLIT_NO,
+    LLM_KV_SPLIT_COUNT,
+    LLM_KV_SPLIT_TENSORS_COUNT,
+
+    LLM_KV_SSM_INNER_SIZE,
+    LLM_KV_SSM_CONV_KERNEL,
+    LLM_KV_SSM_STATE_SIZE,
+    LLM_KV_SSM_TIME_STEP_RANK,
+    LLM_KV_SSM_DT_B_C_RMS,
+
+    LLM_KV_WKV_HEAD_SIZE,
+
+    LLM_KV_TOKENIZER_MODEL,
+    LLM_KV_TOKENIZER_PRE,
+    LLM_KV_TOKENIZER_LIST,
+    LLM_KV_TOKENIZER_TOKEN_TYPE,
+    LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
+    LLM_KV_TOKENIZER_SCORES,
+    LLM_KV_TOKENIZER_MERGES,
+    LLM_KV_TOKENIZER_BOS_ID,
+    LLM_KV_TOKENIZER_EOS_ID,
+    LLM_KV_TOKENIZER_EOT_ID,
+    LLM_KV_TOKENIZER_EOM_ID,
+    LLM_KV_TOKENIZER_UNK_ID,
+    LLM_KV_TOKENIZER_SEP_ID,
+    LLM_KV_TOKENIZER_PAD_ID,
+    LLM_KV_TOKENIZER_CLS_ID,
+    LLM_KV_TOKENIZER_MASK_ID,
+    LLM_KV_TOKENIZER_ADD_BOS,
+    LLM_KV_TOKENIZER_ADD_EOS,
+    LLM_KV_TOKENIZER_ADD_PREFIX,
+    LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
+    LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
+    LLM_KV_TOKENIZER_HF_JSON,
+    LLM_KV_TOKENIZER_RWKV,
+    LLM_KV_TOKENIZER_FIM_PRE_ID,
+    LLM_KV_TOKENIZER_FIM_SUF_ID,
+    LLM_KV_TOKENIZER_FIM_MID_ID,
+    LLM_KV_TOKENIZER_FIM_PAD_ID,
+    LLM_KV_TOKENIZER_FIM_REP_ID,
+    LLM_KV_TOKENIZER_FIM_SEP_ID,
+
+    LLM_KV_ADAPTER_TYPE,
+    LLM_KV_ADAPTER_LORA_ALPHA,
+
+    LLM_KV_POSNET_EMBEDDING_LENGTH,
+    LLM_KV_POSNET_BLOCK_COUNT,
+
+    LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
+    LLM_KV_CONVNEXT_BLOCK_COUNT,
+
+    // deprecated:
+    LLM_KV_TOKENIZER_PREFIX_ID,
+    LLM_KV_TOKENIZER_SUFFIX_ID,
+    LLM_KV_TOKENIZER_MIDDLE_ID,
+};
+
+enum llm_tensor {
+    LLM_TENSOR_TOKEN_EMBD,
+    LLM_TENSOR_TOKEN_EMBD_NORM,
+    LLM_TENSOR_TOKEN_TYPES,
+    LLM_TENSOR_POS_EMBD,
+    LLM_TENSOR_OUTPUT,
+    LLM_TENSOR_OUTPUT_NORM,
+    LLM_TENSOR_ROPE_FREQS,
+    LLM_TENSOR_ROPE_FACTORS_LONG,
+    LLM_TENSOR_ROPE_FACTORS_SHORT,
+    LLM_TENSOR_ATTN_Q,
+    LLM_TENSOR_ATTN_K,
+    LLM_TENSOR_ATTN_V,
+    LLM_TENSOR_ATTN_QKV,
+    LLM_TENSOR_ATTN_OUT,
+    LLM_TENSOR_ATTN_NORM,
+    LLM_TENSOR_ATTN_NORM_2,
+    LLM_TENSOR_ATTN_OUT_NORM,
+    LLM_TENSOR_ATTN_POST_NORM,
+    LLM_TENSOR_ATTN_ROT_EMBD,
+    LLM_TENSOR_FFN_GATE_INP,
+    LLM_TENSOR_FFN_GATE_INP_SHEXP,
+    LLM_TENSOR_FFN_NORM,
+    LLM_TENSOR_FFN_POST_NORM,
+    LLM_TENSOR_FFN_GATE,
+    LLM_TENSOR_FFN_DOWN,
+    LLM_TENSOR_FFN_UP,
+    LLM_TENSOR_FFN_ACT,
+    LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
+    LLM_TENSOR_FFN_GATE_EXP,
+    LLM_TENSOR_FFN_UP_EXP,
+    LLM_TENSOR_FFN_NORM_EXPS,
+    LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
+    LLM_TENSOR_FFN_GATE_EXPS,
+    LLM_TENSOR_FFN_UP_EXPS,
+    LLM_TENSOR_FFN_DOWN_SHEXP,
+    LLM_TENSOR_FFN_GATE_SHEXP,
+    LLM_TENSOR_FFN_UP_SHEXP,
+    LLM_TENSOR_ATTN_Q_NORM,
+    LLM_TENSOR_ATTN_K_NORM,
+    LLM_TENSOR_LAYER_OUT_NORM,
+    LLM_TENSOR_SSM_IN,
+    LLM_TENSOR_SSM_CONV1D,
+    LLM_TENSOR_SSM_X,
+    LLM_TENSOR_SSM_DT,
+    LLM_TENSOR_SSM_A,
+    LLM_TENSOR_SSM_D,
+    LLM_TENSOR_SSM_OUT,
+    LLM_TENSOR_TIME_MIX_W1,
+    LLM_TENSOR_TIME_MIX_W2,
+    LLM_TENSOR_TIME_MIX_LERP_X,
+    LLM_TENSOR_TIME_MIX_LERP_W,
+    LLM_TENSOR_TIME_MIX_LERP_K,
+    LLM_TENSOR_TIME_MIX_LERP_V,
+    LLM_TENSOR_TIME_MIX_LERP_R,
+    LLM_TENSOR_TIME_MIX_LERP_G,
+    LLM_TENSOR_TIME_MIX_FIRST,
+    LLM_TENSOR_TIME_MIX_DECAY,
+    LLM_TENSOR_TIME_MIX_DECAY_W1,
+    LLM_TENSOR_TIME_MIX_DECAY_W2,
+    LLM_TENSOR_TIME_MIX_KEY,
+    LLM_TENSOR_TIME_MIX_VALUE,
+    LLM_TENSOR_TIME_MIX_RECEPTANCE,
+    LLM_TENSOR_TIME_MIX_GATE,
+    LLM_TENSOR_TIME_MIX_LN,
+    LLM_TENSOR_TIME_MIX_OUTPUT,
+    LLM_TENSOR_CHANNEL_MIX_LERP_K,
+    LLM_TENSOR_CHANNEL_MIX_LERP_R,
+    LLM_TENSOR_CHANNEL_MIX_KEY,
+    LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
+    LLM_TENSOR_CHANNEL_MIX_VALUE,
+    LLM_TENSOR_ATTN_Q_A,
+    LLM_TENSOR_ATTN_Q_B,
+    LLM_TENSOR_ATTN_KV_A_MQA,
+    LLM_TENSOR_ATTN_KV_B,
+    LLM_TENSOR_ATTN_Q_A_NORM,
+    LLM_TENSOR_ATTN_KV_A_NORM,
+    LLM_TENSOR_ATTN_SUB_NORM,
+    LLM_TENSOR_FFN_SUB_NORM,
+    LLM_TENSOR_DEC_ATTN_NORM,
+    LLM_TENSOR_DEC_ATTN_Q,
+    LLM_TENSOR_DEC_ATTN_K,
+    LLM_TENSOR_DEC_ATTN_V,
+    LLM_TENSOR_DEC_ATTN_OUT,
+    LLM_TENSOR_DEC_ATTN_REL_B,
+    LLM_TENSOR_DEC_CROSS_ATTN_NORM,
+    LLM_TENSOR_DEC_CROSS_ATTN_Q,
+    LLM_TENSOR_DEC_CROSS_ATTN_K,
+    LLM_TENSOR_DEC_CROSS_ATTN_V,
+    LLM_TENSOR_DEC_CROSS_ATTN_OUT,
+    LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
+    LLM_TENSOR_DEC_FFN_NORM,
+    LLM_TENSOR_DEC_FFN_GATE,
+    LLM_TENSOR_DEC_FFN_DOWN,
+    LLM_TENSOR_DEC_FFN_UP,
+    LLM_TENSOR_DEC_OUTPUT_NORM,
+    LLM_TENSOR_ENC_ATTN_NORM,
+    LLM_TENSOR_ENC_ATTN_Q,
+    LLM_TENSOR_ENC_ATTN_K,
+    LLM_TENSOR_ENC_ATTN_V,
+    LLM_TENSOR_ENC_ATTN_OUT,
+    LLM_TENSOR_ENC_ATTN_REL_B,
+    LLM_TENSOR_ENC_FFN_NORM,
+    LLM_TENSOR_ENC_FFN_GATE,
+    LLM_TENSOR_ENC_FFN_DOWN,
+    LLM_TENSOR_ENC_FFN_UP,
+    LLM_TENSOR_ENC_OUTPUT_NORM,
+    LLM_TENSOR_CLS,
+    LLM_TENSOR_CLS_OUT,
+    LLM_TENSOR_CONV1D,
+    LLM_TENSOR_CONVNEXT_DW,
+    LLM_TENSOR_CONVNEXT_NORM,
+    LLM_TENSOR_CONVNEXT_PW1,
+    LLM_TENSOR_CONVNEXT_PW2,
+    LLM_TENSOR_CONVNEXT_GAMMA,
+    LLM_TENSOR_POS_NET_CONV1,
+    LLM_TENSOR_POS_NET_CONV2,
+    LLM_TENSOR_POS_NET_NORM,
+    LLM_TENSOR_POS_NET_NORM1,
+    LLM_TENSOR_POS_NET_NORM2,
+    LLM_TENSOR_POS_NET_ATTN_NORM,
+    LLM_TENSOR_POS_NET_ATTN_Q,
+    LLM_TENSOR_POS_NET_ATTN_K,
+    LLM_TENSOR_POS_NET_ATTN_V,
+    LLM_TENSOR_POS_NET_ATTN_OUT,
+};
+
+enum llm_tensor_layer {
+    LLM_TENSOR_LAYER_INPUT,
+    LLM_TENSOR_LAYER_REPEATING,
+    LLM_TENSOR_LAYER_OUTPUT,
+};
+
+struct LLM_KV {
+    LLM_KV(llm_arch arch);
+
+    llm_arch arch;
+
+    std::string operator()(llm_kv kv) const;
+};
+
+// helper to handle gguf constants
+// usage:
+//
+//   const auto tn = LLM_TN(LLM_ARCH_LLAMA);
+//
+//   std::string name = tn(LLM_TENSOR_OUTPUT);                     -> "output"
+//   std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias");         -> "token_embd.bias"
+//   std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3);     -> "blk.3.attn_norm.weight"
+//
+struct LLM_TN_IMPL {
+    const llm_arch arch;
+    const llm_tensor tensor;
+    const char * const suffix;
+    const int bid;
+    const int xid;
+
+    std::string str() const;
+
+    operator std::string() const {
+        return str();
+    }
+
+    friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) {
+        return str == tn.str();
+    }
+
+    friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) {
+        return str != tn.str();
+    }
+};
+
+struct LLM_TN {
+    LLM_TN(llm_arch arch) : arch(arch) {}
+
+    llm_arch arch;
+
+    LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
+        return { arch, tensor, suffix, bid, xid };
+    }
+
+    LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
+        return { arch, tensor, nullptr, bid, xid };
+    }
+};
+
+
+struct llm_tensor_info {
+    llm_tensor_layer layer;
+    ggml_op op;
+};
+
+const char * llm_arch_name(llm_arch arch);
+
+llm_arch llm_arch_from_string(const std::string & name);
+
+const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor);
diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
new file mode 100644
index 000000000..01d5ca57f
--- /dev/null
+++ b/src/llama-batch.cpp
@@ -0,0 +1,368 @@
+#include "llama-batch.h"
+
+#include <cstring>
+#include <algorithm>
+
+llama_ubatch llama_sbatch::reserve_ubatch(size_t n_ubatch, bool has_embd) {
+    // clear empty sequences
+    // the previous ubatch is assumed to be gone,
+    // so nothing should refer to values in these sequences anymore.
+    for (size_t i = seq.size(); i-- > 0;) {
+        if (seq[i].length == 0) {
+            seq.pop_back();
+        } else {
+            break;
+        }
+    }
+    ubatch_token.resize(!has_embd ? n_ubatch : 0);
+    ubatch_embd.resize(has_embd ? n_embd * n_ubatch : 0);
+    ubatch_pos.resize(n_ubatch);
+    ubatch_n_seq_id.resize(n_ubatch);
+    ubatch_seq_id.resize(n_ubatch);
+    ubatch_output.resize(n_ubatch);
+    llama_ubatch ubatch = {
+        /*equal_seqs   =*/ true,
+        /*n_tokens     =*/ 0,
+        /*n_seq_tokens =*/ 0,
+        /*n_seqs       =*/ 0,
+        /*token        =*/ !has_embd ? ubatch_token.data() : nullptr,
+        /*embd         =*/ has_embd  ? ubatch_embd.data()  : nullptr,
+        /*pos          =*/ ubatch_pos.data(),
+        /*n_seq_id     =*/ ubatch_n_seq_id.data(),
+        /*seq_id       =*/ ubatch_seq_id.data(),
+        /*output       =*/ ubatch_output.data(),
+    };
+    return ubatch;
+}
+
+void llama_sbatch::add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length) {
+    GGML_ASSERT(batch != nullptr);
+    GGML_ASSERT(length <= seq.length);
+    // Can only add sequences of equal lengths to a batch,
+    // otherwise it isn't clear to which sequence a token belongs
+    GGML_ASSERT(seq.n_seq_id == 0 || ubatch.n_seqs == 0 || length == (size_t) ubatch.n_tokens / ubatch.n_seqs);
+    GGML_ASSERT((seq.n_seq_id != 0) == ubatch.equal_seqs);
+    // NOTE: loops are separated for cache-friendliness
+    if (batch->token) {
+        if (ubatch.equal_seqs) {
+            for (size_t i = 0; i < length; ++i) {
+                ubatch.token[ubatch.n_tokens + i] = batch->token[ids[seq.offset + i]];
+            }
+        } else {
+            // simple split
+            ubatch.token = batch->token + seq.offset;
+        }
+    } else {
+        ubatch.token = nullptr;
+    }
+    if (batch->embd) {
+        if (ubatch.equal_seqs) {
+            for (size_t i = 0; i < length; ++i) {
+                memcpy(
+                        ubatch.embd + (n_embd * (ubatch.n_tokens + i)),
+                        batch->embd + (n_embd * ids[seq.offset + i]),
+                        n_embd * sizeof(float)
+                      );
+            }
+        } else {
+            // simple split
+            ubatch.embd = batch->embd + (n_embd * seq.offset);
+        }
+    } else {
+        ubatch.embd = nullptr;
+    }
+    if (ubatch.equal_seqs) {
+        for (size_t i = 0; i < length; ++i) {
+            ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]];
+        }
+    } else {
+        // simple split
+        ubatch.pos = batch->pos + seq.offset;
+    }
+    if (ubatch.equal_seqs) {
+        ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id;
+        if (seq.seq_id) {
+            ubatch.seq_id[ubatch.n_seqs] = seq.seq_id;
+        }
+    } else {
+        // simple split
+        if (batch->n_seq_id) {
+            ubatch.n_seq_id = batch->n_seq_id + seq.offset;
+        } else {
+            for (size_t i = 0; i < length; ++i) {
+                ubatch.n_seq_id[ubatch.n_seqs + i] = 1;
+            }
+        }
+        if (batch->seq_id) {
+            ubatch.seq_id = batch->seq_id + seq.offset;
+        }
+    }
+    if (logits_all) {
+        for (size_t i = 0; i < length; ++i) {
+            ubatch.output[ubatch.n_tokens + i] = 1;
+            out_ids.push_back(ids[seq.offset + i]);
+        }
+    } else if (batch->logits) {
+        if (ubatch.equal_seqs) {
+            for (size_t i = 0; i < length; ++i) {
+                size_t id = ids[seq.offset + i];
+                int8_t is_output = batch->logits[id];
+                ubatch.output[ubatch.n_tokens + i] = is_output;
+                if (is_output) { out_ids.push_back(id); }
+            }
+        } else {
+            // simple split
+            ubatch.output = batch->logits + seq.offset;
+            for (size_t i = 0; i < length; ++i) {
+                if (ubatch.output[i] != 0) { out_ids.push_back(seq.offset + i); }
+            }
+        }
+    } else {
+        // only get last output
+        for (size_t i = 0; i < length; ++i) {
+            size_t id = ids[seq.offset + i];
+            int8_t is_last = id == ids.size() - 1;
+            ubatch.output[ubatch.n_tokens + i] = is_last;
+            if (is_last) { out_ids.push_back(id); }
+        }
+    }
+    if (ubatch.n_tokens == 0 && ubatch.n_seqs == 0) {
+        ubatch.n_seq_tokens = ubatch.equal_seqs ? length : 1;
+    }
+    ubatch.n_tokens += length;
+    ubatch.n_seqs += ubatch.equal_seqs ? 1 : length; // virtual sequences for simple splits
+    seq.offset += length;
+    seq.length -= length;
+    n_tokens -= length;
+    GGML_ASSERT(ubatch.n_tokens == ubatch.n_seq_tokens * ubatch.n_seqs);
+}
+
+llama_ubatch llama_sbatch::split_simple(size_t n_ubatch) {
+    n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
+    llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
+    ubatch.equal_seqs = false;
+    if (!seq.empty()) {
+        llama_sbatch_seq & s = seq[0];
+        size_t length = s.length < n_ubatch ? s.length : n_ubatch;
+        GGML_ASSERT(seq.size() == 1 && s.n_seq_id == 0); // don't mix with other splits
+        add_seq_to_ubatch(ubatch, s, length);
+    }
+    return ubatch;
+}
+
+llama_ubatch llama_sbatch::split_equal(size_t n_ubatch) {
+    n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
+    llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
+    if (!seq.empty()) {
+        size_t length = 0;
+        size_t n_tokens_in_ubatch = 0;
+        GGML_ASSERT(seq[0].n_seq_id > 0); // should not be mixed with simple splits
+                                          // smallest first, because it's easier to split this way;
+                                          // starting from the end to pop in constant time.
+        for (size_t i = seq.size(); i-- > 0;) {
+            llama_sbatch_seq & s = seq[i];
+            GGML_ASSERT(s.length > 0);
+            if (length == 0) {
+                length = s.length < n_ubatch ? s.length : n_ubatch;
+            }
+            add_seq_to_ubatch(ubatch, s, length);
+            n_tokens_in_ubatch += length;
+            // shared prompts can't be mixed with any of their sequences,
+            // so it's safer to compute them in their own ubatch
+            if (s.n_seq_id > 1) { break; }
+            // stop when there isn't enough space for another sequence
+            if (length + n_tokens_in_ubatch > n_ubatch) { break; }
+        }
+    }
+    return ubatch;
+}
+
+llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) {
+    n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
+    llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
+    if (!seq.empty()) {
+        llama_sbatch_seq & s = seq[seq.size() - 1];
+        size_t length = s.length < n_ubatch ? s.length : n_ubatch;
+        GGML_ASSERT(s.n_seq_id > 0); // should not be mixed with simple splits
+        add_seq_to_ubatch(ubatch, s, length);
+    }
+    return ubatch;
+}
+
+void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
+    GGML_ASSERT(batch.n_tokens >= 0);
+    this->batch = &batch;
+    this->n_embd = n_embd;
+    this->logits_all = logits_all;
+
+    n_tokens = batch.n_tokens;
+    ids.resize(n_tokens);
+    out_ids.clear();
+    // TODO: reserve out_ids and seq
+
+    for (size_t i = 0; i < n_tokens; ++i) {
+        ids[i] = i;
+    }
+    if (simple_split) {
+        seq.resize(1);
+        llama_sbatch_seq & s = seq[0];
+        s.n_seq_id = 0;
+        s.seq_id = nullptr;
+        s.offset = 0;
+        s.length = n_tokens;
+        return;
+    }
+    std::sort(ids.begin(), ids.end(),
+            [&batch](size_t a, size_t b) {
+                int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1;
+                int32_t n_seq_b = batch.n_seq_id ? batch.n_seq_id[b] : 1;
+                // sort by seq_id, then by pos
+                if (n_seq_a == n_seq_b) {
+                    if (batch.seq_id) {
+                        for (int32_t i = 0; i < n_seq_a; ++i) {
+                            llama_seq_id seq_id_a = batch.seq_id[a][i];
+                            llama_seq_id seq_id_b = batch.seq_id[b][i];
+                            // smaller seq_ids go first
+                            if (seq_id_a != seq_id_b) {
+                                return seq_id_a < seq_id_b;
+                            }
+                        }
+                    }
+                    // when all else is equal, sort by pos
+                    if (batch.pos) {
+                        return batch.pos[a] < batch.pos[b];
+                    }
+                    // no pos, sort by id
+                    return a < b;
+                }
+                // shared prompts go first
+                return n_seq_a > n_seq_b;
+            }
+    );
+    // init seq
+    llama_sbatch_seq * last_seq = nullptr;
+
+    for (size_t i = 0; i < n_tokens; ++i) {
+        const size_t bi = ids[i];
+        const int32_t n_seqs = batch.n_seq_id[bi];
+        llama_seq_id * seq_ids = batch.seq_id[bi];
+        if (last_seq != nullptr) {
+            bool same = n_seqs == last_seq->n_seq_id;
+            for (int32_t j = 0; same && j < n_seqs; ++j) {
+                if (seq_ids[j] != last_seq->seq_id[j]) {
+                    same = false;
+                }
+            }
+            if (same) {
+                last_seq->length += 1;
+                continue;
+            }
+        }
+        llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1};
+        seq.push_back(new_seq);
+        last_seq = &seq.back();
+    }
+    // keep shared prompts first at the end, then sort by length descending.
+    std::sort(seq.begin(), seq.end(),
+            [](llama_sbatch_seq & a, llama_sbatch_seq & b) {
+                if (a.n_seq_id == b.n_seq_id) {
+                    return a.length > b.length;
+                }
+                return a.n_seq_id < b.n_seq_id;
+            }
+            );
+}
+
+llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0) {
+    batch = in_batch;
+    GGML_ASSERT(batch.n_tokens > 0);
+    if (!batch.pos) {
+        pos.resize(batch.n_tokens);
+        for (int32_t i = 0; i < batch.n_tokens; i++) {
+            pos[i] = i + p0;
+        }
+        batch.pos = pos.data();
+    }
+    if (!batch.n_seq_id) {
+        n_seq_id.resize(batch.n_tokens);
+        for (int32_t i = 0; i < batch.n_tokens; i++) {
+            n_seq_id[i] = seq_id_0.size();
+        }
+        batch.n_seq_id = n_seq_id.data();
+    }
+    if (!batch.seq_id) {
+        seq_id.resize(batch.n_tokens + 1);
+        seq_id[batch.n_tokens] = NULL;
+        for (int32_t i = 0; i < batch.n_tokens; i++) {
+            seq_id[i] = seq_id_0.data();
+        }
+        batch.seq_id = seq_id.data();
+    }
+    if (!batch.logits) {
+        logits.resize(batch.n_tokens);
+        logits[logits.size() - 1] = true;
+        batch.logits = logits.data();
+    }
+}
+
+//
+// interface implementation
+//
+
+struct llama_batch llama_batch_get_one(
+             llama_token * tokens,
+                 int32_t   n_tokens) {
+    return {
+        /*n_tokens       =*/ n_tokens,
+        /*tokens         =*/ tokens,
+        /*embd           =*/ nullptr,
+        /*pos            =*/ nullptr,
+        /*n_seq_id       =*/ nullptr,
+        /*seq_id         =*/ nullptr,
+        /*logits         =*/ nullptr,
+    };
+}
+
+struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
+    llama_batch batch = {
+        /*n_tokens       =*/ 0,
+        /*tokens         =*/ nullptr,
+        /*embd           =*/ nullptr,
+        /*pos            =*/ nullptr,
+        /*n_seq_id       =*/ nullptr,
+        /*seq_id         =*/ nullptr,
+        /*logits         =*/ nullptr,
+    };
+
+    if (embd) {
+        batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
+    } else {
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
+    }
+
+    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens_alloc);
+    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens_alloc);
+    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
+    for (int i = 0; i < n_tokens_alloc; ++i) {
+        batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
+    }
+    batch.seq_id[n_tokens_alloc] = nullptr;
+
+    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens_alloc);
+
+    return batch;
+}
+
+void llama_batch_free(struct llama_batch batch) {
+    if (batch.token)    free(batch.token);
+    if (batch.embd)     free(batch.embd);
+    if (batch.pos)      free(batch.pos);
+    if (batch.n_seq_id) free(batch.n_seq_id);
+    if (batch.seq_id) {
+        for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
+            free(batch.seq_id[i]);
+        }
+        free(batch.seq_id);
+    }
+    if (batch.logits)   free(batch.logits);
+}
diff --git a/src/llama-batch.h b/src/llama-batch.h
new file mode 100644
index 000000000..773c3808b
--- /dev/null
+++ b/src/llama-batch.h
@@ -0,0 +1,88 @@
+#pragma once
+
+#include "llama.h"
+
+#include <array>
+#include <vector>
+
+// very similar to llama_batch,
+// but has more metadata about sequences
+struct llama_ubatch {
+    bool equal_seqs;
+    // TODO: whole_seqs for embeddings?
+
+    uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
+    uint32_t n_seq_tokens; // tokens per sequence
+    uint32_t n_seqs;
+
+    llama_token  *  token;    // [n_tokens]
+    float        *  embd;     // [n_embd, n_tokens]
+    llama_pos    *  pos;      // [n_tokens]
+    int32_t      *  n_seq_id; // [n_seqs]
+    llama_seq_id ** seq_id;   // [n_seqs]
+    int8_t       *  output;   // [n_tokens]
+};
+
+struct llama_sbatch_seq {
+    int32_t n_seq_id;
+
+    llama_seq_id * seq_id;
+
+    size_t offset;
+    size_t length;
+};
+
+// sequence-length-aware batch splitting
+struct llama_sbatch {
+    // tokens left in this batch
+    size_t n_tokens;
+
+    size_t n_embd;
+
+    bool logits_all; // TODO: remove once lctx.logits_all is removed too
+
+    // sorted indices into the batch
+    std::vector<size_t> ids;
+    // batch indices of the output
+    std::vector<size_t> out_ids;
+    std::vector<llama_sbatch_seq> seq;
+
+    const llama_batch * batch = nullptr;
+
+    // buffers for the ubatch
+    std::vector<llama_token>    ubatch_token;
+    std::vector<float>          ubatch_embd;
+    std::vector<llama_pos>      ubatch_pos;
+    std::vector<int32_t>        ubatch_n_seq_id;
+    std::vector<llama_seq_id *> ubatch_seq_id;
+    std::vector<int8_t>         ubatch_output;
+
+    llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false);
+
+    void add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length);
+
+    // simple split, unknown number of sequences of unequal lengths
+    llama_ubatch split_simple(size_t n_ubatch);
+
+    // make batches of equal-length sequences
+    llama_ubatch split_equal(size_t n_ubatch);
+
+    // sequence-wise split
+    llama_ubatch split_seq(size_t n_ubatch);
+
+    void from_batch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
+};
+
+// temporary allocate memory for the input batch if needed
+struct llama_batch_allocr {
+    struct llama_batch batch;
+
+    std::array<llama_seq_id, 1> seq_id_0 = { 0 }; // default sequence id
+    std::vector<llama_pos>      pos;
+    std::vector<int32_t>        n_seq_id;
+    std::vector<llama_seq_id *> seq_id;
+    std::vector<int8_t>         logits;
+
+    // optionally fulfill the batch returned by llama_batch_get_one
+    llama_batch_allocr(struct llama_batch in_batch, llama_pos p0);
+};
diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
new file mode 100644
index 000000000..a07e9cf00
--- /dev/null
+++ b/src/llama-chat.cpp
@@ -0,0 +1,549 @@
+#include "llama-chat.h"
+
+#include "llama.h"
+
+#include <map>
+#include <sstream>
+
+#if __cplusplus >= 202000L
+    #define LU8(x) (const char*)(u8##x)
+#else
+    #define LU8(x) u8##x
+#endif
+
+// trim whitespace from the beginning and end of a string
+static std::string trim(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && isspace(str[start])) {
+        start += 1;
+    }
+    while (end > start && isspace(str[end - 1])) {
+        end -= 1;
+    }
+    return str.substr(start, end - start);
+}
+
+static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
+    { "chatml",            LLM_CHAT_TEMPLATE_CHATML            },
+    { "llama2",            LLM_CHAT_TEMPLATE_LLAMA_2           },
+    { "llama2-sys",        LLM_CHAT_TEMPLATE_LLAMA_2_SYS       },
+    { "llama2-sys-bos",    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS   },
+    { "llama2-sys-strip",  LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP },
+    { "mistral-v1",        LLM_CHAT_TEMPLATE_MISTRAL_V1        },
+    { "mistral-v3",        LLM_CHAT_TEMPLATE_MISTRAL_V3        },
+    { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
+    { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
+    { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
+    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
+    { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
+    { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
+    { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
+    { "orion",             LLM_CHAT_TEMPLATE_ORION             },
+    { "openchat",          LLM_CHAT_TEMPLATE_OPENCHAT          },
+    { "vicuna",            LLM_CHAT_TEMPLATE_VICUNA            },
+    { "vicuna-orca",       LLM_CHAT_TEMPLATE_VICUNA_ORCA       },
+    { "deepseek",          LLM_CHAT_TEMPLATE_DEEPSEEK          },
+    { "deepseek2",         LLM_CHAT_TEMPLATE_DEEPSEEK_2        },
+    { "command-r",         LLM_CHAT_TEMPLATE_COMMAND_R         },
+    { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
+    { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGML_3         },
+    { "chatglm4",          LLM_CHAT_TEMPLATE_CHATGML_4         },
+    { "minicpm",           LLM_CHAT_TEMPLATE_MINICPM           },
+    { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
+    { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
+    { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
+    { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
+    { "megrez",            LLM_CHAT_TEMPLATE_MEGREZ            },
+};
+
+llm_chat_template llm_chat_template_from_str(const std::string & name) {
+    return LLM_CHAT_TEMPLATES.at(name);
+}
+
+llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
+    try {
+        return llm_chat_template_from_str(tmpl);
+    } catch (const std::out_of_range &) {
+        // ignore
+    }
+
+    auto tmpl_contains = [&tmpl](const char * haystack) -> bool {
+        return tmpl.find(haystack) != std::string::npos;
+    };
+    if (tmpl_contains("<|im_start|>")) {
+        return LLM_CHAT_TEMPLATE_CHATML;
+    } else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
+        if (tmpl_contains("[SYSTEM_PROMPT]")) {
+            return LLM_CHAT_TEMPLATE_MISTRAL_V7;
+        } else if (
+            // catches official 'v1' template
+            tmpl_contains("' [INST] ' + system_message")
+            // catches official 'v3' and 'v3-tekken' templates
+            || tmpl_contains("[AVAILABLE_TOOLS]")
+        ) {
+            // Official mistral 'v1', 'v3' and 'v3-tekken' templates
+            // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
+            // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
+            if (tmpl_contains(" [INST]")) {
+                return LLM_CHAT_TEMPLATE_MISTRAL_V1;
+            } else if (tmpl_contains("\"[INST]\"")) {
+                return LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN;
+            }
+            return LLM_CHAT_TEMPLATE_MISTRAL_V3;
+        } else {
+            // llama2 template and its variants
+            // [variant] support system message
+            // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
+            bool support_system_message = tmpl_contains("<<SYS>>");
+            bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
+            bool strip_message = tmpl_contains("content.strip()");
+            if (strip_message) {
+                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
+            } else if (add_bos_inside_history) {
+                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
+            } else if (support_system_message) {
+                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS;
+            } else {
+                return LLM_CHAT_TEMPLATE_LLAMA_2;
+            }
+        }
+    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
+        return LLM_CHAT_TEMPLATE_PHI_3;
+    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
+        return LLM_CHAT_TEMPLATE_FALCON_3;
+    } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
+        return LLM_CHAT_TEMPLATE_ZEPHYR;
+    } else if (tmpl_contains("bos_token + message['role']")) {
+        return LLM_CHAT_TEMPLATE_MONARCH;
+    } else if (tmpl_contains("<start_of_turn>")) {
+        return LLM_CHAT_TEMPLATE_GEMMA;
+    } else if (tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
+        // OrionStarAI/Orion-14B-Chat
+        return LLM_CHAT_TEMPLATE_ORION;
+    } else if (tmpl_contains("GPT4 Correct ")) {
+        // openchat/openchat-3.5-0106
+        return LLM_CHAT_TEMPLATE_OPENCHAT;
+    } else if (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: ")) {
+        // eachadea/vicuna-13b-1.1 (and Orca variant)
+        if (tmpl_contains("SYSTEM: ")) {
+            return LLM_CHAT_TEMPLATE_VICUNA_ORCA;
+        }
+        return LLM_CHAT_TEMPLATE_VICUNA;
+    } else if (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>")) {
+        // deepseek-ai/deepseek-coder-33b-instruct
+        return LLM_CHAT_TEMPLATE_DEEPSEEK;
+    } else if (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>")) {
+        // CohereForAI/c4ai-command-r-plus
+        return LLM_CHAT_TEMPLATE_COMMAND_R;
+    } else if (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>")) {
+        return LLM_CHAT_TEMPLATE_LLAMA_3;
+    } else if (tmpl_contains("[gMASK]sop")) {
+        // chatglm3-6b
+        return LLM_CHAT_TEMPLATE_CHATGML_3;
+    } else if (tmpl_contains("[gMASK]<sop>")) {
+        return LLM_CHAT_TEMPLATE_CHATGML_4;
+    } else if (tmpl_contains(LU8("<用户>"))) {
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        return LLM_CHAT_TEMPLATE_MINICPM;
+    } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
+        return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
+    } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
+        // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
+        // EXAONE-3.0-7.8B-Instruct
+        return LLM_CHAT_TEMPLATE_EXAONE_3;
+    } else if (tmpl_contains("rwkv-world")) {
+        return LLM_CHAT_TEMPLATE_RWKV_WORLD;
+    } else if (tmpl_contains("<|start_of_role|>")) {
+        return LLM_CHAT_TEMPLATE_GRANITE;
+    } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
+        return LLM_CHAT_TEMPLATE_GIGACHAT;
+    } else if (tmpl_contains("<|role_start|>")) {
+        return LLM_CHAT_TEMPLATE_MEGREZ;
+    }
+    return LLM_CHAT_TEMPLATE_UNKNOWN;
+}
+
+// Simple version of "llama_apply_chat_template" that only works with strings
+// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
+int32_t llm_chat_apply_template(
+    llm_chat_template tmpl,
+    const std::vector<const llama_chat_message *> & chat,
+    std::string & dest, bool add_ass) {
+    // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
+    std::stringstream ss;
+    if (tmpl == LLM_CHAT_TEMPLATE_CHATML) {
+        // chatml template
+        for (auto message : chat) {
+            ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|im_start|>assistant\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
+        // Official mistral 'v7' template
+        // See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
+        for (auto message : chat) {
+            std::string role(message->role);
+            std::string content(message->content);
+            if (role == "system") {
+                ss << "[SYSTEM_PROMPT] " << content << "[/SYSTEM_PROMPT]";
+            } else if (role == "user") {
+                ss << "[INST] " << content << "[/INST]";
+            }
+            else {
+                ss << " " << content << "</s>";
+            }
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
+            || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3
+            || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN) {
+        // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
+        // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
+        std::string leading_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 ? " " : "";
+        std::string trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN ? "" : " ";
+        bool trim_assistant_message = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3;
+        bool is_inside_turn = false;
+        for (auto message : chat) {
+            if (!is_inside_turn) {
+                ss << leading_space << "[INST]" << trailing_space;
+                is_inside_turn = true;
+            }
+            std::string role(message->role);
+            std::string content(message->content);
+            if (role == "system") {
+                ss << content << "\n\n";
+            } else if (role == "user") {
+                ss << content << leading_space << "[/INST]";
+            } else {
+                ss << trailing_space << (trim_assistant_message ? trim(content) : content) << "</s>";
+                is_inside_turn = false;
+            }
+        }
+    } else if (
+            tmpl == LLM_CHAT_TEMPLATE_LLAMA_2
+            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS
+            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS
+            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP) {
+        // llama2 template and its variants
+        // [variant] support system message
+        // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
+        bool support_system_message = tmpl != LLM_CHAT_TEMPLATE_LLAMA_2;
+        // [variant] add BOS inside history
+        bool add_bos_inside_history = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
+        // [variant] trim spaces from the input message
+        bool strip_message = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
+        // construct the prompt
+        bool is_inside_turn = true; // skip BOS at the beginning
+        ss << "[INST] ";
+        for (auto message : chat) {
+            std::string content = strip_message ? trim(message->content) : message->content;
+            std::string role(message->role);
+            if (!is_inside_turn) {
+                is_inside_turn = true;
+                ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
+            }
+            if (role == "system") {
+                if (support_system_message) {
+                    ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
+                } else {
+                    // if the model does not support system message, we still include it in the first message, but without <<SYS>>
+                    ss << content << "\n";
+                }
+            } else if (role == "user") {
+                ss << content << " [/INST]";
+            } else {
+                ss << content << "</s>";
+                is_inside_turn = false;
+            }
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_PHI_3) {
+        // Phi 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
+        // Falcon 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
+        // zephyr template
+        for (auto message : chat) {
+            ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_MONARCH) {
+        // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
+        for (auto message : chat) {
+            std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
+            ss << bos << message->role << "\n" << message->content << "</s>\n";
+        }
+        if (add_ass) {
+            ss << "<s>assistant\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GEMMA) {
+        // google/gemma-7b-it
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
+                system_prompt = trim(message->content);
+                continue;
+            }
+            // in gemma, "assistant" is "model"
+            role = role == "assistant" ? "model" : message->role;
+            ss << "<start_of_turn>" << role << "\n";
+            if (!system_prompt.empty() && role != "model") {
+                ss << system_prompt << "\n\n";
+                system_prompt = "";
+            }
+            ss << trim(message->content) << "<end_of_turn>\n";
+        }
+        if (add_ass) {
+            ss << "<start_of_turn>model\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_ORION) {
+        // OrionStarAI/Orion-14B-Chat
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message support, we will merge it with user prompt
+                system_prompt = message->content;
+                continue;
+            } else if (role == "user") {
+                ss << "Human: ";
+                if (!system_prompt.empty()) {
+                    ss << system_prompt << "\n\n";
+                    system_prompt = "";
+                }
+                ss << message->content << "\n\nAssistant: </s>";
+            } else {
+                ss << message->content << "</s>";
+            }
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_OPENCHAT) {
+        // openchat/openchat-3.5-0106,
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "<|end_of_turn|>";
+            } else {
+                role[0] = toupper(role[0]);
+                ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
+            }
+        }
+        if (add_ass) {
+            ss << "GPT4 Correct Assistant:";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_VICUNA || tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
+        // eachadea/vicuna-13b-1.1 (and Orca variant)
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // Orca-Vicuna variant uses a system prefix
+                if (tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
+                    ss << "SYSTEM: " << message->content << "\n";
+                } else {
+                    ss << message->content << "\n\n";
+                }
+            } else if (role == "user") {
+                ss << "USER: " << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "ASSISTANT: " << message->content << "</s>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "ASSISTANT:";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK) {
+        // deepseek-ai/deepseek-coder-33b-instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content;
+            } else if (role == "user") {
+                ss << "### Instruction:\n" << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "### Response:\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_COMMAND_R) {
+        // CohereForAI/c4ai-command-r-plus
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            } else if (role == "user") {
+                ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            } else if (role == "assistant") {
+                ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+            }
+        }
+        if (add_ass) {
+            ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA_3) {
+        // Llama 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
+        }
+        if (add_ass) {
+            ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) {
+        // chatglm3-6b
+        ss << "[gMASK]" << "sop";
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>" << "\n " << message->content;
+        }
+        if (add_ass) {
+            ss << "<|assistant|>";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) {
+        ss << "[gMASK]" << "<sop>";
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>" << "\n" << message->content;
+        }
+        if (add_ass) {
+            ss << "<|assistant|>";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "user") {
+                ss << LU8("<用户>");
+                ss << trim(message->content);
+                ss << "<AI>";
+            } else {
+                ss << trim(message->content);
+            }
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_2) {
+        // DeepSeek-V2
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "\n\n";
+            } else if (role == "user") {
+                ss << "User: " << message->content << "\n\n";
+            } else if (role == "assistant") {
+                ss << "Assistant: " << message->content << LU8("<｜end▁of▁sentence｜>");
+            }
+        }
+        if (add_ass) {
+            ss << "Assistant:";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
+        // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
+        // EXAONE-3.0-7.8B-Instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n";
+            } else if (role == "user") {
+                ss << "[|user|]" << trim(message->content) << "\n";
+            } else if (role == "assistant") {
+                ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n";
+            }
+        }
+        if (add_ass) {
+            ss << "[|assistant|]";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
+        // this template requires the model to have "\n\n" as EOT token
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "user") {
+                ss << "User: " << message->content << "\n\nAssistant:";
+            } else {
+                ss << message->content << "\n\n";
+            }
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {
+        // IBM Granite template
+        for (const auto & message : chat) {
+            std::string role(message->role);
+            ss << "<|start_of_role|>" << role << "<|end_of_role|>";
+            if (role == "assistant_tool_call") {
+                ss << "<|tool_call|>";
+            }
+            ss << message->content << "<|end_of_text|>\n";
+        }
+        if (add_ass) {
+            ss << "<|start_of_role|>assistant<|end_of_role|>\n";
+        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
+        // GigaChat template
+        bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
+
+        // Handle system message if present
+        if (has_system) {
+            ss << "<s>" << chat[0]->content << "<|message_sep|>";
+        } else {
+            ss << "<s>";
+        }
+
+        // Process remaining messages
+        for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
+            std::string role(chat[i]->role);
+            if (role == "user") {
+                ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
+                << "available functions<|role_sep|>[]<|message_sep|>";
+            } else if (role == "assistant") {
+                ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
+            }
+        }
+
+        // Add generation prompt if needed
+        if (add_ass) {
+            ss << "assistant<|role_sep|>";
+        }
+    }  else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) {
+        // Megrez template
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>";
+        }
+
+        if (add_ass) {
+            ss << "<|role_start|>assistant<|role_end|>";
+        }
+    } else {
+        // template not supported
+        return -1;
+    }
+    dest = ss.str();
+    return dest.size();
+}
+
+// public interface
+
+int32_t llama_chat_builtin_templates(const char ** output, size_t len) {
+    auto it = LLM_CHAT_TEMPLATES.begin();
+    for (size_t i = 0; i < std::min(len, LLM_CHAT_TEMPLATES.size()); i++) {
+        output[i] = it->first.c_str();
+        std::advance(it, 1);
+    }
+    return (int32_t) LLM_CHAT_TEMPLATES.size();
+}
+
diff --git a/src/llama-chat.h b/src/llama-chat.h
new file mode 100644
index 000000000..364318c27
--- /dev/null
+++ b/src/llama-chat.h
@@ -0,0 +1,50 @@
+#pragma once
+
+#include <string>
+#include <vector>
+#include <cstdint>
+
+enum llm_chat_template {
+    LLM_CHAT_TEMPLATE_CHATML,
+    LLM_CHAT_TEMPLATE_LLAMA_2,
+    LLM_CHAT_TEMPLATE_LLAMA_2_SYS,
+    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS,
+    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP,
+    LLM_CHAT_TEMPLATE_MISTRAL_V1,
+    LLM_CHAT_TEMPLATE_MISTRAL_V3,
+    LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
+    LLM_CHAT_TEMPLATE_MISTRAL_V7,
+    LLM_CHAT_TEMPLATE_PHI_3,
+    LLM_CHAT_TEMPLATE_FALCON_3,
+    LLM_CHAT_TEMPLATE_ZEPHYR,
+    LLM_CHAT_TEMPLATE_MONARCH,
+    LLM_CHAT_TEMPLATE_GEMMA,
+    LLM_CHAT_TEMPLATE_ORION,
+    LLM_CHAT_TEMPLATE_OPENCHAT,
+    LLM_CHAT_TEMPLATE_VICUNA,
+    LLM_CHAT_TEMPLATE_VICUNA_ORCA,
+    LLM_CHAT_TEMPLATE_DEEPSEEK,
+    LLM_CHAT_TEMPLATE_DEEPSEEK_2,
+    LLM_CHAT_TEMPLATE_COMMAND_R,
+    LLM_CHAT_TEMPLATE_LLAMA_3,
+    LLM_CHAT_TEMPLATE_CHATGML_3,
+    LLM_CHAT_TEMPLATE_CHATGML_4,
+    LLM_CHAT_TEMPLATE_MINICPM,
+    LLM_CHAT_TEMPLATE_EXAONE_3,
+    LLM_CHAT_TEMPLATE_RWKV_WORLD,
+    LLM_CHAT_TEMPLATE_GRANITE,
+    LLM_CHAT_TEMPLATE_GIGACHAT,
+    LLM_CHAT_TEMPLATE_MEGREZ,
+    LLM_CHAT_TEMPLATE_UNKNOWN,
+};
+
+struct llama_chat_message;
+
+llm_chat_template llm_chat_template_from_str(const std::string & name);
+
+llm_chat_template llm_chat_detect_template(const std::string & tmpl);
+
+int32_t llm_chat_apply_template(
+    llm_chat_template tmpl,
+    const std::vector<const llama_chat_message *> & chat,
+    std::string & dest, bool add_ass);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
new file mode 100644
index 000000000..38a55fb2c
--- /dev/null
+++ b/src/llama-context.cpp
@@ -0,0 +1,1771 @@
+#include "llama-context.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstring>
+#include <stdexcept>
+
+void llama_set_k_shift(struct llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].delta;
+    }
+}
+
+void llama_set_s_copy(struct llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].src;
+    }
+}
+
+// llama input
+
+static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
+    // TODO move to hparams if a T5 variant appears that uses a different value
+    const int64_t max_distance = 128;
+
+    if (bidirectional) {
+        n_buckets >>= 1;
+    }
+
+    const int64_t max_exact = n_buckets >> 1;
+
+    int32_t relative_position = x - y;
+    int32_t relative_bucket = 0;
+    if (bidirectional) {
+        relative_bucket += (relative_position > 0) * n_buckets;
+        relative_position = abs(relative_position);
+    } else {
+        relative_position = -std::min<int32_t>(relative_position, 0);
+    }
+    int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
+    relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
+    relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
+    return relative_bucket;
+}
+
+void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
+    //
+    // set input data
+    //
+
+    const auto & hparams = lctx.model.hparams;
+    const auto & cparams = lctx.cparams;
+    const auto & kv_self = lctx.kv_self;
+
+    if (ubatch.token) {
+        const int64_t n_tokens = ubatch.n_tokens;
+
+        ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
+    }
+
+    if (ubatch.embd) {
+        const int64_t n_embd   = hparams.n_embd;
+        const int64_t n_tokens = ubatch.n_tokens;
+
+        ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+    }
+
+    if (ubatch.pos && lctx.inp_pos) {
+        const int64_t n_tokens = ubatch.n_tokens;
+        auto n_pos = lctx.n_pos_per_token;
+        ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*n_pos*ggml_element_size(lctx.inp_pos));
+    }
+
+    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        //GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
+
+        if (!lctx.inp_out_ids) {
+            LLAMA_LOG_WARN("%s: 'lctx.inp_out_ids' is not created\n", __func__);
+        } else {
+            const int64_t n_tokens = ubatch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+            int32_t * data = (int32_t *) lctx.inp_out_ids->data;
+
+            if (lctx.n_outputs == n_tokens) {
+                for (int i = 0; i < n_tokens; ++i) {
+                    data[i] = i;
+                }
+            } else if (ubatch.output) {
+                int32_t n_outputs = 0;
+                for (int i = 0; i < n_tokens; ++i) {
+                    if (ubatch.output[i]) {
+                        data[n_outputs++] = i;
+                    }
+                }
+                // the graph needs to have been passed the correct number of outputs
+                GGML_ASSERT(lctx.n_outputs == n_outputs);
+            } else if (lctx.n_outputs == 1) {
+                // only keep last output
+                data[0] = n_tokens - 1;
+            } else {
+                GGML_ASSERT(lctx.n_outputs == 0);
+            }
+        }
+    }
+
+    GGML_ASSERT(
+        // (!a || b) is a logical implication (a -> b)
+        // !hparams.causal_attn -> !cparams.causal_attn
+        (hparams.causal_attn || !cparams.causal_attn) &&
+        "causal attention is not supported by this model"
+    );
+
+    if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
+        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
+        if (cparams.causal_attn && !lctx.is_encoding) {
+            const int64_t n_kv         = kv_self.n;
+            const int64_t n_tokens     = ubatch.n_tokens;
+            const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+            const int64_t n_seqs       = ubatch.n_seqs;
+
+
+            float * data     = nullptr;
+            float * data_swa = nullptr;
+
+            if (lctx.inp_KQ_mask) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+                data = (float *) lctx.inp_KQ_mask->data;
+            }
+
+            if (lctx.inp_KQ_mask_swa) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
+                data_swa = (float *) lctx.inp_KQ_mask_swa->data;
+            }
+
+            // For causal attention, use only the previous KV cells
+            // of the correct sequence for each token of the ubatch.
+            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+            for (int h = 0; h < 1; ++h) {
+                for (int s = 0; s < n_seqs; ++s) {
+                    const llama_seq_id seq_id = ubatch.seq_id[s][0];
+
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        const llama_pos pos = ubatch.pos[s*n_seq_tokens + j];
+
+                        for (int i = 0; i < n_kv; ++i) {
+                            float f;
+                            if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                                f = -INFINITY;
+                            } else {
+                                if (hparams.use_alibi) {
+                                    f = -std::abs(kv_self.cells[i].pos - pos);
+                                } else {
+                                    f = 0.0f;
+                                }
+                            }
+
+                            if (data) {
+                                data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+                            }
+
+                            // may need to cut off old tokens for sliding window
+                            if (data_swa) {
+                                if (pos - kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
+                                    f = -INFINITY;
+                                }
+                                data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+                            }
+                        }
+                    }
+                }
+
+                if (data) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
+                    }
+                }
+
+                if (data_swa) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
+                    }
+                }
+            }
+        } else {
+            const int64_t n_tokens     = ubatch.n_tokens;
+            const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+            const int64_t n_seqs       = ubatch.n_seqs;
+            // when using kv cache, the mask needs to match the kv cache size
+            const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+            float * data = (float *) lctx.inp_KQ_mask->data;
+
+            for (int h = 0; h < 1; ++h) {
+                for (int s1 = 0; s1 < n_seqs; ++s1) {
+                    const llama_seq_id seq_id = ubatch.seq_id[s1][0];
+
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        const int32_t tj = s1*n_seq_tokens + j;
+
+                        for (int s0 = 0; s0 < n_seqs; ++s0) {
+                            for (int i = 0; i < n_seq_tokens; ++i) {
+                                const int32_t ti = s0*n_seq_tokens + i;
+                                float f = -INFINITY;
+
+                                for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) {
+                                    if (ubatch.seq_id[s0][s] == seq_id) {
+                                        if (hparams.use_alibi) {
+                                            f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]);
+                                        } else {
+                                            f = 0.0f;
+                                        }
+                                        break;
+                                    }
+                                }
+
+                                data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
+                            }
+                        }
+
+                        for (int i = n_tokens; i < n_stride; ++i) {
+                            data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
+        const int64_t n_tokens     = ubatch.n_tokens;
+        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+        const int64_t n_seqs       = ubatch.n_seqs;
+
+        GGML_ASSERT(lctx.inp_mean);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
+
+        float * data = (float *) lctx.inp_mean->data;
+        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
+
+        std::vector<uint64_t> sum(n_tokens, 0);
+
+        for (int s = 0; s < n_seqs; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+
+            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
+
+            sum[seq_id] += ubatch.n_seq_tokens;
+        }
+
+        std::vector<float> div(n_tokens, 0.0f);
+        for (int i = 0; i < n_tokens; ++i) {
+            const uint64_t s = sum[i];
+            if (s > 0) {
+                div[i] = 1.0f/float(s);
+            }
+        }
+
+        for (int s = 0; s < n_seqs; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+
+            for (int i = 0; i < n_seq_tokens; ++i) {
+                data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id];
+            }
+        }
+    }
+
+    if (cparams.embeddings && (
+                cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
+                cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
+        const int64_t n_tokens     = ubatch.n_tokens;
+        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+        const int64_t n_seqs       = ubatch.n_seqs;
+
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+        for (int s = 0; s < n_seqs; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+
+            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
+
+            for (int i = 0; i < n_seq_tokens; ++i) {
+                const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
+
+                if (pos == 0) {
+                    data[seq_id] = s*n_seq_tokens + i;
+                }
+            }
+        }
+    }
+
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
+        const int64_t n_tokens     = ubatch.n_tokens;
+        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+        const int64_t n_seqs       = ubatch.n_seqs;
+
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+        std::vector<int> last_pos(n_tokens, -1);
+        std::vector<int> last_row(n_tokens, -1);
+
+        for (int s = 0; s < n_seqs; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id[s][0];
+
+            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
+
+            for (int i = 0; i < n_seq_tokens; ++i) {
+                const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
+
+                if (pos >= last_pos[seq_id]) {
+                    last_pos[seq_id] = pos;
+                    last_row[seq_id] = s*n_seq_tokens + i;
+                }
+            }
+        }
+
+        for (int i = 0; i < n_tokens; ++i) {
+            if (last_row[i] >= 0) {
+                data[i] = last_row[i];
+            }
+        }
+    }
+
+    if (kv_self.recurrent) {
+        const int64_t n_kv = kv_self.n;
+
+        if (lctx.inp_s_mask) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
+            float * data = (float *) lctx.inp_s_mask->data;
+
+            // clear unused states
+            for (int i = 0; i < n_kv; ++i) {
+                const uint32_t  cell_id = i + kv_self.head;
+                llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id];
+
+                data[i] = (float) (kv_cell.src >= 0);
+
+                // only clear once
+                if (kv_cell.src < 0) {
+                    kv_cell.src = cell_id;
+                }
+            }
+        }
+
+        if (lctx.inp_s_copy) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+            int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+
+            // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
+            for (uint32_t i = 0; i < n_kv; ++i) {
+                const uint32_t  cell_id = i + kv_self.head;
+                llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id];
+
+                // prevent out-of-bound sources
+                if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self.size) {
+                    kv_cell.src = cell_id;
+                }
+
+                data[i] = kv_cell.src;
+
+                // ensure copy only happens once
+                if (kv_cell.src != (int32_t) cell_id) {
+                    kv_cell.src = cell_id;
+                }
+            }
+        }
+    }
+
+    if (lctx.inp_pos_bucket) {
+        const int64_t n_tokens = ubatch.n_tokens;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
+        GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
+
+        int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
+
+        if (!lctx.is_encoding) {
+            const int64_t n_kv = kv_self.n;
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_kv; ++i) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+                    }
+                }
+            }
+        } else {
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_tokens; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+                    }
+                }
+            }
+        }
+    }
+
+    if (!lctx.is_encoding && lctx.inp_embd_enc) {
+        assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
+        assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size());
+
+        ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
+    }
+
+    if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
+        const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
+        const int64_t n_tokens = ubatch.n_tokens;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
+        GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
+
+        float * data = (float *) lctx.inp_KQ_mask_cross->data;
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                for (int i = 0; i < n_output_enc; ++i) {
+                    float f = -INFINITY;
+                    for (int s = 0; s < ubatch.n_seq_id[j]; ++s) {
+                        const llama_seq_id seq_id = ubatch.seq_id[j][s];
+                        if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
+                            f = 0.0f;
+                        }
+                    }
+                    data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f;
+                }
+            }
+
+            for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                for (int j = 0; j < n_output_enc; ++j) {
+                    data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY;
+                }
+            }
+        }
+    }
+}
+
+// llama output
+
+size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
+    const auto & cparams = lctx.cparams;
+    const auto & hparams = lctx.model.hparams;
+
+    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
+
+    const auto n_batch = cparams.n_batch;
+    const auto n_vocab = hparams.n_vocab;
+    const auto n_embd  = hparams.n_embd;
+
+    // TODO: use a per-batch flag for logits presence instead
+    const bool has_logits = !cparams.embeddings;
+    const bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+
+    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+
+    if (lctx.output_ids.empty()) {
+        // init, never resized afterwards
+        lctx.output_ids.resize(n_batch);
+    }
+
+    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0;
+    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
+
+    // alloc only when more than the current capacity is required
+    // TODO: also consider shrinking the buffer
+    if (!lctx.buf_output || prev_size < new_size) {
+        if (lctx.buf_output) {
+#ifndef NDEBUG
+            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
+            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+#endif
+            lctx.buf_output = nullptr;
+            lctx.logits = nullptr;
+            lctx.embd = nullptr;
+        }
+
+        auto * buft = ggml_backend_cpu_buffer_type();
+        // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
+        auto * output_dev = lctx.model.dev_output.dev;
+        auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
+        if (output_dev_host_buft) {
+            buft = output_dev_host_buft;
+        }
+        lctx.buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size));
+        if (lctx.buf_output == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
+            return 0;
+        }
+    }
+
+    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output.get());
+
+    lctx.logits = has_logits ? output_base               : nullptr;
+    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
+
+    lctx.output_size = n_outputs_max;
+    lctx.logits_size = logits_size;
+    lctx.embd_size   = embd_size;
+
+    // set all ids as invalid (negative)
+    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
+
+    ggml_backend_buffer_clear(lctx.buf_output.get(), 0);
+
+    lctx.n_outputs = 0;
+
+    return n_outputs_max;
+}
+
+void llama_output_reorder(struct llama_context & ctx) {
+    std::vector<size_t> & out_ids = ctx.sbatch.out_ids;
+    if (!out_ids.empty()) {
+        const uint32_t n_vocab = ctx.model.hparams.n_vocab;
+        const uint32_t n_embd  = ctx.model.hparams.n_embd;
+
+        const int32_t n_outputs = ctx.n_outputs;
+        GGML_ASSERT((size_t) n_outputs == out_ids.size());
+
+        // TODO: is there something more efficient which also minimizes swaps?
+        // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
+        for (int32_t i = 0; i < n_outputs - 1; ++i) {
+            int32_t j_min = i;
+            for (int32_t j = i + 1; j < n_outputs; ++j) {
+                if (out_ids[j] < out_ids[j_min]) {
+                    j_min = j;
+                }
+            }
+            if (j_min == i) { continue; }
+            std::swap(out_ids[i], out_ids[j_min]);
+            if (ctx.logits_size > 0) {
+                for (uint32_t k = 0; k < n_vocab; k++) {
+                    std::swap(ctx.logits[i*n_vocab + k], ctx.logits[j_min*n_vocab + k]);
+                }
+            }
+            if (ctx.embd_size > 0) {
+                for (uint32_t k = 0; k < n_embd; k++) {
+                    std::swap(ctx.embd[i*n_embd + k], ctx.embd[j_min*n_embd + k]);
+                }
+            }
+        }
+        std::fill(ctx.output_ids.begin(), ctx.output_ids.end(), -1);
+        for (int32_t i = 0; i < n_outputs; ++i) {
+            ctx.output_ids[out_ids[i]] = i;
+        }
+        out_ids.clear();
+    }
+}
+
+//
+// interface implementation
+//
+
+void llama_free(struct llama_context * ctx) {
+    delete ctx;
+}
+
+uint32_t llama_n_ctx(const struct llama_context * ctx) {
+    return ctx->cparams.n_ctx;
+}
+
+uint32_t llama_n_batch(const struct llama_context * ctx) {
+    return ctx->cparams.n_batch;
+}
+
+uint32_t llama_n_ubatch(const struct llama_context * ctx) {
+    return ctx->cparams.n_ubatch;
+}
+
+uint32_t llama_n_seq_max(const struct llama_context * ctx) {
+    return ctx->kv_self.size;
+}
+
+const struct llama_model * llama_get_model(const struct llama_context * ctx) {
+    return &ctx->model;
+}
+
+enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
+    return ctx->cparams.pooling_type;
+}
+
+void llama_attach_threadpool(
+             struct llama_context * ctx,
+        ggml_threadpool_t   threadpool,
+        ggml_threadpool_t   threadpool_batch) {
+    ctx->threadpool       = threadpool;
+    ctx->threadpool_batch = threadpool_batch ? threadpool_batch : threadpool;
+}
+
+void llama_detach_threadpool(struct llama_context * ctx) {
+    ctx->threadpool       = nullptr;
+    ctx->threadpool_batch = nullptr;
+}
+
+void llama_set_n_threads(struct llama_context * ctx, int32_t n_threads, int32_t n_threads_batch) {
+    ctx->cparams.n_threads       = n_threads;
+    ctx->cparams.n_threads_batch = n_threads_batch;
+}
+
+int32_t llama_n_threads(struct llama_context * ctx) {
+    return ctx->cparams.n_threads;
+}
+
+int32_t llama_n_threads_batch(struct llama_context * ctx) {
+    return ctx->cparams.n_threads_batch;
+}
+
+void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
+    ctx->abort_callback      = abort_callback;
+    ctx->abort_callback_data = abort_callback_data;
+
+    for (auto & backend : ctx->backends) {
+        auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend.get()));
+        auto * set_abort_callback_fn = (ggml_backend_set_abort_callback_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_abort_callback");
+        if (set_abort_callback_fn) {
+            set_abort_callback_fn(backend.get(), ctx->abort_callback, ctx->abort_callback_data);
+        }
+    }
+}
+
+void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
+    ctx->cparams.embeddings = embeddings;
+}
+
+void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
+    ctx->cparams.causal_attn = causal_attn;
+}
+
+void llama_synchronize(struct llama_context * ctx) {
+    ggml_backend_sched_synchronize(ctx->sched.get());
+
+    // FIXME: if multiple single tokens are evaluated without a synchronization,
+    // the stats will be added to the prompt evaluation stats
+    // this should only happen when using batch size 1 to evaluate a batch
+
+    // add the evaluation to the stats
+    if (ctx->n_queued_tokens == 1) {
+        if (!ctx->cparams.no_perf) {
+            ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
+        ctx->n_eval++;
+    } else if (ctx->n_queued_tokens > 1) {
+        if (!ctx->cparams.no_perf) {
+            ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
+        ctx->n_p_eval += ctx->n_queued_tokens;
+    }
+
+    // get a more accurate load time, upon first eval
+    if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
+        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
+        ctx->has_evaluated_once = true;
+    }
+
+    ctx->n_queued_tokens = 0;
+    ctx->t_compute_start_us = 0;
+}
+
+float * llama_get_logits(struct llama_context * ctx) {
+    llama_synchronize(ctx);
+
+    // reorder logits for backward compatibility
+    // TODO: maybe deprecate this
+    llama_output_reorder(*ctx);
+
+    return ctx->logits;
+}
+
+float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
+    int32_t j = -1;
+
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->logits == nullptr) {
+            throw std::runtime_error("no logits");
+        }
+
+        if (i < 0) {
+            j = ctx->n_outputs + i;
+            if (j < 0) {
+                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+            }
+        } else if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size()));
+        } else {
+            j = ctx->output_ids[i];
+        }
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if (j >= ctx->n_outputs) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+        }
+
+        return ctx->logits + j*ctx->model.hparams.n_vocab;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ABORT("fatal error");
+#else
+        return nullptr;
+#endif
+    }
+}
+
+float * llama_get_embeddings(struct llama_context * ctx) {
+    llama_synchronize(ctx);
+
+    // reorder embeddings for backward compatibility
+    // TODO: maybe deprecate this
+    llama_output_reorder(*ctx);
+
+    return ctx->embd;
+}
+
+float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
+    int32_t j = -1;
+
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->embd == nullptr) {
+            throw std::runtime_error("no embeddings");
+        }
+
+        if (i < 0) {
+            j = ctx->n_outputs + i;
+            if (j < 0) {
+                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+            }
+        } else if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size()));
+        } else {
+            j = ctx->output_ids[i];
+        }
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if (j >= ctx->n_outputs) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+        }
+
+        return ctx->embd + j*ctx->model.hparams.n_embd;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ABORT("fatal error");
+#else
+        return nullptr;
+#endif
+    }
+}
+
+float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
+    auto it = ctx->embd_seq.find(seq_id);
+    if (it == ctx->embd_seq.end()) {
+        return nullptr;
+    }
+
+    return it->second.data();
+}
+
+// llama state API
+
+// deprecated
+size_t llama_get_state_size(struct llama_context * ctx) {
+    return llama_state_get_size(ctx);
+}
+
+// deprecated
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    return llama_state_get_data(ctx, dst, -1);
+}
+
+// deprecated
+size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+    return llama_state_set_data(ctx, src, -1);
+}
+
+// deprecated
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+}
+
+// deprecated
+bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    return llama_state_save_file(ctx, path_session, tokens, n_token_count);
+}
+
+// TODO: replace all non-fatal assertions with returned errors or exceptions
+struct llama_data_write {
+    virtual void write(const void * src, size_t size) = 0;
+    virtual void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) = 0;
+    virtual size_t get_size_written() = 0;
+    virtual ~llama_data_write() = default;
+
+    void write_string(const std::string & str) {
+        uint32_t str_size = str.size();
+
+        write(&str_size,  sizeof(str_size));
+        write(str.data(), str_size);
+    }
+
+    void write_model_info(const struct llama_context * ctx) {
+        const std::string arch_str = llm_arch_name(ctx->model.arch);
+        write_string(arch_str);
+        // TODO: add more model-specific info which should prevent loading the session file if not identical
+    }
+
+    //void write_rng(const std::mt19937 & rng) {
+    //    std::ostringstream rng_ss;
+    //    rng_ss << rng;
+
+    //    const std::string & rng_str = rng_ss.str();
+
+    //    write_string(rng_str);
+    //}
+
+    void write_output_ids(struct llama_context * ctx) {
+        llama_output_reorder(*ctx);
+
+        const uint32_t n_outputs = ctx->n_outputs;
+
+        std::vector<int32_t> output_pos;
+
+        const size_t    n_batch = ctx->cparams.n_batch;
+        const auto & output_ids = ctx->output_ids;
+
+        GGML_ASSERT(n_outputs <= ctx->output_size);
+
+        output_pos.resize(n_outputs);
+
+        // build a more compact representation of the output ids
+        for (size_t i = 0; i < n_batch; ++i) {
+            // map an output id to a position in the batch
+            int32_t pos = output_ids[i];
+            if (pos >= 0) {
+                GGML_ASSERT((uint32_t) pos < n_outputs);
+                output_pos[pos] = i;
+            }
+        }
+
+        write(&n_outputs, sizeof(n_outputs));
+
+        if (n_outputs) {
+            write(output_pos.data(), n_outputs * sizeof(int32_t));
+        }
+    }
+
+    void write_logits(const struct llama_context * ctx) {
+        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_vocab);
+
+        write(&logits_size, sizeof(logits_size));
+
+        if (logits_size) {
+            write(ctx->logits, logits_size * sizeof(float));
+        }
+    }
+
+    void write_embeddings(const struct llama_context * ctx) {
+        const uint64_t embeddings_size = std::min((uint64_t) ctx->embd_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_embd);
+
+        write(&embeddings_size, sizeof(embeddings_size));
+
+        if (embeddings_size) {
+            write(ctx->embd, embeddings_size * sizeof(float));
+        }
+    }
+
+    void write_kv_cache_meta(const llama_kv_cache & kv_self, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) {
+        for (const auto & range : cell_ranges) {
+            for (uint32_t i = range.first; i < range.second; ++i) {
+                const auto & cell = kv_self.cells[i];
+                const llama_pos pos      = cell.pos;
+                const uint32_t  n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
+
+                write(&pos,      sizeof(pos));
+                write(&n_seq_id, sizeof(n_seq_id));
+
+                if (n_seq_id) {
+                    for (auto seq_id : cell.seq_id) {
+                        write(&seq_id, sizeof(seq_id));
+                    }
+                }
+            }
+        }
+    }
+
+    void write_kv_cache_data(const struct llama_context * ctx, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) {
+        const struct llama_kv_cache & kv_self = ctx->kv_self;
+        const struct llama_hparams & hparams = ctx->model.hparams;
+
+        const uint32_t v_trans = kv_self.v_trans ? 1 : 0;
+        const uint32_t n_layer = hparams.n_layer;
+
+        write(&v_trans, sizeof(v_trans));
+        write(&n_layer, sizeof(n_layer));
+
+        std::vector<uint8_t> tmp_buf;
+
+        // Iterate and write all the keys first, each row is a cell
+        // Get whole range at a time
+        for (uint32_t il = 0; il < n_layer; ++il) {
+            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+            // Write key type
+            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+            write(&k_type_i, sizeof(k_type_i));
+
+            // Write row size of key
+            const uint64_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+            write(&k_size_row, sizeof(k_size_row));
+
+            // Read each range of cells of k_size length each into tmp_buf and write out
+            for (const auto & range : cell_ranges) {
+                const size_t range_size = range.second - range.first;
+                const size_t buf_size = range_size * k_size_row;
+                write_tensor_data(kv_self.k_l[il], range.first * k_size_row, buf_size);
+            }
+        }
+
+        if (!kv_self.v_trans) {
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+                // Write value type
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                write(&v_type_i, sizeof(v_type_i));
+
+                // Write row size of value
+                const uint64_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+                write(&v_size_row, sizeof(v_size_row));
+
+                // Read each range of cells of v_size length each into tmp_buf and write out
+                for (const auto & range : cell_ranges) {
+                    const size_t range_size = range.second - range.first;
+                    const size_t buf_size = range_size * v_size_row;
+                    write_tensor_data(kv_self.v_l[il], range.first * v_size_row, buf_size);
+                }
+            }
+        } else {
+            // When v is transposed, we also need the element size and get the element ranges from each row
+            const uint32_t kv_size = kv_self.size;
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+                // Write value type
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                write(&v_type_i, sizeof(v_type_i));
+
+                // Write element size
+                const uint32_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+                write(&v_size_el, sizeof(v_size_el));
+
+                // Write GQA embedding size
+                write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+
+                // For each row, we get the element values of each cell
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    // Read each range of cells of v_size_el length each into tmp_buf and write out
+                    for (const auto & range : cell_ranges) {
+                        const size_t range_size = range.second - range.first;
+                        const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+                        const size_t buf_size = range_size * v_size_el;
+                        write_tensor_data(kv_self.v_l[il], src_offset, buf_size);
+                    }
+                }
+            }
+        }
+    }
+
+    void write_kv_cache(const struct llama_context * ctx, llama_seq_id seq_id = -1) {
+        const struct llama_kv_cache & kv_self = ctx->kv_self;
+        std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+        uint32_t cell_count = 0;
+
+        // Count the number of cells with the specified seq_id
+        // Find all the ranges of cells with this seq id (or all, when -1)
+        uint32_t cell_range_begin = kv_self.size;
+        for (uint32_t i = 0; i < kv_self.size; ++i) {
+            const auto & cell = kv_self.cells[i];
+            if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
+                ++cell_count;
+                if (cell_range_begin == kv_self.size) {
+                    cell_range_begin = i;
+                }
+            } else {
+                if (cell_range_begin != kv_self.size) {
+                    cell_ranges.emplace_back(cell_range_begin, i);
+                    cell_range_begin = kv_self.size;
+                }
+            }
+        }
+        if (cell_range_begin != kv_self.size) {
+            cell_ranges.emplace_back(cell_range_begin, kv_self.size);
+        }
+
+        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+        uint32_t cell_count_check = 0;
+        for (const auto & range : cell_ranges) {
+            cell_count_check += range.second - range.first;
+        }
+        GGML_ASSERT(cell_count == cell_count_check);
+
+        write(&cell_count, sizeof(cell_count));
+
+        write_kv_cache_meta(kv_self, cell_ranges, seq_id);
+        write_kv_cache_data(ctx, cell_ranges);
+    }
+};
+
+struct llama_data_read {
+    virtual const uint8_t * read(size_t size) = 0;
+    virtual void read_to(void * dst, size_t size) = 0;
+    virtual size_t get_size_read() = 0;
+    virtual ~llama_data_read() = default;
+
+    void read_string(std::string & str) {
+        uint32_t str_size;
+        read_to(&str_size, sizeof(str_size));
+
+        str.assign((const char *) read(str_size), str_size);
+    }
+
+    // validate model information
+    void read_model_info(const struct llama_context * ctx) {
+        const std::string cur_arch_str = llm_arch_name(ctx->model.arch);
+
+        std::string arch_str;
+        read_string(arch_str);
+        if (cur_arch_str != arch_str) {
+            throw std::runtime_error(format("wrong model arch: '%s' instead of '%s'", arch_str.c_str(), cur_arch_str.c_str()));
+        }
+        // TODO: add more info which needs to be identical but which is not verified otherwise
+    }
+
+    //void read_rng(std::mt19937 & rng) {
+    //    std::string rng_str;
+    //    read_string(rng_str);
+
+    //    std::istringstream rng_ss(rng_str);
+    //    rng_ss >> rng;
+
+    //    if (rng_ss.fail()) {
+    //        throw std::runtime_error("failed to load RNG state");
+    //    }
+    //}
+
+    void read_output_ids(struct llama_context * ctx) {
+        std::vector<int32_t> output_pos;
+
+        uint32_t n_outputs;
+        read_to(&n_outputs, sizeof(n_outputs));
+
+        if (n_outputs > llama_output_reserve(*ctx, n_outputs)) {
+            throw std::runtime_error("could not reserve outputs");
+        }
+
+        if (n_outputs) {
+            output_pos.resize(n_outputs);
+            read_to(output_pos.data(), n_outputs * sizeof(int32_t));
+
+            for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
+                int32_t id = output_pos[i];
+                if ((uint32_t) id >= ctx->cparams.n_batch) {
+                    throw std::runtime_error(format("invalid output id, %d does not fit in batch size of %u", id, ctx->cparams.n_batch));
+                }
+                ctx->output_ids[id] = i;
+            }
+
+            ctx->n_outputs = n_outputs;
+        }
+    }
+
+    void read_logits(struct llama_context * ctx) {
+        uint64_t logits_size;
+        read_to(&logits_size, sizeof(logits_size));
+
+        if (ctx->logits_size < logits_size) {
+            throw std::runtime_error("logits buffer too small");
+        }
+
+        if (logits_size) {
+            read_to(ctx->logits, logits_size * sizeof(float));
+        }
+    }
+
+    void read_embeddings(struct llama_context * ctx) {
+        uint64_t embeddings_size;
+        read_to(&embeddings_size, sizeof(embeddings_size));
+
+        if (ctx->embd_size < embeddings_size) {
+            throw std::runtime_error("embeddings buffer too small");
+        }
+
+        if (embeddings_size) {
+            read_to(ctx->embd, embeddings_size * sizeof(float));
+        }
+    }
+
+    bool read_kv_cache_meta(struct llama_context * ctx, uint32_t cell_count, llama_seq_id dest_seq_id = -1) {
+        struct llama_kv_cache & kv_self = ctx->kv_self;
+
+        if (dest_seq_id != -1) {
+            // single sequence
+
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+
+            llama_ubatch batch = ctx->sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
+            batch.n_tokens = cell_count;
+            batch.n_seq_tokens = cell_count;
+            batch.n_seqs = 1;
+
+            for (uint32_t i = 0; i < cell_count; ++i) {
+                llama_pos pos;
+                uint32_t n_seq_id;
+
+                read_to(&pos, sizeof(pos));
+                read_to(&n_seq_id, sizeof(n_seq_id));
+
+                if (n_seq_id != 0) {
+                    LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
+                    return false;
+                }
+
+                batch.pos[i] = pos;
+            }
+            batch.n_seq_id[0] = 1;
+            batch.seq_id[0] = &dest_seq_id;
+            if (!llama_kv_cache_find_slot(kv_self, batch)) {
+                LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+                return false;
+            }
+
+            // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+            // Assume that this is one contiguous block of cells
+            GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
+            GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
+            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+            GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
+            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
+        } else {
+            // whole KV cache restore
+
+            if (cell_count > kv_self.size) {
+                LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
+                return false;
+            }
+
+            llama_kv_cache_clear(kv_self);
+
+            for (uint32_t i = 0; i < cell_count; ++i) {
+                llama_kv_cell & cell = kv_self.cells[i];
+
+                llama_pos pos;
+                uint32_t  n_seq_id;
+
+                read_to(&pos,      sizeof(pos));
+                read_to(&n_seq_id, sizeof(n_seq_id));
+
+                cell.pos = pos;
+
+                for (uint32_t j = 0; j < n_seq_id; ++j) {
+                    llama_seq_id seq_id;
+                    read_to(&seq_id, sizeof(seq_id));
+
+                    if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
+                        LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
+                        return false;
+                    }
+
+                    cell.seq_id.insert(seq_id);
+
+                    if (kv_self.recurrent) {
+                        int32_t & tail = kv_self.cells[seq_id].tail;
+                        if (tail != -1) {
+                            LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
+                            return false;
+                        }
+                        tail = i;
+                    }
+                }
+            }
+
+            kv_self.head = 0;
+            kv_self.used = cell_count;
+        }
+
+        if (kv_self.recurrent) {
+            for (uint32_t i = 0; i < cell_count; ++i) {
+                uint32_t cell_id = kv_self.head + i;
+                // make sure the recurrent states will keep their restored state
+                kv_self.cells[cell_id].src = cell_id;
+            }
+        }
+
+        return true;
+    }
+
+    bool read_kv_cache_data(struct llama_context * ctx, uint32_t cell_count) {
+        const struct llama_hparams & hparams = ctx->model.hparams;
+        struct llama_kv_cache & kv_self = ctx->kv_self;
+        uint32_t v_trans;
+        uint32_t n_layer;
+        read_to(&v_trans, sizeof(v_trans));
+        read_to(&n_layer, sizeof(n_layer));
+
+        if (n_layer != hparams.n_layer) {
+            LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
+            return false;
+        }
+        if (cell_count > kv_self.size) {
+            LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, kv_self.size);
+            return false;
+        }
+        if (kv_self.v_trans != (bool) v_trans) {
+            LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
+            return false;
+        }
+
+        // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
+        for (uint32_t il = 0; il < n_layer; ++il) {
+            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+            // Read type of key
+            int32_t k_type_i_ref;
+            read_to(&k_type_i_ref, sizeof(k_type_i_ref));
+            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+            if (k_type_i != k_type_i_ref) {
+                LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+                return false;
+            }
+
+            // Read row size of key
+            uint64_t k_size_row_ref;
+            read_to(&k_size_row_ref, sizeof(k_size_row_ref));
+            const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+            if (k_size_row != k_size_row_ref) {
+                LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
+                return false;
+            }
+
+            if (cell_count) {
+                // Read and set the keys for the whole cell range
+                ggml_backend_tensor_set(kv_self.k_l[il], read(cell_count * k_size_row), kv_self.head * k_size_row, cell_count * k_size_row);
+            }
+        }
+
+        if (!kv_self.v_trans) {
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+                // Read type of value
+                int32_t v_type_i_ref;
+                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                if (v_type_i != v_type_i_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                    return false;
+                }
+
+                // Read row size of value
+                uint64_t v_size_row_ref;
+                read_to(&v_size_row_ref, sizeof(v_size_row_ref));
+                const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+                if (v_size_row != v_size_row_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
+                    return false;
+                }
+
+                if (cell_count) {
+                    // Read and set the values for the whole cell range
+                    ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_row), kv_self.head * v_size_row, cell_count * v_size_row);
+                }
+            }
+        } else {
+            // For each layer, read the values for each cell (transposed)
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+                // Read type of value
+                int32_t v_type_i_ref;
+                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                if (v_type_i != v_type_i_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                    return false;
+                }
+
+                // Read element size of value
+                uint32_t v_size_el_ref;
+                read_to(&v_size_el_ref, sizeof(v_size_el_ref));
+                const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+                if (v_size_el != v_size_el_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
+                    return false;
+                }
+
+                // Read GQA embedding size
+                uint32_t n_embd_v_gqa_ref;
+                read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+                if (n_embd_v_gqa != n_embd_v_gqa_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
+                    return false;
+                }
+
+                if (cell_count) {
+                    // For each row in the transposed matrix, read the values for the whole cell range
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const size_t dst_offset = (kv_self.head + j * kv_self.size) * v_size_el;
+                        ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                    }
+                }
+            }
+        }
+        return true;
+    }
+
+    void read_kv_cache(struct llama_context * ctx, llama_seq_id seq_id = -1) {
+        uint32_t cell_count;
+        read_to(&cell_count, sizeof(cell_count));
+
+        bool res = read_kv_cache_meta(ctx, cell_count, seq_id) && read_kv_cache_data(ctx, cell_count);
+
+        if (!res) {
+            if (seq_id == -1) {
+                llama_kv_cache_clear(ctx);
+            } else {
+                llama_kv_cache_seq_rm(ctx, seq_id, -1, -1);
+            }
+            throw std::runtime_error("failed to restore kv cache");
+        }
+    }
+};
+
+struct llama_data_write_dummy : llama_data_write {
+    size_t size_written = 0;
+
+    llama_data_write_dummy() {}
+
+    void write(const void * /* src */, size_t size) override {
+        size_written += size;
+    }
+
+    void write_tensor_data(const struct ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
+        size_written += size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+struct llama_data_write_buffer : llama_data_write {
+    uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_written = 0;
+
+    llama_data_write_buffer(uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
+
+    void write(const void * src, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        memcpy(ptr, src, size);
+        ptr += size;
+        size_written += size;
+        buf_size -= size;
+    }
+
+    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        ggml_backend_tensor_get(tensor, ptr, offset, size);
+        ptr += size;
+        size_written += size;
+        buf_size -= size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+struct llama_data_read_buffer : llama_data_read {
+    const uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_read = 0;
+
+    llama_data_read_buffer(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
+
+    const uint8_t * read(size_t size) override {
+        const uint8_t * base_ptr = ptr;
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        ptr += size;
+        size_read += size;
+        buf_size -= size;
+        return base_ptr;
+    }
+
+    void read_to(void * dst, size_t size) override {
+        memcpy(dst, read(size), size);
+    }
+
+    size_t get_size_read() override {
+        return size_read;
+    }
+};
+
+struct llama_data_write_file : llama_data_write {
+    llama_file * file;
+    size_t size_written = 0;
+    std::vector<uint8_t> temp_buffer;
+
+    llama_data_write_file(llama_file * f) : file(f) {}
+
+    void write(const void * src, size_t size) override {
+        file->write_raw(src, size);
+        size_written += size;
+    }
+
+    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
+        temp_buffer.resize(size);
+        ggml_backend_tensor_get(tensor, temp_buffer.data(), offset, size);
+        write(temp_buffer.data(), temp_buffer.size());
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+struct llama_data_read_file : llama_data_read {
+    llama_file * file;
+    size_t size_read = 0;
+    std::vector<uint8_t> temp_buffer;
+
+    llama_data_read_file(llama_file * f) : file(f) {}
+
+    void read_to(void * dst, size_t size) override {
+        file->read_raw(dst, size);
+        size_read += size;
+    }
+
+    const uint8_t * read(size_t size) override {
+        temp_buffer.resize(size);
+        read_to(temp_buffer.data(), size);
+        return temp_buffer.data();
+    }
+
+    size_t get_size_read() override {
+        return size_read;
+    }
+};
+
+/** copy state data into either a buffer or file depending on the passed in context
+ *
+ * file context:
+ * llama_file file("/path", "wb");
+ * llama_data_write_file data_ctx(&file);
+ * llama_state_get_data_internal(ctx, data_ctx);
+ *
+ * buffer context:
+ * std::vector<uint8_t> buf(max_size, 0);
+ * llama_data_write_buffer data_ctx(buf.data(), max_size);
+ * llama_state_get_data_internal(ctx, data_ctx);
+ *
+*/
+static size_t llama_state_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx) {
+    llama_synchronize(ctx);
+
+    data_ctx.write_model_info(ctx);
+
+    // copy outputs
+    data_ctx.write_output_ids(ctx);
+    data_ctx.write_logits(ctx);
+    data_ctx.write_embeddings(ctx);
+
+    data_ctx.write_kv_cache(ctx);
+
+    return data_ctx.get_size_written();
+}
+
+size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst, size_t size) {
+    llama_data_write_buffer data_ctx(dst, size);
+    try {
+        return llama_state_get_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+// Returns the *actual* size of the state.
+// Intended to be used when saving to state to a buffer.
+size_t llama_state_get_size(struct llama_context * ctx) {
+    llama_data_write_dummy data_ctx;
+    try {
+        return llama_state_get_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+static size_t llama_state_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx) {
+    llama_synchronize(ctx);
+
+    data_ctx.read_model_info(ctx);
+
+    // set outputs
+    data_ctx.read_output_ids(ctx);
+    data_ctx.read_logits(ctx);
+    data_ctx.read_embeddings(ctx);
+
+    data_ctx.read_kv_cache(ctx);
+
+    return data_ctx.get_size_read();
+}
+
+// Sets the state reading from the specified source address
+size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src, size_t size) {
+    llama_data_read_buffer data_ctx(src, size);
+    try {
+        return llama_state_set_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(path_session, "rb");
+
+    // sanity checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
+
+        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
+            LLAMA_LOG_ERROR("%s: unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
+            return false;
+        }
+    }
+
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
+
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s: token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return false;
+        }
+
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
+
+    // restore the context state
+    {
+        const size_t n_state_size_cur = file.size() - file.tell();
+
+        llama_data_read_file data_ctx(&file);
+        const size_t n_read = llama_state_set_data_internal(ctx, data_ctx);
+
+        if (n_read != n_state_size_cur) {
+            LLAMA_LOG_ERROR("%s: did not read all of the session file data! size %zu, got %zu\n", __func__, n_state_size_cur, n_read);
+            return false;
+        }
+    }
+    return true;
+}
+
+bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading session file: %s\n", __func__, err.what());
+        return false;
+    }
+}
+
+static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(path_session, "wb");
+
+    file.write_u32(LLAMA_SESSION_MAGIC);
+    file.write_u32(LLAMA_SESSION_VERSION);
+
+    // save the prompt
+    file.write_u32((uint32_t) n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+    // save the context state using stream saving
+    llama_data_write_file data_ctx(&file);
+    llama_state_get_data_internal(ctx, data_ctx);
+
+    return true;
+}
+
+bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving session file: %s\n", __func__, err.what());
+        return false;
+    }
+}
+
+static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
+    data_ctx.write_kv_cache(ctx, seq_id);
+
+    return data_ctx.get_size_written();
+}
+
+size_t llama_state_seq_get_size(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_data_write_dummy data_ctx;
+    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+}
+
+size_t llama_state_seq_get_data(struct llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id) {
+    llama_data_write_buffer data_ctx(dst, size);
+    try {
+        return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving sequence state: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+static size_t llama_state_seq_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx, llama_seq_id dest_seq_id) {
+    llama_synchronize(ctx);
+
+    data_ctx.read_kv_cache(ctx, dest_seq_id);
+
+    return data_ctx.get_size_read();
+}
+
+size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id dest_seq_id) {
+    llama_data_read_buffer data_ctx(src, size);
+    try {
+        return llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading sequence state: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(filepath, "wb");
+
+    file.write_u32(LLAMA_STATE_SEQ_MAGIC);
+    file.write_u32(LLAMA_STATE_SEQ_VERSION);
+
+    // save the prompt
+    file.write_u32((uint32_t) n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+    // save the context state using stream saving
+    llama_data_write_file data_ctx(&file);
+    llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+
+    const size_t res = file.tell();
+    GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
+    return res;
+}
+
+static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(filepath, "rb");
+
+    // version checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
+
+        if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
+            LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
+            return 0;
+        }
+    }
+
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
+
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return 0;
+        }
+
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
+
+    // restore the context state
+    {
+        const size_t state_size = file.size() - file.tell();
+        llama_data_read_file data_ctx(&file);
+        const size_t nread = llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
+        if (!nread) {
+            LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
+            return 0;
+        }
+        GGML_ASSERT(nread <= state_size);
+        GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
+    }
+
+    return file.tell();
+}
+
+size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving sequence state file: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading sequence state file: %s\n", __func__, err.what());
+        return 0;
+    }
+}
+
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+    struct llama_context * ctx
+) {
+    return ctx->model.tensors_by_name;
+}
diff --git a/src/llama-context.h b/src/llama-context.h
new file mode 100644
index 000000000..0d163c470
--- /dev/null
+++ b/src/llama-context.h
@@ -0,0 +1,128 @@
+#pragma once
+
+#include "llama.h"
+#include "llama-batch.h"
+#include "llama-cparams.h"
+#include "llama-model.h"
+#include "llama-kv-cache.h"
+#include "llama-adapter.h"
+
+#include "ggml-cpp.h"
+
+#include <map>
+#include <unordered_map>
+#include <vector>
+#include <set>
+
+struct llama_context {
+    llama_context(const llama_model & model)
+        : model(model)
+        , t_start_us(model.t_start_us)
+        , t_load_us(model.t_load_us) {}
+
+    const struct llama_model & model;
+
+    struct llama_cparams        cparams;
+    struct llama_sbatch         sbatch;  // TODO: revisit if needed
+    struct llama_kv_cache       kv_self;
+    struct llama_control_vector cvec;
+
+    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
+
+    std::vector<ggml_backend_ptr> backends;
+    std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
+
+    ggml_backend_t backend_cpu = nullptr;
+
+    ggml_threadpool_t threadpool       = nullptr;
+    ggml_threadpool_t threadpool_batch = nullptr;
+
+    bool has_evaluated_once = false;
+
+    mutable int64_t t_start_us;
+    mutable int64_t t_load_us;
+    mutable int64_t t_p_eval_us = 0;
+    mutable int64_t t_eval_us   = 0;
+
+    mutable int64_t t_compute_start_us = 0;
+    mutable int64_t n_queued_tokens = 0;
+
+    mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
+    mutable int32_t n_eval   = 0; // number of eval calls
+
+    // host buffer for the model output (logits and embeddings)
+    ggml_backend_buffer_ptr buf_output;
+
+    // decode output (2-dimensional array: [n_outputs][n_vocab])
+    size_t  logits_size = 0; // capacity (of floats) for logits
+    float * logits      = nullptr;
+
+    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
+    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
+    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch or last logical batch
+
+    bool logits_all = false;
+
+    // embeddings output (2-dimensional array: [n_outputs][n_embd])
+    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
+    size_t  embd_size = 0; // capacity (of floats) for embeddings
+    float * embd      = nullptr;
+
+    // sequence embeddings output (map of [n_embd] vectors)
+    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
+    std::map<llama_seq_id, std::vector<float>> embd_seq;
+
+    // whether we are computing encoder output or decoder output
+    bool is_encoding = false;
+
+    // TODO: find a better way to accommodate mutli-dimension position encoding methods
+    // number of position id each token get, 1 for each token in most cases.
+    // when using m-rope, it will be 3 position ids per token to representing 3 dimension coordinate.
+    int n_pos_per_token = 1;
+
+    // output of the encoder part of the encoder-decoder models
+    std::vector<float> embd_enc;
+    std::vector<std::set<llama_seq_id>> seq_ids_enc;
+
+    // memory buffers used to evaluate the model
+    std::vector<uint8_t> buf_compute_meta;
+    ggml_backend_sched_ptr sched;
+
+    ggml_abort_callback abort_callback      = nullptr;
+    void *              abort_callback_data = nullptr;
+
+    // input tensors
+    struct ggml_tensor * inp_tokens;        // I32 [n_batch]
+    struct ggml_tensor * inp_embd;          // F32 [n_embd, n_batch]
+    struct ggml_tensor * inp_pos;           // I32 [n_batch]
+    struct ggml_tensor * inp_out_ids;       // I32 [n_outputs]
+    struct ggml_tensor * inp_KQ_mask;       // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_KQ_mask_swa;   // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_K_shift;       // I32 [kv_size]
+    struct ggml_tensor * inp_mean;          // F32 [n_batch, n_batch]
+    struct ggml_tensor * inp_cls;           // I32 [n_batch]
+    struct ggml_tensor * inp_s_copy;        // I32 [kv_size]
+    struct ggml_tensor * inp_s_mask;        // F32 [1, n_kv]
+    struct ggml_tensor * inp_s_seq;         // I32 [n_kv, n_batch]
+    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
+    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
+    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+};
+
+// TODO: make these methods of llama_context
+void llama_set_k_shift(struct llama_context & lctx);
+
+void llama_set_s_copy(struct llama_context & lctx);
+
+void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch);
+
+// Make sure enough space is available for outputs.
+// Returns max number of outputs for which space was reserved.
+size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs);
+
+// make the outputs have the same order they had in the user-provided batch
+void llama_output_reorder(struct llama_context & ctx);
+
+// For internal test use
+// TODO: remove
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(struct llama_context * ctx);
diff --git a/src/llama-cparams.cpp b/src/llama-cparams.cpp
new file mode 100644
index 000000000..28369be36
--- /dev/null
+++ b/src/llama-cparams.cpp
@@ -0,0 +1 @@
+#include "llama-cparams.h"
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
new file mode 100644
index 000000000..252012f3d
--- /dev/null
+++ b/src/llama-cparams.h
@@ -0,0 +1,37 @@
+#pragma once
+
+#include "llama.h"
+
+#include <cstdint>
+
+struct llama_cparams {
+    uint32_t n_ctx;           // context size used during inference
+    uint32_t n_batch;
+    uint32_t n_ubatch;
+    uint32_t n_seq_max;
+    int      n_threads;       // number of threads to use for generation
+    int      n_threads_batch; // number of threads to use for batch processing
+
+    float rope_freq_base;
+    float rope_freq_scale;
+
+    uint32_t n_ctx_orig_yarn;
+    // These hyperparameters are not exposed in GGUF, because all
+    // existing YaRN models use the same values for them.
+    float yarn_ext_factor;
+    float yarn_attn_factor;
+    float yarn_beta_fast;
+    float yarn_beta_slow;
+    float defrag_thold;
+
+    bool embeddings;
+    bool causal_attn;
+    bool offload_kqv;
+    bool flash_attn;
+    bool no_perf;
+
+    enum llama_pooling_type pooling_type;
+
+    ggml_backend_sched_eval_callback cb_eval;
+    void * cb_eval_user_data;
+};
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index 76d0cb3a2..186dc9a25 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -1,5 +1,6 @@
 #include "llama-grammar.h"
 
+#include "llama-impl.h"
 #include "llama-vocab.h"
 #include "llama-sampling.h"
 
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index 13e940fb5..f8b40c651 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -1,8 +1,10 @@
 #pragma once
 
-#include "llama-impl.h"
+#include "llama.h"
 
 #include <map>
+#include <string>
+#include <vector>
 
 struct llama_vocab;
 
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
new file mode 100644
index 000000000..c40534696
--- /dev/null
+++ b/src/llama-hparams.cpp
@@ -0,0 +1,71 @@
+#include "llama-hparams.h"
+
+#include "ggml.h"
+
+uint32_t llama_hparams::n_head(uint32_t il) const {
+    if (il < n_layer) {
+        return n_head_arr[il];
+    }
+
+    GGML_ABORT("fatal error");
+}
+
+uint32_t llama_hparams::n_head_kv(uint32_t il) const {
+    if (il < n_layer) {
+        return n_head_kv_arr[il];
+    }
+
+    GGML_ABORT("fatal error");
+}
+
+uint32_t llama_hparams::n_ff(uint32_t il) const {
+    if (il < n_layer) {
+        return n_ff_arr[il];
+    }
+
+    GGML_ABORT("fatal error");
+}
+
+uint32_t llama_hparams::n_gqa(uint32_t il) const {
+    const uint32_t n_head    = this->n_head(il);
+    const uint32_t n_head_kv = this->n_head_kv(il);
+
+    if (n_head_kv == 0) {
+        return 0;
+    }
+
+    return n_head/n_head_kv;
+}
+
+uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
+    const uint32_t n_head_kv = this->n_head_kv(il);
+
+    return n_embd_head_k * n_head_kv;
+}
+
+uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
+    const uint32_t n_head_kv = this->n_head_kv(il);
+
+    return n_embd_head_v * n_head_kv;
+}
+
+uint32_t llama_hparams::n_embd_k_s() const {
+    if (wkv_head_size != 0) {
+        // for RWKV models
+        return 2 * n_embd;
+    }
+
+    // TODO: maybe support other convolution strides than 1
+    // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
+    return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
+}
+
+uint32_t llama_hparams::n_embd_v_s() const {
+    if (wkv_head_size != 0) {
+        // corresponds to RWKV's wkv_states size
+        return n_embd * wkv_head_size;
+    }
+
+    // corresponds to Mamba's ssm_states size
+    return ssm_d_state * ssm_d_inner;
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
new file mode 100644
index 000000000..3a76b71a4
--- /dev/null
+++ b/src/llama-hparams.h
@@ -0,0 +1,132 @@
+#pragma once
+
+#include "llama.h"
+
+#include <array>
+
+// bump if necessary
+#define LLAMA_MAX_LAYERS  512
+#define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
+
+struct llama_hparams_posnet {
+    uint32_t n_embd;
+    uint32_t n_layer;
+};
+
+struct llama_hparams_convnext {
+    uint32_t n_embd;
+    uint32_t n_layer;
+};
+
+struct llama_hparams {
+    bool vocab_only;
+    bool rope_finetuned;
+    bool use_par_res;
+    bool swin_norm;
+
+    uint32_t n_vocab = 0;
+    uint32_t n_ctx_train; // context size the model was trained on
+    uint32_t n_embd;
+    uint32_t n_embd_features = 0;
+    uint32_t n_layer;
+    uint32_t n_rot;
+    uint32_t n_swa = 0; // sliding window attention (SWA)
+    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
+    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
+    uint32_t n_expert = 0;
+    uint32_t n_expert_used = 0;
+    uint32_t n_vocab_type = 0; // for BERT-style token types
+    uint32_t n_rel_attn_bkts = 0;
+
+    // for WavTokenizer
+    struct llama_hparams_posnet   posnet;
+    struct llama_hparams_convnext convnext;
+
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
+
+    uint32_t n_layer_dense_lead = 0;
+    uint32_t n_lora_q           = 0;
+    uint32_t n_lora_kv          = 0;
+    uint32_t n_ff_exp           = 0;
+    uint32_t n_ff_shexp         = 0;
+    uint32_t n_expert_shared    = 0;
+    uint32_t n_norm_groups      = 0;
+
+    float expert_weights_scale = 0.0;
+
+    float f_norm_eps;
+    float f_norm_rms_eps;
+    float f_norm_group_eps;
+
+    float f_attn_logit_softcapping  = 50.0f;
+    float f_final_logit_softcapping = 30.0f;
+
+    // for RWKV
+    uint32_t rescale_every_n_layers = 0;
+    uint32_t time_mix_extra_dim     = 0;
+    uint32_t time_decay_extra_dim   = 0;
+    uint32_t wkv_head_size          = 0;
+
+    float    rope_attn_factor = 1.0f;
+    float    rope_freq_base_train;
+    float    rope_freq_scale_train;
+    uint32_t n_ctx_orig_yarn;
+    float    rope_yarn_log_mul;
+
+    std::array<int, 4> rope_sections;
+
+    // for State Space Models
+    uint32_t ssm_d_conv  = 0;
+    uint32_t ssm_d_inner = 0;
+    uint32_t ssm_d_state = 0;
+    uint32_t ssm_dt_rank = 0;
+
+    bool ssm_dt_b_c_rms = false;
+
+    float f_clamp_kqv      = 0.0f;
+    float f_max_alibi_bias = 0.0f;
+    float f_logit_scale    = 0.0f;
+
+    // Additional scale factors (Granite/Granite MoE)
+    float f_residual_scale  = 0.0f;
+    float f_embedding_scale = 0.0f;
+    float f_attention_scale = 0.0f;
+
+    bool causal_attn   = true;
+    bool use_alibi     = false;
+    bool attn_soft_cap = false;
+
+    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
+    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
+    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
+
+    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
+    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
+    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
+
+    uint32_t n_head(uint32_t il = 0) const;
+
+    uint32_t n_head_kv(uint32_t il = 0) const;
+
+    uint32_t n_ff(uint32_t il = 0) const;
+
+    uint32_t n_gqa(uint32_t il = 0) const;
+
+    // dimension of key embeddings across all k-v heads
+    uint32_t n_embd_k_gqa(uint32_t il = 0) const;
+
+    // dimension of value embeddings across all k-v heads
+    uint32_t n_embd_v_gqa(uint32_t il = 0) const;
+
+    // dimension of the rolling state embeddings
+    // corresponds to Mamba's conv_states size or RWKV's token_shift states size
+    uint32_t n_embd_k_s() const;
+
+    // dimension of the recurrent state embeddings
+    uint32_t n_embd_v_s() const;
+};
+
+static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
+
diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp
new file mode 100644
index 000000000..a05ba4f63
--- /dev/null
+++ b/src/llama-impl.cpp
@@ -0,0 +1,166 @@
+#include "llama-impl.h"
+
+#include "llama.h"
+
+#include <cinttypes>
+#include <climits>
+#include <cstdarg>
+#include <cstring>
+#include <vector>
+#include <sstream>
+
+struct llama_logger_state {
+    ggml_log_callback log_callback = llama_log_callback_default;
+    void * log_callback_user_data = nullptr;
+};
+
+static llama_logger_state g_logger_state;
+
+time_meas::time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}
+
+time_meas::~time_meas() {
+        if (t_start_us >= 0) {
+            t_acc += ggml_time_us() - t_start_us;
+        }
+    }
+
+void llama_log_set(ggml_log_callback log_callback, void * user_data) {
+    ggml_log_set(log_callback, user_data);
+    g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
+    g_logger_state.log_callback_user_data = user_data;
+}
+
+static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
+    va_list args_copy;
+    va_copy(args_copy, args);
+    char buffer[128];
+    int len = vsnprintf(buffer, 128, format, args);
+    if (len < 128) {
+        g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
+    } else {
+        char * buffer2 = new char[len + 1];
+        vsnprintf(buffer2, len + 1, format, args_copy);
+        buffer2[len] = 0;
+        g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
+        delete[] buffer2;
+    }
+    va_end(args_copy);
+}
+
+void llama_log_internal(ggml_log_level level, const char * format, ...) {
+    va_list args;
+    va_start(args, format);
+    llama_log_internal_v(level, format, args);
+    va_end(args);
+}
+
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    fputs(text, stderr);
+    fflush(stderr);
+}
+
+void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    if (search.empty()) {
+        return;
+    }
+    std::string builder;
+    builder.reserve(s.length());
+    size_t pos = 0;
+    size_t last_pos = 0;
+    while ((pos = s.find(search, last_pos)) != std::string::npos) {
+        builder.append(s, last_pos, pos - last_pos);
+        builder.append(replace);
+        last_pos = pos + search.length();
+    }
+    builder.append(s, last_pos, std::string::npos);
+    s = std::move(builder);
+}
+
+std::string format(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
+    char buf[256];
+    snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
+    for (size_t i = 1; i < ne.size(); i++) {
+        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
+    }
+    return buf;
+}
+
+std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
+    char buf[256];
+    snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
+    }
+    return buf;
+}
+
+static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
+    switch (type) {
+        case GGUF_TYPE_UINT8:   return std::to_string(((const uint8_t  *)data)[i]);
+        case GGUF_TYPE_INT8:    return std::to_string(((const int8_t   *)data)[i]);
+        case GGUF_TYPE_UINT16:  return std::to_string(((const uint16_t *)data)[i]);
+        case GGUF_TYPE_INT16:   return std::to_string(((const int16_t  *)data)[i]);
+        case GGUF_TYPE_UINT32:  return std::to_string(((const uint32_t *)data)[i]);
+        case GGUF_TYPE_INT32:   return std::to_string(((const int32_t  *)data)[i]);
+        case GGUF_TYPE_UINT64:  return std::to_string(((const uint64_t *)data)[i]);
+        case GGUF_TYPE_INT64:   return std::to_string(((const int64_t  *)data)[i]);
+        case GGUF_TYPE_FLOAT32: return std::to_string(((const float    *)data)[i]);
+        case GGUF_TYPE_FLOAT64: return std::to_string(((const double   *)data)[i]);
+        case GGUF_TYPE_BOOL:    return ((const bool *)data)[i] ? "true" : "false";
+        default:                return format("unknown type %d", type);
+    }
+}
+
+std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
+    const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
+
+    switch (type) {
+        case GGUF_TYPE_STRING:
+            return gguf_get_val_str(ctx_gguf, i);
+        case GGUF_TYPE_ARRAY:
+            {
+                const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
+                int arr_n = gguf_get_arr_n(ctx_gguf, i);
+                const void * data = gguf_get_arr_data(ctx_gguf, i);
+                std::stringstream ss;
+                ss << "[";
+                for (int j = 0; j < arr_n; j++) {
+                    if (arr_type == GGUF_TYPE_STRING) {
+                        std::string val = gguf_get_arr_str(ctx_gguf, i, j);
+                        // escape quotes
+                        replace_all(val, "\\", "\\\\");
+                        replace_all(val, "\"", "\\\"");
+                        ss << '"' << val << '"';
+                    } else if (arr_type == GGUF_TYPE_ARRAY) {
+                        ss << "???";
+                    } else {
+                        ss << gguf_data_to_str(arr_type, data, j);
+                    }
+                    if (j < arr_n - 1) {
+                        ss << ", ";
+                    }
+                }
+                ss << "]";
+                return ss.str();
+            }
+        default:
+            return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
+    }
+}
diff --git a/src/llama-impl.h b/src/llama-impl.h
index 70f16b61c..12d1fb082 100644
--- a/src/llama-impl.h
+++ b/src/llama-impl.h
@@ -1,10 +1,9 @@
 #pragma once
 
-#include "llama.h"
+#include "ggml.h" // for ggml_log_level
 
 #include <string>
 #include <vector>
-#include <stdexcept>
 
 #ifdef __GNUC__
 #ifdef __MINGW32__
@@ -35,147 +34,28 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
 // helpers
 //
 
-struct time_meas {
-    time_meas(int64_t & t_acc, bool disable = false) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}
+template <typename T>
+struct no_init {
+    T value;
+    no_init() { /* do nothing */ }
+};
 
-    ~time_meas() {
-        if (t_start_us >= 0) {
-            t_acc += ggml_time_us() - t_start_us;
-        }
-    }
+struct time_meas {
+    time_meas(int64_t & t_acc, bool disable = false);
+    ~time_meas();
 
     const int64_t t_start_us;
 
     int64_t & t_acc;
 };
 
-static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    if (search.empty()) {
-        return;
-    }
-    std::string builder;
-    builder.reserve(s.length());
-    size_t pos = 0;
-    size_t last_pos = 0;
-    while ((pos = s.find(search, last_pos)) != std::string::npos) {
-        builder.append(s, last_pos, pos - last_pos);
-        builder.append(replace);
-        last_pos = pos + search.length();
-    }
-    builder.append(s, last_pos, std::string::npos);
-    s = std::move(builder);
-}
+void replace_all(std::string & s, const std::string & search, const std::string & replace);
 
-const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
-    struct llama_context * ctx
-);
+// TODO: rename to llama_format ?
+LLAMA_ATTRIBUTE_FORMAT(1, 2)
+std::string format(const char * fmt, ...);
 
-// the ring buffer works similarly to std::deque, but with a fixed capacity
-template<typename T>
-struct ring_buffer {
-    ring_buffer(size_t cap) : capacity(cap), data(cap) {}
+std::string llama_format_tensor_shape(const std::vector<int64_t> & ne);
+std::string llama_format_tensor_shape(const struct ggml_tensor * t);
 
-    T & front() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[first];
-    }
-
-    const T & front() const {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[first];
-    }
-
-    T & back() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[pos];
-    }
-
-    const T & back() const {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[pos];
-    }
-
-    void push_back(const T & value) {
-        if (capacity == 0) {
-            throw std::runtime_error("ring buffer: capacity is zero");
-        }
-
-        if (sz == capacity) {
-            // advance the start when buffer is full
-            first = (first + 1) % capacity;
-        } else {
-            sz++;
-        }
-        data[pos] = value;
-        pos = (pos + 1) % capacity;
-    }
-
-    T pop_front() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        T value = data[first];
-        first = (first + 1) % capacity;
-        sz--;
-        return value;
-    }
-
-    //T & operator[](size_t i) {
-    //    if (i >= sz) {
-    //        throw std::runtime_error("ring buffer: index out of bounds");
-    //    }
-    //    return data[(first + i) % capacity];
-    //}
-
-    //const T & at(size_t i) const {
-    //    if (i >= sz) {
-    //        throw std::runtime_error("ring buffer: index out of bounds");
-    //    }
-    //    return data[(first + i) % capacity];
-    //}
-
-    const T & rat(size_t i) const {
-        if (i >= sz) {
-            throw std::runtime_error("ring buffer: index out of bounds");
-        }
-        return data[(first + sz - i - 1) % capacity];
-    }
-
-    std::vector<T> to_vector() const {
-        std::vector<T> result;
-        result.reserve(sz);
-        for (size_t i = 0; i < sz; i++) {
-            result.push_back(data[(first + i) % capacity]);
-        }
-        return result;
-    }
-
-    void clear() {
-        // here only reset the status of the buffer
-        sz = 0;
-        first = 0;
-        pos = 0;
-    }
-
-    bool empty() const {
-        return sz == 0;
-    }
-
-    size_t size() const {
-        return sz;
-    }
-
-    size_t capacity = 0;
-    size_t sz = 0;
-    size_t first = 0;
-    size_t pos = 0;
-    std::vector<T> data;
-};
+std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i);
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
new file mode 100644
index 000000000..53379253a
--- /dev/null
+++ b/src/llama-kv-cache.cpp
@@ -0,0 +1,718 @@
+#include "llama-kv-cache.h"
+
+#include "llama-impl.h"
+#include "llama-batch.h"
+#include "llama-cparams.h"
+#include "llama-model.h"
+
+#include <algorithm>
+#include <limits>
+#include <map>
+
+static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
+
+uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
+    // the FA kernels require padding to avoid extra runtime boundary checks
+    return cparams.flash_attn ? 256u : 32u;
+}
+
+bool llama_kv_cache_init(
+             struct llama_kv_cache & cache,
+                 const llama_model & model,
+               const llama_cparams & cparams,
+                         ggml_type   type_k,
+                         ggml_type   type_v,
+                          uint32_t   kv_size,
+                              bool   offload) {
+    const struct llama_hparams & hparams = model.hparams;
+
+    const int32_t n_layer = hparams.n_layer;
+
+    cache.has_shift = false;
+
+    cache.recurrent = llama_model_is_recurrent(&model);
+    cache.v_trans   = !cache.recurrent && !cparams.flash_attn;
+    cache.can_shift = !cache.recurrent && model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA
+
+    LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d\n",
+            __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, cache.can_shift);
+
+    cache.head = 0;
+    cache.size = kv_size;
+    cache.used = 0;
+
+    cache.type_k = type_k;
+    cache.type_v = type_v;
+
+    cache.cells.clear();
+    cache.cells.resize(kv_size);
+
+    // create a context for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            struct ggml_init_params params = {
+                /*.mem_size   =*/ size_t(2u*n_layer*ggml_tensor_overhead()),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+            ggml_context * ctx = ggml_init(params);
+            if (!ctx) {
+                return nullptr;
+            }
+            ctx_map[buft] = ctx;
+            cache.ctxs.emplace_back(ctx);
+            return ctx;
+        }
+        return it->second;
+    };
+
+    cache.k_l.reserve(n_layer);
+    cache.v_l.reserve(n_layer);
+
+    for (int i = 0; i < n_layer; i++) {
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
+
+        LLAMA_LOG_DEBUG("%s: layer %d: n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", __func__, i, n_embd_k_gqa, n_embd_v_gqa);
+
+        ggml_backend_buffer_type_t buft;
+        if (offload) {
+            auto * dev = model.dev_layer.at(i).dev;
+            buft = ggml_backend_dev_buffer_type(dev);
+        } else {
+            buft = ggml_backend_cpu_buffer_type();
+        }
+        ggml_context * ctx = ctx_for_buft(buft);
+
+        if (!ctx) {
+            LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
+            return false;
+        }
+
+        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
+        ggml_format_name(k, "cache_k_l%d", i);
+        ggml_format_name(v, "cache_v_l%d", i);
+        cache.k_l.push_back(k);
+        cache.v_l.push_back(v);
+    }
+
+    // allocate tensors and initialize the buffers to avoid NaNs in the padding
+    for (auto it : ctx_map) {
+        auto * buft = it.first;
+        auto * ctx  = it.second;
+
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        if (!buf) {
+            LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
+            return false;
+        }
+        ggml_backend_buffer_clear(buf, 0);
+        LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+        cache.bufs.emplace_back(buf);
+    }
+
+    return true;
+}
+
+struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
+           struct llama_kv_cache & cache,
+       const struct llama_ubatch & batch) {
+    const uint32_t n_tokens = batch.n_tokens;
+    const uint32_t n_seqs   = batch.n_seqs;
+    const uint32_t n_seq_tokens = batch.n_seq_tokens;
+
+    if (cache.recurrent) {
+        // For recurrent state architectures (like Mamba or RWKV),
+        // each cache cell can store the state for a whole sequence.
+        // A slot should be always be contiguous.
+
+        // can only process batches with an equal number of new tokens in each sequence
+        GGML_ASSERT(batch.equal_seqs);
+
+        int32_t min = cache.size - 1;
+        int32_t max = 0;
+
+        // everything should fit if all seq_ids are smaller than the max
+        for (uint32_t s = 0; s < n_seqs; ++s) {
+            const uint32_t n_seq_id = batch.n_seq_id[s];
+            for (uint32_t j = 0; j < n_seq_id; ++j) {
+                const llama_seq_id seq_id = batch.seq_id[s][j];
+
+                if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
+                    // too big seq_id
+                    // TODO: would it be possible to resize the cache instead?
+                    LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
+                    return llama_kv_cache_slot_info_failed;
+                }
+                if (j > 0) {
+                    llama_kv_cell & seq = cache.cells[seq_id];
+                    if (seq.tail >= 0) {
+                        llama_kv_cell & cell = cache.cells[seq.tail];
+                        // clear cells from seq_ids that become shared
+                        // (should not normally happen, but let's handle it anyway)
+                        cell.seq_id.erase(seq_id);
+                        seq.tail = -1;
+                        if (cell.seq_id.empty()) {
+                            cell.pos = -1;
+                            cell.src = -1;
+                            cache.used -= 1;
+                        }
+                    }
+                }
+            }
+        }
+
+#ifndef NDEBUG
+        {
+            std::vector<int32_t> tails_verif;
+            tails_verif.assign(cache.size, -1);
+            for (uint32_t i = 0; i < cache.size; ++i) {
+                llama_kv_cell & cell = cache.cells[i];
+                for (llama_seq_id seq_id : cell.seq_id) {
+                    if (tails_verif[seq_id] != -1) {
+                        LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
+                    }
+                    tails_verif[seq_id] = i;
+                }
+            }
+            for (uint32_t i = 0; i < cache.size; ++i) {
+                if (tails_verif[i] != cache.cells[i].tail) {
+                    LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cache.cells[i].tail, tails_verif[i]);
+                }
+            }
+        }
+#endif
+
+        // find next empty cell
+        uint32_t next_empty_cell = cache.head;
+
+        for (uint32_t i = 0; i < cache.size; ++i) {
+            if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
+            llama_kv_cell & cell = cache.cells[next_empty_cell];
+            if (cell.is_empty()) { break; }
+            next_empty_cell += 1;
+        }
+
+        // find usable cell range
+        for (uint32_t s = 0; s < n_seqs; ++s) {
+            const llama_seq_id seq_id = batch.seq_id[s][0];
+            llama_kv_cell & seq_meta = cache.cells[seq_id];
+            bool has_cell = false;
+            if (seq_meta.tail >= 0) {
+                llama_kv_cell & cell = cache.cells[seq_meta.tail];
+                GGML_ASSERT(cell.has_seq_id(seq_id));
+                // does this seq_id "own" the cell?
+                if (cell.seq_id.size() == 1) { has_cell = true; }
+            }
+            if (!has_cell) {
+                llama_kv_cell & empty_cell = cache.cells[next_empty_cell];
+                GGML_ASSERT(empty_cell.is_empty());
+                // copy old tail into the empty cell
+                if (seq_meta.tail >= 0) {
+                    llama_kv_cell & orig_cell = cache.cells[seq_meta.tail];
+                    empty_cell.pos = orig_cell.pos;
+                    empty_cell.src = orig_cell.src;
+                    orig_cell.seq_id.erase(seq_id);
+                    empty_cell.seq_id.insert(seq_id); // will be overwritten
+                }
+                seq_meta.tail = next_empty_cell;
+                // find next empty cell
+                if (s + 1 < n_seqs) {
+                    next_empty_cell += 1;
+                    for (uint32_t i = 0; i < cache.size; ++i) {
+                        if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
+                        llama_kv_cell & cell = cache.cells[next_empty_cell];
+                        if (cell.is_empty()) { break; }
+                        next_empty_cell += 1;
+                    }
+                }
+            }
+            if (min > seq_meta.tail) { min = seq_meta.tail; }
+            if (max < seq_meta.tail) { max = seq_meta.tail; }
+        }
+
+        // gather and re-order
+        for (uint32_t s = 0; s < n_seqs; ++s) {
+            int32_t dst_id = s + min;
+            int32_t src_id = cache.cells[batch.seq_id[s][0]].tail;
+            if (dst_id != src_id) {
+                llama_kv_cell & dst_cell = cache.cells[dst_id];
+                llama_kv_cell & src_cell = cache.cells[src_id];
+
+                std::swap(dst_cell.pos, src_cell.pos);
+                std::swap(dst_cell.src, src_cell.src);
+                std::swap(dst_cell.seq_id, src_cell.seq_id);
+
+                // swap tails (assuming they NEVER overlap)
+                for (const llama_seq_id seq_id : src_cell.seq_id) {
+                    cache.cells[seq_id].tail = src_id;
+                }
+                for (const llama_seq_id seq_id : dst_cell.seq_id) {
+                    cache.cells[seq_id].tail = dst_id;
+                }
+            }
+        }
+
+        // update the pos of the used seqs
+        for (uint32_t s = 0; s < n_seqs; ++s) {
+            const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1];
+            int32_t cell_id = s + min;
+            llama_kv_cell & cell = cache.cells[cell_id];
+
+            if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
+                // What should happen when the pos backtracks or skips a value?
+                // Clearing the state mid-batch would require special-casing which isn't done.
+                LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
+                    __func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens);
+            }
+            cell.pos = last_pos;
+            cell.seq_id.clear();
+            for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) {
+                const llama_seq_id seq_id = batch.seq_id[s][j];
+                cell.seq_id.insert(seq_id);
+                cache.cells[seq_id].tail = cell_id;
+            }
+        }
+
+        // allow getting the range of used cells, from head to head + n
+        cache.head = min;
+        cache.n    = max - min + 1;
+        cache.used = std::count_if(cache.cells.begin(), cache.cells.end(),
+            [](const llama_kv_cell& cell){ return !cell.is_empty(); });
+
+        // sanity check
+        return llama_kv_cache_slot_info(cache.n >= n_seqs);
+    }
+    // otherwise, one cell per token.
+
+    if (n_tokens > cache.size) {
+        LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
+        return llama_kv_cache_slot_info_failed;
+    }
+
+    uint32_t n_tested = 0;
+
+    while (true) {
+        if (cache.head + n_tokens > cache.size) {
+            n_tested += cache.size - cache.head;
+            cache.head = 0;
+            continue;
+        }
+
+        bool found = true;
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            if (cache.cells[cache.head + i].pos >= 0) {
+                found = false;
+                cache.head += i + 1;
+                n_tested   += i + 1;
+                break;
+            }
+        }
+
+        if (found) {
+            break;
+        }
+
+        if (n_tested >= cache.size) {
+            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
+            return llama_kv_cache_slot_info_failed;
+        }
+    }
+
+    for (uint32_t s = 0; s < n_seqs; s++) {
+        for (uint32_t i = 0; i < n_seq_tokens; ++i) {
+            uint32_t k = s*n_seq_tokens + i;
+            cache.cells[cache.head + k].pos = batch.pos[k];
+
+            for (int32_t j = 0; j < batch.n_seq_id[s]; j++) {
+                cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]);
+            }
+        }
+    }
+
+    cache.used += n_tokens;
+
+    return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens);
+}
+
+uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
+    for (uint32_t i = cache.size; i > 0; --i) {
+        const llama_kv_cell & cell = cache.cells[i - 1];
+
+        if (cell.pos >= 0 && !cell.is_empty()) {
+            return i;
+        }
+    }
+
+    return 0;
+}
+
+void llama_kv_cache_clear(struct llama_kv_cache & cache) {
+    for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
+        cache.cells[i].pos = -1;
+        cache.cells[i].seq_id.clear();
+        cache.cells[i].src = -1;
+        cache.cells[i].tail = -1;
+    }
+    cache.head = 0;
+    cache.used = 0;
+
+    for (auto & buf : cache.bufs) {
+        ggml_backend_buffer_clear(buf.get(), 0);
+    }
+}
+
+bool llama_kv_cache_seq_rm(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1) {
+    uint32_t new_head = cache.size;
+
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    // models like Mamba or RWKV can't have a state partially erased
+    if (cache.recurrent) {
+        if (seq_id >= (int64_t) cache.size) {
+            // could be fatal
+            return false;
+        }
+        if (0 <= seq_id) {
+            int32_t & tail_id = cache.cells[seq_id].tail;
+            if (tail_id >= 0) {
+                const llama_kv_cell & cell = cache.cells[tail_id];
+                // partial intersection is invalid
+                if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
+                    return false;
+                }
+                // invalidate tails which will be cleared
+                if (p0 <= cell.pos && cell.pos < p1) {
+                    tail_id = -1;
+                }
+            }
+        } else {
+            // seq_id is negative, then the range should include everything or nothing
+            if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+                return false;
+            }
+        }
+    }
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            if (seq_id < 0) {
+                cache.cells[i].seq_id.clear();
+            } else if (cache.cells[i].has_seq_id(seq_id)) {
+                cache.cells[i].seq_id.erase(seq_id);
+            } else {
+                continue;
+            }
+            if (cache.cells[i].is_empty()) {
+                // keep count of the number of used cells
+                if (cache.cells[i].pos >= 0) cache.used--;
+
+                cache.cells[i].pos = -1;
+                cache.cells[i].src = -1;
+                if (new_head == cache.size) new_head = i;
+            }
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+
+    return true;
+}
+
+void llama_kv_cache_seq_cp(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id_src,
+                 llama_seq_id   seq_id_dst,
+                    llama_pos   p0,
+                    llama_pos   p1) {
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    if (cache.recurrent) {
+        if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
+            llama_kv_cell & tail_src = cache.cells[seq_id_src];
+            llama_kv_cell & tail_dst = cache.cells[seq_id_dst];
+            if (tail_dst.tail >= 0) {
+                // clear destination seq_id if it wasn't empty
+                llama_kv_cell & cell_dst = cache.cells[tail_dst.tail];
+
+                cell_dst.seq_id.erase(seq_id_dst);
+                tail_dst.tail = -1;
+                if (cell_dst.seq_id.empty()) {
+                    cell_dst.pos = -1;
+                    cell_dst.delta = -1;
+                    cell_dst.src = -1;
+                    cache.used -= 1;
+                }
+            }
+            if (tail_src.tail >= 0) {
+                llama_kv_cell & cell_src = cache.cells[tail_src.tail];
+
+                cell_src.seq_id.insert(seq_id_dst);
+                tail_dst.tail = tail_src.tail;
+            }
+        }
+
+        return;
+    }
+    // otherwise, this is the KV cache of a Transformer-like model
+
+    cache.head = 0;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.cells[i].seq_id.insert(seq_id_dst);
+        }
+    }
+}
+
+void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+    uint32_t new_head = cache.size;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.recurrent && (llama_seq_id) i != seq_id) {
+            cache.cells[i].tail = -1;
+        }
+        if (!cache.cells[i].has_seq_id(seq_id)) {
+            if (cache.cells[i].pos >= 0) cache.used--;
+            cache.cells[i].pos = -1;
+            cache.cells[i].src = -1;
+            cache.cells[i].seq_id.clear();
+            if (new_head == cache.size) new_head = i;
+        } else {
+            cache.cells[i].seq_id.clear();
+            cache.cells[i].seq_id.insert(seq_id);
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+}
+
+void llama_kv_cache_seq_add(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                    llama_pos   delta) {
+    uint32_t new_head = cache.size;
+
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+    // If there is no range then return early to avoid looping over the cache.
+    if (p0 == p1) return;
+
+    if (cache.recurrent) {
+        // for Mamba-like or RWKV models, only the pos needs to be shifted
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            const int32_t tail_id = cache.cells[seq_id].tail;
+            if (tail_id >= 0) {
+                llama_kv_cell & cell = cache.cells[tail_id];
+                if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                    cell.pos += delta;
+                }
+            }
+        }
+        return;
+    }
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.has_shift = true;
+            cache.cells[i].pos   += delta;
+            cache.cells[i].delta += delta;
+
+            if (cache.cells[i].pos < 0) {
+                if (!cache.cells[i].is_empty()) {
+                    cache.used--;
+                }
+                cache.cells[i].pos = -1;
+                cache.cells[i].seq_id.clear();
+                if (new_head == cache.size) {
+                    new_head = i;
+                }
+            }
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    // Otherwise we just start the next search from the beginning.
+    cache.head = new_head != cache.size ? new_head : 0;
+}
+
+void llama_kv_cache_seq_div(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                          int   d) {
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+    // If there is no range then return early to avoid looping over the cache.
+    if (p0 == p1) return;
+
+    if (cache.recurrent) {
+        // for Mamba-like or RWKV models, only the pos needs to be changed
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            const int32_t tail_id = cache.cells[seq_id].tail;
+            if (tail_id >= 0) {
+                llama_kv_cell & cell = cache.cells[tail_id];
+                if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                    cell.pos /= d;
+                }
+            }
+        }
+        return;
+    }
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.has_shift = true;
+
+            {
+                llama_pos p_old = cache.cells[i].pos;
+                cache.cells[i].pos   /= d;
+                cache.cells[i].delta += cache.cells[i].pos - p_old;
+            }
+        }
+    }
+}
+
+llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+    llama_pos result = 0;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id)) {
+            result = std::max(result, cache.cells[i].pos);
+        }
+    }
+
+    return result;
+}
+
+void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
+    if (!cache.recurrent) {
+        cache.do_defrag = true;
+    }
+}
+
+int32_t llama_get_kv_cache_token_count(const struct llama_kv_cache & kv) {
+    int result = 0;
+
+    for (uint32_t i = 0; i < kv.size; i++) {
+        result += kv.cells[i].seq_id.size();
+    }
+
+    return result;
+}
+
+int32_t llama_get_kv_cache_used_cells(const struct llama_kv_cache & kv) {
+    return kv.used;
+}
+
+bool llama_kv_cache_can_shift(const struct llama_kv_cache & kv) {
+    return kv.can_shift;
+}
+
+//
+// kv cache view
+//
+
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_kv_cache & kv, int32_t n_seq_max) {
+    struct llama_kv_cache_view result = {
+        /*.n_cells            = */ 0,
+        /*.n_seq_max          = */ n_seq_max,
+        /*.token_count        = */ 0,
+        /*.used_cells         = */ llama_get_kv_cache_used_cells(kv),
+        /*.max_contiguous     = */ 0,
+        /*.max_contiguous_idx = */ -1,
+        /*.cells              = */ nullptr,
+        /*.cells_sequences    = */ nullptr,
+    };
+
+    return result;
+}
+
+void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
+    if (view->cells != nullptr) {
+        free(view->cells);
+        view->cells = nullptr;
+    }
+    if (view->cells_sequences != nullptr) {
+        free(view->cells_sequences);
+        view->cells_sequences = nullptr;
+    }
+}
+
+void llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_kv_cache & kv) {
+    if (uint32_t(view->n_cells) < kv.size || view->cells == nullptr) {
+        view->n_cells = int32_t(kv.size);
+        void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
+        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
+        view->cells = (struct llama_kv_cache_view_cell *)p;
+        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
+        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
+        view->cells_sequences = (llama_seq_id *)p;
+    }
+
+    const std::vector<llama_kv_cell> & kv_cells = kv.cells;
+    llama_kv_cache_view_cell * c_curr = view->cells;
+    llama_seq_id * cs_curr = view->cells_sequences;
+    int32_t used_cells = 0;
+    int32_t token_count = 0;
+    int32_t curr_contig_idx = -1;
+    uint32_t max_contig = 0;
+    int32_t max_contig_idx = -1;
+
+    for (int32_t i = 0; i < int32_t(kv.size); i++, c_curr++, cs_curr += view->n_seq_max) {
+        const size_t curr_size = kv_cells[i].seq_id.size();
+        token_count += curr_size;
+        c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
+
+        if (curr_size > 0) {
+            if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
+                max_contig = i - curr_contig_idx;
+                max_contig_idx = curr_contig_idx;
+            }
+            curr_contig_idx = -1;
+        } else if (curr_contig_idx < 0) {
+            curr_contig_idx = i;
+        }
+
+        int seq_idx = 0;
+        for (const llama_seq_id it : kv_cells[i].seq_id) {
+            if (seq_idx >= view->n_seq_max) {
+                break;
+            }
+            cs_curr[seq_idx] = it;
+            seq_idx++;
+        }
+        if (seq_idx != 0) {
+            used_cells++;
+        }
+        for (; seq_idx < view->n_seq_max; seq_idx++) {
+            cs_curr[seq_idx] = -1;
+        }
+    }
+    if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
+        max_contig_idx = curr_contig_idx;
+        max_contig = kv_cells.size() - curr_contig_idx;
+    }
+    view->max_contiguous = max_contig;
+    view->max_contiguous_idx = max_contig_idx;
+    view->token_count = token_count;
+    view->used_cells = used_cells;
+    if (uint32_t(used_cells) != kv.used) {
+        LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
+            __func__, kv.used, used_cells);
+    }
+}
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
new file mode 100644
index 000000000..dca6f3998
--- /dev/null
+++ b/src/llama-kv-cache.h
@@ -0,0 +1,218 @@
+#pragma once
+
+#include "llama.h"
+
+#include "ggml-cpp.h"
+
+#include <set>
+#include <vector>
+
+struct llama_kv_cell {
+    llama_pos pos   = -1;
+    llama_pos delta = 0;
+    int32_t   src   = -1; // used by recurrent state models to copy states
+    int32_t   tail  = -1;
+
+    std::set<llama_seq_id> seq_id;
+
+    bool has_seq_id(const llama_seq_id & id) const {
+        return seq_id.find(id) != seq_id.end();
+    }
+
+    bool is_empty() const {
+        return seq_id.empty();
+    }
+
+    bool is_same_seq(const llama_kv_cell & other) const {
+        return seq_id == other.seq_id;
+    }
+};
+
+// ring-buffer of cached KV data
+struct llama_kv_cache {
+    bool has_shift = false;
+    bool do_defrag = false;
+    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+    bool v_trans   = true;  // the value tensor is transposed
+    bool can_shift = false;
+
+    // Note: The value of head isn't only used to optimize searching
+    // for a free KV slot. llama_decode_internal also uses it, so it
+    // cannot be freely changed after a slot has been allocated.
+    uint32_t head = 0;
+    uint32_t size = 0;
+    uint32_t used = 0; // used cells (i.e. at least one seq_id)
+
+    // computed before each graph build
+    uint32_t n = 0;
+
+    ggml_type type_k = GGML_TYPE_F16;
+    ggml_type type_v = GGML_TYPE_F16;
+
+    std::vector<llama_kv_cell> cells;
+
+    std::vector<struct ggml_tensor *> k_l; // per layer
+    std::vector<struct ggml_tensor *> v_l;
+
+    std::vector<ggml_context_ptr> ctxs;
+    std::vector<ggml_backend_buffer_ptr> bufs;
+
+    size_t total_size() const {
+        size_t size = 0;
+        for (const auto & buf : bufs) {
+            size += ggml_backend_buffer_get_size(buf.get());
+        }
+
+        return size;
+    }
+
+    // TODO: better data structures to reduce the cost of this operation
+    llama_pos max_pos() const {
+        llama_pos max_pos = -1;
+        for (const auto & cell : cells) {
+            max_pos = std::max(max_pos, cell.pos);
+        }
+
+        return max_pos;
+    }
+};
+
+// a structure holds information about the slot found in llama_kv_cache_find_slot
+struct llama_kv_cache_slot_info {
+    std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
+    bool found = false;                       // the slot was found
+
+    explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
+    llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
+
+    operator bool() const { return found; }
+};
+
+// TODO: maybe not needed
+uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams);
+
+bool llama_kv_cache_init(
+        struct llama_kv_cache & cache,
+            const llama_model & model,
+          const llama_cparams & cparams,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                     uint32_t   kv_size,
+                         bool   offload);
+
+// find an empty slot of size "n_tokens" in the cache
+// updates the cache head
+// returns a structure holding information about the slot found
+// Note: On success, it's important that cache.head points
+// to the first cell of the slot.
+struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
+           struct llama_kv_cache & cache,
+       const struct llama_ubatch & batch);
+
+// find how many cells are currently in use
+uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache);
+
+void llama_kv_cache_clear(struct llama_kv_cache & cache);
+
+bool llama_kv_cache_seq_rm(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1);
+
+void llama_kv_cache_seq_cp(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id_src,
+                 llama_seq_id   seq_id_dst,
+                    llama_pos   p0,
+                    llama_pos   p1);
+
+void llama_kv_cache_seq_keep(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id);
+
+void llama_kv_cache_seq_add(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                    llama_pos   delta);
+
+void llama_kv_cache_seq_div(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                          int   d);
+
+llama_pos llama_kv_cache_seq_pos_max(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id);
+
+void llama_kv_cache_defrag(struct llama_kv_cache & cache);
+
+int32_t llama_get_kv_cache_token_count(const struct llama_kv_cache & kv);
+
+int32_t llama_get_kv_cache_used_cells(const struct llama_kv_cache & kv);
+
+bool llama_kv_cache_can_shift(const struct llama_kv_cache & kv);
+
+//
+// kv cache view
+//
+
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_kv_cache & kv, int32_t n_seq_max);
+
+void llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_kv_cache & kv);
+
+//
+// kv cache restore
+//
+
+// saves the kv_cache state for future recovery.
+// used to rollback llama_kv_cache_find_slot changes.
+struct llama_kv_slot_restorer {
+    struct llama_kv_cache_state {
+        uint32_t head = 0;
+        uint32_t n    = 0;
+    } old_state;
+
+    // for non-recurrent models only
+    // list of slots to restore
+    std::vector<std::pair<uint32_t, uint32_t>> slot_boundaries;
+
+    bool do_restore = false;
+
+    explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
+        old_state.head = cache.head;
+        old_state.n    = cache.n;
+    }
+
+    // saves a slot information for future restoration
+    void save(const struct llama_kv_cache_slot_info & slot) {
+        if (slot) {
+            do_restore = true;
+            if (slot.boundaries.first != slot.boundaries.second) {
+                slot_boundaries.push_back(slot.boundaries);
+            }
+        }
+    }
+
+    // must be explicitly called to restore the kv_cache state
+    // and rollback changes from all llama_kv_cache_find_slot calls
+    void restore(struct llama_kv_cache & cache) {
+        if (do_restore) {
+            cache.head = old_state.head;
+            cache.n    = old_state.n;
+
+            if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
+                llama_kv_cache_seq_rm(cache, -1, -1, -1);
+            } else {
+                for (auto & slot : slot_boundaries) {
+                    llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
+                }
+            }
+        }
+    }
+};
+
diff --git a/src/llama-mmap.cpp b/src/llama-mmap.cpp
new file mode 100644
index 000000000..a99326335
--- /dev/null
+++ b/src/llama-mmap.cpp
@@ -0,0 +1,585 @@
+#include "llama-mmap.h"
+
+#include "llama-impl.h"
+
+#include "ggml.h"
+
+#include <cstring>
+#include <climits>
+#include <stdexcept>
+
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/mman.h>
+            #include <fcntl.h>
+        #endif
+        #if defined(_POSIX_MEMLOCK_RANGE)
+            #include <sys/resource.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #ifndef PATH_MAX
+        #define PATH_MAX MAX_PATH
+    #endif
+    #include <io.h>
+#endif
+
+// TODO: consider moving to llama-impl.h if needed in more places
+#if defined(_WIN32)
+std::string llama_format_win_err(DWORD err) {
+    LPSTR buf;
+    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
+    if (!size) {
+        return "FormatMessageA failed";
+    }
+    std::string ret(buf, size);
+    LocalFree(buf);
+    return ret;
+}
+#endif
+
+// llama_file
+
+struct llama_file::impl {
+#if defined(_WIN32)
+    HANDLE fp_win32;
+    std::string GetErrorMessageWin32(DWORD error_code) const {
+        std::string ret;
+        LPSTR lpMsgBuf = NULL;
+        DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                    NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
+        if (!bufLen) {
+            ret = format("Win32 error code: %lx", error_code);
+        } else {
+            ret = lpMsgBuf;
+            LocalFree(lpMsgBuf);
+        }
+
+        return ret;
+    }
+
+    impl(const char * fname, const char * mode) {
+        fp = ggml_fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+        LARGE_INTEGER li;
+        li.QuadPart = 0;
+        BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+
+        return li.QuadPart;
+    }
+
+    void seek(size_t offset, int whence) const {
+        static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN");
+        static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT");
+        static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END");
+
+        LARGE_INTEGER li;
+        li.QuadPart = offset;
+        BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        size_t bytes_read = 0;
+        while (bytes_read < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
+            DWORD chunk_read = 0;
+            BOOL result = ReadFile(fp_win32, reinterpret_cast<char*>(ptr) + bytes_read, chunk_size, &chunk_read, NULL);
+            if (!result) {
+                throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_read < chunk_size || chunk_read == 0) {
+                throw std::runtime_error("unexpectedly reached end of file");
+            }
+
+            bytes_read += chunk_read;
+        }
+    }
+
+    uint32_t read_u32() const {
+        uint32_t val;
+        read_raw(&val, sizeof(val));
+        return val;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        size_t bytes_written = 0;
+        while (bytes_written < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_written, 64*1024*1024);
+            DWORD chunk_written = 0;
+            BOOL result = WriteFile(fp_win32, reinterpret_cast<char const*>(ptr) + bytes_written, chunk_size, &chunk_written, NULL);
+            if (!result) {
+                throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_written < chunk_size || chunk_written == 0) {
+                throw std::runtime_error("unexpectedly failed to write bytes");
+            }
+
+            bytes_written += chunk_written;
+        }
+    }
+
+    void write_u32(uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~impl() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+#else
+    impl(const char * fname, const char * mode) {
+        fp = ggml_fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+// TODO: this ifdef is never true?
+#ifdef _WIN32
+        __int64 ret = _ftelli64(fp);
+#else
+        long ret = std::ftell(fp);
+#endif
+        if (ret == -1) {
+            throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+        }
+
+        return (size_t) ret;
+    }
+
+    void seek(size_t offset, int whence) const {
+// TODO: this ifdef is never true?
+#ifdef _WIN32
+        int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+        int ret = std::fseek(fp, (long) offset, whence);
+#endif
+        if (ret != 0) {
+            throw std::runtime_error(format("seek error: %s", strerror(errno)));
+        }
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        std::size_t ret = std::fread(ptr, len, 1, fp);
+        if (ferror(fp)) {
+            throw std::runtime_error(format("read error: %s", strerror(errno)));
+        }
+        if (ret != 1) {
+            throw std::runtime_error("unexpectedly reached end of file");
+        }
+    }
+
+    uint32_t read_u32() const {
+        uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, len, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~impl() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+#endif
+
+    FILE * fp;
+    size_t size;
+};
+
+llama_file::llama_file(const char * fname, const char * mode) : pimpl(std::make_unique<impl>(fname, mode)) {}
+llama_file::~llama_file() = default;
+
+size_t llama_file::tell() const { return pimpl->tell(); }
+size_t llama_file::size() const { return pimpl->size; }
+
+int llama_file::fileno() const {
+#ifdef _WIN32
+    return _fileno(pimpl->fp);
+#else
+    return ::fileno(pimpl->fp);
+#endif
+}
+
+void llama_file::seek(size_t offset, int whence) const { pimpl->seek(offset, whence); }
+void llama_file::read_raw(void * ptr, size_t len) const { pimpl->read_raw(ptr, len); }
+
+uint32_t llama_file::read_u32() const { return pimpl->read_u32(); }
+
+void llama_file::write_raw(const void * ptr, size_t len) const { pimpl->write_raw(ptr, len); }
+void llama_file::write_u32(uint32_t val) const { pimpl->write_u32(val); }
+
+// llama_mmap
+
+struct llama_mmap::impl {
+#ifdef _POSIX_MAPPED_FILES
+    std::vector<std::pair<size_t, size_t>> mapped_fragments;
+
+    impl(struct llama_file * file, size_t prefetch, bool numa) {
+        size = file->size();
+        int fd = file->fileno();
+        int flags = MAP_SHARED;
+        if (numa) { prefetch = 0; }
+#ifdef __linux__
+        if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
+            LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
+                    strerror(errno));
+        }
+        if (prefetch) { flags |= MAP_POPULATE; }
+#endif
+        addr = mmap(NULL, file->size(), PROT_READ, flags, fd, 0);
+        if (addr == MAP_FAILED) {
+            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
+        }
+
+        if (prefetch > 0) {
+            if (posix_madvise(addr, std::min(file->size(), prefetch), POSIX_MADV_WILLNEED)) {
+                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+        if (numa) {
+            if (posix_madvise(addr, file->size(), POSIX_MADV_RANDOM)) {
+                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+
+        mapped_fragments.emplace_back(0, file->size());
+    }
+
+    static void align_range(size_t * first, size_t * last, size_t page_size) {
+        size_t offset_in_page = *first & (page_size - 1);
+        size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
+        *first += offset_to_page;
+
+        *last = *last & ~(page_size - 1);
+
+        if (*last <= *first) {
+            *last = *first;
+        }
+    }
+
+    void unmap_fragment(size_t first, size_t last) {
+        int page_size = sysconf(_SC_PAGESIZE);
+        align_range(&first, &last, page_size);
+        size_t len = last - first;
+
+        if (len == 0) {
+            return;
+        }
+
+        GGML_ASSERT(first % page_size == 0);
+        GGML_ASSERT(last % page_size == 0);
+        GGML_ASSERT(last > first);
+
+        void * next_page_start = (uint8_t *) addr + first;
+
+        if (munmap(next_page_start, len)) {
+            LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
+        }
+
+        std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
+        for (const auto & frag : mapped_fragments) {
+            if (frag.first < first && frag.second > last) {
+                new_mapped_fragments.emplace_back(frag.first, first);
+                new_mapped_fragments.emplace_back(last, frag.second);
+            } else if (frag.first < first && frag.second > first) {
+                new_mapped_fragments.emplace_back(frag.first, first);
+            } else if (frag.first < last && frag.second > last) {
+                new_mapped_fragments.emplace_back(last, frag.second);
+            } else if (frag.first >= first && frag.second <= last) {
+            } else {
+                new_mapped_fragments.push_back(frag);
+            }
+        }
+        mapped_fragments = std::move(new_mapped_fragments);
+    }
+
+    ~impl() {
+        for (const auto & frag : mapped_fragments) {
+            if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
+                LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
+            }
+        }
+    }
+#elif defined(_WIN32)
+    impl(struct llama_file * file, size_t prefetch, bool numa) {
+        GGML_UNUSED(numa);
+
+        size = file->size();
+
+        HANDLE hFile = (HANDLE) _get_osfhandle(file->fileno());
+
+        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+
+        if (hMapping == NULL) {
+            DWORD error = GetLastError();
+            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+        DWORD error = GetLastError();
+        CloseHandle(hMapping);
+
+        if (addr == NULL) {
+            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        if (prefetch > 0) {
+#if _WIN32_WINNT >= 0x602
+            BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
+            HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
+
+            pPrefetchVirtualMemory = (decltype(pPrefetchVirtualMemory))(void *) GetProcAddress(hKernel32, "PrefetchVirtualMemory");
+
+            if (pPrefetchVirtualMemory) {
+                WIN32_MEMORY_RANGE_ENTRY range;
+                range.VirtualAddress = addr;
+                range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
+                if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
+                    LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
+                            llama_format_win_err(GetLastError()).c_str());
+                }
+            }
+#else
+            throw std::runtime_error("PrefetchVirtualMemory unavailable");
+#endif
+        }
+    }
+
+    void unmap_fragment(size_t first, size_t last) {
+        GGML_UNUSED(first);
+        GGML_UNUSED(last);
+    }
+
+    ~impl() {
+        if (!UnmapViewOfFile(addr)) {
+            LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    impl(struct llama_file * file, size_t prefetch, bool numa) {
+        GGML_UNUSED(file);
+        GGML_UNUSED(prefetch);
+        GGML_UNUSED(numa);
+
+        throw std::runtime_error("mmap not supported");
+    }
+
+    void unmap_fragment(size_t first, size_t last) {
+        GGML_UNUSED(first);
+        GGML_UNUSED(last);
+
+        throw std::runtime_error("mmap not supported");
+    }
+#endif
+
+    void * addr;
+    size_t size;
+};
+
+llama_mmap::llama_mmap(struct llama_file * file, size_t prefetch, bool numa) : pimpl(std::make_unique<impl>(file, prefetch, numa)) {}
+llama_mmap::~llama_mmap() = default;
+
+size_t llama_mmap::size() const { return pimpl->size; }
+void * llama_mmap::addr() const { return pimpl->addr; }
+
+void llama_mmap::unmap_fragment(size_t first, size_t last) { pimpl->unmap_fragment(first, last); }
+
+#if defined(_POSIX_MEMLOCK_RANGE) || defined(_WIN32)
+const bool llama_mmap::SUPPORTED  = true;
+#else
+const bool llama_mmap::SUPPORTED  = false;
+#endif
+
+// llama_mlock
+
+struct llama_mlock::impl {
+#ifdef _POSIX_MEMLOCK_RANGE
+    static size_t lock_granularity() {
+        return (size_t) sysconf(_SC_PAGESIZE);
+    }
+
+    bool raw_lock(const void * addr, size_t size) const {
+        if (!mlock(addr, size)) {
+            return true;
+        }
+
+#ifdef __APPLE__
+#define MLOCK_SUGGESTION \
+        "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
+        "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
+#else
+#define MLOCK_SUGGESTION \
+        "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
+#endif
+
+        char* errmsg = std::strerror(errno);
+        bool suggest = (errno == ENOMEM);
+
+        struct rlimit lock_limit;
+        if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
+            suggest = false;
+        }
+        if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
+            suggest = false;
+        }
+
+        LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+                size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
+        return false;
+    }
+
+    static void raw_unlock(void * addr, size_t size) {
+        if (munlock(addr, size)) {
+            LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
+        }
+    }
+#elif defined(_WIN32)
+    static size_t lock_granularity() {
+        SYSTEM_INFO si;
+        GetSystemInfo(&si);
+        return (size_t) si.dwPageSize;
+    }
+
+    bool raw_lock(void * ptr, size_t len) const {
+        for (int tries = 1; ; tries++) {
+            if (VirtualLock(ptr, len)) {
+                return true;
+            }
+            if (tries == 2) {
+                LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+                    len, size, llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+
+            SIZE_T min_ws_size, max_ws_size;
+            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
+                LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+            size_t increment = len + 1048576;
+            min_ws_size += increment;
+            max_ws_size += increment;
+            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
+                LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+        }
+    }
+
+    static void raw_unlock(void * ptr, size_t len) {
+        if (!VirtualUnlock(ptr, len)) {
+            LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static size_t lock_granularity() {
+        return (size_t) 65536;
+    }
+
+    bool raw_lock(const void * addr, size_t len) const {
+        LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
+        return false;
+    }
+
+    static void raw_unlock(const void * addr, size_t len) {}
+#endif
+
+    impl() : addr(NULL), size(0), failed_already(false) {}
+
+    void init(void * ptr) {
+        GGML_ASSERT(addr == NULL && size == 0);
+        addr = ptr;
+    }
+
+    void grow_to(size_t target_size) {
+        GGML_ASSERT(addr);
+        if (failed_already) {
+            return;
+        }
+        size_t granularity = lock_granularity();
+        target_size = (target_size + granularity - 1) & ~(granularity - 1);
+        if (target_size > size) {
+            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
+                size = target_size;
+            } else {
+                failed_already = true;
+            }
+        }
+    }
+
+    void * addr;
+    size_t size;
+
+    bool failed_already;
+};
+
+llama_mlock::llama_mlock() : pimpl(std::make_unique<impl>()) {}
+llama_mlock::~llama_mlock() = default;
+
+void llama_mlock::init(void * ptr) { pimpl->init(ptr); }
+void llama_mlock::grow_to(size_t target_size) { pimpl->grow_to(target_size); }
+
+#if defined(_POSIX_MEMLOCK_RANGE) || defined(_WIN32)
+const bool llama_mlock::SUPPORTED = true;
+#else
+const bool llama_mlock::SUPPORTED = false;
+#endif
+
+size_t llama_path_max() {
+    return PATH_MAX;
+}
diff --git a/src/llama-mmap.h b/src/llama-mmap.h
new file mode 100644
index 000000000..6bcddee8c
--- /dev/null
+++ b/src/llama-mmap.h
@@ -0,0 +1,67 @@
+#pragma once
+
+#include <memory>
+#include <vector>
+
+struct llama_file;
+struct llama_mmap;
+struct llama_mlock;
+
+using llama_files  = std::vector<std::unique_ptr<llama_file>>;
+using llama_mmaps  = std::vector<std::unique_ptr<llama_mmap>>;
+using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
+
+struct llama_file {
+    llama_file(const char * fname, const char * mode);
+    ~llama_file();
+
+    size_t tell() const;
+    size_t size() const;
+
+    int fileno() const;
+
+    void seek(size_t offset, int whence) const;
+
+    void read_raw(void * ptr, size_t len) const;
+    uint32_t read_u32() const;
+
+    void write_raw(const void * ptr, size_t len) const;
+    void write_u32(uint32_t val) const;
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+};
+
+struct llama_mmap {
+    llama_mmap(const llama_mmap &) = delete;
+    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false);
+    ~llama_mmap();
+
+    size_t size() const;
+    void * addr() const;
+
+    void unmap_fragment(size_t first, size_t last);
+
+    static const bool SUPPORTED;
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+};
+
+struct llama_mlock {
+    llama_mlock();
+    ~llama_mlock();
+
+    void init(void * ptr);
+    void grow_to(size_t target_size);
+
+    static const bool SUPPORTED;
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+};
+
+size_t llama_path_max();
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
new file mode 100644
index 000000000..7743b4652
--- /dev/null
+++ b/src/llama-model-loader.cpp
@@ -0,0 +1,1010 @@
+#include "llama-model-loader.h"
+
+#include "ggml.h"
+
+#include <array>
+#include <cinttypes>
+#include <cstring>
+#include <future>
+
+const char * llama_file_version_name(llama_fver version) {
+    switch (version) {
+        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
+        case GGUF_FILE_VERSION_V2: return "GGUF V2";
+        case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
+    }
+
+    return "unknown";
+}
+
+namespace GGUFMeta {
+    template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
+    struct GKV_Base_Type {
+        static constexpr gguf_type gt = gt_;
+
+        static T getter(const gguf_context * ctx, const int kid) {
+            return gfun(ctx, kid);
+        }
+    };
+
+    template<typename T> struct GKV_Base;
+
+    template<> struct GKV_Base<bool        >: GKV_Base_Type<bool,         GGUF_TYPE_BOOL,    gguf_get_val_bool> {};
+    template<> struct GKV_Base<uint8_t     >: GKV_Base_Type<uint8_t,      GGUF_TYPE_UINT8,   gguf_get_val_u8  > {};
+    template<> struct GKV_Base<uint16_t    >: GKV_Base_Type<uint16_t,     GGUF_TYPE_UINT16,  gguf_get_val_u16 > {};
+    template<> struct GKV_Base<uint32_t    >: GKV_Base_Type<uint32_t,     GGUF_TYPE_UINT32,  gguf_get_val_u32 > {};
+    template<> struct GKV_Base<uint64_t    >: GKV_Base_Type<uint64_t,     GGUF_TYPE_UINT64,  gguf_get_val_u64 > {};
+    template<> struct GKV_Base<int8_t      >: GKV_Base_Type<int8_t,       GGUF_TYPE_INT8,    gguf_get_val_i8  > {};
+    template<> struct GKV_Base<int16_t     >: GKV_Base_Type<int16_t,      GGUF_TYPE_INT16,   gguf_get_val_i16 > {};
+    template<> struct GKV_Base<int32_t     >: GKV_Base_Type<int32_t,      GGUF_TYPE_INT32,   gguf_get_val_i32 > {};
+    template<> struct GKV_Base<int64_t     >: GKV_Base_Type<int64_t,      GGUF_TYPE_INT64,   gguf_get_val_i64 > {};
+    template<> struct GKV_Base<float       >: GKV_Base_Type<float,        GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
+    template<> struct GKV_Base<double      >: GKV_Base_Type<double,       GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
+    template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING,  gguf_get_val_str > {};
+
+    template<> struct GKV_Base<std::string> {
+        static constexpr gguf_type gt = GGUF_TYPE_STRING;
+
+        static std::string getter(const gguf_context * ctx, const int kid) {
+            return gguf_get_val_str(ctx, kid);
+        }
+    };
+
+    struct ArrayInfo {
+        const gguf_type gt;
+        const size_t length;
+        const void * data;
+    };
+
+    template<> struct GKV_Base<ArrayInfo> {
+        public:
+        static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
+        static ArrayInfo getter(const gguf_context *ctx, const int k) {
+            return ArrayInfo {
+                gguf_get_arr_type(ctx, k),
+                size_t(gguf_get_arr_n(ctx, k)),
+                gguf_get_arr_data(ctx, k),
+            };
+        }
+    };
+
+    template<typename T>
+    class GKV : public GKV_Base<T> {
+        GKV() = delete;
+
+        public:
+        static T get_kv(const gguf_context * ctx, const int k) {
+            const enum gguf_type kt = gguf_get_kv_type(ctx, k);
+
+            if (kt != GKV::gt) {
+                throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
+                    gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
+            }
+            return GKV::getter(ctx, k);
+        }
+
+        static const char * override_type_to_str(const llama_model_kv_override_type ty) {
+            switch (ty) {
+                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
+                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
+                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_STR:   return "str";
+            }
+            return "unknown";
+        }
+
+        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
+            if (!ovrd) { return false; }
+            if (ovrd->tag == expected_type) {
+                LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
+                    __func__, override_type_to_str(ovrd->tag), ovrd->key);
+                switch (ovrd->tag) {
+                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
+                    } break;
+                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
+                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
+                    } break;
+                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
+                        LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
+                    } break;
+                    case LLAMA_KV_OVERRIDE_TYPE_STR: {
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_str);
+                    } break;
+                    default:
+                        // Shouldn't be possible to end up here, but just in case...
+                        throw std::runtime_error(
+                            format("Unsupported attempt to override %s type for metadata key %s\n",
+                                override_type_to_str(ovrd->tag), ovrd->key));
+                }
+                return true;
+            }
+            LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
+                __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
+            return false;
+        }
+
+        template<typename OT>
+        static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
+                target = ovrd->val_bool;
+                return true;
+            }
+            return false;
+        }
+
+        template<typename OT>
+        static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
+                target = ovrd->val_i64;
+                return true;
+            }
+            return false;
+        }
+
+        template<typename OT>
+        static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
+                target = ovrd->val_f64;
+                return true;
+            }
+            return false;
+        }
+
+        template<typename OT>
+        static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
+                target = ovrd->val_str;
+                return true;
+            }
+            return false;
+        }
+
+        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            if (try_override<T>(target, ovrd)) {
+                return true;
+            }
+            if (k < 0) { return false; }
+            target = get_kv(ctx, k);
+            return true;
+        }
+
+        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, gguf_find_key(ctx, key), target, ovrd);
+        }
+
+        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, key.c_str(), target, ovrd);
+        }
+    };
+}
+
+    template<typename T>
+    typename std::enable_if<std::is_integral<T>::value, bool>::type
+    llama_model_loader::get_arr_n(const std::string & key, T & result, bool required) {
+        const int kid = gguf_find_key(meta.get(), key.c_str());
+
+        if (kid < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
+
+
+        result = arr_info.length;
+        return true;
+    }
+
+    template<typename T>
+    typename std::enable_if<std::is_integral<T>::value, bool>::type
+    llama_model_loader::get_arr_n(enum llm_kv kid, T & result, bool required) {
+        return get_arr_n(llm_kv(kid), result, required);
+    }
+
+    template bool llama_model_loader::get_arr_n(enum llm_kv kid, uint32_t & result, bool required);
+
+    template<typename T>
+    bool llama_model_loader::get_arr(const std::string & key, std::vector<T> & result, bool required) {
+        const int kid = gguf_find_key(meta.get(), key.c_str());
+
+        if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
+
+        switch (arr_info.gt) {
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
+            default:
+                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+        }
+
+        result.resize(arr_info.length);
+        result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
+
+        return true;
+    }
+
+    template<typename T, size_t N_MAX>
+    bool llama_model_loader::get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required) {
+        const int kid = gguf_find_key(meta.get(), key.c_str());
+
+        if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
+
+        switch (arr_info.gt) {
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
+            default:
+                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+        }
+
+        if (arr_info.length > N_MAX) {
+            throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
+        }
+
+        std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
+
+        return true;
+    }
+
+    template<typename T>
+    bool llama_model_loader::get_arr(enum llm_kv kid, T & result, bool required) {
+        return get_arr(llm_kv(kid), result, required);
+    }
+
+    template<typename T>
+    bool llama_model_loader::get_key(const std::string & key, T & result, bool required) {
+        auto it = kv_overrides.find(key);
+
+        const struct llama_model_kv_override * override =
+            it != kv_overrides.end() ? &it->second : nullptr;
+
+        const bool found = GGUFMeta::GKV<T>::set(meta.get(), key, result, override);
+
+        if (required && !found) {
+            throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+        }
+
+        return found;
+    }
+
+    template<typename T>
+    bool llama_model_loader::get_key(enum llm_kv kid, T & result, bool required) {
+        return get_key(llm_kv(kid), result, required);
+    }
+
+    template bool llama_model_loader::get_key<bool>       (enum llm_kv kid, bool & result,        bool required);
+    template bool llama_model_loader::get_key<float>      (enum llm_kv kid, float & result,       bool required);
+    template bool llama_model_loader::get_key<uint32_t>   (enum llm_kv kid, uint32_t & result,    bool required);
+    template bool llama_model_loader::get_key<std::string>(enum llm_kv kid, std::string & result, bool required);
+
+    template<>
+    bool llama_model_loader::get_key(enum llm_kv kid, enum llama_pooling_type & result, bool required) {
+        uint32_t tmp;
+        const bool found = get_key(kid, tmp, required);
+        if (found) {
+            result = (enum llama_pooling_type) tmp;
+        } else {
+            result = LLAMA_POOLING_TYPE_UNSPECIFIED;
+        }
+        return found;
+    }
+
+    // get array of n <= N_MAX elements, or a single element repeated n times
+    template<typename T, size_t N_MAX>
+    bool llama_model_loader::get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, bool required) {
+        const int kid = gguf_find_key(meta.get(), key.c_str());
+
+        if (kid < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        if (n > N_MAX) {
+            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
+        }
+
+        if (gguf_get_kv_type(meta.get(), kid) == GGUF_TYPE_ARRAY) {
+            struct GGUFMeta::ArrayInfo arr_info =
+                GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
+
+            if (n != arr_info.length) {
+                throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
+            }
+
+            return get_arr(key, result, required);
+        }
+
+        T value;
+
+        bool ok = get_key(key, value, required);
+        if (!ok) {
+            return false;
+        }
+
+        for (uint32_t i = 0; i < n; i++) {
+            result[i] = value;
+        }
+
+        return true;
+    }
+
+    template<typename T>
+    bool llama_model_loader::get_key_or_arr(enum llm_kv kid, T & result, uint32_t n, bool required) {
+        return get_key_or_arr(llm_kv(kid), result, n, required);
+    }
+
+    // TODO: this is not very clever - figure out something better
+    template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
+    template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
+
+llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
+    int trace = 0;
+    if (getenv("LLAMA_TRACE")) {
+        trace = atoi(getenv("LLAMA_TRACE"));
+    }
+
+    if (param_overrides_p != nullptr) {
+        for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
+            kv_overrides.insert({std::string(p->key), *p});
+        }
+    }
+
+    struct ggml_context * ctx = NULL;
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ &ctx,
+    };
+
+    meta.reset(gguf_init_from_file(fname.c_str(), params));
+    if (!meta) {
+        throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
+    }
+
+    get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
+    llm_kv = LLM_KV(llm_arch_from_string(arch_name));
+
+    files.emplace_back(new llama_file(fname.c_str(), "rb"));
+    contexts.emplace_back(ctx);
+
+    // Save tensors data offset of the main file.
+    // For subsidiary files, `meta` tensor data offset must not be used,
+    // so we build a unified tensors index for weights.
+    for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+        std::string tensor_name = std::string(cur->name);
+        // make sure there is no duplicated tensor names
+        if (weights_map.find(tensor_name) != weights_map.end()) {
+            throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
+        }
+        n_elements += ggml_nelements(cur);
+        n_bytes    += ggml_nbytes(cur);
+        weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta.get(), cur));
+    }
+    uint16_t n_split = 0;
+    get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
+
+    // Load additional GGML contexts
+    if (n_split > 1) {
+        uint16_t idx = 0;
+        get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
+        if (idx != 0) {
+            throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
+        }
+
+        std::vector<char> split_prefix(llama_path_max(), 0);
+        if (!llama_split_prefix(split_prefix.data(), split_prefix.size(), fname.c_str(), idx, n_split)) {
+            throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
+        }
+
+        if (trace > 0) {
+            LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
+        }
+
+        std::vector<char> split_path(llama_path_max(), 0);
+        for (idx = 1; idx < n_split; idx++) {
+            llama_split_path(split_path.data(), split_path.size(), split_prefix.data(), idx, n_split);
+
+            struct gguf_init_params split_params = {
+                /*.no_alloc = */ true,
+                /*.ctx      = */ &ctx,
+            };
+            gguf_context_ptr ctx_gguf { gguf_init_from_file(split_path.data(), split_params) };
+            if (!ctx_gguf) {
+                throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path.data()));
+            }
+
+            files.emplace_back(new llama_file(split_path.data(), "rb"));
+            contexts.emplace_back(ctx);
+
+            // Save tensors data offset info of the shard.
+            for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+                std::string tensor_name = std::string(cur->name);
+                // make sure there is no duplicated tensor names
+                if (weights_map.find(tensor_name) != weights_map.end()) {
+                    throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
+                }
+                n_elements += ggml_nelements(cur);
+                n_bytes    += ggml_nbytes(cur);
+                weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur));
+            }
+        }
+
+        get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
+
+        // sanity check
+        {
+            const int n_tensors_loaded = (int) weights_map.size();
+            if (n_tensors != n_tensors_loaded) {
+                throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
+            }
+        }
+
+        LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n",  __func__, n_split - 1);
+    }
+
+    n_kv      = gguf_get_n_kv(meta.get());
+    n_tensors = weights_map.size();
+
+    fver = (enum llama_fver) gguf_get_version(meta.get());
+
+    LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
+            __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
+
+    // determine file type based on the number of tensors for each quantization and print meta data
+    // TODO: make optional
+    {
+        std::map<enum ggml_type, uint32_t> n_type;
+
+        uint32_t n_type_max = 0;
+        enum ggml_type type_max = GGML_TYPE_F32;
+
+        for (const auto & it : weights_map) {
+            const llama_tensor_weight & w = it.second;
+            const ggml_tensor * tensor = w.tensor;
+
+            enum ggml_type type = tensor->type;
+
+            n_type[type]++;
+
+            if (n_type_max < n_type[type]) {
+                n_type_max = n_type[type];
+                type_max   = type;
+            }
+
+            if (trace > 0) {
+                const uint16_t sid = w.idx;
+                LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
+            }
+        }
+
+        switch (type_max) {
+            case GGML_TYPE_F32:     ftype = LLAMA_FTYPE_ALL_F32;        break;
+            case GGML_TYPE_F16:     ftype = LLAMA_FTYPE_MOSTLY_F16;     break;
+            case GGML_TYPE_BF16:    ftype = LLAMA_FTYPE_MOSTLY_BF16;    break;
+            case GGML_TYPE_Q4_0:    ftype = LLAMA_FTYPE_MOSTLY_Q4_0;    break;
+            case GGML_TYPE_Q4_1:    ftype = LLAMA_FTYPE_MOSTLY_Q4_1;    break;
+            case GGML_TYPE_Q5_0:    ftype = LLAMA_FTYPE_MOSTLY_Q5_0;    break;
+            case GGML_TYPE_Q5_1:    ftype = LLAMA_FTYPE_MOSTLY_Q5_1;    break;
+            case GGML_TYPE_Q8_0:    ftype = LLAMA_FTYPE_MOSTLY_Q8_0;    break;
+            case GGML_TYPE_Q2_K:    ftype = LLAMA_FTYPE_MOSTLY_Q2_K;    break;
+            case GGML_TYPE_Q3_K:    ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M;  break;
+            case GGML_TYPE_Q4_K:    ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M;  break;
+            case GGML_TYPE_Q5_K:    ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M;  break;
+            case GGML_TYPE_Q6_K:    ftype = LLAMA_FTYPE_MOSTLY_Q6_K;    break;
+            case GGML_TYPE_TQ1_0:   ftype = LLAMA_FTYPE_MOSTLY_TQ1_0;   break;
+            case GGML_TYPE_TQ2_0:   ftype = LLAMA_FTYPE_MOSTLY_TQ2_0;   break;
+            case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
+            case GGML_TYPE_IQ2_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS;  break;
+            case GGML_TYPE_IQ2_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_S;   break;
+            case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
+            case GGML_TYPE_IQ1_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_S;   break;
+            case GGML_TYPE_IQ1_M:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_M;   break;
+            case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
+            case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
+            case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
+            default:
+                {
+                    LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
+                    ftype = LLAMA_FTYPE_ALL_F32;
+                } break;
+        }
+
+        // this is a way to mark that we have "guessed" the file type
+        ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
+
+        {
+            const int kid = gguf_find_key(meta.get(), "general.file_type"); // TODO: use LLM_KV
+            if (kid >= 0) {
+                ftype = (llama_ftype) gguf_get_val_u32(meta.get(), kid);
+            }
+        }
+
+        LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+
+        for (int i = 0; i < n_kv; i++) {
+            const char * name           = gguf_get_key(meta.get(), i);
+            const enum gguf_type type   = gguf_get_kv_type(meta.get(), i);
+            const std::string type_name =
+                type == GGUF_TYPE_ARRAY
+                ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i))
+                : gguf_type_name(type);
+
+            std::string value          = gguf_kv_to_str(meta.get(), i);
+            const size_t MAX_VALUE_LEN = 40;
+            if (value.size() > MAX_VALUE_LEN) {
+                value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
+            }
+            replace_all(value, "\n", "\\n");
+
+            LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+        }
+
+        // print type counts
+        for (auto & kv : n_type) {
+            if (kv.second == 0) {
+                continue;
+            }
+
+            LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+        }
+    }
+
+    if (!llama_mmap::SUPPORTED) {
+        LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
+        use_mmap = false;
+    }
+
+    this->use_mmap = use_mmap;
+    this->check_tensors = check_tensors;
+}
+
+std::string llama_model_loader::get_arch_name() const {
+    return arch_name;
+}
+
+enum llm_arch llama_model_loader::get_arch() const {
+    return llm_kv.arch;
+}
+
+const llama_model_loader::llama_tensor_weight * llama_model_loader::get_weight(const char * name) const {
+    auto pos = weights_map.find(name);
+    if (pos != weights_map.end()) {
+        return &pos->second;
+    }
+
+    return nullptr;
+}
+
+const llama_model_loader::llama_tensor_weight & llama_model_loader::require_weight(const char * name) const {
+    const llama_tensor_weight * weight = get_weight(name);
+    if (!weight) {
+        throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+    }
+    return *weight;
+}
+
+struct ggml_tensor * llama_model_loader::get_tensor_meta(const char * name) const {
+    const auto * weight = get_weight(name);
+    if (!weight) {
+        return nullptr;
+    }
+    return weight->tensor;
+}
+
+struct ggml_tensor * llama_model_loader::require_tensor_meta(const std::string & name) const {
+    struct ggml_tensor * tensor = get_tensor_meta(name.c_str());
+    if (!tensor) {
+        throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
+    }
+    return tensor;
+}
+
+const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
+    const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
+
+    if (cur == NULL) {
+        if (!required) {
+            return NULL;
+        }
+        throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
+    }
+
+    {
+        bool is_ok = true;
+        for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+            if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
+                is_ok = false;
+                break;
+            }
+        }
+        if (!is_ok) {
+            throw std::runtime_error(
+                    format("%s: tensor '%s' has wrong shape; expected %s, got %s",
+                        __func__, name.c_str(),
+                        llama_format_tensor_shape(ne).c_str(),
+                        llama_format_tensor_shape(cur).c_str()));
+        }
+    }
+
+    return cur;
+}
+
+struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags) {
+    const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
+
+    if (cur == NULL) {
+        return NULL;
+    }
+
+    bool duplicated = flags & TENSOR_DUPLICATED;
+
+    struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
+    ggml_set_name(tensor, ggml_get_name(cur));
+
+    if (duplicated) {
+        size_data += ggml_nbytes(cur);
+    } else {
+        n_created++;
+    }
+
+    return tensor;
+
+}
+
+struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required) {
+    const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+    if (cur == NULL) {
+        return NULL;
+    }
+
+    if (cur->type != base->type) {
+        throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
+    }
+
+    std::array<int64_t, GGML_MAX_DIMS> dims;
+    for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+        dims[i] = i < ne.size() ? ne.begin()[i] : 1;
+    }
+
+    struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
+                                    dims[0], dims[1], dims[2], dims[3],
+                                    cur->nb[1], cur->nb[2], cur->nb[3],
+                                    offset);
+
+    ggml_set_name(tensor, name.c_str());
+
+    n_created++;
+
+    return tensor;
+}
+
+void llama_model_loader::done_getting_tensors() const {
+    if (n_created != n_tensors) {
+        throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
+    }
+}
+
+void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps) {
+    if (use_mmap) {
+        mappings.reserve(files.size());
+        mmaps_used.reserve(files.size());
+        for (const auto & file : files) {
+            auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
+            auto * is_numa_fn = (decltype(ggml_is_numa) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_is_numa");
+            std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, is_numa_fn()));
+            mmaps_used.emplace_back(mapping->size(), 0);
+            if (mlock_mmaps) {
+                std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
+                mlock_mmap->init(mapping->addr());
+                mlock_mmaps->emplace_back(std::move(mlock_mmap));
+            }
+            mappings.emplace_back(std::move(mapping));
+        }
+    }
+
+    // compute the total size of all tensors for progress reporting
+    for (const auto & it : weights_map) {
+        size_data += ggml_nbytes(it.second.tensor);
+    }
+}
+
+void llama_model_loader::get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
+    GGML_ASSERT(!mappings.empty());
+    const auto & mapping = mappings.at(idx);
+
+    *first = mapping->size();
+    *last  = 0;
+    *addr = mapping->addr();
+    for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
+        const auto * weight = get_weight(ggml_get_name(tensor));
+        if (!weight || weight->idx != idx) {
+            continue;
+        }
+        *first = std::min(*first, weight->offs);
+        *last  = std::max(*last,  weight->offs + ggml_nbytes(tensor));
+    }
+}
+
+void llama_model_loader::load_data_for(struct ggml_tensor * cur) const {
+    const auto & w = require_weight(ggml_get_name(cur));
+
+    if (use_mmap) {
+        const auto & mapping = mappings.at(w.idx);
+        if (cur->data == nullptr) {
+            cur->data = (uint8_t *)mapping->addr() + w.offs;
+        } else {
+            memcpy(cur->data, (uint8_t *)mapping->addr() + w.offs, ggml_nbytes(cur));
+        }
+    } else {
+        GGML_ASSERT(cur->data != nullptr);
+        GGML_ASSERT(w.idx < files.size());
+        const auto & file = files.at(w.idx);
+        file->seek(w.offs, SEEK_SET);
+        file->read_raw(cur->data, ggml_nbytes(cur));
+    }
+
+    if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
+        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+    }
+}
+
+bool llama_model_loader::load_all_data(
+        struct ggml_context * ctx,
+        llama_buf_map & bufs,
+        llama_mlocks * lmlocks,
+        llama_progress_callback progress_callback,
+        void * progress_callback_user_data) {
+    GGML_ASSERT(size_data != 0 && "call init_mappings() first");
+
+    std::vector<no_init<uint8_t>> read_buf;
+    std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
+
+    // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
+    // NVMe raid configurations might require more / larger buffers.
+    constexpr size_t n_buffers = 4;
+    constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+
+    std::vector<ggml_backend_buffer_t> host_buffers;
+    std::vector<ggml_backend_event_t> events;
+    std::vector<void *> host_ptrs;
+    size_t buffer_idx = 0; // buffer to use for async loads
+    ggml_backend_t upload_backend = [&](const char * func) -> ggml_backend_t {
+        if (use_mmap || check_tensors) {
+            return nullptr;
+        }
+        // When not using mmaped io use async uploads from pinned memory to GPU memory.
+        // First determine if the backend supports the necessary features for async uploads.
+        auto * buf = bufs.count(0) ? bufs.at(0) : nullptr;
+        if (!buf) {
+            LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", func);
+            return nullptr;
+        }
+
+        auto * buft = ggml_backend_buffer_get_type(buf);
+        auto * dev = ggml_backend_buft_get_device(buft);
+        if (!dev) {
+            LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", func,
+                ggml_backend_buft_name(buft));
+            return nullptr;
+        }
+
+        if (buft != ggml_backend_dev_buffer_type(dev)) {
+            LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", func,
+                ggml_backend_buft_name(buft), ggml_backend_dev_name(dev));
+            return nullptr;
+        }
+
+        ggml_backend_dev_props props;
+        ggml_backend_dev_get_props(dev, &props);
+        if (!props.caps.async || !props.caps.host_buffer || !props.caps.events) {
+            LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", func,
+                ggml_backend_dev_name(dev));
+            return nullptr;
+        }
+
+        auto * host_buft = ggml_backend_dev_host_buffer_type(dev);
+        if (!host_buft) {
+            LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", func,
+                ggml_backend_dev_name(dev));
+            return nullptr;
+        }
+
+        // If the backend is supported, create pinned memory buffers and events for synchronisation.
+        for (size_t idx = 0; idx < n_buffers; ++idx) {
+            auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
+            if (!buf) {
+                LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func,
+                    ggml_backend_dev_name(dev));
+                return nullptr;
+            }
+
+            host_buffers.emplace_back(buf);
+            host_ptrs.emplace_back(ggml_backend_buffer_get_base(buf));
+
+            auto * event = ggml_backend_event_new(dev);
+            if (!event) {
+                LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", func,
+                    ggml_backend_dev_name(dev));
+                return nullptr;
+            }
+
+            events.emplace_back(event);
+        }
+
+        ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
+        if (!backend) {
+            LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", func,
+                ggml_backend_dev_name(dev));
+            return nullptr;
+        }
+
+        return backend;
+    }(__func__);
+
+    if (upload_backend) {
+        LLAMA_LOG_DEBUG("%s: using async uploads for device %s, buffer type %s, backend %s\n", __func__,
+            ggml_backend_dev_name(ggml_backend_get_device(upload_backend)),
+            ggml_backend_buft_name(ggml_backend_buffer_get_type(bufs.at(0))),
+            ggml_backend_name(upload_backend));
+    }
+
+    for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+        const auto * weight = get_weight(ggml_get_name(cur));
+        if (weight == nullptr) {
+            // this can happen with split experts models
+            continue;
+        }
+
+        if (progress_callback) {
+            if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
+                return false;
+            }
+        }
+
+        size_t n_size = ggml_nbytes(cur);
+
+        if (use_mmap) {
+            const auto & mapping = mappings.at(weight->idx);
+            ggml_backend_buffer_t buf_mmap = nullptr;
+            if (bufs.count(weight->idx)) {
+                buf_mmap = bufs.at(weight->idx);
+            }
+            uint8_t * data = (uint8_t *) mapping->addr() + weight->offs;
+
+            if (check_tensors) {
+                validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
+                    return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
+                }));
+            }
+
+            GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
+            if (buf_mmap && cur->data == nullptr) {
+                ggml_backend_tensor_alloc(buf_mmap, cur, data);
+                if (lmlocks) {
+                    const auto & lmlock = lmlocks->at(weight->idx);
+                    lmlock->grow_to(weight->offs + n_size);
+                }
+
+                auto & mmap_used = mmaps_used[weight->idx];
+                mmap_used.first  = std::min(mmap_used.first,  weight->offs);
+                mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
+            } else {
+                ggml_backend_tensor_set(cur, data, 0, n_size);
+            }
+        } else {
+            const auto & file = files.at(weight->idx);
+            if (ggml_backend_buffer_is_host(cur->buffer)) {
+                file->seek(weight->offs, SEEK_SET);
+                file->read_raw(cur->data, n_size);
+                if (check_tensors) {
+                    validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
+                        return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
+                    }));
+                }
+            } else {
+                // If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
+                if (upload_backend) {
+                    file->seek(weight->offs, SEEK_SET);
+
+                    size_t bytes_read = 0;
+
+                    while (bytes_read < n_size) {
+                        size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
+
+                        ggml_backend_event_synchronize(events[buffer_idx]);
+                        file->read_raw(host_ptrs[buffer_idx], read_iteration);
+                        ggml_backend_tensor_set_async(upload_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+                        ggml_backend_event_record(events[buffer_idx], upload_backend);
+
+                        bytes_read += read_iteration;
+                        ++buffer_idx;
+                        buffer_idx %= n_buffers;
+                    }
+                } else {
+                    read_buf.resize(n_size);
+                    file->seek(weight->offs, SEEK_SET);
+                    file->read_raw(read_buf.data(), n_size);
+                    ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+                    if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+                        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+                    }
+                }
+            }
+        }
+
+        size_done += n_size;
+    }
+
+    // free temporary resources used for async uploads
+    for (auto * event : events) {
+        ggml_backend_event_synchronize(event);
+        ggml_backend_event_free(event);
+    }
+    for (auto * buf : host_buffers) {
+        ggml_backend_buffer_free(buf);
+    }
+    ggml_backend_free(upload_backend);
+
+    // check validation results
+    bool validation_failed = false;
+    for (auto & future : validation_result) {
+        auto result = future.get();
+        if (!result.second) {
+            LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
+            validation_failed = true;
+        }
+    }
+    if (validation_failed) {
+        throw std::runtime_error("found tensors with invalid data");
+    }
+
+    // check if this is the last call and do final cleanup
+    if (size_done >= size_data) {
+        // unmap offloaded tensors and metadata
+        if (use_mmap) {
+            for (uint32_t idx = 0; idx < mappings.size(); idx++) {
+                const auto & mmap_used = mmaps_used.at(idx);
+                auto & mapping = mappings.at(idx);
+                mapping->unmap_fragment(0, mmap_used.first);
+                if (mmap_used.second != 0) {
+                    mapping->unmap_fragment(mmap_used.second, mapping->size());
+                }
+            }
+        }
+        if (progress_callback) {
+            // Even though the model is done loading, we still honor
+            // cancellation since we need to free allocations.
+            return progress_callback(1.0f, progress_callback_user_data);
+        }
+    }
+
+    return true;
+}
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
new file mode 100644
index 000000000..1ec478195
--- /dev/null
+++ b/src/llama-model-loader.h
@@ -0,0 +1,158 @@
+#pragma once
+
+#include "llama.h"
+
+#include "llama-impl.h"
+#include "llama-arch.h"
+#include "llama-mmap.h"
+
+#include "ggml-cpp.h"
+
+#include <cstddef>
+#include <map>
+#include <stdexcept>
+#include <unordered_map>
+
+using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
+
+enum llama_fver {
+    GGUF_FILE_VERSION_V1 = 1,
+    GGUF_FILE_VERSION_V2 = 2,
+    GGUF_FILE_VERSION_V3 = 3,
+};
+
+const char * llama_file_version_name(llama_fver version);
+
+struct llama_model_loader {
+    // Holds information on a model weight
+    struct llama_tensor_weight {
+        uint16_t  idx; // source file index
+        size_t   offs; // tensor data offset in the original file
+
+        ggml_tensor * tensor;
+
+        llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
+            const int tensor_idx = gguf_find_tensor(gguf_ctx,  ggml_get_name(tensor));
+            if (tensor_idx < 0) {
+                throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor)));
+            }
+
+            offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
+            if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size()) {
+                throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor)));
+            }
+        }
+    };
+
+    // custom comparator to sort weights more nicely by layer
+    struct weight_name_comparer {
+        bool operator()(const std::string & a, const std::string & b) const {
+            int a_layer = -1;
+            int b_layer = -1;
+            sscanf(a.c_str(), "blk.%d.", &a_layer);
+            sscanf(b.c_str(), "blk.%d.", &b_layer);
+            if (a_layer != b_layer) {
+                return a_layer < b_layer;
+            }
+            return a < b;
+        }
+    };
+
+    static const int TENSOR_NOT_REQUIRED = 1;
+    static const int TENSOR_DUPLICATED   = 2;
+
+    int n_kv      = 0;
+    int n_tensors = 0;
+    int n_created = 0;
+
+    uint64_t n_elements = 0;
+    size_t   n_bytes    = 0;
+
+    bool use_mmap = false;
+    bool check_tensors;
+
+    llama_files files;
+    llama_ftype ftype;
+    llama_fver  fver;
+
+    llama_mmaps mappings;
+
+    std::map<std::string, struct llama_tensor_weight, weight_name_comparer> weights_map;
+    std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
+
+    gguf_context_ptr meta;
+    std::vector<ggml_context_ptr> contexts;
+
+    std::string arch_name;
+    LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);
+
+    size_t size_done = 0;
+    size_t size_data = 0;
+    std::vector<std::pair<size_t, size_t>> mmaps_used;
+
+    llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p);
+
+    template<typename T>
+    typename std::enable_if<std::is_integral<T>::value, bool>::type
+    get_arr_n(const std::string & key, T & result, bool required = true);
+
+    template<typename T>
+    typename std::enable_if<std::is_integral<T>::value, bool>::type
+    get_arr_n(enum llm_kv kid, T & result, bool required = true);
+
+    template<typename T>
+    bool get_arr(const std::string & key, std::vector<T> & result, bool required = true);
+
+    template<typename T, size_t N_MAX>
+    bool get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required = true);
+
+    template<typename T>
+    bool get_arr(enum llm_kv kid, T & result, bool required = true);
+
+    template<typename T>
+    bool get_key(const std::string & key, T & result, bool required = true);
+
+    template<typename T>
+    bool get_key(enum llm_kv kid, T & result, bool required = true);
+
+    template<typename T, size_t N_MAX>
+    bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, bool required = true);
+
+    template<typename T>
+    bool get_key_or_arr(enum llm_kv kid, T & result, uint32_t n, bool required = true);
+
+    std::string get_arch_name() const;
+
+    enum llm_arch get_arch() const;
+
+    const llama_tensor_weight * get_weight(const char * name) const;
+
+    const llama_tensor_weight & require_weight(const char * name) const;
+
+    struct ggml_tensor * get_tensor_meta(const char * name) const;
+
+    struct ggml_tensor * require_tensor_meta(const std::string & name) const;
+
+    const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const;
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags = 0);
+
+    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true);
+
+    void done_getting_tensors() const;
+
+    void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);
+
+    void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const;
+
+    // for backwards compatibility, does not support ggml-backend
+    void load_data_for(struct ggml_tensor * cur) const;
+
+    // Returns false if cancelled by progress_callback
+    bool load_all_data(
+            struct ggml_context * ctx,
+            llama_buf_map & bufs,
+            llama_mlocks * lmlocks,
+            llama_progress_callback progress_callback,
+            void * progress_callback_user_data);
+};
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
new file mode 100644
index 000000000..ace0ba262
--- /dev/null
+++ b/src/llama-model.cpp
@@ -0,0 +1,2164 @@
+#include "llama-model.h"
+
+#include "llama-impl.h"
+#include "llama-model-loader.h"
+
+#include "unicode.h" // TODO: remove
+
+#include <algorithm>
+#include <cassert>
+#include <functional>
+#include <sstream>
+#include <stdexcept>
+
+static const size_t kiB = 1024;
+static const size_t MiB = 1024*kiB;
+static const size_t GiB = 1024*MiB;
+
+const char * llm_type_name(llm_type type) {
+    switch (type) {
+        case MODEL_14M:           return "14M";
+        case MODEL_17M:           return "17M";
+        case MODEL_22M:           return "22M";
+        case MODEL_33M:           return "33M";
+        case MODEL_60M:           return "60M";
+        case MODEL_70M:           return "70M";
+        case MODEL_80M:           return "80M";
+        case MODEL_109M:          return "109M";
+        case MODEL_137M:          return "137M";
+        case MODEL_160M:          return "160M";
+        case MODEL_220M:          return "220M";
+        case MODEL_250M:          return "250M";
+        case MODEL_270M:          return "270M";
+        case MODEL_335M:          return "335M";
+        case MODEL_410M:          return "410M";
+        case MODEL_450M:          return "450M";
+        case MODEL_770M:          return "770M";
+        case MODEL_780M:          return "780M";
+        case MODEL_0_5B:          return "0.5B";
+        case MODEL_1B:            return "1B";
+        case MODEL_1_3B:          return "1.3B";
+        case MODEL_1_4B:          return "1.4B";
+        case MODEL_1_5B:          return "1.5B";
+        case MODEL_1_6B:          return "1.6B";
+        case MODEL_2B:            return "2B";
+        case MODEL_2_8B:          return "2.8B";
+        case MODEL_3B:            return "3B";
+        case MODEL_4B:            return "4B";
+        case MODEL_6B:            return "6B";
+        case MODEL_6_9B:          return "6.9B";
+        case MODEL_7B:            return "7B";
+        case MODEL_8B:            return "8B";
+        case MODEL_9B:            return "9B";
+        case MODEL_11B:           return "11B";
+        case MODEL_12B:           return "12B";
+        case MODEL_13B:           return "13B";
+        case MODEL_14B:           return "14B";
+        case MODEL_15B:           return "15B";
+        case MODEL_16B:           return "16B";
+        case MODEL_20B:           return "20B";
+        case MODEL_30B:           return "30B";
+        case MODEL_32B:           return "32B";
+        case MODEL_34B:           return "34B";
+        case MODEL_35B:           return "35B";
+        case MODEL_40B:           return "40B";
+        case MODEL_65B:           return "65B";
+        case MODEL_70B:           return "70B";
+        case MODEL_236B:          return "236B";
+        case MODEL_314B:          return "314B";
+        case MODEL_SMALL:         return "0.1B";
+        case MODEL_MEDIUM:        return "0.4B";
+        case MODEL_LARGE:         return "0.8B";
+        case MODEL_XL:            return "1.5B";
+        case MODEL_A1_7B:         return "A1.7B";
+        case MODEL_A2_7B:         return "A2.7B";
+        case MODEL_8x7B:          return "8x7B";
+        case MODEL_8x22B:         return "8x22B";
+        case MODEL_16x12B:        return "16x12B";
+        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
+        case MODEL_57B_A14B:      return "57B.A14B";
+        case MODEL_27B:           return "27B";
+        default:                  return "?B";
+    }
+}
+
+static std::string llama_model_ftype_name(llama_ftype ftype) {
+    if (ftype & LLAMA_FTYPE_GUESSED) {
+        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
+    }
+
+    switch (ftype) {
+        case LLAMA_FTYPE_ALL_F32:         return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
+        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
+        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
+        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
+        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
+        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
+        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
+        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
+
+        default: return "unknown, may not work";
+    }
+}
+
+std::string llama_model_arch_name (const llama_model & model) {
+    return llm_arch_name(model.arch);
+}
+
+std::string llama_model_type_name (const llama_model & model) {
+    return llm_type_name(model.type);
+}
+
+std::string llama_model_ftype_name(const llama_model & model) {
+    return llama_model_ftype_name(model.ftype);
+}
+
+template<typename F>
+static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*8,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context_ptr ctx { ggml_init(params) };
+    if (!ctx) {
+        throw std::runtime_error(format("failed to create ggml context"));
+    }
+
+    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
+    ggml_tensor * op_tensor = fn(ctx.get());
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op_tensor->src[i] != nullptr) {
+            assert(op_tensor->src[i]->buffer == nullptr);
+            op_tensor->src[i]->buffer = buf.get();
+        }
+    }
+
+    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
+
+    return op_supported;
+}
+
+template<typename F>
+static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
+    for (const auto & cur : buft_list) {
+        ggml_backend_dev_t cur_dev = cur.first;
+        ggml_backend_buffer_type_t cur_buft = cur.second;
+        if (buft_supported(cur_buft, cur_dev, fn)) {
+            return cur_buft;
+        }
+    }
+
+    throw std::runtime_error(format("no suitable buffer type found"));
+}
+
+ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il) {
+    return select_buft(
+            *model.dev_layer.at(il).buft_list,
+            [&](ggml_context * ctx) {
+                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
+                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
+                return ggml_add(ctx, cur, layer_dir);
+            });
+}
+
+struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name) {
+    auto it = std::find_if(model.tensors_by_name.begin(), model.tensors_by_name.end(),
+            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
+                return it.first == name;
+            });
+    if (it == model.tensors_by_name.end()) {
+        return nullptr;
+    }
+
+    return it->second;
+}
+
+size_t llama_model_max_nodes(const llama_model & model) {
+    return std::max<size_t>(8192, model.tensors_by_name.size()*5);
+}
+
+static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
+    { LLAMA_ROPE_SCALING_TYPE_NONE,       "none"       },
+    { LLAMA_ROPE_SCALING_TYPE_LINEAR,     "linear"     },
+    { LLAMA_ROPE_SCALING_TYPE_YARN,       "yarn"       },
+    { LLAMA_ROPE_SCALING_TYPE_LONGROPE,   "longrope"   },
+};
+
+static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
+    for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
+        if (kv.second == name) {
+            return (llama_rope_scaling_type) kv.first;
+        }
+    }
+
+    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+}
+
+// NOTE: avoid ever using this except for building the token_to_piece caches
+static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
+    std::string piece;
+    piece.resize(piece.capacity());  // using string internal cache
+    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+    if (n_chars < 0) {
+        piece.resize(-n_chars);
+        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+        GGML_ASSERT(check == -n_chars);
+    }
+    else {
+        piece.resize(n_chars);
+    }
+
+    return piece;
+}
+
+void llm_load_stats(llama_model_loader & ml, llama_model & model) {
+    model.n_elements = ml.n_elements;
+    model.n_bytes = ml.n_bytes;
+}
+
+void llm_load_arch(llama_model_loader & ml, llama_model & model) {
+    model.arch = ml.get_arch();
+    if (model.arch == LLM_ARCH_UNKNOWN) {
+        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
+    }
+}
+
+void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
+    auto & hparams = model.hparams;
+    const gguf_context * ctx = ml.meta.get();
+
+    // get metadata as string
+    for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
+        enum gguf_type type = gguf_get_kv_type(ctx, i);
+        if (type == GGUF_TYPE_ARRAY) {
+            continue;
+        }
+        const char * name = gguf_get_key(ctx, i);
+        const std::string value = gguf_kv_to_str(ctx, i);
+        model.gguf_kv.emplace(name, value);
+    }
+
+    // get general kv
+    ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
+
+    // get hparams kv
+    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false);
+
+    // everything past this point is not vocab-related
+    if (hparams.vocab_only) {
+        return;
+    }
+
+    ml.get_key(LLM_KV_CONTEXT_LENGTH,    hparams.n_ctx_train);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH,  hparams.n_embd);
+    ml.get_key(LLM_KV_BLOCK_COUNT,       hparams.n_layer);
+    ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
+
+    if (model.arch == LLM_ARCH_WAVTOKENIZER_DEC) {
+        ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
+
+        ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
+        ml.get_key(LLM_KV_POSNET_BLOCK_COUNT,      hparams.posnet.n_layer);
+
+        ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
+        ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT,      hparams.convnext.n_layer);
+    }
+
+    GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
+    GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
+    if (hparams.n_expert > 0) {
+        GGML_ASSERT(hparams.n_expert_used > 0);
+    } else {
+        GGML_ASSERT(hparams.n_expert_used == 0);
+    }
+
+    // zero-out the array hparams
+    std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
+    std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
+    std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
+
+    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
+
+    // n_head_kv is optional, default to n_head
+    hparams.n_head_kv_arr = hparams.n_head_arr;
+
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
+
+    bool rope_finetuned = false;
+    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
+    hparams.rope_finetuned = rope_finetuned;
+
+    hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
+    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
+
+    // rope_freq_base (optional)
+    hparams.rope_freq_base_train = 10000.0f;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
+
+    std::string rope_scaling("linear");
+    ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
+    hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
+    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
+
+    // rope_freq_scale (inverse of the kv) is optional
+    float ropescale = 0.0f;
+    if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
+        // try the old key name
+        ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
+    }
+    hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
+
+    ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
+
+    // non-transformer models do not have attention heads
+    if (hparams.n_head() > 0) {
+        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
+        // gpt-j n_rot = rotary_dim
+
+        hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+        hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+
+        // sanity check for n_rot (optional)
+        hparams.n_rot = hparams.n_embd_head_k;
+
+        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
+
+        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
+            if (hparams.n_rot != hparams.n_embd_head_k) {
+                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
+            }
+        }
+    } else {
+        hparams.n_rot = 0;
+        hparams.n_embd_head_k = 0;
+        hparams.n_embd_head_v = 0;
+    }
+
+    using e_model = llm_type; // TMP
+
+    // arch-specific KVs
+    switch (model.arch) {
+        case LLM_ARCH_LLAMA:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                if (hparams.n_expert == 8) {
+                    switch (hparams.n_layer) {
+                        case 32: model.type = e_model::MODEL_8x7B; break;
+                        case 56: model.type = e_model::MODEL_8x22B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                } else {
+                    switch (hparams.n_layer) {
+                        case 16: model.type = e_model::MODEL_1B; break; // Llama 3.2 1B
+                        case 22: model.type = e_model::MODEL_1B; break;
+                        case 26: model.type = e_model::MODEL_3B; break;
+                        case 28: model.type = e_model::MODEL_3B; break; // Llama 3.2 3B
+                        // granite uses a vocab with len 49152
+                        case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
+                        case 36: model.type = e_model::MODEL_8B; break; // granite
+                        case 40: model.type = e_model::MODEL_13B; break;
+                        case 48: model.type = e_model::MODEL_34B; break;
+                        case 60: model.type = e_model::MODEL_30B; break;
+                        case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                }
+            } break;
+        case LLM_ARCH_DECI:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_MINICPM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
+                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+
+                switch (hparams.n_layer) {
+                    case 52: model.type = e_model::MODEL_1B; break;
+                    case 40: model.type = e_model::MODEL_2B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_MINICPM3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+
+                switch (hparams.n_layer) {
+                    case 62: model.type = e_model::MODEL_4B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GROK:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 64: model.type = e_model::MODEL_314B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_FALCON:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 60: model.type = e_model::MODEL_40B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_BAICHUAN:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+
+                if (model.type == e_model::MODEL_13B) {
+                    // TODO: become GGUF KV parameter
+                    hparams.f_max_alibi_bias = 8.0f;
+                }
+            } break;
+        case LLM_ARCH_STARCODER:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 36: model.type = e_model::MODEL_3B; break;
+                    case 42: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_15B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_REFACT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_1B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+
+                // TODO: become GGUF KV parameter
+                hparams.f_max_alibi_bias = 8.0f;
+            } break;
+        case LLM_ARCH_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+
+                switch (hparams.n_layer) {
+                    case 3:
+                        model.type = e_model::MODEL_17M; break; // bge-micro
+                    case 6:
+                        model.type = e_model::MODEL_22M; break; // MiniLM-L6
+                    case 12:
+                        switch (hparams.n_embd) {
+                            case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
+                            case 768: model.type = e_model::MODEL_109M; break; // bge-base
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        model.type = e_model::MODEL_335M; break; // bge-large
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_JINA_BERT_V2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+                hparams.f_max_alibi_bias = 8.0f;
+
+                switch (hparams.n_layer) {
+                    case 4:  model.type = e_model::MODEL_33M;  break; // jina-embeddings-small
+                    case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_NOMIC_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
+
+                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+                    model.type = e_model::MODEL_137M;
+                }
+            } break;
+        case LLM_ARCH_BLOOM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 30:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            case 4096: model.type = e_model::MODEL_7B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+
+                // TODO: become GGUF KV parameter
+                hparams.f_max_alibi_bias = 8.0f;
+            } break;
+        case LLM_ARCH_MPT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
+                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 48: model.type = e_model::MODEL_30B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_STABLELM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_12B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_QWEN:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_QWEN2VL:
+            {
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+            }
+            // fall through
+        case LLM_ARCH_QWEN2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
+                    case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 36: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
+                    case 48: model.type = e_model::MODEL_14B; break;
+                    case 64: model.type = e_model::MODEL_32B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_QWEN2MOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_A2_7B; break;
+                    case 28: model.type = e_model::MODEL_57B_A14B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_PHI2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_PHI3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_14B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+
+                // for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
+                if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) {
+                    // default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct
+                    hparams.n_swa = 2047;
+                } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
+                    // default value for Phi-3-mini-128k-instruct
+                    hparams.n_swa = 262144;
+                } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
+                    // default value for Phi-3-medium-128k-instruct
+                    hparams.n_swa = 131072;
+                }
+                bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                if (!found_swa && hparams.n_swa == 0) {
+                    throw std::runtime_error("invalid value for sliding_window");
+                }
+            } break;
+        case LLM_ARCH_PLAMO:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_GPT2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 12: model.type = e_model::MODEL_SMALL; break;
+                    case 24: model.type = e_model::MODEL_MEDIUM; break;
+                    case 36: model.type = e_model::MODEL_LARGE; break;
+                    case 48: model.type = e_model::MODEL_XL; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_CODESHELL:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 42: model.type = e_model::MODEL_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_ORION:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_14B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_INTERNLM2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 48: model.type = e_model::MODEL_20B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GEMMA:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 18: model.type = e_model::MODEL_2B; break;
+                    case 28: model.type = e_model::MODEL_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_GEMMA2:
+            {
+                hparams.n_swa = 4096; // default value of gemma 2
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
+                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
+                hparams.attn_soft_cap = true;
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_2B; break;
+                    case 42: model.type = e_model::MODEL_9B; break;
+                    case 46: model.type = e_model::MODEL_27B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 30: model.type = e_model::MODEL_3B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_15B; break;
+                    case 52: model.type = e_model::MODEL_20B; break; // granite
+                    case 88: model.type = e_model::MODEL_34B; break; // granite
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+                ml.get_key(LLM_KV_SSM_DT_B_C_RMS,     hparams.ssm_dt_b_c_rms, false);
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 24:
+                        switch (hparams.n_embd) {
+                            case 768: model.type = e_model::MODEL_SMALL; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 48:
+                        switch (hparams.n_embd) {
+                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
+                            case 1536: model.type = e_model::MODEL_LARGE; break;
+                            case 2048: model.type = e_model::MODEL_XL; break;
+                            default:   model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 64:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    case 80: model.type = e_model::MODEL_65B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_35B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DBRX:
+        {
+            ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+            ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv);
+
+            switch (hparams.n_layer) {
+                case 40: model.type = e_model::MODEL_16x12B; break;
+                default: model.type = e_model::MODEL_UNKNOWN;
+            }
+        } break;
+        case LLM_ARCH_OLMO:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
+
+                switch (hparams.n_layer) {
+                    case 22: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_OLMO2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 16: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_OLMOE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 16: model.type = e_model::MODEL_A1_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_OPENELM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                case 16: model.type = e_model::MODEL_270M; break;
+                case 20: model.type = e_model::MODEL_450M; break;
+                case 28: model.type = e_model::MODEL_1B; break;
+                case 36: model.type = e_model::MODEL_3B; break;
+                default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GPTNEOX:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+                switch (hparams.n_layer) {
+                    case 6:
+                        switch (hparams.n_ff()) {
+                            case 512: model.type = e_model::MODEL_14M; break;
+                            case 2048: model.type = e_model::MODEL_70M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 12:
+                        switch (hparams.n_ff()) {
+                            case 3072: model.type = e_model::MODEL_160M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 16:
+                        switch (hparams.n_ff()) {
+                            case 8192: model.type = e_model::MODEL_1B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff()) {
+                            case 4096: model.type = e_model::MODEL_410M; break;
+                            case 8192: model.type = e_model::MODEL_1_4B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 32:
+                        switch (hparams.n_ff()) {
+                            case 10240: model.type = e_model::MODEL_2_8B; break;
+                            case 16384: model.type = e_model::MODEL_6_9B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 36:
+                        switch (hparams.n_ff()) {
+                            case 20480: model.type = e_model::MODEL_12B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 44:
+                        switch (hparams.n_ff()) {
+                            case 24576: model.type = e_model::MODEL_20B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_ARCTIC:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                if (hparams.n_expert == 128) {
+                    switch (hparams.n_layer) {
+                        case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                } else {
+                    model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DEEPSEEK:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+
+                switch (hparams.n_layer) {
+                    case 28: model.type = e_model::MODEL_20B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                bool is_lite = (hparams.n_layer == 27);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                if (!is_lite) {
+                    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+                }
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
+
+                switch (hparams.n_layer) {
+                    case 27: model.type = e_model::MODEL_16B; break;
+                    case 60: model.type = e_model::MODEL_236B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_CHATGLM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 28: model.type = e_model::MODEL_6B; break;
+                    case 40: model.type = e_model::MODEL_9B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_BITNET:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_T5:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+
+                uint32_t dec_start_token_id;
+                if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
+                    hparams.dec_start_token_id = dec_start_token_id;
+                }
+
+                switch (hparams.n_layer) {
+                    case 6:  model.type = e_model::MODEL_60M;  break; // t5-small
+                    case 8:  model.type = e_model::MODEL_80M;  break; // flan-t5-small
+                    case 12:
+                        switch (hparams.n_ff()) {
+                            case 3072: model.type = e_model::MODEL_220M; break; // t5-base
+                            case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff()) {
+                            case 4096:  model.type = e_model::MODEL_770M; break; // t5-large
+                            case 2816:  model.type = e_model::MODEL_780M; break; // flan-t5-large
+                            case 16384: model.type = e_model::MODEL_3B;   break; // t5-3b
+                            case 5120:  model.type = e_model::MODEL_3B;   break; // flan-t5-xl
+                            case 65536: model.type = e_model::MODEL_11B;  break; // t5-11b
+                            case 10240: model.type = e_model::MODEL_11B;  break; // flan-t5-xxl
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_T5ENCODER:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+                model.type = e_model::MODEL_UNKNOWN;
+            } break;
+        case LLM_ARCH_JAIS:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1_3B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    /* TODO: add variants */
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_NEMOTRON:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_4B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_EXAONE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_8B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_RWKV6:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
+                ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
+                ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
+                ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1_6B; break;
+                    case 32:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            case 4096: model.type = e_model::MODEL_7B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 61: model.type = e_model::MODEL_14B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GRANITE:
+        case LLM_ARCH_GRANITE_MOE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
+                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
+                ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_3B; break;
+                    // Add additional layer/vocab/etc checks here for other model sizes
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_CHAMELEON:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default
+                ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 48: model.type = e_model::MODEL_34B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
+                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+            } break;
+        default: throw std::runtime_error("unsupported model architecture");
+    }
+
+    model.ftype = ml.ftype;
+
+    if (hparams.f_max_alibi_bias > 0.0f) {
+        hparams.use_alibi = true;
+    }
+
+    hparams.rope_type = llama_rope_type(&model);
+}
+
+void llm_load_vocab(llama_model_loader & ml, llama_model & model) {
+    auto & vocab = model.vocab;
+
+    struct gguf_context * ctx = ml.meta.get();
+
+    const auto kv = LLM_KV(model.arch);
+
+    // determine vocab type
+    {
+        std::string tokenizer_model;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
+        ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
+
+        if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
+            vocab.type = LLAMA_VOCAB_TYPE_NONE;
+
+            // default special tokens
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
+            vocab.linefeed_id     = LLAMA_TOKEN_NULL;
+
+            // read vocab size from metadata
+            if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) {
+                vocab.n_vocab = 0;
+                LLAMA_LOG_WARN("%s: there is no vocab_size in metadata, vocab.n_vocab will be set to %u\n", __func__, vocab.n_vocab);
+            }
+            return;
+        }
+
+        if (tokenizer_model == "llama") {
+            vocab.type = LLAMA_VOCAB_TYPE_SPM;
+
+            // default special tokens
+            vocab.special_bos_id  = 1;
+            vocab.special_eos_id  = 2;
+            vocab.special_unk_id  = 0;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
+        } else if (tokenizer_model == "bert") {
+            vocab.type = LLAMA_VOCAB_TYPE_WPM;
+
+            // default special tokens
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_unk_id  = 100;
+            vocab.special_sep_id  = 102;
+            vocab.special_pad_id  = 0;
+            vocab.special_cls_id  = 101;
+            vocab.special_mask_id = 103;
+        } else if (tokenizer_model == "gpt2") {
+            vocab.type = LLAMA_VOCAB_TYPE_BPE;
+
+            // read bpe merges and populate bpe ranks
+            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+
+            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+            for (int i = 0; i < n_merges; i++) {
+                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
+
+                std::string first;
+                std::string second;
+
+                const size_t pos = word.find(' ', 1);
+
+                if (pos != std::string::npos) {
+                    first  = word.substr(0, pos);
+                    second = word.substr(pos + 1);
+                }
+
+                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
+            }
+
+            // default special tokens
+            vocab.special_bos_id  = 11;
+            vocab.special_eos_id  = 11;
+            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
+        } else if (tokenizer_model == "t5") {
+            vocab.type = LLAMA_VOCAB_TYPE_UGM;
+
+            // default special tokens
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id  = 1;
+            vocab.special_unk_id  = 2;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = 0;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
+
+            const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
+            if (precompiled_charsmap_keyidx != -1) {
+                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+                const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
+                vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
+#ifdef IS_BIG_ENDIAN
+                // correct endiannes of data in precompiled_charsmap binary blob
+                uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
+                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
+                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
+                size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
+                uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
+                for (size_t i = 0; i < xcda_array_size; ++i) {
+                    xcda_array[i] = __builtin_bswap32(xcda_array[i]);
+                }
+#endif
+            }
+        } else if (tokenizer_model == "rwkv") {
+            vocab.type = LLAMA_VOCAB_TYPE_RWKV;
+
+            // default special tokens
+            vocab.special_bos_id = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id = LLAMA_TOKEN_NULL;
+            vocab.special_unk_id = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id = LLAMA_TOKEN_NULL;
+        } else {
+            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
+        }
+
+        // for now, only BPE models have pre-tokenizers
+        if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
+            vocab.tokenizer_add_space_prefix = false;
+            vocab.tokenizer_clean_spaces = true;
+            if (tokenizer_pre.empty()) {
+                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
+                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (tokenizer_pre == "default") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (
+                    tokenizer_pre == "llama3"   ||
+                    tokenizer_pre == "llama-v3" ||
+                    tokenizer_pre == "llama-bpe"||
+                    tokenizer_pre == "falcon3") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
+            } else if (
+                    tokenizer_pre == "deepseek-llm") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "deepseek-coder") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "falcon") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
+            } else if (
+                    tokenizer_pre == "mpt") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
+            } else if (
+                    tokenizer_pre == "starcoder") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
+            } else if (
+                    tokenizer_pre == "gpt-2"   ||
+                    tokenizer_pre == "phi-2"   ||
+                    tokenizer_pre == "jina-es" ||
+                    tokenizer_pre == "jina-de" ||
+                    tokenizer_pre == "gigachat"   ||
+                    tokenizer_pre == "jina-v1-en" ||
+                    tokenizer_pre == "jina-v2-es" ||
+                    tokenizer_pre == "jina-v2-de" ||
+                    tokenizer_pre == "jina-v2-code" ||
+                    tokenizer_pre == "roberta-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
+            } else if (
+                    tokenizer_pre == "refact") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
+            } else if (
+                tokenizer_pre == "command-r") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "qwen2") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "stablelm2") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
+            } else if (
+                tokenizer_pre == "olmo") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
+            } else if (
+                tokenizer_pre == "dbrx") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
+            } else if (
+                tokenizer_pre == "smaug-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+            } else if (
+                tokenizer_pre == "poro-chat") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "chatglm-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
+                vocab.special_bos_id = LLAMA_TOKEN_NULL;
+            } else if (
+                tokenizer_pre == "viking") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "jais") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
+            } else if (
+                tokenizer_pre == "tekken") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
+                vocab.tokenizer_clean_spaces = false;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
+            } else if (
+                tokenizer_pre == "smollm") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "codeshell") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
+            } else if (
+                tokenizer_pre == "bloom") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_BLOOM;
+            } else if (
+                tokenizer_pre == "gpt3-finnish") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
+            } else if (
+                tokenizer_pre == "exaone") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
+            } else if (
+                tokenizer_pre == "chameleon") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
+                vocab.tokenizer_add_bos = true;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "minerva-7b") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA;
+            } else if (
+                tokenizer_pre == "megrez") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+            } else {
+                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+            }
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_space_prefix = true;
+            vocab.tokenizer_clean_spaces = false;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_space_prefix = false;
+            vocab.tokenizer_clean_spaces = true;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_bos = false;
+            vocab.tokenizer_add_eos = true;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_space_prefix = false;
+            vocab.tokenizer_clean_spaces = false;
+            vocab.tokenizer_add_bos = false;
+            vocab.tokenizer_add_eos = false;
+        } else {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+        }
+
+        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      vocab.tokenizer_add_space_prefix,         false);
+        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
+    }
+
+    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
+    if (token_idx == -1) {
+        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+    }
+
+    const float * scores = nullptr;
+    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
+    if (score_idx != -1) {
+        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+    }
+
+    const int * toktypes = nullptr;
+    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
+    if (toktype_idx != -1) {
+        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+    }
+
+    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+
+    vocab.n_vocab = n_vocab;
+    vocab.id_to_token.resize(n_vocab);
+
+    for (uint32_t i = 0; i < n_vocab; i++) {
+        std::string word = gguf_get_arr_str(ctx, token_idx, i);
+        if (word.empty()) {
+            LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
+            word = "[EMPTY_" + std::to_string(i) + "]";
+        }
+
+        vocab.token_to_id[word] = i;
+        vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
+
+        auto & token_data = vocab.id_to_token[i];
+        token_data.text  = std::move(word);
+        token_data.score = scores ? scores[i] : 0.0f;
+        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
+
+        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
+            switch(toktypes[i]) {
+                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
+                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
+                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
+                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
+                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
+                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+            }
+        }
+    }
+    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
+
+    vocab.init_tokenizer();
+
+    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
+    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        try {
+            vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
+        } catch (const std::exception & e) {
+            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
+            vocab.linefeed_id = vocab.special_pad_id;
+        }
+    } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+        vocab.linefeed_id = vocab.special_pad_id;
+    } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
+        const std::vector<int> ids = llama_tokenize_internal(vocab, "\n", false);
+        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+        vocab.linefeed_id = ids[0];
+    } else {
+        const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
+
+        //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+        if (ids.empty()) {
+            LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
+            vocab.linefeed_id = vocab.special_pad_id;
+        } else {
+            vocab.linefeed_id = ids[0];
+        }
+    }
+
+    // special tokens
+    {
+        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
+            { LLM_KV_TOKENIZER_BOS_ID,     vocab.special_bos_id     },
+            { LLM_KV_TOKENIZER_EOS_ID,     vocab.special_eos_id     },
+            { LLM_KV_TOKENIZER_EOT_ID,     vocab.special_eot_id     },
+            { LLM_KV_TOKENIZER_EOM_ID,     vocab.special_eom_id     },
+            { LLM_KV_TOKENIZER_UNK_ID,     vocab.special_unk_id     },
+            { LLM_KV_TOKENIZER_SEP_ID,     vocab.special_sep_id     },
+            { LLM_KV_TOKENIZER_PAD_ID,     vocab.special_pad_id     },
+            { LLM_KV_TOKENIZER_CLS_ID,     vocab.special_cls_id     },
+            { LLM_KV_TOKENIZER_MASK_ID,    vocab.special_mask_id    },
+            { LLM_KV_TOKENIZER_FIM_PRE_ID, vocab.special_fim_pre_id },
+            { LLM_KV_TOKENIZER_FIM_SUF_ID, vocab.special_fim_suf_id },
+            { LLM_KV_TOKENIZER_FIM_MID_ID, vocab.special_fim_mid_id },
+            { LLM_KV_TOKENIZER_FIM_PAD_ID, vocab.special_fim_pad_id },
+            { LLM_KV_TOKENIZER_FIM_REP_ID, vocab.special_fim_rep_id },
+            { LLM_KV_TOKENIZER_FIM_SEP_ID, vocab.special_fim_sep_id },
+
+            // deprecated
+            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_fim_pre_id },
+            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_fim_suf_id },
+            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_fim_mid_id },
+        };
+
+        for (const auto & it : special_token_types) {
+            const std::string & key = kv(std::get<0>(it));
+            int32_t & id = std::get<1>(it);
+
+            uint32_t new_id;
+            if (!ml.get_key(std::get<0>(it), new_id, false)) {
+                continue;
+            }
+            if (new_id >= vocab.id_to_token.size()) {
+                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
+                    __func__, key.c_str(), new_id, id);
+            } else {
+                id = new_id;
+            }
+        }
+
+        // Handle add_bos_token and add_eos_token
+        {
+            bool temp = true;
+
+            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
+                vocab.tokenizer_add_bos = temp;
+            }
+            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
+                vocab.tokenizer_add_eos = temp;
+            }
+        }
+
+        // auto-detect special tokens by text
+        // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
+        //       for now, we apply this workaround to find the tokens based on their text
+
+        for (const auto & t : vocab.token_to_id) {
+            // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
+            if (vocab.special_eot_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|eot_id|>"
+                        || t.first == "<|im_end|>"
+                        || t.first == "<|end|>"
+                        || t.first == "<end_of_turn>"
+                        || t.first == "<|endoftext|>"
+                        || t.first == "<EOT>"
+                        || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
+                   ) {
+                    vocab.special_eot_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find EOM token: "<|eom_id|>"
+            if (vocab.special_eom_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|eom_id|>"
+                        ) {
+                    vocab.special_eom_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
+            if (vocab.special_fim_pre_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_prefix|>"  // Qwen
+                        || t.first == "<fim-prefix>"
+                        || t.first == "<｜fim▁begin｜>" // DeepSeek
+                        || t.first == "<PRE>"
+                        ) {
+                    vocab.special_fim_pre_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
+            if (vocab.special_fim_suf_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_suffix|>" // Qwen
+                        || t.first == "<fim-suffix>"
+                        || t.first == "<｜fim▁hole｜>" // DeepSeek
+                        || t.first == "<SUF>"
+                        ) {
+                    vocab.special_fim_suf_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
+            if (vocab.special_fim_mid_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_middle|>" // Qwen
+                        || t.first == "<fim-middle>"
+                        || t.first == "<｜fim▁end｜>"  // DeepSeek
+                        || t.first == "<MID>"
+                        ) {
+                    vocab.special_fim_mid_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
+            if (vocab.special_fim_pad_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_pad|>" // Qwen
+                        || t.first == "<fim-pad>"
+                        || t.first == "<PAD>"
+                        ) {
+                    vocab.special_fim_pad_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
+            if (vocab.special_fim_rep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_repo|>"  // Qwen
+                        || t.first == "<|repo_name|>"
+                        || t.first == "<fim-repo>"
+                        || t.first == "<REPO>"
+                        ) {
+                    vocab.special_fim_rep_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SEP token: "<|file_sep|>"
+            if (vocab.special_fim_sep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|file_sep|>" // Qwen
+                        ) {
+                    vocab.special_fim_sep_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+        }
+
+        // maintain a list of tokens that cause end-of-generation
+        // this is currently determined based on the token text, which is obviously not ideal
+        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
+        vocab.special_eog_ids.clear();
+
+        if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_pad_id);
+        }
+
+        if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_rep_id);
+        }
+
+        if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_sep_id);
+        }
+
+        for (const auto & t : vocab.token_to_id) {
+            if (false
+                    || t.first == "<|eot_id|>"
+                    || t.first == "<|im_end|>"
+                    || t.first == "<|end|>"
+                    || t.first == "<end_of_turn>"
+                    || t.first == "<|endoftext|>"
+                    || t.first == "<|eom_id|>"
+                    || t.first == "<EOT>"
+               ) {
+                vocab.special_eog_ids.insert(t.second);
+                if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                            __func__, t.second, t.first.c_str());
+                    vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                }
+            } else {
+                // token is control, but not marked as EOG -> print a debug log
+                if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) {
+                    LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
+                            __func__, t.second, t.first.c_str());
+                }
+            }
+        }
+
+        // sanity checks
+        if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_eos_id);
+            LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+
+        if (vocab.special_eot_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_eot_id);
+            LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+
+        if (vocab.special_eom_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_eom_id);
+            LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+    }
+
+    // build special tokens cache
+    {
+        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
+            if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
+                vocab.cache_special_tokens.push_back(id);
+            }
+        }
+
+        std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
+            [&] (const llama_vocab::id a, const llama_vocab::id b) {
+                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
+            }
+        );
+
+        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
+    }
+
+    // build token to piece cache
+    {
+        size_t size_cache = 0;
+
+        std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
+
+        for (uint32_t id = 0; id < n_vocab; ++id) {
+            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
+
+            size_cache += cache_token_to_piece[id].size();
+        }
+
+        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
+
+        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+    }
+
+    // Handle per token attributes
+    //NOTE: Each model customizes per token attributes.
+    //NOTE: Per token attributes are missing from the GGUF file.
+    //TODO: Extract attributes from GGUF file.
+    {
+        auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
+            for (auto substr : substrs) {
+                if (str.find(substr) < std::string::npos) {
+                    return true;
+                }
+            }
+            return false;
+        };
+
+        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+            uint32_t current = vocab.id_to_token.at(id).attr;
+            current = value ? (current | attr) : (current & ~attr);
+            vocab.id_to_token[id].attr = (llama_token_attr) current;
+        };
+
+        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+        };
+
+        std::string model_name;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+        // model name to lowercase
+        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+            [] (const std::string::value_type x) {
+                return std::tolower(x);
+            }
+        );
+
+        // set attributes by model/tokenizer name
+        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
+            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+            for (auto id : vocab.cache_special_tokens) {
+                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"</s>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+            }
+        }
+    }
+}
+
+void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
+    const auto & hparams = model.hparams;
+    const auto & vocab   = model.vocab;
+
+    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+
+    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
+        bool is_var = false;
+
+        std::vector<uint32_t> v;
+        for (uint32_t i = 0; i < n; ++i) {
+            v.push_back(f(i));
+            if (v[i] != v[0]) {
+                is_var = true;
+            }
+        }
+
+        std::stringstream ss;
+
+        if (is_var) {
+            ss << "[";
+            for (uint32_t i = 0; i < n; ++i) {
+                ss << v[i];
+                if (i < n - 1) {
+                    ss << ", ";
+                }
+            }
+            ss << "]";
+        } else {
+            ss << v[0];
+        }
+
+        return ss.str();
+    };
+
+    // hparams
+    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, llm_arch_name(model.arch));
+    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
+    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
+    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
+    LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
+
+    if (!hparams.vocab_only) {
+        LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
+        LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
+        LLAMA_LOG_INFO("%s: n_head           = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
+        LLAMA_LOG_INFO("%s: n_swa            = %u\n",     __func__, hparams.n_swa);
+        LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
+        LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
+        LLAMA_LOG_INFO("%s: n_gqa            = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
+        LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
+        LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
+        LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+        LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
+        LLAMA_LOG_INFO("%s: n_ff             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
+        LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
+        LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
+        LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
+        LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
+        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
+        LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
+        LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
+        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+        LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
+        LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
+        LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
+        LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+        LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms   = %d\n",     __func__, hparams.ssm_dt_b_c_rms);
+    }
+
+    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model).c_str());
+    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model).c_str());
+    if (ml.n_elements >= 1e12) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
+    } else if (ml.n_elements >= 1e9) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
+    } else if (ml.n_elements >= 1e6) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
+    } else {
+        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
+    }
+    if (ml.n_bytes < GiB) {
+        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
+    } else {
+        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+    }
+
+    // general kv
+    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
+
+    // special tokens
+    if (vocab.special_bos_id  != -1)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
+    if (vocab.special_eos_id  != -1)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
+    if (vocab.special_eot_id  != -1)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
+    if (vocab.special_eom_id  != -1)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
+    if (vocab.special_unk_id  != -1)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
+    if (vocab.special_sep_id  != -1)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
+    if (vocab.special_pad_id  != -1)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
+    if (vocab.special_cls_id  != -1)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
+    if (vocab.special_mask_id != -1)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+
+    if (vocab.linefeed_id != -1)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
+
+    if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
+    if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
+    if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
+    if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
+    if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
+    if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
+
+    for (const auto & id : vocab.special_eog_ids) {
+        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
+    }
+
+    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
+
+    if (model.arch == LLM_ARCH_DEEPSEEK) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+    }
+
+    if (model.arch == LLM_ARCH_DEEPSEEK2) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
+        LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
+    }
+
+    if (model.arch == LLM_ARCH_QWEN2MOE) {
+        LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
+    }
+
+    if (model.arch == LLM_ARCH_MINICPM || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
+        LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
+        LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
+        LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
+    }
+}
+
+//
+// interface implementation
+//
+
+struct llama_model_params llama_model_default_params() {
+    struct llama_model_params result = {
+        /*.devices                     =*/ nullptr,
+        /*.n_gpu_layers                =*/ 0,
+        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
+        /*.main_gpu                    =*/ 0,
+        /*.tensor_split                =*/ nullptr,
+        /*.rpc_servers                 =*/ nullptr,
+        /*.progress_callback           =*/ nullptr,
+        /*.progress_callback_user_data =*/ nullptr,
+        /*.kv_overrides                =*/ nullptr,
+        /*.vocab_only                  =*/ false,
+        /*.use_mmap                    =*/ true,
+        /*.use_mlock                   =*/ false,
+        /*.check_tensors               =*/ false,
+    };
+
+#ifdef GGML_USE_METAL
+    // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
+    result.n_gpu_layers = 999;
+#endif
+
+    return result;
+}
+
+void llama_free_model(struct llama_model * model) {
+    delete model;
+}
+
+enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
+    return model->vocab.type;
+}
+
+int32_t llama_n_vocab(const struct llama_model * model) {
+    return model->hparams.n_vocab;
+}
+
+int32_t llama_n_ctx_train(const struct llama_model * model) {
+    return model->hparams.n_ctx_train;
+}
+
+int32_t llama_n_embd(const struct llama_model * model) {
+    return model->hparams.n_embd;
+}
+
+int32_t llama_n_layer(const struct llama_model * model) {
+    return model->hparams.n_layer;
+}
+
+int32_t llama_n_head(const struct llama_model * model) {
+    return model->hparams.n_head();
+}
+
+enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+    switch (model->arch) {
+        // these models do not use RoPE
+        case LLM_ARCH_GPT2:
+        case LLM_ARCH_GPTJ:
+        case LLM_ARCH_MPT:
+        case LLM_ARCH_REFACT:
+        case LLM_ARCH_BLOOM:
+        case LLM_ARCH_MAMBA:
+        case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_T5:
+        case LLM_ARCH_T5ENCODER:
+        case LLM_ARCH_JAIS:
+        case LLM_ARCH_RWKV6:
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            return LLAMA_ROPE_TYPE_NONE;
+
+        // use what we call a normal RoPE, operating on pairs of consecutive head values
+        case LLM_ARCH_LLAMA:
+        case LLM_ARCH_DECI:
+        case LLM_ARCH_BAICHUAN:
+        case LLM_ARCH_STARCODER:
+        case LLM_ARCH_PLAMO:
+        case LLM_ARCH_ORION:
+        case LLM_ARCH_INTERNLM2:
+        case LLM_ARCH_MINICPM:
+        case LLM_ARCH_XVERSE:
+        case LLM_ARCH_COMMAND_R:
+        case LLM_ARCH_OLMO:
+        case LLM_ARCH_ARCTIC:
+        case LLM_ARCH_DEEPSEEK:
+        case LLM_ARCH_DEEPSEEK2:
+        case LLM_ARCH_CHATGLM:
+        case LLM_ARCH_GRANITE:
+        case LLM_ARCH_GRANITE_MOE:
+        case LLM_ARCH_CHAMELEON:
+            return LLAMA_ROPE_TYPE_NORM;
+
+        // the pairs of head values are offset by n_rot/2
+        case LLM_ARCH_FALCON:
+        case LLM_ARCH_GROK:
+        case LLM_ARCH_DBRX:
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_NOMIC_BERT:
+        case LLM_ARCH_STABLELM:
+        case LLM_ARCH_BITNET:
+        case LLM_ARCH_QWEN:
+        case LLM_ARCH_QWEN2:
+        case LLM_ARCH_QWEN2MOE:
+        case LLM_ARCH_OLMO2:
+        case LLM_ARCH_OLMOE:
+        case LLM_ARCH_PHI2:
+        case LLM_ARCH_PHI3:
+        case LLM_ARCH_GEMMA:
+        case LLM_ARCH_GEMMA2:
+        case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_OPENELM:
+        case LLM_ARCH_GPTNEOX:
+        case LLM_ARCH_CODESHELL:
+        case LLM_ARCH_NEMOTRON:
+        case LLM_ARCH_EXAONE:
+        case LLM_ARCH_MINICPM3:
+            return LLAMA_ROPE_TYPE_NEOX;
+
+        case LLM_ARCH_QWEN2VL:
+            return LLAMA_ROPE_TYPE_MROPE;
+
+        // all model arches should be listed explicitly here
+        case LLM_ARCH_UNKNOWN:
+            GGML_ABORT("unknown architecture");
+    }
+
+    return LLAMA_ROPE_TYPE_NONE;
+}
+
+float llama_rope_freq_scale_train(const struct llama_model * model) {
+    return model->hparams.rope_freq_scale_train;
+}
+
+int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
+    const auto & it = model->gguf_kv.find(key);
+    if (it == model->gguf_kv.end()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
+int32_t llama_model_meta_count(const struct llama_model * model) {
+    return (int)model->gguf_kv.size();
+}
+
+int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+    if (i < 0 || i >= (int)model->gguf_kv.size()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    auto it = model->gguf_kv.begin();
+    std::advance(it, i);
+    return snprintf(buf, buf_size, "%s", it->first.c_str());
+}
+
+int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
+    if (i < 0 || i >= (int)model->gguf_kv.size()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    auto it = model->gguf_kv.begin();
+    std::advance(it, i);
+    return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
+int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
+    return snprintf(buf, buf_size, "%s %s %s",
+            llama_model_arch_name (*model).c_str(),
+            llama_model_type_name (*model).c_str(),
+            llama_model_ftype_name(*model).c_str());
+}
+
+uint64_t llama_model_size(const struct llama_model * model) {
+    return model->n_bytes;
+}
+
+uint64_t llama_model_n_params(const struct llama_model * model) {
+    return model->n_elements;
+}
+
+bool llama_model_has_encoder(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_T5:        return true;
+        case LLM_ARCH_T5ENCODER: return true;
+        default:                 return false;
+    }
+}
+
+bool llama_model_has_decoder(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_T5ENCODER: return false;
+        default:                 return true;
+    }
+}
+
+llama_token llama_model_decoder_start_token(const struct llama_model * model) {
+    return model->hparams.dec_start_token_id;
+}
+
+bool llama_model_is_recurrent(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_MAMBA:  return true;
+        case LLM_ARCH_RWKV6:  return true;
+        default:              return false;
+    }
+}
diff --git a/src/llama-model.h b/src/llama-model.h
new file mode 100644
index 000000000..01c780c41
--- /dev/null
+++ b/src/llama-model.h
@@ -0,0 +1,389 @@
+#pragma once
+
+#include "llama.h"
+#include "llama-arch.h"
+#include "llama-hparams.h"
+#include "llama-vocab.h"
+#include "llama-mmap.h"
+
+#include "ggml-cpp.h"
+
+#include <vector>
+
+// available models
+// TODO: this enum does not follow the enum naming convention
+enum llm_type {
+    MODEL_UNKNOWN,
+    MODEL_14M,
+    MODEL_17M,
+    MODEL_22M,
+    MODEL_33M,
+    MODEL_60M,
+    MODEL_70M,
+    MODEL_80M,
+    MODEL_109M,
+    MODEL_137M,
+    MODEL_160M,
+    MODEL_220M,
+    MODEL_250M,
+    MODEL_270M,
+    MODEL_335M,
+    MODEL_410M,
+    MODEL_450M,
+    MODEL_770M,
+    MODEL_780M,
+    MODEL_0_5B,
+    MODEL_1B,
+    MODEL_1_3B,
+    MODEL_1_4B,
+    MODEL_1_5B,
+    MODEL_1_6B,
+    MODEL_2B,
+    MODEL_2_8B,
+    MODEL_3B,
+    MODEL_4B,
+    MODEL_6B,
+    MODEL_6_9B,
+    MODEL_7B,
+    MODEL_8B,
+    MODEL_9B,
+    MODEL_11B,
+    MODEL_12B,
+    MODEL_13B,
+    MODEL_14B,
+    MODEL_15B,
+    MODEL_16B,
+    MODEL_20B,
+    MODEL_30B,
+    MODEL_32B,
+    MODEL_34B,
+    MODEL_35B,
+    MODEL_40B,
+    MODEL_65B,
+    MODEL_70B,
+    MODEL_236B,
+    MODEL_314B,
+    MODEL_SMALL,
+    MODEL_MEDIUM,
+    MODEL_LARGE,
+    MODEL_XL,
+    MODEL_A1_7B,
+    MODEL_A2_7B,
+    MODEL_8x7B,
+    MODEL_8x22B,
+    MODEL_16x12B,
+    MODEL_10B_128x3_66B,
+    MODEL_57B_A14B,
+    MODEL_27B,
+};
+
+struct llama_layer_posnet {
+    // resnet
+    struct ggml_tensor * norm1   = nullptr;
+    struct ggml_tensor * norm1_b = nullptr;
+
+    struct ggml_tensor * conv1   = nullptr;
+    struct ggml_tensor * conv1_b = nullptr;
+
+    struct ggml_tensor * norm2   = nullptr;
+    struct ggml_tensor * norm2_b = nullptr;
+
+    struct ggml_tensor * conv2   = nullptr;
+    struct ggml_tensor * conv2_b = nullptr;
+
+    // attention
+    struct ggml_tensor * attn_norm   = nullptr;
+    struct ggml_tensor * attn_norm_b = nullptr;
+
+    struct ggml_tensor * attn_q   = nullptr;
+    struct ggml_tensor * attn_q_b = nullptr;
+
+    struct ggml_tensor * attn_k   = nullptr;
+    struct ggml_tensor * attn_k_b = nullptr;
+
+    struct ggml_tensor * attn_v   = nullptr;
+    struct ggml_tensor * attn_v_b = nullptr;
+
+    struct ggml_tensor * attn_o   = nullptr;
+    struct ggml_tensor * attn_o_b = nullptr;
+
+    // normalize
+    struct ggml_tensor * norm   = nullptr;
+    struct ggml_tensor * norm_b = nullptr;
+};
+
+struct llama_layer_convnext {
+    struct ggml_tensor * dw   = nullptr;
+    struct ggml_tensor * dw_b = nullptr;
+
+    struct ggml_tensor * norm   = nullptr;
+    struct ggml_tensor * norm_b = nullptr;
+
+    struct ggml_tensor * pw1   = nullptr;
+    struct ggml_tensor * pw1_b = nullptr;
+
+    struct ggml_tensor * pw2   = nullptr;
+    struct ggml_tensor * pw2_b = nullptr;
+
+    struct ggml_tensor * gamma = nullptr;
+};
+
+struct llama_layer {
+    // normalization
+    struct ggml_tensor * attn_norm       = nullptr;
+    struct ggml_tensor * attn_norm_b     = nullptr;
+    struct ggml_tensor * attn_norm_2     = nullptr;
+    struct ggml_tensor * attn_norm_2_b   = nullptr;
+    struct ggml_tensor * attn_q_norm     = nullptr;
+    struct ggml_tensor * attn_q_norm_b   = nullptr;
+    struct ggml_tensor * attn_k_norm     = nullptr;
+    struct ggml_tensor * attn_k_norm_b   = nullptr;
+    struct ggml_tensor * attn_out_norm   = nullptr;
+    struct ggml_tensor * attn_out_norm_b = nullptr;
+    struct ggml_tensor * attn_q_a_norm   = nullptr;
+    struct ggml_tensor * attn_kv_a_norm  = nullptr;
+    struct ggml_tensor * attn_sub_norm   = nullptr;
+    struct ggml_tensor * attn_post_norm  = nullptr;
+    struct ggml_tensor * ffn_sub_norm    = nullptr;
+    struct ggml_tensor * attn_norm_cross = nullptr;
+    struct ggml_tensor * attn_norm_enc   = nullptr;
+
+    // attention
+    struct ggml_tensor * wq        = nullptr;
+    struct ggml_tensor * wk        = nullptr;
+    struct ggml_tensor * wv        = nullptr;
+    struct ggml_tensor * wo        = nullptr;
+    struct ggml_tensor * wqkv      = nullptr;
+    struct ggml_tensor * wq_a      = nullptr;
+    struct ggml_tensor * wq_b      = nullptr;
+    struct ggml_tensor * wkv_a_mqa = nullptr;
+    struct ggml_tensor * wkv_b     = nullptr;
+    struct ggml_tensor * wq_cross  = nullptr;
+    struct ggml_tensor * wk_cross  = nullptr;
+    struct ggml_tensor * wv_cross  = nullptr;
+    struct ggml_tensor * wo_cross  = nullptr;
+    struct ggml_tensor * wq_enc    = nullptr;
+    struct ggml_tensor * wk_enc    = nullptr;
+    struct ggml_tensor * wv_enc    = nullptr;
+    struct ggml_tensor * wo_enc    = nullptr;
+
+    // attention bias
+    struct ggml_tensor * bq   = nullptr;
+    struct ggml_tensor * bk   = nullptr;
+    struct ggml_tensor * bv   = nullptr;
+    struct ggml_tensor * bo   = nullptr;
+    struct ggml_tensor * bqkv = nullptr;
+
+    // relative position bias
+    struct ggml_tensor * attn_rel_b       = nullptr;
+    struct ggml_tensor * attn_rel_b_enc   = nullptr;
+    struct ggml_tensor * attn_rel_b_cross = nullptr;
+
+    // normalization
+    struct ggml_tensor * ffn_norm         = nullptr;
+    struct ggml_tensor * ffn_norm_b       = nullptr;
+    struct ggml_tensor * ffn_post_norm    = nullptr;
+    struct ggml_tensor * layer_out_norm   = nullptr;
+    struct ggml_tensor * layer_out_norm_b = nullptr;
+    struct ggml_tensor * ffn_norm_exps    = nullptr;
+    struct ggml_tensor * ffn_norm_enc     = nullptr;
+
+    // ff
+    struct ggml_tensor * ffn_gate     = nullptr; // w1
+    struct ggml_tensor * ffn_down     = nullptr; // w2
+    struct ggml_tensor * ffn_up       = nullptr; // w3
+    struct ggml_tensor * ffn_gate_enc = nullptr;
+    struct ggml_tensor * ffn_down_enc = nullptr;
+    struct ggml_tensor * ffn_up_enc   = nullptr;
+
+    // ff MoE
+    struct ggml_tensor * ffn_gate_inp  = nullptr;
+    struct ggml_tensor * ffn_gate_exps = nullptr;
+    struct ggml_tensor * ffn_down_exps = nullptr;
+    struct ggml_tensor * ffn_up_exps   = nullptr;
+
+    // ff shared expert (shexp)
+    struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
+    struct ggml_tensor * ffn_gate_shexp     = nullptr;
+    struct ggml_tensor * ffn_down_shexp     = nullptr;
+    struct ggml_tensor * ffn_up_shexp       = nullptr;
+
+    // ff bias
+    struct ggml_tensor * ffn_gate_b = nullptr;
+    struct ggml_tensor * ffn_down_b = nullptr; // b2
+    struct ggml_tensor * ffn_up_b   = nullptr; // b3
+    struct ggml_tensor * ffn_act    = nullptr;
+
+    // mamba proj
+    struct ggml_tensor * ssm_in  = nullptr;
+    struct ggml_tensor * ssm_x   = nullptr;
+    struct ggml_tensor * ssm_dt  = nullptr;
+    struct ggml_tensor * ssm_out = nullptr;
+
+    // mamba
+    struct ggml_tensor * ssm_conv1d = nullptr;
+    struct ggml_tensor * ssm_a      = nullptr;
+    struct ggml_tensor * ssm_d      = nullptr;
+
+    // mamba bias
+    struct ggml_tensor * ssm_conv1d_b = nullptr;
+    struct ggml_tensor * ssm_dt_b     = nullptr;
+
+    // rwkv
+    struct ggml_tensor * time_mix_w1         = nullptr;
+    struct ggml_tensor * time_mix_w2         = nullptr;
+    struct ggml_tensor * time_mix_lerp_x     = nullptr;
+    struct ggml_tensor * time_mix_lerp_w     = nullptr;
+    struct ggml_tensor * time_mix_lerp_k     = nullptr;
+    struct ggml_tensor * time_mix_lerp_v     = nullptr;
+    struct ggml_tensor * time_mix_lerp_r     = nullptr;
+    struct ggml_tensor * time_mix_lerp_g     = nullptr;
+
+    struct ggml_tensor * time_mix_first      = nullptr;
+    struct ggml_tensor * time_mix_decay      = nullptr;
+    struct ggml_tensor * time_mix_decay_w1   = nullptr;
+    struct ggml_tensor * time_mix_decay_w2   = nullptr;
+    struct ggml_tensor * time_mix_key        = nullptr;
+    struct ggml_tensor * time_mix_value      = nullptr;
+    struct ggml_tensor * time_mix_receptance = nullptr;
+    struct ggml_tensor * time_mix_gate       = nullptr;
+
+    struct ggml_tensor * time_mix_ln     = nullptr;
+    struct ggml_tensor * time_mix_ln_b   = nullptr;
+    struct ggml_tensor * time_mix_output = nullptr;
+
+    struct ggml_tensor * channel_mix_lerp_k = nullptr;
+    struct ggml_tensor * channel_mix_lerp_r = nullptr;
+
+    struct ggml_tensor * channel_mix_key        = nullptr;
+    struct ggml_tensor * channel_mix_receptance = nullptr;
+    struct ggml_tensor * channel_mix_value      = nullptr;
+
+    // long rope factors
+    struct ggml_tensor * rope_long  = nullptr;
+    struct ggml_tensor * rope_short = nullptr;
+    struct ggml_tensor * rope_freqs = nullptr;
+
+    // bitnet scale
+    struct ggml_tensor * wq_scale       = nullptr;
+    struct ggml_tensor * wk_scale       = nullptr;
+    struct ggml_tensor * wv_scale       = nullptr;
+    struct ggml_tensor * wo_scale       = nullptr;
+    struct ggml_tensor * ffn_gate_scale = nullptr;
+    struct ggml_tensor * ffn_up_scale   = nullptr;
+    struct ggml_tensor * ffn_down_scale = nullptr;
+
+    struct llama_layer_posnet posnet;
+
+    struct llama_layer_convnext convnext;
+};
+
+struct llama_model {
+    llm_type type = MODEL_UNKNOWN;
+    llm_arch arch = LLM_ARCH_UNKNOWN;
+
+    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
+
+    std::string name = "n/a";
+
+    llama_hparams hparams = {};
+    llama_vocab   vocab;
+
+    struct ggml_tensor * tok_embd   = nullptr;
+    struct ggml_tensor * type_embd  = nullptr;
+    struct ggml_tensor * pos_embd   = nullptr;
+    struct ggml_tensor * tok_norm   = nullptr;
+    struct ggml_tensor * tok_norm_b = nullptr;
+
+    struct ggml_tensor * output_norm     = nullptr;
+    struct ggml_tensor * output_norm_b   = nullptr;
+    struct ggml_tensor * output          = nullptr;
+    struct ggml_tensor * output_b        = nullptr;
+    struct ggml_tensor * output_norm_enc = nullptr;
+
+    // classifier
+    struct ggml_tensor * cls       = nullptr;
+    struct ggml_tensor * cls_b     = nullptr;
+    struct ggml_tensor * cls_out   = nullptr;
+    struct ggml_tensor * cls_out_b = nullptr;
+
+    struct ggml_tensor * conv1d   = nullptr;
+    struct ggml_tensor * conv1d_b = nullptr;
+
+    std::vector<llama_layer> layers;
+
+    // gguf metadata
+    std::unordered_map<std::string, std::string> gguf_kv;
+
+    llama_split_mode split_mode;
+    int main_gpu;
+    int n_gpu_layers;
+
+    std::vector<std::string> rpc_servers;
+
+    // list of devices used in this model
+    std::vector<ggml_backend_dev_t> devices;
+
+
+    // lists of buffer types used for each layer
+    using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
+    buft_list_t cpu_buft_list;
+    std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
+
+    struct layer_dev {
+        ggml_backend_dev_t dev;
+        buft_list_t * buft_list;
+    };
+
+    layer_dev dev_input = {};
+    layer_dev dev_output = {};
+    std::vector<layer_dev> dev_layer;
+
+    // contexts where the model tensors metadata is stored
+    std::vector<ggml_context_ptr> ctxs;
+
+    // the model memory buffers for the tensor data
+    std::vector<ggml_backend_buffer_ptr> bufs;
+
+    // model memory mapped files
+    llama_mmaps mappings;
+
+    // objects representing data potentially being locked in memory
+    llama_mlocks mlock_bufs;
+    llama_mlocks mlock_mmaps;
+
+    // for quantize-stats only
+    std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
+
+    int64_t t_load_us  = 0;
+    int64_t t_start_us = 0;
+
+    // total number of parameters in the model
+    uint64_t n_elements = 0;
+
+    // total size of all the tensors in the model in bytes
+    size_t  n_bytes     = 0;
+};
+
+const char * llm_type_name(llm_type type);
+
+std::string llama_model_arch_name (const llama_model & model);
+std::string llama_model_type_name (const llama_model & model);
+std::string llama_model_ftype_name(const llama_model & model);
+
+// used by llama_adapter_cvec
+ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il);
+
+// used by llama_adapter_lora
+struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name);
+
+size_t llama_model_max_nodes(const llama_model & model);
+
+struct llama_model_loader;
+
+// TODO: become llama_model methods
+void llm_load_stats     (llama_model_loader & ml, llama_model & model);
+void llm_load_arch      (llama_model_loader & ml, llama_model & model);
+void llm_load_hparams   (llama_model_loader & ml, llama_model & model);
+void llm_load_vocab     (llama_model_loader & ml, llama_model & model);
+void llm_load_print_meta(llama_model_loader & ml, llama_model & model);
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
new file mode 100644
index 000000000..42974f8f1
--- /dev/null
+++ b/src/llama-quant.cpp
@@ -0,0 +1,929 @@
+#include "llama-quant.h"
+
+#include "llama-impl.h"
+#include "llama-model.h"
+#include "llama-model-loader.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <mutex>
+#include <thread>
+#include <unordered_map>
+
+// TODO: replace with ggml API call
+#define QK_K 256
+
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
+
+struct quantize_state_internal {
+    const llama_model                 & model;
+    const llama_model_quantize_params * params;
+
+    int n_attention_wv = 0;
+    int n_ffn_down     = 0;
+    int n_ffn_gate     = 0;
+    int n_ffn_up       = 0;
+    int i_attention_wv = 0;
+    int i_ffn_down     = 0;
+    int i_ffn_gate     = 0;
+    int i_ffn_up       = 0;
+
+    int n_k_quantized = 0;
+    int n_fallback    = 0;
+
+    bool has_imatrix = false;
+
+    // used to figure out if a model shares tok_embd with the output weight
+    bool has_output = false;
+
+    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
+        : model(model)
+        , params(params)
+        {}
+};
+
+static void llama_tensor_dequantize_internal(
+    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
+    const size_t nelements, const int nthread
+) {
+    if (output.size() < nelements) {
+        output.resize(nelements);
+    }
+    float * f32_output = (float *) output.data();
+
+    const ggml_type_traits * qtype = ggml_get_type_traits(tensor->type);
+    if (ggml_is_quantized(tensor->type)) {
+        if (qtype->to_float == NULL) {
+            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
+        }
+    } else if (tensor->type != GGML_TYPE_F16 &&
+               tensor->type != GGML_TYPE_BF16) {
+        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
+    }
+
+    if (nthread < 2) {
+        if (tensor->type == GGML_TYPE_F16) {
+            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
+        } else if (tensor->type == GGML_TYPE_BF16) {
+            ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
+        } else if (ggml_is_quantized(tensor->type)) {
+            qtype->to_float(tensor->data, f32_output, nelements);
+        } else {
+            GGML_ABORT("fatal error"); // unreachable
+        }
+        return;
+    }
+
+    size_t block_size;
+    if (tensor->type == GGML_TYPE_F16 ||
+        tensor->type == GGML_TYPE_BF16) {
+        block_size = 1;
+    } else {
+        block_size = (size_t)ggml_blck_size(tensor->type);
+    }
+
+    size_t block_size_bytes = ggml_type_size(tensor->type);
+
+    GGML_ASSERT(nelements % block_size == 0);
+    size_t nblocks = nelements / block_size;
+    size_t blocks_per_thread = nblocks / nthread;
+    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
+
+    size_t in_buff_offs = 0;
+    size_t out_buff_offs = 0;
+
+    for (int tnum = 0; tnum < nthread; tnum++) {
+        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
+        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
+        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
+
+        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
+            if (typ == GGML_TYPE_F16) {
+                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
+            } else if (typ == GGML_TYPE_BF16) {
+                ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
+            } else {
+                qtype->to_float(inbuf, outbuf, nels);
+            }
+        };
+        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
+        in_buff_offs += thr_block_bytes;
+        out_buff_offs += thr_elems;
+    }
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+}
+
+static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+    const std::string name = ggml_get_name(tensor);
+
+    // TODO: avoid hardcoded tensor names - use the TN_* constants
+    const llm_arch arch = qs.model.arch;
+    const auto       tn = LLM_TN(arch);
+
+    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
+        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
+    };
+    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
+    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
+        if (n_expert > 1) {
+            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but occasionally randomly
+            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
+            // for getting the current layer as I initially thought, and we need to resort to parsing the
+            // tensor name.
+            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
+                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
+            }
+            if (i_layer < 0 || i_layer >= n_layer) {
+                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
+            }
+        }
+        return std::make_pair(i_layer, n_layer);
+    };
+
+    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
+    // with the quantization of the output tensor
+    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
+        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->output_tensor_type;
+        } else {
+            int nx = tensor->ne[0];
+            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q5_K;
+            }
+            else if (new_type != GGML_TYPE_Q8_0) {
+                new_type = GGML_TYPE_Q6_K;
+            }
+        }
+    } else if (name == "token_embd.weight") {
+        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->token_embedding_type;
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q2_K;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+        }
+    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+        if (name.find("attn_v.weight") != std::string::npos) {
+            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
+            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            ++qs.i_attention_wv;
+        }
+        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (name.find("ffn_down") != std::string::npos) {
+            if (qs.i_ffn_down < qs.n_ffn_down/8) {
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
+            ++qs.i_ffn_down;
+        }
+        else if (name.find("attn_output.weight") != std::string::npos) {
+            if (qs.model.hparams.n_expert == 8) {
+                new_type = GGML_TYPE_Q5_K;
+            } else {
+                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+            }
+        }
+    } else if (name.find("attn_v.weight") != std::string::npos) {
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
+                use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
+        if (qs.model.type == MODEL_70B) {
+            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+            // nearly negligible increase in model size by quantizing this tensor with more bits:
+            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+        }
+        if (qs.model.hparams.n_expert == 8) {
+            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+            // TODO: explore better strategies
+            new_type = GGML_TYPE_Q8_0;
+        }
+        ++qs.i_attention_wv;
+    } else if (name.find("attn_k.weight") != std::string::npos) {
+        if (qs.model.hparams.n_expert == 8) {
+            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+            // TODO: explore better strategies
+            new_type = GGML_TYPE_Q8_0;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
+        }
+    } else if (name.find("attn_q.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
+        }
+    } else if (name.find("ffn_down") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
+            if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
+            new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+            new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
+                     : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
+                     : GGML_TYPE_Q3_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
+                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+            new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+            if (arch == LLM_ARCH_FALCON) {
+                new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
+                           use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+            } else {
+                if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
+            }
+        }
+        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
+                && qs.has_imatrix && i_layer < n_layer/8) {
+            // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
+            // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
+            // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
+            new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
+        }
+        ++qs.i_ffn_down;
+    } else if (name.find("attn_output.weight") != std::string::npos) {
+        if (arch != LLM_ARCH_FALCON) {
+            if (qs.model.hparams.n_expert == 8) {
+                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
+                    new_type = GGML_TYPE_Q5_K;
+                }
+            } else {
+                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
+            }
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+        }
+    }
+    else if (name.find("attn_qkv.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
+    }
+    else if (name.find("ffn_gate") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        ++qs.i_ffn_gate;
+    }
+    else if (name.find("ffn_up") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        ++qs.i_ffn_up;
+    }
+
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    //}
+    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
+    //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    //}
+    // This can be used to reduce the size of the Q5_K_S model.
+    // The associated PPL increase is fully in line with the size reduction
+    //else {
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
+    //}
+    bool convert_incompatible_tensor = false;
+    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
+        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K    || new_type == GGML_TYPE_IQ4_XS ||
+        new_type == GGML_TYPE_IQ2_XS  || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S  ||
+        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S   || new_type == GGML_TYPE_IQ3_S  ||
+        new_type == GGML_TYPE_IQ1_M) {
+        int nx = tensor->ne[0];
+        int ny = tensor->ne[1];
+        if (nx % QK_K != 0) {
+            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
+            convert_incompatible_tensor = true;
+        } else {
+            ++qs.n_k_quantized;
+        }
+    }
+    if (convert_incompatible_tensor) {
+        switch (new_type) {
+            case GGML_TYPE_TQ1_0:
+            case GGML_TYPE_TQ2_0:  new_type = GGML_TYPE_Q4_0; break;  // TODO: use a symmetric type instead
+            case GGML_TYPE_IQ2_XXS:
+            case GGML_TYPE_IQ2_XS:
+            case GGML_TYPE_IQ2_S:
+            case GGML_TYPE_IQ3_XXS:
+            case GGML_TYPE_IQ3_S:
+            case GGML_TYPE_IQ1_S:
+            case GGML_TYPE_IQ1_M:
+            case GGML_TYPE_Q2_K:
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
+            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
+            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
+            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
+            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
+        }
+        if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
+            new_type = GGML_TYPE_F16;
+        }
+        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
+        ++qs.n_fallback;
+    }
+
+    return new_type;
+}
+
+static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+    if (nthread < 2) {
+        // single-thread
+        size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
+        if (!ggml_validate_row_data(new_type, new_data, new_size)) {
+            throw std::runtime_error("quantized data validation failed");
+        }
+        return new_size;
+    }
+
+    std::mutex mutex;
+    int64_t counter = 0;
+    size_t new_size = 0;
+    bool valid = true;
+    auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
+            nrows, n_per_row, imatrix]() {
+        const int64_t nrows_per_chunk = chunk_size / n_per_row;
+        size_t local_size = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int64_t first_row = counter; counter += nrows_per_chunk;
+            if (first_row >= nrows) {
+                if (local_size > 0) {
+                    new_size += local_size;
+                }
+                break;
+            }
+            lock.unlock();
+            const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
+            size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+            local_size += this_size;
+
+            // validate the quantized data
+            const size_t row_size  = ggml_row_size(new_type, n_per_row);
+            void * this_data = (char *) new_data + first_row * row_size;
+            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
+                std::unique_lock<std::mutex> lock(mutex);
+                valid = false;
+                break;
+            }
+        }
+    };
+    for (int it = 0; it < nthread - 1; ++it) {
+        workers.emplace_back(compute);
+    }
+    compute();
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+    if (!valid) {
+        throw std::runtime_error("quantized data validation failed");
+    }
+    return new_size;
+}
+
+static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
+    ggml_type default_type;
+    llama_ftype ftype = params->ftype;
+
+    switch (params->ftype) {
+        case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
+        case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
+        case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break;
+        case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;
+
+        // K-quants
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q2_K:    default_type = GGML_TYPE_Q2_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  default_type = GGML_TYPE_Q5_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:    default_type = GGML_TYPE_Q6_K;    break;
+        case LLAMA_FTYPE_MOSTLY_TQ1_0:   default_type = GGML_TYPE_TQ1_0;   break;
+        case LLAMA_FTYPE_MOSTLY_TQ2_0:   default_type = GGML_TYPE_TQ2_0;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:   default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:   default_type = GGML_TYPE_IQ2_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:   default_type = GGML_TYPE_IQ1_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:   default_type = GGML_TYPE_IQ1_M;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
+
+        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
+    }
+
+    int nthread = params->nthread;
+
+    if (nthread <= 0) {
+        nthread = std::thread::hardware_concurrency();
+    }
+
+    // mmap consistently increases speed Linux, and also increases speed on Windows with
+    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
+#if defined(__linux__) || defined(_WIN32)
+    constexpr bool use_mmap = true;
+#else
+    constexpr bool use_mmap = false;
+#endif
+
+    llama_model_kv_override * kv_overrides = nullptr;
+    if (params->kv_overrides) {
+        auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+        kv_overrides = v->data();
+    }
+    llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
+    ml.init_mappings(false); // no prefetching
+
+    llama_model model;
+    llm_load_arch   (ml, model);
+    llm_load_hparams(ml, model);
+    llm_load_stats  (ml, model);
+
+    struct quantize_state_internal qs(model, params);
+
+    if (params->only_copy) {
+        ftype = model.ftype;
+    }
+    const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
+    if (params->imatrix) {
+        imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
+        if (imatrix_data) {
+            LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
+            qs.has_imatrix = true;
+            // check imatrix for nans or infs
+            for (const auto & kv : *imatrix_data) {
+                for (float f : kv.second) {
+                    if (!std::isfinite(f)) {
+                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
+                    }
+                }
+            }
+        }
+    }
+
+    const size_t align = GGUF_DEFAULT_ALIGNMENT;
+    gguf_context_ptr ctx_out { gguf_init_empty() };
+
+    // copy the KV pairs from the input file
+    gguf_set_kv     (ctx_out.get(), ml.meta.get());
+    gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
+    gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV
+
+    // Remove split metadata
+    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
+    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
+    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
+
+    if (params->kv_overrides) {
+        const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
+        for (const auto & o : overrides) {
+            if (o.key[0] == 0) break;
+            if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
+                gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
+                gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
+                gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
+                gguf_set_val_str(ctx_out.get(), o.key, o.val_str);
+            } else {
+                LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
+            }
+        }
+    }
+
+    // make a list of weights
+    std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
+    tensors.reserve(ml.weights_map.size());
+    for (const auto & it : ml.weights_map) {
+        tensors.push_back(&it.second);
+    }
+
+    // keep_split requires that the weights are sorted by split index
+    if (params->keep_split) {
+        std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
+            if (a->idx == b->idx) {
+                return a->offs < b->offs;
+            }
+            return a->idx < b->idx;
+        });
+    }
+
+    for (const auto * it : tensors) {
+        const struct ggml_tensor * tensor = it->tensor;
+
+        const std::string name = ggml_get_name(tensor);
+
+        // TODO: avoid hardcoded tensor names - use the TN_* constants
+        if (name.find("attn_v.weight")   != std::string::npos ||
+            name.find("attn_qkv.weight") != std::string::npos ||
+            name.find("attn_kv_b.weight")!= std::string::npos) {
+            ++qs.n_attention_wv;
+        } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
+            qs.has_output = true;
+        }
+    }
+
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
+
+    // sanity checks
+    {
+        const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
+        // attention layers have a non-zero number of kv heads
+        int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
+        if (llama_model_has_encoder(&model)) {
+            n_attn_layer *= 3;
+        }
+        GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
+    }
+
+    size_t total_size_org = 0;
+    size_t total_size_new = 0;
+
+    std::vector<std::thread> workers;
+    workers.reserve(nthread);
+
+    int idx = 0;
+
+    std::vector<no_init<uint8_t>> read_data;
+    std::vector<no_init<uint8_t>> work;
+    std::vector<no_init<float>> f32_conv_buf;
+
+    uint16_t n_split = 1;
+
+    // Assume split index is continuous
+    if (params->keep_split) {
+        for (const auto * it : tensors) {
+            n_split = std::max(uint16_t(it->idx + 1), n_split);
+        }
+    }
+    std::vector<gguf_context_ptr> ctx_outs(n_split);
+    ctx_outs[0] = std::move(ctx_out);
+
+    // populate the original tensors so we get an initial meta data
+    for (const auto * it : tensors) {
+        uint16_t i_split = params->keep_split ? it->idx : 0;
+        struct ggml_tensor * tensor = it->tensor;
+        if (!ctx_outs[i_split]) {
+            ctx_outs[i_split].reset(gguf_init_empty());
+        }
+        gguf_add_tensor(ctx_outs[i_split].get(), tensor);
+    }
+
+    // Set split info if needed
+    if (n_split > 1) {
+        for (size_t i = 0; i < ctx_outs.size(); ++i) {
+            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
+            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
+            gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
+        }
+    }
+
+    int cur_split = -1;
+    std::ofstream fout;
+    auto close_ofstream = [&]() {
+        // Write metadata and close file handler
+        if (fout.is_open()) {
+            fout.seekp(0);
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split].get()));
+            gguf_get_meta_data(ctx_outs[cur_split].get(), data.data());
+            fout.write((const char *) data.data(), data.size());
+            fout.close();
+        }
+    };
+    auto new_ofstream = [&](int index) {
+        cur_split = index;
+        GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
+        std::string fname = fname_out;
+        if (params->keep_split) {
+            std::vector<char> split_path(llama_path_max(), 0);
+            llama_split_path(split_path.data(), split_path.size(), fname_out.c_str(), cur_split, n_split);
+            fname = std::string(split_path.data());
+        }
+
+        fout = std::ofstream(fname, std::ios::binary);
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+        const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get());
+        // placeholder for the meta data
+        ::zeros(fout, meta_size);
+    };
+
+    const auto tn = LLM_TN(model.arch);
+    new_ofstream(0);
+    for (const auto * it : tensors) {
+        const auto & weight = *it;
+        struct ggml_tensor * tensor = weight.tensor;
+        if (weight.idx != cur_split && params->keep_split) {
+            close_ofstream();
+            new_ofstream(weight.idx);
+        }
+
+        const std::string name = ggml_get_name(tensor);
+
+        if (!ml.use_mmap) {
+            if (read_data.size() < ggml_nbytes(tensor)) {
+                read_data.resize(ggml_nbytes(tensor));
+            }
+            tensor->data = read_data.data();
+        }
+        ml.load_data_for(tensor);
+
+        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
+               ++idx, ml.n_tensors,
+               ggml_get_name(tensor),
+               llama_format_tensor_shape(tensor).c_str(),
+               ggml_type_name(tensor->type));
+
+        // This used to be a regex, but <regex> has an extreme cost to compile times.
+        bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+
+        // quantize only 2D and 3D tensors (experts)
+        quantize &= (ggml_n_dims(tensor) >= 2);
+
+        // do not quantize norm tensors
+        quantize &= name.find("_norm.weight") == std::string::npos;
+
+        quantize &= params->quantize_output_tensor || name != "output.weight";
+        quantize &= !params->only_copy;
+
+        // do not quantize expert gating tensors
+        // NOTE: can't use LLM_TN here because the layer number is not known
+        quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
+
+        // do not quantize positional embeddings and token types (BERT)
+        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
+        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
+
+        // do not quantize Mamba's small yet 2D weights
+        // NOTE: can't use LLM_TN here because the layer number is not known
+        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+
+        // do not quantize RWKV's time_mix_first tensors
+        quantize &= name.find("time_mix_first.weight") == std::string::npos;
+        quantize &= name.find("time_mix_w1.weight") == std::string::npos;
+        quantize &= name.find("time_mix_w2.weight") == std::string::npos;
+        quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
+        quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
+
+        // do not quantize relative position bias (T5)
+        quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+
+        enum ggml_type new_type;
+        void * new_data;
+        size_t new_size;
+
+        if (quantize) {
+            new_type = default_type;
+
+            // get more optimal quantization type based on the tensor shape, layer, etc.
+            if (!params->pure && ggml_is_quantized(default_type)) {
+                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+            }
+            if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
+                new_type = params->token_embedding_type;
+            }
+            if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+                new_type = params->output_tensor_type;
+            }
+
+            // If we've decided to quantize to the same type the tensor is already
+            // in then there's nothing to do.
+            quantize = tensor->type != new_type;
+        }
+
+        if (!quantize) {
+            new_type = tensor->type;
+            new_data = tensor->data;
+            new_size = ggml_nbytes(tensor);
+            LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
+        } else {
+            const int64_t nelements = ggml_nelements(tensor);
+
+            const float * imatrix = nullptr;
+            if (imatrix_data) {
+                auto it = imatrix_data->find(tensor->name);
+                if (it == imatrix_data->end()) {
+                    LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
+                } else {
+                    if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
+                        imatrix = it->second.data();
+                    } else {
+                        LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
+                                int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
+
+                        // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
+                        // this is a significant error and it may be good idea to abort the process if this happens,
+                        // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
+                        // tok_embd should be ignored in this case, since it always causes this warning
+                        if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
+                            throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
+                                    int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
+                        }
+                    }
+                }
+            }
+            if ((new_type == GGML_TYPE_IQ2_XXS ||
+                 new_type == GGML_TYPE_IQ2_XS  ||
+                 new_type == GGML_TYPE_IQ2_S   ||
+                 new_type == GGML_TYPE_IQ1_S   ||
+                (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
+                (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
+                LLAMA_LOG_ERROR("\n\n============================================================\n");
+                LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
+                LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
+                LLAMA_LOG_ERROR("============================================================\n\n");
+                throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
+            }
+
+            float * f32_data;
+
+            if (tensor->type == GGML_TYPE_F32) {
+                f32_data = (float *) tensor->data;
+            } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
+                throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
+            } else {
+                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+                f32_data = (float *) f32_conv_buf.data();
+            }
+
+            LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
+            fflush(stdout);
+
+            if (work.size() < (size_t)nelements * 4) {
+                work.resize(nelements * 4); // upper bound on size
+            }
+            new_data = work.data();
+
+            const int64_t n_per_row = tensor->ne[0];
+            const int64_t nrows = tensor->ne[1];
+
+            static const int64_t min_chunk_size = 32 * 512;
+            const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row));
+
+            const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
+            const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
+            const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
+
+            // quantize each expert separately since they have different importance matrices
+            new_size = 0;
+            for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
+                const float * f32_data_03 = f32_data + i03 * nelements_matrix;
+                void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
+                const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
+
+                new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
+            }
+            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
+        }
+        total_size_org += ggml_nbytes(tensor);
+        total_size_new += new_size;
+
+        // update the gguf meta data as we go
+        gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
+        gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data, new_size);
+
+        // write tensor data + padding
+        fout.write((const char *) new_data, new_size);
+        zeros(fout, GGML_PAD(new_size, align) - new_size);
+    }
+    close_ofstream();
+
+    LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
+    LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
+
+    if (qs.n_fallback > 0) {
+        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
+                __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
+    }
+}
+
+//
+// interface implementation
+//
+
+struct llama_model_quantize_params llama_model_quantize_default_params() {
+    struct llama_model_quantize_params result = {
+        /*.nthread                     =*/ 0,
+        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+        /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
+        /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
+        /*.allow_requantize            =*/ false,
+        /*.quantize_output_tensor      =*/ true,
+        /*.only_copy                   =*/ false,
+        /*.pure                        =*/ false,
+        /*.keep_split                  =*/ false,
+        /*.imatrix                     =*/ nullptr,
+        /*.kv_overrides                =*/ nullptr,
+    };
+
+    return result;
+}
+
+uint32_t llama_model_quantize(
+        const char * fname_inp,
+        const char * fname_out,
+        const llama_model_quantize_params * params) {
+    try {
+        llama_model_quantize_internal(fname_inp, fname_out, params);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
+        return 1;
+    }
+
+    return 0;
+}
diff --git a/src/llama-quant.h b/src/llama-quant.h
new file mode 100644
index 000000000..6f70f09be
--- /dev/null
+++ b/src/llama-quant.h
@@ -0,0 +1 @@
+#pragma once
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index bebff77cf..69cea2f14 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1,5 +1,6 @@
 #include "llama-sampling.h"
 
+#include "llama-impl.h"
 #include "llama-vocab.h"
 #include "llama-grammar.h"
 
@@ -14,6 +15,118 @@
 #include <numeric>
 #include <random>
 #include <unordered_map>
+#include <stdexcept>
+
+// the ring buffer works similarly to std::deque, but with a fixed capacity
+template<typename T>
+struct ring_buffer {
+    ring_buffer(size_t cap) : capacity(cap), data(cap) {}
+
+    T & front() {
+        if (sz == 0) {
+            throw std::runtime_error("ring buffer is empty");
+        }
+        return data[first];
+    }
+
+    const T & front() const {
+        if (sz == 0) {
+            throw std::runtime_error("ring buffer is empty");
+        }
+        return data[first];
+    }
+
+    T & back() {
+        if (sz == 0) {
+            throw std::runtime_error("ring buffer is empty");
+        }
+        return data[pos];
+    }
+
+    const T & back() const {
+        if (sz == 0) {
+            throw std::runtime_error("ring buffer is empty");
+        }
+        return data[pos];
+    }
+
+    void push_back(const T & value) {
+        if (capacity == 0) {
+            throw std::runtime_error("ring buffer: capacity is zero");
+        }
+
+        if (sz == capacity) {
+            // advance the start when buffer is full
+            first = (first + 1) % capacity;
+        } else {
+            sz++;
+        }
+        data[pos] = value;
+        pos = (pos + 1) % capacity;
+    }
+
+    T pop_front() {
+        if (sz == 0) {
+            throw std::runtime_error("ring buffer is empty");
+        }
+        T value = data[first];
+        first = (first + 1) % capacity;
+        sz--;
+        return value;
+    }
+
+    //T & operator[](size_t i) {
+    //    if (i >= sz) {
+    //        throw std::runtime_error("ring buffer: index out of bounds");
+    //    }
+    //    return data[(first + i) % capacity];
+    //}
+
+    //const T & at(size_t i) const {
+    //    if (i >= sz) {
+    //        throw std::runtime_error("ring buffer: index out of bounds");
+    //    }
+    //    return data[(first + i) % capacity];
+    //}
+
+    const T & rat(size_t i) const {
+        if (i >= sz) {
+            throw std::runtime_error("ring buffer: index out of bounds");
+        }
+        return data[(first + sz - i - 1) % capacity];
+    }
+
+    std::vector<T> to_vector() const {
+        std::vector<T> result;
+        result.reserve(sz);
+        for (size_t i = 0; i < sz; i++) {
+            result.push_back(data[(first + i) % capacity]);
+        }
+        return result;
+    }
+
+    void clear() {
+        // here only reset the status of the buffer
+        sz = 0;
+        first = 0;
+        pos = 0;
+    }
+
+    bool empty() const {
+        return sz == 0;
+    }
+
+    size_t size() const {
+        return sz;
+    }
+
+    size_t capacity = 0;
+    size_t sz = 0;
+    size_t first = 0;
+    size_t pos = 0;
+
+    std::vector<T> data;
+};
 
 static int llama_sample_dist(llama_token_data_array * cur_p, std::mt19937 & rng) {
     // iterator for the probabilities
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 0a477d6dd..909e04871 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1,5 +1,7 @@
 #include "llama-vocab.h"
 
+#include "llama-impl.h"
+
 #include "unicode.h"
 
 #include <algorithm>
@@ -16,22 +18,6 @@
 // helpers
 //
 
-LLAMA_ATTRIBUTE_FORMAT(1, 2)
-static std::string format(const char * fmt, ...) {
-    va_list ap;
-    va_list ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
 struct naive_trie {
     naive_trie() : has_value(false), value(0) {
     }
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index a9b0da5ef..0d00086da 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#include "llama-impl.h"
+#include "llama.h"
 
 #include <string>
 #include <vector>
@@ -8,6 +8,18 @@
 #include <map>
 #include <set>
 
+static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
+    switch (type) {
+        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
+        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
+        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
+        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
+        case LLAMA_VOCAB_TYPE_UGM:  return "UGM";
+        case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
+        default:                    return "unknown";
+    }
+}
+
 struct llm_tokenizer;
 
 struct llama_vocab {
diff --git a/src/llama.cpp b/src/llama.cpp
index 4d41602fe..d7110b90b 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1,48 +1,20 @@
 #include "llama-impl.h"
+
+#include "llama-chat.h"
+#include "llama-mmap.h"
+#include "llama-context.h"
 #include "llama-vocab.h"
 #include "llama-sampling.h"
-
-#include "unicode.h"
+#include "llama-kv-cache.h"
+#include "llama-model-loader.h"
+#include "llama-model.h"
+#include "llama-quant.h"
 
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml-cpp.h"
 
-// TODO: replace with ggml API call
-#define QK_K 256
-
-#ifdef __has_include
-    #if __has_include(<unistd.h>)
-        #include <unistd.h>
-        #if defined(_POSIX_MAPPED_FILES)
-            #include <sys/mman.h>
-            #include <fcntl.h>
-        #endif
-        #if defined(_POSIX_MEMLOCK_RANGE)
-            #include <sys/resource.h>
-        #endif
-    #endif
-#endif
-
-#if defined(_WIN32)
-    #define WIN32_LEAN_AND_MEAN
-    #ifndef NOMINMAX
-        #define NOMINMAX
-    #endif
-    #include <windows.h>
-    #ifndef PATH_MAX
-        #define PATH_MAX MAX_PATH
-    #endif
-    #include <io.h>
-#endif
-
-#if __cplusplus >= 202000L
-    #define LU8(x) (const char*)(u8##x)
-#else
-    #define LU8(x) u8##x
-#endif
-
 #include <algorithm>
 #include <array>
 #include <cassert>
@@ -57,7416 +29,25 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <fstream>
 #include <functional>
-#include <future>
 #include <initializer_list>
 #include <locale>
 #include <map>
-#include <memory>
-#include <mutex>
 #include <numeric>
-#include <set>
-#include <sstream>
-#include <thread>
 #include <type_traits>
-#include <unordered_map>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-// bump if necessary
-#define LLAMA_MAX_LAYERS  512
-#define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
-
 //
-// helpers
+// tensor loading (TODO: add llama_tesor_loader?)
 //
 
-// trim whitespace from the beginning and end of a string
-static std::string trim(const std::string & str) {
-    size_t start = 0;
-    size_t end = str.size();
-    while (start < end && isspace(str[start])) {
-        start += 1;
-    }
-    while (end > start && isspace(str[end - 1])) {
-        end -= 1;
-    }
-    return str.substr(start, end - start);
-}
-
-static bool is_float_close(float a, float b, float abs_tol) {
-    // Check for non-negative tolerance
-    if (abs_tol < 0.0) {
-        throw std::invalid_argument("Tolerance must be non-negative");
-    }
-
-    // Exact equality check
-    if (a == b) {
-        return true;
-    }
-
-    // Check for infinities
-    if (std::isinf(a) || std::isinf(b)) {
-        return false;
-    }
-
-    // Regular comparison using the provided absolute tolerance
-    return std::fabs(b - a) <= abs_tol;
-}
-
-static void zeros(std::ofstream & file, size_t n) {
-    char zero = 0;
-    for (size_t i = 0; i < n; ++i) {
-        file.write(&zero, 1);
-    }
-}
-
-LLAMA_ATTRIBUTE_FORMAT(1, 2)
-static std::string format(const char * fmt, ...) {
-    va_list ap;
-    va_list ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
-//
-// gguf constants (sync with gguf.py)
-//
-
-enum llm_arch {
-    LLM_ARCH_LLAMA,
-    LLM_ARCH_DECI,
-    LLM_ARCH_FALCON,
-    LLM_ARCH_BAICHUAN,
-    LLM_ARCH_GROK,
-    LLM_ARCH_GPT2,
-    LLM_ARCH_GPTJ,
-    LLM_ARCH_GPTNEOX,
-    LLM_ARCH_MPT,
-    LLM_ARCH_STARCODER,
-    LLM_ARCH_REFACT,
-    LLM_ARCH_BERT,
-    LLM_ARCH_NOMIC_BERT,
-    LLM_ARCH_JINA_BERT_V2,
-    LLM_ARCH_BLOOM,
-    LLM_ARCH_STABLELM,
-    LLM_ARCH_QWEN,
-    LLM_ARCH_QWEN2,
-    LLM_ARCH_QWEN2MOE,
-    LLM_ARCH_QWEN2VL,
-    LLM_ARCH_PHI2,
-    LLM_ARCH_PHI3,
-    LLM_ARCH_PLAMO,
-    LLM_ARCH_CODESHELL,
-    LLM_ARCH_ORION,
-    LLM_ARCH_INTERNLM2,
-    LLM_ARCH_MINICPM,
-    LLM_ARCH_MINICPM3,
-    LLM_ARCH_GEMMA,
-    LLM_ARCH_GEMMA2,
-    LLM_ARCH_STARCODER2,
-    LLM_ARCH_MAMBA,
-    LLM_ARCH_XVERSE,
-    LLM_ARCH_COMMAND_R,
-    LLM_ARCH_DBRX,
-    LLM_ARCH_OLMO,
-    LLM_ARCH_OLMO2,
-    LLM_ARCH_OLMOE,
-    LLM_ARCH_OPENELM,
-    LLM_ARCH_ARCTIC,
-    LLM_ARCH_DEEPSEEK,
-    LLM_ARCH_DEEPSEEK2,
-    LLM_ARCH_CHATGLM,
-    LLM_ARCH_BITNET,
-    LLM_ARCH_T5,
-    LLM_ARCH_T5ENCODER,
-    LLM_ARCH_JAIS,
-    LLM_ARCH_NEMOTRON,
-    LLM_ARCH_EXAONE,
-    LLM_ARCH_RWKV6,
-    LLM_ARCH_GRANITE,
-    LLM_ARCH_GRANITE_MOE,
-    LLM_ARCH_CHAMELEON,
-    LLM_ARCH_WAVTOKENIZER_DEC,
-    LLM_ARCH_UNKNOWN,
-};
-
-static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-    { LLM_ARCH_LLAMA,            "llama"            },
-    { LLM_ARCH_DECI,             "deci"            },
-    { LLM_ARCH_FALCON,           "falcon"           },
-    { LLM_ARCH_GROK,             "grok"             },
-    { LLM_ARCH_GPT2,             "gpt2"             },
-    { LLM_ARCH_GPTJ,             "gptj"             },
-    { LLM_ARCH_GPTNEOX,          "gptneox"          },
-    { LLM_ARCH_MPT,              "mpt"              },
-    { LLM_ARCH_BAICHUAN,         "baichuan"         },
-    { LLM_ARCH_STARCODER,        "starcoder"        },
-    { LLM_ARCH_REFACT,           "refact"           },
-    { LLM_ARCH_BERT,             "bert"             },
-    { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
-    { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
-    { LLM_ARCH_BLOOM,            "bloom"            },
-    { LLM_ARCH_STABLELM,         "stablelm"         },
-    { LLM_ARCH_QWEN,             "qwen"             },
-    { LLM_ARCH_QWEN2,            "qwen2"            },
-    { LLM_ARCH_QWEN2MOE,         "qwen2moe"         },
-    { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
-    { LLM_ARCH_PHI2,             "phi2"             },
-    { LLM_ARCH_PHI3,             "phi3"             },
-    { LLM_ARCH_PLAMO,            "plamo"            },
-    { LLM_ARCH_CODESHELL,        "codeshell"        },
-    { LLM_ARCH_ORION,            "orion"            },
-    { LLM_ARCH_INTERNLM2,        "internlm2"        },
-    { LLM_ARCH_MINICPM,          "minicpm"          },
-    { LLM_ARCH_MINICPM3,         "minicpm3"         },
-    { LLM_ARCH_GEMMA,            "gemma"            },
-    { LLM_ARCH_GEMMA2,           "gemma2"           },
-    { LLM_ARCH_STARCODER2,       "starcoder2"       },
-    { LLM_ARCH_MAMBA,            "mamba"            },
-    { LLM_ARCH_XVERSE,           "xverse"           },
-    { LLM_ARCH_COMMAND_R,        "command-r"        },
-    { LLM_ARCH_DBRX,             "dbrx"             },
-    { LLM_ARCH_OLMO,             "olmo"             },
-    { LLM_ARCH_OLMO2,            "olmo2"            },
-    { LLM_ARCH_OLMOE,            "olmoe"            },
-    { LLM_ARCH_OPENELM,          "openelm"          },
-    { LLM_ARCH_ARCTIC,           "arctic"           },
-    { LLM_ARCH_DEEPSEEK,         "deepseek"         },
-    { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
-    { LLM_ARCH_CHATGLM,          "chatglm"          },
-    { LLM_ARCH_BITNET,           "bitnet"           },
-    { LLM_ARCH_T5,               "t5"               },
-    { LLM_ARCH_T5ENCODER,        "t5encoder"        },
-    { LLM_ARCH_JAIS,             "jais"             },
-    { LLM_ARCH_NEMOTRON,         "nemotron"         },
-    { LLM_ARCH_EXAONE,           "exaone"           },
-    { LLM_ARCH_RWKV6,            "rwkv6"            },
-    { LLM_ARCH_GRANITE,          "granite"          },
-    { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
-    { LLM_ARCH_CHAMELEON,        "chameleon"        },
-    { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
-    { LLM_ARCH_UNKNOWN,          "(unknown)"        },
-};
-
-enum llm_kv {
-    LLM_KV_GENERAL_TYPE,
-    LLM_KV_GENERAL_ARCHITECTURE,
-    LLM_KV_GENERAL_QUANTIZATION_VERSION,
-    LLM_KV_GENERAL_ALIGNMENT,
-    LLM_KV_GENERAL_NAME,
-    LLM_KV_GENERAL_AUTHOR,
-    LLM_KV_GENERAL_VERSION,
-    LLM_KV_GENERAL_URL,
-    LLM_KV_GENERAL_DESCRIPTION,
-    LLM_KV_GENERAL_LICENSE,
-    LLM_KV_GENERAL_SOURCE_URL,
-    LLM_KV_GENERAL_SOURCE_HF_REPO,
-
-    LLM_KV_VOCAB_SIZE,
-    LLM_KV_CONTEXT_LENGTH,
-    LLM_KV_EMBEDDING_LENGTH,
-    LLM_KV_FEATURES_LENGTH,
-    LLM_KV_BLOCK_COUNT,
-    LLM_KV_LEADING_DENSE_BLOCK_COUNT,
-    LLM_KV_FEED_FORWARD_LENGTH,
-    LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
-    LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
-    LLM_KV_USE_PARALLEL_RESIDUAL,
-    LLM_KV_TENSOR_DATA_LAYOUT,
-    LLM_KV_EXPERT_COUNT,
-    LLM_KV_EXPERT_USED_COUNT,
-    LLM_KV_EXPERT_SHARED_COUNT,
-    LLM_KV_EXPERT_WEIGHTS_SCALE,
-    LLM_KV_POOLING_TYPE,
-    LLM_KV_LOGIT_SCALE,
-    LLM_KV_DECODER_START_TOKEN_ID,
-    LLM_KV_ATTN_LOGIT_SOFTCAPPING,
-    LLM_KV_FINAL_LOGIT_SOFTCAPPING,
-    LLM_KV_SWIN_NORM,
-    LLM_KV_RESCALE_EVERY_N_LAYERS,
-    LLM_KV_TIME_MIX_EXTRA_DIM,
-    LLM_KV_TIME_DECAY_EXTRA_DIM,
-    LLM_KV_RESIDUAL_SCALE,
-    LLM_KV_EMBEDDING_SCALE,
-
-    LLM_KV_ATTENTION_HEAD_COUNT,
-    LLM_KV_ATTENTION_HEAD_COUNT_KV,
-    LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
-    LLM_KV_ATTENTION_CLAMP_KQV,
-    LLM_KV_ATTENTION_KEY_LENGTH,
-    LLM_KV_ATTENTION_VALUE_LENGTH,
-    LLM_KV_ATTENTION_LAYERNORM_EPS,
-    LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
-    LLM_KV_ATTENTION_GROUPNORM_EPS,
-    LLM_KV_ATTENTION_GROUPNORM_GROUPS,
-    LLM_KV_ATTENTION_CAUSAL,
-    LLM_KV_ATTENTION_Q_LORA_RANK,
-    LLM_KV_ATTENTION_KV_LORA_RANK,
-    LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
-    LLM_KV_ATTENTION_SLIDING_WINDOW,
-    LLM_KV_ATTENTION_SCALE,
-
-    LLM_KV_ROPE_DIMENSION_COUNT,
-    LLM_KV_ROPE_DIMENSION_SECTIONS,
-    LLM_KV_ROPE_FREQ_BASE,
-    LLM_KV_ROPE_SCALE_LINEAR,
-    LLM_KV_ROPE_SCALING_TYPE,
-    LLM_KV_ROPE_SCALING_FACTOR,
-    LLM_KV_ROPE_SCALING_ATTN_FACTOR,
-    LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
-    LLM_KV_ROPE_SCALING_FINETUNED,
-    LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
-
-    LLM_KV_SPLIT_NO,
-    LLM_KV_SPLIT_COUNT,
-    LLM_KV_SPLIT_TENSORS_COUNT,
-
-    LLM_KV_SSM_INNER_SIZE,
-    LLM_KV_SSM_CONV_KERNEL,
-    LLM_KV_SSM_STATE_SIZE,
-    LLM_KV_SSM_TIME_STEP_RANK,
-    LLM_KV_SSM_DT_B_C_RMS,
-
-    LLM_KV_WKV_HEAD_SIZE,
-
-    LLM_KV_TOKENIZER_MODEL,
-    LLM_KV_TOKENIZER_PRE,
-    LLM_KV_TOKENIZER_LIST,
-    LLM_KV_TOKENIZER_TOKEN_TYPE,
-    LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
-    LLM_KV_TOKENIZER_SCORES,
-    LLM_KV_TOKENIZER_MERGES,
-    LLM_KV_TOKENIZER_BOS_ID,
-    LLM_KV_TOKENIZER_EOS_ID,
-    LLM_KV_TOKENIZER_EOT_ID,
-    LLM_KV_TOKENIZER_EOM_ID,
-    LLM_KV_TOKENIZER_UNK_ID,
-    LLM_KV_TOKENIZER_SEP_ID,
-    LLM_KV_TOKENIZER_PAD_ID,
-    LLM_KV_TOKENIZER_CLS_ID,
-    LLM_KV_TOKENIZER_MASK_ID,
-    LLM_KV_TOKENIZER_ADD_BOS,
-    LLM_KV_TOKENIZER_ADD_EOS,
-    LLM_KV_TOKENIZER_ADD_PREFIX,
-    LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
-    LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
-    LLM_KV_TOKENIZER_HF_JSON,
-    LLM_KV_TOKENIZER_RWKV,
-    LLM_KV_TOKENIZER_FIM_PRE_ID,
-    LLM_KV_TOKENIZER_FIM_SUF_ID,
-    LLM_KV_TOKENIZER_FIM_MID_ID,
-    LLM_KV_TOKENIZER_FIM_PAD_ID,
-    LLM_KV_TOKENIZER_FIM_REP_ID,
-    LLM_KV_TOKENIZER_FIM_SEP_ID,
-
-    LLM_KV_ADAPTER_TYPE,
-    LLM_KV_ADAPTER_LORA_ALPHA,
-
-    LLM_KV_POSNET_EMBEDDING_LENGTH,
-    LLM_KV_POSNET_BLOCK_COUNT,
-
-    LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
-    LLM_KV_CONVNEXT_BLOCK_COUNT,
-
-    // deprecated:
-    LLM_KV_TOKENIZER_PREFIX_ID,
-    LLM_KV_TOKENIZER_SUFFIX_ID,
-    LLM_KV_TOKENIZER_MIDDLE_ID,
-};
-
-static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
-    { LLM_KV_GENERAL_TYPE,                  "general.type"                          },
-    { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
-    { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
-    { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
-    { LLM_KV_GENERAL_NAME,                  "general.name"                          },
-    { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
-    { LLM_KV_GENERAL_VERSION,               "general.version"                       },
-    { LLM_KV_GENERAL_URL,                   "general.url"                           },
-    { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
-    { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
-    { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
-    { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
-
-    { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
-    { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
-    { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
-    { LLM_KV_FEATURES_LENGTH,                   "%s.features_length"                   },
-    { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
-    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
-    { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
-    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
-    { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
-    { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
-    { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
-    { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
-    { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
-    { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
-    { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
-    { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
-    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
-    { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
-    { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
-    { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
-    { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },
-    { LLM_KV_RESCALE_EVERY_N_LAYERS,            "%s.rescale_every_n_layers"            },
-    { LLM_KV_TIME_MIX_EXTRA_DIM,                "%s.time_mix_extra_dim"                },
-    { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
-    { LLM_KV_RESIDUAL_SCALE,                    "%s.residual_scale"                    },
-    { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
-
-    { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"             },
-    { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"          },
-    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,         "%s.attention.max_alibi_bias"         },
-    { LLM_KV_ATTENTION_CLAMP_KQV,              "%s.attention.clamp_kqv"              },
-    { LLM_KV_ATTENTION_KEY_LENGTH,             "%s.attention.key_length"             },
-    { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"           },
-    { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"     },
-    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon" },
-    { LLM_KV_ATTENTION_GROUPNORM_EPS,          "%s.attention.group_norm_epsilon"     },
-    { LLM_KV_ATTENTION_GROUPNORM_GROUPS,       "%s.attention.group_norm_groups"      },
-    { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                 },
-    { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
-    { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
-    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
-    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
-    { LLM_KV_ATTENTION_SCALE,                  "%s.attention.scale"                  },
-
-    { LLM_KV_ROPE_DIMENSION_COUNT,             "%s.rope.dimension_count"                 },
-    { LLM_KV_ROPE_DIMENSION_SECTIONS,          "%s.rope.dimension_sections"              },
-    { LLM_KV_ROPE_FREQ_BASE,                   "%s.rope.freq_base"                       },
-    { LLM_KV_ROPE_SCALE_LINEAR,                "%s.rope.scale_linear"                    },
-    { LLM_KV_ROPE_SCALING_TYPE,                "%s.rope.scaling.type"                    },
-    { LLM_KV_ROPE_SCALING_FACTOR,              "%s.rope.scaling.factor"                  },
-    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,         "%s.rope.scaling.attn_factor"             },
-    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,        "%s.rope.scaling.original_context_length" },
-    { LLM_KV_ROPE_SCALING_FINETUNED,           "%s.rope.scaling.finetuned"               },
-    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,        "%s.rope.scaling.yarn_log_multiplier"     },
-
-    { LLM_KV_SPLIT_NO,                         "split.no"            },
-    { LLM_KV_SPLIT_COUNT,                      "split.count"         },
-    { LLM_KV_SPLIT_TENSORS_COUNT,              "split.tensors.count" },
-
-    { LLM_KV_SSM_CONV_KERNEL,                  "%s.ssm.conv_kernel"    },
-    { LLM_KV_SSM_INNER_SIZE,                   "%s.ssm.inner_size"     },
-    { LLM_KV_SSM_STATE_SIZE,                   "%s.ssm.state_size"     },
-    { LLM_KV_SSM_TIME_STEP_RANK,               "%s.ssm.time_step_rank" },
-    { LLM_KV_SSM_DT_B_C_RMS,                   "%s.ssm.dt_b_c_rms"     },
-
-    { LLM_KV_WKV_HEAD_SIZE,                    "%s.wkv.head_size" },
-
-    { LLM_KV_POSNET_EMBEDDING_LENGTH,          "%s.posnet.embedding_length" },
-    { LLM_KV_POSNET_BLOCK_COUNT,               "%s.posnet.block_count"      },
-
-    { LLM_KV_CONVNEXT_EMBEDDING_LENGTH,        "%s.convnext.embedding_length" },
-    { LLM_KV_CONVNEXT_BLOCK_COUNT,             "%s.convnext.block_count"      },
-
-    { LLM_KV_TOKENIZER_MODEL,                  "tokenizer.ggml.model"                    },
-    { LLM_KV_TOKENIZER_PRE,                    "tokenizer.ggml.pre"                      },
-    { LLM_KV_TOKENIZER_LIST,                   "tokenizer.ggml.tokens"                   },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE,             "tokenizer.ggml.token_type"               },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,       "tokenizer.ggml.token_type_count"         },
-    { LLM_KV_TOKENIZER_SCORES,                 "tokenizer.ggml.scores"                   },
-    { LLM_KV_TOKENIZER_MERGES,                 "tokenizer.ggml.merges"                   },
-    { LLM_KV_TOKENIZER_BOS_ID,                 "tokenizer.ggml.bos_token_id"             },
-    { LLM_KV_TOKENIZER_EOS_ID,                 "tokenizer.ggml.eos_token_id"             },
-    { LLM_KV_TOKENIZER_EOT_ID,                 "tokenizer.ggml.eot_token_id"             },
-    { LLM_KV_TOKENIZER_EOM_ID,                 "tokenizer.ggml.eom_token_id"             },
-    { LLM_KV_TOKENIZER_UNK_ID,                 "tokenizer.ggml.unknown_token_id"         },
-    { LLM_KV_TOKENIZER_SEP_ID,                 "tokenizer.ggml.seperator_token_id"       },
-    { LLM_KV_TOKENIZER_PAD_ID,                 "tokenizer.ggml.padding_token_id"         },
-    { LLM_KV_TOKENIZER_CLS_ID,                 "tokenizer.ggml.cls_token_id"             },
-    { LLM_KV_TOKENIZER_MASK_ID,                "tokenizer.ggml.mask_token_id"            },
-    { LLM_KV_TOKENIZER_ADD_BOS,                "tokenizer.ggml.add_bos_token"            },
-    { LLM_KV_TOKENIZER_ADD_EOS,                "tokenizer.ggml.add_eos_token"            },
-    { LLM_KV_TOKENIZER_ADD_PREFIX,             "tokenizer.ggml.add_space_prefix"         },
-    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,        "tokenizer.ggml.remove_extra_whitespaces" },
-    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,   "tokenizer.ggml.precompiled_charsmap"     },
-    { LLM_KV_TOKENIZER_HF_JSON,                "tokenizer.huggingface.json"              },
-    { LLM_KV_TOKENIZER_RWKV,                   "tokenizer.rwkv.world"                    },
-    { LLM_KV_TOKENIZER_FIM_PRE_ID,             "tokenizer.ggml.fim_pre_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SUF_ID,             "tokenizer.ggml.fim_suf_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_MID_ID,             "tokenizer.ggml.fim_mid_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_PAD_ID,             "tokenizer.ggml.fim_pad_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_REP_ID,             "tokenizer.ggml.fim_rep_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SEP_ID,             "tokenizer.ggml.fim_sep_token_id"         },
-
-    { LLM_KV_ADAPTER_TYPE,                     "adapter.type"       },
-    { LLM_KV_ADAPTER_LORA_ALPHA,               "adapter.lora.alpha" },
-
-    // deprecated
-    { LLM_KV_TOKENIZER_PREFIX_ID,              "tokenizer.ggml.prefix_token_id" },
-    { LLM_KV_TOKENIZER_SUFFIX_ID,              "tokenizer.ggml.suffix_token_id" },
-    { LLM_KV_TOKENIZER_MIDDLE_ID,              "tokenizer.ggml.middle_token_id" },
-};
-
-struct LLM_KV {
-    LLM_KV(llm_arch arch) : arch(arch) {}
-
-    llm_arch arch;
-
-    std::string operator()(llm_kv kv) const {
-        return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
-    }
-};
-
-enum llm_tensor {
-    LLM_TENSOR_TOKEN_EMBD,
-    LLM_TENSOR_TOKEN_EMBD_NORM,
-    LLM_TENSOR_TOKEN_TYPES,
-    LLM_TENSOR_POS_EMBD,
-    LLM_TENSOR_OUTPUT,
-    LLM_TENSOR_OUTPUT_NORM,
-    LLM_TENSOR_ROPE_FREQS,
-    LLM_TENSOR_ROPE_FACTORS_LONG,
-    LLM_TENSOR_ROPE_FACTORS_SHORT,
-    LLM_TENSOR_ATTN_Q,
-    LLM_TENSOR_ATTN_K,
-    LLM_TENSOR_ATTN_V,
-    LLM_TENSOR_ATTN_QKV,
-    LLM_TENSOR_ATTN_OUT,
-    LLM_TENSOR_ATTN_NORM,
-    LLM_TENSOR_ATTN_NORM_2,
-    LLM_TENSOR_ATTN_OUT_NORM,
-    LLM_TENSOR_ATTN_POST_NORM,
-    LLM_TENSOR_ATTN_ROT_EMBD,
-    LLM_TENSOR_FFN_GATE_INP,
-    LLM_TENSOR_FFN_GATE_INP_SHEXP,
-    LLM_TENSOR_FFN_NORM,
-    LLM_TENSOR_FFN_POST_NORM,
-    LLM_TENSOR_FFN_GATE,
-    LLM_TENSOR_FFN_DOWN,
-    LLM_TENSOR_FFN_UP,
-    LLM_TENSOR_FFN_ACT,
-    LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
-    LLM_TENSOR_FFN_GATE_EXP,
-    LLM_TENSOR_FFN_UP_EXP,
-    LLM_TENSOR_FFN_NORM_EXPS,
-    LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
-    LLM_TENSOR_FFN_GATE_EXPS,
-    LLM_TENSOR_FFN_UP_EXPS,
-    LLM_TENSOR_FFN_DOWN_SHEXP,
-    LLM_TENSOR_FFN_GATE_SHEXP,
-    LLM_TENSOR_FFN_UP_SHEXP,
-    LLM_TENSOR_ATTN_Q_NORM,
-    LLM_TENSOR_ATTN_K_NORM,
-    LLM_TENSOR_LAYER_OUT_NORM,
-    LLM_TENSOR_SSM_IN,
-    LLM_TENSOR_SSM_CONV1D,
-    LLM_TENSOR_SSM_X,
-    LLM_TENSOR_SSM_DT,
-    LLM_TENSOR_SSM_A,
-    LLM_TENSOR_SSM_D,
-    LLM_TENSOR_SSM_OUT,
-    LLM_TENSOR_TIME_MIX_W1,
-    LLM_TENSOR_TIME_MIX_W2,
-    LLM_TENSOR_TIME_MIX_LERP_X,
-    LLM_TENSOR_TIME_MIX_LERP_W,
-    LLM_TENSOR_TIME_MIX_LERP_K,
-    LLM_TENSOR_TIME_MIX_LERP_V,
-    LLM_TENSOR_TIME_MIX_LERP_R,
-    LLM_TENSOR_TIME_MIX_LERP_G,
-    LLM_TENSOR_TIME_MIX_FIRST,
-    LLM_TENSOR_TIME_MIX_DECAY,
-    LLM_TENSOR_TIME_MIX_DECAY_W1,
-    LLM_TENSOR_TIME_MIX_DECAY_W2,
-    LLM_TENSOR_TIME_MIX_KEY,
-    LLM_TENSOR_TIME_MIX_VALUE,
-    LLM_TENSOR_TIME_MIX_RECEPTANCE,
-    LLM_TENSOR_TIME_MIX_GATE,
-    LLM_TENSOR_TIME_MIX_LN,
-    LLM_TENSOR_TIME_MIX_OUTPUT,
-    LLM_TENSOR_CHANNEL_MIX_LERP_K,
-    LLM_TENSOR_CHANNEL_MIX_LERP_R,
-    LLM_TENSOR_CHANNEL_MIX_KEY,
-    LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
-    LLM_TENSOR_CHANNEL_MIX_VALUE,
-    LLM_TENSOR_ATTN_Q_A,
-    LLM_TENSOR_ATTN_Q_B,
-    LLM_TENSOR_ATTN_KV_A_MQA,
-    LLM_TENSOR_ATTN_KV_B,
-    LLM_TENSOR_ATTN_Q_A_NORM,
-    LLM_TENSOR_ATTN_KV_A_NORM,
-    LLM_TENSOR_ATTN_SUB_NORM,
-    LLM_TENSOR_FFN_SUB_NORM,
-    LLM_TENSOR_DEC_ATTN_NORM,
-    LLM_TENSOR_DEC_ATTN_Q,
-    LLM_TENSOR_DEC_ATTN_K,
-    LLM_TENSOR_DEC_ATTN_V,
-    LLM_TENSOR_DEC_ATTN_OUT,
-    LLM_TENSOR_DEC_ATTN_REL_B,
-    LLM_TENSOR_DEC_CROSS_ATTN_NORM,
-    LLM_TENSOR_DEC_CROSS_ATTN_Q,
-    LLM_TENSOR_DEC_CROSS_ATTN_K,
-    LLM_TENSOR_DEC_CROSS_ATTN_V,
-    LLM_TENSOR_DEC_CROSS_ATTN_OUT,
-    LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
-    LLM_TENSOR_DEC_FFN_NORM,
-    LLM_TENSOR_DEC_FFN_GATE,
-    LLM_TENSOR_DEC_FFN_DOWN,
-    LLM_TENSOR_DEC_FFN_UP,
-    LLM_TENSOR_DEC_OUTPUT_NORM,
-    LLM_TENSOR_ENC_ATTN_NORM,
-    LLM_TENSOR_ENC_ATTN_Q,
-    LLM_TENSOR_ENC_ATTN_K,
-    LLM_TENSOR_ENC_ATTN_V,
-    LLM_TENSOR_ENC_ATTN_OUT,
-    LLM_TENSOR_ENC_ATTN_REL_B,
-    LLM_TENSOR_ENC_FFN_NORM,
-    LLM_TENSOR_ENC_FFN_GATE,
-    LLM_TENSOR_ENC_FFN_DOWN,
-    LLM_TENSOR_ENC_FFN_UP,
-    LLM_TENSOR_ENC_OUTPUT_NORM,
-    LLM_TENSOR_CLS,
-    LLM_TENSOR_CLS_OUT,
-    LLM_TENSOR_CONV1D,
-    LLM_TENSOR_CONVNEXT_DW,
-    LLM_TENSOR_CONVNEXT_NORM,
-    LLM_TENSOR_CONVNEXT_PW1,
-    LLM_TENSOR_CONVNEXT_PW2,
-    LLM_TENSOR_CONVNEXT_GAMMA,
-    LLM_TENSOR_POS_NET_CONV1,
-    LLM_TENSOR_POS_NET_CONV2,
-    LLM_TENSOR_POS_NET_NORM,
-    LLM_TENSOR_POS_NET_NORM1,
-    LLM_TENSOR_POS_NET_NORM2,
-    LLM_TENSOR_POS_NET_ATTN_NORM,
-    LLM_TENSOR_POS_NET_ATTN_Q,
-    LLM_TENSOR_POS_NET_ATTN_K,
-    LLM_TENSOR_POS_NET_ATTN_V,
-    LLM_TENSOR_POS_NET_ATTN_OUT,
-};
-
-static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
-    {
-        LLM_ARCH_LLAMA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_DECI,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_BAICHUAN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_FALCON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GROK,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GPT2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_GPTJ,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-        },
-    },
-    {
-        LLM_ARCH_GPTNEOX,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MPT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output"},
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
-        },
-    },
-    {
-        LLM_ARCH_STARCODER,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_REFACT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_BERT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
-            { LLM_TENSOR_CLS_OUT,         "cls.output" },
-        },
-    },
-    {
-        LLM_ARCH_NOMIC_BERT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_JINA_BERT_V2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
-        },
-    },
-    {
-        LLM_ARCH_BLOOM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_STABLELM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2VL,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_PHI2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_PHI3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_PLAMO,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_CODESHELL,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_ORION,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_INTERNLM2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MINICPM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-        },
-    },
-    {
-        LLM_ARCH_MINICPM3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
-            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
-            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
-            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
-            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_STARCODER2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MAMBA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-        },
-    },
-    {
-        LLM_ARCH_XVERSE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_COMMAND_R,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_DBRX,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_OLMO,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_OLMO2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_OLMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_OPENELM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_ARCTIC,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_NORM_EXPS,   "blk.%d.ffn_norm_exps" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_DEEPSEEK,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,      "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_DEEPSEEK2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
-            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
-            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
-            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
-            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_CHATGLM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_BITNET,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_SUB_NORM,      "blk.%d.attn_sub_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_SUB_NORM,       "blk.%d.ffn_sub_norm" },
-        },
-    },
-    {
-        LLM_ARCH_T5,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
-            { LLM_TENSOR_OUTPUT,               "output" },
-            { LLM_TENSOR_DEC_OUTPUT_NORM,      "dec.output_norm" },
-            { LLM_TENSOR_DEC_ATTN_NORM,        "dec.blk.%d.attn_norm" },
-            { LLM_TENSOR_DEC_ATTN_Q,           "dec.blk.%d.attn_q" },
-            { LLM_TENSOR_DEC_ATTN_K,           "dec.blk.%d.attn_k" },
-            { LLM_TENSOR_DEC_ATTN_V,           "dec.blk.%d.attn_v" },
-            { LLM_TENSOR_DEC_ATTN_OUT,         "dec.blk.%d.attn_o" },
-            { LLM_TENSOR_DEC_ATTN_REL_B,       "dec.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "dec.blk.%d.cross_attn_norm" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_Q,     "dec.blk.%d.cross_attn_q" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_K,     "dec.blk.%d.cross_attn_k" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_V,     "dec.blk.%d.cross_attn_v" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_OUT,   "dec.blk.%d.cross_attn_o" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
-            { LLM_TENSOR_DEC_FFN_NORM,         "dec.blk.%d.ffn_norm" },
-            { LLM_TENSOR_DEC_FFN_GATE,         "dec.blk.%d.ffn_gate" },
-            { LLM_TENSOR_DEC_FFN_DOWN,         "dec.blk.%d.ffn_down" },
-            { LLM_TENSOR_DEC_FFN_UP,           "dec.blk.%d.ffn_up" },
-            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
-            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
-            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
-            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
-            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
-            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
-            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
-            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
-            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
-            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_T5ENCODER,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
-            { LLM_TENSOR_OUTPUT,               "output" },
-            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
-            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
-            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
-            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
-            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
-            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
-            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
-            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
-            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
-            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_JAIS,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_NEMOTRON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_EXAONE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_RWKV6,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
-            { LLM_TENSOR_OUTPUT,                    "output" },
-            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_NORM_2,               "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
-            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
-            { LLM_TENSOR_TIME_MIX_LERP_X,           "blk.%d.time_mix_lerp_x" },
-            { LLM_TENSOR_TIME_MIX_LERP_W,           "blk.%d.time_mix_lerp_w" },
-            { LLM_TENSOR_TIME_MIX_LERP_K,           "blk.%d.time_mix_lerp_k" },
-            { LLM_TENSOR_TIME_MIX_LERP_V,           "blk.%d.time_mix_lerp_v" },
-            { LLM_TENSOR_TIME_MIX_LERP_R,           "blk.%d.time_mix_lerp_r" },
-            { LLM_TENSOR_TIME_MIX_LERP_G,           "blk.%d.time_mix_lerp_g" },
-            { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
-            { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W2,         "blk.%d.time_mix_decay_w2" },
-            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
-            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
-            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
-            { LLM_TENSOR_TIME_MIX_GATE,             "blk.%d.time_mix_gate" },
-            { LLM_TENSOR_TIME_MIX_LN,               "blk.%d.time_mix_ln" },
-            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
-            { LLM_TENSOR_CHANNEL_MIX_LERP_K,        "blk.%d.channel_mix_lerp_k" },
-            { LLM_TENSOR_CHANNEL_MIX_LERP_R,        "blk.%d.channel_mix_lerp_r" },
-            { LLM_TENSOR_CHANNEL_MIX_KEY,           "blk.%d.channel_mix_key" },
-            { LLM_TENSOR_CHANNEL_MIX_VALUE,         "blk.%d.channel_mix_value" },
-            { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,    "blk.%d.channel_mix_receptance" },
-        },
-    },
-    {
-        LLM_ARCH_GRANITE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GRANITE_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_CHAMELEON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_WAVTOKENIZER_DEC,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
-            { LLM_TENSOR_CONV1D,            "conv1d" },
-            { LLM_TENSOR_CONVNEXT_DW,       "convnext.%d.dw" },
-            { LLM_TENSOR_CONVNEXT_NORM,     "convnext.%d.norm" },
-            { LLM_TENSOR_CONVNEXT_PW1,      "convnext.%d.pw1" },
-            { LLM_TENSOR_CONVNEXT_PW2,      "convnext.%d.pw2" },
-            { LLM_TENSOR_CONVNEXT_GAMMA,    "convnext.%d.gamma" },
-            { LLM_TENSOR_OUTPUT_NORM,       "output_norm" },
-            { LLM_TENSOR_OUTPUT,            "output" },
-            { LLM_TENSOR_POS_NET_CONV1,     "posnet.%d.conv1" },
-            { LLM_TENSOR_POS_NET_CONV2,     "posnet.%d.conv2" },
-            { LLM_TENSOR_POS_NET_NORM,      "posnet.%d.norm" },
-            { LLM_TENSOR_POS_NET_NORM1,     "posnet.%d.norm1" },
-            { LLM_TENSOR_POS_NET_NORM2,     "posnet.%d.norm2" },
-            { LLM_TENSOR_POS_NET_ATTN_NORM, "posnet.%d.attn_norm" },
-            { LLM_TENSOR_POS_NET_ATTN_Q,    "posnet.%d.attn_q" },
-            { LLM_TENSOR_POS_NET_ATTN_K,    "posnet.%d.attn_k" },
-            { LLM_TENSOR_POS_NET_ATTN_V,    "posnet.%d.attn_v" },
-            { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
-        },
-    },
-    {
-        LLM_ARCH_UNKNOWN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-        },
-    },
-};
-
-enum llm_chat_template {
-    LLM_CHAT_TEMPLATE_CHATML,
-    LLM_CHAT_TEMPLATE_LLAMA_2,
-    LLM_CHAT_TEMPLATE_LLAMA_2_SYS,
-    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS,
-    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP,
-    LLM_CHAT_TEMPLATE_MISTRAL_V1,
-    LLM_CHAT_TEMPLATE_MISTRAL_V3,
-    LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
-    LLM_CHAT_TEMPLATE_MISTRAL_V7,
-    LLM_CHAT_TEMPLATE_PHI_3,
-    LLM_CHAT_TEMPLATE_FALCON_3,
-    LLM_CHAT_TEMPLATE_ZEPHYR,
-    LLM_CHAT_TEMPLATE_MONARCH,
-    LLM_CHAT_TEMPLATE_GEMMA,
-    LLM_CHAT_TEMPLATE_ORION,
-    LLM_CHAT_TEMPLATE_OPENCHAT,
-    LLM_CHAT_TEMPLATE_VICUNA,
-    LLM_CHAT_TEMPLATE_VICUNA_ORCA,
-    LLM_CHAT_TEMPLATE_DEEPSEEK,
-    LLM_CHAT_TEMPLATE_DEEPSEEK_2,
-    LLM_CHAT_TEMPLATE_COMMAND_R,
-    LLM_CHAT_TEMPLATE_LLAMA_3,
-    LLM_CHAT_TEMPLATE_CHATGML_3,
-    LLM_CHAT_TEMPLATE_CHATGML_4,
-    LLM_CHAT_TEMPLATE_MINICPM,
-    LLM_CHAT_TEMPLATE_EXAONE_3,
-    LLM_CHAT_TEMPLATE_RWKV_WORLD,
-    LLM_CHAT_TEMPLATE_GRANITE,
-    LLM_CHAT_TEMPLATE_GIGACHAT,
-    LLM_CHAT_TEMPLATE_MEGREZ,
-    LLM_CHAT_TEMPLATE_UNKNOWN,
-};
-
-static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
-    { "chatml",            LLM_CHAT_TEMPLATE_CHATML            },
-    { "llama2",            LLM_CHAT_TEMPLATE_LLAMA_2           },
-    { "llama2-sys",        LLM_CHAT_TEMPLATE_LLAMA_2_SYS       },
-    { "llama2-sys-bos",    LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS   },
-    { "llama2-sys-strip",  LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP },
-    { "mistral-v1",        LLM_CHAT_TEMPLATE_MISTRAL_V1        },
-    { "mistral-v3",        LLM_CHAT_TEMPLATE_MISTRAL_V3        },
-    { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
-    { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
-    { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
-    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
-    { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
-    { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
-    { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
-    { "orion",             LLM_CHAT_TEMPLATE_ORION             },
-    { "openchat",          LLM_CHAT_TEMPLATE_OPENCHAT          },
-    { "vicuna",            LLM_CHAT_TEMPLATE_VICUNA            },
-    { "vicuna-orca",       LLM_CHAT_TEMPLATE_VICUNA_ORCA       },
-    { "deepseek",          LLM_CHAT_TEMPLATE_DEEPSEEK          },
-    { "deepseek2",         LLM_CHAT_TEMPLATE_DEEPSEEK_2        },
-    { "command-r",         LLM_CHAT_TEMPLATE_COMMAND_R         },
-    { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
-    { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGML_3         },
-    { "chatglm4",          LLM_CHAT_TEMPLATE_CHATGML_4         },
-    { "minicpm",           LLM_CHAT_TEMPLATE_MINICPM           },
-    { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
-    { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
-    { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
-    { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
-    { "megrez",            LLM_CHAT_TEMPLATE_MEGREZ            },
-};
-
-static llm_arch llm_arch_from_string(const std::string & name) {
-    for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
-        if (kv.second == name) {
-            return kv.first;
-        }
-    }
-
-    return LLM_ARCH_UNKNOWN;
-}
-
-// helper to handle gguf constants
-// usage:
-//
-//   const auto tn = LLM_TN(LLM_ARCH_LLAMA);
-//
-//   std::string name = tn(LLM_TENSOR_OUTPUT);                     -> "output"
-//   std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias");         -> "token_embd.bias"
-//   std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3);     -> "blk.3.attn_norm.weight"
-//
-struct LLM_TN_IMPL {
-    const llm_arch arch;
-    const llm_tensor tensor;
-    const char * const suffix;
-    const int bid;
-    const int xid;
-
-    std::string str() const {
-        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
-            return "__missing__";
-        }
-
-        std::string name = ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid);
-
-        if (suffix != nullptr) {
-            name += ".";
-            name += suffix;
-        }
-
-        return name;
-    }
-
-    operator std::string() const {
-        return str();
-    }
-
-    friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) {
-        return str == tn.str();
-    }
-
-    friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) {
-        return str != tn.str();
-    }
-};
-
-struct LLM_TN {
-    LLM_TN(llm_arch arch) : arch(arch) {}
-
-    llm_arch arch;
-
-    LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
-        return { arch, tensor, suffix, bid, xid };
-    }
-
-    LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
-        return { arch, tensor, nullptr, bid, xid };
-    }
-};
-
-//
-// gguf helpers
-//
-
-static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
-    { LLAMA_ROPE_SCALING_TYPE_NONE,       "none"       },
-    { LLAMA_ROPE_SCALING_TYPE_LINEAR,     "linear"     },
-    { LLAMA_ROPE_SCALING_TYPE_YARN,       "yarn"       },
-    { LLAMA_ROPE_SCALING_TYPE_LONGROPE,   "longrope"   },
-};
-
-static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
-    for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
-        if (kv.second == name) {
-            return (llama_rope_scaling_type) kv.first;
-        }
-    }
-
-    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-}
-
-static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
-    switch (type) {
-        case GGUF_TYPE_UINT8:   return std::to_string(((const uint8_t  *)data)[i]);
-        case GGUF_TYPE_INT8:    return std::to_string(((const int8_t   *)data)[i]);
-        case GGUF_TYPE_UINT16:  return std::to_string(((const uint16_t *)data)[i]);
-        case GGUF_TYPE_INT16:   return std::to_string(((const int16_t  *)data)[i]);
-        case GGUF_TYPE_UINT32:  return std::to_string(((const uint32_t *)data)[i]);
-        case GGUF_TYPE_INT32:   return std::to_string(((const int32_t  *)data)[i]);
-        case GGUF_TYPE_UINT64:  return std::to_string(((const uint64_t *)data)[i]);
-        case GGUF_TYPE_INT64:   return std::to_string(((const int64_t  *)data)[i]);
-        case GGUF_TYPE_FLOAT32: return std::to_string(((const float    *)data)[i]);
-        case GGUF_TYPE_FLOAT64: return std::to_string(((const double   *)data)[i]);
-        case GGUF_TYPE_BOOL:    return ((const bool *)data)[i] ? "true" : "false";
-        default:                return format("unknown type %d", type);
-    }
-}
-
-static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
-    const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
-
-    switch (type) {
-        case GGUF_TYPE_STRING:
-            return gguf_get_val_str(ctx_gguf, i);
-        case GGUF_TYPE_ARRAY:
-            {
-                const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
-                int arr_n = gguf_get_arr_n(ctx_gguf, i);
-                const void * data = gguf_get_arr_data(ctx_gguf, i);
-                std::stringstream ss;
-                ss << "[";
-                for (int j = 0; j < arr_n; j++) {
-                    if (arr_type == GGUF_TYPE_STRING) {
-                        std::string val = gguf_get_arr_str(ctx_gguf, i, j);
-                        // escape quotes
-                        replace_all(val, "\\", "\\\\");
-                        replace_all(val, "\"", "\\\"");
-                        ss << '"' << val << '"';
-                    } else if (arr_type == GGUF_TYPE_ARRAY) {
-                        ss << "???";
-                    } else {
-                        ss << gguf_data_to_str(arr_type, data, j);
-                    }
-                    if (j < arr_n - 1) {
-                        ss << ", ";
-                    }
-                }
-                ss << "]";
-                return ss.str();
-            }
-        default:
-            return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
-    }
-}
-
-//
-// llama helpers
-//
-
-#if defined(_WIN32)
-static std::string llama_format_win_err(DWORD err) {
-    LPSTR buf;
-    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
-                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
-    if (!size) {
-        return "FormatMessageA failed";
-    }
-    std::string ret(buf, size);
-    LocalFree(buf);
-    return ret;
-}
-#endif
-
-template <typename T>
-struct no_init {
-    T value;
-    no_init() { /* do nothing */ }
-};
-
-struct llama_file {
-
-#if defined(_WIN32)
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    HANDLE fp_win32;
-    size_t size;
-
-private:
-    std::string GetErrorMessageWin32(DWORD error_code) const {
-        std::string ret;
-        LPSTR lpMsgBuf = NULL;
-        DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
-                                    NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
-        if (!bufLen) {
-            ret = format("Win32 error code: %lx", error_code);
-        } else {
-            ret = lpMsgBuf;
-            LocalFree(lpMsgBuf);
-        }
-
-        return ret;
-    }
-
-public:
-
-    llama_file(const char * fname, const char * mode) {
-        fp = ggml_fopen(fname, mode);
-        if (fp == NULL) {
-            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
-        }
-        fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
-        seek(0, SEEK_END);
-        size = tell();
-        seek(0, SEEK_SET);
-    }
-
-    size_t tell() const {
-        // SetFilePointerEx returns the current position when seeking relative 0 bytes
-        LARGE_INTEGER li;
-        li.QuadPart = 0;
-        BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT);
-        if (!ret) {
-            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
-        }
-
-        return li.QuadPart;
-    }
-
-    void seek(size_t offset, int whence) const {
-        // no need to convert SEEK_* to FILE_*. The enums are the same.
-        // Still, keep static asserts to avoid failures in the future.
-        static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN");
-        static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT");
-        static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END");
-
-        LARGE_INTEGER li;
-        li.QuadPart = offset;
-        BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence);
-        if (!ret) {
-            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
-        }
-    }
-
-    void read_raw(void * ptr, size_t len) const {
-        // On Win32 ReadFile is significant faster than fread which is again significant faster than std::fstream. Thus
-        // use the Win32 API to do file io instead of the C/C++ library functions.
-
-        // There are conditions under which ReadFile cannot read chunks >64MB.
-        // Thus split the operation into smaller chunks if len exceeds this limit.
-        size_t bytes_read = 0;
-        while (bytes_read < len) {
-            size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
-            DWORD chunk_read = 0;
-            BOOL result = ReadFile(fp_win32, reinterpret_cast<char*>(ptr) + bytes_read, chunk_size, &chunk_read, NULL);
-            if (!result) {
-                throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
-            }
-            if (chunk_read < chunk_size || chunk_read == 0) {
-                throw std::runtime_error("unexpectedly reached end of file");
-            }
-
-            bytes_read += chunk_read;
-        } ;
-    }
-
-    uint32_t read_u32() const {
-        uint32_t val;
-        read_raw(&val, sizeof(val));
-        return val;
-    }
-
-    void write_raw(const void * ptr, size_t len) const {
-        // There are conditions under which WriteFile cannot write chunks >64MB.
-        // Thus split the operation into smaller chunks if len exceeds this limit.
-        size_t bytes_written = 0;
-        while (bytes_written < len) {
-            size_t chunk_size = std::min<size_t>(len - bytes_written, 64*1024*1024);
-            DWORD chunk_written = 0;
-            BOOL result = WriteFile(fp_win32, reinterpret_cast<char const*>(ptr) + bytes_written, chunk_size, &chunk_written, NULL);
-            if (!result) {
-                throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
-            }
-            if (chunk_written < chunk_size || chunk_written == 0) {
-                throw std::runtime_error("unexpectedly failed to write bytes");
-            }
-
-            bytes_written += chunk_written;
-        }
-    }
-
-    void write_u32(std::uint32_t val) const {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-#else
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = ggml_fopen(fname, mode);
-        if (fp == NULL) {
-            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
-        }
-        seek(0, SEEK_END);
-        size = tell();
-        seek(0, SEEK_SET);
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        if (ret == -1) {
-            throw std::runtime_error(format("ftell error: %s", strerror(errno)));
-        }
-
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) const {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        if (ret != 0) {
-            throw std::runtime_error(format("seek error: %s", strerror(errno)));
-        }
-    }
-
-    void read_raw(void * ptr, size_t len) const {
-        if (len == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, len, 1, fp);
-        if (ferror(fp)) {
-            throw std::runtime_error(format("read error: %s", strerror(errno)));
-        }
-        if (ret != 1) {
-            throw std::runtime_error("unexpectedly reached end of file");
-        }
-    }
-
-    uint32_t read_u32() const {
-        uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    void write_raw(const void * ptr, size_t len) const {
-        if (len == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, len, 1, fp);
-        if (ret != 1) {
-            throw std::runtime_error(format("write error: %s", strerror(errno)));
-        }
-    }
-
-    void write_u32(std::uint32_t val) const {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-#endif
-};
-using llama_files = std::vector<std::unique_ptr<llama_file>>;
-
-struct llama_mmap {
-    void * addr;
-    size_t size;
-
-    llama_mmap(const llama_mmap &) = delete;
-
-#ifdef _POSIX_MAPPED_FILES
-    static constexpr bool SUPPORTED = true;
-
-    // list of mapped fragments (first_offset, last_offset)
-    std::vector<std::pair<size_t, size_t>> mapped_fragments;
-
-    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
-        size = file->size;
-        int fd = fileno(file->fp);
-        int flags = MAP_SHARED;
-        // prefetch/readahead impairs performance on NUMA systems
-        if (numa)  { prefetch = 0; }
-#ifdef __linux__
-        // advise the kernel to read the file sequentially (increases readahead)
-        if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
-            LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
-                    strerror(errno));
-        }
-        if (prefetch) { flags |= MAP_POPULATE; }
-#endif
-        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
-        if (addr == MAP_FAILED) { // NOLINT
-            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
-        }
-
-        if (prefetch > 0) {
-            // advise the kernel to preload the mapped memory
-            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
-                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
-                        strerror(errno));
-            }
-        }
-        if (numa) {
-            // advise the kernel not to use readahead
-            // (because the next page might not belong on the same node)
-            if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
-                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
-                        strerror(errno));
-            }
-        }
-
-        // initialize list of mapped_fragments
-        mapped_fragments.emplace_back(0, file->size);
-    }
-
-    static void align_range(size_t * first, size_t * last, size_t page_size) {
-        // align first to the next page
-        size_t offset_in_page = *first & (page_size - 1);
-        size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
-        *first += offset_to_page;
-
-        // align last to the previous page
-        *last = *last & ~(page_size - 1);
-
-        if (*last <= *first) {
-            *last = *first;
-        }
-    }
-
-    // partially unmap the file in the range [first, last)
-    void unmap_fragment(size_t first, size_t last) {
-        // note: this function must not be called multiple times with overlapping ranges
-        // otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings
-        int page_size = sysconf(_SC_PAGESIZE);
-        align_range(&first, &last, page_size);
-        size_t len = last - first;
-
-        if (len == 0) {
-            return;
-        }
-
-        GGML_ASSERT(first % page_size == 0);
-        GGML_ASSERT(last % page_size == 0);
-        GGML_ASSERT(last > first);
-
-        void * next_page_start = (uint8_t *) addr + first;
-
-        // unmap the range
-        if (munmap(next_page_start, len)) {
-            LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
-        }
-
-        // update the list of mapped fragments to avoid unmapping the same range again in the destructor
-        std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
-        for (const auto & frag : mapped_fragments) {
-            if (frag.first < first && frag.second > last) {
-                // the range is in the middle of the fragment, split it
-                new_mapped_fragments.emplace_back(frag.first, first);
-                new_mapped_fragments.emplace_back(last, frag.second);
-            } else if (frag.first < first && frag.second > first) {
-                // the range starts in the middle of the fragment
-                new_mapped_fragments.emplace_back(frag.first, first);
-            } else if (frag.first < last && frag.second > last) {
-                // the range ends in the middle of the fragment
-                new_mapped_fragments.emplace_back(last, frag.second);
-            } else if (frag.first >= first && frag.second <= last) {
-                // the range covers the entire fragment
-            } else {
-                // the range is outside the fragment
-                new_mapped_fragments.push_back(frag);
-            }
-        }
-        mapped_fragments = std::move(new_mapped_fragments);
-    }
-
-    ~llama_mmap() {
-        for (const auto & frag : mapped_fragments) {
-            if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
-                LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
-            }
-        }
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
-        GGML_UNUSED(numa);
-
-        size = file->size;
-
-        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
-
-        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
-
-        if (hMapping == NULL) {
-            DWORD error = GetLastError();
-            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
-        }
-
-        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
-        DWORD error = GetLastError();
-        CloseHandle(hMapping);
-
-        if (addr == NULL) {
-            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
-        }
-
-        if (prefetch > 0) {
-#if _WIN32_WINNT >= 0x602
-            // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
-            BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
-            HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
-
-            // may fail on pre-Windows 8 systems
-            pPrefetchVirtualMemory = (decltype(pPrefetchVirtualMemory))(void *) GetProcAddress(hKernel32, "PrefetchVirtualMemory");
-
-            if (pPrefetchVirtualMemory) {
-                // advise the kernel to preload the mapped memory
-                WIN32_MEMORY_RANGE_ENTRY range;
-                range.VirtualAddress = addr;
-                range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
-                if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
-                    LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
-                            llama_format_win_err(GetLastError()).c_str());
-                }
-            }
-#else
-            throw std::runtime_error("PrefetchVirtualMemory unavailable");
-#endif
-        }
-    }
-
-    void unmap_fragment(size_t first, size_t last) {
-        // not supported
-        GGML_UNUSED(first);
-        GGML_UNUSED(last);
-    }
-
-    ~llama_mmap() {
-        if (!UnmapViewOfFile(addr)) {
-            LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
-        GGML_UNUSED(file);
-        GGML_UNUSED(prefetch);
-        GGML_UNUSED(numa);
-
-        throw std::runtime_error("mmap not supported");
-    }
-
-    void unmap_fragment(size_t first, size_t last) {
-        GGML_UNUSED(first);
-        GGML_UNUSED(last);
-
-        throw std::runtime_error("mmap not supported");
-    }
-#endif
-};
-using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
-
-// Represents some region of memory being locked using mlock or VirtualLock;
-// will automatically unlock on destruction.
-struct llama_mlock {
-    void * addr = NULL;
-    size_t size = 0;
-
-    bool failed_already = false;
-
-    llama_mlock() {}
-    llama_mlock(const llama_mlock &) = delete;
-
-    ~llama_mlock() {
-        if (size) {
-            raw_unlock(addr, size);
-        }
-    }
-
-    void init(void * ptr) {
-        GGML_ASSERT(addr == NULL && size == 0); // NOLINT
-        addr = ptr;
-    }
-
-    void grow_to(size_t target_size) {
-        GGML_ASSERT(addr);
-        if (failed_already) {
-            return;
-        }
-        size_t granularity = lock_granularity();
-        target_size = (target_size + granularity - 1) & ~(granularity - 1);
-        if (target_size > size) {
-            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
-                size = target_size;
-            } else {
-                failed_already = true;
-            }
-        }
-    }
-
-#ifdef _POSIX_MEMLOCK_RANGE
-    static constexpr bool SUPPORTED = true;
-
-    static size_t lock_granularity() {
-        return (size_t) sysconf(_SC_PAGESIZE);
-    }
-
-    #ifdef __APPLE__
-        #define MLOCK_SUGGESTION \
-            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
-            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
-    #else
-        #define MLOCK_SUGGESTION \
-            "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
-    #endif
-
-    bool raw_lock(const void * addr, size_t size) const {
-        if (!mlock(addr, size)) {
-            return true;
-        }
-
-        char* errmsg = std::strerror(errno);
-        bool suggest = (errno == ENOMEM);
-
-        // Check if the resource limit is fine after all
-        struct rlimit lock_limit;
-        if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
-            suggest = false;
-        }
-        if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
-            suggest = false;
-        }
-
-        LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
-                size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
-        return false;
-    }
-
-    #undef MLOCK_SUGGESTION
-
-    static void raw_unlock(void * addr, size_t size) {
-        if (munlock(addr, size)) {
-            LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
-        }
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    static size_t lock_granularity() {
-        SYSTEM_INFO si;
-        GetSystemInfo(&si);
-        return (size_t) si.dwPageSize;
-    }
-
-    bool raw_lock(void * ptr, size_t len) const {
-        for (int tries = 1; ; tries++) {
-            if (VirtualLock(ptr, len)) {
-                return true;
-            }
-            if (tries == 2) {
-                LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
-                    len, size, llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-
-            // It failed but this was only the first try; increase the working
-            // set size and try again.
-            SIZE_T min_ws_size, max_ws_size;
-            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
-                LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-            // Per MSDN: "The maximum number of pages that a process can lock
-            // is equal to the number of pages in its minimum working set minus
-            // a small overhead."
-            // Hopefully a megabyte is enough overhead:
-            size_t increment = len + 1048576;
-            // The minimum must be <= the maximum, so we need to increase both:
-            min_ws_size += increment;
-            max_ws_size += increment;
-            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
-                LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-        }
-    }
-
-    static void raw_unlock(void * ptr, size_t len) {
-        if (!VirtualUnlock(ptr, len)) {
-            LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    static size_t lock_granularity() {
-        return (size_t) 65536;
-    }
-
-    bool raw_lock(const void * addr, size_t len) const {
-        LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
-        return false;
-    }
-
-    static void raw_unlock(const void * addr, size_t len) {}
-#endif
-};
-using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
-
-// NOTE: avoid ever using this except for building the token_to_piece caches
-static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
-    std::string piece;
-    piece.resize(piece.capacity());  // using string internal cache
-    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
-    if (n_chars < 0) {
-        piece.resize(-n_chars);
-        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
-        GGML_ASSERT(check == -n_chars);
-    }
-    else {
-        piece.resize(n_chars);
-    }
-
-    return piece;
-}
-
-//
-// globals
-//
-
-struct llama_logger_state {
-    ggml_log_callback log_callback = llama_log_callback_default;
-    void * log_callback_user_data = nullptr;
-};
-
-static llama_logger_state g_logger_state;
-
-// available llama models
-enum e_model {
-    MODEL_UNKNOWN,
-    MODEL_14M,
-    MODEL_17M,
-    MODEL_22M,
-    MODEL_33M,
-    MODEL_60M,
-    MODEL_70M,
-    MODEL_80M,
-    MODEL_109M,
-    MODEL_137M,
-    MODEL_160M,
-    MODEL_220M,
-    MODEL_250M,
-    MODEL_270M,
-    MODEL_335M,
-    MODEL_410M,
-    MODEL_450M,
-    MODEL_770M,
-    MODEL_780M,
-    MODEL_0_5B,
-    MODEL_1B,
-    MODEL_1_3B,
-    MODEL_1_4B,
-    MODEL_1_5B,
-    MODEL_1_6B,
-    MODEL_2B,
-    MODEL_2_8B,
-    MODEL_3B,
-    MODEL_4B,
-    MODEL_6B,
-    MODEL_6_9B,
-    MODEL_7B,
-    MODEL_8B,
-    MODEL_9B,
-    MODEL_11B,
-    MODEL_12B,
-    MODEL_13B,
-    MODEL_14B,
-    MODEL_15B,
-    MODEL_16B,
-    MODEL_20B,
-    MODEL_30B,
-    MODEL_32B,
-    MODEL_34B,
-    MODEL_35B,
-    MODEL_40B,
-    MODEL_65B,
-    MODEL_70B,
-    MODEL_236B,
-    MODEL_314B,
-    MODEL_SMALL,
-    MODEL_MEDIUM,
-    MODEL_LARGE,
-    MODEL_XL,
-    MODEL_A1_7B,
-    MODEL_A2_7B,
-    MODEL_8x7B,
-    MODEL_8x22B,
-    MODEL_16x12B,
-    MODEL_10B_128x3_66B,
-    MODEL_57B_A14B,
-    MODEL_27B,
-};
-
-static const size_t kiB = 1024;
-static const size_t MiB = 1024*kiB;
-static const size_t GiB = 1024*MiB;
-
-struct llama_hparams_posnet {
-    uint32_t n_embd;
-    uint32_t n_layer;
-};
-
-struct llama_hparams_convnext {
-    uint32_t n_embd;
-    uint32_t n_layer;
-};
-
-struct llama_hparams {
-    bool vocab_only;
-    bool rope_finetuned;
-    bool use_par_res;
-    bool swin_norm;
-
-    uint32_t n_vocab = 0;
-    uint32_t n_ctx_train; // context size the model was trained on
-    uint32_t n_embd;
-    uint32_t n_embd_features = 0;
-    uint32_t n_layer;
-    uint32_t n_rot;
-    uint32_t n_swa = 0; // sliding window attention (SWA)
-    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
-    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
-    uint32_t n_expert = 0;
-    uint32_t n_expert_used = 0;
-    uint32_t n_vocab_type = 0; // for BERT-style token types
-    uint32_t n_rel_attn_bkts = 0;
-
-    // for WavTokenizer
-    struct llama_hparams_posnet   posnet;
-    struct llama_hparams_convnext convnext;
-
-    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
-    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
-    std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
-
-    uint32_t n_layer_dense_lead = 0;
-    uint32_t n_lora_q = 0;
-    uint32_t n_lora_kv = 0;
-    uint32_t n_ff_exp = 0;
-    uint32_t n_ff_shexp = 0;
-    uint32_t n_expert_shared = 0;
-    float    expert_weights_scale = 0.0;
-
-    float f_norm_eps;
-    float f_norm_rms_eps;
-    float f_norm_group_eps;
-
-    uint32_t n_norm_groups;
-
-    float f_attn_logit_softcapping = 50.0f;
-    float f_final_logit_softcapping = 30.0f;
-
-    // for RWKV
-    uint32_t rescale_every_n_layers = 0;
-    uint32_t time_mix_extra_dim = 0;
-    uint32_t time_decay_extra_dim = 0;
-    uint32_t wkv_head_size = 0;
-
-    float     rope_attn_factor = 1.0f;
-    float     rope_freq_base_train;
-    float     rope_freq_scale_train;
-    uint32_t  n_ctx_orig_yarn;
-    float     rope_yarn_log_mul;
-    int       rope_sections[4];
-
-    // for State Space Models
-    uint32_t ssm_d_conv  = 0;
-    uint32_t ssm_d_inner = 0;
-    uint32_t ssm_d_state = 0;
-    uint32_t ssm_dt_rank = 0;
-    bool ssm_dt_b_c_rms = false;
-
-    float f_clamp_kqv      = 0.0f;
-    float f_max_alibi_bias = 0.0f;
-    float f_logit_scale    = 0.0f;
-
-    // Additional scale factors (Granite/Granite MoE)
-    float f_residual_scale  = 0.0f;
-    float f_embedding_scale = 0.0f;
-    float f_attention_scale = 0.0f;
-
-    bool causal_attn   = true;
-    bool use_alibi     = false;
-    bool attn_soft_cap = false;
-
-    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
-    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
-    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
-
-    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
-    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
-    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
-
-    uint32_t n_head(uint32_t il = 0) const {
-        if (il < n_layer) {
-            return n_head_arr[il];
-        }
-
-        GGML_ABORT("fatal error");
-    }
-
-    uint32_t n_head_kv(uint32_t il = 0) const {
-        if (il < n_layer) {
-            return n_head_kv_arr[il];
-        }
-
-        GGML_ABORT("fatal error");
-    }
-
-    uint32_t n_ff(uint32_t il = 0) const {
-        if (il < n_layer) {
-            return n_ff_arr[il];
-        }
-
-        GGML_ABORT("fatal error");
-    }
-
-    uint32_t n_gqa(uint32_t il = 0) const {
-        const uint32_t n_head    = this->n_head(il);
-        const uint32_t n_head_kv = this->n_head_kv(il);
-
-        if (n_head_kv == 0) {
-            return 0;
-        }
-
-        return n_head/n_head_kv;
-    }
-
-    uint32_t n_embd_k_gqa(uint32_t il = 0) const { // dimension of key embeddings across all k-v heads
-        const uint32_t n_head_kv = this->n_head_kv(il);
-
-        return n_embd_head_k * n_head_kv;
-    }
-
-    uint32_t n_embd_v_gqa(uint32_t il = 0) const { // dimension of value embeddings across all k-v heads
-        const uint32_t n_head_kv = this->n_head_kv(il);
-
-        return n_embd_head_v * n_head_kv;
-    }
-
-    uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
-        // corresponds to Mamba's conv_states size or RWKV's token_shift states size
-        if (wkv_head_size != 0) {
-            // for RWKV models
-            return 2 * n_embd;
-        }
-
-        // TODO: maybe support other convolution strides than 1
-        // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
-        return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
-    }
-
-    uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
-        if (wkv_head_size != 0) {
-            // corresponds to RWKV's wkv_states size
-            return n_embd * wkv_head_size;
-        }
-
-        // corresponds to Mamba's ssm_states size
-        return ssm_d_state * ssm_d_inner;
-    }
-};
-
-static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
-
-struct llama_cparams {
-    uint32_t n_ctx;           // context size used during inference
-    uint32_t n_batch;
-    uint32_t n_ubatch;
-    uint32_t n_seq_max;
-    int      n_threads;       // number of threads to use for generation
-    int      n_threads_batch; // number of threads to use for batch processing
-
-    float rope_freq_base;
-    float rope_freq_scale;
-
-    uint32_t n_ctx_orig_yarn;
-    // These hyperparameters are not exposed in GGUF, because all
-    // existing YaRN models use the same values for them.
-    float yarn_ext_factor;
-    float yarn_attn_factor;
-    float yarn_beta_fast;
-    float yarn_beta_slow;
-    float defrag_thold;
-
-    bool embeddings;
-    bool causal_attn;
-    bool offload_kqv;
-    bool flash_attn;
-    bool no_perf;
-
-    enum llama_pooling_type pooling_type;
-
-    ggml_backend_sched_eval_callback cb_eval;
-    void * cb_eval_user_data;
-};
-
-struct llama_layer_posnet {
-    // resnet
-    struct ggml_tensor * norm1   = nullptr;
-    struct ggml_tensor * norm1_b = nullptr;
-
-    struct ggml_tensor * conv1   = nullptr;
-    struct ggml_tensor * conv1_b = nullptr;
-
-    struct ggml_tensor * norm2   = nullptr;
-    struct ggml_tensor * norm2_b = nullptr;
-
-    struct ggml_tensor * conv2   = nullptr;
-    struct ggml_tensor * conv2_b = nullptr;
-
-    // attention
-    struct ggml_tensor * attn_norm   = nullptr;
-    struct ggml_tensor * attn_norm_b = nullptr;
-
-    struct ggml_tensor * attn_q   = nullptr;
-    struct ggml_tensor * attn_q_b = nullptr;
-
-    struct ggml_tensor * attn_k   = nullptr;
-    struct ggml_tensor * attn_k_b = nullptr;
-
-    struct ggml_tensor * attn_v   = nullptr;
-    struct ggml_tensor * attn_v_b = nullptr;
-
-    struct ggml_tensor * attn_o   = nullptr;
-    struct ggml_tensor * attn_o_b = nullptr;
-
-    // normalize
-    struct ggml_tensor * norm   = nullptr;
-    struct ggml_tensor * norm_b = nullptr;
-};
-
-struct llama_layer_convnext {
-    struct ggml_tensor * dw   = nullptr;
-    struct ggml_tensor * dw_b = nullptr;
-
-    struct ggml_tensor * norm   = nullptr;
-    struct ggml_tensor * norm_b = nullptr;
-
-    struct ggml_tensor * pw1   = nullptr;
-    struct ggml_tensor * pw1_b = nullptr;
-
-    struct ggml_tensor * pw2   = nullptr;
-    struct ggml_tensor * pw2_b = nullptr;
-
-    struct ggml_tensor * gamma = nullptr;
-};
-
-struct llama_layer {
-    // normalization
-    struct ggml_tensor * attn_norm       = nullptr;
-    struct ggml_tensor * attn_norm_b     = nullptr;
-    struct ggml_tensor * attn_norm_2     = nullptr;
-    struct ggml_tensor * attn_norm_2_b   = nullptr;
-    struct ggml_tensor * attn_q_norm     = nullptr;
-    struct ggml_tensor * attn_q_norm_b   = nullptr;
-    struct ggml_tensor * attn_k_norm     = nullptr;
-    struct ggml_tensor * attn_k_norm_b   = nullptr;
-    struct ggml_tensor * attn_out_norm   = nullptr;
-    struct ggml_tensor * attn_out_norm_b = nullptr;
-    struct ggml_tensor * attn_q_a_norm   = nullptr;
-    struct ggml_tensor * attn_kv_a_norm  = nullptr;
-    struct ggml_tensor * attn_sub_norm   = nullptr;
-    struct ggml_tensor * attn_post_norm  = nullptr;
-    struct ggml_tensor * ffn_sub_norm    = nullptr;
-    struct ggml_tensor * attn_norm_cross = nullptr;
-    struct ggml_tensor * attn_norm_enc   = nullptr;
-
-    // attention
-    struct ggml_tensor * wq        = nullptr;
-    struct ggml_tensor * wk        = nullptr;
-    struct ggml_tensor * wv        = nullptr;
-    struct ggml_tensor * wo        = nullptr;
-    struct ggml_tensor * wqkv      = nullptr;
-    struct ggml_tensor * wq_a      = nullptr;
-    struct ggml_tensor * wq_b      = nullptr;
-    struct ggml_tensor * wkv_a_mqa = nullptr;
-    struct ggml_tensor * wkv_b     = nullptr;
-    struct ggml_tensor * wq_cross  = nullptr;
-    struct ggml_tensor * wk_cross  = nullptr;
-    struct ggml_tensor * wv_cross  = nullptr;
-    struct ggml_tensor * wo_cross  = nullptr;
-    struct ggml_tensor * wq_enc    = nullptr;
-    struct ggml_tensor * wk_enc    = nullptr;
-    struct ggml_tensor * wv_enc    = nullptr;
-    struct ggml_tensor * wo_enc    = nullptr;
-
-    // attention bias
-    struct ggml_tensor * bq   = nullptr;
-    struct ggml_tensor * bk   = nullptr;
-    struct ggml_tensor * bv   = nullptr;
-    struct ggml_tensor * bo   = nullptr;
-    struct ggml_tensor * bqkv = nullptr;
-
-    // relative position bias
-    struct ggml_tensor * attn_rel_b       = nullptr;
-    struct ggml_tensor * attn_rel_b_enc   = nullptr;
-    struct ggml_tensor * attn_rel_b_cross = nullptr;
-
-    // normalization
-    struct ggml_tensor * ffn_norm         = nullptr;
-    struct ggml_tensor * ffn_norm_b       = nullptr;
-    struct ggml_tensor * ffn_post_norm    = nullptr;
-    struct ggml_tensor * layer_out_norm   = nullptr;
-    struct ggml_tensor * layer_out_norm_b = nullptr;
-    struct ggml_tensor * ffn_norm_exps    = nullptr;
-    struct ggml_tensor * ffn_norm_enc     = nullptr;
-
-    // ff
-    struct ggml_tensor * ffn_gate     = nullptr; // w1
-    struct ggml_tensor * ffn_down     = nullptr; // w2
-    struct ggml_tensor * ffn_up       = nullptr; // w3
-    struct ggml_tensor * ffn_gate_enc = nullptr;
-    struct ggml_tensor * ffn_down_enc = nullptr;
-    struct ggml_tensor * ffn_up_enc   = nullptr;
-
-    // ff MoE
-    struct ggml_tensor * ffn_gate_inp  = nullptr;
-    struct ggml_tensor * ffn_gate_exps = nullptr;
-    struct ggml_tensor * ffn_down_exps = nullptr;
-    struct ggml_tensor * ffn_up_exps   = nullptr;
-
-    // ff shared expert (shexp)
-    struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
-    struct ggml_tensor * ffn_gate_shexp     = nullptr;
-    struct ggml_tensor * ffn_down_shexp     = nullptr;
-    struct ggml_tensor * ffn_up_shexp       = nullptr;
-
-    // ff bias
-    struct ggml_tensor * ffn_gate_b = nullptr;
-    struct ggml_tensor * ffn_down_b = nullptr; // b2
-    struct ggml_tensor * ffn_up_b   = nullptr; // b3
-    struct ggml_tensor * ffn_act    = nullptr;
-
-    // mamba proj
-    struct ggml_tensor * ssm_in  = nullptr;
-    struct ggml_tensor * ssm_x   = nullptr;
-    struct ggml_tensor * ssm_dt  = nullptr;
-    struct ggml_tensor * ssm_out = nullptr;
-
-    // mamba
-    struct ggml_tensor * ssm_conv1d = nullptr;
-    struct ggml_tensor * ssm_a      = nullptr;
-    struct ggml_tensor * ssm_d      = nullptr;
-
-    // mamba bias
-    struct ggml_tensor * ssm_conv1d_b = nullptr;
-    struct ggml_tensor * ssm_dt_b     = nullptr;
-
-    // rwkv
-    struct ggml_tensor * time_mix_w1         = nullptr;
-    struct ggml_tensor * time_mix_w2         = nullptr;
-    struct ggml_tensor * time_mix_lerp_x     = nullptr;
-    struct ggml_tensor * time_mix_lerp_w     = nullptr;
-    struct ggml_tensor * time_mix_lerp_k     = nullptr;
-    struct ggml_tensor * time_mix_lerp_v     = nullptr;
-    struct ggml_tensor * time_mix_lerp_r     = nullptr;
-    struct ggml_tensor * time_mix_lerp_g     = nullptr;
-
-    struct ggml_tensor * time_mix_first      = nullptr;
-    struct ggml_tensor * time_mix_decay      = nullptr;
-    struct ggml_tensor * time_mix_decay_w1   = nullptr;
-    struct ggml_tensor * time_mix_decay_w2   = nullptr;
-    struct ggml_tensor * time_mix_key        = nullptr;
-    struct ggml_tensor * time_mix_value      = nullptr;
-    struct ggml_tensor * time_mix_receptance = nullptr;
-    struct ggml_tensor * time_mix_gate       = nullptr;
-
-    struct ggml_tensor * time_mix_ln     = nullptr;
-    struct ggml_tensor * time_mix_ln_b   = nullptr;
-    struct ggml_tensor * time_mix_output = nullptr;
-
-    struct ggml_tensor * channel_mix_lerp_k = nullptr;
-    struct ggml_tensor * channel_mix_lerp_r = nullptr;
-
-    struct ggml_tensor * channel_mix_key        = nullptr;
-    struct ggml_tensor * channel_mix_receptance = nullptr;
-    struct ggml_tensor * channel_mix_value      = nullptr;
-
-    // long rope factors
-    struct ggml_tensor * rope_long  = nullptr;
-    struct ggml_tensor * rope_short = nullptr;
-    struct ggml_tensor * rope_freqs = nullptr;
-
-    // bitnet scale
-    struct ggml_tensor * wq_scale       = nullptr;
-    struct ggml_tensor * wk_scale       = nullptr;
-    struct ggml_tensor * wv_scale       = nullptr;
-    struct ggml_tensor * wo_scale       = nullptr;
-    struct ggml_tensor * ffn_gate_scale = nullptr;
-    struct ggml_tensor * ffn_up_scale   = nullptr;
-    struct ggml_tensor * ffn_down_scale = nullptr;
-
-    struct llama_layer_posnet posnet;
-
-    struct llama_layer_convnext convnext;
-};
-
-// very similar to llama_batch,
-// but has more metadata about sequences
-struct llama_ubatch {
-    bool equal_seqs;
-    // TODO: whole_seqs for embeddings?
-
-    uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
-    uint32_t n_seq_tokens; // tokens per sequence
-    uint32_t n_seqs;
-
-    llama_token  *  token;    // [n_tokens]
-    float        *  embd;     // [n_embd, n_tokens]
-    llama_pos    *  pos;      // [n_tokens]
-    int32_t      *  n_seq_id; // [n_seqs]
-    llama_seq_id ** seq_id;   // [n_seqs]
-    int8_t       *  output;   // [n_tokens]
-};
-
-struct llama_kv_cell {
-    llama_pos pos   = -1;
-    llama_pos delta = 0;
-    int32_t   src   = -1; // used by recurrent state models to copy states
-    int32_t   tail  = -1;
-
-    std::set<llama_seq_id> seq_id;
-
-    bool has_seq_id(const llama_seq_id & id) const {
-        return seq_id.find(id) != seq_id.end();
-    }
-
-    bool is_empty() const {
-        return seq_id.empty();
-    }
-
-    bool is_same_seq(const llama_kv_cell & other) const {
-        return seq_id == other.seq_id;
-    }
-};
-
-// ring-buffer of cached KV data
-struct llama_kv_cache {
-    bool has_shift = false;
-    bool do_defrag = false;
-    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
-    bool v_trans   = true;  // the value tensor is transposed
-
-    // Note: The value of head isn't only used to optimize searching
-    // for a free KV slot. llama_decode_internal also uses it, so it
-    // cannot be freely changed after a slot has been allocated.
-    uint32_t head = 0;
-    uint32_t size = 0;
-    uint32_t used = 0; // used cells (i.e. at least one seq_id)
-
-    // computed before each graph build
-    uint32_t n = 0;
-
-    ggml_type type_k = GGML_TYPE_F16;
-    ggml_type type_v = GGML_TYPE_F16;
-
-    std::vector<llama_kv_cell> cells;
-
-    std::vector<struct ggml_tensor *> k_l; // per layer
-    std::vector<struct ggml_tensor *> v_l;
-
-    std::vector<ggml_context_ptr> ctxs;
-    std::vector<ggml_backend_buffer_ptr> bufs;
-
-    size_t total_size() {
-        size_t size = 0;
-        for (auto & buf : bufs) {
-            size += ggml_backend_buffer_get_size(buf.get());
-        }
-        return size;
-    }
-};
-
-struct llama_control_vector {
-    std::vector<struct ggml_tensor *> tensors; // per layer
-    std::vector<ggml_context_ptr> ctxs;
-    std::vector<ggml_backend_buffer_ptr> bufs;
-
-    int32_t layer_start = -1;
-    int32_t layer_end   = -1;
-
-    struct ggml_tensor * tensor_for(int il) const {
-        if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
-            return nullptr;
-        }
-        return tensors[il];
-    }
-
-    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
-        ggml_tensor * layer_dir = tensor_for(il);
-        if (layer_dir != nullptr) {
-            cur = ggml_add(ctx, cur, layer_dir);
-        }
-        return cur;
-    }
-};
-
-struct llama_model {
-    e_model     type  = MODEL_UNKNOWN;
-    llm_arch    arch  = LLM_ARCH_UNKNOWN;
-    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
-    std::string name = "n/a";
-
-    llama_hparams hparams = {};
-    llama_vocab   vocab;
-
-    struct ggml_tensor * tok_embd = nullptr;
-    struct ggml_tensor * type_embd = nullptr;
-    struct ggml_tensor * pos_embd = nullptr;
-    struct ggml_tensor * tok_norm = nullptr;
-    struct ggml_tensor * tok_norm_b = nullptr;
-
-    struct ggml_tensor * output_norm = nullptr;
-    struct ggml_tensor * output_norm_b = nullptr;
-    struct ggml_tensor * output = nullptr;
-    struct ggml_tensor * output_b = nullptr;
-    struct ggml_tensor * output_norm_enc = nullptr;
-
-    // classifier
-    struct ggml_tensor * cls = nullptr;
-    struct ggml_tensor * cls_b = nullptr;
-    struct ggml_tensor * cls_out   = nullptr;
-    struct ggml_tensor * cls_out_b = nullptr;
-
-    struct ggml_tensor * conv1d = nullptr;
-    struct ggml_tensor * conv1d_b = nullptr;
-
-    std::vector<llama_layer> layers;
-
-    // gguf metadata
-    std::unordered_map<std::string, std::string> gguf_kv;
-
-    llama_split_mode split_mode;
-    int main_gpu;
-    int n_gpu_layers;
-
-    std::vector<std::string> rpc_servers;
-
-    // list of devices used in this model
-    std::vector<ggml_backend_dev_t> devices;
-
-
-    // lists of buffer types used for each layer
-    using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
-    buft_list_t cpu_buft_list;
-    std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
-
-    struct layer_dev {
-        ggml_backend_dev_t dev;
-        buft_list_t * buft_list;
-    };
-    layer_dev dev_input = {};
-    layer_dev dev_output = {};
-    std::vector<layer_dev> dev_layer;
-
-    // contexts where the model tensors metadata is stored
-    std::vector<ggml_context_ptr> ctxs;
-
-    // the model memory buffers for the tensor data
-    std::vector<ggml_backend_buffer_ptr> bufs;
-
-    // model memory mapped files
-    llama_mmaps mappings;
-
-    // objects representing data potentially being locked in memory
-    llama_mlocks mlock_bufs;
-    llama_mlocks mlock_mmaps;
-
-    // for quantize-stats only
-    std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
-
-    int64_t t_load_us  = 0;
-    int64_t t_start_us = 0;
-
-    // total number of parameters in the model
-    uint64_t n_elements = 0;
-
-    // total size of all the tensors in the model in bytes
-    size_t  n_bytes     = 0;
-
-    // keep track of loaded lora adapters
-    std::set<struct llama_lora_adapter *> lora_adapters;
-
-    ~llama_model() {
-       while (!lora_adapters.empty()) {
-            llama_lora_adapter_free(*lora_adapters.begin());
-        }
-    }
-};
-
-struct llama_sbatch_seq {
-    int32_t n_seq_id;
-    llama_seq_id * seq_id;
-    size_t offset;
-    size_t length;
-};
-
-// sequence-length-aware batch splitting
-struct llama_sbatch {
-    // tokens left in this batch
-    size_t n_tokens;
-
-    size_t n_embd;
-
-    bool logits_all; // TODO: remove once lctx.logits_all is removed too
-
-    // sorted indices into the batch
-    std::vector<size_t> ids;
-    // batch indices of the output
-    std::vector<size_t> out_ids;
-    std::vector<llama_sbatch_seq> seq;
-
-    const llama_batch * batch = nullptr;
-
-    // buffers for the ubatch
-    std::vector<llama_token>    ubatch_token;
-    std::vector<float>          ubatch_embd;
-    std::vector<llama_pos>      ubatch_pos;
-    std::vector<int32_t>        ubatch_n_seq_id;
-    std::vector<llama_seq_id *> ubatch_seq_id;
-    std::vector<int8_t>         ubatch_output;
-
-    llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false) {
-        // clear empty sequences
-        // the previous ubatch is assumed to be gone,
-        // so nothing should refer to values in these sequences anymore.
-        for (size_t i = seq.size(); i-- > 0;) {
-            if (seq[i].length == 0) {
-                seq.pop_back();
-            } else {
-                break;
-            }
-        }
-        ubatch_token.resize(!has_embd ? n_ubatch : 0);
-        ubatch_embd.resize(has_embd ? n_embd * n_ubatch : 0);
-        ubatch_pos.resize(n_ubatch);
-        ubatch_n_seq_id.resize(n_ubatch);
-        ubatch_seq_id.resize(n_ubatch);
-        ubatch_output.resize(n_ubatch);
-        llama_ubatch ubatch = {
-            /*equal_seqs   =*/ true,
-            /*n_tokens     =*/ 0,
-            /*n_seq_tokens =*/ 0,
-            /*n_seqs       =*/ 0,
-            /*token        =*/ !has_embd ? ubatch_token.data() : nullptr,
-            /*embd         =*/ has_embd  ? ubatch_embd.data()  : nullptr,
-            /*pos          =*/ ubatch_pos.data(),
-            /*n_seq_id     =*/ ubatch_n_seq_id.data(),
-            /*seq_id       =*/ ubatch_seq_id.data(),
-            /*output       =*/ ubatch_output.data(),
-        };
-        return ubatch;
-    }
-
-    void add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length) {
-        GGML_ASSERT(batch != nullptr);
-        GGML_ASSERT(length <= seq.length);
-        // Can only add sequences of equal lengths to a batch,
-        // otherwise it isn't clear to which sequence a token belongs
-        GGML_ASSERT(seq.n_seq_id == 0 || ubatch.n_seqs == 0 || length == (size_t) ubatch.n_tokens / ubatch.n_seqs);
-        GGML_ASSERT((seq.n_seq_id != 0) == ubatch.equal_seqs);
-        // NOTE: loops are separated for cache-friendliness
-        if (batch->token) {
-            if (ubatch.equal_seqs) {
-                for (size_t i = 0; i < length; ++i) {
-                    ubatch.token[ubatch.n_tokens + i] = batch->token[ids[seq.offset + i]];
-                }
-            } else {
-                // simple split
-                ubatch.token = batch->token + seq.offset;
-            }
-        } else {
-            ubatch.token = nullptr;
-        }
-        if (batch->embd) {
-            if (ubatch.equal_seqs) {
-                for (size_t i = 0; i < length; ++i) {
-                    memcpy(
-                        ubatch.embd + n_embd * (ubatch.n_tokens + i),
-                        batch->embd + n_embd * ids[seq.offset + i],
-                        n_embd * sizeof(float)
-                    );
-                }
-            } else {
-                // simple split
-                ubatch.embd = batch->embd + (n_embd * seq.offset);
-            }
-        } else {
-            ubatch.embd = nullptr;
-        }
-        if (ubatch.equal_seqs) {
-            for (size_t i = 0; i < length; ++i) {
-                ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]];
-            }
-        } else {
-            // simple split
-            ubatch.pos = batch->pos + seq.offset;
-        }
-        if (ubatch.equal_seqs) {
-            ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id;
-            if (seq.seq_id) {
-                ubatch.seq_id[ubatch.n_seqs] = seq.seq_id;
-            }
-        } else {
-            // simple split
-            if (batch->n_seq_id) {
-                ubatch.n_seq_id = batch->n_seq_id + seq.offset;
-            } else {
-                for (size_t i = 0; i < length; ++i) {
-                    ubatch.n_seq_id[ubatch.n_seqs + i] = 1;
-                }
-            }
-            if (batch->seq_id) {
-                ubatch.seq_id = batch->seq_id + seq.offset;
-            }
-        }
-        if (logits_all) {
-            for (size_t i = 0; i < length; ++i) {
-                ubatch.output[ubatch.n_tokens + i] = 1;
-                out_ids.push_back(ids[seq.offset + i]);
-            }
-        } else if (batch->logits) {
-            if (ubatch.equal_seqs) {
-                for (size_t i = 0; i < length; ++i) {
-                    size_t id = ids[seq.offset + i];
-                    int8_t is_output = batch->logits[id];
-                    ubatch.output[ubatch.n_tokens + i] = is_output;
-                    if (is_output) { out_ids.push_back(id); }
-                }
-            } else {
-                // simple split
-                ubatch.output = batch->logits + seq.offset;
-                for (size_t i = 0; i < length; ++i) {
-                    if (ubatch.output[i] != 0) { out_ids.push_back(seq.offset + i); }
-                }
-            }
-        } else {
-            // only get last output
-            for (size_t i = 0; i < length; ++i) {
-                size_t id = ids[seq.offset + i];
-                int8_t is_last = id == ids.size() - 1;
-                ubatch.output[ubatch.n_tokens + i] = is_last;
-                if (is_last) { out_ids.push_back(id); }
-            }
-        }
-        if (ubatch.n_tokens == 0 && ubatch.n_seqs == 0) {
-            ubatch.n_seq_tokens = ubatch.equal_seqs ? length : 1;
-        }
-        ubatch.n_tokens += length;
-        ubatch.n_seqs += ubatch.equal_seqs ? 1 : length; // virtual sequences for simple splits
-        seq.offset += length;
-        seq.length -= length;
-        n_tokens -= length;
-        GGML_ASSERT(ubatch.n_tokens == ubatch.n_seq_tokens * ubatch.n_seqs);
-    }
-
-    // simple split, unknown number of sequences of unequal lengths
-    llama_ubatch split_simple(size_t n_ubatch) {
-        n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
-        llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
-        ubatch.equal_seqs = false;
-        if (!seq.empty()) {
-            llama_sbatch_seq & s = seq[0];
-            size_t length = s.length < n_ubatch ? s.length : n_ubatch;
-            GGML_ASSERT(seq.size() == 1 && s.n_seq_id == 0); // don't mix with other splits
-            add_seq_to_ubatch(ubatch, s, length);
-        }
-        return ubatch;
-    }
-
-    // make batches of equal-length sequences
-    llama_ubatch split_equal(size_t n_ubatch) {
-        n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
-        llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
-        if (!seq.empty()) {
-            size_t length = 0;
-            size_t n_tokens_in_ubatch = 0;
-            GGML_ASSERT(seq[0].n_seq_id > 0); // should not be mixed with simple splits
-            // smallest first, because it's easier to split this way;
-            // starting from the end to pop in constant time.
-            for (size_t i = seq.size(); i-- > 0;) {
-                llama_sbatch_seq & s = seq[i];
-                GGML_ASSERT(s.length > 0);
-                if (length == 0) {
-                    length = s.length < n_ubatch ? s.length : n_ubatch;
-                }
-                add_seq_to_ubatch(ubatch, s, length);
-                n_tokens_in_ubatch += length;
-                // shared prompts can't be mixed with any of their sequences,
-                // so it's safer to compute them in their own ubatch
-                if (s.n_seq_id > 1) { break; }
-                // stop when there isn't enough space for another sequence
-                if (length + n_tokens_in_ubatch > n_ubatch) { break; }
-            }
-        }
-        return ubatch;
-    }
-
-    // sequence-wise split
-    llama_ubatch split_seq(size_t n_ubatch) {
-        n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch;
-        llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr);
-        if (!seq.empty()) {
-            llama_sbatch_seq & s = seq[seq.size() - 1];
-            size_t length = s.length < n_ubatch ? s.length : n_ubatch;
-            GGML_ASSERT(s.n_seq_id > 0); // should not be mixed with simple splits
-            add_seq_to_ubatch(ubatch, s, length);
-        }
-        return ubatch;
-    }
-
-    void from_batch(const llama_batch & batch, const size_t n_embd, const bool simple_split = false, const bool logits_all = false) {
-        GGML_ASSERT(batch.n_tokens >= 0);
-        this->batch = &batch;
-        this->n_embd = n_embd;
-        this->logits_all = logits_all;
-
-        n_tokens = batch.n_tokens;
-        ids.resize(n_tokens);
-        out_ids.clear();
-        // TODO: reserve out_ids and seq
-
-        for (size_t i = 0; i < n_tokens; ++i) {
-            ids[i] = i;
-        }
-        if (simple_split) {
-            seq.resize(1);
-            llama_sbatch_seq & s = seq[0];
-            s.n_seq_id = 0;
-            s.seq_id = nullptr;
-            s.offset = 0;
-            s.length = n_tokens;
-            return;
-        }
-        std::sort(ids.begin(), ids.end(),
-            [&batch](size_t a, size_t b) {
-                int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1;
-                int32_t n_seq_b = batch.n_seq_id ? batch.n_seq_id[b] : 1;
-                // sort by seq_id, then by pos
-                if (n_seq_a == n_seq_b) {
-                    if (batch.seq_id) {
-                        for (int32_t i = 0; i < n_seq_a; ++i) {
-                            llama_seq_id seq_id_a = batch.seq_id[a][i];
-                            llama_seq_id seq_id_b = batch.seq_id[b][i];
-                            // smaller seq_ids go first
-                            if (seq_id_a != seq_id_b) {
-                                return seq_id_a < seq_id_b;
-                            }
-                        }
-                    }
-                    // when all else is equal, sort by pos
-                    if (batch.pos) {
-                        return batch.pos[a] < batch.pos[b];
-                    }
-                    // no pos, sort by id
-                    return a < b;
-                }
-                // shared prompts go first
-                return n_seq_a > n_seq_b;
-            }
-        );
-        // init seq
-        llama_sbatch_seq * last_seq = nullptr;
-
-        for (size_t i = 0; i < n_tokens; ++i) {
-            const size_t bi = ids[i];
-            const int32_t n_seqs = batch.n_seq_id[bi];
-            llama_seq_id * seq_ids = batch.seq_id[bi];
-            if (last_seq != nullptr) {
-                bool same = n_seqs == last_seq->n_seq_id;
-                for (int32_t j = 0; same && j < n_seqs; ++j) {
-                    if (seq_ids[j] != last_seq->seq_id[j]) {
-                        same = false;
-                    }
-                }
-                if (same) {
-                    last_seq->length += 1;
-                    continue;
-                }
-            }
-            llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1};
-            seq.push_back(new_seq);
-            last_seq = &seq.back();
-        }
-        // keep shared prompts first at the end, then sort by length descending.
-        std::sort(seq.begin(), seq.end(),
-            [](llama_sbatch_seq & a, llama_sbatch_seq & b) {
-                if (a.n_seq_id == b.n_seq_id) {
-                    return a.length > b.length;
-                }
-                return a.n_seq_id < b.n_seq_id;
-            }
-        );
-    }
-};
-
-struct llama_context {
-    llama_context(const llama_model & model)
-        : model(model)
-        , t_start_us(model.t_start_us)
-        , t_load_us(model.t_load_us) {}
-
-    const struct llama_model & model;
-
-    struct llama_cparams        cparams;
-    struct llama_sbatch         sbatch;
-    struct llama_kv_cache       kv_self;
-    struct llama_control_vector cvec;
-
-    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
-
-    std::vector<ggml_backend_ptr> backends;
-    std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
-
-    ggml_backend_t backend_cpu = nullptr;
-
-    ggml_threadpool_t threadpool       = nullptr;
-    ggml_threadpool_t threadpool_batch = nullptr;
-
-    bool has_evaluated_once = false;
-
-    mutable int64_t t_start_us;
-    mutable int64_t t_load_us;
-    mutable int64_t t_p_eval_us = 0;
-    mutable int64_t t_eval_us   = 0;
-
-    mutable int64_t t_compute_start_us = 0;
-    mutable int64_t n_queued_tokens = 0;
-
-    mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
-    mutable int32_t n_eval   = 0; // number of eval calls
-
-    // host buffer for the model output (logits and embeddings)
-    ggml_backend_buffer_ptr buf_output;
-
-    // decode output (2-dimensional array: [n_outputs][n_vocab])
-    size_t  logits_size = 0; // capacity (of floats) for logits
-    float * logits      = nullptr;
-
-    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
-    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
-    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch or last logical batch
-
-    bool logits_all = false;
-
-    // embeddings output (2-dimensional array: [n_outputs][n_embd])
-    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
-    size_t  embd_size = 0; // capacity (of floats) for embeddings
-    float * embd      = nullptr;
-
-    // sequence embeddings output (map of [n_embd] vectors)
-    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
-    std::map<llama_seq_id, std::vector<float>> embd_seq;
-
-    // whether we are computing encoder output or decoder output
-    bool is_encoding = false;
-
-    // TODO: find a better way to accommodate mutli-dimension position encoding methods
-    // number of position id each token get, 1 for each token in most cases.
-    // when using m-rope, it will be 3 position ids per token to representing 3 dimension coordinate.
-    int n_pos_per_token = 1;
-
-    // output of the encoder part of the encoder-decoder models
-    std::vector<float> embd_enc;
-    std::vector<std::set<llama_seq_id>> seq_ids_enc;
-
-    // memory buffers used to evaluate the model
-    std::vector<uint8_t> buf_compute_meta;
-    ggml_backend_sched_ptr sched;
-
-    ggml_abort_callback abort_callback      = nullptr;
-    void *              abort_callback_data = nullptr;
-
-    // input tensors
-    struct ggml_tensor * inp_tokens;      // I32 [n_batch]
-    struct ggml_tensor * inp_embd;        // F32 [n_embd, n_batch]
-    struct ggml_tensor * inp_pos;         // I32 [n_batch]
-    struct ggml_tensor * inp_out_ids;     // I32 [n_outputs]
-    struct ggml_tensor * inp_KQ_mask;     // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_K_shift;     // I32 [kv_size]
-    struct ggml_tensor * inp_mean;        // F32 [n_batch, n_batch]
-    struct ggml_tensor * inp_cls;         // I32 [n_batch]
-    struct ggml_tensor * inp_s_copy;      // I32 [kv_size]
-    struct ggml_tensor * inp_s_mask;      // F32 [1, n_kv]
-    struct ggml_tensor * inp_s_seq;       // I32 [n_kv, n_batch]
-    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
-    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
-    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
-};
-
-struct llama_lora_weight {
-    struct ggml_tensor * a = nullptr;
-    struct ggml_tensor * b = nullptr;
-    llama_lora_weight() = default;
-    llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
-};
-
-struct llama_lora_adapter {
-    struct llama_model * base_model;
-    // map tensor name to lora_a_b
-    std::unordered_map<std::string, struct llama_lora_weight> ab_map;
-    std::vector<ggml_context_ptr> ctxs;
-    std::vector<ggml_backend_buffer_ptr> bufs;
-
-    float alpha;
-
-    llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
-        base_model->lora_adapters.insert(this);
-    }
-
-    llama_lora_weight * get_weight(struct ggml_tensor * w) {
-        std::string name(w->name);
-        auto pos = ab_map.find(name);
-        if (ab_map.find(name) != ab_map.end()) {
-            return &pos->second;
-        }
-        return nullptr;
-    }
-
-    ~llama_lora_adapter() {
-        auto pos = base_model->lora_adapters.find(this);
-        if (pos != base_model->lora_adapters.end()) {
-            base_model->lora_adapters.erase(pos);
-        }
-    }
-};
-
 static int llama_get_device_count(const llama_model & model) {
     return (int) model.devices.size();
 }
 
-static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) {
-    auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
-            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
-                return it.first == name;
-            });
-    if (it == model->tensors_by_name.end()) {
-        return nullptr;
-    }
-    return it->second;
-}
-
-template<typename F>
-static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*8,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-    ggml_context_ptr ctx { ggml_init(params) };
-    if (!ctx) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-
-    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
-    ggml_tensor * op_tensor = fn(ctx.get());
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op_tensor->src[i] != nullptr) {
-            assert(op_tensor->src[i]->buffer == nullptr);
-            op_tensor->src[i]->buffer = buf.get();
-        }
-    }
-    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
-
-    return op_supported;
-}
-
-template<typename F>
-static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
-    for (const auto & cur : buft_list) {
-        ggml_backend_dev_t cur_dev = cur.first;
-        ggml_backend_buffer_type_t cur_buft = cur.second;
-        if (buft_supported(cur_buft, cur_dev, fn)) {
-            return cur_buft;
-        }
-    }
-    throw std::runtime_error(format("no suitable buffer type found"));
-}
-
-//
-// kv cache helpers
-//
-
-static bool llama_kv_cache_init(
-             struct llama_kv_cache & cache,
-               const llama_context * ctx,
-                         ggml_type   type_k,
-                         ggml_type   type_v,
-                          uint32_t   kv_size,
-                              bool   offload) {
-    const llama_model & model = ctx->model;
-    const llama_cparams & cparams = ctx->cparams;
-
-    const struct llama_hparams & hparams = model.hparams;
-
-    const int32_t n_layer = hparams.n_layer;
-
-    LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d\n", __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
-
-    cache.has_shift = false;
-
-    cache.recurrent = llama_model_is_recurrent(&model);
-    cache.v_trans   = !cache.recurrent && !cparams.flash_attn;
-
-    cache.head = 0;
-    cache.size = kv_size;
-    cache.used = 0;
-
-    cache.type_k = type_k;
-    cache.type_v = type_v;
-
-    cache.cells.clear();
-    cache.cells.resize(kv_size);
-
-    // create a context for each buffer type
-    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
-    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
-        auto it = ctx_map.find(buft);
-        if (it == ctx_map.end()) {
-            struct ggml_init_params params = {
-                /*.mem_size   =*/ size_t(2u*n_layer*ggml_tensor_overhead()),
-                /*.mem_buffer =*/ NULL,
-                /*.no_alloc   =*/ true,
-            };
-            ggml_context * ctx = ggml_init(params);
-            if (!ctx) {
-                return nullptr;
-            }
-            ctx_map[buft] = ctx;
-            cache.ctxs.emplace_back(ctx);
-            return ctx;
-        }
-        return it->second;
-    };
-
-    cache.k_l.reserve(n_layer);
-    cache.v_l.reserve(n_layer);
-
-    for (int i = 0; i < n_layer; i++) {
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
-
-        LLAMA_LOG_DEBUG("%s: layer %d: n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", __func__, i, n_embd_k_gqa, n_embd_v_gqa);
-
-        ggml_backend_buffer_type_t buft;
-        if (offload) {
-            auto * dev = model.dev_layer.at(i).dev;
-            buft = ggml_backend_dev_buffer_type(dev);
-        } else {
-            buft = ggml_backend_cpu_buffer_type();
-        }
-        ggml_context * ctx = ctx_for_buft(buft);
-
-        if (!ctx) {
-            LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
-            return false;
-        }
-
-        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
-        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
-        ggml_format_name(k, "cache_k_l%d", i);
-        ggml_format_name(v, "cache_v_l%d", i);
-        cache.k_l.push_back(k);
-        cache.v_l.push_back(v);
-    }
-
-    // allocate tensors and initialize the buffers to avoid NaNs in the padding
-    for (auto it : ctx_map) {
-        auto * buft = it.first;
-        auto * ctx  = it.second;
-
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
-        if (!buf) {
-            LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
-            return false;
-        }
-        ggml_backend_buffer_clear(buf, 0);
-        LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
-        cache.bufs.emplace_back(buf);
-    }
-
-    return true;
-}
-
-// a structure holds information about the slot found in llama_kv_cache_find_slot
-struct llama_kv_cache_slot_info {
-    std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
-    bool found = false;                       // the slot was found
-
-    explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
-    llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
-
-    operator bool() const { return found; }
-};
-static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
-
-// find an empty slot of size "n_tokens" in the cache
-// updates the cache head
-// returns a structure holding information about the slot found
-// Note: On success, it's important that cache.head points
-// to the first cell of the slot.
-static struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
-           struct llama_kv_cache & cache,
-       const struct llama_ubatch & batch) {
-    const uint32_t n_tokens = batch.n_tokens;
-    const uint32_t n_seqs   = batch.n_seqs;
-    const uint32_t n_seq_tokens = batch.n_seq_tokens;
-
-    if (cache.recurrent) {
-        // For recurrent state architectures (like Mamba or RWKV),
-        // each cache cell can store the state for a whole sequence.
-        // A slot should be always be contiguous.
-
-        // can only process batches with an equal number of new tokens in each sequence
-        GGML_ASSERT(batch.equal_seqs);
-
-        int32_t min = cache.size - 1;
-        int32_t max = 0;
-
-        // everything should fit if all seq_ids are smaller than the max
-        for (uint32_t s = 0; s < n_seqs; ++s) {
-            const uint32_t n_seq_id = batch.n_seq_id[s];
-            for (uint32_t j = 0; j < n_seq_id; ++j) {
-                const llama_seq_id seq_id = batch.seq_id[s][j];
-
-                if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
-                    // too big seq_id
-                    // TODO: would it be possible to resize the cache instead?
-                    LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
-                    return llama_kv_cache_slot_info_failed;
-                }
-                if (j > 0) {
-                    llama_kv_cell & seq = cache.cells[seq_id];
-                    if (seq.tail >= 0) {
-                        llama_kv_cell & cell = cache.cells[seq.tail];
-                        // clear cells from seq_ids that become shared
-                        // (should not normally happen, but let's handle it anyway)
-                        cell.seq_id.erase(seq_id);
-                        seq.tail = -1;
-                        if (cell.seq_id.empty()) {
-                            cell.pos = -1;
-                            cell.src = -1;
-                            cache.used -= 1;
-                        }
-                    }
-                }
-            }
-        }
-
-#ifndef NDEBUG
-        {
-            std::vector<int32_t> tails_verif;
-            tails_verif.assign(cache.size, -1);
-            for (uint32_t i = 0; i < cache.size; ++i) {
-                llama_kv_cell & cell = cache.cells[i];
-                for (llama_seq_id seq_id : cell.seq_id) {
-                    if (tails_verif[seq_id] != -1) {
-                        LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
-                    }
-                    tails_verif[seq_id] = i;
-                }
-            }
-            for (uint32_t i = 0; i < cache.size; ++i) {
-                if (tails_verif[i] != cache.cells[i].tail) {
-                    LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cache.cells[i].tail, tails_verif[i]);
-                }
-            }
-        }
-#endif
-
-        // find next empty cell
-        uint32_t next_empty_cell = cache.head;
-
-        for (uint32_t i = 0; i < cache.size; ++i) {
-            if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
-            llama_kv_cell & cell = cache.cells[next_empty_cell];
-            if (cell.is_empty()) { break; }
-            next_empty_cell += 1;
-        }
-
-        // find usable cell range
-        for (uint32_t s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = batch.seq_id[s][0];
-            llama_kv_cell & seq_meta = cache.cells[seq_id];
-            bool has_cell = false;
-            if (seq_meta.tail >= 0) {
-                llama_kv_cell & cell = cache.cells[seq_meta.tail];
-                GGML_ASSERT(cell.has_seq_id(seq_id));
-                // does this seq_id "own" the cell?
-                if (cell.seq_id.size() == 1) { has_cell = true; }
-            }
-            if (!has_cell) {
-                llama_kv_cell & empty_cell = cache.cells[next_empty_cell];
-                GGML_ASSERT(empty_cell.is_empty());
-                // copy old tail into the empty cell
-                if (seq_meta.tail >= 0) {
-                    llama_kv_cell & orig_cell = cache.cells[seq_meta.tail];
-                    empty_cell.pos = orig_cell.pos;
-                    empty_cell.src = orig_cell.src;
-                    orig_cell.seq_id.erase(seq_id);
-                    empty_cell.seq_id.insert(seq_id); // will be overwritten
-                }
-                seq_meta.tail = next_empty_cell;
-                // find next empty cell
-                if (s + 1 < n_seqs) {
-                    next_empty_cell += 1;
-                    for (uint32_t i = 0; i < cache.size; ++i) {
-                        if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
-                        llama_kv_cell & cell = cache.cells[next_empty_cell];
-                        if (cell.is_empty()) { break; }
-                        next_empty_cell += 1;
-                    }
-                }
-            }
-            if (min > seq_meta.tail) { min = seq_meta.tail; }
-            if (max < seq_meta.tail) { max = seq_meta.tail; }
-        }
-
-        // gather and re-order
-        for (uint32_t s = 0; s < n_seqs; ++s) {
-            int32_t dst_id = s + min;
-            int32_t src_id = cache.cells[batch.seq_id[s][0]].tail;
-            if (dst_id != src_id) {
-                llama_kv_cell & dst_cell = cache.cells[dst_id];
-                llama_kv_cell & src_cell = cache.cells[src_id];
-
-                std::swap(dst_cell.pos, src_cell.pos);
-                std::swap(dst_cell.src, src_cell.src);
-                std::swap(dst_cell.seq_id, src_cell.seq_id);
-
-                // swap tails (assuming they NEVER overlap)
-                for (const llama_seq_id seq_id : src_cell.seq_id) {
-                    cache.cells[seq_id].tail = src_id;
-                }
-                for (const llama_seq_id seq_id : dst_cell.seq_id) {
-                    cache.cells[seq_id].tail = dst_id;
-                }
-            }
-        }
-
-        // update the pos of the used seqs
-        for (uint32_t s = 0; s < n_seqs; ++s) {
-            const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1];
-            int32_t cell_id = s + min;
-            llama_kv_cell & cell = cache.cells[cell_id];
-
-            if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
-                // What should happen when the pos backtracks or skips a value?
-                // Clearing the state mid-batch would require special-casing which isn't done.
-                LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
-                    __func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens);
-            }
-            cell.pos = last_pos;
-            cell.seq_id.clear();
-            for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) {
-                const llama_seq_id seq_id = batch.seq_id[s][j];
-                cell.seq_id.insert(seq_id);
-                cache.cells[seq_id].tail = cell_id;
-            }
-        }
-
-        // allow getting the range of used cells, from head to head + n
-        cache.head = min;
-        cache.n    = max - min + 1;
-        cache.used = std::count_if(cache.cells.begin(), cache.cells.end(),
-            [](const llama_kv_cell& cell){ return !cell.is_empty(); });
-
-        // sanity check
-        return llama_kv_cache_slot_info(cache.n >= n_seqs);
-    }
-    // otherwise, one cell per token.
-
-    if (n_tokens > cache.size) {
-        LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
-        return llama_kv_cache_slot_info_failed;
-    }
-
-    uint32_t n_tested = 0;
-
-    while (true) {
-        if (cache.head + n_tokens > cache.size) {
-            n_tested += cache.size - cache.head;
-            cache.head = 0;
-            continue;
-        }
-
-        bool found = true;
-        for (uint32_t i = 0; i < n_tokens; i++) {
-            if (cache.cells[cache.head + i].pos >= 0) {
-                found = false;
-                cache.head += i + 1;
-                n_tested   += i + 1;
-                break;
-            }
-        }
-
-        if (found) {
-            break;
-        }
-
-        if (n_tested >= cache.size) {
-            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
-            return llama_kv_cache_slot_info_failed;
-        }
-    }
-
-    for (uint32_t s = 0; s < n_seqs; s++) {
-        for (uint32_t i = 0; i < n_seq_tokens; ++i) {
-            uint32_t k = s*n_seq_tokens + i;
-            cache.cells[cache.head + k].pos = batch.pos[k];
-
-            for (int32_t j = 0; j < batch.n_seq_id[s]; j++) {
-                cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]);
-            }
-        }
-    }
-
-    cache.used += n_tokens;
-
-    return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens);
-}
-
-// find how many cells are currently in use
-static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
-    for (uint32_t i = cache.size; i > 0; --i) {
-        const llama_kv_cell & cell = cache.cells[i - 1];
-
-        if (cell.pos >= 0 && !cell.is_empty()) {
-            return i;
-        }
-    }
-
-    return 0;
-}
-
-static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
-    for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
-        cache.cells[i].pos = -1;
-        cache.cells[i].seq_id.clear();
-        cache.cells[i].src = -1;
-        cache.cells[i].tail = -1;
-    }
-    cache.head = 0;
-    cache.used = 0;
-
-    for (auto & buf : cache.bufs) {
-        ggml_backend_buffer_clear(buf.get(), 0);
-    }
-}
-
-static bool llama_kv_cache_seq_rm(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1) {
-    uint32_t new_head = cache.size;
-
-    if (p0 < 0) p0 = 0;
-    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
-
-    // models like Mamba or RWKV can't have a state partially erased
-    if (cache.recurrent) {
-        if (seq_id >= (int64_t) cache.size) {
-            // could be fatal
-            return false;
-        }
-        if (0 <= seq_id) {
-            int32_t & tail_id = cache.cells[seq_id].tail;
-            if (tail_id >= 0) {
-                const llama_kv_cell & cell = cache.cells[tail_id];
-                // partial intersection is invalid
-                if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
-                    return false;
-                }
-                // invalidate tails which will be cleared
-                if (p0 <= cell.pos && cell.pos < p1) {
-                    tail_id = -1;
-                }
-            }
-        } else {
-            // seq_id is negative, then the range should include everything or nothing
-            if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
-                return false;
-            }
-        }
-    }
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            if (seq_id < 0) {
-                cache.cells[i].seq_id.clear();
-            } else if (cache.cells[i].has_seq_id(seq_id)) {
-                cache.cells[i].seq_id.erase(seq_id);
-            } else {
-                continue;
-            }
-            if (cache.cells[i].is_empty()) {
-                // keep count of the number of used cells
-                if (cache.cells[i].pos >= 0) cache.used--;
-
-                cache.cells[i].pos = -1;
-                cache.cells[i].src = -1;
-                if (new_head == cache.size) new_head = i;
-            }
-        }
-    }
-
-    // If we freed up a slot, set head to it so searching can start there.
-    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
-
-    return true;
-}
-
-static void llama_kv_cache_seq_cp(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id_src,
-                 llama_seq_id   seq_id_dst,
-                    llama_pos   p0,
-                    llama_pos   p1) {
-    if (p0 < 0) p0 = 0;
-    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
-
-    if (cache.recurrent) {
-        if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
-            llama_kv_cell & tail_src = cache.cells[seq_id_src];
-            llama_kv_cell & tail_dst = cache.cells[seq_id_dst];
-            if (tail_dst.tail >= 0) {
-                // clear destination seq_id if it wasn't empty
-                llama_kv_cell & cell_dst = cache.cells[tail_dst.tail];
-
-                cell_dst.seq_id.erase(seq_id_dst);
-                tail_dst.tail = -1;
-                if (cell_dst.seq_id.empty()) {
-                    cell_dst.pos = -1;
-                    cell_dst.delta = -1;
-                    cell_dst.src = -1;
-                    cache.used -= 1;
-                }
-            }
-            if (tail_src.tail >= 0) {
-                llama_kv_cell & cell_src = cache.cells[tail_src.tail];
-
-                cell_src.seq_id.insert(seq_id_dst);
-                tail_dst.tail = tail_src.tail;
-            }
-        }
-
-        return;
-    }
-    // otherwise, this is the KV cache of a Transformer-like model
-
-    cache.head = 0;
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            cache.cells[i].seq_id.insert(seq_id_dst);
-        }
-    }
-}
-
-static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
-    uint32_t new_head = cache.size;
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.recurrent && (llama_seq_id) i != seq_id) {
-            cache.cells[i].tail = -1;
-        }
-        if (!cache.cells[i].has_seq_id(seq_id)) {
-            if (cache.cells[i].pos >= 0) cache.used--;
-            cache.cells[i].pos = -1;
-            cache.cells[i].src = -1;
-            cache.cells[i].seq_id.clear();
-            if (new_head == cache.size) new_head = i;
-        } else {
-            cache.cells[i].seq_id.clear();
-            cache.cells[i].seq_id.insert(seq_id);
-        }
-    }
-
-    // If we freed up a slot, set head to it so searching can start there.
-    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
-}
-
-static void llama_kv_cache_seq_add(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1,
-                    llama_pos   delta) {
-    uint32_t new_head = cache.size;
-
-    if (p0 < 0) p0 = 0;
-    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
-    // If there is no range then return early to avoid looping over the cache.
-    if (p0 == p1) return;
-
-    if (cache.recurrent) {
-        // for Mamba-like or RWKV models, only the pos needs to be shifted
-        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
-            const int32_t tail_id = cache.cells[seq_id].tail;
-            if (tail_id >= 0) {
-                llama_kv_cell & cell = cache.cells[tail_id];
-                if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
-                    cell.pos += delta;
-                }
-            }
-        }
-        return;
-    }
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            cache.has_shift = true;
-            cache.cells[i].pos   += delta;
-            cache.cells[i].delta += delta;
-
-            if (cache.cells[i].pos < 0) {
-                if (!cache.cells[i].is_empty()) {
-                    cache.used--;
-                }
-                cache.cells[i].pos = -1;
-                cache.cells[i].seq_id.clear();
-                if (new_head == cache.size) {
-                    new_head = i;
-                }
-            }
-        }
-    }
-
-    // If we freed up a slot, set head to it so searching can start there.
-    // Otherwise we just start the next search from the beginning.
-    cache.head = new_head != cache.size ? new_head : 0;
-}
-
-static void llama_kv_cache_seq_div(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1,
-                          int   d) {
-    if (p0 < 0) p0 = 0;
-    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
-    // If there is no range then return early to avoid looping over the cache.
-    if (p0 == p1) return;
-
-    if (cache.recurrent) {
-        // for Mamba-like or RWKV models, only the pos needs to be changed
-        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
-            const int32_t tail_id = cache.cells[seq_id].tail;
-            if (tail_id >= 0) {
-                llama_kv_cell & cell = cache.cells[tail_id];
-                if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
-                    cell.pos /= d;
-                }
-            }
-        }
-        return;
-    }
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            cache.has_shift = true;
-
-            {
-                llama_pos p_old = cache.cells[i].pos;
-                cache.cells[i].pos   /= d;
-                cache.cells[i].delta += cache.cells[i].pos - p_old;
-            }
-        }
-    }
-}
-
-static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
-    llama_pos result = 0;
-
-    for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].has_seq_id(seq_id)) {
-            result = std::max(result, cache.cells[i].pos);
-        }
-    }
-
-    return result;
-}
-
-static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
-    if (!cache.recurrent) {
-        cache.do_defrag = true;
-    }
-}
-
-static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
-    // the FA kernels require padding to avoid extra runtime boundary checks
-    return cparams.flash_attn ? 256u : 32u;
-}
-
-// saves the kv_cache state for future recovery.
-// used to rollback llama_kv_cache_find_slot changes.
-struct llama_kv_slot_restorer {
-    struct llama_kv_cache_state {
-        uint32_t head = 0;
-        uint32_t n    = 0;
-    } old_state;
-
-    // for non-recurrent models only
-    // list of slots to restore
-    std::vector<std::pair<uint32_t, uint32_t>> slot_boundaries;
-
-    bool do_restore = false;
-
-    explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
-        old_state.head  = cache.head;
-        old_state.n     = cache.n;
-    }
-
-    // saves a slot information for future restoration
-    void save(const struct llama_kv_cache_slot_info & slot) {
-        if (slot) {
-            do_restore = true;
-            if (slot.boundaries.first != slot.boundaries.second) {
-                slot_boundaries.push_back(slot.boundaries);
-            }
-        }
-    }
-
-    // must be explicitly called to restore the kv_cache state
-    // and rollback changes from all llama_kv_cache_find_slot calls
-    void restore(struct llama_kv_cache & cache) {
-        if (do_restore) {
-            cache.head  = old_state.head;
-            cache.n     = old_state.n;
-
-            if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
-                llama_kv_cache_seq_rm(cache, -1, -1, -1);
-            } else {
-                for (auto & slot : slot_boundaries) {
-                    llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
-                }
-            }
-        }
-    }
-};
-
-//
-// model loading and saving
-//
-
-enum llama_fver {
-    GGUF_FILE_VERSION_V1 = 1,
-    GGUF_FILE_VERSION_V2 = 2,
-    GGUF_FILE_VERSION_V3 = 3,
-};
-
-static const char * llama_file_version_name(llama_fver version) {
-    switch (version) {
-        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
-        case GGUF_FILE_VERSION_V2: return "GGUF V2";
-        case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
-    }
-
-    return "unknown";
-}
-
-static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
-    char buf[256];
-    snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
-    for (size_t i = 1; i < ne.size(); i++) {
-        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
-    }
-    return buf;
-}
-
-static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
-    char buf[256];
-    snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
-    }
-    return buf;
-}
-
-namespace GGUFMeta {
-    template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
-    struct GKV_Base_Type {
-        static constexpr gguf_type gt = gt_;
-
-        static T getter(const gguf_context * ctx, const int kid) {
-            return gfun(ctx, kid);
-        }
-    };
-
-    template<typename T> struct GKV_Base;
-
-    template<> struct GKV_Base<bool        >: GKV_Base_Type<bool,         GGUF_TYPE_BOOL,    gguf_get_val_bool> {};
-    template<> struct GKV_Base<uint8_t     >: GKV_Base_Type<uint8_t,      GGUF_TYPE_UINT8,   gguf_get_val_u8  > {};
-    template<> struct GKV_Base<uint16_t    >: GKV_Base_Type<uint16_t,     GGUF_TYPE_UINT16,  gguf_get_val_u16 > {};
-    template<> struct GKV_Base<uint32_t    >: GKV_Base_Type<uint32_t,     GGUF_TYPE_UINT32,  gguf_get_val_u32 > {};
-    template<> struct GKV_Base<uint64_t    >: GKV_Base_Type<uint64_t,     GGUF_TYPE_UINT64,  gguf_get_val_u64 > {};
-    template<> struct GKV_Base<int8_t      >: GKV_Base_Type<int8_t,       GGUF_TYPE_INT8,    gguf_get_val_i8  > {};
-    template<> struct GKV_Base<int16_t     >: GKV_Base_Type<int16_t,      GGUF_TYPE_INT16,   gguf_get_val_i16 > {};
-    template<> struct GKV_Base<int32_t     >: GKV_Base_Type<int32_t,      GGUF_TYPE_INT32,   gguf_get_val_i32 > {};
-    template<> struct GKV_Base<int64_t     >: GKV_Base_Type<int64_t,      GGUF_TYPE_INT64,   gguf_get_val_i64 > {};
-    template<> struct GKV_Base<float       >: GKV_Base_Type<float,        GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
-    template<> struct GKV_Base<double      >: GKV_Base_Type<double,       GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
-    template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING,  gguf_get_val_str > {};
-
-    template<> struct GKV_Base<std::string> {
-        static constexpr gguf_type gt = GGUF_TYPE_STRING;
-
-        static std::string getter(const gguf_context * ctx, const int kid) {
-            return gguf_get_val_str(ctx, kid);
-        }
-    };
-
-    struct ArrayInfo {
-        const gguf_type gt;
-        const size_t length;
-        const void * data;
-    };
-
-    template<> struct GKV_Base<ArrayInfo> {
-        public:
-        static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
-        static ArrayInfo getter(const gguf_context *ctx, const int k) {
-            return ArrayInfo {
-                gguf_get_arr_type(ctx, k),
-                size_t(gguf_get_arr_n(ctx, k)),
-                gguf_get_arr_data(ctx, k),
-            };
-        }
-    };
-
-    template<typename T>
-    class GKV : public GKV_Base<T> {
-        GKV() = delete;
-
-        public:
-        static T get_kv(const gguf_context * ctx, const int k) {
-            const enum gguf_type kt = gguf_get_kv_type(ctx, k);
-
-            if (kt != GKV::gt) {
-                throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
-                    gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
-            }
-            return GKV::getter(ctx, k);
-        }
-
-        static const char * override_type_to_str(const llama_model_kv_override_type ty) {
-            switch (ty) {
-                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
-                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
-                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
-                case LLAMA_KV_OVERRIDE_TYPE_STR:   return "str";
-            }
-            return "unknown";
-        }
-
-        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
-            if (!ovrd) { return false; }
-            if (ovrd->tag == expected_type) {
-                LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
-                    __func__, override_type_to_str(ovrd->tag), ovrd->key);
-                switch (ovrd->tag) {
-                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
-                        LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
-                    } break;
-                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
-                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
-                    } break;
-                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
-                        LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
-                    } break;
-                    case LLAMA_KV_OVERRIDE_TYPE_STR: {
-                        LLAMA_LOG_INFO("%s\n", ovrd->val_str);
-                    } break;
-                    default:
-                        // Shouldn't be possible to end up here, but just in case...
-                        throw std::runtime_error(
-                            format("Unsupported attempt to override %s type for metadata key %s\n",
-                                override_type_to_str(ovrd->tag), ovrd->key));
-                }
-                return true;
-            }
-            LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
-                __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
-            return false;
-        }
-
-        template<typename OT>
-        static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
-            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
-                target = ovrd->val_bool;
-                return true;
-            }
-            return false;
-        }
-
-        template<typename OT>
-        static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
-            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
-                target = ovrd->val_i64;
-                return true;
-            }
-            return false;
-        }
-
-        template<typename OT>
-        static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override * ovrd) {
-            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
-                target = ovrd->val_f64;
-                return true;
-            }
-            return false;
-        }
-
-        template<typename OT>
-        static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override * ovrd) {
-            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
-                target = ovrd->val_str;
-                return true;
-            }
-            return false;
-        }
-
-        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
-            if (try_override<T>(target, ovrd)) {
-                return true;
-            }
-            if (k < 0) { return false; }
-            target = get_kv(ctx, k);
-            return true;
-        }
-
-        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
-            return set(ctx, gguf_find_key(ctx, key), target, ovrd);
-        }
-
-        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
-            return set(ctx, key.c_str(), target, ovrd);
-        }
-    };
-}
-
-using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
-
-static size_t llama_model_max_nodes(const llama_model & model) {
-    return std::max<size_t>(8192, model.tensors_by_name.size()*5);
-}
-
-struct llama_model_loader {
-    int n_kv      = 0;
-    int n_tensors = 0;
-    int n_created = 0;
-
-    uint64_t n_elements = 0;
-    size_t  n_bytes     = 0;
-
-    bool use_mmap = false;
-    bool check_tensors;
-
-    llama_files files;
-    llama_ftype ftype;
-    llama_fver  fver;
-
-    llama_mmaps mappings;
-
-    // Holds information on a model weight
-    struct llama_tensor_weight {
-        uint16_t  idx; // source file index
-        size_t   offs; // tensor data offset in the original file
-
-        ggml_tensor * tensor;
-
-        llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
-            const int tensor_idx = gguf_find_tensor(gguf_ctx,  ggml_get_name(tensor));
-            if (tensor_idx < 0) {
-                throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor)));
-            }
-
-            offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
-            if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
-                throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor)));
-            }
-        }
-    };
-
-    // custom comparator to sort weights more nicely by layer
-    struct weight_name_comparer {
-        bool operator()(const std::string & a, const std::string & b) const {
-            int a_layer = -1;
-            int b_layer = -1;
-            sscanf(a.c_str(), "blk.%d.", &a_layer);
-            sscanf(b.c_str(), "blk.%d.", &b_layer);
-            if (a_layer != b_layer) {
-                return a_layer < b_layer;
-            }
-            return a < b;
-        }
-    };
-
-    std::map<std::string, struct llama_tensor_weight, weight_name_comparer> weights_map;
-    std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
-
-    gguf_context_ptr meta;
-    std::vector<ggml_context_ptr> contexts;
-
-    std::string arch_name;
-    LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);
-
-    llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
-        int trace = 0;
-        if (getenv("LLAMA_TRACE")) {
-            trace = atoi(getenv("LLAMA_TRACE"));
-        }
-
-        if (param_overrides_p != nullptr) {
-            for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
-                kv_overrides.insert({std::string(p->key), *p});
-            }
-        }
-
-        struct ggml_context * ctx = NULL;
-        struct gguf_init_params params = {
-            /*.no_alloc = */ true,
-            /*.ctx      = */ &ctx,
-        };
-
-        meta.reset(gguf_init_from_file(fname.c_str(), params));
-        if (!meta) {
-            throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
-        }
-
-        get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
-        llm_kv = LLM_KV(llm_arch_from_string(arch_name));
-
-        files.emplace_back(new llama_file(fname.c_str(), "rb"));
-        contexts.emplace_back(ctx);
-
-        // Save tensors data offset of the main file.
-        // For subsidiary files, `meta` tensor data offset must not be used,
-        // so we build a unified tensors index for weights.
-        for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-            std::string tensor_name = std::string(cur->name);
-            // make sure there is no duplicated tensor names
-            if (weights_map.find(tensor_name) != weights_map.end()) {
-                throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
-            }
-            n_elements += ggml_nelements(cur);
-            n_bytes    += ggml_nbytes(cur);
-            weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta.get(), cur));
-        }
-        uint16_t n_split = 0;
-        get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
-
-        // Load additional GGML contexts
-        if (n_split > 1) {
-            uint16_t idx = 0;
-            get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
-            if (idx != 0) {
-                throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
-            }
-
-            char split_prefix[PATH_MAX] = {0};
-            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
-                throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
-            }
-
-            if (trace > 0) {
-                LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
-            }
-
-            char split_path[PATH_MAX] = {0};
-            for (idx = 1; idx < n_split; idx++) {
-                llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
-
-                struct gguf_init_params split_params = {
-                    /*.no_alloc = */ true,
-                    /*.ctx      = */ &ctx,
-                };
-                gguf_context_ptr ctx_gguf { gguf_init_from_file(split_path, split_params) };
-                if (!ctx_gguf) {
-                    throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
-                }
-
-                files.emplace_back(new llama_file(split_path, "rb"));
-                contexts.emplace_back(ctx);
-
-                // Save tensors data offset info of the shard.
-                for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-                    std::string tensor_name = std::string(cur->name);
-                    // make sure there is no duplicated tensor names
-                    if (weights_map.find(tensor_name) != weights_map.end()) {
-                        throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
-                    }
-                    n_elements += ggml_nelements(cur);
-                    n_bytes    += ggml_nbytes(cur);
-                    weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur));
-                }
-            }
-
-            get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
-
-            // sanity check
-            {
-                const int n_tensors_loaded = (int) weights_map.size();
-                if (n_tensors != n_tensors_loaded) {
-                    throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
-                }
-            }
-
-            LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n",  __func__, n_split - 1);
-        }
-
-        n_kv      = gguf_get_n_kv(meta.get());
-        n_tensors = weights_map.size();
-
-        fver = (enum llama_fver) gguf_get_version(meta.get());
-
-        LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
-                __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
-
-        // determine file type based on the number of tensors for each quantization and print meta data
-        // TODO: make optional
-        {
-            std::map<enum ggml_type, uint32_t> n_type;
-
-            uint32_t n_type_max = 0;
-            enum ggml_type type_max = GGML_TYPE_F32;
-
-            for (const auto & it : weights_map) {
-                const llama_tensor_weight & w = it.second;
-                const ggml_tensor * tensor = w.tensor;
-
-                enum ggml_type type = tensor->type;
-
-                n_type[type]++;
-
-                if (n_type_max < n_type[type]) {
-                    n_type_max = n_type[type];
-                    type_max   = type;
-                }
-
-                if (trace > 0) {
-                    const uint16_t sid = w.idx;
-                    LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
-                }
-            }
-
-            switch (type_max) {
-                case GGML_TYPE_F32:     ftype = LLAMA_FTYPE_ALL_F32;        break;
-                case GGML_TYPE_F16:     ftype = LLAMA_FTYPE_MOSTLY_F16;     break;
-                case GGML_TYPE_BF16:    ftype = LLAMA_FTYPE_MOSTLY_BF16;    break;
-                case GGML_TYPE_Q4_0:    ftype = LLAMA_FTYPE_MOSTLY_Q4_0;    break;
-                case GGML_TYPE_Q4_1:    ftype = LLAMA_FTYPE_MOSTLY_Q4_1;    break;
-                case GGML_TYPE_Q5_0:    ftype = LLAMA_FTYPE_MOSTLY_Q5_0;    break;
-                case GGML_TYPE_Q5_1:    ftype = LLAMA_FTYPE_MOSTLY_Q5_1;    break;
-                case GGML_TYPE_Q8_0:    ftype = LLAMA_FTYPE_MOSTLY_Q8_0;    break;
-                case GGML_TYPE_Q2_K:    ftype = LLAMA_FTYPE_MOSTLY_Q2_K;    break;
-                case GGML_TYPE_Q3_K:    ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M;  break;
-                case GGML_TYPE_Q4_K:    ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M;  break;
-                case GGML_TYPE_Q5_K:    ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M;  break;
-                case GGML_TYPE_Q6_K:    ftype = LLAMA_FTYPE_MOSTLY_Q6_K;    break;
-                case GGML_TYPE_TQ1_0:   ftype = LLAMA_FTYPE_MOSTLY_TQ1_0;   break;
-                case GGML_TYPE_TQ2_0:   ftype = LLAMA_FTYPE_MOSTLY_TQ2_0;   break;
-                case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
-                case GGML_TYPE_IQ2_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS;  break;
-                case GGML_TYPE_IQ2_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_S;   break;
-                case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
-                case GGML_TYPE_IQ1_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_S;   break;
-                case GGML_TYPE_IQ1_M:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_M;   break;
-                case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
-                case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
-                case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
-                default:
-                    {
-                        LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
-                        ftype = LLAMA_FTYPE_ALL_F32;
-                    } break;
-            }
-
-            // this is a way to mark that we have "guessed" the file type
-            ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
-
-            {
-                const int kid = gguf_find_key(meta.get(), "general.file_type"); // TODO: use LLM_KV
-                if (kid >= 0) {
-                    ftype = (llama_ftype) gguf_get_val_u32(meta.get(), kid);
-                }
-            }
-
-            LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
-
-            for (int i = 0; i < n_kv; i++) {
-                const char * name           = gguf_get_key(meta.get(), i);
-                const enum gguf_type type   = gguf_get_kv_type(meta.get(), i);
-                const std::string type_name =
-                    type == GGUF_TYPE_ARRAY
-                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i))
-                    : gguf_type_name(type);
-
-                std::string value          = gguf_kv_to_str(meta.get(), i);
-                const size_t MAX_VALUE_LEN = 40;
-                if (value.size() > MAX_VALUE_LEN) {
-                    value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
-                }
-                replace_all(value, "\n", "\\n");
-
-                LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
-            }
-
-            // print type counts
-            for (auto & kv : n_type) {
-                if (kv.second == 0) {
-                    continue;
-                }
-
-                LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
-            }
-        }
-
-        if (!llama_mmap::SUPPORTED) {
-            LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
-            use_mmap = false;
-        }
-
-        this->use_mmap = use_mmap;
-        this->check_tensors = check_tensors;
-    }
-
-    template<typename T>
-    typename std::enable_if<std::is_integral<T>::value, bool>::type
-    get_arr_n(const std::string & key, T & result, const bool required = true) {
-        const int kid = gguf_find_key(meta.get(), key.c_str());
-
-        if (kid < 0) {
-            if (required) {
-                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
-            }
-            return false;
-        }
-
-        struct GGUFMeta::ArrayInfo arr_info =
-            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
-
-
-        result = arr_info.length;
-        return true;
-    }
-
-    template<typename T>
-    typename std::enable_if<std::is_integral<T>::value, bool>::type
-    get_arr_n(const enum llm_kv kid, T & result, const bool required = true) {
-        return get_arr_n(llm_kv(kid), result, required);
-    }
-
-    template<typename T>
-    bool get_arr(const std::string & key, std::vector<T> & result, const bool required = true) {
-        const int kid = gguf_find_key(meta.get(), key.c_str());
-
-        if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) {
-            if (required) {
-                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
-            }
-            return false;
-        }
-
-        struct GGUFMeta::ArrayInfo arr_info =
-            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
-
-        switch (arr_info.gt) {
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
-            case GGUF_TYPE_INT32:   GGML_ASSERT(
-                                            (std::is_same<T,  int32_t>::value) ||
-                                            (std::is_same<T, uint32_t>::value));  break;
-            default:
-                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
-        }
-
-        result.resize(arr_info.length);
-        result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
-
-        return true;
-    }
-
-    template<typename T, size_t N_MAX>
-    bool get_arr(const std::string & key, std::array<T, N_MAX> & result, const bool required = true) {
-        const int kid = gguf_find_key(meta.get(), key.c_str());
-
-        if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) {
-            if (required) {
-                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
-            }
-            return false;
-        }
-
-        struct GGUFMeta::ArrayInfo arr_info =
-            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
-
-        switch (arr_info.gt) {
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
-            case GGUF_TYPE_INT32:   GGML_ASSERT(
-                                            (std::is_same<T,  int32_t>::value) ||
-                                            (std::is_same<T, uint32_t>::value));  break;
-            default:
-                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
-        }
-
-        if (arr_info.length > N_MAX) {
-            throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
-        }
-
-        std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
-
-        return true;
-    }
-
-    template<typename T>
-    bool get_arr(const enum llm_kv kid, T & result, const bool required = true) {
-        return get_arr(llm_kv(kid), result, required);
-    }
-
-    template<typename T>
-    bool get_key(const std::string & key, T & result, const bool required = true) {
-        auto it = kv_overrides.find(key);
-
-        const struct llama_model_kv_override * override =
-            it != kv_overrides.end() ? &it->second : nullptr;
-
-        const bool found = GGUFMeta::GKV<T>::set(meta.get(), key, result, override);
-
-        if (required && !found) {
-            throw std::runtime_error(format("key not found in model: %s", key.c_str()));
-        }
-
-        return found;
-    }
-
-    template<typename T>
-    bool get_key(const enum llm_kv kid, T & result, const bool required = true) {
-        return get_key(llm_kv(kid), result, required);
-    }
-
-    // get array of n <= N_MAX elements, or a single element repeated n times
-    template<typename T, size_t N_MAX>
-    bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required = true) {
-        const int kid = gguf_find_key(meta.get(), key.c_str());
-
-        if (kid < 0) {
-            if (required) {
-                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
-            }
-            return false;
-        }
-
-        if (n > N_MAX) {
-            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
-        }
-
-        if (gguf_get_kv_type(meta.get(), kid) == GGUF_TYPE_ARRAY) {
-            struct GGUFMeta::ArrayInfo arr_info =
-                GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
-
-            if (n != arr_info.length) {
-                throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
-            }
-
-            return get_arr(key, result, required);
-        } else {
-            T value;
-
-            bool ok = get_key(key, value, required);
-            if (!ok) {
-                return false;
-            }
-
-            for (uint32_t i = 0; i < n; i++) {
-                result[i] = value;
-            }
-
-            return true;
-        }
-    }
-
-    template<typename T>
-    bool get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required = true) {
-        return get_key_or_arr(llm_kv(kid), result, n, required);
-    }
-
-    std::string get_arch_name() const {
-        return arch_name;
-    }
-
-    enum llm_arch get_arch() const {
-        return llm_kv.arch;
-    }
-
-    const llama_tensor_weight * get_weight(const char * name) const {
-        auto pos = weights_map.find(name);
-        if (pos != weights_map.end()) {
-            return &pos->second;
-        }
-
-        return nullptr;
-    }
-
-    const llama_tensor_weight & require_weight(const char * name) const {
-        const llama_tensor_weight * weight = get_weight(name);
-        if (!weight) {
-            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
-        }
-        return *weight;
-    }
-
-    struct ggml_tensor * get_tensor_meta(const char * name) const {
-        const auto * weight = get_weight(name);
-        if (!weight) {
-            return nullptr;
-        }
-        return weight->tensor;
-    }
-
-    struct ggml_tensor * require_tensor_meta(const std::string & name) const {
-        struct ggml_tensor * tensor = get_tensor_meta(name.c_str());
-        if (!tensor) {
-            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
-        }
-        return tensor;
-    }
-
-    const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
-        const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
-
-        if (cur == NULL) {
-            if (!required) {
-                return NULL;
-            }
-            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
-        }
-
-        {
-            bool is_ok = true;
-            for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
-                if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
-                    is_ok = false;
-                    break;
-                }
-            }
-            if (!is_ok) {
-                throw std::runtime_error(
-                        format("%s: tensor '%s' has wrong shape; expected %s, got %s",
-                            __func__, name.c_str(),
-                            llama_format_tensor_shape(ne).c_str(),
-                            llama_format_tensor_shape(cur).c_str()));
-            }
-        }
-
-        return cur;
-    }
-
-    static const int TENSOR_NOT_REQUIRED = 1;
-    static const int TENSOR_DUPLICATED   = 2;
-
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags = 0) {
-        const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
-
-        if (cur == NULL) {
-            return NULL;
-        }
-
-        bool duplicated = flags & TENSOR_DUPLICATED;
-
-        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
-        ggml_set_name(tensor, ggml_get_name(cur));
-
-        if (duplicated) {
-            size_data += ggml_nbytes(cur);
-        } else {
-            n_created++;
-        }
-
-        return tensor;
-
-    }
-
-    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true) {
-        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
-
-        if (cur == NULL) {
-            return NULL;
-        }
-
-        if (cur->type != base->type) {
-            throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
-        }
-
-        std::array<int64_t, GGML_MAX_DIMS> dims;
-        for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
-            dims[i] = i < ne.size() ? ne.begin()[i] : 1;
-        }
-
-        struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
-                                        dims[0], dims[1], dims[2], dims[3],
-                                        cur->nb[1], cur->nb[2], cur->nb[3],
-                                        offset);
-
-        ggml_set_name(tensor, name.c_str());
-
-        n_created++;
-
-        return tensor;
-    }
-
-    void done_getting_tensors() const {
-        if (n_created != n_tensors) {
-            throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
-        }
-    }
-
-    void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
-        if (use_mmap) {
-            mappings.reserve(files.size());
-            mmaps_used.reserve(files.size());
-            for (const auto & file : files) {
-                auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
-                auto * is_numa_fn = (decltype(ggml_is_numa) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_is_numa");
-                std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, is_numa_fn()));
-                mmaps_used.emplace_back(mapping->size, 0);
-                if (mlock_mmaps) {
-                    std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
-                    mlock_mmap->init(mapping->addr);
-                    mlock_mmaps->emplace_back(std::move(mlock_mmap));
-                }
-                mappings.emplace_back(std::move(mapping));
-            }
-        }
-
-        // compute the total size of all tensors for progress reporting
-        for (const auto & it : weights_map) {
-            size_data += ggml_nbytes(it.second.tensor);
-        }
-    }
-
-    void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
-        GGML_ASSERT(!mappings.empty());
-        const auto & mapping = mappings.at(idx);
-
-        *first = mapping->size;
-        *last  = 0;
-        *addr = mapping->addr;
-        for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
-            const auto * weight = get_weight(ggml_get_name(tensor));
-            if (!weight || weight->idx != idx) {
-                continue;
-            }
-            *first = std::min(*first, weight->offs);
-            *last  = std::max(*last,  weight->offs + ggml_nbytes(tensor));
-        }
-    }
-
-    // for backwards compatibility, does not support ggml-backend
-    void load_data_for(struct ggml_tensor * cur) const {
-        const auto & w = require_weight(ggml_get_name(cur));
-
-        if (use_mmap) {
-            const auto & mapping = mappings.at(w.idx);
-            if (cur->data == nullptr) {
-                cur->data = (uint8_t *)mapping->addr + w.offs;
-            } else {
-                memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
-            }
-        } else {
-            GGML_ASSERT(cur->data != nullptr);
-            GGML_ASSERT(w.idx < files.size());
-            const auto & file = files.at(w.idx);
-            file->seek(w.offs, SEEK_SET);
-            file->read_raw(cur->data, ggml_nbytes(cur));
-        }
-
-        if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
-            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
-        }
-    }
-
-    size_t size_done = 0;
-    size_t size_data = 0;
-    std::vector<std::pair<size_t, size_t>> mmaps_used;
-
-    // Returns false if cancelled by progress_callback
-    bool load_all_data(
-            struct ggml_context * ctx,
-            llama_buf_map & bufs,
-            llama_mlocks * lmlocks,
-            llama_progress_callback progress_callback,
-            void * progress_callback_user_data) {
-        GGML_ASSERT(size_data != 0 && "call init_mappings() first");
-
-        std::vector<no_init<uint8_t>> read_buf;
-        std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
-
-        // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
-        // NVMe raid configurations might require more / larger buffers.
-        constexpr size_t n_buffers = 4;
-        constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
-
-        std::vector<ggml_backend_buffer_t> host_buffers;
-        std::vector<ggml_backend_event_t> events;
-        std::vector<void *> host_ptrs;
-        size_t buffer_idx = 0; // buffer to use for async loads
-        ggml_backend_t upload_backend = [&](const char * func) -> ggml_backend_t {
-            if (use_mmap || check_tensors) {
-                return nullptr;
-            }
-            // When not using mmaped io use async uploads from pinned memory to GPU memory.
-            // First determine if the backend supports the necessary features for async uploads.
-            auto * buf = bufs.count(0) ? bufs.at(0) : nullptr;
-            if (!buf) {
-                LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", func);
-                return nullptr;
-            }
-
-            auto * buft = ggml_backend_buffer_get_type(buf);
-            auto * dev = ggml_backend_buft_get_device(buft);
-            if (!dev) {
-                LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", func,
-                    ggml_backend_buft_name(buft));
-                return nullptr;
-            }
-
-            if (buft != ggml_backend_dev_buffer_type(dev)) {
-                LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", func,
-                    ggml_backend_buft_name(buft), ggml_backend_dev_name(dev));
-                return nullptr;
-            }
-
-            ggml_backend_dev_props props;
-            ggml_backend_dev_get_props(dev, &props);
-            if (!props.caps.async || !props.caps.host_buffer || !props.caps.events) {
-                LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", func,
-                    ggml_backend_dev_name(dev));
-                return nullptr;
-            }
-
-            auto * host_buft = ggml_backend_dev_host_buffer_type(dev);
-            if (!host_buft) {
-                LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", func,
-                    ggml_backend_dev_name(dev));
-                return nullptr;
-            }
-
-            // If the backend is supported, create pinned memory buffers and events for synchronisation.
-            for (size_t idx = 0; idx < n_buffers; ++idx) {
-                auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
-                if (!buf) {
-                    LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func,
-                        ggml_backend_dev_name(dev));
-                    return nullptr;
-                }
-
-                host_buffers.emplace_back(buf);
-                host_ptrs.emplace_back(ggml_backend_buffer_get_base(buf));
-
-                auto * event = ggml_backend_event_new(dev);
-                if (!event) {
-                    LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", func,
-                        ggml_backend_dev_name(dev));
-                    return nullptr;
-                }
-
-                events.emplace_back(event);
-            }
-
-            ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
-            if (!backend) {
-                LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", func,
-                    ggml_backend_dev_name(dev));
-                return nullptr;
-            }
-
-            return backend;
-        }(__func__);
-
-        if (upload_backend) {
-            LLAMA_LOG_DEBUG("%s: using async uploads for device %s, buffer type %s, backend %s\n", __func__,
-                ggml_backend_dev_name(ggml_backend_get_device(upload_backend)),
-                ggml_backend_buft_name(ggml_backend_buffer_get_type(bufs.at(0))),
-                ggml_backend_name(upload_backend));
-        }
-
-        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
-            const auto * weight = get_weight(ggml_get_name(cur));
-            if (weight == nullptr) {
-                // this can happen with split experts models
-                continue;
-            }
-
-            if (progress_callback) {
-                if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
-                    return false;
-                }
-            }
-
-            size_t n_size = ggml_nbytes(cur);
-
-            if (use_mmap) {
-                const auto & mapping = mappings.at(weight->idx);
-                ggml_backend_buffer_t buf_mmap = nullptr;
-                if (bufs.count(weight->idx)) {
-                    buf_mmap = bufs.at(weight->idx);
-                }
-                uint8_t * data = (uint8_t *) mapping->addr + weight->offs;
-
-                if (check_tensors) {
-                    validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
-                        return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
-                    }));
-                }
-
-                GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
-                if (buf_mmap && cur->data == nullptr) {
-                    ggml_backend_tensor_alloc(buf_mmap, cur, data);
-                    if (lmlocks) {
-                        const auto & lmlock = lmlocks->at(weight->idx);
-                        lmlock->grow_to(weight->offs + n_size);
-                    }
-
-                    auto & mmap_used = mmaps_used[weight->idx];
-                    mmap_used.first  = std::min(mmap_used.first,  weight->offs);
-                    mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
-                } else {
-                    ggml_backend_tensor_set(cur, data, 0, n_size);
-                }
-            } else {
-                const auto & file = files.at(weight->idx);
-                if (ggml_backend_buffer_is_host(cur->buffer)) {
-                    file->seek(weight->offs, SEEK_SET);
-                    file->read_raw(cur->data, n_size);
-                    if (check_tensors) {
-                        validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
-                            return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
-                        }));
-                    }
-                } else {
-                    // If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
-                    if (upload_backend) {
-                        file->seek(weight->offs, SEEK_SET);
-
-                        size_t bytes_read = 0;
-
-                        while (bytes_read < n_size) {
-                            size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
-
-                            ggml_backend_event_synchronize(events[buffer_idx]);
-                            file->read_raw(host_ptrs[buffer_idx], read_iteration);
-                            ggml_backend_tensor_set_async(upload_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
-                            ggml_backend_event_record(events[buffer_idx], upload_backend);
-
-                            bytes_read += read_iteration;
-                            ++buffer_idx;
-                            buffer_idx %= n_buffers;
-                        }
-                    } else {
-                        read_buf.resize(n_size);
-                        file->seek(weight->offs, SEEK_SET);
-                        file->read_raw(read_buf.data(), n_size);
-                        ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
-                        if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
-                            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
-                        }
-                    }
-                }
-            }
-
-            size_done += n_size;
-        }
-
-        // free temporary resources used for async uploads
-        for (auto * event : events) {
-            ggml_backend_event_synchronize(event);
-            ggml_backend_event_free(event);
-        }
-        for (auto * buf : host_buffers) {
-            ggml_backend_buffer_free(buf);
-        }
-        ggml_backend_free(upload_backend);
-
-        // check validation results
-        bool validation_failed = false;
-        for (auto & future : validation_result) {
-            auto result = future.get();
-            if (!result.second) {
-                LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
-                validation_failed = true;
-            }
-        }
-        if (validation_failed) {
-            throw std::runtime_error("found tensors with invalid data");
-        }
-
-        // check if this is the last call and do final cleanup
-        if (size_done >= size_data) {
-            // unmap offloaded tensors and metadata
-            if (use_mmap) {
-                for (uint32_t idx = 0; idx < mappings.size(); idx++) {
-                    const auto & mmap_used = mmaps_used.at(idx);
-                    auto & mapping = mappings.at(idx);
-                    mapping->unmap_fragment(0, mmap_used.first);
-                    if (mmap_used.second != 0) {
-                        mapping->unmap_fragment(mmap_used.second, mapping->size);
-                    }
-                }
-            }
-            if (progress_callback) {
-                // Even though the model is done loading, we still honor
-                // cancellation since we need to free allocations.
-                return progress_callback(1.0f, progress_callback_user_data);
-            }
-        }
-
-        return true;
-    }
-};
-
-// temporary allocate memory for the input batch if needed
-static const llama_seq_id batch_default_seq_id = 0;
-struct llama_batch_allocr {
-    std::array<llama_seq_id, 1> seq_id_0 = {batch_default_seq_id};
-    std::vector<llama_pos>      pos;
-    std::vector<int32_t>        n_seq_id;
-    std::vector<llama_seq_id *> seq_id;
-    std::vector<int8_t>         logits;
-    struct llama_batch          batch;
-    // optionally fulfill the batch returned by llama_batch_get_one
-    llama_batch_allocr(llama_context & ctx, struct llama_batch in_batch) {
-        batch = in_batch;
-        GGML_ASSERT(batch.n_tokens > 0);
-        if (!batch.pos) {
-            // determine the last position in KV cache
-            llama_pos last_pos = -1;
-            for (const auto & cell : ctx.kv_self.cells) {
-                if (cell.has_seq_id(batch_default_seq_id)) {
-                    last_pos = std::max(last_pos, cell.pos);
-                }
-            }
-            last_pos++; // next position
-            pos.resize(batch.n_tokens);
-            for (int32_t i = 0; i < batch.n_tokens; i++) {
-                pos[i] = i+last_pos;
-            }
-            batch.pos = pos.data();
-        }
-        if (!batch.n_seq_id) {
-            n_seq_id.resize(batch.n_tokens);
-            for (int32_t i = 0; i < batch.n_tokens; i++) {
-                n_seq_id[i] = seq_id_0.size();
-            }
-            batch.n_seq_id = n_seq_id.data();
-        }
-        if (!batch.seq_id) {
-            seq_id.resize(batch.n_tokens + 1);
-            seq_id[batch.n_tokens] = NULL;
-            for (int32_t i = 0; i < batch.n_tokens; i++) {
-                seq_id[i] = seq_id_0.data();
-            }
-            batch.seq_id = seq_id.data();
-        }
-        if (!batch.logits) {
-            logits.resize(batch.n_tokens);
-            logits[logits.size() - 1] = true;
-            batch.logits = logits.data();
-        }
-    }
-};
-
-template<>
-bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
-    uint32_t tmp;
-    const bool found = get_key(kid, tmp, required);
-    if (found) {
-        result = (enum llama_pooling_type) tmp;
-    } else {
-        result = LLAMA_POOLING_TYPE_UNSPECIFIED;
-    }
-    return found;
-}
-
-
-//
-// load LLaMA models
-//
-
-static const char * llama_model_arch_name(llm_arch arch) {
-    auto it = LLM_ARCH_NAMES.find(arch);
-    if (it == LLM_ARCH_NAMES.end()) {
-        return "unknown";
-    }
-    return it->second;
-}
-
-static std::string llama_model_ftype_name(llama_ftype ftype) {
-    if (ftype & LLAMA_FTYPE_GUESSED) {
-        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
-    }
-
-    switch (ftype) {
-        case LLAMA_FTYPE_ALL_F32:         return "all F32";
-        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
-        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
-        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
-        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
-        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
-        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
-        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
-
-        default: return "unknown, may not work";
-    }
-}
-
-static const char * llama_model_type_name(e_model type) {
-    switch (type) {
-        case MODEL_14M:           return "14M";
-        case MODEL_17M:           return "17M";
-        case MODEL_22M:           return "22M";
-        case MODEL_33M:           return "33M";
-        case MODEL_60M:           return "60M";
-        case MODEL_70M:           return "70M";
-        case MODEL_80M:           return "80M";
-        case MODEL_109M:          return "109M";
-        case MODEL_137M:          return "137M";
-        case MODEL_160M:          return "160M";
-        case MODEL_220M:          return "220M";
-        case MODEL_250M:          return "250M";
-        case MODEL_270M:          return "270M";
-        case MODEL_335M:          return "335M";
-        case MODEL_410M:          return "410M";
-        case MODEL_450M:          return "450M";
-        case MODEL_770M:          return "770M";
-        case MODEL_780M:          return "780M";
-        case MODEL_0_5B:          return "0.5B";
-        case MODEL_1B:            return "1B";
-        case MODEL_1_3B:          return "1.3B";
-        case MODEL_1_4B:          return "1.4B";
-        case MODEL_1_5B:          return "1.5B";
-        case MODEL_1_6B:          return "1.6B";
-        case MODEL_2B:            return "2B";
-        case MODEL_2_8B:          return "2.8B";
-        case MODEL_3B:            return "3B";
-        case MODEL_4B:            return "4B";
-        case MODEL_6B:            return "6B";
-        case MODEL_6_9B:          return "6.9B";
-        case MODEL_7B:            return "7B";
-        case MODEL_8B:            return "8B";
-        case MODEL_9B:            return "9B";
-        case MODEL_11B:           return "11B";
-        case MODEL_12B:           return "12B";
-        case MODEL_13B:           return "13B";
-        case MODEL_14B:           return "14B";
-        case MODEL_15B:           return "15B";
-        case MODEL_16B:           return "16B";
-        case MODEL_20B:           return "20B";
-        case MODEL_30B:           return "30B";
-        case MODEL_32B:           return "32B";
-        case MODEL_34B:           return "34B";
-        case MODEL_35B:           return "35B";
-        case MODEL_40B:           return "40B";
-        case MODEL_65B:           return "65B";
-        case MODEL_70B:           return "70B";
-        case MODEL_236B:          return "236B";
-        case MODEL_314B:          return "314B";
-        case MODEL_SMALL:         return "0.1B";
-        case MODEL_MEDIUM:        return "0.4B";
-        case MODEL_LARGE:         return "0.8B";
-        case MODEL_XL:            return "1.5B";
-        case MODEL_A1_7B:         return "A1.7B";
-        case MODEL_A2_7B:         return "A2.7B";
-        case MODEL_8x7B:          return "8x7B";
-        case MODEL_8x22B:         return "8x22B";
-        case MODEL_16x12B:        return "16x12B";
-        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
-        case MODEL_57B_A14B:      return "57B.A14B";
-        case MODEL_27B:           return "27B";
-        default:                  return "?B";
-    }
-}
-
-static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
-    switch (type) {
-        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
-        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
-        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
-        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
-        case LLAMA_VOCAB_TYPE_UGM:  return "UGM";
-        case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
-        default:                    return "unknown";
-    }
-}
-
-static void llm_load_stats(llama_model_loader & ml, llama_model & model) {
-    model.n_elements = ml.n_elements;
-    model.n_bytes = ml.n_bytes;
-}
-
-static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
-    model.arch = ml.get_arch();
-    if (model.arch == LLM_ARCH_UNKNOWN) {
-        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
-    }
-}
-
-static void llm_load_hparams(
-        llama_model_loader & ml,
-        llama_model & model) {
-    auto & hparams = model.hparams;
-    const gguf_context * ctx = ml.meta.get();
-
-    // get metadata as string
-    for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
-        enum gguf_type type = gguf_get_kv_type(ctx, i);
-        if (type == GGUF_TYPE_ARRAY) {
-            continue;
-        }
-        const char * name = gguf_get_key(ctx, i);
-        const std::string value = gguf_kv_to_str(ctx, i);
-        model.gguf_kv.emplace(name, value);
-    }
-
-    // get general kv
-    ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
-
-    // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false);
-
-    // everything past this point is not vocab-related
-    if (hparams.vocab_only) {
-        return;
-    }
-
-    ml.get_key(LLM_KV_CONTEXT_LENGTH,    hparams.n_ctx_train);
-    ml.get_key(LLM_KV_EMBEDDING_LENGTH,  hparams.n_embd);
-    ml.get_key(LLM_KV_BLOCK_COUNT,       hparams.n_layer);
-    ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
-
-    if (model.arch == LLM_ARCH_WAVTOKENIZER_DEC) {
-        ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
-
-        ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
-        ml.get_key(LLM_KV_POSNET_BLOCK_COUNT,      hparams.posnet.n_layer);
-
-        ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
-        ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT,      hparams.convnext.n_layer);
-    }
-
-    GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
-    GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
-    if (hparams.n_expert > 0) {
-        GGML_ASSERT(hparams.n_expert_used > 0);
-    } else {
-        GGML_ASSERT(hparams.n_expert_used == 0);
-    }
-
-    // zero-out the array hparams
-    std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
-    std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
-    std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
-
-    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
-
-    // n_head_kv is optional, default to n_head
-    hparams.n_head_kv_arr = hparams.n_head_arr;
-
-    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
-
-    bool rope_finetuned = false;
-    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-    hparams.rope_finetuned = rope_finetuned;
-
-    hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
-    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
-
-    // rope_freq_base (optional)
-    hparams.rope_freq_base_train = 10000.0f;
-    ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
-
-    std::string rope_scaling("linear");
-    ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
-    hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
-    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
-
-    // rope_freq_scale (inverse of the kv) is optional
-    float ropescale = 0.0f;
-    if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
-        // try the old key name
-        ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
-    }
-    hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
-
-    ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
-
-    // non-transformer models do not have attention heads
-    if (hparams.n_head() > 0) {
-        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
-        // gpt-j n_rot = rotary_dim
-
-        hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
-        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
-
-        hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
-        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
-
-        // sanity check for n_rot (optional)
-        hparams.n_rot = hparams.n_embd_head_k;
-
-        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
-
-        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
-            if (hparams.n_rot != hparams.n_embd_head_k) {
-                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
-            }
-        }
-    } else {
-        hparams.n_rot = 0;
-        hparams.n_embd_head_k = 0;
-        hparams.n_embd_head_v = 0;
-    }
-
-    // arch-specific KVs
-    switch (model.arch) {
-        case LLM_ARCH_LLAMA:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                if (hparams.n_expert == 8) {
-                    switch (hparams.n_layer) {
-                        case 32: model.type = e_model::MODEL_8x7B; break;
-                        case 56: model.type = e_model::MODEL_8x22B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
-                    }
-                } else {
-                    switch (hparams.n_layer) {
-                        case 16: model.type = e_model::MODEL_1B; break; // Llama 3.2 1B
-                        case 22: model.type = e_model::MODEL_1B; break;
-                        case 26: model.type = e_model::MODEL_3B; break;
-                        case 28: model.type = e_model::MODEL_3B; break; // Llama 3.2 3B
-                        // granite uses a vocab with len 49152
-                        case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
-                        case 36: model.type = e_model::MODEL_8B; break; // granite
-                        case 40: model.type = e_model::MODEL_13B; break;
-                        case 48: model.type = e_model::MODEL_34B; break;
-                        case 60: model.type = e_model::MODEL_30B; break;
-                        case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
-                    }
-                }
-            } break;
-        case LLM_ARCH_DECI:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MINICPM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
-
-                switch (hparams.n_layer) {
-                    case 52: model.type = e_model::MODEL_1B; break;
-                    case 40: model.type = e_model::MODEL_2B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MINICPM3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
-
-                switch (hparams.n_layer) {
-                    case 62: model.type = e_model::MODEL_4B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GROK:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 64: model.type = e_model::MODEL_314B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_FALCON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 60: model.type = e_model::MODEL_40B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_BAICHUAN:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-
-                if (model.type == e_model::MODEL_13B) {
-                    // TODO: become GGUF KV parameter
-                    hparams.f_max_alibi_bias = 8.0f;
-                }
-            } break;
-        case LLM_ARCH_STARCODER:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 36: model.type = e_model::MODEL_3B; break;
-                    case 42: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_15B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_REFACT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_1B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-
-                // TODO: become GGUF KV parameter
-                hparams.f_max_alibi_bias = 8.0f;
-            } break;
-        case LLM_ARCH_BERT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
-
-                switch (hparams.n_layer) {
-                    case 3:
-                        model.type = e_model::MODEL_17M; break; // bge-micro
-                    case 6:
-                        model.type = e_model::MODEL_22M; break; // MiniLM-L6
-                    case 12:
-                        switch (hparams.n_embd) {
-                            case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
-                            case 768: model.type = e_model::MODEL_109M; break; // bge-base
-                        } break;
-                    case 24:
-                        model.type = e_model::MODEL_335M; break; // bge-large
-                }
-            } break;
-        case LLM_ARCH_JINA_BERT_V2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
-                hparams.f_max_alibi_bias = 8.0f;
-
-                switch (hparams.n_layer) {
-                    case 4:  model.type = e_model::MODEL_33M;  break; // jina-embeddings-small
-                    case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
-                }
-            } break;
-        case LLM_ARCH_NOMIC_BERT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
-
-                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
-                    model.type = e_model::MODEL_137M;
-                }
-            } break;
-        case LLM_ARCH_BLOOM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 30:
-                        switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            case 4096: model.type = e_model::MODEL_7B; break;
-                        } break;
-                }
-
-                // TODO: become GGUF KV parameter
-                hparams.f_max_alibi_bias = 8.0f;
-            } break;
-        case LLM_ARCH_MPT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
-                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_30B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_STABLELM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_12B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_QWEN:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN2VL:
-            {
-                std::array<int, 4> section_dims;
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, section_dims, 4, true);
-                std::copy(section_dims.begin(), section_dims.begin() + 4, std::begin(hparams.rope_sections));
-            }
-            // fall through
-        case LLM_ARCH_QWEN2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
-                    case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 36: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
-                    case 48: model.type = e_model::MODEL_14B; break;
-                    case 64: model.type = e_model::MODEL_32B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN2MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_A2_7B; break;
-                    case 28: model.type = e_model::MODEL_57B_A14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PHI2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PHI3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-
-                // for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
-                if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) {
-                    // default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct
-                    hparams.n_swa = 2047;
-                } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
-                    // default value for Phi-3-mini-128k-instruct
-                    hparams.n_swa = 262144;
-                } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
-                    // default value for Phi-3-medium-128k-instruct
-                    hparams.n_swa = 131072;
-                }
-                bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (!found_swa && hparams.n_swa == 0) {
-                    throw std::runtime_error("invalid value for sliding_window");
-                }
-            } break;
-        case LLM_ARCH_PLAMO:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_GPT2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 12: model.type = e_model::MODEL_SMALL; break;
-                    case 24: model.type = e_model::MODEL_MEDIUM; break;
-                    case 36: model.type = e_model::MODEL_LARGE; break;
-                    case 48: model.type = e_model::MODEL_XL; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_CODESHELL:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 42: model.type = e_model::MODEL_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_ORION:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_INTERNLM2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_20B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GEMMA:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 18: model.type = e_model::MODEL_2B; break;
-                    case 28: model.type = e_model::MODEL_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_GEMMA2:
-            {
-                hparams.n_swa = 4096; // default value of gemma 2
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
-                hparams.attn_soft_cap = true;
-
-                switch (hparams.n_layer) {
-                    case 26: model.type = e_model::MODEL_2B; break;
-                    case 42: model.type = e_model::MODEL_9B; break;
-                    case 46: model.type = e_model::MODEL_27B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_STARCODER2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 30: model.type = e_model::MODEL_3B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_15B; break;
-                    case 52: model.type = e_model::MODEL_20B; break; // granite
-                    case 88: model.type = e_model::MODEL_34B; break; // granite
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MAMBA:
-            {
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 24:
-                        switch (hparams.n_embd) {
-                            case 768: model.type = e_model::MODEL_SMALL; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 48:
-                        switch (hparams.n_embd) {
-                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
-                            case 1536: model.type = e_model::MODEL_LARGE; break;
-                            case 2048: model.type = e_model::MODEL_XL; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 64:
-                        switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_XVERSE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    case 80: model.type = e_model::MODEL_65B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_COMMAND_R:
-            {
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_35B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DBRX:
-        {
-            ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
-            ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv);
-
-            switch (hparams.n_layer) {
-                case 40: model.type = e_model::MODEL_16x12B; break;
-                default: model.type = e_model::MODEL_UNKNOWN;
-            }
-        } break;
-        case LLM_ARCH_OLMO:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
-
-                switch (hparams.n_layer) {
-                    case 22: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OLMO2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 16: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OLMOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 16: model.type = e_model::MODEL_A1_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OPENELM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                case 16: model.type = e_model::MODEL_270M; break;
-                case 20: model.type = e_model::MODEL_450M; break;
-                case 28: model.type = e_model::MODEL_1B; break;
-                case 36: model.type = e_model::MODEL_3B; break;
-                default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GPTNEOX:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
-                switch (hparams.n_layer) {
-                    case 6:
-                        switch (hparams.n_ff()) {
-                            case 512: model.type = e_model::MODEL_14M; break;
-                            case 2048: model.type = e_model::MODEL_70M; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 12:
-                        switch (hparams.n_ff()) {
-                            case 3072: model.type = e_model::MODEL_160M; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 16:
-                        switch (hparams.n_ff()) {
-                            case 8192: model.type = e_model::MODEL_1B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 24:
-                        switch (hparams.n_ff()) {
-                            case 4096: model.type = e_model::MODEL_410M; break;
-                            case 8192: model.type = e_model::MODEL_1_4B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 32:
-                        switch (hparams.n_ff()) {
-                            case 10240: model.type = e_model::MODEL_2_8B; break;
-                            case 16384: model.type = e_model::MODEL_6_9B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 36:
-                        switch (hparams.n_ff()) {
-                            case 20480: model.type = e_model::MODEL_12B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 44:
-                        switch (hparams.n_ff()) {
-                            case 24576: model.type = e_model::MODEL_20B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_ARCTIC:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                if (hparams.n_expert == 128) {
-                    switch (hparams.n_layer) {
-                        case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
-                    }
-                } else {
-                    model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DEEPSEEK:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
-
-                switch (hparams.n_layer) {
-                    case 28: model.type = e_model::MODEL_20B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DEEPSEEK2:
-            {
-                bool is_lite = (hparams.n_layer == 27);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
-                if (!is_lite) {
-                    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
-                }
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
-
-                switch (hparams.n_layer) {
-                    case 27: model.type = e_model::MODEL_16B; break;
-                    case 60: model.type = e_model::MODEL_236B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_CHATGLM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 28: model.type = e_model::MODEL_6B; break;
-                    case 40: model.type = e_model::MODEL_9B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_BITNET:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 26: model.type = e_model::MODEL_3B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_T5:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
-
-                uint32_t dec_start_token_id;
-                if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
-                    hparams.dec_start_token_id = dec_start_token_id;
-                }
-
-                switch (hparams.n_layer) {
-                    case 6:  model.type = e_model::MODEL_60M;  break; // t5-small
-                    case 8:  model.type = e_model::MODEL_80M;  break; // flan-t5-small
-                    case 12:
-                        switch (hparams.n_ff()) {
-                            case 3072: model.type = e_model::MODEL_220M; break; // t5-base
-                            case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 24:
-                        switch (hparams.n_ff()) {
-                            case 4096:  model.type = e_model::MODEL_770M; break; // t5-large
-                            case 2816:  model.type = e_model::MODEL_780M; break; // flan-t5-large
-                            case 16384: model.type = e_model::MODEL_3B;   break; // t5-3b
-                            case 5120:  model.type = e_model::MODEL_3B;   break; // flan-t5-xl
-                            case 65536: model.type = e_model::MODEL_11B;  break; // t5-11b
-                            case 10240: model.type = e_model::MODEL_11B;  break; // flan-t5-xxl
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_T5ENCODER:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
-                model.type = e_model::MODEL_UNKNOWN;
-            } break;
-        case LLM_ARCH_JAIS:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1_3B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    /* TODO: add variants */
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_NEMOTRON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_4B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_EXAONE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_RWKV6:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
-                ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
-                ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
-                ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
-
-                switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1_6B; break;
-                    case 32:
-                        switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            case 4096: model.type = e_model::MODEL_7B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
-                        } break;
-                    case 61: model.type = e_model::MODEL_14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
-                ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_3B; break;
-                    // Add additional layer/vocab/etc checks here for other model sizes
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_CHAMELEON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default
-                ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
-
-                switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_34B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_WAVTOKENIZER_DEC:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
-                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-            } break;
-        default: (void)0;
-    }
-
-    model.ftype = ml.ftype;
-
-    if (hparams.f_max_alibi_bias > 0.0f) {
-        hparams.use_alibi = true;
-    }
-
-    hparams.rope_type = llama_rope_type(&model);
-}
-
-static void llm_load_vocab(
-        llama_model_loader & ml,
-        llama_model & model) {
-    auto & vocab = model.vocab;
-
-    struct gguf_context * ctx = ml.meta.get();
-
-    const auto kv = LLM_KV(model.arch);
-
-    // determine vocab type
-    {
-        std::string tokenizer_model;
-        std::string tokenizer_pre;
-
-        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
-        ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
-
-        if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
-            vocab.type = LLAMA_VOCAB_TYPE_NONE;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-            vocab.linefeed_id     = LLAMA_TOKEN_NULL;
-
-            // read vocab size from metadata
-            if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) {
-                vocab.n_vocab = 0;
-                LLAMA_LOG_WARN("%s: there is no vocab_size in metadata, vocab.n_vocab will be set to %u\n", __func__, vocab.n_vocab);
-            }
-            return;
-        }
-
-        if (tokenizer_model == "llama") {
-            vocab.type = LLAMA_VOCAB_TYPE_SPM;
-
-            // default special tokens
-            vocab.special_bos_id  = 1;
-            vocab.special_eos_id  = 2;
-            vocab.special_unk_id  = 0;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-        } else if (tokenizer_model == "bert") {
-            vocab.type = LLAMA_VOCAB_TYPE_WPM;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id  = 100;
-            vocab.special_sep_id  = 102;
-            vocab.special_pad_id  = 0;
-            vocab.special_cls_id  = 101;
-            vocab.special_mask_id = 103;
-        } else if (tokenizer_model == "gpt2") {
-            vocab.type = LLAMA_VOCAB_TYPE_BPE;
-
-            // read bpe merges and populate bpe ranks
-            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
-            if (merges_keyidx == -1) {
-                throw std::runtime_error("cannot find tokenizer merges in model file\n");
-            }
-
-            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
-            for (int i = 0; i < n_merges; i++) {
-                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
-                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
-
-                std::string first;
-                std::string second;
-
-                const size_t pos = word.find(' ', 1);
-
-                if (pos != std::string::npos) {
-                    first  = word.substr(0, pos);
-                    second = word.substr(pos + 1);
-                }
-
-                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
-            }
-
-            // default special tokens
-            vocab.special_bos_id  = 11;
-            vocab.special_eos_id  = 11;
-            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-        } else if (tokenizer_model == "t5") {
-            vocab.type = LLAMA_VOCAB_TYPE_UGM;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = 1;
-            vocab.special_unk_id  = 2;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = 0;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-
-            const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
-            if (precompiled_charsmap_keyidx != -1) {
-                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
-                const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
-                vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
-#ifdef IS_BIG_ENDIAN
-                // correct endiannes of data in precompiled_charsmap binary blob
-                uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
-                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
-                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
-                size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
-                uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
-                for (size_t i = 0; i < xcda_array_size; ++i) {
-                    xcda_array[i] = __builtin_bswap32(xcda_array[i]);
-                }
-#endif
-            }
-        } else if (tokenizer_model == "rwkv") {
-            vocab.type = LLAMA_VOCAB_TYPE_RWKV;
-
-            // default special tokens
-            vocab.special_bos_id = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id = LLAMA_TOKEN_NULL;
-        } else {
-            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
-        }
-
-        // for now, only BPE models have pre-tokenizers
-        if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = true;
-            if (tokenizer_pre.empty()) {
-                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
-                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (tokenizer_pre == "default") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (
-                    tokenizer_pre == "llama3"   ||
-                    tokenizer_pre == "llama-v3" ||
-                    tokenizer_pre == "llama-bpe"||
-                    tokenizer_pre == "falcon3") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
-            } else if (
-                    tokenizer_pre == "deepseek-llm") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                    tokenizer_pre == "deepseek-coder") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                    tokenizer_pre == "falcon") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
-            } else if (
-                    tokenizer_pre == "mpt") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
-            } else if (
-                    tokenizer_pre == "starcoder") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
-            } else if (
-                    tokenizer_pre == "gpt-2"   ||
-                    tokenizer_pre == "phi-2"   ||
-                    tokenizer_pre == "jina-es" ||
-                    tokenizer_pre == "jina-de" ||
-                    tokenizer_pre == "gigachat"   ||
-                    tokenizer_pre == "jina-v1-en" ||
-                    tokenizer_pre == "jina-v2-es" ||
-                    tokenizer_pre == "jina-v2-de" ||
-                    tokenizer_pre == "jina-v2-code" ||
-                    tokenizer_pre == "roberta-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
-            } else if (
-                    tokenizer_pre == "refact") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
-            } else if (
-                tokenizer_pre == "command-r") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "qwen2") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "stablelm2") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
-            } else if (
-                tokenizer_pre == "olmo") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
-            } else if (
-                tokenizer_pre == "dbrx") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
-            } else if (
-                tokenizer_pre == "smaug-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
-            } else if (
-                tokenizer_pre == "poro-chat") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "chatglm-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
-                vocab.special_bos_id = LLAMA_TOKEN_NULL;
-            } else if (
-                tokenizer_pre == "viking") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "jais") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
-            } else if (
-                tokenizer_pre == "tekken") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
-                vocab.tokenizer_clean_spaces = false;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
-            } else if (
-                tokenizer_pre == "smollm") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "codeshell") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
-            } else if (
-                tokenizer_pre == "bloom") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_BLOOM;
-            } else if (
-                tokenizer_pre == "gpt3-finnish") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
-            } else if (
-                tokenizer_pre == "exaone") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
-            } else if (
-                tokenizer_pre == "chameleon") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
-                vocab.tokenizer_add_bos = true;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "minerva-7b") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA;
-            } else if (
-                tokenizer_pre == "megrez") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
-            } else {
-                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
-            }
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = true;
-            vocab.tokenizer_clean_spaces = false;
-            vocab.tokenizer_add_bos = true;
-            vocab.tokenizer_add_eos = false;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = true;
-            vocab.tokenizer_add_bos = true;
-            vocab.tokenizer_add_eos = false;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_bos = false;
-            vocab.tokenizer_add_eos = true;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = false;
-            vocab.tokenizer_add_bos = false;
-            vocab.tokenizer_add_eos = false;
-        } else {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-        }
-
-        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      vocab.tokenizer_add_space_prefix,         false);
-        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
-    }
-
-    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
-    if (token_idx == -1) {
-        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
-    }
-
-    const float * scores = nullptr;
-    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
-    if (score_idx != -1) {
-        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
-    }
-
-    const int * toktypes = nullptr;
-    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
-    if (toktype_idx != -1) {
-        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
-    }
-
-    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
-
-    vocab.n_vocab = n_vocab;
-    vocab.id_to_token.resize(n_vocab);
-
-    for (uint32_t i = 0; i < n_vocab; i++) {
-        std::string word = gguf_get_arr_str(ctx, token_idx, i);
-
-        //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
-        if (word.empty()) {
-            LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
-            word = "[EMPTY_" + std::to_string(i) + "]";
-        }
-
-        vocab.token_to_id[word] = i;
-        vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
-
-        auto & token_data = vocab.id_to_token[i];
-        token_data.text  = std::move(word);
-        token_data.score = scores ? scores[i] : 0.0f;
-        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
-
-        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
-            switch(toktypes[i]) {
-                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
-                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
-                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
-                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
-                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
-                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
-                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
-                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
-            }
-        }
-    }
-    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
-
-    vocab.init_tokenizer();
-
-    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
-    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-        try {
-            vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
-        } catch (const std::exception & e) {
-            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
-            vocab.linefeed_id = vocab.special_pad_id;
-        }
-    } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
-        vocab.linefeed_id = vocab.special_pad_id;
-    } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\n", false);
-        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
-        vocab.linefeed_id = ids[0];
-    } else {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
-
-        //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
-        if (ids.empty()) {
-            LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
-            vocab.linefeed_id = vocab.special_pad_id;
-        } else {
-            vocab.linefeed_id = ids[0];
-        }
-    }
-
-    // special tokens
-    {
-        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
-            { LLM_KV_TOKENIZER_BOS_ID,     vocab.special_bos_id     },
-            { LLM_KV_TOKENIZER_EOS_ID,     vocab.special_eos_id     },
-            { LLM_KV_TOKENIZER_EOT_ID,     vocab.special_eot_id     },
-            { LLM_KV_TOKENIZER_EOM_ID,     vocab.special_eom_id     },
-            { LLM_KV_TOKENIZER_UNK_ID,     vocab.special_unk_id     },
-            { LLM_KV_TOKENIZER_SEP_ID,     vocab.special_sep_id     },
-            { LLM_KV_TOKENIZER_PAD_ID,     vocab.special_pad_id     },
-            { LLM_KV_TOKENIZER_CLS_ID,     vocab.special_cls_id     },
-            { LLM_KV_TOKENIZER_MASK_ID,    vocab.special_mask_id    },
-            { LLM_KV_TOKENIZER_FIM_PRE_ID, vocab.special_fim_pre_id },
-            { LLM_KV_TOKENIZER_FIM_SUF_ID, vocab.special_fim_suf_id },
-            { LLM_KV_TOKENIZER_FIM_MID_ID, vocab.special_fim_mid_id },
-            { LLM_KV_TOKENIZER_FIM_PAD_ID, vocab.special_fim_pad_id },
-            { LLM_KV_TOKENIZER_FIM_REP_ID, vocab.special_fim_rep_id },
-            { LLM_KV_TOKENIZER_FIM_SEP_ID, vocab.special_fim_sep_id },
-
-            // deprecated
-            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_fim_pre_id },
-            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_fim_suf_id },
-            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_fim_mid_id },
-        };
-
-        for (const auto & it : special_token_types) {
-            const std::string & key = kv(std::get<0>(it));
-            int32_t & id = std::get<1>(it);
-
-            uint32_t new_id;
-            if (!ml.get_key(std::get<0>(it), new_id, false)) {
-                continue;
-            }
-            if (new_id >= vocab.id_to_token.size()) {
-                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
-                    __func__, key.c_str(), new_id, id);
-            } else {
-                id = new_id;
-            }
-        }
-
-        // Handle add_bos_token and add_eos_token
-        {
-            bool temp = true;
-
-            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
-                vocab.tokenizer_add_bos = temp;
-            }
-            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
-                vocab.tokenizer_add_eos = temp;
-            }
-        }
-
-        // auto-detect special tokens by text
-        // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
-        //       for now, we apply this workaround to find the tokens based on their text
-
-        for (const auto & t : vocab.token_to_id) {
-            // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
-            if (vocab.special_eot_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|eot_id|>"
-                        || t.first == "<|im_end|>"
-                        || t.first == "<|end|>"
-                        || t.first == "<end_of_turn>"
-                        || t.first == "<|endoftext|>"
-                        || t.first == "<EOT>"
-                        || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
-                   ) {
-                    vocab.special_eot_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find EOM token: "<|eom_id|>"
-            if (vocab.special_eom_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|eom_id|>"
-                        ) {
-                    vocab.special_eom_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
-            if (vocab.special_fim_pre_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_prefix|>"  // Qwen
-                        || t.first == "<fim-prefix>"
-                        || t.first == "<｜fim▁begin｜>" // DeepSeek
-                        || t.first == "<PRE>"
-                        ) {
-                    vocab.special_fim_pre_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
-            if (vocab.special_fim_suf_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_suffix|>" // Qwen
-                        || t.first == "<fim-suffix>"
-                        || t.first == "<｜fim▁hole｜>" // DeepSeek
-                        || t.first == "<SUF>"
-                        ) {
-                    vocab.special_fim_suf_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
-            if (vocab.special_fim_mid_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_middle|>" // Qwen
-                        || t.first == "<fim-middle>"
-                        || t.first == "<｜fim▁end｜>"  // DeepSeek
-                        || t.first == "<MID>"
-                        ) {
-                    vocab.special_fim_mid_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
-            if (vocab.special_fim_pad_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_pad|>" // Qwen
-                        || t.first == "<fim-pad>"
-                        || t.first == "<PAD>"
-                        ) {
-                    vocab.special_fim_pad_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
-            if (vocab.special_fim_rep_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_repo|>"  // Qwen
-                        || t.first == "<|repo_name|>"
-                        || t.first == "<fim-repo>"
-                        || t.first == "<REPO>"
-                        ) {
-                    vocab.special_fim_rep_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-
-            // find FIM_SEP token: "<|file_sep|>"
-            if (vocab.special_fim_sep_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|file_sep|>" // Qwen
-                        ) {
-                    vocab.special_fim_sep_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
-        }
-
-        // maintain a list of tokens that cause end-of-generation
-        // this is currently determined based on the token text, which is obviously not ideal
-        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
-        vocab.special_eog_ids.clear();
-
-        if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_pad_id);
-        }
-
-        if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_rep_id);
-        }
-
-        if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_sep_id);
-        }
-
-        for (const auto & t : vocab.token_to_id) {
-            if (false
-                    || t.first == "<|eot_id|>"
-                    || t.first == "<|im_end|>"
-                    || t.first == "<|end|>"
-                    || t.first == "<end_of_turn>"
-                    || t.first == "<|endoftext|>"
-                    || t.first == "<|eom_id|>"
-                    || t.first == "<EOT>"
-               ) {
-                vocab.special_eog_ids.insert(t.second);
-                if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                    LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                            __func__, t.second, t.first.c_str());
-                    vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                }
-            } else {
-                // token is control, but not marked as EOG -> print a debug log
-                if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) {
-                    LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
-                            __func__, t.second, t.first.c_str());
-                }
-            }
-        }
-
-        // sanity checks
-        if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eos_id);
-            LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eot_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eot_id);
-            LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eom_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eom_id);
-            LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-    }
-
-    // build special tokens cache
-    {
-        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
-            if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
-                vocab.cache_special_tokens.push_back(id);
-            }
-        }
-
-        std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
-            [&] (const llama_vocab::id a, const llama_vocab::id b) {
-                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
-            }
-        );
-
-        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
-    }
-
-    // build token to piece cache
-    {
-        size_t size_cache = 0;
-
-        std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
-
-        for (uint32_t id = 0; id < n_vocab; ++id) {
-            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
-
-            size_cache += cache_token_to_piece[id].size();
-        }
-
-        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
-
-        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
-    }
-
-    // Handle per token attributes
-    //NOTE: Each model customizes per token attributes.
-    //NOTE: Per token attributes are missing from the GGUF file.
-    //TODO: Extract attributes from GGUF file.
-    {
-        auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
-            for (auto substr : substrs) {
-                if (str.find(substr) < std::string::npos) {
-                    return true;
-                }
-            }
-            return false;
-        };
-
-        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
-            uint32_t current = vocab.id_to_token.at(id).attr;
-            current = value ? (current | attr) : (current & ~attr);
-            vocab.id_to_token[id].attr = (llama_token_attr) current;
-        };
-
-        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
-            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
-        };
-
-        std::string model_name;
-        std::string tokenizer_pre;
-
-        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
-        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
-
-        // model name to lowercase
-        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
-            [] (const std::string::value_type x) {
-                return std::tolower(x);
-            }
-        );
-
-        // set attributes by model/tokenizer name
-        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
-            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
-        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
-            for (auto id : vocab.cache_special_tokens) {
-                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
-            }
-            for (auto token : {"</s>"}) {
-                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
-            }
-            for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
-                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
-            }
-        }
-    }
-}
-
-static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
-    const auto & hparams = model.hparams;
-    const auto & vocab   = model.vocab;
-
-    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
-
-    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
-        bool is_var = false;
-
-        std::vector<uint32_t> v;
-        for (uint32_t i = 0; i < n; ++i) {
-            v.push_back(f(i));
-            if (v[i] != v[0]) {
-                is_var = true;
-            }
-        }
-
-        std::stringstream ss;
-
-        if (is_var) {
-            ss << "[";
-            for (uint32_t i = 0; i < n; ++i) {
-                ss << v[i];
-                if (i < n - 1) {
-                    ss << ", ";
-                }
-            }
-            ss << "]";
-        } else {
-            ss << v[0];
-        }
-
-        return ss.str();
-    };
-
-    // hparams
-    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
-    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
-    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
-    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
-    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
-    LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
-
-    if (!hparams.vocab_only) {
-        LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
-        LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
-        LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
-        LLAMA_LOG_INFO("%s: n_head           = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
-        LLAMA_LOG_INFO("%s: n_swa            = %u\n",     __func__, hparams.n_swa);
-        LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
-        LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
-        LLAMA_LOG_INFO("%s: n_gqa            = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
-        LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
-        LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
-        LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
-        LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
-        LLAMA_LOG_INFO("%s: n_ff             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
-        LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-        LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
-        LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
-        LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
-        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
-        LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
-        LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
-        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
-        LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
-        LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
-        LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
-        LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
-        LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
-        LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms   = %d\n",     __func__, hparams.ssm_dt_b_c_rms);
-    }
-
-    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
-    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
-    if (ml.n_elements >= 1e12) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
-    } else if (ml.n_elements >= 1e9) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
-    } else if (ml.n_elements >= 1e6) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
-    } else {
-        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
-    }
-    if (ml.n_bytes < GiB) {
-        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
-    } else {
-        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
-    }
-
-    // general kv
-    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
-
-    // special tokens
-    if (vocab.special_bos_id  != -1)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
-    if (vocab.special_eos_id  != -1)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
-    if (vocab.special_eot_id  != -1)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
-    if (vocab.special_eom_id  != -1)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
-    if (vocab.special_unk_id  != -1)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
-    if (vocab.special_sep_id  != -1)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
-    if (vocab.special_pad_id  != -1)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
-    if (vocab.special_cls_id  != -1)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
-    if (vocab.special_mask_id != -1)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
-
-    if (vocab.linefeed_id != -1)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
-
-    if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
-    if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
-    if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
-    if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
-    if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
-    if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
-
-    for (const auto & id : vocab.special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
-    }
-
-    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
-
-    if (model.arch == LLM_ARCH_DEEPSEEK) {
-        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
-        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
-        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
-        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
-    }
-
-    if (model.arch == LLM_ARCH_DEEPSEEK2) {
-        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
-        LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
-        LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
-        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
-        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
-        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
-        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
-    }
-
-    if (model.arch == LLM_ARCH_QWEN2MOE) {
-        LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
-        LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
-    }
-
-    if (model.arch == LLM_ARCH_MINICPM || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
-        LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
-        LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
-        LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
-    }
-}
-
-enum llm_tensor_layer {
-    LLM_TENSOR_LAYER_INPUT,
-    LLM_TENSOR_LAYER_REPEATING,
-    LLM_TENSOR_LAYER_OUTPUT,
-};
-
-struct llm_tensor_info {
-    llm_tensor_layer layer;
-    ggml_op op;
-};
-
-static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
-    {LLM_TENSOR_TOKEN_EMBD,                 {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_POS_EMBD,                   {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_TOKEN_TYPES,                {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
-    {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
-    {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
-    {LLM_TENSOR_ROPE_FREQS,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
-    {LLM_TENSOR_ROPE_FACTORS_LONG,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
-    {LLM_TENSOR_ROPE_FACTORS_SHORT,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
-    {LLM_TENSOR_ATTN_Q,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_K,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_V,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_QKV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_OUT,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_DOWN,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_UP,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_DOWN_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_UP_SHEXP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_Q_A,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_Q_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_KV_A_MQA,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_Q,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_K,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_V,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_QKV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_OUT,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_DOWN,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_UP,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_DOWN_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE_SHEXP,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_UP_SHEXP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_Q_A,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_Q_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_KV_A_MQA,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_V,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_ATTN_OUT,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_CROSS_ATTN_Q,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_CROSS_ATTN_K,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_CROSS_ATTN_V,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_CROSS_ATTN_OUT,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_FFN_GATE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_FFN_DOWN,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_DEC_FFN_UP,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_ATTN_V,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_ATTN_OUT,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_FFN_GATE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_FFN_DOWN,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ENC_FFN_UP,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE_INP_SHEXP,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_GATE_INP,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_SSM_IN,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_SSM_X,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_SSM_DT,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_SSM_OUT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_W1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_W2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_DECAY_W1,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_DECAY_W2,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_KEY,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_VALUE,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_RECEPTANCE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_GATE,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_TIME_MIX_OUTPUT,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CHANNEL_MIX_KEY,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CHANNEL_MIX_VALUE,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_FFN_ACT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}},
-    {LLM_TENSOR_SSM_CONV1D,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
-    {LLM_TENSOR_SSM_A,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}},
-    {LLM_TENSOR_SSM_D,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_TIME_MIX_LERP_X,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_TIME_MIX_LN,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_CHANNEL_MIX_LERP_K,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_CHANNEL_MIX_LERP_R,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_TIME_MIX_LERP_W,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_LERP_K,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_LERP_V,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_LERP_R,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_LERP_G,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_DECAY,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
-    {LLM_TENSOR_TIME_MIX_FIRST,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV6}},
-    {LLM_TENSOR_ATTN_NORM,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_NORM_2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_OUT_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_POST_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_FFN_NORM,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_FFN_POST_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_FFN_NORM_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_Q_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_K_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_LAYER_OUT_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_Q_A_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_KV_A_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ATTN_SUB_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_FFN_SUB_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_DEC_ATTN_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_DEC_CROSS_ATTN_NORM,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_DEC_FFN_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ENC_ATTN_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_ENC_FFN_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_DEC_ATTN_REL_B,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_ENC_ATTN_REL_B,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_FFN_DOWN_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
-    {LLM_TENSOR_FFN_GATE_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
-    {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
-    // this tensor is loaded for T5, but never used
-    {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-    {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
-    {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_POS_NET_NORM2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_POS_NET_CONV1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
-    {LLM_TENSOR_POS_NET_CONV2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
-    {LLM_TENSOR_POS_NET_ATTN_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_POS_NET_ATTN_Q,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_POS_NET_ATTN_K,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_POS_NET_ATTN_V,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_POS_NET_ATTN_OUT,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CONVNEXT_DW,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
-    {LLM_TENSOR_CONVNEXT_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-    {LLM_TENSOR_CONVNEXT_PW1,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CONVNEXT_PW2,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_CONVNEXT_GAMMA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-};
-
 // checks if the weight tensor can be used with the specified buffer type and device
 static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) {
     GGML_ASSERT(w != nullptr);
@@ -7841,11 +422,12 @@ static bool llm_load_tensors(
                 tn_tensor = LLM_TENSOR_OUTPUT;
             }
 
-            auto it = llm_tensor_info_mapping.find(tn_tensor);
-            if (it == llm_tensor_info_mapping.end()) {
+            llm_tensor_info info;
+            try {
+                info = llm_tensor_info_for(tn_tensor);
+            } catch (const std::out_of_range & e) {
                 throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str()));
             }
-            const auto & info = it->second;
 
             // tensors with "bias" suffix are always used with GGML_OP_ADD
             ggml_op op;
@@ -14644,9 +7226,9 @@ struct llm_build_context {
 
                 // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
                 switch (model.type) {
-                    case e_model::MODEL_2B:
-                    case e_model::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
-                    case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+                    case llm_type::MODEL_2B:
+                    case llm_type::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
+                    case llm_type::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
                     default: GGML_ABORT("fatal error");
                 };
                 cb(Qcur, "Qcur_scaled", il);
@@ -17896,572 +10478,6 @@ static struct ggml_cgraph * llama_build_graph(
     return result;
 }
 
-static void llama_set_k_shift(llama_context & lctx) {
-    const int64_t kv_size = lctx.kv_self.size;
-
-    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
-
-    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
-
-    for (int i = 0; i < kv_size; ++i) {
-        data[i] = lctx.kv_self.cells[i].delta;
-    }
-}
-
-static void llama_set_s_copy(llama_context & lctx) {
-    const int64_t kv_size = lctx.kv_self.size;
-
-    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
-
-    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
-
-    for (int i = 0; i < kv_size; ++i) {
-        data[i] = lctx.kv_self.cells[i].src;
-    }
-}
-
-static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
-    // TODO move to hparams if a T5 variant appears that uses a different value
-    const int64_t max_distance = 128;
-
-    if (bidirectional) {
-        n_buckets >>= 1;
-    }
-
-    const int64_t max_exact = n_buckets >> 1;
-
-    int32_t relative_position = x - y;
-    int32_t relative_bucket = 0;
-    if (bidirectional) {
-        relative_bucket += (relative_position > 0) * n_buckets;
-        relative_position = abs(relative_position);
-    } else {
-        relative_position = -std::min<int32_t>(relative_position, 0);
-    }
-    int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
-    relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
-    relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
-    return relative_bucket;
-}
-
-static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
-    //
-    // set input data
-    //
-
-    const auto & hparams = lctx.model.hparams;
-    const auto & cparams = lctx.cparams;
-    const auto & kv_self = lctx.kv_self;
-
-    if (ubatch.token) {
-        const int64_t n_tokens = ubatch.n_tokens;
-
-        ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
-    }
-
-    if (ubatch.embd) {
-        const int64_t n_embd   = hparams.n_embd;
-        const int64_t n_tokens = ubatch.n_tokens;
-
-        ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
-    }
-
-    if (ubatch.pos && lctx.inp_pos) {
-        const int64_t n_tokens = ubatch.n_tokens;
-        auto n_pos = lctx.n_pos_per_token;
-        ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*n_pos*ggml_element_size(lctx.inp_pos));
-    }
-
-    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
-        //GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
-
-        if (!lctx.inp_out_ids) {
-            LLAMA_LOG_WARN("%s: 'lctx.inp_out_ids' is not created\n", __func__);
-        } else {
-            const int64_t n_tokens = ubatch.n_tokens;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
-            int32_t * data = (int32_t *) lctx.inp_out_ids->data;
-
-            if (lctx.n_outputs == n_tokens) {
-                for (int i = 0; i < n_tokens; ++i) {
-                    data[i] = i;
-                }
-            } else if (ubatch.output) {
-                int32_t n_outputs = 0;
-                for (int i = 0; i < n_tokens; ++i) {
-                    if (ubatch.output[i]) {
-                        data[n_outputs++] = i;
-                    }
-                }
-                // the graph needs to have been passed the correct number of outputs
-                GGML_ASSERT(lctx.n_outputs == n_outputs);
-            } else if (lctx.n_outputs == 1) {
-                // only keep last output
-                data[0] = n_tokens - 1;
-            } else {
-                GGML_ASSERT(lctx.n_outputs == 0);
-            }
-        }
-    }
-
-    GGML_ASSERT(
-        // (!a || b) is a logical implication (a -> b)
-        // !hparams.causal_attn -> !cparams.causal_attn
-        (hparams.causal_attn || !cparams.causal_attn) &&
-        "causal attention is not supported by this model"
-    );
-
-    if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
-        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
-        if (cparams.causal_attn && !lctx.is_encoding) {
-            const int64_t n_kv         = kv_self.n;
-            const int64_t n_tokens     = ubatch.n_tokens;
-            const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-            const int64_t n_seqs       = ubatch.n_seqs;
-
-
-            float * data     = nullptr;
-            float * data_swa = nullptr;
-
-            if (lctx.inp_KQ_mask) {
-                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
-                data = (float *) lctx.inp_KQ_mask->data;
-            }
-
-            if (lctx.inp_KQ_mask_swa) {
-                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
-                data_swa = (float *) lctx.inp_KQ_mask_swa->data;
-            }
-
-            // For causal attention, use only the previous KV cells
-            // of the correct sequence for each token of the ubatch.
-            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
-            for (int h = 0; h < 1; ++h) {
-                for (int s = 0; s < n_seqs; ++s) {
-                    const llama_seq_id seq_id = ubatch.seq_id[s][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const llama_pos pos = ubatch.pos[s*n_seq_tokens + j];
-
-                        for (int i = 0; i < n_kv; ++i) {
-                            float f;
-                            if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                                f = -INFINITY;
-                            } else {
-                                if (hparams.use_alibi) {
-                                    f = -std::abs(kv_self.cells[i].pos - pos);
-                                } else {
-                                    f = 0.0f;
-                                }
-                            }
-
-                            if (data) {
-                                data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
-                            }
-
-                            // may need to cut off old tokens for sliding window
-                            if (data_swa) {
-                                if (pos - kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
-                                    f = -INFINITY;
-                                }
-                                data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
-                            }
-                        }
-                    }
-                }
-
-                if (data) {
-                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                        for (int j = 0; j < n_kv; ++j) {
-                            data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
-                        }
-                    }
-                }
-
-                if (data_swa) {
-                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                        for (int j = 0; j < n_kv; ++j) {
-                            data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
-                        }
-                    }
-                }
-            }
-        } else {
-            const int64_t n_tokens     = ubatch.n_tokens;
-            const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-            const int64_t n_seqs       = ubatch.n_seqs;
-            // when using kv cache, the mask needs to match the kv cache size
-            const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
-
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
-
-            float * data = (float *) lctx.inp_KQ_mask->data;
-
-            for (int h = 0; h < 1; ++h) {
-                for (int s1 = 0; s1 < n_seqs; ++s1) {
-                    const llama_seq_id seq_id = ubatch.seq_id[s1][0];
-
-                    for (int j = 0; j < n_seq_tokens; ++j) {
-                        const int32_t tj = s1*n_seq_tokens + j;
-
-                        for (int s0 = 0; s0 < n_seqs; ++s0) {
-                            for (int i = 0; i < n_seq_tokens; ++i) {
-                                const int32_t ti = s0*n_seq_tokens + i;
-                                float f = -INFINITY;
-
-                                for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) {
-                                    if (ubatch.seq_id[s0][s] == seq_id) {
-                                        if (hparams.use_alibi) {
-                                            f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]);
-                                        } else {
-                                            f = 0.0f;
-                                        }
-                                        break;
-                                    }
-                                }
-
-                                data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
-                            }
-                        }
-
-                        for (int i = n_tokens; i < n_stride; ++i) {
-                            data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
-        const int64_t n_tokens     = ubatch.n_tokens;
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-        const int64_t n_seqs       = ubatch.n_seqs;
-
-        GGML_ASSERT(lctx.inp_mean);
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
-
-        float * data = (float *) lctx.inp_mean->data;
-        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
-
-        std::vector<uint64_t> sum(n_tokens, 0);
-
-        for (int s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = ubatch.seq_id[s][0];
-
-            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
-
-            sum[seq_id] += ubatch.n_seq_tokens;
-        }
-
-        std::vector<float> div(n_tokens, 0.0f);
-        for (int i = 0; i < n_tokens; ++i) {
-            const uint64_t s = sum[i];
-            if (s > 0) {
-                div[i] = 1.0f/float(s);
-            }
-        }
-
-        for (int s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = ubatch.seq_id[s][0];
-
-            for (int i = 0; i < n_seq_tokens; ++i) {
-                data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id];
-            }
-        }
-    }
-
-    if (cparams.embeddings && (
-                cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
-                cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
-        const int64_t n_tokens     = ubatch.n_tokens;
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-        const int64_t n_seqs       = ubatch.n_seqs;
-
-        GGML_ASSERT(lctx.inp_cls);
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
-
-        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
-        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
-
-        for (int s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = ubatch.seq_id[s][0];
-
-            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
-
-            for (int i = 0; i < n_seq_tokens; ++i) {
-                const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
-
-                if (pos == 0) {
-                    data[seq_id] = s*n_seq_tokens + i;
-                }
-            }
-        }
-    }
-
-    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
-        const int64_t n_tokens     = ubatch.n_tokens;
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-        const int64_t n_seqs       = ubatch.n_seqs;
-
-        GGML_ASSERT(lctx.inp_cls);
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
-
-        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
-        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
-
-        std::vector<int> last_pos(n_tokens, -1);
-        std::vector<int> last_row(n_tokens, -1);
-
-        for (int s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = ubatch.seq_id[s][0];
-
-            // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
-
-            for (int i = 0; i < n_seq_tokens; ++i) {
-                const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
-
-                if (pos >= last_pos[seq_id]) {
-                    last_pos[seq_id] = pos;
-                    last_row[seq_id] = s*n_seq_tokens + i;
-                }
-            }
-        }
-
-        for (int i = 0; i < n_tokens; ++i) {
-            if (last_row[i] >= 0) {
-                data[i] = last_row[i];
-            }
-        }
-    }
-
-    if (kv_self.recurrent) {
-        const int64_t n_kv = kv_self.n;
-
-        if (lctx.inp_s_mask) {
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
-            float * data = (float *) lctx.inp_s_mask->data;
-
-            // clear unused states
-            for (int i = 0; i < n_kv; ++i) {
-                const uint32_t  cell_id = i + kv_self.head;
-                llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id];
-
-                data[i] = (float) (kv_cell.src >= 0);
-
-                // only clear once
-                if (kv_cell.src < 0) {
-                    kv_cell.src = cell_id;
-                }
-            }
-        }
-
-        if (lctx.inp_s_copy) {
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
-            int32_t * data = (int32_t *) lctx.inp_s_copy->data;
-
-            // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
-            for (uint32_t i = 0; i < n_kv; ++i) {
-                const uint32_t  cell_id = i + kv_self.head;
-                llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id];
-
-                // prevent out-of-bound sources
-                if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self.size) {
-                    kv_cell.src = cell_id;
-                }
-
-                data[i] = kv_cell.src;
-
-                // ensure copy only happens once
-                if (kv_cell.src != (int32_t) cell_id) {
-                    kv_cell.src = cell_id;
-                }
-            }
-        }
-    }
-
-    if (lctx.inp_pos_bucket) {
-        const int64_t n_tokens = ubatch.n_tokens;
-
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
-        GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
-
-        int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
-
-        if (!lctx.is_encoding) {
-            const int64_t n_kv = kv_self.n;
-            for (int h = 0; h < 1; ++h) {
-                for (int j = 0; j < n_tokens; ++j) {
-                    for (int i = 0; i < n_kv; ++i) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
-                    }
-                }
-            }
-        } else {
-            for (int h = 0; h < 1; ++h) {
-                for (int j = 0; j < n_tokens; ++j) {
-                    for (int i = 0; i < n_tokens; ++i) {
-                        data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
-                    }
-                }
-            }
-        }
-    }
-
-    if (!lctx.is_encoding && lctx.inp_embd_enc) {
-        assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
-        assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size());
-
-        ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
-    }
-
-    if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
-        const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
-        const int64_t n_tokens = ubatch.n_tokens;
-
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
-        GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
-
-        float * data = (float *) lctx.inp_KQ_mask_cross->data;
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                for (int i = 0; i < n_output_enc; ++i) {
-                    float f = -INFINITY;
-                    for (int s = 0; s < ubatch.n_seq_id[j]; ++s) {
-                        const llama_seq_id seq_id = ubatch.seq_id[j][s];
-                        if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
-                            f = 0.0f;
-                        }
-                    }
-                    data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f;
-                }
-            }
-
-            for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                for (int j = 0; j < n_output_enc; ++j) {
-                    data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY;
-                }
-            }
-        }
-    }
-}
-
-// Make sure enough space is available for outputs.
-// Returns max number of outputs for which space was reserved.
-static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
-    const auto & cparams = lctx.cparams;
-    const auto & hparams = lctx.model.hparams;
-
-    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
-
-    const auto n_batch = cparams.n_batch;
-    const auto n_vocab = hparams.n_vocab;
-    const auto n_embd  = hparams.n_embd;
-
-    // TODO: use a per-batch flag for logits presence instead
-    const bool has_logits = !cparams.embeddings;
-    const bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
-
-    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
-
-    if (lctx.output_ids.empty()) {
-        // init, never resized afterwards
-        lctx.output_ids.resize(n_batch);
-    }
-
-    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0;
-    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
-
-    // alloc only when more than the current capacity is required
-    // TODO: also consider shrinking the buffer
-    if (!lctx.buf_output || prev_size < new_size) {
-        if (lctx.buf_output) {
-#ifndef NDEBUG
-            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
-            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
-#endif
-            lctx.buf_output = nullptr;
-            lctx.logits = nullptr;
-            lctx.embd = nullptr;
-        }
-
-        auto * buft = ggml_backend_cpu_buffer_type();
-        // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
-        auto * output_dev = lctx.model.dev_output.dev;
-        auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
-        if (output_dev_host_buft) {
-            buft = output_dev_host_buft;
-        }
-        lctx.buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size));
-        if (lctx.buf_output == nullptr) {
-            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
-            return 0;
-        }
-    }
-
-    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output.get());
-
-    lctx.logits = has_logits ? output_base               : nullptr;
-    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
-
-    lctx.output_size = n_outputs_max;
-    lctx.logits_size = logits_size;
-    lctx.embd_size   = embd_size;
-
-    // set all ids as invalid (negative)
-    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
-
-    ggml_backend_buffer_clear(lctx.buf_output.get(), 0);
-
-    lctx.n_outputs = 0;
-
-    return n_outputs_max;
-}
-
-// make the outputs have the same order they had in the user-provided batch
-static void llama_output_reorder(struct llama_context * ctx) {
-    std::vector<size_t> & out_ids = ctx->sbatch.out_ids;
-    if (!out_ids.empty()) {
-        uint32_t n_vocab = ctx->model.hparams.n_vocab;
-        uint32_t n_embd  = ctx->model.hparams.n_embd;
-        int32_t n_outputs = ctx->n_outputs;
-        GGML_ASSERT((size_t) n_outputs == out_ids.size());
-        // TODO: is there something more efficient which also minimizes swaps?
-        // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
-        for (int32_t i = 0; i < n_outputs - 1; ++i) {
-            int32_t j_min = i;
-            for (int32_t j = i + 1; j < n_outputs; ++j) {
-                if (out_ids[j] < out_ids[j_min]) {
-                    j_min = j;
-                }
-            }
-            if (j_min == i) { continue; }
-            std::swap(out_ids[i], out_ids[j_min]);
-            if (ctx->logits_size > 0) {
-                for (uint32_t k = 0; k < n_vocab; k++) {
-                    std::swap(ctx->logits[i*n_vocab + k], ctx->logits[j_min*n_vocab + k]);
-                }
-            }
-            if (ctx->embd_size > 0) {
-                for (uint32_t k = 0; k < n_embd; k++) {
-                    std::swap(ctx->embd[i*n_embd + k], ctx->embd[j_min*n_embd + k]);
-                }
-            }
-        }
-        std::fill(ctx->output_ids.begin(), ctx->output_ids.end(), -1);
-        for (int32_t i = 0; i < n_outputs; ++i) {
-            ctx->output_ids[out_ids[i]] = i;
-        }
-        out_ids.clear();
-    }
-}
-
 // returns the result of ggml_backend_sched_graph_compute_async execution
 static enum ggml_status llama_graph_compute(
           llama_context & lctx,
@@ -18513,7 +10529,8 @@ static int llama_decode_internal(
     }
 
     // temporary allocate memory for the input batch if needed
-    llama_batch_allocr batch_allocr(lctx, inp_batch);
+    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.kv_self.max_pos() + 1);
+
     const llama_batch & batch = batch_allocr.batch;
     const uint32_t n_tokens_all = batch.n_tokens;
 
@@ -18847,7 +10864,8 @@ static int llama_encode_internal(
     }
 
     // temporary allocate memory for the input batch if needed
-    llama_batch_allocr batch_allocr(lctx, inp_batch);
+    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.kv_self.max_pos() + 1);
+
     const llama_batch & batch = batch_allocr.batch;
     const uint32_t n_tokens = batch.n_tokens;
 
@@ -19297,1046 +11315,6 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
     }
 }
 
-//
-// quantization
-//
-
-struct quantize_state_internal {
-    const llama_model                 & model;
-    const llama_model_quantize_params * params;
-
-    int n_attention_wv    = 0;
-    int n_ffn_down        = 0;
-    int n_ffn_gate        = 0;
-    int n_ffn_up          = 0;
-    int i_attention_wv    = 0;
-    int i_ffn_down        = 0;
-    int i_ffn_gate        = 0;
-    int i_ffn_up          = 0;
-
-    int n_k_quantized     = 0;
-    int n_fallback        = 0;
-
-    bool has_imatrix      = false;
-
-    // used to figure out if a model shares tok_embd with the output weight
-    bool has_output       = false;
-
-    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
-        : model(model)
-        , params(params)
-        {}
-};
-
-static void llama_tensor_dequantize_internal(
-    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
-    const size_t nelements, const int nthread
-) {
-    if (output.size() < nelements) {
-        output.resize(nelements);
-    }
-    float * f32_output = (float *) output.data();
-
-    const ggml_type_traits * qtype = ggml_get_type_traits(tensor->type);
-    if (ggml_is_quantized(tensor->type)) {
-        if (qtype->to_float == NULL) {
-            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
-        }
-    } else if (tensor->type != GGML_TYPE_F16 &&
-               tensor->type != GGML_TYPE_BF16) {
-        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
-    }
-
-    if (nthread < 2) {
-        if (tensor->type == GGML_TYPE_F16) {
-            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
-        } else if (tensor->type == GGML_TYPE_BF16) {
-            ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
-        } else if (ggml_is_quantized(tensor->type)) {
-            qtype->to_float(tensor->data, f32_output, nelements);
-        } else {
-            GGML_ABORT("fatal error"); // unreachable
-        }
-        return;
-    }
-
-    size_t block_size;
-    if (tensor->type == GGML_TYPE_F16 ||
-        tensor->type == GGML_TYPE_BF16) {
-        block_size = 1;
-    } else {
-        block_size = (size_t)ggml_blck_size(tensor->type);
-    }
-
-    size_t block_size_bytes = ggml_type_size(tensor->type);
-
-    GGML_ASSERT(nelements % block_size == 0);
-    size_t nblocks = nelements / block_size;
-    size_t blocks_per_thread = nblocks / nthread;
-    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
-
-    size_t in_buff_offs = 0;
-    size_t out_buff_offs = 0;
-
-    for (int tnum = 0; tnum < nthread; tnum++) {
-        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
-        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
-        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
-
-        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
-            if (typ == GGML_TYPE_F16) {
-                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
-            } else if (typ == GGML_TYPE_BF16) {
-                ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
-            } else {
-                qtype->to_float(inbuf, outbuf, nels);
-            }
-        };
-        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
-        in_buff_offs += thr_block_bytes;
-        out_buff_offs += thr_elems;
-    }
-    for (auto & w : workers) { w.join(); }
-    workers.clear();
-}
-
-static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
-    const std::string name = ggml_get_name(tensor);
-
-    // TODO: avoid hardcoded tensor names - use the TN_* constants
-    const llm_arch arch = qs.model.arch;
-    const auto       tn = LLM_TN(arch);
-
-    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
-        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
-    };
-    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
-    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
-        if (n_expert > 1) {
-            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but occasionally randomly
-            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
-            // for getting the current layer as I initially thought, and we need to resort to parsing the
-            // tensor name.
-            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
-                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
-            }
-            if (i_layer < 0 || i_layer >= n_layer) {
-                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
-            }
-        }
-        return std::make_pair(i_layer, n_layer);
-    };
-
-    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
-    // with the quantization of the output tensor
-    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
-        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
-            new_type = qs.params->output_tensor_type;
-        } else {
-            int nx = tensor->ne[0];
-            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
-                new_type = GGML_TYPE_Q8_0;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
-                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
-                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-                new_type = GGML_TYPE_Q5_K;
-            }
-            else if (new_type != GGML_TYPE_Q8_0) {
-                new_type = GGML_TYPE_Q6_K;
-            }
-        }
-    } else if (name == "token_embd.weight") {
-        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
-            new_type = qs.params->token_embedding_type;
-        } else {
-            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
-                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-                new_type = GGML_TYPE_Q2_K;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
-                new_type = GGML_TYPE_IQ3_S;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-                new_type = GGML_TYPE_IQ3_S;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0) {
-                new_type = GGML_TYPE_Q4_K;
-            }
-        }
-    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
-               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-        if (name.find("attn_v.weight") != std::string::npos) {
-            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
-            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
-            ++qs.i_attention_wv;
-        }
-        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (name.find("ffn_down") != std::string::npos) {
-            if (qs.i_ffn_down < qs.n_ffn_down/8) {
-                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
-            }
-            ++qs.i_ffn_down;
-        }
-        else if (name.find("attn_output.weight") != std::string::npos) {
-            if (qs.model.hparams.n_expert == 8) {
-                new_type = GGML_TYPE_Q5_K;
-            } else {
-                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
-            }
-        }
-    } else if (name.find("attn_v.weight") != std::string::npos) {
-        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
-            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
-        }
-        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
-        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q5_K;
-        }
-        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
-                use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
-        if (qs.model.type == MODEL_70B) {
-            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
-            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
-            // nearly negligible increase in model size by quantizing this tensor with more bits:
-            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
-        }
-        if (qs.model.hparams.n_expert == 8) {
-            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
-            // TODO: explore better strategies
-            new_type = GGML_TYPE_Q8_0;
-        }
-        ++qs.i_attention_wv;
-    } else if (name.find("attn_k.weight") != std::string::npos) {
-        if (qs.model.hparams.n_expert == 8) {
-            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
-            // TODO: explore better strategies
-            new_type = GGML_TYPE_Q8_0;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
-            new_type = GGML_TYPE_IQ3_XXS;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_IQ2_S;
-        }
-    } else if (name.find("attn_q.weight") != std::string::npos) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
-            new_type = GGML_TYPE_IQ3_XXS;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_IQ2_S;
-        }
-    } else if (name.find("ffn_down") != std::string::npos) {
-        auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
-        int i_layer = info.first, n_layer = info.second;
-        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
-            if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
-            new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
-                     : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
-                     : GGML_TYPE_Q3_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
-                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
-            new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
-            if (arch == LLM_ARCH_FALCON) {
-                new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
-                           use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
-            } else {
-                if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
-            }
-        }
-        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
-            new_type = GGML_TYPE_Q5_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
-            new_type = GGML_TYPE_Q5_K;
-        }
-        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
-                && qs.has_imatrix && i_layer < n_layer/8) {
-            // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
-            // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
-            // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
-            new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
-        }
-        ++qs.i_ffn_down;
-    } else if (name.find("attn_output.weight") != std::string::npos) {
-        if (arch != LLM_ARCH_FALCON) {
-            if (qs.model.hparams.n_expert == 8) {
-                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
-                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
-                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
-                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
-                    new_type = GGML_TYPE_Q5_K;
-                }
-            } else {
-                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
-            }
-        } else {
-            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
-        }
-    }
-    else if (name.find("attn_qkv.weight") != std::string::npos) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
-    }
-    else if (name.find("ffn_gate") != std::string::npos) {
-        auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
-        int i_layer = info.first, n_layer = info.second;
-        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
-            new_type = GGML_TYPE_IQ3_XXS;
-        }
-        ++qs.i_ffn_gate;
-    }
-    else if (name.find("ffn_up") != std::string::npos) {
-        auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
-        int i_layer = info.first, n_layer = info.second;
-        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
-            new_type = GGML_TYPE_IQ3_XXS;
-        }
-        ++qs.i_ffn_up;
-    }
-
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-    //}
-    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
-    //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-    //}
-    // This can be used to reduce the size of the Q5_K_S model.
-    // The associated PPL increase is fully in line with the size reduction
-    //else {
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
-    //}
-    bool convert_incompatible_tensor = false;
-    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
-        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K    || new_type == GGML_TYPE_IQ4_XS ||
-        new_type == GGML_TYPE_IQ2_XS  || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S  ||
-        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S   || new_type == GGML_TYPE_IQ3_S  ||
-        new_type == GGML_TYPE_IQ1_M) {
-        int nx = tensor->ne[0];
-        int ny = tensor->ne[1];
-        if (nx % QK_K != 0) {
-            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
-            convert_incompatible_tensor = true;
-        } else {
-            ++qs.n_k_quantized;
-        }
-    }
-    if (convert_incompatible_tensor) {
-        switch (new_type) {
-            case GGML_TYPE_TQ1_0:
-            case GGML_TYPE_TQ2_0:  new_type = GGML_TYPE_Q4_0; break;  // TODO: use a symmetric type instead
-            case GGML_TYPE_IQ2_XXS:
-            case GGML_TYPE_IQ2_XS:
-            case GGML_TYPE_IQ2_S:
-            case GGML_TYPE_IQ3_XXS:
-            case GGML_TYPE_IQ3_S:
-            case GGML_TYPE_IQ1_S:
-            case GGML_TYPE_IQ1_M:
-            case GGML_TYPE_Q2_K:
-            case GGML_TYPE_Q3_K:
-            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
-            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
-            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
-            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
-            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
-        }
-        if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
-            new_type = GGML_TYPE_F16;
-        }
-        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
-        ++qs.n_fallback;
-    }
-
-    return new_type;
-}
-
-static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
-    if (nthread < 2) {
-        // single-thread
-        size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
-        if (!ggml_validate_row_data(new_type, new_data, new_size)) {
-            throw std::runtime_error("quantized data validation failed");
-        }
-        return new_size;
-    }
-
-    std::mutex mutex;
-    int64_t counter = 0;
-    size_t new_size = 0;
-    bool valid = true;
-    auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
-            nrows, n_per_row, imatrix]() {
-        const int64_t nrows_per_chunk = chunk_size / n_per_row;
-        size_t local_size = 0;
-        while (true) {
-            std::unique_lock<std::mutex> lock(mutex);
-            int64_t first_row = counter; counter += nrows_per_chunk;
-            if (first_row >= nrows) {
-                if (local_size > 0) {
-                    new_size += local_size;
-                }
-                break;
-            }
-            lock.unlock();
-            const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
-            size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
-            local_size += this_size;
-
-            // validate the quantized data
-            const size_t row_size  = ggml_row_size(new_type, n_per_row);
-            void * this_data = (char *) new_data + first_row * row_size;
-            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
-                std::unique_lock<std::mutex> lock(mutex);
-                valid = false;
-                break;
-            }
-        }
-    };
-    for (int it = 0; it < nthread - 1; ++it) {
-        workers.emplace_back(compute);
-    }
-    compute();
-    for (auto & w : workers) { w.join(); }
-    workers.clear();
-    if (!valid) {
-        throw std::runtime_error("quantized data validation failed");
-    }
-    return new_size;
-}
-
-static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
-    ggml_type default_type;
-    llama_ftype ftype = params->ftype;
-
-    switch (params->ftype) {
-        case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
-        case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
-        case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break;
-        case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;
-
-        // K-quants
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q2_K:    default_type = GGML_TYPE_Q2_K;    break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  default_type = GGML_TYPE_IQ3_S;   break;
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  default_type = GGML_TYPE_Q5_K;    break;
-        case LLAMA_FTYPE_MOSTLY_Q6_K:    default_type = GGML_TYPE_Q6_K;    break;
-        case LLAMA_FTYPE_MOSTLY_TQ1_0:   default_type = GGML_TYPE_TQ1_0;   break;
-        case LLAMA_FTYPE_MOSTLY_TQ2_0:   default_type = GGML_TYPE_TQ2_0;   break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  default_type = GGML_TYPE_IQ2_XS;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:   default_type = GGML_TYPE_IQ2_XS;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:   default_type = GGML_TYPE_IQ2_S;   break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
-        case LLAMA_FTYPE_MOSTLY_IQ1_S:   default_type = GGML_TYPE_IQ1_S;   break;
-        case LLAMA_FTYPE_MOSTLY_IQ1_M:   default_type = GGML_TYPE_IQ1_M;   break;
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
-
-        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
-    }
-
-    int nthread = params->nthread;
-
-    if (nthread <= 0) {
-        nthread = std::thread::hardware_concurrency();
-    }
-
-    // mmap consistently increases speed Linux, and also increases speed on Windows with
-    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
-#if defined(__linux__) || defined(_WIN32)
-    constexpr bool use_mmap = true;
-#else
-    constexpr bool use_mmap = false;
-#endif
-
-    llama_model_kv_override * kv_overrides = nullptr;
-    if (params->kv_overrides) {
-        auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
-        kv_overrides = v->data();
-    }
-    llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
-    ml.init_mappings(false); // no prefetching
-
-    llama_model model;
-    llm_load_arch(ml, model);
-    llm_load_hparams(ml, model);
-    llm_load_stats(ml, model);
-
-    struct quantize_state_internal qs(model, params);
-
-    if (params->only_copy) {
-        ftype = model.ftype;
-    }
-    const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
-    if (params->imatrix) {
-        imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
-        if (imatrix_data) {
-            LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
-            qs.has_imatrix = true;
-            // check imatrix for nans or infs
-            for (const auto & kv : *imatrix_data) {
-                for (float f : kv.second) {
-                    if (!std::isfinite(f)) {
-                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
-                    }
-                }
-            }
-        }
-    }
-
-    const size_t align = GGUF_DEFAULT_ALIGNMENT;
-    gguf_context_ptr ctx_out { gguf_init_empty() };
-
-    // copy the KV pairs from the input file
-    gguf_set_kv     (ctx_out.get(), ml.meta.get());
-    gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
-    gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV
-
-    // Remove split metadata
-    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
-    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
-    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
-
-    if (params->kv_overrides) {
-        const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
-        for (const auto & o : overrides) {
-            if (o.key[0] == 0) break;
-            if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
-                gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64);
-            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
-                gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64);
-            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
-                gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool);
-            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
-                gguf_set_val_str(ctx_out.get(), o.key, o.val_str);
-            } else {
-                LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
-            }
-        }
-    }
-
-    // make a list of weights
-    std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
-    tensors.reserve(ml.weights_map.size());
-    for (const auto & it : ml.weights_map) {
-        tensors.push_back(&it.second);
-    }
-
-    // keep_split requires that the weights are sorted by split index
-    if (params->keep_split) {
-        std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
-            if (a->idx == b->idx) {
-                return a->offs < b->offs;
-            }
-            return a->idx < b->idx;
-        });
-    }
-
-    for (const auto * it : tensors) {
-        const struct ggml_tensor * tensor = it->tensor;
-
-        const std::string name = ggml_get_name(tensor);
-
-        // TODO: avoid hardcoded tensor names - use the TN_* constants
-        if (name.find("attn_v.weight")   != std::string::npos ||
-            name.find("attn_qkv.weight") != std::string::npos ||
-            name.find("attn_kv_b.weight")!= std::string::npos) {
-            ++qs.n_attention_wv;
-        } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
-            qs.has_output = true;
-        }
-    }
-
-    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
-
-    // sanity checks
-    {
-        const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
-        // attention layers have a non-zero number of kv heads
-        int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
-        if (llama_model_has_encoder(&model)) {
-            n_attn_layer *= 3;
-        }
-        GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
-    }
-
-    size_t total_size_org = 0;
-    size_t total_size_new = 0;
-
-    std::vector<std::thread> workers;
-    workers.reserve(nthread);
-
-    int idx = 0;
-
-    std::vector<no_init<uint8_t>> read_data;
-    std::vector<no_init<uint8_t>> work;
-    std::vector<no_init<float>> f32_conv_buf;
-
-    uint16_t n_split = 1;
-
-    // Assume split index is continuous
-    if (params->keep_split) {
-        for (const auto * it : tensors) {
-            n_split = std::max(uint16_t(it->idx + 1), n_split);
-        }
-    }
-    std::vector<gguf_context_ptr> ctx_outs(n_split);
-    ctx_outs[0] = std::move(ctx_out);
-
-    // populate the original tensors so we get an initial meta data
-    for (const auto * it : tensors) {
-        uint16_t i_split = params->keep_split ? it->idx : 0;
-        struct ggml_tensor * tensor = it->tensor;
-        if (!ctx_outs[i_split]) {
-            ctx_outs[i_split].reset(gguf_init_empty());
-        }
-        gguf_add_tensor(ctx_outs[i_split].get(), tensor);
-    }
-
-    // Set split info if needed
-    if (n_split > 1) {
-        for (size_t i = 0; i < ctx_outs.size(); ++i) {
-            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
-            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
-            gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
-        }
-    }
-
-    int cur_split = -1;
-    std::ofstream fout;
-    auto close_ofstream = [&]() {
-        // Write metadata and close file handler
-        if (fout.is_open()) {
-            fout.seekp(0);
-            std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split].get()));
-            gguf_get_meta_data(ctx_outs[cur_split].get(), data.data());
-            fout.write((const char *) data.data(), data.size());
-            fout.close();
-        }
-    };
-    auto new_ofstream = [&](int index) {
-        cur_split = index;
-        GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
-        std::string fname = fname_out;
-        if (params->keep_split) {
-            char split_path[PATH_MAX] = {0};
-            llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split);
-            fname = std::string(split_path);
-        }
-
-        fout = std::ofstream(fname, std::ios::binary);
-        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
-        const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get());
-        // placeholder for the meta data
-        ::zeros(fout, meta_size);
-    };
-
-    const auto tn = LLM_TN(model.arch);
-    new_ofstream(0);
-    for (const auto * it : tensors) {
-        const auto & weight = *it;
-        struct ggml_tensor * tensor = weight.tensor;
-        if (weight.idx != cur_split && params->keep_split) {
-            close_ofstream();
-            new_ofstream(weight.idx);
-        }
-
-        const std::string name = ggml_get_name(tensor);
-
-        if (!ml.use_mmap) {
-            if (read_data.size() < ggml_nbytes(tensor)) {
-                read_data.resize(ggml_nbytes(tensor));
-            }
-            tensor->data = read_data.data();
-        }
-        ml.load_data_for(tensor);
-
-        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
-               ++idx, ml.n_tensors,
-               ggml_get_name(tensor),
-               llama_format_tensor_shape(tensor).c_str(),
-               ggml_type_name(tensor->type));
-
-        // This used to be a regex, but <regex> has an extreme cost to compile times.
-        bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
-
-        // quantize only 2D and 3D tensors (experts)
-        quantize &= (ggml_n_dims(tensor) >= 2);
-
-        // do not quantize norm tensors
-        quantize &= name.find("_norm.weight") == std::string::npos;
-
-        quantize &= params->quantize_output_tensor || name != "output.weight";
-        quantize &= !params->only_copy;
-
-        // do not quantize expert gating tensors
-        // NOTE: can't use LLM_TN here because the layer number is not known
-        quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
-
-        // do not quantize positional embeddings and token types (BERT)
-        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
-        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
-
-        // do not quantize Mamba's small yet 2D weights
-        // NOTE: can't use LLM_TN here because the layer number is not known
-        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
-
-        // do not quantize RWKV's time_mix_first tensors
-        quantize &= name.find("time_mix_first.weight") == std::string::npos;
-        quantize &= name.find("time_mix_w1.weight") == std::string::npos;
-        quantize &= name.find("time_mix_w2.weight") == std::string::npos;
-        quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
-        quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
-
-        // do not quantize relative position bias (T5)
-        quantize &= name.find("attn_rel_b.weight") == std::string::npos;
-
-        enum ggml_type new_type;
-        void * new_data;
-        size_t new_size;
-
-        if (quantize) {
-            new_type = default_type;
-
-            // get more optimal quantization type based on the tensor shape, layer, etc.
-            if (!params->pure && ggml_is_quantized(default_type)) {
-                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
-            }
-            if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
-                new_type = params->token_embedding_type;
-            }
-            if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
-                new_type = params->output_tensor_type;
-            }
-
-            // If we've decided to quantize to the same type the tensor is already
-            // in then there's nothing to do.
-            quantize = tensor->type != new_type;
-        }
-
-        if (!quantize) {
-            new_type = tensor->type;
-            new_data = tensor->data;
-            new_size = ggml_nbytes(tensor);
-            LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
-        } else {
-            const int64_t nelements = ggml_nelements(tensor);
-
-            const float * imatrix = nullptr;
-            if (imatrix_data) {
-                auto it = imatrix_data->find(tensor->name);
-                if (it == imatrix_data->end()) {
-                    LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
-                } else {
-                    if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
-                        imatrix = it->second.data();
-                    } else {
-                        LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
-                                int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
-
-                        // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
-                        // this is a significant error and it may be good idea to abort the process if this happens,
-                        // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
-                        // tok_embd should be ignored in this case, since it always causes this warning
-                        if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
-                            throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
-                                    int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
-                        }
-                    }
-                }
-            }
-            if ((new_type == GGML_TYPE_IQ2_XXS ||
-                 new_type == GGML_TYPE_IQ2_XS  ||
-                 new_type == GGML_TYPE_IQ2_S   ||
-                 new_type == GGML_TYPE_IQ1_S   ||
-                (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
-                (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
-                LLAMA_LOG_ERROR("\n\n============================================================\n");
-                LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
-                LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
-                LLAMA_LOG_ERROR("============================================================\n\n");
-                throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
-            }
-
-            float * f32_data;
-
-            if (tensor->type == GGML_TYPE_F32) {
-                f32_data = (float *) tensor->data;
-            } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
-                throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
-            } else {
-                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
-                f32_data = (float *) f32_conv_buf.data();
-            }
-
-            LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
-            fflush(stdout);
-
-            if (work.size() < (size_t)nelements * 4) {
-                work.resize(nelements * 4); // upper bound on size
-            }
-            new_data = work.data();
-
-            const int64_t n_per_row = tensor->ne[0];
-            const int64_t nrows = tensor->ne[1];
-
-            static const int64_t min_chunk_size = 32 * 512;
-            const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row));
-
-            const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
-            const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
-            const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
-
-            // quantize each expert separately since they have different importance matrices
-            new_size = 0;
-            for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
-                const float * f32_data_03 = f32_data + i03 * nelements_matrix;
-                void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
-                const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
-
-                new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
-            }
-            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
-        }
-        total_size_org += ggml_nbytes(tensor);
-        total_size_new += new_size;
-
-        // update the gguf meta data as we go
-        gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
-        gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data, new_size);
-
-        // write tensor data + padding
-        fout.write((const char *) new_data, new_size);
-        zeros(fout, GGML_PAD(new_size, align) - new_size);
-    }
-    close_ofstream();
-
-    LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
-    LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
-
-    if (qs.n_fallback > 0) {
-        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
-                __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
-    }
-}
-
-static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
-    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
-
-    ggml_context * ctx_init;
-    struct gguf_init_params meta_gguf_params = {
-        /* .no_alloc = */ true,
-        /* .ctx      = */ &ctx_init,
-    };
-
-    gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
-    if (!ctx_gguf) {
-        throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
-    }
-
-    ggml_context_ptr ctx { ctx_init };
-
-    // check metadata
-    {
-        auto get_kv_str = [&](const std::string & key) -> std::string {
-            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
-            return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
-        };
-        auto get_kv_f32 = [&](const std::string & key) -> float {
-            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
-            return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
-        };
-        LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
-
-        auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
-        if (general_type != "adapter") {
-            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
-        }
-
-        auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
-        auto general_arch = llm_arch_from_string(general_arch_str);
-        if (general_arch != model->arch) {
-            throw std::runtime_error("model arch and LoRA arch mismatch");
-        }
-
-        auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
-        if (adapter_type != "lora") {
-            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
-        }
-
-        adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
-    }
-
-    int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
-
-    // contexts for each buffer type
-    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
-    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
-        auto it = ctx_map.find(buft);
-        if (it == ctx_map.end()) {
-            // add a new context
-            struct ggml_init_params params = {
-                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),
-                /*.mem_buffer =*/ NULL,
-                /*.no_alloc   =*/ true,
-            };
-            ggml_context * buft_ctx = ggml_init(params);
-            if (!buft_ctx) {
-                return nullptr;
-            }
-            ctx_map[buft] = buft_ctx;
-            adapter.ctxs.emplace_back(buft_ctx);
-            return buft_ctx;
-        };
-        return it->second;
-    };
-
-    // bundle lora_a and lora_b into pairs
-    std::map<std::string, llama_lora_weight> ab_map;
-    auto str_endswith = [](const std::string & str, const std::string & suffix) {
-        return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
-    };
-    for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
-        std::string name(cur->name);
-        if (str_endswith(name, ".lora_a")) {
-            replace_all(name, ".lora_a", "");
-            if (ab_map.find(name) == ab_map.end()) {
-                ab_map[name] = llama_lora_weight(cur, nullptr);
-            } else {
-                ab_map[name].a = cur;
-            }
-        } else if (str_endswith(name, ".lora_b")) {
-            replace_all(name, ".lora_b", "");
-            if (ab_map.find(name) == ab_map.end()) {
-                ab_map[name] = llama_lora_weight(nullptr, cur);
-            } else {
-                ab_map[name].b = cur;
-            }
-        } else {
-            throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
-        }
-    }
-
-    // add tensors
-    for (auto & it : ab_map) {
-        const std::string & name = it.first;
-        llama_lora_weight & w = it.second;
-
-        if (!w.a || !w.b) {
-            throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
-        }
-
-        // device buft and device ctx
-        auto * model_tensor = llama_get_model_tensor(model, name.c_str());
-        if (!model_tensor) {
-            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
-        }
-        struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
-        // validate tensor shape
-        if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
-            throw std::runtime_error("tensor '" + name + "' has incorrect shape");
-        }
-        if (w.a->ne[1] != w.b->ne[0]) {
-            throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
-        }
-        // save tensor to adapter
-        struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
-        struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
-        ggml_set_name(tensor_a, w.a->name);
-        ggml_set_name(tensor_b, w.b->name);
-        adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
-    }
-
-    // allocate tensors / buffers and zero
-    {
-        adapter.ctxs.reserve(ctx_map.size());
-        adapter.bufs.reserve(ctx_map.size());
-        for (auto & it : ctx_map) {
-            ggml_backend_buffer_type_t buft = it.first;
-            ggml_context * ctx_dev = it.second;
-            ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
-            if (!buf) {
-                throw std::runtime_error("failed to allocate buffer for lora adapter\n");
-            }
-            LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
-            adapter.bufs.emplace_back(std::move(buf));
-        }
-    }
-
-    // set tensor data
-    {
-        llama_file gguf_file(path_lora, "rb");
-        std::vector<uint8_t> read_buf;
-        auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
-            size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
-            size_t size = ggml_nbytes(orig);
-            read_buf.resize(size);
-            gguf_file.seek(offs, SEEK_SET);
-            gguf_file.read_raw(read_buf.data(), size);
-            ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
-        };
-        for (auto & it : adapter.ab_map) {
-            auto orig = ab_map[it.first];
-            auto dev  = it.second;
-            set_tensor(orig.a, dev.a);
-            set_tensor(orig.b, dev.b);
-        }
-    }
-
-    LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
-}
-
 int32_t llama_lora_adapter_set(
             struct llama_context * ctx,
             struct llama_lora_adapter * adapter,
@@ -20345,7 +11323,9 @@ int32_t llama_lora_adapter_set(
         LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
         return -1;
     }
+
     ctx->lora_adapters[adapter] = scale;
+
     return 0;
 }
 
@@ -20357,6 +11337,7 @@ int32_t llama_lora_adapter_remove(
         ctx->lora_adapters.erase(pos);
         return 0;
     }
+
     return -1;
 }
 
@@ -20364,37 +11345,20 @@ void llama_lora_adapter_clear(struct llama_context * ctx) {
     ctx->lora_adapters.clear();
 }
 
-void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
-    delete adapter;
+// TODO: tmp
+int32_t llama_control_vector_apply(
+        struct llama_context * lctx,
+                 const float * data,
+                      size_t   len,
+                     int32_t   n_embd,
+                     int32_t   il_start,
+                     int32_t   il_end) {
+    return llama_control_vector_apply(lctx->cvec, lctx->model, data, len, n_embd, il_start, il_end);
 }
 
 //
 // interface implementation
 //
-struct llama_model_params llama_model_default_params() {
-    struct llama_model_params result = {
-        /*.devices                     =*/ nullptr,
-        /*.n_gpu_layers                =*/ 0,
-        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
-        /*.main_gpu                    =*/ 0,
-        /*.tensor_split                =*/ nullptr,
-        /*.rpc_servers                 =*/ nullptr,
-        /*.progress_callback           =*/ nullptr,
-        /*.progress_callback_user_data =*/ nullptr,
-        /*.kv_overrides                =*/ nullptr,
-        /*.vocab_only                  =*/ false,
-        /*.use_mmap                    =*/ true,
-        /*.use_mlock                   =*/ false,
-        /*.check_tensors               =*/ false,
-    };
-
-#ifdef GGML_USE_METAL
-    // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
-    result.n_gpu_layers = 999;
-#endif
-
-    return result;
-}
 
 struct llama_context_params llama_context_default_params() {
     struct llama_context_params result = {
@@ -20439,24 +11403,6 @@ struct llama_sampler_chain_params llama_sampler_chain_default_params() {
     return result;
 }
 
-struct llama_model_quantize_params llama_model_quantize_default_params() {
-    struct llama_model_quantize_params result = {
-        /*.nthread                     =*/ 0,
-        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
-        /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
-        /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
-        /*.allow_requantize            =*/ false,
-        /*.quantize_output_tensor      =*/ true,
-        /*.only_copy                   =*/ false,
-        /*.pure                        =*/ false,
-        /*.keep_split                  =*/ false,
-        /*.imatrix                     =*/ nullptr,
-        /*.kv_overrides                =*/ nullptr,
-    };
-
-    return result;
-}
-
 size_t llama_max_devices(void) {
     return 16;
 }
@@ -20499,19 +11445,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) {
     }
 }
 
-void llama_attach_threadpool(
-             struct llama_context * ctx,
-        ggml_threadpool_t   threadpool,
-        ggml_threadpool_t   threadpool_batch) {
-    ctx->threadpool       = threadpool;
-    ctx->threadpool_batch = threadpool_batch ? threadpool_batch : threadpool;
-}
-
-void llama_detach_threadpool(struct llama_context * ctx) {
-    ctx->threadpool       = nullptr;
-    ctx->threadpool_batch = nullptr;
-}
-
 void llama_backend_free(void) {
     ggml_quantize_free();
 }
@@ -20522,7 +11455,7 @@ int64_t llama_time_us(void) {
 
 struct llama_model * llama_load_model_from_file(
         const char * path_model,
-        struct llama_model_params   params) {
+        struct llama_model_params params) {
     ggml_time_init();
 
     llama_model * model = new llama_model;
@@ -20633,6 +11566,7 @@ struct llama_model * llama_load_model_from_file(
         } else if (status == -2) {
             LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
         }
+
         llama_free_model(model);
         return nullptr;
     }
@@ -20640,10 +11574,6 @@ struct llama_model * llama_load_model_from_file(
     return model;
 }
 
-void llama_free_model(struct llama_model * model) {
-    delete model;
-}
-
 struct llama_context * llama_new_context_with_model(
                  struct llama_model * model,
         struct llama_context_params   params) {
@@ -20844,7 +11774,7 @@ struct llama_context * llama_new_context_with_model(
 
         llama_set_abort_callback(ctx, params.abort_callback, params.abort_callback_data);
 
-        if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
+        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, ctx->cparams, type_k, type_v, kv_size, cparams.offload_kqv)) {
             LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
             llama_free(ctx);
             return nullptr;
@@ -20995,442 +11925,26 @@ struct llama_context * llama_new_context_with_model(
     return ctx;
 }
 
-void llama_free(struct llama_context * ctx) {
-    delete ctx;
-}
+//
+// kv cache
+//
 
-uint32_t llama_n_ctx(const struct llama_context * ctx) {
-    return ctx->cparams.n_ctx;
-}
-
-uint32_t llama_n_batch(const struct llama_context * ctx) {
-    return ctx->cparams.n_batch;
-}
-
-uint32_t llama_n_ubatch(const struct llama_context * ctx) {
-    return ctx->cparams.n_ubatch;
-}
-
-uint32_t llama_n_seq_max(const struct llama_context * ctx) {
-    return ctx->kv_self.size;
-}
-
-enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
-    return model->vocab.type;
-}
-
-int32_t llama_n_vocab(const struct llama_model * model) {
-    return model->hparams.n_vocab;
-}
-
-int32_t llama_n_ctx_train(const struct llama_model * model) {
-    return model->hparams.n_ctx_train;
-}
-
-int32_t llama_n_embd(const struct llama_model * model) {
-    return model->hparams.n_embd;
-}
-
-int32_t llama_n_layer(const struct llama_model * model) {
-    return model->hparams.n_layer;
-}
-
-int32_t llama_n_head(const struct llama_model * model) {
-    return model->hparams.n_head();
-}
-
-const struct llama_model * llama_get_model(const struct llama_context * ctx) {
-    return &ctx->model;
-}
-
-enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
-    return ctx->cparams.pooling_type;
-}
-
-enum llama_rope_type llama_rope_type(const struct llama_model * model) {
-    switch (model->arch) {
-        // these models do not use RoPE
-        case LLM_ARCH_GPT2:
-        case LLM_ARCH_GPTJ:
-        case LLM_ARCH_MPT:
-        case LLM_ARCH_REFACT:
-        case LLM_ARCH_BLOOM:
-        case LLM_ARCH_MAMBA:
-        case LLM_ARCH_JINA_BERT_V2:
-        case LLM_ARCH_T5:
-        case LLM_ARCH_T5ENCODER:
-        case LLM_ARCH_JAIS:
-        case LLM_ARCH_RWKV6:
-        case LLM_ARCH_WAVTOKENIZER_DEC:
-            return LLAMA_ROPE_TYPE_NONE;
-
-        // use what we call a normal RoPE, operating on pairs of consecutive head values
-        case LLM_ARCH_LLAMA:
-        case LLM_ARCH_DECI:
-        case LLM_ARCH_BAICHUAN:
-        case LLM_ARCH_STARCODER:
-        case LLM_ARCH_PLAMO:
-        case LLM_ARCH_ORION:
-        case LLM_ARCH_INTERNLM2:
-        case LLM_ARCH_MINICPM:
-        case LLM_ARCH_XVERSE:
-        case LLM_ARCH_COMMAND_R:
-        case LLM_ARCH_OLMO:
-        case LLM_ARCH_ARCTIC:
-        case LLM_ARCH_DEEPSEEK:
-        case LLM_ARCH_DEEPSEEK2:
-        case LLM_ARCH_CHATGLM:
-        case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
-        case LLM_ARCH_CHAMELEON:
-            return LLAMA_ROPE_TYPE_NORM;
-
-        // the pairs of head values are offset by n_rot/2
-        case LLM_ARCH_FALCON:
-        case LLM_ARCH_GROK:
-        case LLM_ARCH_DBRX:
-        case LLM_ARCH_BERT:
-        case LLM_ARCH_NOMIC_BERT:
-        case LLM_ARCH_STABLELM:
-        case LLM_ARCH_BITNET:
-        case LLM_ARCH_QWEN:
-        case LLM_ARCH_QWEN2:
-        case LLM_ARCH_QWEN2MOE:
-        case LLM_ARCH_OLMO2:
-        case LLM_ARCH_OLMOE:
-        case LLM_ARCH_PHI2:
-        case LLM_ARCH_PHI3:
-        case LLM_ARCH_GEMMA:
-        case LLM_ARCH_GEMMA2:
-        case LLM_ARCH_STARCODER2:
-        case LLM_ARCH_OPENELM:
-        case LLM_ARCH_GPTNEOX:
-        case LLM_ARCH_CODESHELL:
-        case LLM_ARCH_NEMOTRON:
-        case LLM_ARCH_EXAONE:
-        case LLM_ARCH_MINICPM3:
-            return LLAMA_ROPE_TYPE_NEOX;
-
-        case LLM_ARCH_QWEN2VL:
-            return LLAMA_ROPE_TYPE_MROPE;
-
-        // all model arches should be listed explicitly here
-        case LLM_ARCH_UNKNOWN:
-            GGML_ABORT("unknown architecture");
-    }
-
-    return LLAMA_ROPE_TYPE_NONE;
-}
-
-float llama_rope_freq_scale_train(const struct llama_model * model) {
-    return model->hparams.rope_freq_scale_train;
-}
-
-int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
-    const auto & it = model->gguf_kv.find(key);
-    if (it == model->gguf_kv.end()) {
-        if (buf_size > 0) {
-            buf[0] = '\0';
-        }
-        return -1;
-    }
-    return snprintf(buf, buf_size, "%s", it->second.c_str());
-}
-
-int32_t llama_model_meta_count(const struct llama_model * model) {
-    return (int)model->gguf_kv.size();
-}
-
-int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
-    if (i < 0 || i >= (int)model->gguf_kv.size()) {
-        if (buf_size > 0) {
-            buf[0] = '\0';
-        }
-        return -1;
-    }
-    auto it = model->gguf_kv.begin();
-    std::advance(it, i);
-    return snprintf(buf, buf_size, "%s", it->first.c_str());
-}
-
-int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
-    if (i < 0 || i >= (int)model->gguf_kv.size()) {
-        if (buf_size > 0) {
-            buf[0] = '\0';
-        }
-        return -1;
-    }
-    auto it = model->gguf_kv.begin();
-    std::advance(it, i);
-    return snprintf(buf, buf_size, "%s", it->second.c_str());
-}
-
-int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
-    return snprintf(buf, buf_size, "%s %s %s",
-            llama_model_arch_name(model->arch),
-            llama_model_type_name(model->type),
-            llama_model_ftype_name(model->ftype).c_str());
-}
-
-uint64_t llama_model_size(const struct llama_model * model) {
-    return model->n_bytes;
-}
-
-uint64_t llama_model_n_params(const struct llama_model * model) {
-    return model->n_elements;
-}
-
-bool llama_model_has_encoder(const struct llama_model * model) {
-    switch (model->arch) {
-        case LLM_ARCH_T5:        return true;
-        case LLM_ARCH_T5ENCODER: return true;
-        default:                 return false;
-    }
-}
-
-bool llama_model_has_decoder(const struct llama_model * model) {
-    switch (model->arch) {
-        case LLM_ARCH_T5ENCODER: return false;
-        default:                 return true;
-    }
-}
-
-llama_token llama_model_decoder_start_token(const struct llama_model * model) {
-    return model->hparams.dec_start_token_id;
-}
-
-bool llama_model_is_recurrent(const struct llama_model * model) {
-    switch (model->arch) {
-        case LLM_ARCH_MAMBA:  return true;
-        case LLM_ARCH_RWKV6:  return true;
-        default:              return false;
-    }
-}
-
-uint32_t llama_model_quantize(
-        const char * fname_inp,
-        const char * fname_out,
-        const llama_model_quantize_params * params) {
-    try {
-        llama_model_quantize_internal(fname_inp, fname_out, params);
-        return 0;
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
-        return 1;
-    }
-}
-
-struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
-    try {
-        struct llama_lora_adapter * adapter = new llama_lora_adapter(model);
-        llama_lora_adapter_init_internal(model, path_lora, *adapter);
-        return adapter;
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-        return nullptr;
-    }
-}
-
-static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
-    GGML_ASSERT(cvec.tensors.empty());
-    GGML_ASSERT(cvec.ctxs.empty());
-    GGML_ASSERT(cvec.bufs.empty());
-
-    // create a context for each buffer type
-    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
-    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
-        auto it = ctx_map.find(buft);
-        if (it == ctx_map.end()) {
-            struct ggml_init_params params = {
-                /*.mem_size   =*/ model.hparams.n_layer*ggml_tensor_overhead(),
-                /*.mem_buffer =*/ NULL,
-                /*.no_alloc   =*/ true,
-            };
-            ggml_context * ctx = ggml_init(params);
-            if (!ctx) {
-                return nullptr;
-            }
-            ctx_map[buft] = ctx;
-            cvec.ctxs.emplace_back(ctx);
-            return ctx;
-        }
-        return it->second;
-    };
-
-    // make tensors
-    cvec.tensors.reserve(model.hparams.n_layer);
-    cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
-    for (size_t il = 1; il < model.hparams.n_layer; il++) {
-        ggml_backend_buffer_type_t buft = select_buft(*model.dev_layer.at(il).buft_list,
-            [&](ggml_context * ctx) {
-                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
-                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
-                return ggml_add(ctx, cur, layer_dir);
-            });
-        ggml_context * ctx = ctx_for_buft(buft);
-        if (!ctx) {
-            LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
-            return false;
-        }
-        ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
-        cvec.tensors.push_back(tensor);
-    }
-
-    // allocate tensors / buffers and zero
-    cvec.bufs.reserve(ctx_map.size());
-    for (auto it : ctx_map) {
-        ggml_backend_buffer_type_t buft = it.first;
-        ggml_context * ctx = it.second;
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
-        if (!buf) {
-            LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
-            return false;
-        }
-        ggml_backend_buffer_clear(buf, 0);
-        cvec.bufs.emplace_back(buf);
-    }
-
-    return true;
-}
-
-int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
-    const llama_model & model = lctx->model;
-    llama_control_vector & cvec = lctx->cvec;
-
-    if (data == nullptr) {
-        // disable the current control vector (but leave allocated for later)
-        cvec.layer_start = -1;
-        cvec.layer_end   = -1;
-        return 0;
-    }
-
-    if (n_embd != (int) model.hparams.n_embd) {
-        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
-        return 1;
-    }
-
-    if (cvec.tensors.empty()) {
-        if (!llama_control_vector_init(cvec, model)) {
-            return 1;
-        }
-    }
-
-    cvec.layer_start = il_start;
-    cvec.layer_end   = il_end;
-
-    for (size_t il = 1; il < model.hparams.n_layer; il++) {
-        assert(cvec.tensors[il] != nullptr);
-
-        const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
-        if (off + n_embd <= len) {
-            ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
-        }
-    }
-
-    return 0;
-}
+// TODO: tmp bridges below until `struct llama_kv_cache` is exposed through the public API
 
 struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
-    struct llama_kv_cache_view result = {
-        /*.n_cells            = */ 0,
-        /*.n_seq_max          = */ n_seq_max,
-        /*.token_count        = */ 0,
-        /*.used_cells         = */ llama_get_kv_cache_used_cells(ctx),
-        /*.max_contiguous     = */ 0,
-        /*.max_contiguous_idx = */ -1,
-        /*.cells              = */ nullptr,
-        /*.cells_sequences    = */ nullptr,
-    };
-    return result;
-}
-
-void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
-    if (view->cells != nullptr) {
-        free(view->cells);
-        view->cells = nullptr;
-    }
-    if (view->cells_sequences != nullptr) {
-        free(view->cells_sequences);
-        view->cells_sequences = nullptr;
-    }
+    return llama_kv_cache_view_init(ctx->kv_self, n_seq_max);
 }
 
 void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
-    if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) {
-        view->n_cells = int32_t(ctx->kv_self.size);
-        void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
-        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
-        view->cells = (struct llama_kv_cache_view_cell *)p;
-        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
-        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
-        view->cells_sequences = (llama_seq_id *)p;
-    }
-
-    const std::vector<llama_kv_cell> & kv_cells = ctx->kv_self.cells;
-    llama_kv_cache_view_cell * c_curr = view->cells;
-    llama_seq_id * cs_curr = view->cells_sequences;
-    int32_t used_cells = 0;
-    int32_t token_count = 0;
-    int32_t curr_contig_idx = -1;
-    uint32_t max_contig = 0;
-    int32_t max_contig_idx = -1;
-
-    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
-        const size_t curr_size = kv_cells[i].seq_id.size();
-        token_count += curr_size;
-        c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
-
-        if (curr_size > 0) {
-            if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
-                max_contig = i - curr_contig_idx;
-                max_contig_idx = curr_contig_idx;
-            }
-            curr_contig_idx = -1;
-        } else if (curr_contig_idx < 0) {
-            curr_contig_idx = i;
-        }
-
-        int seq_idx = 0;
-        for (const llama_seq_id it : kv_cells[i].seq_id) {
-            if (seq_idx >= view->n_seq_max) {
-                break;
-            }
-            cs_curr[seq_idx] = it;
-            seq_idx++;
-        }
-        if (seq_idx != 0) {
-            used_cells++;
-        }
-        for (; seq_idx < view->n_seq_max; seq_idx++) {
-            cs_curr[seq_idx] = -1;
-        }
-    }
-    if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
-        max_contig_idx = curr_contig_idx;
-        max_contig = kv_cells.size() - curr_contig_idx;
-    }
-    view->max_contiguous = max_contig;
-    view->max_contiguous_idx = max_contig_idx;
-    view->token_count = token_count;
-    view->used_cells = used_cells;
-    if (uint32_t(used_cells) != ctx->kv_self.used) {
-        LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
-            __func__, ctx->kv_self.used, used_cells);
-    }
+    llama_kv_cache_view_update(view, ctx->kv_self);
 }
 
 int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
-    int result = 0;
-
-    for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
-        result += ctx->kv_self.cells[i].seq_id.size();
-    }
-
-    return result;
+    return llama_get_kv_cache_token_count(ctx->kv_self);
 }
 
 int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
-    return ctx->kv_self.used;
+    return llama_get_kv_cache_used_cells(ctx->kv_self);
 }
 
 void llama_kv_cache_clear(struct llama_context * ctx) {
@@ -21481,1068 +11995,10 @@ void llama_kv_cache_update(struct llama_context * ctx) {
 }
 
 bool llama_kv_cache_can_shift(struct llama_context * ctx) {
-    return !ctx->kv_self.recurrent && ctx->model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA
+    return llama_kv_cache_can_shift(ctx->kv_self);
 }
 
-// deprecated
-size_t llama_get_state_size(struct llama_context * ctx) {
-    return llama_state_get_size(ctx);
-}
-
-// deprecated
-size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
-    return llama_state_get_data(ctx, dst, -1);
-}
-
-// deprecated
-size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
-    return llama_state_set_data(ctx, src, -1);
-}
-
-// deprecated
-bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
-}
-
-// deprecated
-bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
-    return llama_state_save_file(ctx, path_session, tokens, n_token_count);
-}
-
-// TODO: replace all non-fatal assertions with returned errors or exceptions
-struct llama_data_write {
-    virtual void write(const void * src, size_t size) = 0;
-    virtual void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) = 0;
-    virtual size_t get_size_written() = 0;
-    virtual ~llama_data_write() = default;
-
-    void write_string(const std::string & str) {
-        uint32_t str_size = str.size();
-
-        write(&str_size,  sizeof(str_size));
-        write(str.data(), str_size);
-    }
-
-    void write_model_info(const struct llama_context * ctx) {
-        std::string arch_str = LLM_ARCH_NAMES.at(ctx->model.arch);
-        write_string(arch_str);
-        // TODO: add more model-specific info which should prevent loading the session file if not identical
-    }
-
-    //void write_rng(const std::mt19937 & rng) {
-    //    std::ostringstream rng_ss;
-    //    rng_ss << rng;
-
-    //    const std::string & rng_str = rng_ss.str();
-
-    //    write_string(rng_str);
-    //}
-
-    void write_output_ids(struct llama_context * ctx) {
-        llama_output_reorder(ctx);
-
-        const uint32_t n_outputs = ctx->n_outputs;
-
-        std::vector<int32_t> output_pos;
-
-        const size_t    n_batch = ctx->cparams.n_batch;
-        const auto & output_ids = ctx->output_ids;
-
-        GGML_ASSERT(n_outputs <= ctx->output_size);
-
-        output_pos.resize(n_outputs);
-
-        // build a more compact representation of the output ids
-        for (size_t i = 0; i < n_batch; ++i) {
-            // map an output id to a position in the batch
-            int32_t pos = output_ids[i];
-            if (pos >= 0) {
-                GGML_ASSERT((uint32_t) pos < n_outputs);
-                output_pos[pos] = i;
-            }
-        }
-
-        write(&n_outputs, sizeof(n_outputs));
-
-        if (n_outputs) {
-            write(output_pos.data(), n_outputs * sizeof(int32_t));
-        }
-    }
-
-    void write_logits(const struct llama_context * ctx) {
-        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_vocab);
-
-        write(&logits_size, sizeof(logits_size));
-
-        if (logits_size) {
-            write(ctx->logits, logits_size * sizeof(float));
-        }
-    }
-
-    void write_embeddings(const struct llama_context * ctx) {
-        const uint64_t embeddings_size = std::min((uint64_t) ctx->embd_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_embd);
-
-        write(&embeddings_size, sizeof(embeddings_size));
-
-        if (embeddings_size) {
-            write(ctx->embd, embeddings_size * sizeof(float));
-        }
-    }
-
-    void write_kv_cache_meta(const llama_kv_cache & kv_self, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) {
-
-        for (const auto & range : cell_ranges) {
-            for (uint32_t i = range.first; i < range.second; ++i) {
-                const auto & cell = kv_self.cells[i];
-                const llama_pos pos      = cell.pos;
-                const uint32_t  n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
-
-                write(&pos,      sizeof(pos));
-                write(&n_seq_id, sizeof(n_seq_id));
-
-                if (n_seq_id) {
-                    for (auto seq_id : cell.seq_id) {
-                        write(&seq_id, sizeof(seq_id));
-                    }
-                }
-            }
-        }
-    }
-
-    void write_kv_cache_data(const struct llama_context * ctx, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) {
-        const struct llama_kv_cache & kv_self = ctx->kv_self;
-        const struct llama_hparams & hparams = ctx->model.hparams;
-
-        const uint32_t v_trans = kv_self.v_trans ? 1 : 0;
-        const uint32_t n_layer = hparams.n_layer;
-
-        write(&v_trans, sizeof(v_trans));
-        write(&n_layer, sizeof(n_layer));
-
-        std::vector<uint8_t> tmp_buf;
-
-        // Iterate and write all the keys first, each row is a cell
-        // Get whole range at a time
-        for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
-
-            // Write key type
-            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
-            write(&k_type_i, sizeof(k_type_i));
-
-            // Write row size of key
-            const uint64_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-            write(&k_size_row, sizeof(k_size_row));
-
-            // Read each range of cells of k_size length each into tmp_buf and write out
-            for (const auto & range : cell_ranges) {
-                const size_t range_size = range.second - range.first;
-                const size_t buf_size = range_size * k_size_row;
-                write_tensor_data(kv_self.k_l[il], range.first * k_size_row, buf_size);
-            }
-        }
-
-        if (!kv_self.v_trans) {
-            for (uint32_t il = 0; il < n_layer; ++il) {
-                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
-
-                // Write value type
-                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-                write(&v_type_i, sizeof(v_type_i));
-
-                // Write row size of value
-                const uint64_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
-                write(&v_size_row, sizeof(v_size_row));
-
-                // Read each range of cells of v_size length each into tmp_buf and write out
-                for (const auto & range : cell_ranges) {
-                    const size_t range_size = range.second - range.first;
-                    const size_t buf_size = range_size * v_size_row;
-                    write_tensor_data(kv_self.v_l[il], range.first * v_size_row, buf_size);
-                }
-            }
-        } else {
-            // When v is transposed, we also need the element size and get the element ranges from each row
-            const uint32_t kv_size = kv_self.size;
-            for (uint32_t il = 0; il < n_layer; ++il) {
-                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
-
-                // Write value type
-                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-                write(&v_type_i, sizeof(v_type_i));
-
-                // Write element size
-                const uint32_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-                write(&v_size_el, sizeof(v_size_el));
-
-                // Write GQA embedding size
-                write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
-
-                // For each row, we get the element values of each cell
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    // Read each range of cells of v_size_el length each into tmp_buf and write out
-                    for (const auto & range : cell_ranges) {
-                        const size_t range_size = range.second - range.first;
-                        const size_t src_offset = (range.first + j * kv_size) * v_size_el;
-                        const size_t buf_size = range_size * v_size_el;
-                        write_tensor_data(kv_self.v_l[il], src_offset, buf_size);
-                    }
-                }
-            }
-        }
-    }
-
-    void write_kv_cache(const struct llama_context * ctx, llama_seq_id seq_id = -1) {
-        const struct llama_kv_cache & kv_self = ctx->kv_self;
-        std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
-        uint32_t cell_count = 0;
-
-        // Count the number of cells with the specified seq_id
-        // Find all the ranges of cells with this seq id (or all, when -1)
-        uint32_t cell_range_begin = kv_self.size;
-        for (uint32_t i = 0; i < kv_self.size; ++i) {
-            const auto & cell = kv_self.cells[i];
-            if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
-                ++cell_count;
-                if (cell_range_begin == kv_self.size) {
-                    cell_range_begin = i;
-                }
-            } else {
-                if (cell_range_begin != kv_self.size) {
-                    cell_ranges.emplace_back(cell_range_begin, i);
-                    cell_range_begin = kv_self.size;
-                }
-            }
-        }
-        if (cell_range_begin != kv_self.size) {
-            cell_ranges.emplace_back(cell_range_begin, kv_self.size);
-        }
-
-        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
-        uint32_t cell_count_check = 0;
-        for (const auto & range : cell_ranges) {
-            cell_count_check += range.second - range.first;
-        }
-        GGML_ASSERT(cell_count == cell_count_check);
-
-        write(&cell_count, sizeof(cell_count));
-
-        write_kv_cache_meta(kv_self, cell_ranges, seq_id);
-        write_kv_cache_data(ctx, cell_ranges);
-    }
-};
-
-struct llama_data_read {
-    virtual const uint8_t * read(size_t size) = 0;
-    virtual void read_to(void * dst, size_t size) = 0;
-    virtual size_t get_size_read() = 0;
-    virtual ~llama_data_read() = default;
-
-    void read_string(std::string & str) {
-        uint32_t str_size;
-        read_to(&str_size, sizeof(str_size));
-
-        str.assign((const char *) read(str_size), str_size);
-    }
-
-    // validate model information
-    void read_model_info(const struct llama_context * ctx) {
-        std::string cur_arch_str = LLM_ARCH_NAMES.at(ctx->model.arch);
-        std::string arch_str;
-        read_string(arch_str);
-        if (cur_arch_str != arch_str) {
-            throw std::runtime_error(format("wrong model arch: '%s' instead of '%s'", arch_str.c_str(), cur_arch_str.c_str()));
-        }
-        // TODO: add more info which needs to be identical but which is not verified otherwise
-    }
-
-    //void read_rng(std::mt19937 & rng) {
-    //    std::string rng_str;
-    //    read_string(rng_str);
-
-    //    std::istringstream rng_ss(rng_str);
-    //    rng_ss >> rng;
-
-    //    if (rng_ss.fail()) {
-    //        throw std::runtime_error("failed to load RNG state");
-    //    }
-    //}
-
-    void read_output_ids(struct llama_context * ctx) {
-        std::vector<int32_t> output_pos;
-
-        uint32_t n_outputs;
-        read_to(&n_outputs, sizeof(n_outputs));
-
-        if (n_outputs > llama_output_reserve(*ctx, n_outputs)) {
-            throw std::runtime_error("could not reserve outputs");
-        }
-
-        if (n_outputs) {
-            output_pos.resize(n_outputs);
-            read_to(output_pos.data(), n_outputs * sizeof(int32_t));
-
-            for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
-                int32_t id = output_pos[i];
-                if ((uint32_t) id >= ctx->cparams.n_batch) {
-                    throw std::runtime_error(format("invalid output id, %d does not fit in batch size of %u", id, ctx->cparams.n_batch));
-                }
-                ctx->output_ids[id] = i;
-            }
-
-            ctx->n_outputs = n_outputs;
-        }
-    }
-
-    void read_logits(struct llama_context * ctx) {
-        uint64_t logits_size;
-        read_to(&logits_size, sizeof(logits_size));
-
-        if (ctx->logits_size < logits_size) {
-            throw std::runtime_error("logits buffer too small");
-        }
-
-        if (logits_size) {
-            read_to(ctx->logits, logits_size * sizeof(float));
-        }
-    }
-
-    void read_embeddings(struct llama_context * ctx) {
-        uint64_t embeddings_size;
-        read_to(&embeddings_size, sizeof(embeddings_size));
-
-        if (ctx->embd_size < embeddings_size) {
-            throw std::runtime_error("embeddings buffer too small");
-        }
-
-        if (embeddings_size) {
-            read_to(ctx->embd, embeddings_size * sizeof(float));
-        }
-    }
-
-    bool read_kv_cache_meta(struct llama_context * ctx, uint32_t cell_count, llama_seq_id dest_seq_id = -1) {
-        struct llama_kv_cache & kv_self = ctx->kv_self;
-
-        if (dest_seq_id != -1) {
-            // single sequence
-
-            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-
-            llama_ubatch batch = ctx->sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
-            batch.n_tokens = cell_count;
-            batch.n_seq_tokens = cell_count;
-            batch.n_seqs = 1;
-
-            for (uint32_t i = 0; i < cell_count; ++i) {
-                llama_pos pos;
-                uint32_t n_seq_id;
-
-                read_to(&pos, sizeof(pos));
-                read_to(&n_seq_id, sizeof(n_seq_id));
-
-                if (n_seq_id != 0) {
-                    LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
-                    return false;
-                }
-
-                batch.pos[i] = pos;
-            }
-            batch.n_seq_id[0] = 1;
-            batch.seq_id[0] = &dest_seq_id;
-            if (!llama_kv_cache_find_slot(kv_self, batch)) {
-                LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
-                return false;
-            }
-
-            // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
-            // Assume that this is one contiguous block of cells
-            GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
-            GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
-            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
-            GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
-            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
-        } else {
-            // whole KV cache restore
-
-            if (cell_count > kv_self.size) {
-                LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
-                return false;
-            }
-
-            llama_kv_cache_clear(kv_self);
-
-            for (uint32_t i = 0; i < cell_count; ++i) {
-                llama_kv_cell & cell = kv_self.cells[i];
-
-                llama_pos pos;
-                uint32_t  n_seq_id;
-
-                read_to(&pos,      sizeof(pos));
-                read_to(&n_seq_id, sizeof(n_seq_id));
-
-                cell.pos = pos;
-
-                for (uint32_t j = 0; j < n_seq_id; ++j) {
-                    llama_seq_id seq_id;
-                    read_to(&seq_id, sizeof(seq_id));
-
-                    if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
-                        LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
-                        return false;
-                    }
-
-                    cell.seq_id.insert(seq_id);
-
-                    if (kv_self.recurrent) {
-                        int32_t & tail = kv_self.cells[seq_id].tail;
-                        if (tail != -1) {
-                            LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
-                            return false;
-                        }
-                        tail = i;
-                    }
-                }
-            }
-
-            kv_self.head = 0;
-            kv_self.used = cell_count;
-        }
-
-        if (kv_self.recurrent) {
-            for (uint32_t i = 0; i < cell_count; ++i) {
-                uint32_t cell_id = kv_self.head + i;
-                // make sure the recurrent states will keep their restored state
-                kv_self.cells[cell_id].src = cell_id;
-            }
-        }
-
-        return true;
-    }
-
-    bool read_kv_cache_data(struct llama_context * ctx, uint32_t cell_count) {
-        const struct llama_hparams & hparams = ctx->model.hparams;
-        struct llama_kv_cache & kv_self = ctx->kv_self;
-        uint32_t v_trans;
-        uint32_t n_layer;
-        read_to(&v_trans, sizeof(v_trans));
-        read_to(&n_layer, sizeof(n_layer));
-
-        if (n_layer != hparams.n_layer) {
-            LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
-            return false;
-        }
-        if (cell_count > kv_self.size) {
-            LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, kv_self.size);
-            return false;
-        }
-        if (kv_self.v_trans != (bool) v_trans) {
-            LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
-            return false;
-        }
-
-        // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
-        for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
-
-            // Read type of key
-            int32_t k_type_i_ref;
-            read_to(&k_type_i_ref, sizeof(k_type_i_ref));
-            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
-            if (k_type_i != k_type_i_ref) {
-                LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
-                return false;
-            }
-
-            // Read row size of key
-            uint64_t k_size_row_ref;
-            read_to(&k_size_row_ref, sizeof(k_size_row_ref));
-            const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-            if (k_size_row != k_size_row_ref) {
-                LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
-                return false;
-            }
-
-            if (cell_count) {
-                // Read and set the keys for the whole cell range
-                ggml_backend_tensor_set(kv_self.k_l[il], read(cell_count * k_size_row), kv_self.head * k_size_row, cell_count * k_size_row);
-            }
-        }
-
-        if (!kv_self.v_trans) {
-            for (uint32_t il = 0; il < n_layer; ++il) {
-                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
-
-                // Read type of value
-                int32_t v_type_i_ref;
-                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
-                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-                if (v_type_i != v_type_i_ref) {
-                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
-                    return false;
-                }
-
-                // Read row size of value
-                uint64_t v_size_row_ref;
-                read_to(&v_size_row_ref, sizeof(v_size_row_ref));
-                const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
-                if (v_size_row != v_size_row_ref) {
-                    LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
-                    return false;
-                }
-
-                if (cell_count) {
-                    // Read and set the values for the whole cell range
-                    ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_row), kv_self.head * v_size_row, cell_count * v_size_row);
-                }
-            }
-        } else {
-            // For each layer, read the values for each cell (transposed)
-            for (uint32_t il = 0; il < n_layer; ++il) {
-                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
-
-                // Read type of value
-                int32_t v_type_i_ref;
-                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
-                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-                if (v_type_i != v_type_i_ref) {
-                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
-                    return false;
-                }
-
-                // Read element size of value
-                uint32_t v_size_el_ref;
-                read_to(&v_size_el_ref, sizeof(v_size_el_ref));
-                const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-                if (v_size_el != v_size_el_ref) {
-                    LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
-                    return false;
-                }
-
-                // Read GQA embedding size
-                uint32_t n_embd_v_gqa_ref;
-                read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
-                if (n_embd_v_gqa != n_embd_v_gqa_ref) {
-                    LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
-                    return false;
-                }
-
-                if (cell_count) {
-                    // For each row in the transposed matrix, read the values for the whole cell range
-                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                        const size_t dst_offset = (kv_self.head + j * kv_self.size) * v_size_el;
-                        ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
-                    }
-                }
-            }
-        }
-        return true;
-    }
-
-    void read_kv_cache(struct llama_context * ctx, llama_seq_id seq_id = -1) {
-        uint32_t cell_count;
-        read_to(&cell_count, sizeof(cell_count));
-
-        bool res = read_kv_cache_meta(ctx, cell_count, seq_id) && read_kv_cache_data(ctx, cell_count);
-
-        if (!res) {
-            if (seq_id == -1) {
-                llama_kv_cache_clear(ctx);
-            } else {
-                llama_kv_cache_seq_rm(ctx, seq_id, -1, -1);
-            }
-            throw std::runtime_error("failed to restore kv cache");
-        }
-    }
-};
-
-struct llama_data_write_dummy : llama_data_write {
-    size_t size_written = 0;
-
-    llama_data_write_dummy() {}
-
-    void write(const void * /* src */, size_t size) override {
-        size_written += size;
-    }
-
-    void write_tensor_data(const struct ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
-        size_written += size;
-    }
-
-    size_t get_size_written() override {
-        return size_written;
-    }
-};
-
-struct llama_data_write_buffer : llama_data_write {
-    uint8_t * ptr;
-    size_t buf_size = 0;
-    size_t size_written = 0;
-
-    llama_data_write_buffer(uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
-
-    void write(const void * src, size_t size) override {
-        if (size > buf_size) {
-            throw std::runtime_error("unexpectedly reached end of buffer");
-        }
-        memcpy(ptr, src, size);
-        ptr += size;
-        size_written += size;
-        buf_size -= size;
-    }
-
-    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
-        if (size > buf_size) {
-            throw std::runtime_error("unexpectedly reached end of buffer");
-        }
-        ggml_backend_tensor_get(tensor, ptr, offset, size);
-        ptr += size;
-        size_written += size;
-        buf_size -= size;
-    }
-
-    size_t get_size_written() override {
-        return size_written;
-    }
-};
-
-struct llama_data_read_buffer : llama_data_read {
-    const uint8_t * ptr;
-    size_t buf_size = 0;
-    size_t size_read = 0;
-
-    llama_data_read_buffer(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
-
-    const uint8_t * read(size_t size) override {
-        const uint8_t * base_ptr = ptr;
-        if (size > buf_size) {
-            throw std::runtime_error("unexpectedly reached end of buffer");
-        }
-        ptr += size;
-        size_read += size;
-        buf_size -= size;
-        return base_ptr;
-    }
-
-    void read_to(void * dst, size_t size) override {
-        memcpy(dst, read(size), size);
-    }
-
-    size_t get_size_read() override {
-        return size_read;
-    }
-};
-
-struct llama_data_write_file : llama_data_write {
-    llama_file * file;
-    size_t size_written = 0;
-    std::vector<uint8_t> temp_buffer;
-
-    llama_data_write_file(llama_file * f) : file(f) {}
-
-    void write(const void * src, size_t size) override {
-        file->write_raw(src, size);
-        size_written += size;
-    }
-
-    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
-        temp_buffer.resize(size);
-        ggml_backend_tensor_get(tensor, temp_buffer.data(), offset, size);
-        write(temp_buffer.data(), temp_buffer.size());
-    }
-
-    size_t get_size_written() override {
-        return size_written;
-    }
-};
-
-struct llama_data_read_file : llama_data_read {
-    llama_file * file;
-    size_t size_read = 0;
-    std::vector<uint8_t> temp_buffer;
-
-    llama_data_read_file(llama_file * f) : file(f) {}
-
-    void read_to(void * dst, size_t size) override {
-        file->read_raw(dst, size);
-        size_read += size;
-    }
-
-    const uint8_t * read(size_t size) override {
-        temp_buffer.resize(size);
-        read_to(temp_buffer.data(), size);
-        return temp_buffer.data();
-    }
-
-    size_t get_size_read() override {
-        return size_read;
-    }
-};
-
-/** copy state data into either a buffer or file depending on the passed in context
- *
- * file context:
- * llama_file file("/path", "wb");
- * llama_data_write_file data_ctx(&file);
- * llama_state_get_data_internal(ctx, data_ctx);
- *
- * buffer context:
- * std::vector<uint8_t> buf(max_size, 0);
- * llama_data_write_buffer data_ctx(buf.data(), max_size);
- * llama_state_get_data_internal(ctx, data_ctx);
- *
-*/
-static size_t llama_state_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx) {
-    llama_synchronize(ctx);
-
-    data_ctx.write_model_info(ctx);
-
-    // copy outputs
-    data_ctx.write_output_ids(ctx);
-    data_ctx.write_logits(ctx);
-    data_ctx.write_embeddings(ctx);
-
-    data_ctx.write_kv_cache(ctx);
-
-    return data_ctx.get_size_written();
-}
-
-size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst, size_t size) {
-    llama_data_write_buffer data_ctx(dst, size);
-    try {
-        return llama_state_get_data_internal(ctx, data_ctx);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-// Returns the *actual* size of the state.
-// Intended to be used when saving to state to a buffer.
-size_t llama_state_get_size(struct llama_context * ctx) {
-    llama_data_write_dummy data_ctx;
-    try {
-        return llama_state_get_data_internal(ctx, data_ctx);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-static size_t llama_state_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx) {
-    llama_synchronize(ctx);
-
-    data_ctx.read_model_info(ctx);
-
-    // set outputs
-    data_ctx.read_output_ids(ctx);
-    data_ctx.read_logits(ctx);
-    data_ctx.read_embeddings(ctx);
-
-    data_ctx.read_kv_cache(ctx);
-
-    return data_ctx.get_size_read();
-}
-
-// Sets the state reading from the specified source address
-size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src, size_t size) {
-    llama_data_read_buffer data_ctx(src, size);
-    try {
-        return llama_state_set_data_internal(ctx, data_ctx);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    llama_file file(path_session, "rb");
-
-    // sanity checks
-    {
-        const uint32_t magic   = file.read_u32();
-        const uint32_t version = file.read_u32();
-
-        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
-            LLAMA_LOG_ERROR("%s: unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
-            return false;
-        }
-    }
-
-    // load the prompt
-    {
-        const uint32_t n_token_count = file.read_u32();
-
-        if (n_token_count > n_token_capacity) {
-            LLAMA_LOG_ERROR("%s: token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
-            return false;
-        }
-
-        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
-        *n_token_count_out = n_token_count;
-    }
-
-    // restore the context state
-    {
-        const size_t n_state_size_cur = file.size - file.tell();
-
-        llama_data_read_file data_ctx(&file);
-        const size_t n_read = llama_state_set_data_internal(ctx, data_ctx);
-
-        if (n_read != n_state_size_cur) {
-            LLAMA_LOG_ERROR("%s: did not read all of the session file data! size %zu, got %zu\n", __func__, n_state_size_cur, n_read);
-            return false;
-        }
-    }
-    return true;
-}
-
-bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    try {
-        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading session file: %s\n", __func__, err.what());
-        return false;
-    }
-}
-
-static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
-    llama_file file(path_session, "wb");
-
-    file.write_u32(LLAMA_SESSION_MAGIC);
-    file.write_u32(LLAMA_SESSION_VERSION);
-
-    // save the prompt
-    file.write_u32((uint32_t) n_token_count);
-    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
-
-    // save the context state using stream saving
-    llama_data_write_file data_ctx(&file);
-    llama_state_get_data_internal(ctx, data_ctx);
-
-    return true;
-}
-
-bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
-    try {
-        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error saving session file: %s\n", __func__, err.what());
-        return false;
-    }
-}
-
-static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx, llama_seq_id seq_id) {
-    llama_synchronize(ctx);
-
-    data_ctx.write_kv_cache(ctx, seq_id);
-
-    return data_ctx.get_size_written();
-}
-
-size_t llama_state_seq_get_size(struct llama_context * ctx, llama_seq_id seq_id) {
-    llama_data_write_dummy data_ctx;
-    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
-}
-
-size_t llama_state_seq_get_data(struct llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id) {
-    llama_data_write_buffer data_ctx(dst, size);
-    try {
-        return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error saving sequence state: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-static size_t llama_state_seq_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx, llama_seq_id dest_seq_id) {
-    llama_synchronize(ctx);
-
-    data_ctx.read_kv_cache(ctx, dest_seq_id);
-
-    return data_ctx.get_size_read();
-}
-
-size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id dest_seq_id) {
-    llama_data_read_buffer data_ctx(src, size);
-    try {
-        return llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading sequence state: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
-    llama_file file(filepath, "wb");
-
-    file.write_u32(LLAMA_STATE_SEQ_MAGIC);
-    file.write_u32(LLAMA_STATE_SEQ_VERSION);
-
-    // save the prompt
-    file.write_u32((uint32_t) n_token_count);
-    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
-
-    // save the context state using stream saving
-    llama_data_write_file data_ctx(&file);
-    llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
-
-    const size_t res = file.tell();
-    GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
-    return res;
-}
-
-static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    llama_file file(filepath, "rb");
-
-    // version checks
-    {
-        const uint32_t magic   = file.read_u32();
-        const uint32_t version = file.read_u32();
-
-        if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
-            LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
-            return 0;
-        }
-    }
-
-    // load the prompt
-    {
-        const uint32_t n_token_count = file.read_u32();
-
-        if (n_token_count > n_token_capacity) {
-            LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
-            return 0;
-        }
-
-        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
-        *n_token_count_out = n_token_count;
-    }
-
-    // restore the context state
-    {
-        const size_t state_size = file.size - file.tell();
-        llama_data_read_file data_ctx(&file);
-        const size_t nread = llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
-        if (!nread) {
-            LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
-            return 0;
-        }
-        GGML_ASSERT(nread <= state_size);
-        GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
-    }
-
-    return file.tell();
-}
-
-size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
-    try {
-        return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error saving sequence state file: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    try {
-        return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading sequence state file: %s\n", __func__, err.what());
-        return 0;
-    }
-}
-
-void llama_set_n_threads(struct llama_context * ctx, int32_t n_threads, int32_t n_threads_batch) {
-    ctx->cparams.n_threads       = n_threads;
-    ctx->cparams.n_threads_batch = n_threads_batch;
-}
-
-int32_t llama_n_threads(struct llama_context * ctx) {
-    return ctx->cparams.n_threads;
-}
-
-int32_t llama_n_threads_batch(struct llama_context * ctx) {
-    return ctx->cparams.n_threads_batch;
-}
-
-void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
-    ctx->abort_callback      = abort_callback;
-    ctx->abort_callback_data = abort_callback_data;
-
-    for (auto & backend : ctx->backends) {
-        auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend.get()));
-        auto * set_abort_callback_fn = (ggml_backend_set_abort_callback_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_abort_callback");
-        if (set_abort_callback_fn) {
-            set_abort_callback_fn(backend.get(), ctx->abort_callback, ctx->abort_callback_data);
-        }
-    }
-}
-
-void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
-    ctx->cparams.embeddings = embeddings;
-}
-
-void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
-    ctx->cparams.causal_attn = causal_attn;
-}
-
-struct llama_batch llama_batch_get_one(
-             llama_token * tokens,
-                 int32_t   n_tokens) {
-    return {
-        /*n_tokens       =*/ n_tokens,
-        /*tokens         =*/ tokens,
-        /*embd           =*/ nullptr,
-        /*pos            =*/ nullptr,
-        /*n_seq_id       =*/ nullptr,
-        /*seq_id         =*/ nullptr,
-        /*logits         =*/ nullptr,
-    };
-}
-
-struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
-    llama_batch batch = {
-        /*n_tokens       =*/ 0,
-        /*tokens         =*/ nullptr,
-        /*embd           =*/ nullptr,
-        /*pos            =*/ nullptr,
-        /*n_seq_id       =*/ nullptr,
-        /*seq_id         =*/ nullptr,
-        /*logits         =*/ nullptr,
-    };
-
-    if (embd) {
-        batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
-    } else {
-        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
-    }
-
-    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens_alloc);
-    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens_alloc);
-    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
-    for (int i = 0; i < n_tokens_alloc; ++i) {
-        batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
-    }
-    batch.seq_id[n_tokens_alloc] = nullptr;
-
-    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens_alloc);
-
-    return batch;
-}
-
-void llama_batch_free(struct llama_batch batch) {
-    if (batch.token)    free(batch.token);
-    if (batch.embd)     free(batch.embd);
-    if (batch.pos)      free(batch.pos);
-    if (batch.n_seq_id) free(batch.n_seq_id);
-    if (batch.seq_id) {
-        for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
-            free(batch.seq_id[i]);
-        }
-        free(batch.seq_id);
-    }
-    if (batch.logits)   free(batch.logits);
-}
+///
 
 int32_t llama_encode(
         struct llama_context * ctx,
@@ -22566,150 +12022,12 @@ int32_t llama_decode(
     return ret;
 }
 
-void llama_synchronize(struct llama_context * ctx) {
-    ggml_backend_sched_synchronize(ctx->sched.get());
-
-    // FIXME: if multiple single tokens are evaluated without a synchronization,
-    // the stats will be added to the prompt evaluation stats
-    // this should only happen when using batch size 1 to evaluate a batch
-
-    // add the evaluation to the stats
-    if (ctx->n_queued_tokens == 1) {
-        if (!ctx->cparams.no_perf) {
-            ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
-        }
-        ctx->n_eval++;
-    } else if (ctx->n_queued_tokens > 1) {
-        if (!ctx->cparams.no_perf) {
-            ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
-        }
-        ctx->n_p_eval += ctx->n_queued_tokens;
-    }
-
-    // get a more accurate load time, upon first eval
-    if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
-        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
-        ctx->has_evaluated_once = true;
-    }
-
-    ctx->n_queued_tokens = 0;
-    ctx->t_compute_start_us = 0;
-}
-
-float * llama_get_logits(struct llama_context * ctx) {
-    llama_synchronize(ctx);
-
-    // reorder logits for backward compatibility
-    // TODO: maybe deprecate this
-    llama_output_reorder(ctx);
-
-    return ctx->logits;
-}
-
-float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
-    int32_t j = -1;
-    llama_synchronize(ctx);
-
-    try {
-        if (ctx->logits == nullptr) {
-            throw std::runtime_error("no logits");
-        }
-
-        if (i < 0) {
-            j = ctx->n_outputs + i;
-            if (j < 0) {
-                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
-            }
-        } else if ((size_t) i >= ctx->output_ids.size()) {
-            throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size()));
-        } else {
-            j = ctx->output_ids[i];
-        }
-
-        if (j < 0) {
-            throw std::runtime_error(format("batch.logits[%d] != true", i));
-        }
-        if (j >= ctx->n_outputs) {
-            // This should not happen
-            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
-        }
-
-        return ctx->logits + j*ctx->model.hparams.n_vocab;
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
-#ifndef NDEBUG
-        GGML_ABORT("fatal error");
-#else
-        return nullptr;
-#endif
-    }
-}
-
-float * llama_get_embeddings(struct llama_context * ctx) {
-    llama_synchronize(ctx);
-
-    // reorder embeddings for backward compatibility
-    // TODO: maybe deprecate this
-    llama_output_reorder(ctx);
-
-    return ctx->embd;
-}
-
-float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
-    int32_t j = -1;
-
-    llama_synchronize(ctx);
-
-    try {
-        if (ctx->embd == nullptr) {
-            throw std::runtime_error("no embeddings");
-        }
-
-        if (i < 0) {
-            j = ctx->n_outputs + i;
-            if (j < 0) {
-                throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
-            }
-        } else if ((size_t) i >= ctx->output_ids.size()) {
-            throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size()));
-        } else {
-            j = ctx->output_ids[i];
-        }
-
-        if (j < 0) {
-            throw std::runtime_error(format("batch.logits[%d] != true", i));
-        }
-        if (j >= ctx->n_outputs) {
-            // This should not happen
-            throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
-        }
-
-        return ctx->embd + j*ctx->model.hparams.n_embd;
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
-#ifndef NDEBUG
-        GGML_ABORT("fatal error");
-#else
-        return nullptr;
-#endif
-    }
-}
-
-float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
-    llama_synchronize(ctx);
-
-    auto it = ctx->embd_seq.find(seq_id);
-    if (it == ctx->embd_seq.end()) {
-        return nullptr;
-    }
-
-    return it->second.data();
-}
-
 //
 // vocab
 //
 
+// TODO: tmp bridges below until `struct llama_vocab` is exposed through the public API
+
 const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
     return llama_token_get_text_impl(model->vocab, token);
 }
@@ -22842,478 +12160,6 @@ int32_t llama_detokenize(
 // chat templates
 //
 
-static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
-    if (LLM_CHAT_TEMPLATES.find(tmpl) != LLM_CHAT_TEMPLATES.end()) {
-        return LLM_CHAT_TEMPLATES.at(tmpl);
-    }
-    auto tmpl_contains = [&tmpl](const char * haystack) -> bool {
-        return tmpl.find(haystack) != std::string::npos;
-    };
-    if (tmpl_contains("<|im_start|>")) {
-        return LLM_CHAT_TEMPLATE_CHATML;
-    } else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
-        if (tmpl_contains("[SYSTEM_PROMPT]")) {
-            return LLM_CHAT_TEMPLATE_MISTRAL_V7;
-        } else if (
-            // catches official 'v1' template
-            tmpl_contains("' [INST] ' + system_message")
-            // catches official 'v3' and 'v3-tekken' templates
-            || tmpl_contains("[AVAILABLE_TOOLS]")
-        ) {
-            // Official mistral 'v1', 'v3' and 'v3-tekken' templates
-            // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
-            // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
-            if (tmpl_contains(" [INST]")) {
-                return LLM_CHAT_TEMPLATE_MISTRAL_V1;
-            } else if (tmpl_contains("\"[INST]\"")) {
-                return LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN;
-            }
-            return LLM_CHAT_TEMPLATE_MISTRAL_V3;
-        } else {
-            // llama2 template and its variants
-            // [variant] support system message
-            // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
-            bool support_system_message = tmpl_contains("<<SYS>>");
-            bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
-            bool strip_message = tmpl_contains("content.strip()");
-            if (strip_message) {
-                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
-            } else if (add_bos_inside_history) {
-                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
-            } else if (support_system_message) {
-                return LLM_CHAT_TEMPLATE_LLAMA_2_SYS;
-            } else {
-                return LLM_CHAT_TEMPLATE_LLAMA_2;
-            }
-        }
-    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
-        return LLM_CHAT_TEMPLATE_PHI_3;
-    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
-        return LLM_CHAT_TEMPLATE_FALCON_3;
-    } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
-        return LLM_CHAT_TEMPLATE_ZEPHYR;
-    } else if (tmpl_contains("bos_token + message['role']")) {
-        return LLM_CHAT_TEMPLATE_MONARCH;
-    } else if (tmpl_contains("<start_of_turn>")) {
-        return LLM_CHAT_TEMPLATE_GEMMA;
-    } else if (tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
-        // OrionStarAI/Orion-14B-Chat
-        return LLM_CHAT_TEMPLATE_ORION;
-    } else if (tmpl_contains("GPT4 Correct ")) {
-        // openchat/openchat-3.5-0106
-        return LLM_CHAT_TEMPLATE_OPENCHAT;
-    } else if (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: ")) {
-        // eachadea/vicuna-13b-1.1 (and Orca variant)
-        if (tmpl_contains("SYSTEM: ")) {
-            return LLM_CHAT_TEMPLATE_VICUNA_ORCA;
-        }
-        return LLM_CHAT_TEMPLATE_VICUNA;
-    } else if (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>")) {
-        // deepseek-ai/deepseek-coder-33b-instruct
-        return LLM_CHAT_TEMPLATE_DEEPSEEK;
-    } else if (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>")) {
-        // CohereForAI/c4ai-command-r-plus
-        return LLM_CHAT_TEMPLATE_COMMAND_R;
-    } else if (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>")) {
-        return LLM_CHAT_TEMPLATE_LLAMA_3;
-    } else if (tmpl_contains("[gMASK]sop")) {
-        // chatglm3-6b
-        return LLM_CHAT_TEMPLATE_CHATGML_3;
-    } else if (tmpl_contains("[gMASK]<sop>")) {
-        return LLM_CHAT_TEMPLATE_CHATGML_4;
-    } else if (tmpl_contains(LU8("<用户>"))) {
-        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
-        return LLM_CHAT_TEMPLATE_MINICPM;
-    } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
-        return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
-    } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
-        // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
-        // EXAONE-3.0-7.8B-Instruct
-        return LLM_CHAT_TEMPLATE_EXAONE_3;
-    } else if (tmpl_contains("rwkv-world")) {
-        return LLM_CHAT_TEMPLATE_RWKV_WORLD;
-    } else if (tmpl_contains("<|start_of_role|>")) {
-        return LLM_CHAT_TEMPLATE_GRANITE;
-    } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
-        return LLM_CHAT_TEMPLATE_GIGACHAT;
-    } else if (tmpl_contains("<|role_start|>")) {
-        return LLM_CHAT_TEMPLATE_MEGREZ;
-    }
-    return LLM_CHAT_TEMPLATE_UNKNOWN;
-}
-
-// Simple version of "llama_apply_chat_template" that only works with strings
-// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
-static int32_t llama_chat_apply_template_internal(
-    const llm_chat_template tmpl,
-    const std::vector<const llama_chat_message *> & chat,
-    std::string & dest, bool add_ass) {
-    // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
-    std::stringstream ss;
-    if (tmpl == LLM_CHAT_TEMPLATE_CHATML) {
-        // chatml template
-        for (auto message : chat) {
-            ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
-        }
-        if (add_ass) {
-            ss << "<|im_start|>assistant\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
-        // Official mistral 'v7' template
-        // See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
-        for (auto message : chat) {
-            std::string role(message->role);
-            std::string content(message->content);
-            if (role == "system") {
-                ss << "[SYSTEM_PROMPT] " << content << "[/SYSTEM_PROMPT]";
-            } else if (role == "user") {
-                ss << "[INST] " << content << "[/INST]";
-            }
-            else {
-                ss << " " << content << "</s>";
-            }
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
-            || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3
-            || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN) {
-        // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
-        // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
-        std::string leading_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 ? " " : "";
-        std::string trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN ? "" : " ";
-        bool trim_assistant_message = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3;
-        bool is_inside_turn = false;
-        for (auto message : chat) {
-            if (!is_inside_turn) {
-                ss << leading_space << "[INST]" << trailing_space;
-                is_inside_turn = true;
-            }
-            std::string role(message->role);
-            std::string content(message->content);
-            if (role == "system") {
-                ss << content << "\n\n";
-            } else if (role == "user") {
-                ss << content << leading_space << "[/INST]";
-            } else {
-                ss << trailing_space << (trim_assistant_message ? trim(content) : content) << "</s>";
-                is_inside_turn = false;
-            }
-        }
-    } else if (
-            tmpl == LLM_CHAT_TEMPLATE_LLAMA_2
-            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS
-            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS
-            || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP) {
-        // llama2 template and its variants
-        // [variant] support system message
-        // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
-        bool support_system_message = tmpl != LLM_CHAT_TEMPLATE_LLAMA_2;
-        // [variant] add BOS inside history
-        bool add_bos_inside_history = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
-        // [variant] trim spaces from the input message
-        bool strip_message = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
-        // construct the prompt
-        bool is_inside_turn = true; // skip BOS at the beginning
-        ss << "[INST] ";
-        for (auto message : chat) {
-            std::string content = strip_message ? trim(message->content) : message->content;
-            std::string role(message->role);
-            if (!is_inside_turn) {
-                is_inside_turn = true;
-                ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
-            }
-            if (role == "system") {
-                if (support_system_message) {
-                    ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
-                } else {
-                    // if the model does not support system message, we still include it in the first message, but without <<SYS>>
-                    ss << content << "\n";
-                }
-            } else if (role == "user") {
-                ss << content << " [/INST]";
-            } else {
-                ss << content << "</s>";
-                is_inside_turn = false;
-            }
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_PHI_3) {
-        // Phi 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
-        // Falcon 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>\n" << message->content << "\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
-        // zephyr template
-        for (auto message : chat) {
-            ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_MONARCH) {
-        // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
-        for (auto message : chat) {
-            std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
-            ss << bos << message->role << "\n" << message->content << "</s>\n";
-        }
-        if (add_ass) {
-            ss << "<s>assistant\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_GEMMA) {
-        // google/gemma-7b-it
-        std::string system_prompt = "";
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
-                system_prompt = trim(message->content);
-                continue;
-            }
-            // in gemma, "assistant" is "model"
-            role = role == "assistant" ? "model" : message->role;
-            ss << "<start_of_turn>" << role << "\n";
-            if (!system_prompt.empty() && role != "model") {
-                ss << system_prompt << "\n\n";
-                system_prompt = "";
-            }
-            ss << trim(message->content) << "<end_of_turn>\n";
-        }
-        if (add_ass) {
-            ss << "<start_of_turn>model\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_ORION) {
-        // OrionStarAI/Orion-14B-Chat
-        std::string system_prompt = "";
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                // there is no system message support, we will merge it with user prompt
-                system_prompt = message->content;
-                continue;
-            } else if (role == "user") {
-                ss << "Human: ";
-                if (!system_prompt.empty()) {
-                    ss << system_prompt << "\n\n";
-                    system_prompt = "";
-                }
-                ss << message->content << "\n\nAssistant: </s>";
-            } else {
-                ss << message->content << "</s>";
-            }
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_OPENCHAT) {
-        // openchat/openchat-3.5-0106,
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << message->content << "<|end_of_turn|>";
-            } else {
-                role[0] = toupper(role[0]);
-                ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
-            }
-        }
-        if (add_ass) {
-            ss << "GPT4 Correct Assistant:";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_VICUNA || tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
-        // eachadea/vicuna-13b-1.1 (and Orca variant)
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                // Orca-Vicuna variant uses a system prefix
-                if (tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
-                    ss << "SYSTEM: " << message->content << "\n";
-                } else {
-                    ss << message->content << "\n\n";
-                }
-            } else if (role == "user") {
-                ss << "USER: " << message->content << "\n";
-            } else if (role == "assistant") {
-                ss << "ASSISTANT: " << message->content << "</s>\n";
-            }
-        }
-        if (add_ass) {
-            ss << "ASSISTANT:";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK) {
-        // deepseek-ai/deepseek-coder-33b-instruct
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << message->content;
-            } else if (role == "user") {
-                ss << "### Instruction:\n" << message->content << "\n";
-            } else if (role == "assistant") {
-                ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
-            }
-        }
-        if (add_ass) {
-            ss << "### Response:\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_COMMAND_R) {
-        // CohereForAI/c4ai-command-r-plus
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
-            } else if (role == "user") {
-                ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
-            } else if (role == "assistant") {
-                ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
-            }
-        }
-        if (add_ass) {
-            ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA_3) {
-        // Llama 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
-        }
-        if (add_ass) {
-            ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) {
-        // chatglm3-6b
-        ss << "[gMASK]" << "sop";
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>" << "\n " << message->content;
-        }
-        if (add_ass) {
-            ss << "<|assistant|>";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) {
-        ss << "[gMASK]" << "<sop>";
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>" << "\n" << message->content;
-        }
-        if (add_ass) {
-            ss << "<|assistant|>";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
-        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "user") {
-                ss << LU8("<用户>");
-                ss << trim(message->content);
-                ss << "<AI>";
-            } else {
-                ss << trim(message->content);
-            }
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_2) {
-        // DeepSeek-V2
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << message->content << "\n\n";
-            } else if (role == "user") {
-                ss << "User: " << message->content << "\n\n";
-            } else if (role == "assistant") {
-                ss << "Assistant: " << message->content << LU8("<｜end▁of▁sentence｜>");
-            }
-        }
-        if (add_ass) {
-            ss << "Assistant:";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
-        // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
-        // EXAONE-3.0-7.8B-Instruct
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n";
-            } else if (role == "user") {
-                ss << "[|user|]" << trim(message->content) << "\n";
-            } else if (role == "assistant") {
-                ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n";
-            }
-        }
-        if (add_ass) {
-            ss << "[|assistant|]";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
-        // this template requires the model to have "\n\n" as EOT token
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "user") {
-                ss << "User: " << message->content << "\n\nAssistant:";
-            } else {
-                ss << message->content << "\n\n";
-            }
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {
-        // IBM Granite template
-        for (const auto & message : chat) {
-            std::string role(message->role);
-            ss << "<|start_of_role|>" << role << "<|end_of_role|>";
-            if (role == "assistant_tool_call") {
-                ss << "<|tool_call|>";
-            }
-            ss << message->content << "<|end_of_text|>\n";
-        }
-        if (add_ass) {
-            ss << "<|start_of_role|>assistant<|end_of_role|>\n";
-        }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
-        // GigaChat template
-        bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
-
-        // Handle system message if present
-        if (has_system) {
-            ss << "<s>" << chat[0]->content << "<|message_sep|>";
-        } else {
-            ss << "<s>";
-        }
-
-        // Process remaining messages
-        for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
-            std::string role(chat[i]->role);
-            if (role == "user") {
-                ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
-                << "available functions<|role_sep|>[]<|message_sep|>";
-            } else if (role == "assistant") {
-                ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
-            }
-        }
-
-        // Add generation prompt if needed
-        if (add_ass) {
-            ss << "assistant<|role_sep|>";
-        }
-    }  else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) {
-        // Megrez template
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>";
-        }
-
-        if (add_ass) {
-            ss << "<|role_start|>assistant<|role_end|>";
-        }
-    } else {
-        // template not supported
-        return -1;
-    }
-    dest = ss.str();
-    return dest.size();
-}
-
 int32_t llama_chat_apply_template(
                 const struct llama_model * model,
                               const char * tmpl,
@@ -23333,7 +12179,7 @@ int32_t llama_chat_apply_template(
         }
         else {
             // worst case: there is no information about template, we will use chatml by default
-            curr_tmpl = "chatml";  // see llama_chat_apply_template_internal
+            curr_tmpl = "chatml";  // see llm_chat_apply_template
         }
     }
 
@@ -23345,11 +12191,11 @@ int32_t llama_chat_apply_template(
     }
 
     std::string formatted_chat;
-    llm_chat_template detected_tmpl = llama_chat_detect_template(curr_tmpl);
+    llm_chat_template detected_tmpl = llm_chat_detect_template(curr_tmpl);
     if (detected_tmpl == LLM_CHAT_TEMPLATE_UNKNOWN) {
         return -1;
     }
-    int32_t res = llama_chat_apply_template_internal(detected_tmpl, chat_vec, formatted_chat, add_ass);
+    int32_t res = llm_chat_apply_template(detected_tmpl, chat_vec, formatted_chat, add_ass);
     if (res < 0) {
         return res;
     }
@@ -23359,15 +12205,6 @@ int32_t llama_chat_apply_template(
     return res;
 }
 
-int32_t llama_chat_builtin_templates(const char ** output, size_t len) {
-    auto it = LLM_CHAT_TEMPLATES.begin();
-    for (size_t i = 0; i < std::min(len, LLM_CHAT_TEMPLATES.size()); i++) {
-        output[i] = it->first.c_str();
-        std::advance(it, 1);
-    }
-    return (int32_t) LLM_CHAT_TEMPLATES.size();
-}
-
 //
 // sampling
 //
@@ -23435,6 +12272,10 @@ const char * llama_print_system_info(void) {
     return s.c_str();
 }
 
+//
+// perf
+//
+
 struct llama_perf_context_data llama_perf_context(const struct llama_context * ctx) {
     struct llama_perf_context_data data = {};
 
@@ -23470,47 +12311,3 @@ void llama_perf_context_reset(struct llama_context * ctx) {
     ctx->t_eval_us   = ctx->n_eval = 0;
     ctx->t_p_eval_us = ctx->n_p_eval = 0;
 }
-
-// For internal test use
-const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
-    struct llama_context * ctx
-) {
-    return ctx->model.tensors_by_name;
-}
-
-void llama_log_set(ggml_log_callback log_callback, void * user_data) {
-    ggml_log_set(log_callback, user_data);
-    g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
-    g_logger_state.log_callback_user_data = user_data;
-}
-
-static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
-    va_list args_copy;
-    va_copy(args_copy, args);
-    char buffer[128];
-    int len = vsnprintf(buffer, 128, format, args);
-    if (len < 128) {
-        g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
-    } else {
-        char * buffer2 = new char[len + 1];
-        vsnprintf(buffer2, len + 1, format, args_copy);
-        buffer2[len] = 0;
-        g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
-        delete[] buffer2;
-    }
-    va_end(args_copy);
-}
-
-void llama_log_internal(ggml_log_level level, const char * format, ...) {
-    va_list args;
-    va_start(args, format);
-    llama_log_internal_v(level, format, args);
-    va_end(args);
-}
-
-void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
-    (void) level;
-    (void) user_data;
-    fputs(text, stderr);
-    fflush(stderr);
-}

From e7da954eccdf39ee795a6135bdb86f0978902681 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 3 Jan 2025 11:26:14 +0200
Subject: [PATCH 104/376] metal : avoid uint (#11019)

---
 ggml/src/ggml-metal/ggml-metal.m | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 28f590f92..a85502ee0 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -2067,8 +2067,8 @@ static void ggml_metal_encode_node(
                 GGML_ASSERT(ne12 % ne02 == 0);
                 GGML_ASSERT(ne13 % ne03 == 0);
 
-                const uint r2 = ne12/ne02;
-                const uint r3 = ne13/ne03;
+                const uint32_t r2 = ne12/ne02;
+                const uint32_t r3 = ne13/ne03;
 
                 // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                 // to the matrix-vector kernel

From 4b0c638b9a68f577cb2066b638c9f622d91ee661 Mon Sep 17 00:00:00 2001
From: Molly Sophia <mollysophia379@gmail.com>
Date: Fri, 3 Jan 2025 20:13:18 +0800
Subject: [PATCH 105/376] common : disable KV cache shifting automatically for
 unsupported models (#11053)

* Disable KV cache shifting automatically for unsupported models

instead of exiting directly

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update common/common.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/common.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 3e37039ca..4bb140ee2 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -889,9 +889,8 @@ struct common_init_result common_init_from_params(common_params & params) {
     }
 
     if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
-        LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__);
-        llama_free_model(model);
-        return iparams;
+        LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__);
+        params.ctx_shift = false;
     }
 
     if (!params.control_vectors.empty()) {

From c31fc8b966817b2f0b277fd28e04a189e388972a Mon Sep 17 00:00:00 2001
From: "Gilad S." <7817232+giladgd@users.noreply.github.com>
Date: Sat, 4 Jan 2025 10:17:31 +0200
Subject: [PATCH 106/376] fix: Vulkan shader gen binary path (#11037)

---
 ggml/src/ggml-vulkan/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt
index 6d46e5f24..9501de736 100644
--- a/ggml/src/ggml-vulkan/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/CMakeLists.txt
@@ -73,7 +73,7 @@ if (Vulkan_FOUND)
         OUTPUT ${_ggml_vk_header}
                 ${_ggml_vk_source}
 
-        COMMAND ${_ggml_vk_genshaders_cmd}
+        COMMAND "$<TARGET_FILE_DIR:vulkan-shaders-gen>/${_ggml_vk_genshaders_cmd}"
             --glslc      ${Vulkan_GLSLC_EXECUTABLE}
             --input-dir  ${_ggml_vk_input_dir}
             --output-dir ${_ggml_vk_output_dir}

From db68c93b57bfdf6da1fbdae81080382d6998cbc9 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 19 Dec 2024 03:50:12 +0100
Subject: [PATCH 107/376] ggml : improve inputs log sched_print_assignments
 (ggml/1053)

This commit attempts to improve the log message for the inputs of the
splits in the sched_print_assignments function.

The motivation for this change is that currently even if there are no
inputs a colon is displayed at the end of the line, which can make it a
little confusing when reading the output as it could be interpreted as
the line below are inputs when they are in fact nodes. With this change
the colon will only be printed if there actually are inputs.
---
 ggml/src/ggml-backend.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index fdb4b986f..e2d6c4056 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -795,9 +795,12 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
     for (int i = 0; i < graph->n_nodes; i++) {
         if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
             ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
-            GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
+            GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs", cur_split, ggml_backend_name(split_backend),
                 sched->splits[cur_split].n_inputs);
             for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
+                if (j == 0) {
+                    GGML_LOG_DEBUG(": ");
+                }
                 GGML_LOG_DEBUG("[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
                     fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
             }

From 5e3b08d606b5b0caaea16541b504c3bba8f3ec1d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 4 Jan 2025 10:53:54 +0200
Subject: [PATCH 108/376] ggml : do not install metal source when embed library
 (ggml/1054)

---
 ggml/CMakeLists.txt                | 20 --------------------
 ggml/src/ggml-metal/CMakeLists.txt | 16 ++++++++++++++++
 2 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index e33d97482..393506533 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -252,26 +252,6 @@ set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
 install(TARGETS ggml LIBRARY PUBLIC_HEADER)
 install(TARGETS ggml-base LIBRARY)
 
-# FIXME: this should be done in the backend cmake files
-if (GGML_METAL)
-    # FIXME: does this need to be installed with GGML_METAL_EMBED_LIBRARY?
-    install(
-        FILES src/ggml-metal/ggml-metal.metal
-        PERMISSIONS
-            OWNER_READ
-            OWNER_WRITE
-            GROUP_READ
-            WORLD_READ
-        DESTINATION ${CMAKE_INSTALL_BINDIR})
-
-    if (NOT GGML_METAL_EMBED_LIBRARY)
-        install(
-            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            DESTINATION ${CMAKE_INSTALL_BINDIR}
-        )
-    endif()
-endif()
-
 if (GGML_STANDALONE)
     configure_file(${CMAKE_CURRENT_SOURCE_DIR}/ggml.pc.in
         ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
diff --git a/ggml/src/ggml-metal/CMakeLists.txt b/ggml/src/ggml-metal/CMakeLists.txt
index 1bad27206..89fcde2fa 100644
--- a/ggml/src/ggml-metal/CMakeLists.txt
+++ b/ggml/src/ggml-metal/CMakeLists.txt
@@ -103,3 +103,19 @@ else()
         DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
         )
 endif() # GGML_METAL_EMBED_LIBRARY
+
+if (NOT GGML_METAL_EMBED_LIBRARY)
+    install(
+        FILES src/ggml-metal/ggml-metal.metal
+        PERMISSIONS
+            OWNER_READ
+            OWNER_WRITE
+            GROUP_READ
+            WORLD_READ
+        DESTINATION ${CMAKE_INSTALL_BINDIR})
+
+        install(
+            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            DESTINATION ${CMAKE_INSTALL_BINDIR}
+        )
+endif()

From 78c678517530d411b4263341cdb4dc28c9d117c8 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 4 Jan 2025 10:54:01 +0200
Subject: [PATCH 109/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index b4ac38bbf..b67445ecd 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-e6d93f40dffe8733d5d72f1d8fa6b3ca27ae899f
+a2af72be7baf5b1f4a33d34e77e509e5e85b7cd7

From 46be942214e295cd34660bbbd6b846155d1c36a0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?DAN=E2=84=A2?= <dranger003@gmail.com>
Date: Sat, 4 Jan 2025 09:33:31 -0500
Subject: [PATCH 110/376] llama : add support for the cohere2 model
 architecture (#10900)

---
 convert_hf_to_gguf.py     |  18 +++++
 gguf-py/gguf/constants.py |  14 ++++
 src/llama-arch.cpp        |  16 ++++
 src/llama-arch.h          |   1 +
 src/llama-model.cpp       |  11 +++
 src/llama.cpp             | 161 ++++++++++++++++++++++++++++++++++++++
 6 files changed, 221 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 4e6c0f60c..d4441bbe9 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3373,6 +3373,24 @@ class CommandR2Model(Model):
         self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
 
 
+@Model.register("Cohere2ForCausalLM")
+class Cohere2Model(Model):
+    model_arch = gguf.MODEL_ARCH.COHERE2
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        rotary_pct = self.hparams["rotary_pct"]
+        hidden_size = self.hparams["hidden_size"]
+        num_attention_heads = self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+
 @Model.register("OlmoForCausalLM")
 @Model.register("OLMoForCausalLM")
 class OlmoModel(Model):
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 273370370..cdf79673b 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -255,6 +255,7 @@ class MODEL_ARCH(IntEnum):
     MAMBA            = auto()
     XVERSE           = auto()
     COMMAND_R        = auto()
+    COHERE2          = auto()
     DBRX             = auto()
     OLMO             = auto()
     OLMO2            = auto()
@@ -437,6 +438,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.MAMBA:            "mamba",
     MODEL_ARCH.XVERSE:           "xverse",
     MODEL_ARCH.COMMAND_R:        "command-r",
+    MODEL_ARCH.COHERE2:          "cohere2",
     MODEL_ARCH.DBRX:             "dbrx",
     MODEL_ARCH.OLMO:             "olmo",
     MODEL_ARCH.OLMO2:            "olmo2",
@@ -1136,6 +1138,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_K_NORM,
         MODEL_TENSOR.ATTN_Q_NORM,
     ],
+    MODEL_ARCH.COHERE2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.DBRX: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index a60038385..fea4b21d3 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -39,6 +39,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_MAMBA,            "mamba"            },
     { LLM_ARCH_XVERSE,           "xverse"           },
     { LLM_ARCH_COMMAND_R,        "command-r"        },
+    { LLM_ARCH_COHERE2,          "cohere2"          },
     { LLM_ARCH_DBRX,             "dbrx"             },
     { LLM_ARCH_OLMO,             "olmo"             },
     { LLM_ARCH_OLMO2,            "olmo2"            },
@@ -807,6 +808,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
         },
     },
+    {
+        LLM_ARCH_COHERE2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_DBRX,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 446e72eeb..10bd619a4 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -43,6 +43,7 @@ enum llm_arch {
     LLM_ARCH_MAMBA,
     LLM_ARCH_XVERSE,
     LLM_ARCH_COMMAND_R,
+    LLM_ARCH_COHERE2,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
     LLM_ARCH_OLMO2,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index ace0ba262..c356abded 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -786,6 +786,16 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_COHERE2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_8B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_DBRX:
         {
             ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2031,6 +2041,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_MINICPM:
         case LLM_ARCH_XVERSE:
         case LLM_ARCH_COMMAND_R:
+        case LLM_ARCH_COHERE2:
         case LLM_ARCH_OLMO:
         case LLM_ARCH_ARCTIC:
         case LLM_ARCH_DEEPSEEK:
diff --git a/src/llama.cpp b/src/llama.cpp
index d7110b90b..50e9191fa 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1552,6 +1552,32 @@ static bool llm_load_tensors(
                         layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
                 } break;
+            case LLM_ARCH_COHERE2:
+                {
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    // init output from the input tok embed
+                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
+                                                      llama_model_loader::TENSOR_DUPLICATED);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
+                    }
+                }
+                break;
             case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
                 {
                     model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -7633,6 +7659,137 @@ struct llm_build_context {
 
     }
 
+    struct ggml_cgraph * build_cohere2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        const float f_logit_scale = hparams.f_logit_scale;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        // cohere2 requires different mask for layers using sliding window (SWA)
+        struct ggml_tensor * KQ_mask     = build_inp_KQ_mask();
+        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
+
+        // sliding window switch pattern
+        const int32_t sliding_window_pattern = 4;
+
+        for (int il = 0; il < n_layer; ++il) {
+            // three layers sliding window attention (window size 4096) and ROPE
+            // fourth layer uses global attention without positional embeddings
+            const bool           is_sliding = il % sliding_window_pattern < (sliding_window_pattern - 1);
+            struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+            struct ggml_tensor * ffn_inp = cur;
+
+            // self-attention
+            {
+                // rope freq factors for 128k context
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                if (is_sliding) {
+                    Qcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+                                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor,
+                                        beta_fast, beta_slow);
+                    cb(Qcur, "Qcur", il);
+
+                    Kcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                                        rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
+                                        attn_factor, beta_fast, beta_slow);
+                    cb(Kcur, "Kcur", il);
+                } else {
+                    // For non-sliding layers, just reshape without applying RoPE
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                    cb(Qcur, "Qcur", il);
+
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur,
+                                   KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f / sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur                              = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpL                             = ggml_get_rows(ctx0, inpL, inp_out_ids);
+                ffn_inp                          = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            }
+
+            struct ggml_tensor * attn_out = cur;
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, lctx, ffn_inp, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate,
+                                    NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR,
+                                    cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // add together residual + FFN + self-attention
+            cur = ggml_add(ctx0, cur, inpL);
+            cur = ggml_add(ctx0, cur, attn_out);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        if (f_logit_scale) {
+            cur = ggml_scale(ctx0, cur, f_logit_scale);
+        }
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     // ref: https://allenai.org/olmo
     // based on the original build_llama() function, changes:
     //   * non-parametric layer norm
@@ -10384,6 +10541,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_command_r();
             } break;
+        case LLM_ARCH_COHERE2:
+            {
+                result = llm.build_cohere2();
+            } break;
         case LLM_ARCH_DBRX:
             {
                 result = llm.build_dbrx();

From f922a9c542ee117550a168395c63ea79261f5c99 Mon Sep 17 00:00:00 2001
From: matt23654 <matthew.webber@protonmail.com>
Date: Sat, 4 Jan 2025 16:10:30 +0000
Subject: [PATCH 111/376] [GGML][RPC] Support for models with non-512-aligned
 tensors over RPC. (#11047)

* Added init tensor calling code

* Added get_alloc_size forwarding

* Cleaned up and improved type/error handling.

* fix: remove trailing whitespaces.

* Cleanup and use GGML error logging functions.

* Handle potentially dangerous edge cases.

* Apply suggestions from code review

Co-authored-by: Diego Devesa <slarengh@gmail.com>

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 ggml/src/ggml-rpc/ggml-rpc.cpp | 140 +++++++++++++++++++++++++++++++--
 1 file changed, 134 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 431082426..2213aba9f 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -93,9 +93,23 @@ enum rpc_cmd {
     RPC_CMD_COPY_TENSOR,
     RPC_CMD_GRAPH_COMPUTE,
     RPC_CMD_GET_DEVICE_MEMORY,
+    RPC_CMD_INIT_TENSOR,
+    RPC_CMD_GET_ALLOC_SIZE,
     RPC_CMD_COUNT,
 };
 
+struct rpc_msg_get_alloc_size_req {
+    rpc_tensor tensor;
+};
+
+struct rpc_msg_get_alloc_size_rsp {
+    uint64_t alloc_size;
+};
+
+struct rpc_msg_init_tensor_req {
+    rpc_tensor tensor;
+};
+
 struct rpc_msg_alloc_buffer_req {
     uint64_t size;
 };
@@ -461,10 +475,18 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
 }
 
 static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-    UNUSED(buffer);
-    if (ggml_is_quantized(tensor->type)) {
-        // TODO: this check is due to MATRIX_ROW_PADDING in CUDA and should be generalized
-        GGML_ASSERT(tensor->ne[0] % 512 == 0 && "unsupported quantized tensor");
+    ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
+
+    // CUDA backend on the server pads everything to 512 due to CUDA limitations.
+    // Due to bandwidth constraints, we only call the server init tensor functions if necessary.
+    // In particular, only quantized tensors need padding
+    if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) {
+        rpc_msg_init_tensor_req request;
+
+        request.tensor = serialize_tensor(tensor);
+
+        bool status = send_rpc_cmd(ctx->sock, RPC_CMD_INIT_TENSOR, &request, sizeof(request), nullptr, 0);
+        GGML_ASSERT(status);
     }
 }
 
@@ -577,8 +599,23 @@ static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) {
 }
 
 static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    UNUSED(buft);
-    return ggml_nbytes(tensor);
+    // See comments in init_tensor.
+    if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) {
+        ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
+        auto sock = get_socket(buft_ctx->endpoint);
+
+        rpc_msg_get_alloc_size_req request;
+
+        request.tensor = serialize_tensor(tensor);
+
+        rpc_msg_get_alloc_size_rsp response;
+        bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALLOC_SIZE, &request, sizeof(request), &response, sizeof(response));
+        GGML_ASSERT(status);
+
+        return response.alloc_size;
+    } else {
+        return ggml_nbytes(tensor);
+    }
 }
 
 static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = {
@@ -757,6 +794,8 @@ public:
     bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
     bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response);
     bool graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph_compute_rsp & response);
+    bool init_tensor(const rpc_msg_init_tensor_req & request);
+    bool get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response);
 
 private:
     ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor);
@@ -770,6 +809,36 @@ private:
     std::unordered_set<ggml_backend_buffer_t> buffers;
 };
 
+bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response) {
+    ggml_backend_buffer_type_t buft;
+    struct ggml_init_params params {
+        /*.mem_size   =*/ ggml_tensor_overhead(),
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx = ggml_init(params);
+    ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
+
+    if (tensor == nullptr) {
+        GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n");
+        ggml_free(ctx);
+        return false;
+    }
+
+    if (tensor->buffer == nullptr) {
+        //No buffer allocated.
+        buft = ggml_backend_get_default_buffer_type(backend);
+    } else {
+        buft = tensor->buffer->buft;
+    }
+
+    response.alloc_size = ggml_backend_buft_get_alloc_size(buft,tensor);
+
+    ggml_free(ctx);
+    return true;
+}
+
 void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response) {
     ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
     ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, request.size);
@@ -905,6 +974,40 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
     return true;
 }
 
+bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
+    struct ggml_init_params params {
+        /*.mem_size   =*/ ggml_tensor_overhead(),
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+    struct ggml_context * ctx = ggml_init(params);
+    ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
+    if (tensor == nullptr) {
+        GGML_LOG_ERROR("Null tensor pointer passed to server init_tensor function.\n");
+        ggml_free(ctx);
+        return false;
+    }
+
+    // Call the backend's buffer_init_tensor function
+    ggml_backend_buffer_t buffer = tensor->buffer;
+    if (buffer && buffer->iface.init_tensor) {
+        buffer->iface.init_tensor(buffer, tensor);
+    } else {
+        GGML_LOG_ERROR("Null buffer for tensor passed to init_tensor function\n");
+    }
+
+    if (tensor->extra != nullptr) {
+        // This pointer can either be passed around client/server, or probably better stored server-side and kept track of.
+        // Currently unimplemented.
+        GGML_LOG_ERROR("tensor->extra populated by the backend, this is currently unsupported.\n");
+        ggml_free(ctx);
+        return false;
+    }
+
+    ggml_free(ctx);
+    return true;
+}
+
 bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response) {
     struct ggml_init_params params {
         /*.mem_size   =*/ ggml_tensor_overhead(),
@@ -1058,6 +1161,18 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
                 }
                 break;
             }
+            case RPC_CMD_GET_ALLOC_SIZE: {
+                rpc_msg_get_alloc_size_req request;
+                if (!recv_msg(sockfd, &request, sizeof(request))) {
+                    return;
+                }
+                rpc_msg_get_alloc_size_rsp response;
+                server.get_alloc_size(request, response);
+                if (!send_msg(sockfd, &response, sizeof(response))) {
+                    return;
+                }
+                break;
+            }
             case RPC_CMD_GET_ALIGNMENT: {
                 if (!recv_msg(sockfd, nullptr, 0)) {
                     return;
@@ -1133,6 +1248,19 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
                 }
                 break;
             }
+            case RPC_CMD_INIT_TENSOR: {
+                rpc_msg_init_tensor_req request;
+                if (!recv_msg(sockfd, &request,sizeof(request))) {
+                    return;
+                }
+                if (!server.init_tensor(request)) {
+                    return;
+                }
+                if (!send_msg(sockfd, nullptr, 0)) {
+                    return;
+                }
+                break;
+            }
             case RPC_CMD_GET_TENSOR: {
                 rpc_msg_get_tensor_req request;
                 if (!recv_msg(sockfd, &request, sizeof(request))) {

From 9394bbd484f802ce80d2858033583af3ef700d25 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Sat, 4 Jan 2025 21:06:11 +0100
Subject: [PATCH 112/376] llama : Add support for DeepSeek V3 (#11049)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type

* vocab : add DeepSeek V3 pre-tokenizer regexes

* unicode : handle ACCENT_MARK and SYMBOL categories in regex

* llama : add DeepSeek V3 chat template, handle new model parameters and tensor types

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 convert_hf_to_gguf.py          | 23 +++++++++++++++++
 convert_hf_to_gguf_update.py   |  1 +
 gguf-py/gguf/constants.py      | 10 ++++++++
 gguf-py/gguf/gguf_writer.py    |  7 ++++++
 gguf-py/gguf/tensor_mapping.py |  4 +++
 include/llama.h                |  1 +
 src/llama-arch.cpp             |  4 +++
 src/llama-arch.h               |  3 +++
 src/llama-chat.cpp             | 18 ++++++++++++++
 src/llama-chat.h               |  1 +
 src/llama-hparams.h            | 12 +++++++--
 src/llama-model.cpp            | 23 +++++++++++++++++
 src/llama-model.h              |  2 ++
 src/llama-vocab.cpp            |  7 ++++++
 src/llama.cpp                  | 45 +++++++++++++++++++++++++++++++---
 src/unicode.cpp                |  6 +++++
 16 files changed, 162 insertions(+), 5 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index d4441bbe9..01b58f976 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -687,6 +687,9 @@ class Model:
         if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
             # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
             res = "megrez"
+        if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
+            # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
+            res = "deepseek-v3"
 
         if res is None:
             logger.warning("\n")
@@ -3849,6 +3852,7 @@ class DeepseekModel(Model):
 
 
 @Model.register("DeepseekV2ForCausalLM")
+@Model.register("DeepseekV3ForCausalLM")
 class DeepseekV2Model(Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -3870,6 +3874,15 @@ class DeepseekV2Model(Model):
         self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
         self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
         self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+
+        if hparams["scoring_func"] == "sigmoid":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        elif hparams["scoring_func"] == "softmax":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}")
+
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
         if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
@@ -3882,6 +3895,16 @@ class DeepseekV2Model(Model):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # rename e_score_correction_bias tensors
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # skip Multi-Token Prediction (MTP) layers
+        block_count = self.hparams["num_hidden_layers"]
+        match = re.match(r"model.layers.(\d+)", name)
+        if match and int(match.group(1)) >= block_count:
+            return []
+
         # process the experts separately
         if name.find("mlp.experts") != -1:
             n_experts = self.hparams["n_routed_experts"]
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index fea23ddb4..56edc64a7 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -107,6 +107,7 @@ models = [
     {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
     {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
     {"name": "megrez",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
+    {"name": "deepseek-v3",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
 ]
 
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index cdf79673b..9d0e7489f 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -102,6 +102,8 @@ class Keys:
         EXPERT_USED_COUNT                 = "{arch}.expert_used_count"
         EXPERT_SHARED_COUNT               = "{arch}.expert_shared_count"
         EXPERT_WEIGHTS_SCALE              = "{arch}.expert_weights_scale"
+        EXPERT_WEIGHTS_NORM               = "{arch}.expert_weights_norm"
+        EXPERT_GATING_FUNC                = "{arch}.expert_gating_func"
         POOLING_TYPE                      = "{arch}.pooling_type"
         LOGIT_SCALE                       = "{arch}.logit_scale"
         DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
@@ -313,6 +315,7 @@ class MODEL_TENSOR(IntEnum):
     FFN_GATE_SHEXP       = auto()
     FFN_DOWN_SHEXP       = auto()
     FFN_UP_SHEXP         = auto()
+    FFN_EXP_PROBS_B      = auto()
     ATTN_Q_NORM          = auto()
     ATTN_K_NORM          = auto()
     LAYER_OUT_NORM       = auto()
@@ -498,6 +501,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_GATE_EXP:              "blk.{bid}.ffn_gate_exps",
     MODEL_TENSOR.FFN_DOWN_EXP:              "blk.{bid}.ffn_down_exps",
     MODEL_TENSOR.FFN_UP_EXP:                "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.FFN_EXP_PROBS_B:           "blk.{bid}.exp_probs_b",
     MODEL_TENSOR.LAYER_OUT_NORM:            "blk.{bid}.layer_output_norm",
     MODEL_TENSOR.SSM_IN:                    "blk.{bid}.ssm_in",
     MODEL_TENSOR.SSM_CONV1D:                "blk.{bid}.ssm_conv1d",
@@ -1290,6 +1294,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_GATE_SHEXP,
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
     ],
     MODEL_ARCH.CHATGLM : [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -1590,6 +1595,11 @@ class GGMLQuantizationType(IntEnum):
     TQ2_0   = 35
 
 
+class ExpertGatingFuncType(IntEnum):
+    SOFTMAX  = 1
+    SIGMOID  = 2
+
+
 # TODO: add GGMLFileType from ggml_ftype in ggml.h
 
 
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 3023b539a..4a0a65e3c 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -26,6 +26,7 @@ from .constants import (
     RopeScalingType,
     PoolingType,
     TokenType,
+    ExpertGatingFuncType,
 )
 
 from .quants import quant_shape_from_byte_shape
@@ -715,6 +716,12 @@ class GGUFWriter:
     def add_expert_weights_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
 
+    def add_expert_weights_norm(self, value: bool) -> None:
+        self.add_bool(Keys.LLM.EXPERT_WEIGHTS_NORM.format(arch=self.arch), value)
+
+    def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
+
     def add_swin_norm(self, value: bool) -> None:
         self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 7009a11d4..efe2a4aa4 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -276,6 +276,10 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe
         ),
 
+        MODEL_TENSOR.FFN_EXP_PROBS_B: (
+            "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3
+        ),
+
         # Feed-forward up
         MODEL_TENSOR.FFN_UP: (
             "gpt_neox.layers.{bid}.mlp.dense_h_to_4h",                # gptneox
diff --git a/include/llama.h b/include/llama.h
index 7b305b299..a0d5ba5dd 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -105,6 +105,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_EXAONE         = 25,
         LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
         LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
+        LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
     };
 
     enum llama_rope_type {
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index fea4b21d3..007d79f82 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -92,6 +92,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
     { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
     { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
+    { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
+    { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
     { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
     { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
     { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
@@ -984,6 +986,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
             { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
         },
     },
     {
@@ -1366,6 +1369,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_DOWN_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_GATE_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_EXP_PROBS_B,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     // this tensor is loaded for T5, but never used
     {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
     {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 10bd619a4..45e458bb9 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -96,6 +96,8 @@ enum llm_kv {
     LLM_KV_EXPERT_USED_COUNT,
     LLM_KV_EXPERT_SHARED_COUNT,
     LLM_KV_EXPERT_WEIGHTS_SCALE,
+    LLM_KV_EXPERT_WEIGHTS_NORM,
+    LLM_KV_EXPERT_GATING_FUNC,
     LLM_KV_POOLING_TYPE,
     LLM_KV_LOGIT_SCALE,
     LLM_KV_DECODER_START_TOKEN_ID,
@@ -231,6 +233,7 @@ enum llm_tensor {
     LLM_TENSOR_FFN_DOWN_SHEXP,
     LLM_TENSOR_FFN_GATE_SHEXP,
     LLM_TENSOR_FFN_UP_SHEXP,
+    LLM_TENSOR_FFN_EXP_PROBS_B,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,
     LLM_TENSOR_LAYER_OUT_NORM,
diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index a07e9cf00..44670d3d8 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -45,6 +45,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "vicuna-orca",       LLM_CHAT_TEMPLATE_VICUNA_ORCA       },
     { "deepseek",          LLM_CHAT_TEMPLATE_DEEPSEEK          },
     { "deepseek2",         LLM_CHAT_TEMPLATE_DEEPSEEK_2        },
+    { "deepseek3",         LLM_CHAT_TEMPLATE_DEEPSEEK_3        },
     { "command-r",         LLM_CHAT_TEMPLATE_COMMAND_R         },
     { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
     { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGML_3         },
@@ -148,6 +149,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_MINICPM;
     } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
         return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
+    } else if (tmpl_contains(LU8("'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'"))) {
+        return LLM_CHAT_TEMPLATE_DEEPSEEK_3;
     } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
         // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
         // EXAONE-3.0-7.8B-Instruct
@@ -453,6 +456,21 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "Assistant:";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_3) {
+        // DeepSeek-V3
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "\n\n";
+            } else if (role == "user") {
+                ss << LU8("<｜User｜>") << message->content;
+            } else if (role == "assistant") {
+                ss << LU8("<｜Assistant｜>") << message->content << LU8("<｜end▁of▁sentence｜>");
+            }
+        }
+        if (add_ass) {
+            ss << LU8("<｜Assistant｜>");
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
         // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
         // EXAONE-3.0-7.8B-Instruct
diff --git a/src/llama-chat.h b/src/llama-chat.h
index 364318c27..b8e94d9ef 100644
--- a/src/llama-chat.h
+++ b/src/llama-chat.h
@@ -25,6 +25,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_VICUNA_ORCA,
     LLM_CHAT_TEMPLATE_DEEPSEEK,
     LLM_CHAT_TEMPLATE_DEEPSEEK_2,
+    LLM_CHAT_TEMPLATE_DEEPSEEK_3,
     LLM_CHAT_TEMPLATE_COMMAND_R,
     LLM_CHAT_TEMPLATE_LLAMA_3,
     LLM_CHAT_TEMPLATE_CHATGML_3,
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 3a76b71a4..a29f20ec4 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -6,7 +6,13 @@
 
 // bump if necessary
 #define LLAMA_MAX_LAYERS  512
-#define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
+#define LLAMA_MAX_EXPERTS 256  // DeepSeekV3
+
+enum llama_expert_gating_func_type {
+    LLAMA_EXPERT_GATING_FUNC_TYPE_NONE    = 0,
+    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX = 1,
+    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2,
+};
 
 struct llama_hparams_posnet {
     uint32_t n_embd;
@@ -54,7 +60,9 @@ struct llama_hparams {
     uint32_t n_expert_shared    = 0;
     uint32_t n_norm_groups      = 0;
 
-    float expert_weights_scale = 0.0;
+    float    expert_weights_scale = 0.0;
+    bool     expert_weights_norm  = false;
+    uint32_t expert_gating_func   = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
 
     float f_norm_eps;
     float f_norm_rms_eps;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c356abded..405e0528f 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -66,6 +66,7 @@ const char * llm_type_name(llm_type type) {
         case MODEL_70B:           return "70B";
         case MODEL_236B:          return "236B";
         case MODEL_314B:          return "314B";
+        case MODEL_671B:          return "671B";
         case MODEL_SMALL:         return "0.1B";
         case MODEL_MEDIUM:        return "0.4B";
         case MODEL_LARGE:         return "0.8B";
@@ -125,6 +126,14 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
     }
 }
 
+static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) {
+    switch (type) {
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
+        default:                                    return "unknown";
+    }
+}
+
 std::string llama_model_arch_name (const llama_model & model) {
     return llm_arch_name(model.arch);
 }
@@ -933,11 +942,19 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
                 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
                 ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    // for compatibility with existing DeepSeek V2 and V2.5 GGUFs
+                    // that have no expert_gating_func model parameter set
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
+                }
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
 
                 switch (hparams.n_layer) {
                     case 27: model.type = e_model::MODEL_16B; break;
                     case 60: model.type = e_model::MODEL_236B; break;
+                    case 61: model.type = e_model::MODEL_671B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -1259,6 +1276,10 @@ void llm_load_vocab(llama_model_loader & ml, llama_model & model) {
                     tokenizer_pre == "deepseek-coder") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
                 vocab.tokenizer_clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "deepseek-v3") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                     tokenizer_pre == "falcon") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
@@ -1941,6 +1962,8 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
         LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
         LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
         LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+        LLAMA_LOG_INFO("%s: expert_weights_norm  = %d\n",     __func__, hparams.expert_weights_norm);
+        LLAMA_LOG_INFO("%s: expert_gating_func   = %s\n",     __func__, llama_expert_gating_func_name((enum llama_expert_gating_func_type) hparams.expert_gating_func));
         LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
     }
 
diff --git a/src/llama-model.h b/src/llama-model.h
index 01c780c41..ce038932d 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -63,6 +63,7 @@ enum llm_type {
     MODEL_70B,
     MODEL_236B,
     MODEL_314B,
+    MODEL_671B,
     MODEL_SMALL,
     MODEL_MEDIUM,
     MODEL_LARGE,
@@ -213,6 +214,7 @@ struct llama_layer {
     struct ggml_tensor * ffn_down_b = nullptr; // b2
     struct ggml_tensor * ffn_up_b   = nullptr; // b3
     struct ggml_tensor * ffn_act    = nullptr;
+    struct ggml_tensor * ffn_exp_probs_b = nullptr;
 
     // mamba proj
     struct ggml_tensor * ssm_in  = nullptr;
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 909e04871..3fcfcaa3f 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -382,6 +382,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "\\p{N}+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
+                regex_exprs = {
+                    "\\p{N}{1,3}",
+                    "[一-龥぀-ゟ゠-ヿ]+",
+                    "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
                 regex_exprs = {
                     "[\r\n]",
diff --git a/src/llama.cpp b/src/llama.cpp
index 50e9191fa..ea78ea487 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1857,6 +1857,7 @@ static bool llm_load_tensors(
                             layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                         } else {
                             layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                             if (n_expert == 0) {
                                 throw std::runtime_error("n_expert must be > 0");
@@ -2837,12 +2838,14 @@ static struct ggml_tensor * llm_build_moe_ffn(
          struct ggml_tensor * up_exps,
          struct ggml_tensor * gate_exps,
          struct ggml_tensor * down_exps,
+         struct ggml_tensor * exp_probs_b,
                     int64_t   n_expert,
                     int64_t   n_expert_used,
             llm_ffn_op_type   type_op,
                        bool   norm_w,
                        bool   scale_w,
                       float   w_scale,
+llama_expert_gating_func_type gating_op,
          const llm_build_cb & cb,
                         int   il) {
     int64_t n_embd = cur->ne[0];
@@ -2851,11 +2854,31 @@ static struct ggml_tensor * llm_build_moe_ffn(
     ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
     cb(logits, "ffn_moe_logits", il);
 
-    ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
+    ggml_tensor * probs = nullptr;
+    switch (gating_op) {
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX:
+            {
+                probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
+            } break;
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID:
+            {
+                probs = ggml_sigmoid(ctx, logits); // [n_expert, n_tokens]
+            } break;
+        default:
+            GGML_ABORT("fatal error");
+    }
     cb(probs, "ffn_moe_probs", il);
 
+    // add experts selection bias - introduced in DeepSeek V3
+    // leave probs unbiased as it's later used to get expert weights
+    ggml_tensor * selection_probs = probs;
+    if (exp_probs_b != nullptr) {
+        selection_probs = ggml_add(ctx, probs, exp_probs_b);
+        cb(selection_probs, "ffn_moe_probs_biased", il);
+    }
+
     // select experts
-    ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
+    ggml_tensor * selected_experts = ggml_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
     cb(selected_experts->src[0], "ffn_moe_argsort", il);
     cb(selected_experts, "ffn_moe_topk", il);
 
@@ -3976,9 +3999,11 @@ struct llm_build_context {
                         model.layers[il].ffn_up_exps,
                         model.layers[il].ffn_gate_exps,
                         model.layers[il].ffn_down_exps,
+                        nullptr,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, true,
                         false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                         cb, il);
                 cb(cur, "ffn_moe_out", il);
             }
@@ -4628,9 +4653,11 @@ struct llm_build_context {
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
+                    nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_GELU, true,
                     false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -4769,9 +4796,11 @@ struct llm_build_context {
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
+                    nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
                     false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -6017,9 +6046,11 @@ struct llm_build_context {
                         model.layers[il].ffn_up_exps,
                         model.layers[il].ffn_gate_exps,
                         model.layers[il].ffn_down_exps,
+                        nullptr,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, false,
                         false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                         cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -8142,9 +8173,11 @@ struct llm_build_context {
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
+                    nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, false,
                     false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -8539,9 +8572,11 @@ struct llm_build_context {
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
+                    nullptr,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
                     false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -8680,9 +8715,11 @@ struct llm_build_context {
                             model.layers[il].ffn_up_exps,
                             model.layers[il].ffn_gate_exps,
                             model.layers[il].ffn_down_exps,
+                            nullptr,
                             n_expert, n_expert_used,
                             LLM_FFN_SILU, false,
                             false, hparams.expert_weights_scale,
+                            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                             cb, il);
                 cb(moe_out, "ffn_moe_out", il);
 
@@ -8909,9 +8946,11 @@ struct llm_build_context {
                             model.layers[il].ffn_up_exps,
                             model.layers[il].ffn_gate_exps,
                             model.layers[il].ffn_down_exps,
+                            model.layers[il].ffn_exp_probs_b,
                             n_expert, n_expert_used,
-                            LLM_FFN_SILU, false,
+                            LLM_FFN_SILU, hparams.expert_weights_norm,
                             true, hparams.expert_weights_scale,
+                            (enum llama_expert_gating_func_type) hparams.expert_gating_func,
                             cb, il);
                 cb(moe_out, "ffn_moe_out", il);
 
diff --git a/src/unicode.cpp b/src/unicode.cpp
index 8ed6b1a51..7aca6544b 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -667,18 +667,24 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
         { "\\p{N}", unicode_cpt_flags::NUMBER },
         { "\\p{L}", unicode_cpt_flags::LETTER },
         { "\\p{P}", unicode_cpt_flags::PUNCTUATION },
+        { "\\p{M}", unicode_cpt_flags::ACCENT_MARK },
+        { "\\p{S}", unicode_cpt_flags::SYMBOL },
     };
 
     static const std::map<int, int> k_ucat_cpt = {
         { unicode_cpt_flags::NUMBER,      0xD1 },
         { unicode_cpt_flags::LETTER,      0xD2 },
         { unicode_cpt_flags::PUNCTUATION, 0xD3 },
+        { unicode_cpt_flags::ACCENT_MARK, 0xD4 },
+        { unicode_cpt_flags::SYMBOL,      0xD5 },
     };
 
     static const std::map<int, std::string> k_ucat_map = {
         { unicode_cpt_flags::NUMBER,      "\x30-\x39" }, // 0-9
         { unicode_cpt_flags::LETTER,      "\x41-\x5A\x61-\x7A" }, // A-Za-z
         { unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
+        { unicode_cpt_flags::ACCENT_MARK, "" }, // no sub-128 codepoints
+        { unicode_cpt_flags::SYMBOL,      "\\\x24\\\x2B\x3C-\x3E\x5E\x60\\\x7C" }, // $+<=>^`|
     };
 
     // compute collapsed codepoints only if needed by at least one regex

From b56f079e28fda692f11a8b59200ceb815b05d419 Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Sat, 4 Jan 2025 21:09:59 +0100
Subject: [PATCH 113/376] Vulkan: Add device-specific blacklist for coopmat for
 the AMD proprietary driver (#11074)

* Vulkan: Add device-specific blacklist for coopmat for the AMD proprietary driver

* Add (TM) to AMD name check
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 30 +++++++++++++++++++++-------
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 020e61280..d75cd6d61 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2040,6 +2040,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
     std::cerr << "Done!" << std::endl;
 }
 
+static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props);
+
 static vk_device ggml_vk_get_device(size_t idx) {
     VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")");
 
@@ -2175,9 +2177,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
 
-        if (device->vendor_id == VK_VENDOR_ID_INTEL || (device->vendor_id == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
-            // Intel drivers don't support coopmat properly yet
-            // Only RADV supports coopmat properly on AMD
+        if (!ggml_vk_khr_cooperative_matrix_support(device->properties, driver_props)) {
             device->coopmat_support = false;
         }
 
@@ -2515,7 +2515,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
     return vk_instance.devices[idx];
 }
 
-
 static void ggml_vk_print_gpu_info(size_t idx) {
     GGML_ASSERT(idx < vk_instance.device_indices.size());
     size_t dev_num = vk_instance.device_indices[idx];
@@ -2565,9 +2564,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
         }
     }
 
-    if (props2.properties.vendorID == VK_VENDOR_ID_INTEL || (props2.properties.vendorID == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
-        // Intel drivers don't support coopmat properly yet
-        // Only RADV supports coopmat properly on AMD
+    if (!ggml_vk_khr_cooperative_matrix_support(props2.properties, driver_props)) {
         coopmat_support = false;
     }
 
@@ -8088,6 +8085,25 @@ static bool ggml_vk_instance_portability_enumeration_ext_available(const std::ve
     UNUSED(instance_extensions);
 }
 
+static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props) {
+    switch (props.vendorID) {
+    case VK_VENDOR_ID_INTEL:
+        // Intel drivers don't support coopmat properly yet
+        return false;
+    case VK_VENDOR_ID_AMD:
+        if (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource) {
+            // Workaround for AMD proprietary driver reporting support on all GPUs
+            const std::string name = props.deviceName;
+            return name.rfind("AMD Radeon RX 7", 0) == 0   || name.rfind("AMD Radeon(TM) RX 7", 0) == 0   || // RDNA 3 consumer GPUs
+                   name.rfind("AMD Radeon PRO W7", 0) == 0 || name.rfind("AMD Radeon(TM) PRO W7", 0) == 0 || // RDNA 3 workstation GPUs
+                   name.rfind("AMD Radeon 7", 0) == 0      || name.rfind("AMD Radeon(TM) 7", 0) == 0;        // RDNA 3 APUs
+        }
+        return true;
+    default:
+        return true;
+    }
+}
+
 // checks
 
 #ifdef GGML_VULKAN_CHECK_RESULTS

From 46e3556e01b824e52395fb050b29804b6cff2a7c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 6 Jan 2025 02:33:52 +0100
Subject: [PATCH 114/376] CUDA: add BF16 support (#11093)

* CUDA: add BF16 support
---
 ggml/src/ggml-cuda/convert.cu     |   2 +
 ggml/src/ggml-cuda/ggml-cuda.cu   |   3 +-
 ggml/src/ggml-cuda/mmv.cu         | 114 ++++++++++++++++++++----------
 ggml/src/ggml-cuda/vendors/cuda.h |   1 +
 ggml/src/ggml-cuda/vendors/hip.h  |   3 +
 ggml/src/ggml-cuda/vendors/musa.h |   3 +
 6 files changed, 87 insertions(+), 39 deletions(-)

diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index 3896f956d..5b0dfacef 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -680,6 +680,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
             return dequantize_row_iq3_s_cuda;
         case GGML_TYPE_F16:
             return convert_unary_cuda<half>;
+        case GGML_TYPE_BF16:
+            return convert_unary_cuda<nv_bfloat16>;
         default:
             return nullptr;
     }
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index c180adc84..0b06be729 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1728,7 +1728,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
 static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft);
 
-    bool use_mul_mat_vec   = src0->type == GGML_TYPE_F16
+    bool use_mul_mat_vec   = (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
         && src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
     bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
@@ -2869,6 +2869,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                     case GGML_TYPE_IQ3_XXS:
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_IQ4_XS:
+                    case GGML_TYPE_BF16:
 #ifdef GGML_USE_MUSA
                         if (a->type == GGML_TYPE_Q3_K) {
                             return false;
diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu
index a4b4f6bc1..ac45f2d17 100644
--- a/ggml/src/ggml-cuda/mmv.cu
+++ b/ggml/src/ggml-cuda/mmv.cu
@@ -1,9 +1,9 @@
 #include "common.cuh"
 #include "mmv.cuh"
 
-template <typename type_acc, int block_size>
+template <typename T, typename type_acc, int block_size>
 static __global__ void mul_mat_vec(
-        const half * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
+        const T * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
         const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) {
     const int64_t row     = blockIdx.x;
     const int64_t channel = blockIdx.z;
@@ -13,7 +13,6 @@ static __global__ void mul_mat_vec(
     y   +=  channel               *stride_channel_y;
     dst +=  channel               *stride_channel_dst;
 
-    const half2  * x2 = (const half2  *) x;
     const float2 * y2 = (const float2 *) y;
 
     extern __shared__ char data_mmv[];
@@ -28,28 +27,44 @@ static __global__ void mul_mat_vec(
 
     float sumf;
 
-    if (std::is_same<type_acc, float>::value) {
+    if constexpr (std::is_same<T, half>::value) {
+        const half2 * x2 = (const half2 *) x;
+
+        if (std::is_same<type_acc, float>::value) {
+            sumf = 0.0f;
+
+            for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
+                const float2 tmpx = __half22float2(x2[col2]);
+                const float2 tmpy = y2[col2];
+                sumf += tmpx.x * tmpy.x;
+                sumf += tmpx.y * tmpy.y;
+            }
+        } else {
+#ifdef FP16_AVAILABLE
+            half2 sumh2 = make_half2(0.0f, 0.0f);
+
+            for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
+                const float2 tmp = y2[col2];
+                sumh2 += x2[col2] * make_half2(tmp.x, tmp.y);
+            }
+
+            sumf = __low2float(sumh2) + __high2float(sumh2);
+#else
+            NO_DEVICE_CODE;
+#endif // FP16_AVAILABLE
+        }
+    } else if constexpr (std::is_same<T, nv_bfloat16>::value) {
+        const int * x2 = (const int *) x;
         sumf = 0.0f;
 
         for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
-            const float2 tmpx = __half22float2(x2[col2]);
+            const int    tmpx = x2[col2];
             const float2 tmpy = y2[col2];
-            sumf += tmpx.x * tmpy.x;
-            sumf += tmpx.y * tmpy.y;
+            sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
+            sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
         }
     } else {
-#ifdef FP16_AVAILABLE
-        half2 sumh2 = make_half2(0.0f, 0.0f);
-
-        for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
-            const float2 tmp = y2[col2];
-            sumh2 += x2[col2] * make_half2(tmp.x, tmp.y);
-        }
-
-        sumf = __low2float(sumh2) + __high2float(sumh2);
-#else
-        NO_DEVICE_CODE;
-#endif // FP16_AVAILABLE
+        static_assert(std::is_same<T, void>::value, "unsupported type");
     }
 
     sumf = warp_reduce_sum(sumf);
@@ -71,9 +86,9 @@ static __global__ void mul_mat_vec(
     dst[row] = sumf;
 }
 
-template <typename type_acc>
+template <typename T, typename type_acc>
 static void launch_mul_mat_vec_cuda(
-        const half * x, const float * y, float * dst,
+        const T * x, const float * y, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
         cudaStream_t stream) {
@@ -97,35 +112,35 @@ static void launch_mul_mat_vec_cuda(
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
-            mul_mat_vec<type_acc,  32><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc,  32><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case   64: {
-            mul_mat_vec<type_acc,  64><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc,  64><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case   96: {
-            mul_mat_vec<type_acc,  96><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc,  96><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case  128: {
-            mul_mat_vec<type_acc, 128><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc, 128><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case  160: {
-            mul_mat_vec<type_acc, 160><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc, 160><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case  192: {
-            mul_mat_vec<type_acc, 192><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc, 192><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case  224: {
-            mul_mat_vec<type_acc, 224><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc, 224><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         case  256: {
-            mul_mat_vec<type_acc, 256><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec<T, type_acc, 256><<<block_nums, block_dims, smem, stream>>>
                 (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
         } break;
         default: {
@@ -134,25 +149,25 @@ static void launch_mul_mat_vec_cuda(
     }
 }
 
+template<typename T>
 static void mul_mat_vec_cuda(
-        const half * x, const float * y, float * dst,
+        const T * x, const float * y, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
         enum ggml_prec prec, cudaStream_t stream) {
     switch (prec) {
         case GGML_PREC_DEFAULT: {
-            launch_mul_mat_vec_cuda<half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
+            launch_mul_mat_vec_cuda<T, half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
                 stride_channel_x, stride_channel_y, stride_channel_dst, stream);
         } break;
         case GGML_PREC_F32: {
-            launch_mul_mat_vec_cuda<float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
+            launch_mul_mat_vec_cuda<T, float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
                 stride_channel_x, stride_channel_y, stride_channel_dst, stream);
         } break;
     }
 }
 
 void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type  == GGML_TYPE_F32);
 
@@ -164,7 +179,6 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
 
-    const half  * src0_d = (const half  *) src0->data;
     const float * src1_d = (const float *) src1->data;
     float       *  dst_d = (float       *)  dst->data;
 
@@ -181,7 +195,20 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     const int64_t channel_stride_y   = src1->nb[2] / ggml_type_size(src1->type);
     const int64_t channel_stride_dst =  dst->nb[2] / ggml_type_size( dst->type);
 
-    mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12, channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
+    switch (src0->type) {
+        case GGML_TYPE_F16: {
+            const half * src0_d = (const half *) src0->data;
+            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
+                channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
+        } break;
+        case GGML_TYPE_BF16: {
+            const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
+            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
+                channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
+        } break;
+        default:
+            GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
+    }
 }
 
 void ggml_cuda_op_mul_mat_vec(
@@ -190,7 +217,6 @@ void ggml_cuda_op_mul_mat_vec(
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream) {
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type  == GGML_TYPE_F32);
 
@@ -211,8 +237,20 @@ void ggml_cuda_op_mul_mat_vec(
     const int64_t channel_stride_y   = 0;
     const int64_t channel_stride_dst = 0;
 
-    mul_mat_vec_cuda((const half *) src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
-        nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
+    switch (src0->type) {
+        case GGML_TYPE_F16: {
+            const half * src0_d = (const half *) src0_dd_i;
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
+                nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
+        } break;
+        case GGML_TYPE_BF16: {
+            const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
+                nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
+        } break;
+        default:
+            GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
+    }
 
     GGML_UNUSED(ctx);
     GGML_UNUSED(src1);
diff --git a/ggml/src/ggml-cuda/vendors/cuda.h b/ggml/src/ggml-cuda/vendors/cuda.h
index db9f6a165..1746b0732 100644
--- a/ggml/src/ggml-cuda/vendors/cuda.h
+++ b/ggml/src/ggml-cuda/vendors/cuda.h
@@ -3,6 +3,7 @@
 #include <cuda_runtime.h>
 #include <cuda.h>
 #include <cublas_v2.h>
+#include <cuda_bf16.h>
 #include <cuda_fp16.h>
 
 #if CUDART_VERSION < 11020
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 3205534d6..c905b15d7 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -3,6 +3,7 @@
 #include <hip/hip_runtime.h>
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
+#include <hip/hip_bfloat16.h>
 #ifdef __HIP_PLATFORM_AMD__
 // for rocblas_initialize()
 #include "rocblas/rocblas.h"
@@ -121,6 +122,8 @@
     #define __has_builtin(x) 0
 #endif
 
+typedef hip_bfloat16 nv_bfloat16;
+
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
 static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
index 1604b8229..6cc1b69ee 100644
--- a/ggml/src/ggml-cuda/vendors/musa.h
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -3,6 +3,7 @@
 #include <musa_runtime.h>
 #include <musa.h>
 #include <mublas.h>
+#include <musa_bf16.h>
 #include <musa_fp16.h>
 #define CUBLAS_COMPUTE_16F CUDA_R_16F
 #define CUBLAS_COMPUTE_32F CUDA_R_32F
@@ -132,3 +133,5 @@
 #define cudaKernelNodeParams musaKernelNodeParams
 #define cudaStreamCaptureModeRelaxed musaStreamCaptureModeRelaxed
 #define cudaStreamEndCapture musaStreamEndCapture
+
+typedef mt_bfloat16 nv_bfloat16;

From 5047dd3546951dea3d65c02257d06c46c8662338 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 10:52:01 +0200
Subject: [PATCH 115/376] llama : use _impl suffix instead of _internal
 (#11060)

ggml-ci
---
 src/llama-quant.cpp | 20 ++++++++++----------
 src/llama.cpp       | 16 ++++++++--------
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 42974f8f1..104f90343 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -22,7 +22,7 @@ static void zeros(std::ofstream & file, size_t n) {
     }
 }
 
-struct quantize_state_internal {
+struct quantize_state_impl {
     const llama_model                 & model;
     const llama_model_quantize_params * params;
 
@@ -43,13 +43,13 @@ struct quantize_state_internal {
     // used to figure out if a model shares tok_embd with the output weight
     bool has_output = false;
 
-    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
+    quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params)
         : model(model)
         , params(params)
         {}
 };
 
-static void llama_tensor_dequantize_internal(
+static void llama_tensor_dequantize_impl(
     struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
     const size_t nelements, const int nthread
 ) {
@@ -121,7 +121,7 @@ static void llama_tensor_dequantize_internal(
     workers.clear();
 }
 
-static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
     const std::string name = ggml_get_name(tensor);
 
     // TODO: avoid hardcoded tensor names - use the TN_* constants
@@ -410,7 +410,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     return new_type;
 }
 
-static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
     if (nthread < 2) {
         // single-thread
         size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
@@ -464,7 +464,7 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
     return new_size;
 }
 
-static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
+static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
     ggml_type default_type;
     llama_ftype ftype = params->ftype;
 
@@ -534,7 +534,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     llm_load_hparams(ml, model);
     llm_load_stats  (ml, model);
 
-    struct quantize_state_internal qs(model, params);
+    struct quantize_state_impl qs(model, params);
 
     if (params->only_copy) {
         ftype = model.ftype;
@@ -837,7 +837,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
                 throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
             } else {
-                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+                llama_tensor_dequantize_impl(tensor, f32_conv_buf, workers, nelements, nthread);
                 f32_data = (float *) f32_conv_buf.data();
             }
 
@@ -866,7 +866,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
                 const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
 
-                new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
+                new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
             }
             LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
         }
@@ -919,7 +919,7 @@ uint32_t llama_model_quantize(
         const char * fname_out,
         const llama_model_quantize_params * params) {
     try {
-        llama_model_quantize_internal(fname_inp, fname_out, params);
+        llama_model_quantize_impl(fname_inp, fname_out, params);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
         return 1;
diff --git a/src/llama.cpp b/src/llama.cpp
index ea78ea487..4a6798f41 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -10717,7 +10717,7 @@ static enum ggml_status llama_graph_compute(
 // return positive int on warning
 // return negative int on error
 //
-static int llama_decode_internal(
+static int llama_decode_impl(
          llama_context & lctx,
            llama_batch   inp_batch) {
 
@@ -11052,7 +11052,7 @@ static int llama_decode_internal(
 // return positive int on warning
 // return negative int on error
 //
-static int llama_encode_internal(
+static int llama_encode_impl(
          llama_context & lctx,
            llama_batch   inp_batch) {
 
@@ -11234,7 +11234,7 @@ static int llama_encode_internal(
 }
 
 // find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
-static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
+static void llama_kv_cache_defrag_impl(struct llama_context & lctx) {
     auto & kv_self = lctx.kv_self;
 
     const auto & hparams = lctx.model.hparams;
@@ -11454,7 +11454,7 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
     //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
 }
 
-static void llama_kv_cache_update_internal(struct llama_context & lctx) {
+static void llama_kv_cache_update_impl(struct llama_context & lctx) {
     bool need_reserve = false;
 
     if (lctx.kv_self.has_shift) {
@@ -11490,7 +11490,7 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
 
     // defragment the KV cache if needed
     if (lctx.kv_self.do_defrag) {
-        llama_kv_cache_defrag_internal(lctx);
+        llama_kv_cache_defrag_impl(lctx);
 
         need_reserve = true;
 
@@ -12191,7 +12191,7 @@ void llama_kv_cache_defrag(struct llama_context * ctx) {
 }
 
 void llama_kv_cache_update(struct llama_context * ctx) {
-    llama_kv_cache_update_internal(*ctx);
+    llama_kv_cache_update_impl(*ctx);
 }
 
 bool llama_kv_cache_can_shift(struct llama_context * ctx) {
@@ -12203,7 +12203,7 @@ bool llama_kv_cache_can_shift(struct llama_context * ctx) {
 int32_t llama_encode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
-    const int ret = llama_encode_internal(*ctx, batch);
+    const int ret = llama_encode_impl(*ctx, batch);
     if (ret != 0) {
         LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
     }
@@ -12214,7 +12214,7 @@ int32_t llama_encode(
 int32_t llama_decode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
-    const int ret = llama_decode_internal(*ctx, batch);
+    const int ret = llama_decode_impl(*ctx, batch);
     if (ret != 0) {
         LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
     }

From 727368c60f2ebf2d6a7473a4a9f80957ab063a8e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 10:52:15 +0200
Subject: [PATCH 116/376] llama : use LLAMA_TOKEN_NULL (#11062)

ggml-ci
---
 common/common.cpp                             |  2 +-
 common/ngram-cache.cpp                        | 24 +++++++-------
 common/ngram-cache.h                          |  4 +--
 examples/batched/batched.cpp                  |  2 +-
 .../convert-llama2c-to-ggml.cpp               |  4 +--
 examples/main/main.cpp                        |  4 +--
 examples/server/utils.hpp                     |  2 +-
 include/llama.h                               |  1 -
 src/llama-model.cpp                           | 32 +++++++++----------
 src/llama-sampling.cpp                        |  8 ++---
 src/llama-vocab.cpp                           | 24 +++++++-------
 11 files changed, 53 insertions(+), 54 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 4bb140ee2..d6a7ab753 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -982,7 +982,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         if (llama_model_has_encoder(model)) {
             llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
             llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-            if (decoder_start_token_id == -1) {
+            if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
                 decoder_start_token_id = bos;
             }
             tmp.clear();
diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp
index a9dfb6714..a057ae45f 100644
--- a/common/ngram-cache.cpp
+++ b/common/ngram-cache.cpp
@@ -65,13 +65,13 @@ constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
 static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
     common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
     if (part_static_it == nc_static.end()) {
-        return -1;
+        return LLAMA_TOKEN_NULL;
     }
     const common_ngram_cache_part part_static = part_static_it->second;
 
     int max_count_static  = 0;
     int sum_count_static  = 0;
-    llama_token max_token = -1;
+    llama_token max_token = LLAMA_TOKEN_NULL;
 
     for (std::pair<llama_token, int> token_count_static : part_static) {
         const llama_token token = token_count_static.first;
@@ -85,10 +85,10 @@ static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram
     }
 
     if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
-        return -1;
+        return LLAMA_TOKEN_NULL;
     }
     if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
-        return -1;
+        return LLAMA_TOKEN_NULL;
     }
     return max_token;
 }
@@ -98,9 +98,9 @@ static llama_token try_draft(
     common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
     const int * min_sample_size, const int * min_percent) {
 
-    llama_token drafted_token = -1;
+    llama_token drafted_token = LLAMA_TOKEN_NULL;
 
-    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
+    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
         const common_ngram ngram_primary = ngrams_primary[i];
 
         common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
@@ -112,7 +112,7 @@ static llama_token try_draft(
         int max_count_primary = 0;
         int max_count_static  = 0;
         int sum_count_primary = 0;
-        llama_token max_token = -1;
+        llama_token max_token = LLAMA_TOKEN_NULL;
 
         for (std::pair<llama_token, int> token_count_primary : part_primary) {
             const llama_token token = token_count_primary.first;
@@ -154,7 +154,7 @@ void common_ngram_cache_draft(
     }
 
     while ((int) draft.size()-1 < n_draft) {
-        llama_token drafted_token = -1;
+        llama_token drafted_token = LLAMA_TOKEN_NULL;
 
         const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
         common_ngram ngram_static;
@@ -177,17 +177,17 @@ void common_ngram_cache_draft(
             }
             ngrams_cd.push_back(ngram_cd);
         }
-        if (drafted_token == -1) {
+        if (drafted_token == LLAMA_TOKEN_NULL) {
             drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
         }
-        if (drafted_token == -1) {
+        if (drafted_token == LLAMA_TOKEN_NULL) {
             drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
         }
-        if (drafted_token == -1) {
+        if (drafted_token == LLAMA_TOKEN_NULL) {
             drafted_token = try_draft(nc_static, ngram_static);
         }
 
-        if (drafted_token == -1) {
+        if (drafted_token == LLAMA_TOKEN_NULL) {
             break;
         }
 
diff --git a/common/ngram-cache.h b/common/ngram-cache.h
index 09c2b0319..dfe012abe 100644
--- a/common/ngram-cache.h
+++ b/common/ngram-cache.h
@@ -17,13 +17,13 @@ struct common_ngram {
 
     common_ngram() {
         for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = -1;
+            tokens[i] = LLAMA_TOKEN_NULL;
         }
     }
 
     common_ngram(const llama_token * input, const int ngram_size) {
         for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = i < ngram_size ? input[i] : -1;
+            tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
         }
     }
 
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index e2e01f2d5..2e25b62f6 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -120,7 +120,7 @@ int main(int argc, char ** argv) {
         }
 
         llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-        if (decoder_start_token_id == -1) {
+        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
             decoder_start_token_id = llama_token_bos(model);
         }
 
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 736035d78..9c3a0c367 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -689,8 +689,8 @@ static void save_as_llama_model(
     gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
     gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
     gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
-    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
-    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL);
 
     gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
     gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index b5e477f5b..aaee47e32 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -494,7 +494,7 @@ int main(int argc, char ** argv) {
         }
 
         llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-        if (decoder_start_token_id == -1) {
+        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
             decoder_start_token_id = llama_token_bos(model);
         }
 
@@ -831,7 +831,7 @@ int main(int argc, char ** argv) {
                     // if user stop generation mid-way, we must add EOT to finish model's last response
                     if (need_insert_eot && format_chat) {
                         llama_token eot = llama_token_eot(model);
-                        embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot);
+                        embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_token_eos(model) : eot);
                         need_insert_eot = false;
                     }
 
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index dc6e6e67e..ad130d490 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -507,7 +507,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
 
 // format incomplete utf-8 multibyte character for output
 static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
-    std::string out = token == -1 ? "" : common_token_to_piece(ctx, token);
+    std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token);
 
     // if the size is 1 and first bit is 1, meaning it's a partial character
     //   (size > 1 meaning it's already a known token)
diff --git a/include/llama.h b/include/llama.h
index a0d5ba5dd..0f619aa19 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -34,7 +34,6 @@
 
 #define LLAMA_DEFAULT_SEED 0xFFFFFFFF
 
-// TODO: use everywhere in the implementation
 #define LLAMA_TOKEN_NULL -1
 
 #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 405e0528f..22596499a 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1923,24 +1923,24 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
 
     // special tokens
-    if (vocab.special_bos_id  != -1)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
-    if (vocab.special_eos_id  != -1)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
-    if (vocab.special_eot_id  != -1)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
-    if (vocab.special_eom_id  != -1)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
-    if (vocab.special_unk_id  != -1)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
-    if (vocab.special_sep_id  != -1)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
-    if (vocab.special_pad_id  != -1)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
-    if (vocab.special_cls_id  != -1)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
-    if (vocab.special_mask_id != -1)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+    if (vocab.special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
+    if (vocab.special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
+    if (vocab.special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
+    if (vocab.special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
+    if (vocab.special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
+    if (vocab.special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
+    if (vocab.special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
+    if (vocab.special_cls_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
+    if (vocab.special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
 
-    if (vocab.linefeed_id != -1)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
+    if (vocab.linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
 
-    if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
-    if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
-    if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
-    if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
-    if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
-    if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
+    if (vocab.special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
+    if (vocab.special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
+    if (vocab.special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
+    if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
+    if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
+    if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
 
     for (const auto & id : vocab.special_eog_ids) {
         LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 69cea2f14..ef5a576cc 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -257,7 +257,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
             for (int i = 0; i < (int)cur_p->size; ++i) {
                 const float val = cur_p->data[i].logit;
                 int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
-                ib = std::max(0, std::min(nbuckets-1, ib));
+                ib = std::max(0, std::min(nbuckets - 1, ib));
                 bucket_idx[i] = ib;
                 ++histo[ib];
             }
@@ -280,13 +280,13 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
             for (int i = 0; i < (int)cur_p->size; ++i) {
                 int j = bucket_idx[i];
                 if (j >= ib) {
-                    *bucket_ptrs[nbuckets-1-j]++ = cur_p->data[i];
+                    *bucket_ptrs[nbuckets - 1 - j]++ = cur_p->data[i];
                 }
             }
 
             ptr = tmp_tokens.data();
             int ndone = 0;
-            for (int j = nbuckets-1; j > ib; --j) {
+            for (int j = nbuckets - 1; j > ib; --j) {
                 std::sort(ptr, ptr + histo[j], comp);
                 ptr += histo[j];
                 ndone += histo[j];
@@ -1832,7 +1832,7 @@ static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_dat
                 ctx->dry_repeat_count[last - k] = std::min(n, rep_limit);
                 if (n > 0) {
                     lt = k;
-                    rt = k+n-1;
+                    rt = k + n - 1;
                 }
             } else {
                 // If k is inside the current Z-box, consider two cases.
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 3fcfcaa3f..a4c015484 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -497,7 +497,7 @@ struct llm_tokenizer_bpe_session {
 
     bool append_bos(std::vector<llama_vocab::id> & output) const {
         if (vocab.tokenizer_add_bos) {
-            GGML_ASSERT(vocab.special_bos_id != -1);
+            GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
             output.push_back(vocab.special_bos_id);
             return true;
         }
@@ -506,7 +506,7 @@ struct llm_tokenizer_bpe_session {
 
     bool append_eos(std::vector<llama_vocab::id> & output) const {
         if (vocab.tokenizer_add_eos) {
-            GGML_ASSERT(vocab.special_eos_id != -1);
+            GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
             output.push_back(vocab.special_eos_id);
             return true;
         }
@@ -1403,7 +1403,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                         if (source == 0) {
                             buffer.erase_after(buffer.before_begin());
                         } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                            buffer.erase_after(std::next(buffer.begin(), (source - 1)));
                         }
 
                         // repeat for the right side
@@ -1417,7 +1417,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                         if (source == 0) {
                             buffer.erase_after(buffer.before_begin());
                         } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                            buffer.erase_after(std::next(buffer.begin(), (source - 1)));
                         }
                         break;
                     }
@@ -1454,7 +1454,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                 bool is_prev_special = true;  // prefix with space if first token
 
                 if (add_special && vocab.tokenizer_add_bos) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_bos_id);
                     is_prev_special = true;
                 }
@@ -1489,7 +1489,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                 }
 
                 if (add_special && vocab.tokenizer_add_eos) {
-                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_eos_id);
                 }
             } break;
@@ -1522,7 +1522,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
         case LLAMA_VOCAB_TYPE_WPM:
             {
                 if (add_special) {
-                    GGML_ASSERT(vocab.special_cls_id != -1);
+                    GGML_ASSERT(vocab.special_cls_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_cls_id);
                 }
 
@@ -1542,14 +1542,14 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                 }
 
                 if (add_special) {
-                    GGML_ASSERT(vocab.special_sep_id != -1);
+                    GGML_ASSERT(vocab.special_sep_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_sep_id);
                 }
             } break;
         case LLAMA_VOCAB_TYPE_UGM:
             {
                 if (add_special && vocab.tokenizer_add_bos) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_bos_id);
                 }
                 llm_tokenizer_ugm_session session(vocab);
@@ -1574,7 +1574,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
                 }
 
                 if (add_special && vocab.tokenizer_add_eos) {
-                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
                     output.push_back(vocab.special_eos_id);
                 }
             } break;
@@ -1642,7 +1642,7 @@ llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, lla
 }
 
 bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
-    return token != -1 && vocab.special_eog_ids.count(token) > 0;
+    return token != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(token) > 0;
 }
 
 bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
@@ -1881,7 +1881,7 @@ int32_t llama_detokenize_impl(
     }
 
     if (remove_special && vocab.tokenizer_add_eos) {
-        if (n_tokens > 0 && tokens[n_tokens-1] == vocab.special_eos_id) {
+        if (n_tokens > 0 && tokens[n_tokens - 1] == vocab.special_eos_id) {
             n_tokens--;
         }
     }

From ae2f606bb598b287f5fb69c9fdfc98b86598c6cc Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 10:52:38 +0200
Subject: [PATCH 117/376] mmap : fix fileno macro clash (#11076)

* mmap : fix fileno macro clash

ggml-ci

* cont

ggml-ci
---
 src/llama-mmap.cpp | 10 +++++++---
 src/llama-mmap.h   |  2 +-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/llama-mmap.cpp b/src/llama-mmap.cpp
index a99326335..a8cb9439b 100644
--- a/src/llama-mmap.cpp
+++ b/src/llama-mmap.cpp
@@ -241,12 +241,16 @@ llama_file::~llama_file() = default;
 size_t llama_file::tell() const { return pimpl->tell(); }
 size_t llama_file::size() const { return pimpl->size; }
 
-int llama_file::fileno() const {
+int llama_file::file_id() const {
 #ifdef _WIN32
     return _fileno(pimpl->fp);
+#else
+#if defined(fileno)
+    return fileno(pimpl->fp);
 #else
     return ::fileno(pimpl->fp);
 #endif
+#endif
 }
 
 void llama_file::seek(size_t offset, int whence) const { pimpl->seek(offset, whence); }
@@ -265,7 +269,7 @@ struct llama_mmap::impl {
 
     impl(struct llama_file * file, size_t prefetch, bool numa) {
         size = file->size();
-        int fd = file->fileno();
+        int fd = file->file_id();
         int flags = MAP_SHARED;
         if (numa) { prefetch = 0; }
 #ifdef __linux__
@@ -357,7 +361,7 @@ struct llama_mmap::impl {
 
         size = file->size();
 
-        HANDLE hFile = (HANDLE) _get_osfhandle(file->fileno());
+        HANDLE hFile = (HANDLE) _get_osfhandle(file->file_id());
 
         HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
 
diff --git a/src/llama-mmap.h b/src/llama-mmap.h
index 6bcddee8c..1da9ecb6b 100644
--- a/src/llama-mmap.h
+++ b/src/llama-mmap.h
@@ -18,7 +18,7 @@ struct llama_file {
     size_t tell() const;
     size_t size() const;
 
-    int fileno() const;
+    int file_id() const; // fileno overload
 
     void seek(size_t offset, int whence) const;
 

From 3e6e7a6bc2c4b980a0cf0fcb5cb3b79a965b5f14 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 10:54:25 +0200
Subject: [PATCH 118/376] tokenize : escape the prompt (#11058)

* tokenize : escape the prompt

* tokenize : update help
---
 examples/tokenize/tokenize.cpp | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index c97e22724..57d9d4312 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -31,6 +31,7 @@ static void print_usage_information(const char * argv0) {
     printf("    -p PROMPT, --prompt PROMPT           read prompt from the argument.\n");
     printf("    --stdin                              read prompt from standard input.\n");
     printf("    --no-bos                             do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
+    printf("    --no-escape                          do not escape input (such as \\n, \\t, etc.).\n");
     printf("    --no-parse-special                   do not parse control tokens.\n");
     printf("    --log-disable                        disable logs. Makes stderr quiet when loading the model.\n");
     printf("    --show-count                         print the total number of tokens.\n");
@@ -198,6 +199,7 @@ int main(int raw_argc, char ** raw_argv) {
     // variables where to put any arguments we see.
     bool printing_ids = false;
     bool no_bos = false;
+    bool no_escape = false;
     bool no_parse_special = false;
     bool disable_logging = false;
     bool show_token_count = false;
@@ -233,6 +235,9 @@ int main(int raw_argc, char ** raw_argv) {
         else if (arg == "--no-bos") {
             no_bos = true;
         }
+        else if (arg == "--no-escape") {
+            no_escape = true;
+        }
         else if (arg == "--no-parse-special") {
             no_parse_special = true;
         }
@@ -363,6 +368,11 @@ int main(int raw_argc, char ** raw_argv) {
     const bool model_wants_add_bos = llama_add_bos_token(model);
     const bool add_bos = model_wants_add_bos && !no_bos;
     const bool parse_special = !no_parse_special;
+    const bool escape = !no_escape;
+
+    if (escape) {
+        string_process_escapes(prompt);
+    }
 
     std::vector<llama_token> tokens;
     tokens = common_tokenize(model, prompt, add_bos, parse_special);

From 47182dd03fe04a4ffda5d7f4c8a109ae0056cf56 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 10:55:18 +0200
Subject: [PATCH 119/376] llama : update llama_model API names (#11063)

* llama : deprecate llama_free_model, add llama_model_free

ggml-ci

* llama : change `llama_load_model_from_file` -> `llama_model_load_from_file`

ggml-ci
---
 common/common.cpp                          | 14 +++++++-------
 examples/batched-bench/batched-bench.cpp   |  4 ++--
 examples/batched/batched.cpp               |  4 ++--
 examples/gritlm/gritlm.cpp                 |  4 ++--
 examples/llama-bench/llama-bench.cpp       |  8 ++++----
 examples/llava/llava-cli.cpp               |  6 +++---
 examples/llava/minicpmv-cli.cpp            |  4 ++--
 examples/llava/qwen2vl-cli.cpp             |  6 +++---
 examples/passkey/passkey.cpp               |  4 ++--
 examples/quantize-stats/quantize-stats.cpp |  8 ++++----
 examples/run/run.cpp                       |  2 +-
 examples/simple-chat/simple-chat.cpp       |  4 ++--
 examples/simple/simple.cpp                 |  4 ++--
 examples/tokenize/tokenize.cpp             |  4 ++--
 include/llama-cpp.h                        |  2 +-
 include/llama.h                            | 13 ++++++++++---
 src/llama-model.cpp                        |  4 ++++
 src/llama.cpp                              | 16 +++++++++++-----
 tests/test-autorelease.cpp                 |  4 ++--
 tests/test-model-load-cancel.cpp           |  2 +-
 tests/test-tokenizer-0.cpp                 |  6 +++---
 tests/test-tokenizer-1-bpe.cpp             |  6 +++---
 tests/test-tokenizer-1-spm.cpp             |  6 +++---
 23 files changed, 76 insertions(+), 59 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index d6a7ab753..4fd36105e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -846,7 +846,7 @@ struct common_init_result common_init_from_params(common_params & params) {
     } else if (!params.model_url.empty()) {
         model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams);
     } else {
-        model = llama_load_model_from_file(params.model.c_str(), mparams);
+        model = llama_model_load_from_file(params.model.c_str(), mparams);
     }
 
     if (model == NULL) {
@@ -873,7 +873,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         }
 
         if (!ok) {
-            llama_free_model(model);
+            llama_model_free(model);
 
             return iparams;
         }
@@ -884,7 +884,7 @@ struct common_init_result common_init_from_params(common_params & params) {
     llama_context * lctx = llama_new_context_with_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
-        llama_free_model(model);
+        llama_model_free(model);
         return iparams;
     }
 
@@ -900,7 +900,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
             llama_free(lctx);
-            llama_free_model(model);
+            llama_model_free(model);
 
             return iparams;
         }
@@ -913,7 +913,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                                              params.control_vector_layer_end);
         if (err) {
             llama_free(lctx);
-            llama_free_model(model);
+            llama_model_free(model);
 
             return iparams;
         }
@@ -926,7 +926,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
             llama_free(lctx);
-            llama_free_model(model);
+            llama_model_free(model);
             return iparams;
         }
 
@@ -1411,7 +1411,7 @@ struct llama_model * common_load_model_from_url(
         }
     }
 
-    return llama_load_model_from_file(local_path.c_str(), params);
+    return llama_model_load_from_file(local_path.c_str(), params);
 }
 
 struct llama_model * common_load_model_from_hf(
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index a3b21ad6b..dd75ff9f1 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -38,7 +38,7 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = common_model_params_to_llama(params);
 
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@@ -194,7 +194,7 @@ int main(int argc, char ** argv) {
     llama_batch_free(batch);
 
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     llama_backend_free();
 
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 2e25b62f6..d34b03099 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -41,7 +41,7 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = common_model_params_to_llama(params);
 
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         LOG_ERR("%s: error: unable to load model\n" , __func__);
@@ -236,7 +236,7 @@ int main(int argc, char ** argv) {
 
     llama_sampler_free(smpl);
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     llama_backend_free();
 
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
index 18a945b33..4d2db5624 100644
--- a/examples/gritlm/gritlm.cpp
+++ b/examples/gritlm/gritlm.cpp
@@ -165,7 +165,7 @@ int main(int argc, char * argv[]) {
 
     llama_backend_init();
 
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
+    llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams);
 
     // create generation context
     llama_context * ctx = llama_new_context_with_model(model, cparams);
@@ -219,7 +219,7 @@ int main(int argc, char * argv[]) {
 
     llama_sampler_free(smpl);
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
     llama_backend_free();
 
     return 0;
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 2338ad106..2a0916766 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -1526,10 +1526,10 @@ int main(int argc, char ** argv) {
         // keep the same model between tests when possible
         if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
             if (lmodel) {
-                llama_free_model(lmodel);
+                llama_model_free(lmodel);
             }
 
-            lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams());
+            lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams());
             if (lmodel == NULL) {
                 fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
                 return 1;
@@ -1540,7 +1540,7 @@ int main(int argc, char ** argv) {
         llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams());
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
-            llama_free_model(lmodel);
+            llama_model_free(lmodel);
             return 1;
         }
 
@@ -1626,7 +1626,7 @@ int main(int argc, char ** argv) {
         ggml_threadpool_free_fn(threadpool);
     }
 
-    llama_free_model(lmodel);
+    llama_model_free(lmodel);
 
     if (p) {
         p->print_footer();
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index 2691c6e6b..27215a42e 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -221,7 +221,7 @@ static struct llama_model * llava_init(common_params * params) {
 
     llama_model_params model_params = common_model_params_to_llama(*params);
 
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n" , __func__);
         return NULL;
@@ -265,7 +265,7 @@ static void llava_free(struct llava_context * ctx_llava) {
     }
 
     llama_free(ctx_llava->ctx_llama);
-    llama_free_model(ctx_llava->model);
+    llama_model_free(ctx_llava->model);
     llama_backend_free();
 }
 
@@ -323,7 +323,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    llama_free_model(model);
+    llama_model_free(model);
 
     return 0;
 }
diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp
index e9cbb51ed..2342bdd09 100644
--- a/examples/llava/minicpmv-cli.cpp
+++ b/examples/llava/minicpmv-cli.cpp
@@ -31,7 +31,7 @@ static struct llama_model * llava_init(common_params * params) {
 
     llama_model_params model_params = common_model_params_to_llama(*params);
 
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n" , __func__);
         return NULL;
@@ -75,7 +75,7 @@ static void llava_free(struct llava_context * ctx_llava) {
     }
 
     llama_free(ctx_llava->ctx_llama);
-    llama_free_model(ctx_llava->model);
+    llama_model_free(ctx_llava->model);
     llama_backend_free();
 }
 
diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp
index e86a60280..f3e5d66e2 100644
--- a/examples/llava/qwen2vl-cli.cpp
+++ b/examples/llava/qwen2vl-cli.cpp
@@ -310,7 +310,7 @@ static struct llama_model * llava_init(common_params * params) {
 
     llama_model_params model_params = common_model_params_to_llama(*params);
 
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n" , __func__);
         return NULL;
@@ -354,7 +354,7 @@ static void llava_free(struct llava_context * ctx_llava) {
     }
 
     llama_free(ctx_llava->ctx_llama);
-    llama_free_model(ctx_llava->model);
+    llama_model_free(ctx_llava->model);
     llama_backend_free();
 }
 
@@ -575,7 +575,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    llama_free_model(model);
+    llama_model_free(model);
 
     return 0;
 }
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index 09bba708f..ea91f376c 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = common_model_params_to_llama(params);
 
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n" , __func__);
@@ -266,7 +266,7 @@ int main(int argc, char ** argv) {
     llama_batch_free(batch);
 
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     llama_backend_free();
 
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index ab91d0b40..9bfbb8862 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -309,7 +309,7 @@ int main(int argc, char ** argv) {
         auto mparams = llama_model_default_params();
         mparams.use_mlock  = false;
 
-        model = llama_load_model_from_file(params.model.c_str(), mparams);
+        model = llama_model_load_from_file(params.model.c_str(), mparams);
 
         if (model == NULL) {
             fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
@@ -323,7 +323,7 @@ int main(int argc, char ** argv) {
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
-            llama_free_model(model);
+            llama_model_free(model);
             return 1;
         }
     }
@@ -347,7 +347,7 @@ int main(int argc, char ** argv) {
             fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
                 "this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
             llama_free(ctx);
-            llama_free_model(model);
+            llama_model_free(model);
             return 1;
         }
         included_layers++;
@@ -409,7 +409,7 @@ int main(int argc, char ** argv) {
 
 
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
     // report timing
     {
         const int64_t t_main_end_us = ggml_time_us();
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 75b817272..c52a7961f 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -664,7 +664,7 @@ class LlamaData {
             "\r%*s"
             "\rLoading model",
             get_terminal_width(), " ");
-        llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), opt.model_params));
+        llama_model_ptr model(llama_model_load_from_file(opt.model_.c_str(), opt.model_params));
         if (!model) {
             printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index 7f4da666b..d72f5bcdd 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -69,7 +69,7 @@ int main(int argc, char ** argv) {
     llama_model_params model_params = llama_model_default_params();
     model_params.n_gpu_layers = ngl;
 
-    llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
     if (!model) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
         return 1;
@@ -194,7 +194,7 @@ int main(int argc, char ** argv) {
     }
     llama_sampler_free(smpl);
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     return 0;
 }
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 3288c0250..f69117890 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -83,7 +83,7 @@ int main(int argc, char ** argv) {
     llama_model_params model_params = llama_model_default_params();
     model_params.n_gpu_layers = ngl;
 
-    llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params);
+    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@@ -199,7 +199,7 @@ int main(int argc, char ** argv) {
 
     llama_sampler_free(smpl);
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     return 0;
 }
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 57d9d4312..684ca054a 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -338,7 +338,7 @@ int main(int raw_argc, char ** raw_argv) {
 
     llama_model_params model_params = llama_model_default_params();
     model_params.vocab_only = true;
-    llama_model * model = llama_load_model_from_file(model_path, model_params);
+    llama_model * model = llama_model_load_from_file(model_path, model_params);
     if (!model) {
         fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path);
         return 1;
@@ -408,7 +408,7 @@ int main(int raw_argc, char ** raw_argv) {
     }
     // silence valgrind
     llama_free(ctx);
-    llama_free_model(model);
+    llama_model_free(model);
 
     return 0;
 }
diff --git a/include/llama-cpp.h b/include/llama-cpp.h
index 1500cb2fc..11306b17f 100644
--- a/include/llama-cpp.h
+++ b/include/llama-cpp.h
@@ -9,7 +9,7 @@
 #include "llama.h"
 
 struct llama_model_deleter {
-    void operator()(llama_model * model) { llama_free_model(model); }
+    void operator()(llama_model * model) { llama_model_free(model); }
 };
 
 struct llama_context_deleter {
diff --git a/include/llama.h b/include/llama.h
index 0f619aa19..0295a51fb 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -413,12 +413,19 @@ extern "C" {
     // Call once at the end of the program - currently only used for MPI
     LLAMA_API void llama_backend_free(void);
 
-    LLAMA_API struct llama_model * llama_load_model_from_file(
+    DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
+                             const char * path_model,
+              struct llama_model_params   params),
+            "use llama_model_load_from_file instead");
+
+    LLAMA_API struct llama_model * llama_model_load_from_file(
                              const char * path_model,
               struct llama_model_params   params);
 
-    // TODO: rename to llama_model_free
-    LLAMA_API void llama_free_model(struct llama_model * model);
+    DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
+            "use llama_model_free instead");
+
+    LLAMA_API void llama_model_free(struct llama_model * model);
 
     // TODO: rename to llama_init_from_model
     LLAMA_API struct llama_context * llama_new_context_with_model(
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 22596499a..7deb3683b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2009,6 +2009,10 @@ struct llama_model_params llama_model_default_params() {
 }
 
 void llama_free_model(struct llama_model * model) {
+    llama_model_free(model);
+}
+
+void llama_model_free(struct llama_model * model) {
     delete model;
 }
 
diff --git a/src/llama.cpp b/src/llama.cpp
index 4a6798f41..7337c34ce 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -11656,6 +11656,12 @@ int64_t llama_time_us(void) {
 struct llama_model * llama_load_model_from_file(
         const char * path_model,
         struct llama_model_params params) {
+    return llama_model_load_from_file(path_model, params);
+}
+
+struct llama_model * llama_model_load_from_file(
+        const char * path_model,
+        struct llama_model_params params) {
     ggml_time_init();
 
     llama_model * model = new llama_model;
@@ -11694,7 +11700,7 @@ struct llama_model * llama_load_model_from_file(
         ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
         if (!rpc_reg) {
             LLAMA_LOG_ERROR("%s: failed to find RPC backend\n", __func__);
-            llama_free_model(model);
+            llama_model_free(model);
             return nullptr;
         }
 
@@ -11702,7 +11708,7 @@ struct llama_model * llama_load_model_from_file(
         ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
         if (!ggml_backend_rpc_add_device_fn) {
             LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__);
-            llama_free_model(model);
+            llama_model_free(model);
             return nullptr;
         }
 
@@ -11712,7 +11718,7 @@ struct llama_model * llama_load_model_from_file(
                 model->devices.push_back(dev);
             } else {
                 LLAMA_LOG_ERROR("%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
-                llama_free_model(model);
+                llama_model_free(model);
                 return nullptr;
             }
         }
@@ -11744,7 +11750,7 @@ struct llama_model * llama_load_model_from_file(
     if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
         if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) {
             LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size());
-            llama_free_model(model);
+            llama_model_free(model);
             return nullptr;
         }
         ggml_backend_dev_t main_gpu = model->devices[params.main_gpu];
@@ -11767,7 +11773,7 @@ struct llama_model * llama_load_model_from_file(
             LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
         }
 
-        llama_free_model(model);
+        llama_model_free(model);
         return nullptr;
     }
 
diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp
index 57fa00011..ba084a91a 100644
--- a/tests/test-autorelease.cpp
+++ b/tests/test-autorelease.cpp
@@ -13,10 +13,10 @@ int main(int argc, char ** argv) {
 
     std::thread([&model_path]() {
         llama_backend_init();
-        auto * model = llama_load_model_from_file(model_path, llama_model_default_params());
+        auto * model = llama_model_load_from_file(model_path, llama_model_default_params());
         auto * ctx = llama_new_context_with_model(model, llama_context_default_params());
         llama_free(ctx);
-        llama_free_model(model);
+        llama_model_free(model);
         llama_backend_free();
     }).join();
 
diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp
index 858535c3c..9095826fa 100644
--- a/tests/test-model-load-cancel.cpp
+++ b/tests/test-model-load-cancel.cpp
@@ -21,7 +21,7 @@ int main(int argc, char *argv[] ) {
         (void) ctx;
         return progress > 0.50;
     };
-    auto * model = llama_load_model_from_file(model_path, params);
+    auto * model = llama_model_load_from_file(model_path, params);
     llama_backend_free();
     return model == nullptr ? EXIT_SUCCESS : EXIT_FAILURE;
 }
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
index 0af85f002..121c2c60c 100644
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -152,7 +152,7 @@ int main(int argc, char **argv) {
 
         mparams.vocab_only = true;
 
-        model = llama_load_model_from_file(fname.c_str(), mparams);
+        model = llama_model_load_from_file(fname.c_str(), mparams);
 
         if (model == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
@@ -165,7 +165,7 @@ int main(int argc, char **argv) {
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
+            llama_model_free(model);
             return 1;
         }
     }
@@ -300,7 +300,7 @@ int main(int argc, char **argv) {
         fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
     }
 
-    llama_free_model(model);
+    llama_model_free(model);
     llama_free(ctx);
 
     llama_backend_free();
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 0ff7fc833..5718fab04 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -46,7 +46,7 @@ int main(int argc, char **argv) {
 
         mparams.vocab_only = true;
 
-        model = llama_load_model_from_file(fname.c_str(), mparams);
+        model = llama_model_load_from_file(fname.c_str(), mparams);
 
         if (model == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
@@ -59,7 +59,7 @@ int main(int argc, char **argv) {
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
+            llama_model_free(model);
             return 1;
         }
     }
@@ -143,7 +143,7 @@ int main(int argc, char **argv) {
         }
     }
 
-    llama_free_model(model);
+    llama_model_free(model);
     llama_free(ctx);
 
     llama_backend_free();
diff --git a/tests/test-tokenizer-1-spm.cpp b/tests/test-tokenizer-1-spm.cpp
index 9b0716a43..ac05387c9 100644
--- a/tests/test-tokenizer-1-spm.cpp
+++ b/tests/test-tokenizer-1-spm.cpp
@@ -34,7 +34,7 @@ int main(int argc, char ** argv) {
 
         mparams.vocab_only = true;
 
-        model = llama_load_model_from_file(fname.c_str(), mparams);
+        model = llama_model_load_from_file(fname.c_str(), mparams);
 
         if (model == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
@@ -47,7 +47,7 @@ int main(int argc, char ** argv) {
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
+            llama_model_free(model);
             return 1;
         }
     }
@@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    llama_free_model(model);
+    llama_model_free(model);
     llama_free(ctx);
 
     llama_backend_free();

From 6369f867a410416239d9f20ec27c2b1d6a9fee52 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 6 Jan 2025 10:28:17 +0100
Subject: [PATCH 120/376] llama : rename missed batch params/vars to ubatch
 (#10059)

This commit renames the `batch` parameter to `ubatch` in the
`llama_kv_cache_find_slot`, `llm_build_inp_embd`, and
`llm_build_mamba` functions.

The motivation for this is that this should have been done as part of
Commit 19d900a7565b8f6b0a708836a57d26966cb9efe2 ("llama : rename batch
to ubatch (#9950)") but for some reason I missed these functions in
that commit and only noticed them now (sorry).
---
 src/llama-kv-cache.cpp | 32 ++++++++++++++++----------------
 src/llama.cpp          | 18 +++++++++---------
 2 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 53379253a..90b6c56ed 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -119,10 +119,10 @@ bool llama_kv_cache_init(
 
 struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
            struct llama_kv_cache & cache,
-       const struct llama_ubatch & batch) {
-    const uint32_t n_tokens = batch.n_tokens;
-    const uint32_t n_seqs   = batch.n_seqs;
-    const uint32_t n_seq_tokens = batch.n_seq_tokens;
+       const struct llama_ubatch & ubatch) {
+    const uint32_t n_tokens = ubatch.n_tokens;
+    const uint32_t n_seqs   = ubatch.n_seqs;
+    const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
 
     if (cache.recurrent) {
         // For recurrent state architectures (like Mamba or RWKV),
@@ -130,16 +130,16 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
         // A slot should be always be contiguous.
 
         // can only process batches with an equal number of new tokens in each sequence
-        GGML_ASSERT(batch.equal_seqs);
+        GGML_ASSERT(ubatch.equal_seqs);
 
         int32_t min = cache.size - 1;
         int32_t max = 0;
 
         // everything should fit if all seq_ids are smaller than the max
         for (uint32_t s = 0; s < n_seqs; ++s) {
-            const uint32_t n_seq_id = batch.n_seq_id[s];
+            const uint32_t n_seq_id = ubatch.n_seq_id[s];
             for (uint32_t j = 0; j < n_seq_id; ++j) {
-                const llama_seq_id seq_id = batch.seq_id[s][j];
+                const llama_seq_id seq_id = ubatch.seq_id[s][j];
 
                 if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
                     // too big seq_id
@@ -198,7 +198,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
 
         // find usable cell range
         for (uint32_t s = 0; s < n_seqs; ++s) {
-            const llama_seq_id seq_id = batch.seq_id[s][0];
+            const llama_seq_id seq_id = ubatch.seq_id[s][0];
             llama_kv_cell & seq_meta = cache.cells[seq_id];
             bool has_cell = false;
             if (seq_meta.tail >= 0) {
@@ -237,7 +237,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
         // gather and re-order
         for (uint32_t s = 0; s < n_seqs; ++s) {
             int32_t dst_id = s + min;
-            int32_t src_id = cache.cells[batch.seq_id[s][0]].tail;
+            int32_t src_id = cache.cells[ubatch.seq_id[s][0]].tail;
             if (dst_id != src_id) {
                 llama_kv_cell & dst_cell = cache.cells[dst_id];
                 llama_kv_cell & src_cell = cache.cells[src_id];
@@ -258,7 +258,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
 
         // update the pos of the used seqs
         for (uint32_t s = 0; s < n_seqs; ++s) {
-            const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1];
+            const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
             int32_t cell_id = s + min;
             llama_kv_cell & cell = cache.cells[cell_id];
 
@@ -266,12 +266,12 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
                 // What should happen when the pos backtracks or skips a value?
                 // Clearing the state mid-batch would require special-casing which isn't done.
                 LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
-                    __func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens);
+                    __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
             }
             cell.pos = last_pos;
             cell.seq_id.clear();
-            for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) {
-                const llama_seq_id seq_id = batch.seq_id[s][j];
+            for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
+                const llama_seq_id seq_id = ubatch.seq_id[s][j];
                 cell.seq_id.insert(seq_id);
                 cache.cells[seq_id].tail = cell_id;
             }
@@ -325,10 +325,10 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
     for (uint32_t s = 0; s < n_seqs; s++) {
         for (uint32_t i = 0; i < n_seq_tokens; ++i) {
             uint32_t k = s*n_seq_tokens + i;
-            cache.cells[cache.head + k].pos = batch.pos[k];
+            cache.cells[cache.head + k].pos = ubatch.pos[k];
 
-            for (int32_t j = 0; j < batch.n_seq_id[s]; j++) {
-                cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]);
+            for (int32_t j = 0; j < ubatch.n_seq_id[s]; j++) {
+                cache.cells[cache.head + k].seq_id.insert(ubatch.seq_id[s][j]);
             }
         }
     }
diff --git a/src/llama.cpp b/src/llama.cpp
index 7337c34ce..60728e5bb 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2540,21 +2540,21 @@ static struct ggml_tensor * llm_build_inp_embd(
         struct ggml_context * ctx,
        struct llama_context & lctx,
         const llama_hparams & hparams,
-         const llama_ubatch & batch,
+         const llama_ubatch & ubatch,
          struct ggml_tensor * tok_embd,
          const llm_build_cb & cb) {
     const int64_t n_embd = hparams.n_embd;
 
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
+    if (ubatch.token) {
+        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ubatch.n_tokens);
         cb(lctx.inp_tokens, "inp_tokens", -1);
         ggml_set_input(lctx.inp_tokens);
 
         inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
     } else {
-        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
         inpL = lctx.inp_embd;
         ggml_set_input(lctx.inp_embd);
     }
@@ -3149,7 +3149,7 @@ static struct ggml_tensor * llm_build_copy_mask_state(
 static struct ggml_tensor * llm_build_mamba(
         struct ggml_context * ctx,
        struct llama_context & lctx,
-         const llama_ubatch & batch,
+         const llama_ubatch & ubatch,
          struct ggml_cgraph * graph,
          struct ggml_tensor * cur,
          struct ggml_tensor * state_copy,
@@ -3165,17 +3165,17 @@ static struct ggml_tensor * llm_build_mamba(
     const int64_t d_inner = hparams.ssm_d_inner;
     const int64_t d_state = hparams.ssm_d_state;
     const int64_t dt_rank = hparams.ssm_dt_rank;
-    const int64_t n_seqs  = batch.n_seqs;
+    const int64_t n_seqs  = ubatch.n_seqs;
     // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
     const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
     // Use the same RMS norm as the final layer norm
     const float norm_rms_eps = hparams.f_norm_rms_eps;
 
-    const int64_t n_seq_tokens = batch.n_seq_tokens;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
 
     GGML_ASSERT(n_seqs != 0);
-    GGML_ASSERT(batch.equal_seqs);
-    GGML_ASSERT(batch.n_tokens == n_seq_tokens * n_seqs);
+    GGML_ASSERT(ubatch.equal_seqs);
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
 
     struct ggml_tensor * conv_states_all = kv.k_l[il];
     struct ggml_tensor * ssm_states_all  = kv.v_l[il];

From 96a1dc27c3f09bf1ed83a26292d571795bcf27fa Mon Sep 17 00:00:00 2001
From: Asghar Ghorbani <a-ghorbani@users.noreply.github.com>
Date: Mon, 6 Jan 2025 12:21:46 +0100
Subject: [PATCH 121/376] llama : prevent system info string accumulation
 across calls (#11101)

---
 src/llama.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/llama.cpp b/src/llama.cpp
index 60728e5bb..c162c31a6 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -12458,6 +12458,8 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int
 
 const char * llama_print_system_info(void) {
     static std::string s;
+    s.clear(); // Clear the string, since it's static, otherwise it will accumulate data from previous calls.
+
 
     for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
         auto * reg = ggml_backend_reg_get(i);

From 09186fabbe05236f2b9446ba6c643cb737540d10 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 6 Jan 2025 13:41:12 +0100
Subject: [PATCH 122/376] llama : remove check flash_attn with lora (#11104)

---
 src/llama.cpp | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index c162c31a6..ebd6e3b29 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -11519,13 +11519,7 @@ int32_t llama_lora_adapter_set(
             struct llama_context * ctx,
             struct llama_lora_adapter * adapter,
             float scale) {
-    if (ctx->cparams.flash_attn) {
-        LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
-        return -1;
-    }
-
     ctx->lora_adapters[adapter] = scale;
-
     return 0;
 }
 

From e6e7c75d94adf4d39e846d30807c531ff22865e7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 15:36:08 +0200
Subject: [PATCH 123/376] server : fix extra BOS in infill endpoint (#11106)

* server : fix extra BOS in infill endpoing

ggml-ci

* server : update infill tests
---
 examples/server/server.cpp                | 2 +-
 examples/server/tests/unit/test_infill.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c2e62ba69..127323e77 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3797,7 +3797,7 @@ int main(int argc, char ** argv) {
         data["input_extra"] = input_extra; // default to empty array if it's not exist
 
         std::string prompt = json_value(data, "prompt", std::string());
-        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
+        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, false, true);
         SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
         data["prompt"] = format_infill(
             ctx_server.ctx,
diff --git a/examples/server/tests/unit/test_infill.py b/examples/server/tests/unit/test_infill.py
index ad4b8192a..10554db0f 100644
--- a/examples/server/tests/unit/test_infill.py
+++ b/examples/server/tests/unit/test_infill.py
@@ -18,7 +18,7 @@ def test_infill_without_input_extra():
         "input_suffix": "}\n",
     })
     assert res.status_code == 200
-    assert match_regex("(Ann|small|shiny)+", res.body["content"])
+    assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"])
 
 
 def test_infill_with_input_extra():

From 96be8c32649378a23031630a48c440f3a5d0839b Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 6 Jan 2025 16:34:49 +0100
Subject: [PATCH 124/376] github : add cmd line field to bug report (#11090)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* github : cmd line to bug report

* codeowners : (@ngxson) only watch dockerfile

* Apply suggestions from code review [no ci]

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* rm cmd in log output [no ci]

* rm 2 [no ci]

* no need backticks [no ci]

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 .github/ISSUE_TEMPLATE/010-bug-compilation.yml | 12 +++++++++++-
 .github/ISSUE_TEMPLATE/019-bug-misc.yml        | 12 +++++++++++-
 CODEOWNERS                                     |  2 +-
 3 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
index f10b3a2b2..b85bf5741 100644
--- a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
+++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
@@ -65,12 +65,22 @@ body:
         If possible, please do a git bisect and identify the exact commit that introduced the bug.
     validations:
       required: false
+  - type: textarea
+    id: command
+    attributes:
+      label: Compile command
+      description: >
+        Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`.
+        This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
   - type: textarea
     id: logs
     attributes:
       label: Relevant log output
       description: >
-          Please copy and paste any relevant log output, including the command that you entered and any generated text.
+          Please copy and paste any relevant log output, including any generated text.
           This will be automatically formatted into code, so no need for backticks.
       render: shell
     validations:
diff --git a/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/.github/ISSUE_TEMPLATE/019-bug-misc.yml
index d157ea307..1904e31fd 100644
--- a/.github/ISSUE_TEMPLATE/019-bug-misc.yml
+++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml
@@ -52,6 +52,16 @@ body:
         - Other (Please specify in the next section)
     validations:
       required: false
+  - type: textarea
+    id: command
+    attributes:
+      label: Command line
+      description: >
+        Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc.
+        This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: false
   - type: textarea
     id: info
     attributes:
@@ -74,7 +84,7 @@ body:
     attributes:
       label: Relevant log output
       description: >
-          If applicable, please copy and paste any relevant log output, including the command that you entered and any generated text.
+          If applicable, please copy and paste any relevant log output, including any generated text.
           This will be automatically formatted into code, so no need for backticks.
       render: shell
     validations:
diff --git a/CODEOWNERS b/CODEOWNERS
index adeba5395..c9fa34761 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,5 +1,5 @@
 # collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
 
 /ci/ @ggerganov
-/.devops/ @ngxson
+/.devops/*.Dockerfile @ngxson
 /examples/server/ @ngxson

From ecebbd292d741ac084cf248146b2cfb17002aa1d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 6 Jan 2025 17:52:35 +0200
Subject: [PATCH 125/376] llama : remove unused headers (#11109)

ggml-ci
---
 src/llama.cpp | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index ebd6e3b29..8ea6686c9 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8,7 +8,6 @@
 #include "llama-kv-cache.h"
 #include "llama-model-loader.h"
 #include "llama-model.h"
-#include "llama-quant.h"
 
 #include "ggml.h"
 #include "ggml-alloc.h"
@@ -18,12 +17,8 @@
 #include <algorithm>
 #include <array>
 #include <cassert>
-#include <cctype>
 #include <cfloat>
-#include <cinttypes>
-#include <climits>
 #include <cmath>
-#include <cstdarg>
 #include <cstddef>
 #include <cstdint>
 #include <cstdio>
@@ -31,10 +26,7 @@
 #include <ctime>
 #include <functional>
 #include <initializer_list>
-#include <locale>
 #include <map>
-#include <numeric>
-#include <type_traits>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -12434,16 +12426,16 @@ int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix,
     return 0;
 }
 
-int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
+int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count) {
     std::string str_split_path(split_path);
     char postfix[32];
     snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
     std::string str_postfix(postfix);
 
-    // check if dest ends with postfix
+    // check if split_prefix ends with postfix
     int size_prefix = str_split_path.size() - str_postfix.size();
     if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
-        snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
+        snprintf(split_prefix, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
         return size_prefix;
     }
 

From dc7cef9f373f2a24b851f0df7a618c5209e593fa Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Mon, 6 Jan 2025 22:45:28 +0000
Subject: [PATCH 126/376] llama-run : fix context size (#11094)

Set `n_ctx` equal to `n_batch` in `Opt` class. Now context size is
a more reasonable 2048.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index c52a7961f..2888fcfed 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -83,6 +83,7 @@ class Opt {
         }
 
         ctx_params.n_batch        = context_size >= 0 ? context_size : context_size_default;
+        ctx_params.n_ctx          = ctx_params.n_batch;
         model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
         temperature               = temperature >= 0 ? temperature : temperature_default;
 

From c0d6f790d07aa78be15584ec394ac20739ade93b Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Tue, 7 Jan 2025 11:56:07 +0530
Subject: [PATCH 127/376] SYCL: Use get_multi_ptr instead of deprecated
 get_pointer in wkv6 (#11087)

* SYCL: Use get_multi_ptr instead of deprecated get_pointer in wkv6

* Revert "SYCL: Use get_multi_ptr instead of deprecated get_pointer in wkv6"

This reverts commit f62dc45f318e48d375e7734b34cbddee81deed52.

* Reland: Use get_multi_ptr instead of deprecated get_pointer in wkv6
---
 ggml/src/ggml-sycl/wkv6.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp
index 75ddfb86a..105db6f03 100644
--- a/ggml/src/ggml-sycl/wkv6.cpp
+++ b/ggml/src/ggml-sycl/wkv6.cpp
@@ -131,7 +131,7 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* s
             [=](sycl::nd_item<3> item_ct1) {
                 rwkv_wkv_f32_kernel(
                     B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
-                    item_ct1, shared_mem_acc.get_pointer()
+                    item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
                 );
             });
     });

From a4dd490069a66ae56b42127048f06757fc4de4f7 Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Tue, 7 Jan 2025 08:37:02 +0200
Subject: [PATCH 128/376] rpc : code cleanup (#11107)

Remove duplicated macros, use GGML_LOG_ERROR for errors
---
 ggml/src/ggml-rpc/ggml-rpc.cpp | 49 ++++++++++++++--------------------
 1 file changed, 20 insertions(+), 29 deletions(-)

diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 2213aba9f..63da2b86b 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -27,15 +27,6 @@
 #endif
 #include <cstring>
 
-#define UNUSED GGML_UNUSED
-
-#define GGML_DEBUG 0
-#if (GGML_DEBUG >= 1)
-#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
-#else
-#define GGML_PRINT_DEBUG(...)
-#endif
-
 #ifdef _WIN32
 typedef SOCKET sockfd_t;
 using ssize_t = __int64;
@@ -411,7 +402,7 @@ static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
         initialized = true;
     }
 #else
-    UNUSED(initialized);
+    GGML_UNUSED(initialized);
 #endif
     auto sock = socket_connect(host.c_str(), port);
     if (sock == nullptr) {
@@ -640,7 +631,7 @@ static void ggml_backend_rpc_free(ggml_backend_t backend) {
 }
 
 static void ggml_backend_rpc_synchronize(ggml_backend_t backend) {
-    UNUSED(backend);
+    GGML_UNUSED(backend);
     // this is no-op because we don't have any async operations
 }
 
@@ -850,7 +841,7 @@ void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_
         GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size);
         buffers.insert(buffer);
     } else {
-        GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
+        GGML_LOG_ERROR("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
     }
 }
 
@@ -872,7 +863,7 @@ bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rp
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
     if (buffers.find(buffer) == buffers.end()) {
-        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
         return false;
     }
     void * base = ggml_backend_buffer_get_base(buffer);
@@ -884,7 +875,7 @@ bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) {
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
     if (buffers.find(buffer) == buffers.end()) {
-        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
         return false;
     }
     ggml_backend_buffer_free(buffer);
@@ -896,7 +887,7 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) {
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
     if (buffers.find(buffer) == buffers.end()) {
-        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
         return false;
     }
     ggml_backend_buffer_clear(buffer, request.value);
@@ -952,7 +943,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
     struct ggml_context * ctx = ggml_init(params);
     ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
     if (tensor == nullptr) {
-        GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
+        GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
         ggml_free(ctx);
         return false;
     }
@@ -1017,7 +1008,7 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<
     struct ggml_context * ctx = ggml_init(params);
     ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
     if (tensor == nullptr) {
-        GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
+        GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
         ggml_free(ctx);
         return false;
     }
@@ -1051,7 +1042,7 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
     ggml_tensor * src = deserialize_tensor(ctx, &request.src);
     ggml_tensor * dst = deserialize_tensor(ctx, &request.dst);
     if (src == nullptr || dst == nullptr) {
-        GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__);
+        GGML_LOG_ERROR("[%s] error deserializing tensors\n", __func__);
         ggml_free(ctx);
         return false;
     }
@@ -1385,14 +1376,14 @@ static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t *
 
     ggml_backend_rpc_get_device_memory(ctx->endpoint.c_str(), free, total);
 
-    UNUSED(dev);
+    GGML_UNUSED(dev);
 }
 
 static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) {
     // TODO: obtain value from the server
     return GGML_BACKEND_DEVICE_TYPE_GPU;
 
-    UNUSED(dev);
+    GGML_UNUSED(dev);
 }
 
 static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
@@ -1413,7 +1404,7 @@ static ggml_backend_t ggml_backend_rpc_device_init(ggml_backend_dev_t dev, const
 
     return ggml_backend_rpc_init(ctx->endpoint.c_str());
 
-    UNUSED(params);
+    GGML_UNUSED(params);
 }
 
 static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_backend_dev_t dev) {
@@ -1421,12 +1412,12 @@ static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_b
 
     return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
 
-    UNUSED(dev);
+    GGML_UNUSED(dev);
 }
 
 static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
-    UNUSED(dev);
-    UNUSED(op);
+    GGML_UNUSED(dev);
+    GGML_UNUSED(op);
     //TODO: call the remote backend and cache the results
     return true;
 }
@@ -1463,20 +1454,20 @@ static const struct ggml_backend_device_i ggml_backend_rpc_device_i = {
 static const char * ggml_backend_rpc_reg_get_name(ggml_backend_reg_t reg) {
     return "RPC";
 
-    UNUSED(reg);
+    GGML_UNUSED(reg);
 }
 
 static size_t ggml_backend_rpc_reg_get_device_count(ggml_backend_reg_t reg) {
     return 0;
 
-    UNUSED(reg);
+    GGML_UNUSED(reg);
 }
 
 static ggml_backend_dev_t ggml_backend_rpc_reg_get_device(ggml_backend_reg_t reg, size_t index) {
     GGML_ABORT("The RPC backend does not have enumerated devices - use ggml_backend_add_device instead");
 
-    UNUSED(reg);
-    UNUSED(index);
+    GGML_UNUSED(reg);
+    GGML_UNUSED(index);
 }
 
 static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const char * name) {
@@ -1485,7 +1476,7 @@ static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const ch
     }
     return NULL;
 
-    UNUSED(reg);
+    GGML_UNUSED(reg);
 }
 
 static const struct ggml_backend_reg_i ggml_backend_rpc_reg_i = {

From a3d50bc022bedd6c7754c24749a1fef4d2d60c7c Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Tue, 7 Jan 2025 12:38:05 +0100
Subject: [PATCH 129/376] ggml-backend : only offload from host buffers
 (#11120)

---
 ggml/src/ggml-backend.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index e2d6c4056..d034f8b7f 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -761,7 +761,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
         }
         // skip ROPE since the rope freqs tensor is too small to choose a backend based on it
         // not an ideal solution
-        if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
+        if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS && ggml_backend_buffer_is_host(src->buffer)) {
             int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
             // check if a backend with higher prio wants to offload the op
             if (src_backend_id == sched->n_backends - 1) {

From 017cc5f446863316d05522a87f25ec48713a9492 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Tue, 7 Jan 2025 16:11:57 +0100
Subject: [PATCH 130/376] ggml-backend : only offload from host buffers (fix)
 (#11124)

---
 ggml/src/ggml-backend.cpp              | 4 ++--
 ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index d034f8b7f..dba7be33b 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -761,10 +761,10 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
         }
         // skip ROPE since the rope freqs tensor is too small to choose a backend based on it
         // not an ideal solution
-        if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS && ggml_backend_buffer_is_host(src->buffer)) {
+        if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
             int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
             // check if a backend with higher prio wants to offload the op
-            if (src_backend_id == sched->n_backends - 1) {
+            if (src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
                 for (int b = 0; b < src_backend_id; b++) {
                     if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
                         SET_CAUSE(tensor, "1.off");
diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
index 622c63f1f..b311a5b1c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
@@ -4169,6 +4169,8 @@ static ggml_backend_buffer_t ggml_backend_cpu_aarch64_buffer_type_alloc_buffer(g
     buffer->buft              = buft;
     buffer->iface.init_tensor = ggml_backend_cpu_aarch64_buffer_init_tensor;
     buffer->iface.set_tensor  = ggml_backend_cpu_aarch64_buffer_set_tensor;
+    buffer->iface.get_tensor  = nullptr;
+    buffer->iface.cpy_tensor  = nullptr;
     return buffer;
 }
 

From 53ff6b9b9fb25ed0ec0a213e05534fe7c3d0040f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 7 Jan 2025 18:01:58 +0100
Subject: [PATCH 131/376] GGUF: C++ refactor, backend support, misc fixes
 (#11030)

* GGUF: C++ refactor, backend support, misc fixes

remove ggml_tensor.backend

update CODEOWNERS [no ci]

remove gguf_get_data from API

revise GGUF API data types
---
 CODEOWNERS                                    |    6 +
 common/common.cpp                             |    3 +
 .../convert-llama2c-to-ggml.cpp               |    2 +
 .../cvector-generator/cvector-generator.cpp   |    4 +-
 examples/export-lora/export-lora.cpp          |    6 +-
 examples/gguf-hash/gguf-hash.cpp              |    1 +
 examples/gguf-split/gguf-split.cpp            |   14 +-
 examples/gguf/gguf.cpp                        |   16 +-
 examples/llava/clip.cpp                       |    6 +-
 ggml/CMakeLists.txt                           |    3 +-
 ggml/include/ggml-cpp.h                       |    1 +
 ggml/include/ggml.h                           |  140 --
 ggml/include/gguf.h                           |  202 +++
 ggml/src/CMakeLists.txt                       |    4 +-
 ggml/src/ggml-impl.h                          |   27 +-
 ggml/src/ggml.c                               | 1276 ----------------
 ggml/src/gguf.cpp                             | 1325 +++++++++++++++++
 src/llama-impl.cpp                            |    3 +-
 src/llama-model-loader.cpp                    |    9 +-
 src/llama-quant.cpp                           |    3 +-
 tests/test-gguf.cpp                           |  371 ++---
 21 files changed, 1795 insertions(+), 1627 deletions(-)
 create mode 100644 ggml/include/gguf.h
 create mode 100644 ggml/src/gguf.cpp

diff --git a/CODEOWNERS b/CODEOWNERS
index c9fa34761..72d594b46 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -3,3 +3,9 @@
 /ci/ @ggerganov
 /.devops/*.Dockerfile @ngxson
 /examples/server/ @ngxson
+/ggml/src/ggml-cuda/fattn* @JohannesGaessler
+/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
+/ggml/src/ggml-cuda/mmv.* @JohannesGaessler
+/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
+/ggml/src/ggml-opt.cpp @JohannesGaessler
+/ggml/src/gguf.cpp @JohannesGaessler
diff --git a/common/common.cpp b/common/common.cpp
index 4fd36105e..86e4e1e24 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -2,6 +2,9 @@
 #define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
 #endif
 
+#include "ggml.h"
+#include "gguf.h"
+
 #include "common.h"
 #include "log.h"
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 9c3a0c367..1256abb17 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -1,4 +1,6 @@
 #include "ggml.h"
+#include "gguf.h"
+
 #include "llama.h"
 #include "common.h"
 #include "log.h"
diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp
index 7c9f50228..e899c1078 100644
--- a/examples/cvector-generator/cvector-generator.cpp
+++ b/examples/cvector-generator/cvector-generator.cpp
@@ -1,7 +1,9 @@
+#include "ggml.h"
+#include "gguf.h"
+
 #include "arg.h"
 #include "common.h"
 #include "llama.h"
-#include "ggml.h"
 #include "pca.hpp"
 #include "mean.hpp"
 
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 058b5cc86..d5dcd20a0 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -1,7 +1,9 @@
-#include "arg.h"
-#include "common.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
+#include "gguf.h"
+
+#include "arg.h"
+#include "common.h"
 
 #include <map>
 #include <vector>
diff --git a/examples/gguf-hash/gguf-hash.cpp b/examples/gguf-hash/gguf-hash.cpp
index e96c75117..9523ec122 100644
--- a/examples/gguf-hash/gguf-hash.cpp
+++ b/examples/gguf-hash/gguf-hash.cpp
@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "gguf.h"
 
 #include <cstdlib>   /* abort() */
 #include <cstddef>
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
index 9e3d44984..ef3ceb686 100644
--- a/examples/gguf-split/gguf-split.cpp
+++ b/examples/gguf-split/gguf-split.cpp
@@ -1,16 +1,18 @@
+#include "ggml.h"
+#include "gguf.h"
 #include "llama.h"
 #include "common.h"
 
 #include <algorithm>
+#include <cinttypes>
+#include <climits>
+#include <cstdio>
 #include <cstdlib>
+#include <stdexcept>
+#include <cstring>
 #include <fstream>
 #include <string>
 #include <vector>
-#include <climits>
-
-#include <cstdio>
-#include <cstring>
-#include <stdexcept>
 
 #if defined(_WIN32)
     #include <windows.h>
@@ -296,7 +298,7 @@ struct split_strategy {
                 total_size += ggml_nbytes(t);
             }
             total_size = total_size / 1000 / 1000; // convert to megabytes
-            printf("split %05d: n_tensors = %d, total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            printf("split %05d: n_tensors = %" PRIi64 ", total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
             i_split++;
         }
     }
diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index 7498f85ef..f31989c8c 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -1,10 +1,9 @@
 #include "ggml.h"
+#include "gguf.h"
 
 #include <cstdio>
-#include <cinttypes>
 #include <string>
 #include <sstream>
-#include <fstream>
 #include <vector>
 
 #undef MIN
@@ -135,9 +134,10 @@ static bool gguf_ex_read_0(const std::string & fname) {
 
         for (int i = 0; i < n_tensors; ++i) {
             const char * name   = gguf_get_tensor_name  (ctx, i);
+            const size_t size   = gguf_get_tensor_size  (ctx, i);
             const size_t offset = gguf_get_tensor_offset(ctx, i);
 
-            printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
+            printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset);
         }
     }
 
@@ -182,9 +182,10 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
 
         for (int i = 0; i < n_tensors; ++i) {
             const char * name   = gguf_get_tensor_name  (ctx, i);
+            const size_t size   = gguf_get_tensor_size  (ctx, i);
             const size_t offset = gguf_get_tensor_offset(ctx, i);
 
-            printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
+            printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset);
         }
     }
 
@@ -199,7 +200,8 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
 
             struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
 
-            printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data);
+            printf("%s: tensor[%d]: n_dims = %d, ne = (%d, %d, %d, %d), name = %s, data = %p\n",
+                __func__, i, ggml_n_dims(cur), int(cur->ne[0]), int(cur->ne[1]), int(cur->ne[2]), int(cur->ne[3]), cur->name, cur->data);
 
             // print first 10 elements
             const float * data = (const float *) cur->data;
@@ -215,7 +217,7 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
                 const float * data = (const float *) cur->data;
                 for (int j = 0; j < ggml_nelements(cur); ++j) {
                     if (data[j] != 100 + i) {
-                        fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
+                        fprintf(stderr, "%s: tensor[%d], data[%d]: found %f, expected %f\n", __func__, i, j, data[j], float(100 + i));
                         gguf_free(ctx);
                         return false;
                     }
@@ -245,6 +247,8 @@ int main(int argc, char ** argv) {
         check_data = false;
     }
 
+    srand(123456);
+
     const std::string fname(argv[1]);
     const std::string mode (argv[2]);
 
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 3cd0d2fa8..7a8a3156b 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -7,6 +7,7 @@
 #include "ggml-cpu.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
+#include "gguf.h"
 
 //#ifdef GGML_USE_CUDA
 //#include "ggml-cuda.h"
@@ -262,7 +263,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
             {
                 const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
                 int arr_n = gguf_get_arr_n(ctx_gguf, i);
-                const void * data = gguf_get_arr_data(ctx_gguf, i);
+                const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i);
                 std::stringstream ss;
                 ss << "[";
                 for (int j = 0; j < arr_n; j++) {
@@ -2734,7 +2735,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
         total_size_org += orig_size;
         total_size_new += new_size;
         gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
-        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
+        GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size);
+        gguf_set_tensor_data(ctx_out, name.c_str(), new_data);
         fout.write((const char *)new_data, new_size);
         size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
         for (size_t j = 0; j < pad; ++j) {
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 393506533..fe8acc803 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -243,7 +243,8 @@ set(GGML_PUBLIC_HEADERS
     include/ggml-metal.h
     include/ggml-rpc.h
     include/ggml-sycl.h
-    include/ggml-vulkan.h)
+    include/ggml-vulkan.h
+    include/gguf.h)
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
 #if (GGML_METAL)
diff --git a/ggml/include/ggml-cpp.h b/ggml/include/ggml-cpp.h
index 219361af4..a12342c25 100644
--- a/ggml/include/ggml-cpp.h
+++ b/ggml/include/ggml-cpp.h
@@ -7,6 +7,7 @@
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
+#include "gguf.h"
 #include <memory>
 
 // Smart pointers for ggml types
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index c714fc8c8..8630d92c5 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -241,12 +241,6 @@
 #define GGML_ROPE_TYPE_MROPE  8
 #define GGML_ROPE_TYPE_VISION 24
 
-#define GGUF_MAGIC "GGUF"
-
-#define GGUF_VERSION 3
-
-#define GGUF_DEFAULT_ALIGNMENT 32
-
 #define GGML_UNUSED(x) (void)(x)
 
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
@@ -403,12 +397,6 @@ extern "C" {
         GGML_PREC_F32,
     };
 
-    enum ggml_backend_type {
-        GGML_BACKEND_TYPE_CPU = 0,
-        GGML_BACKEND_TYPE_GPU = 10,
-        GGML_BACKEND_TYPE_GPU_SPLIT = 20,
-    };
-
     // model file types
     enum ggml_ftype {
         GGML_FTYPE_UNKNOWN        = -1,
@@ -587,8 +575,6 @@ extern "C" {
     struct ggml_tensor {
         enum ggml_type type;
 
-        GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor");
-
         struct ggml_backend_buffer * buffer;
 
         int64_t ne[GGML_MAX_DIMS]; // number of elements
@@ -2111,132 +2097,6 @@ extern "C" {
                    int64_t   n_per_row,
                const float * imatrix);
 
-    //
-    // gguf
-    //
-
-    enum gguf_type {
-        GGUF_TYPE_UINT8   = 0,
-        GGUF_TYPE_INT8    = 1,
-        GGUF_TYPE_UINT16  = 2,
-        GGUF_TYPE_INT16   = 3,
-        GGUF_TYPE_UINT32  = 4,
-        GGUF_TYPE_INT32   = 5,
-        GGUF_TYPE_FLOAT32 = 6,
-        GGUF_TYPE_BOOL    = 7,
-        GGUF_TYPE_STRING  = 8,
-        GGUF_TYPE_ARRAY   = 9,
-        GGUF_TYPE_UINT64  = 10,
-        GGUF_TYPE_INT64   = 11,
-        GGUF_TYPE_FLOAT64 = 12,
-        GGUF_TYPE_COUNT,       // marks the end of the enum
-    };
-
-    struct gguf_context;
-
-    struct gguf_init_params {
-        bool no_alloc;
-
-        // if not NULL, create a ggml_context and allocate the tensor data in it
-        struct ggml_context ** ctx;
-    };
-
-    GGML_API struct gguf_context * gguf_init_empty(void);
-    GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
-    //GGML_API struct gguf_context * gguf_init_from_buffer(..);
-
-    GGML_API void gguf_free(struct gguf_context * ctx);
-
-    GGML_API const char * gguf_type_name(enum gguf_type type);
-
-    GGML_API int    gguf_get_version    (const struct gguf_context * ctx);
-    GGML_API size_t gguf_get_alignment  (const struct gguf_context * ctx);
-    GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
-    GGML_API void * gguf_get_data       (const struct gguf_context * ctx);
-
-    GGML_API int          gguf_get_n_kv(const struct gguf_context * ctx);
-    GGML_API int          gguf_find_key(const struct gguf_context * ctx, const char * key);
-    GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int key_id);
-
-    GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int key_id);
-    GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id);
-
-    // will abort if the wrong type is used for the key
-    GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int key_id);
-    GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int key_id);
-    GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int key_id);
-    GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int key_id);
-    GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int key_id);
-    GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int key_id);
-    GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int key_id);
-    GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int key_id);
-    GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int key_id);
-    GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int key_id);
-    GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int key_id);
-    GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id);
-    GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id);
-    GGML_API int          gguf_get_arr_n   (const struct gguf_context * ctx, int key_id);
-    GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
-    GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
-
-    GGML_API int            gguf_get_n_tensors    (const struct gguf_context * ctx);
-    GGML_API int            gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
-    GGML_API size_t         gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
-    GGML_API char *         gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
-    GGML_API enum ggml_type gguf_get_tensor_type  (const struct gguf_context * ctx, int i);
-
-    // removes key if it exists
-    GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key);
-
-    // overrides existing values or adds a new one
-    GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);
-    GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t   val);
-    GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
-    GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t  val);
-    GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
-    GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t  val);
-    GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float    val);
-    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
-    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t  val);
-    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double   val);
-    GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool     val);
-    GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
-    GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
-    GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
-
-    // set or add KV pairs from another context
-    GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
-
-    // manage tensor info
-    GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
-    GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
-    GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
-
-    // writing gguf files can be done in 2 ways:
-    //
-    // - write the entire gguf_context to a binary file in a single pass:
-    //
-    //   gguf_write_to_file(ctx, fname);
-    //
-    // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
-    //
-    //   FILE * f = fopen(fname, "wb");
-    //   fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
-    //   fwrite(f, ...);
-    //   void * data = gguf_meta_get_meta_data(ctx);
-    //   fseek(f, 0, SEEK_SET);
-    //   fwrite(f, data, gguf_get_meta_size(ctx));
-    //   free(data);
-    //   fclose(f);
-    //
-
-    // write the entire context to a binary file
-    GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
-
-    // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
-    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
-    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
-
 #ifdef __cplusplus
     // restrict not standard in C++
 #    if defined(__GNUC__)
diff --git a/ggml/include/gguf.h b/ggml/include/gguf.h
new file mode 100644
index 000000000..79ee20206
--- /dev/null
+++ b/ggml/include/gguf.h
@@ -0,0 +1,202 @@
+// This file contains functionality related to "GGUF" files, the binary file format used by ggml.
+// GGUF files have the following structure:
+//
+// 1. File magic "GGUF" (4 bytes).
+// 2. File version (uint32_t).
+// 3. Number of ggml tensors in file (int64_t).
+// 4. Number of key-value-pairs in file (int64_t).
+// 5. For each KV pair:
+//   1. The key (string).
+//   2. The value type (gguf_type).
+//   3a. If the value type is GGUF_TYPE_ARRAY:
+//     1. The type of the array (gguf_type).
+//     2. The number of elements in the array (uint64_t).
+//     3. The binary representation of each element in the array.
+//   3b. Otherwise:
+//     1. The binary representation of the value.
+// 6. For each ggml tensor:
+//   1. The tensor name (string).
+//   2. The number of dimensions of the tensor (uint32_t).
+//   3. For each dimension:
+//     1. The size of the tensor in the dimension (int64_t).
+//   4. The tensor data type (ggml_type).
+//   5. The tensor data offset in the tensor data binary blob (uint64_t).
+// 7. The tensor data binary blob (optional, aligned).
+//
+// Strings are serialized as the string length (uint64_t) followed by the C string without the null terminator.
+// All enums are stored as int32_t.
+// All bool values are stored as int8_t.
+// If the special key "general.alignment" (uint32_t) is defined it is used for alignment,
+//   otherwise GGUF_DEFAULT_ALIGNMENT is used.
+//
+// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
+
+#pragma once
+
+#include "ggml.h"
+
+#include <stdbool.h>
+#include <stdint.h>
+
+#define GGUF_MAGIC   "GGUF"
+#define GGUF_VERSION 3
+
+#define GGUF_KEY_GENERAL_ALIGNMENT "general.alignment"
+
+#define GGUF_DEFAULT_ALIGNMENT 32
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    // types that can be stored as GGUF KV data
+    enum gguf_type {
+        GGUF_TYPE_UINT8   = 0,
+        GGUF_TYPE_INT8    = 1,
+        GGUF_TYPE_UINT16  = 2,
+        GGUF_TYPE_INT16   = 3,
+        GGUF_TYPE_UINT32  = 4,
+        GGUF_TYPE_INT32   = 5,
+        GGUF_TYPE_FLOAT32 = 6,
+        GGUF_TYPE_BOOL    = 7,
+        GGUF_TYPE_STRING  = 8,
+        GGUF_TYPE_ARRAY   = 9,
+        GGUF_TYPE_UINT64  = 10,
+        GGUF_TYPE_INT64   = 11,
+        GGUF_TYPE_FLOAT64 = 12,
+        GGUF_TYPE_COUNT,       // marks the end of the enum
+    };
+
+    struct gguf_context;
+
+    struct gguf_init_params {
+        bool no_alloc;
+
+        // if not NULL, create a ggml_context and allocate the tensor data in it
+        struct ggml_context ** ctx;
+    };
+
+    GGML_API struct gguf_context * gguf_init_empty(void);
+    GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
+    //GGML_API struct gguf_context * gguf_init_from_buffer(..);
+
+    GGML_API void gguf_free(struct gguf_context * ctx);
+
+    GGML_API const char * gguf_type_name(enum gguf_type type);
+
+    GGML_API uint32_t gguf_get_version    (const struct gguf_context * ctx);
+    GGML_API size_t   gguf_get_alignment  (const struct gguf_context * ctx);
+    GGML_API size_t   gguf_get_data_offset(const struct gguf_context * ctx);
+
+    GGML_API int64_t      gguf_get_n_kv(const struct gguf_context * ctx);
+    GGML_API int64_t      gguf_find_key(const struct gguf_context * ctx, const char * key); // returns -1 if key is not found
+    GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int64_t key_id);
+
+    GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id);
+
+    // will abort if the wrong type is used for the key
+    GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id);
+    GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id);
+    GGML_API size_t       gguf_get_arr_n   (const struct gguf_context * ctx, int64_t key_id);
+
+    // get raw pointer to the first element of the array with the given key_id
+    // for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
+    GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
+
+    // get ith C string from array with given key_id
+    GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);
+
+    GGML_API int64_t        gguf_get_n_tensors    (const struct gguf_context * ctx);
+    GGML_API int64_t        gguf_find_tensor      (const struct gguf_context * ctx, const char * name); // returns -1 if the tensor is not found
+    GGML_API size_t         gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API const char *   gguf_get_tensor_name  (const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API enum ggml_type gguf_get_tensor_type  (const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API size_t         gguf_get_tensor_size  (const struct gguf_context * ctx, int64_t tensor_id);
+
+    // removes key if it exists, returns id that the key had prior to removal (-1 if it didn't exist)
+    GGML_API int64_t gguf_remove_key(struct gguf_context * ctx, const char * key);
+
+    // overrides an existing KV pair or adds a new one, the new KV pair is always at the back
+    GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t      val);
+    GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t       val);
+    GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t     val);
+    GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t      val);
+    GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t     val);
+    GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t      val);
+    GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float        val);
+    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t     val);
+    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t      val);
+    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double       val);
+    GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool         val);
+    GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
+
+    // creates a new array with n elements of the given type and copies the corresponding number of bytes from data
+    GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n);
+
+    // creates a new array with n strings and copies the corresponding strings from data
+    GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, size_t n);
+
+    // set or add KV pairs from another context
+    GGML_API void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src);
+
+    // add tensor to GGUF context, tensor name must be unique
+    GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
+
+    // after changing a tensor's type, the offsets of all tensors with higher indices are immediately recalculated
+    //   in such a way that the tensor data remains as one contiguous block (except for padding)
+    GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
+
+    // assumes that at least gguf_get_tensor_size bytes can be read from data
+    GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data);
+
+    // writing gguf files can be done in 3 ways:
+    //
+    // - write the entire gguf_context to a binary file in a single pass:
+    //
+    //   gguf_write_to_file(ctx, fname, /*only_meta =*/ false);
+    //
+    // - write only the meta data to a file, then re-open the file and append the tensor data:
+    //
+    //   gguf_write_to_file(ctx, fname, /*only_meta =*/ true);
+    //   FILE * f = fopen(fname, "ab");
+    //   fwrite(f, ...); // write tensor data
+    //   fclose(f);
+    //
+    // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
+    //
+    //   FILE * f = fopen(fname, "wb");
+    //   const size_t size_meta = gguf_get_meta_size(ctx);
+    //   fseek(f, size_meta, SEEK_SET);
+    //   fwrite(f, ...); // write tensor data
+    //   void * data = malloc(size_meta);
+    //   gguf_get_meta_data(ctx, data);
+    //   rewind(f);
+    //   fwrite(data, 1, data, f);
+    //   free(data);
+    //   fclose(f);
+    //
+
+    // write the entire context to a binary file
+    GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
+
+    // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
+    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
+
+    // writes the meta data to pointer "data"
+    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 84101c32c..ae1cd2337 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -208,6 +208,7 @@ add_library(ggml-base
             ../include/ggml-backend.h
             ../include/ggml-cpp.h
             ../include/ggml-opt.h
+            ../include/gguf.h
             ggml.c
             ggml-alloc.c
             ggml-backend.cpp
@@ -215,7 +216,8 @@ add_library(ggml-base
             ggml-threading.cpp
             ggml-threading.h
             ggml-quants.c
-            ggml-quants.h)
+            ggml-quants.h
+            gguf.cpp)
 
 target_include_directories(ggml-base PRIVATE .)
 
diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index 549772c57..eab017889 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -3,6 +3,8 @@
 // GGML internal header
 
 #include "ggml.h"
+#include "gguf.h"
+
 #include <assert.h>
 #include <math.h>
 #include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
@@ -551,22 +553,15 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
 #define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
 #define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
 
-// expose GGUF internals for test code
-
-GGML_API size_t gguf_type_size(enum gguf_type type);
-
-GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
-
-struct gguf_buf {
-    void * data;
-    size_t size;
-    size_t offset;
-};
-GGML_API struct gguf_buf gguf_buf_init(size_t size);
-GGML_API void gguf_buf_free(struct gguf_buf buf);
-
-GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta);
-
 #ifdef __cplusplus
 }
 #endif
+
+#ifdef __cplusplus
+#include <vector>
+
+// expose GGUF internals for test code
+GGML_API size_t gguf_type_size(enum gguf_type type);
+GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
+GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta);
+#endif // __cplusplus
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 2bbe5f482..90abc6ad4 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1588,15 +1588,8 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 
     struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs);
 
-#ifdef __clang__
-    // temporary until ggml_tensor::backend is removed
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wdeprecated-declarations"
-#endif
-
     *result = (struct ggml_tensor) {
         /*.type         =*/ type,
-        /*.backend      =*/ GGML_BACKEND_TYPE_CPU,
         /*.buffer       =*/ NULL,
         /*.ne           =*/ { 1, 1, 1, 1 },
         /*.nb           =*/ { 0, 0, 0, 0 },
@@ -1612,10 +1605,6 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         /*.padding      =*/ { 0 },
     };
 
-#ifdef __clang__
-    #pragma clang diagnostic pop
-#endif
-
     // TODO: this should not be needed as long as we don't rely on aligned SIMD loads
     //GGML_ASSERT_ALIGNED(result->data);
 
@@ -6417,1271 +6406,6 @@ size_t ggml_quantize_chunk(
 
 ////////////////////////////////////////////////////////////////////////////////
 
-struct gguf_str {
-    uint64_t n;  // GGUFv2
-    char * data;
-};
-
-static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = {
-    [GGUF_TYPE_UINT8]   = sizeof(uint8_t),
-    [GGUF_TYPE_INT8]    = sizeof(int8_t),
-    [GGUF_TYPE_UINT16]  = sizeof(uint16_t),
-    [GGUF_TYPE_INT16]   = sizeof(int16_t),
-    [GGUF_TYPE_UINT32]  = sizeof(uint32_t),
-    [GGUF_TYPE_INT32]   = sizeof(int32_t),
-    [GGUF_TYPE_FLOAT32] = sizeof(float),
-    [GGUF_TYPE_BOOL]    = sizeof(bool),
-    [GGUF_TYPE_STRING]  = sizeof(struct gguf_str),
-    [GGUF_TYPE_UINT64]  = sizeof(uint64_t),
-    [GGUF_TYPE_INT64]   = sizeof(int64_t),
-    [GGUF_TYPE_FLOAT64] = sizeof(double),
-    [GGUF_TYPE_ARRAY]   = 0, // undefined
-};
-static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
-
-static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = {
-    [GGUF_TYPE_UINT8]   = "u8",
-    [GGUF_TYPE_INT8]    = "i8",
-    [GGUF_TYPE_UINT16]  = "u16",
-    [GGUF_TYPE_INT16]   = "i16",
-    [GGUF_TYPE_UINT32]  = "u32",
-    [GGUF_TYPE_INT32]   = "i32",
-    [GGUF_TYPE_FLOAT32] = "f32",
-    [GGUF_TYPE_BOOL]    = "bool",
-    [GGUF_TYPE_STRING]  = "str",
-    [GGUF_TYPE_ARRAY]   = "arr",
-    [GGUF_TYPE_UINT64]  = "u64",
-    [GGUF_TYPE_INT64]   = "i64",
-    [GGUF_TYPE_FLOAT64] = "f64",
-};
-static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
-
-union gguf_value {
-    uint8_t  uint8;
-    int8_t   int8;
-    uint16_t uint16;
-    int16_t  int16;
-    uint32_t uint32;
-    int32_t  int32;
-    float    float32;
-    uint64_t uint64;
-    int64_t  int64;
-    double   float64;
-    bool     bool_;
-
-    struct gguf_str str;
-
-    struct {
-        enum gguf_type type;
-
-        uint64_t n;  // GGUFv2
-        void * data;
-    } arr;
-};
-
-struct gguf_kv {
-    struct gguf_str key;
-
-    enum  gguf_type  type;
-    union gguf_value value;
-};
-
-struct gguf_header {
-    char magic[4];
-
-    uint32_t version;
-    uint64_t n_tensors; // GGUFv2
-    uint64_t n_kv;      // GGUFv2
-};
-
-struct gguf_tensor_info {
-    struct gguf_str name;
-
-    uint32_t n_dims;
-    uint64_t ne[GGML_MAX_DIMS];
-
-    enum ggml_type type;
-
-    uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT`
-
-    // for writing API
-    const void * data;
-    size_t size;
-};
-
-struct gguf_context {
-    struct gguf_header header;
-
-    struct gguf_kv          * kv;
-    struct gguf_tensor_info * infos;
-
-    size_t alignment;
-    size_t offset;    // offset of `data` from beginning of file
-    size_t size;      // size of `data` in bytes
-
-    //uint8_t * padding;
-    void * data;
-};
-
-size_t gguf_type_size(enum gguf_type type) {
-    GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT);
-    return GGUF_TYPE_SIZE[type];
-}
-
-static bool gguf_tensor_info_sanitize(struct gguf_tensor_info * info) {
-    if (info->n_dims > GGML_MAX_DIMS) {
-        fprintf(stderr, "%s: invalid number of dimensions (%" PRIu32 ")\n", __func__, info->n_dims);
-        return false;
-    }
-
-    if (info->type < 0 || info->type >= GGML_TYPE_COUNT) {
-        fprintf(stderr, "%s: invalid type (%d)\n", __func__, info->type);
-        return false;
-    }
-
-    if (strlen(info->name.data) >= GGML_MAX_NAME) {
-        fprintf(stderr, "%s: tensor '%s' name is too long\n", __func__, info->name.data);
-        return false;
-    }
-
-    for (uint32_t i = 0; i < info->n_dims; ++i) {
-        if (info->ne[i] <= 0) {
-            fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[i]);
-            return false;
-        }
-    }
-
-    // prevent overflow for total number of elements
-    if (INT64_MAX/info->ne[1] <= info->ne[0]) {
-        fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[1]);
-        return false;
-    }
-
-    if (INT64_MAX/info->ne[2] <= info->ne[0]*info->ne[1]) {
-        fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[2]);
-        return false;
-    }
-
-    if (INT64_MAX/info->ne[3] <= info->ne[0]*info->ne[1]*info->ne[2]) {
-        fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[3]);
-        return false;
-    }
-
-    return true;
-}
-
-static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) {
-    const size_t n = fread(dst, 1, size, file);
-    *offset += n;
-    return n == size;
-}
-
-static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
-    p->n    = 0;
-    p->data = NULL;
-
-    bool ok = true;
-
-    ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset);
-
-    // early exit if string length is invalid, prevents from integer overflow
-    if (p->n == SIZE_MAX) {
-        fprintf(stderr, "%s: invalid string length (%" PRIu64 ")\n", __func__, p->n);
-        return false;
-    }
-
-    p->data = calloc(p->n + 1, 1);
-    if (!p->data) {
-        fprintf(stderr, "%s: failed to allocate memory for string of length %" PRIu64 "\n", __func__, p->n);
-        return false;
-    }
-
-    ok = ok && gguf_fread_el(file,  p->data, p->n, offset);
-
-    return ok;
-}
-
-static void gguf_free_kv(struct gguf_kv * kv) {
-    if (kv->key.data) {
-        GGML_FREE(kv->key.data);
-    }
-
-    if (kv->type == GGUF_TYPE_STRING) {
-        if (kv->value.str.data) {
-            GGML_FREE(kv->value.str.data);
-        }
-    }
-
-    if (kv->type == GGUF_TYPE_ARRAY) {
-        if (kv->value.arr.data) {
-            if (kv->value.arr.type == GGUF_TYPE_STRING) {
-                for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
-                    struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
-                    if (str->data) {
-                        GGML_FREE(str->data);
-                    }
-                }
-            }
-            GGML_FREE(kv->value.arr.data);
-        }
-    }
-}
-
-struct gguf_context * gguf_init_empty(void) {
-    struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
-    if (!ctx) {
-        fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
-        return NULL;
-    }
-
-    memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
-    ctx->header.version   = GGUF_VERSION;
-    ctx->header.n_tensors = 0;
-    ctx->header.n_kv      = 0;
-
-    ctx->kv    = NULL;
-    ctx->infos = NULL;
-
-    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;
-    ctx->offset    = 0;
-    ctx->size      = 0;
-
-    ctx->data = NULL;
-
-    return ctx;
-}
-
-struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
-    // offset from start of file
-    size_t offset = 0;
-
-    char magic[4];
-
-    // check the magic before making allocations
-    {
-        gguf_fread_el(file, &magic, sizeof(magic), &offset);
-
-        for (uint32_t i = 0; i < sizeof(magic); i++) {
-            if (magic[i] != GGUF_MAGIC[i]) {
-                fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
-                return NULL;
-            }
-        }
-    }
-
-    bool ok = true;
-
-    struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
-    if (!ctx) {
-        fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
-        return NULL;
-    }
-
-    // read the header
-    {
-        strncpy(ctx->header.magic, magic, 4);
-
-        ctx->kv    = NULL;
-        ctx->infos = NULL;
-        ctx->data  = NULL;
-
-        ok = ok && gguf_fread_el(file, &ctx->header.version,   sizeof(ctx->header.version),   &offset);
-        ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
-        ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
-
-        if (ctx->header.version == 1) {
-            fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__);
-            gguf_free(ctx);
-            return NULL;
-        }
-
-        // sanity-checks to prevent from integer/buffer overflows
-
-        ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/sizeof(struct gguf_tensor_info));
-        ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/ggml_tensor_overhead());
-        ok = ok && (ctx->header.n_kv      < (SIZE_MAX/2)/sizeof(struct gguf_kv));
-
-        if (!ok) {
-            fprintf(stderr, "%s: failed to read header\n", __func__);
-            gguf_free(ctx);
-            return NULL;
-        }
-    }
-
-    // read the kv pairs
-    {
-        const uint64_t n_kv = ctx->header.n_kv;
-
-        if (n_kv > 0) {
-            ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
-            if (!ctx->kv) {
-                fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
-                gguf_free(ctx);
-                return NULL;
-            }
-        }
-
-        for (uint64_t i = 0; i < n_kv; ++i) {
-            struct gguf_kv * kv = &ctx->kv[i];
-
-            //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
-
-            ok = ok && gguf_fread_str(file, &kv->key,                    &offset);
-            ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset);
-
-            //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data);
-
-            switch (kv->type) {
-                case GGUF_TYPE_UINT8:   ok = ok && gguf_fread_el (file, &kv->value.uint8,   sizeof(kv->value.uint8),   &offset); break;
-                case GGUF_TYPE_INT8:    ok = ok && gguf_fread_el (file, &kv->value.int8,    sizeof(kv->value.int8),    &offset); break;
-                case GGUF_TYPE_UINT16:  ok = ok && gguf_fread_el (file, &kv->value.uint16,  sizeof(kv->value.uint16),  &offset); break;
-                case GGUF_TYPE_INT16:   ok = ok && gguf_fread_el (file, &kv->value.int16,   sizeof(kv->value.int16),   &offset); break;
-                case GGUF_TYPE_UINT32:  ok = ok && gguf_fread_el (file, &kv->value.uint32,  sizeof(kv->value.uint32),  &offset); break;
-                case GGUF_TYPE_INT32:   ok = ok && gguf_fread_el (file, &kv->value.int32,   sizeof(kv->value.int32),   &offset); break;
-                case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (file, &kv->value.float32, sizeof(kv->value.float32), &offset); break;
-                case GGUF_TYPE_UINT64:  ok = ok && gguf_fread_el (file, &kv->value.uint64,  sizeof(kv->value.uint64),  &offset); break;
-                case GGUF_TYPE_INT64:   ok = ok && gguf_fread_el (file, &kv->value.int64,   sizeof(kv->value.int64),   &offset); break;
-                case GGUF_TYPE_FLOAT64: ok = ok && gguf_fread_el (file, &kv->value.float64, sizeof(kv->value.float64), &offset); break;
-                case GGUF_TYPE_BOOL:    ok = ok && gguf_fread_el (file, &kv->value.bool_,   sizeof(kv->value.bool_),   &offset); break;
-                case GGUF_TYPE_STRING:  ok = ok && gguf_fread_str(file, &kv->value.str,                                &offset); break;
-                case GGUF_TYPE_ARRAY:
-                    {
-                        ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);
-                        ok = ok && gguf_fread_el(file, &kv->value.arr.n,    sizeof(kv->value.arr.n),    &offset);
-
-                        switch (kv->value.arr.type) {
-                            case GGUF_TYPE_UINT8:
-                            case GGUF_TYPE_INT8:
-                            case GGUF_TYPE_UINT16:
-                            case GGUF_TYPE_INT16:
-                            case GGUF_TYPE_UINT32:
-                            case GGUF_TYPE_INT32:
-                            case GGUF_TYPE_FLOAT32:
-                            case GGUF_TYPE_UINT64:
-                            case GGUF_TYPE_INT64:
-                            case GGUF_TYPE_FLOAT64:
-                            case GGUF_TYPE_BOOL:
-                                {
-                                    // prevent from integer overflow in the malloc below
-                                    if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) {
-                                        fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
-                                        gguf_free(ctx);
-                                        return NULL;
-                                    }
-
-                                    kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
-                                    if (!kv->value.arr.data) {
-                                        fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
-                                        gguf_free(ctx);
-                                        return NULL;
-                                    }
-
-                                    ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
-                                } break;
-                            case GGUF_TYPE_STRING:
-                                {
-                                    // prevent from integer overflow in the malloc below
-                                    if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) {
-                                        fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
-                                        gguf_free(ctx);
-                                        return NULL;
-                                    }
-
-                                    kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str));
-                                    if (!kv->value.arr.data) {
-                                        fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
-                                        gguf_free(ctx);
-                                        return NULL;
-                                    }
-
-                                    for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
-                                        ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
-                                    }
-                                } break;
-                            case GGUF_TYPE_ARRAY:
-                            default:
-                                {
-                                    fprintf(stderr, "%s: invalid array type %d\n", __func__, kv->value.arr.type);
-                                    ok = false;
-                                } break;
-                        }
-                    } break;
-                default:
-                    {
-                        fprintf(stderr, "%s: invalid type %d\n", __func__, kv->type);
-                        ok = false;
-                    } break;
-            }
-
-            if (!ok) {
-                break;
-            }
-        }
-
-        if (!ok) {
-            fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
-            gguf_free(ctx);
-            return NULL;
-        }
-    }
-
-    // read the tensor infos
-    if (ctx->header.n_tensors > 0) {
-        ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
-        if (!ctx->infos) {
-            fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__);
-            gguf_free(ctx);
-            return NULL;
-        }
-
-        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
-            struct gguf_tensor_info * info = &ctx->infos[i];
-
-            for (int j = 0; j < GGML_MAX_DIMS; ++j) {
-                info->ne[j] = 1;
-            }
-
-            ok = ok && gguf_fread_str(file, &info->name,                          &offset);
-            ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims),  &offset);
-
-            ok = ok && (info->n_dims <= GGML_MAX_DIMS);
-
-            for (uint32_t j = 0; j < info->n_dims; ++j) {
-                ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
-            }
-
-            ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
-            ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
-
-            ok = ok && gguf_tensor_info_sanitize(info);
-
-            // make sure there is no duplicated tensor names
-            for (uint64_t j = 0; j < i && ok; ++j) {
-                if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
-                    fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
-                    ok = false;
-                }
-            }
-
-            if (!ok) {
-                fprintf(stderr, "%s: failed to read tensor info\n", __func__);
-                gguf_free(ctx);
-                return NULL;
-            }
-        }
-    }
-
-    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;
-
-    int alignment_idx = gguf_find_key(ctx, "general.alignment");
-    if (alignment_idx != -1) {
-        ctx->alignment = gguf_get_val_u32(ctx, alignment_idx);
-    }
-
-    // we require the data section to be aligned, so take into account any padding
-    {
-        const size_t offset_pad = offset % ctx->alignment;
-
-        if (offset_pad != 0) {
-            offset += ctx->alignment - offset_pad;
-            fseek(file, offset, SEEK_SET);
-        }
-    }
-
-    // store the current file offset - this is where the data section starts
-    ctx->offset = offset;
-
-    // compute the total size of the data section, taking into account the alignment
-    {
-        ctx->size = 0;
-        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
-            struct gguf_tensor_info * info = &ctx->infos[i];
-
-            const int64_t ne =
-                (int64_t) info->ne[0] *
-                (int64_t) info->ne[1] *
-                (int64_t) info->ne[2] *
-                (int64_t) info->ne[3];
-
-            if (ggml_blck_size(info->type) == 0 ) {
-                // this tensor type support have been removed:
-                fprintf(stderr, "%s: tensor '%s' of type %d: %s\n",
-                        __func__, info->name.data, (int) info->type, ggml_type_name(info->type));
-                gguf_free(ctx);
-                return NULL;
-            }
-
-            if (ne % ggml_blck_size(info->type) != 0) {
-                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
-                        __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
-                gguf_free(ctx);
-                return NULL;
-            }
-
-            const size_t size_cur = ggml_row_size(info->type, ne);
-
-            ctx->size += GGML_PAD(size_cur, ctx->alignment);
-        }
-    }
-
-    // load the tensor data only if requested
-    if (params.ctx != NULL) {
-        // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob
-        // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of
-        // the ggml_tensor structs to the appropriate locations in the binary blob
-
-        // compute the exact size needed for the new ggml_context
-        const size_t mem_size =
-            params.no_alloc ?
-            (ctx->header.n_tensors    )*ggml_tensor_overhead() :
-            (ctx->header.n_tensors + 1)*ggml_tensor_overhead() + ctx->size;
-
-        struct ggml_init_params pdata = {
-            .mem_size   = mem_size,
-            .mem_buffer = NULL,
-            .no_alloc   = params.no_alloc,
-        };
-
-        *params.ctx = ggml_init(pdata);
-        if (*params.ctx == NULL) {
-            fprintf(stderr, "%s: failed to initialize context\n", __func__);
-            gguf_free(ctx);
-            return NULL;
-        }
-
-        struct ggml_context * ctx_data = *params.ctx;
-
-        struct ggml_tensor * data = NULL;
-
-        if (!params.no_alloc) {
-            data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
-
-            ok = ok && data != NULL;
-
-            // read the binary blob with the tensor data
-            ok = ok && gguf_fread_el(file, data->data, ctx->size, &offset);
-
-            if (!ok) {
-                fprintf(stderr, "%s: failed to read tensor data\n", __func__);
-                ggml_free(ctx_data);
-                gguf_free(ctx);
-                return NULL;
-            }
-
-            ctx->data = data->data;
-        }
-
-        ggml_set_no_alloc(ctx_data, true);
-
-        // create the tensors
-        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
-            const int64_t ne[GGML_MAX_DIMS] = {
-                ctx->infos[i].ne[0],
-                ctx->infos[i].ne[1],
-                ctx->infos[i].ne[2],
-                ctx->infos[i].ne[3],
-            };
-
-            struct ggml_tensor * cur = ggml_new_tensor(ctx_data, ctx->infos[i].type, ctx->infos[i].n_dims, ne);
-
-            ok = ok && cur != NULL;
-
-            if (!ok) {
-                break;
-            }
-
-            ggml_set_name(cur, ctx->infos[i].name.data);
-
-            // point the data member to the appropriate location in the binary blob using the tensor infos
-            if (!params.no_alloc) {
-              //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
-                cur->data = (char *) data->data + ctx->infos[i].offset;               // offset from data
-            }
-        }
-
-        if (!ok) {
-            fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
-            ggml_free(ctx_data);
-            gguf_free(ctx);
-            return NULL;
-        }
-
-        ggml_set_no_alloc(ctx_data, params.no_alloc);
-    }
-
-    return ctx;
-}
-
-struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
-    FILE * file = ggml_fopen(fname, "rb");
-    if (!file) {
-        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
-        return NULL;
-    }
-
-    struct gguf_context * result = gguf_init_from_file_impl(file, params);
-    fclose(file);
-    return result;
-}
-
-void gguf_free(struct gguf_context * ctx) {
-    if (ctx == NULL) {
-        return;
-    }
-
-    if (ctx->kv) {
-        // free string memory - not great..
-        for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
-            gguf_free_kv(&ctx->kv[i]);
-        }
-
-        GGML_FREE(ctx->kv);
-    }
-
-    if (ctx->infos) {
-        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
-            struct gguf_tensor_info * info = &ctx->infos[i];
-
-            if (info->name.data) {
-                GGML_FREE(info->name.data);
-            }
-        }
-
-        GGML_FREE(ctx->infos);
-    }
-
-    GGML_FREE(ctx);
-}
-
-const char * gguf_type_name(enum gguf_type type) {
-    return GGUF_TYPE_NAME[type];
-}
-
-int gguf_get_version(const struct gguf_context * ctx) {
-    return ctx->header.version;
-}
-
-size_t gguf_get_alignment(const struct gguf_context * ctx) {
-    return ctx->alignment;
-}
-
-size_t gguf_get_data_offset(const struct gguf_context * ctx) {
-    return ctx->offset;
-}
-
-void * gguf_get_data(const struct gguf_context * ctx) {
-    return ctx->data;
-}
-
-int gguf_get_n_kv(const struct gguf_context * ctx) {
-    return ctx->header.n_kv;
-}
-
-int gguf_find_key(const struct gguf_context * ctx, const char * key) {
-    // return -1 if key not found
-    int keyfound = -1;
-
-    const int n_kv = gguf_get_n_kv(ctx);
-
-    for (int i = 0; i < n_kv; ++i) {
-        if (strcmp(key, gguf_get_key(ctx, i)) == 0) {
-            keyfound = i;
-            break;
-        }
-    }
-
-    return keyfound;
-}
-
-const char * gguf_get_key(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    return ctx->kv[key_id].key.data;
-}
-
-enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    return ctx->kv[key_id].type;
-}
-
-enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
-    return ctx->kv[key_id].value.arr.type;
-}
-
-const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
-    return ctx->kv[key_id].value.arr.data;
-}
-
-const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
-    struct gguf_kv * kv = &ctx->kv[key_id];
-    struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i];
-    return str->data;
-}
-
-int gguf_get_arr_n(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
-    return ctx->kv[key_id].value.arr.n;
-}
-
-uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT8);
-    return ctx->kv[key_id].value.uint8;
-}
-
-int8_t gguf_get_val_i8(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT8);
-    return ctx->kv[key_id].value.int8;
-}
-
-uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT16);
-    return ctx->kv[key_id].value.uint16;
-}
-
-int16_t gguf_get_val_i16(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT16);
-    return ctx->kv[key_id].value.int16;
-}
-
-uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT32);
-    return ctx->kv[key_id].value.uint32;
-}
-
-int32_t gguf_get_val_i32(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT32);
-    return ctx->kv[key_id].value.int32;
-}
-
-float gguf_get_val_f32(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT32);
-    return ctx->kv[key_id].value.float32;
-}
-
-uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT64);
-    return ctx->kv[key_id].value.uint64;
-}
-
-int64_t gguf_get_val_i64(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT64);
-    return ctx->kv[key_id].value.int64;
-}
-
-double gguf_get_val_f64(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT64);
-    return ctx->kv[key_id].value.float64;
-}
-
-bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_BOOL);
-    return ctx->kv[key_id].value.bool_;
-}
-
-const char * gguf_get_val_str(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_STRING);
-    return ctx->kv[key_id].value.str.data;
-}
-
-const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id) {
-    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_ARRAY);
-    GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_STRING);
-    return &ctx->kv[key_id].value;
-}
-
-int gguf_get_n_tensors(const struct gguf_context * ctx) {
-    return ctx->header.n_tensors;
-}
-
-int gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
-    // return -1 if tensor not found
-    int tensorfound = -1;
-
-    const int n_tensors = gguf_get_n_tensors(ctx);
-
-    for (int i = 0; i < n_tensors; ++i) {
-        if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) {
-            tensorfound = i;
-            break;
-        }
-    }
-
-    return tensorfound;
-}
-
-size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i) {
-    return ctx->infos[i].offset;
-}
-
-char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) {
-    return ctx->infos[i].name.data;
-}
-
-enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int i) {
-    return ctx->infos[i].type;
-}
-
-// returns the index
-static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
-    const int idx = gguf_find_key(ctx, key);
-    if (idx >= 0) {
-        return idx;
-    }
-
-    const int n_kv = gguf_get_n_kv(ctx);
-
-    ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv));
-    ctx->kv[n_kv].key.n    = strlen(key);
-    ctx->kv[n_kv].key.data = strdup(key);
-    ctx->header.n_kv++;
-
-    return n_kv;
-}
-
-void gguf_remove_key(struct gguf_context * ctx, const char * key) {
-    const int idx = gguf_find_key(ctx, key);
-    if (idx >= 0) {
-        const int n_kv = gguf_get_n_kv(ctx);
-        gguf_free_kv(&ctx->kv[idx]);
-        for (int i = idx; i < n_kv-1; ++i) {
-            ctx->kv[i] = ctx->kv[i+1];
-        }
-        ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv));
-        ctx->header.n_kv--;
-    }
-}
-
-void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type        = GGUF_TYPE_UINT8;
-    ctx->kv[idx].value.uint8 = val;
-}
-
-void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type       = GGUF_TYPE_INT8;
-    ctx->kv[idx].value.int8 = val;
-}
-
-void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type         = GGUF_TYPE_UINT16;
-    ctx->kv[idx].value.uint16 = val;
-}
-
-void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type        = GGUF_TYPE_INT16;
-    ctx->kv[idx].value.int16 = val;
-}
-
-void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type         = GGUF_TYPE_UINT32;
-    ctx->kv[idx].value.uint32 = val;
-}
-
-void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type        = GGUF_TYPE_INT32;
-    ctx->kv[idx].value.int32 = val;
-}
-
-void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type          = GGUF_TYPE_FLOAT32;
-    ctx->kv[idx].value.float32 = val;
-}
-
-void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type         = GGUF_TYPE_UINT64;
-    ctx->kv[idx].value.uint64 = val;
-}
-
-void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type        = GGUF_TYPE_INT64;
-    ctx->kv[idx].value.int64 = val;
-}
-
-void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type          = GGUF_TYPE_FLOAT64;
-    ctx->kv[idx].value.float64 = val;
-}
-
-void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type        = GGUF_TYPE_BOOL;
-    ctx->kv[idx].value.bool_ = val;
-}
-
-void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type           = GGUF_TYPE_STRING;
-    ctx->kv[idx].value.str.n    = strlen(val);
-    ctx->kv[idx].value.str.data = strdup(val);
-}
-
-void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
-    ctx->kv[idx].value.arr.type = type;
-    ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type));
-    memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type));
-}
-
-void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) {
-    const int idx = gguf_get_or_add_key(ctx, key);
-
-    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
-    ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
-    ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str));
-    for (int i = 0; i < n; i++) {
-        struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
-        str->n    = strlen(data[i]);
-        str->data = strdup(data[i]);
-    }
-}
-
-// set or add KV pairs from another context
-void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
-    for (uint32_t i = 0; i < src->header.n_kv; i++) {
-        switch (src->kv[i].type) {
-            case GGUF_TYPE_UINT8:   gguf_set_val_u8  (ctx, src->kv[i].key.data, src->kv[i].value.uint8);    break;
-            case GGUF_TYPE_INT8:    gguf_set_val_i8  (ctx, src->kv[i].key.data, src->kv[i].value.int8);     break;
-            case GGUF_TYPE_UINT16:  gguf_set_val_u16 (ctx, src->kv[i].key.data, src->kv[i].value.uint16);   break;
-            case GGUF_TYPE_INT16:   gguf_set_val_i16 (ctx, src->kv[i].key.data, src->kv[i].value.int16);    break;
-            case GGUF_TYPE_UINT32:  gguf_set_val_u32 (ctx, src->kv[i].key.data, src->kv[i].value.uint32);   break;
-            case GGUF_TYPE_INT32:   gguf_set_val_i32 (ctx, src->kv[i].key.data, src->kv[i].value.int32);    break;
-            case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, src->kv[i].key.data, src->kv[i].value.float32);  break;
-            case GGUF_TYPE_UINT64:  gguf_set_val_u64 (ctx, src->kv[i].key.data, src->kv[i].value.uint64);   break;
-            case GGUF_TYPE_INT64:   gguf_set_val_i64 (ctx, src->kv[i].key.data, src->kv[i].value.int64);    break;
-            case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, src->kv[i].key.data, src->kv[i].value.float64);  break;
-            case GGUF_TYPE_BOOL:    gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_);    break;
-            case GGUF_TYPE_STRING:  gguf_set_val_str (ctx, src->kv[i].key.data, src->kv[i].value.str.data); break;
-            case GGUF_TYPE_ARRAY:
-                {
-                    if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
-                        const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *));
-                        for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
-                            data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
-                        }
-                        gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
-                        GGML_FREE((void *)data);
-                    } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
-                        GGML_ABORT("nested arrays not supported");
-                    } else {
-                        gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n);
-                    }
-                } break;
-            default: GGML_ABORT("invalid type");
-        }
-    }
-}
-
-void gguf_add_tensor(
-             struct gguf_context * ctx,
-        const struct ggml_tensor * tensor) {
-    GGML_ASSERT(tensor);
-    if (gguf_find_tensor(ctx, tensor->name) != -1) {
-        GGML_ABORT("duplicated tensor name");
-    }
-
-    const int idx = ctx->header.n_tensors;
-    ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));
-
-    ctx->infos[idx].name.n    = strlen(tensor->name);
-    ctx->infos[idx].name.data = strdup(tensor->name);
-
-    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
-        ctx->infos[idx].ne[i] = 1;
-    }
-
-    ctx->infos[idx].n_dims = ggml_n_dims(tensor);
-    for (uint32_t i = 0; i < ctx->infos[idx].n_dims; i++) {
-        ctx->infos[idx].ne[i] = tensor->ne[i];
-    }
-
-    ctx->infos[idx].type   = tensor->type;
-    ctx->infos[idx].offset = 0;
-    ctx->infos[idx].data   = tensor->data;
-    ctx->infos[idx].size   = ggml_nbytes(tensor);
-
-    if (ctx->header.n_tensors > 0) {
-        ctx->infos[idx].offset = ctx->infos[idx - 1].offset + GGML_PAD(ctx->infos[idx - 1].size, ctx->alignment);
-    }
-
-    ctx->header.n_tensors++;
-}
-
-void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
-    const int idx = gguf_find_tensor(ctx, name);
-    if (idx < 0) {
-        GGML_ABORT("tensor not found");
-    }
-
-    ctx->infos[idx].type = type;
-}
-
-void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size) {
-    const int idx = gguf_find_tensor(ctx, name);
-    if (idx < 0) {
-        GGML_ABORT("tensor not found");
-    }
-
-    ctx->infos[idx].data = data;
-    ctx->infos[idx].size = size;
-
-    // update offsets
-    for (uint32_t i = idx + 1; i < ctx->header.n_tensors; ++i) {
-        ctx->infos[i].offset = ctx->infos[i - 1].offset + GGML_PAD(ctx->infos[i - 1].size, ctx->alignment);
-    }
-}
-
-//static void gguf_fwrite_str(FILE * file, const struct gguf_str * val) {
-//    fwrite(&val->n,   sizeof(val->n),    1, file);
-//    fwrite(val->data, sizeof(char), val->n, file);
-//}
-//
-//static void gguf_fwrite_el(FILE * file, const void * val, size_t size) {
-//    fwrite(val, sizeof(char), size, file);
-//}
-
-struct gguf_buf gguf_buf_init(size_t size) {
-    struct gguf_buf buf = {
-        /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
-        /*buf.size   =*/ size,
-        /*buf.offset =*/ 0,
-    };
-
-    return buf;
-}
-
-void gguf_buf_free(struct gguf_buf buf) {
-    if (buf.data) {
-        GGML_FREE(buf.data);
-    }
-}
-
-static void gguf_buf_grow(struct gguf_buf * buf, size_t size) {
-    if (buf->offset + size > buf->size) {
-        buf->size = 1.5*(buf->offset + size);
-        if (buf->data) {
-            buf->data = realloc(buf->data, buf->size);
-        }
-    }
-}
-
-static void gguf_bwrite_str(struct gguf_buf * buf, const struct gguf_str * val) {
-    gguf_buf_grow(buf, sizeof(val->n) + val->n);
-
-    if (buf->data) {
-        memcpy((char *) buf->data + buf->offset, &val->n, sizeof(val->n));
-    }
-    buf->offset += sizeof(val->n);
-
-    if (buf->data) {
-        memcpy((char *) buf->data + buf->offset, val->data, val->n);
-    }
-    buf->offset += val->n;
-}
-
-static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_size) {
-    gguf_buf_grow(buf, el_size);
-
-    if (buf->data) {
-        memcpy((char *) buf->data + buf->offset, val, el_size);
-    }
-    buf->offset += el_size;
-}
-
-void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
-    // write header
-    gguf_bwrite_el(buf, &ctx->header.magic,     sizeof(ctx->header.magic));
-    gguf_bwrite_el(buf, &ctx->header.version,   sizeof(ctx->header.version));
-    gguf_bwrite_el(buf, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors));
-    gguf_bwrite_el(buf, &ctx->header.n_kv,      sizeof(ctx->header.n_kv));
-
-    // write key-value pairs
-    for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
-        struct gguf_kv * kv = &ctx->kv[i];
-
-        gguf_bwrite_str(buf, &kv->key);
-        gguf_bwrite_el (buf, &kv->type, sizeof(kv->type));
-
-        switch (kv->type) {
-            case GGUF_TYPE_UINT8:   gguf_bwrite_el( buf, &kv->value.uint8,   sizeof(kv->value.uint8)  ); break;
-            case GGUF_TYPE_INT8:    gguf_bwrite_el (buf, &kv->value.int8,    sizeof(kv->value.int8)   ); break;
-            case GGUF_TYPE_UINT16:  gguf_bwrite_el (buf, &kv->value.uint16,  sizeof(kv->value.uint16) ); break;
-            case GGUF_TYPE_INT16:   gguf_bwrite_el (buf, &kv->value.int16,   sizeof(kv->value.int16)  ); break;
-            case GGUF_TYPE_UINT32:  gguf_bwrite_el (buf, &kv->value.uint32,  sizeof(kv->value.uint32) ); break;
-            case GGUF_TYPE_INT32:   gguf_bwrite_el (buf, &kv->value.int32,   sizeof(kv->value.int32)  ); break;
-            case GGUF_TYPE_FLOAT32: gguf_bwrite_el (buf, &kv->value.float32, sizeof(kv->value.float32)); break;
-            case GGUF_TYPE_UINT64:  gguf_bwrite_el (buf, &kv->value.uint64,  sizeof(kv->value.uint64) ); break;
-            case GGUF_TYPE_INT64:   gguf_bwrite_el (buf, &kv->value.int64,   sizeof(kv->value.int64)  ); break;
-            case GGUF_TYPE_FLOAT64: gguf_bwrite_el (buf, &kv->value.float64, sizeof(kv->value.float64)); break;
-            case GGUF_TYPE_BOOL:    gguf_bwrite_el (buf, &kv->value.bool_,   sizeof(kv->value.bool_)  ); break;
-            case GGUF_TYPE_STRING:  gguf_bwrite_str(buf, &kv->value.str                               ); break;
-            case GGUF_TYPE_ARRAY:
-                {
-                    gguf_bwrite_el(buf, &kv->value.arr.type, sizeof(kv->value.arr.type));
-                    gguf_bwrite_el(buf, &kv->value.arr.n,    sizeof(kv->value.arr.n)   );
-
-                    switch (kv->value.arr.type) {
-                        case GGUF_TYPE_UINT8:
-                        case GGUF_TYPE_INT8:
-                        case GGUF_TYPE_UINT16:
-                        case GGUF_TYPE_INT16:
-                        case GGUF_TYPE_UINT32:
-                        case GGUF_TYPE_INT32:
-                        case GGUF_TYPE_FLOAT32:
-                        case GGUF_TYPE_UINT64:
-                        case GGUF_TYPE_INT64:
-                        case GGUF_TYPE_FLOAT64:
-                        case GGUF_TYPE_BOOL:
-                            {
-                                gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type));
-                            } break;
-                        case GGUF_TYPE_STRING:
-                            {
-                                for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
-                                    gguf_bwrite_str(buf, &((struct gguf_str *) kv->value.arr.data)[j]);
-                                }
-                            } break;
-                        case GGUF_TYPE_ARRAY:
-                        default: GGML_ABORT("invalid type");
-                    }
-                } break;
-            default: GGML_ABORT("invalid type");
-        }
-    }
-
-    // write tensor infos
-    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
-        struct gguf_tensor_info * info = &ctx->infos[i];
-
-        gguf_bwrite_str(buf, &info->name);
-        gguf_bwrite_el (buf, &info->n_dims, sizeof(info->n_dims));
-        for (uint32_t j = 0; j < info->n_dims; ++j) {
-            gguf_bwrite_el(buf, &info->ne[j], sizeof(info->ne[j]));
-        }
-        gguf_bwrite_el(buf, &info->type,   sizeof(info->type));
-        gguf_bwrite_el(buf, &info->offset, sizeof(info->offset));
-    }
-
-    // we require the data section to be aligned, so take into account any padding
-    {
-        const size_t offset     = buf->offset;
-        const size_t offset_pad = GGML_PAD(offset, ctx->alignment);
-
-        if (offset_pad != offset) {
-            uint8_t pad = 0;
-            for (size_t i = 0; i < offset_pad - offset; ++i) {
-                gguf_bwrite_el(buf, &pad, sizeof(pad));
-            }
-        }
-    }
-
-    if (only_meta) {
-        return;
-    }
-
-    size_t offset = 0;
-
-    // write tensor data
-    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
-        struct gguf_tensor_info * info = &ctx->infos[i];
-
-        const size_t size     = info->size;
-        const size_t size_pad = GGML_PAD(size, ctx->alignment);
-
-        gguf_bwrite_el(buf, info->data, size);
-
-        if (size_pad != size) {
-            uint8_t pad = 0;
-            for (size_t j = 0; j < size_pad - size; ++j) {
-                gguf_bwrite_el(buf, &pad, sizeof(pad));
-            }
-        }
-
-        GGML_ASSERT(offset == info->offset);
-
-        offset += size_pad;
-    }
-}
-
-void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
-    FILE * file = ggml_fopen(fname, "wb");
-    if (!file) {
-        GGML_ABORT("failed to open file for writing");
-    }
-
-    struct gguf_buf buf = gguf_buf_init(16*1024);
-
-    gguf_write_to_buf(ctx, &buf, only_meta);
-
-    fwrite(buf.data, 1, buf.offset, file);
-
-    gguf_buf_free(buf);
-
-    fclose(file);
-}
-
-size_t gguf_get_meta_size(const struct gguf_context * ctx) {
-    // no allocs - only compute size
-    struct gguf_buf buf = gguf_buf_init(0);
-
-    gguf_write_to_buf(ctx, &buf, true);
-
-    return buf.offset;
-}
-
-void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
-    struct gguf_buf buf = gguf_buf_init(16*1024);
-
-    gguf_write_to_buf(ctx, &buf, true);
-
-    memcpy(data, buf.data, buf.offset);
-
-    gguf_buf_free(buf);
-}
-
 void ggml_log_set(ggml_log_callback log_callback, void * user_data) {
     g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default;
     g_logger_state.log_callback_user_data = user_data;
diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp
new file mode 100644
index 000000000..655ed600a
--- /dev/null
+++ b/ggml/src/gguf.cpp
@@ -0,0 +1,1325 @@
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "ggml-impl.h"
+#include "gguf.h"
+
+#include <cinttypes>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <map>
+#include <new>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+template <typename T>
+struct type_to_gguf_type;
+
+template <>
+struct type_to_gguf_type<uint8_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_UINT8;
+};
+
+template <>
+struct type_to_gguf_type<int8_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_INT8;
+};
+
+template <>
+struct type_to_gguf_type<uint16_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_UINT16;
+};
+
+template <>
+struct type_to_gguf_type<int16_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_INT16;
+};
+
+template <>
+struct type_to_gguf_type<uint32_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_UINT32;
+};
+
+template <>
+struct type_to_gguf_type<int32_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_INT32;
+};
+
+template <>
+struct type_to_gguf_type<float> {
+    static constexpr enum gguf_type value = GGUF_TYPE_FLOAT32;
+};
+
+template <>
+struct type_to_gguf_type<bool> {
+    static constexpr enum gguf_type value = GGUF_TYPE_BOOL;
+};
+
+template <>
+struct type_to_gguf_type<std::string> {
+    static constexpr enum gguf_type value = GGUF_TYPE_STRING;
+};
+
+template <>
+struct type_to_gguf_type<uint64_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_UINT64;
+};
+
+template <>
+struct type_to_gguf_type<int64_t> {
+    static constexpr enum gguf_type value = GGUF_TYPE_INT64;
+};
+
+template <>
+struct type_to_gguf_type<double> {
+    static constexpr enum gguf_type value = GGUF_TYPE_FLOAT64;
+};
+
+static const std::map<gguf_type, size_t> GGUF_TYPE_SIZE = {
+    {GGUF_TYPE_UINT8,   sizeof(uint8_t)},
+    {GGUF_TYPE_INT8,    sizeof(int8_t)},
+    {GGUF_TYPE_UINT16,  sizeof(uint16_t)},
+    {GGUF_TYPE_INT16,   sizeof(int16_t)},
+    {GGUF_TYPE_UINT32,  sizeof(uint32_t)},
+    {GGUF_TYPE_INT32,   sizeof(int32_t)},
+    {GGUF_TYPE_FLOAT32, sizeof(float)},
+    {GGUF_TYPE_BOOL,    sizeof(int8_t)},
+    {GGUF_TYPE_STRING,  0}, // undefined
+    {GGUF_TYPE_ARRAY,   0}, // undefined
+    {GGUF_TYPE_UINT64,  sizeof(uint64_t)},
+    {GGUF_TYPE_INT64,   sizeof(int64_t)},
+    {GGUF_TYPE_FLOAT64, sizeof(double)},
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+static const std::map<gguf_type, const char *> GGUF_TYPE_NAME = {
+    {GGUF_TYPE_UINT8,   "u8"},
+    {GGUF_TYPE_INT8,    "i8"},
+    {GGUF_TYPE_UINT16,  "u16"},
+    {GGUF_TYPE_INT16,   "i16"},
+    {GGUF_TYPE_UINT32,  "u32"},
+    {GGUF_TYPE_INT32,   "i32"},
+    {GGUF_TYPE_FLOAT32, "f32"},
+    {GGUF_TYPE_BOOL,    "bool"},
+    {GGUF_TYPE_STRING,  "str"},
+    {GGUF_TYPE_ARRAY,   "arr"},
+    {GGUF_TYPE_UINT64,  "u64"},
+    {GGUF_TYPE_INT64,   "i64"},
+    {GGUF_TYPE_FLOAT64, "f64"},
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+size_t gguf_type_size(enum gguf_type type) {
+    auto it = GGUF_TYPE_SIZE.find(type);
+    return it == GGUF_TYPE_SIZE.end() ? 0 : it->second;
+}
+
+struct gguf_kv {
+    std::string key;
+
+    bool is_array;
+    enum gguf_type type;
+
+    std::vector<int8_t>      data;
+    std::vector<std::string> data_string;
+
+    template <typename T>
+    gguf_kv(const std::string & key, const T value)
+            : key(key), is_array(false), type(type_to_gguf_type<T>::value) {
+        GGML_ASSERT(!key.empty());
+        data.resize(sizeof(T));
+        memcpy(data.data(), &value, sizeof(T));
+    }
+
+    template <typename T>
+    gguf_kv(const std::string & key, const std::vector<T> & value)
+            : key(key), is_array(true), type(type_to_gguf_type<T>::value) {
+        GGML_ASSERT(!key.empty());
+        data.resize(value.size()*sizeof(T));
+        for (size_t i = 0; i < value.size(); ++i) {
+            const T tmp = value[i];
+            memcpy(data.data() + i*sizeof(T), &tmp, sizeof(T));
+        }
+    }
+
+    gguf_kv(const std::string & key, const std::string & value)
+            : key(key), is_array(false), type(GGUF_TYPE_STRING) {
+        GGML_ASSERT(!key.empty());
+        data_string.push_back(value);
+    }
+
+    gguf_kv(const std::string & key, const std::vector<std::string> & value)
+            : key(key), is_array(true), type(GGUF_TYPE_STRING) {
+        GGML_ASSERT(!key.empty());
+        data_string = value;
+    }
+
+    const std::string & get_key() const {
+        return key;
+    }
+
+    const enum gguf_type & get_type() const {
+        return type;
+    }
+
+    size_t get_ne() const {
+        if (type == GGUF_TYPE_STRING) {
+            const size_t ne = data_string.size();
+            GGML_ASSERT(is_array || ne == 1);
+            return ne;
+        }
+        const size_t type_size = gguf_type_size(type);
+        GGML_ASSERT(data.size() % type_size == 0);
+        const size_t ne = data.size() / type_size;
+        GGML_ASSERT(is_array || ne == 1);
+        return ne;
+    }
+
+    template <typename T>
+    const T & get_val(const size_t i = 0) const {
+        GGML_ASSERT(type_to_gguf_type<T>::value == type);
+        if constexpr (std::is_same<T, std::string>::value) {
+            GGML_ASSERT(data_string.size() >= i+1);
+            return data_string[i];
+        }
+        const size_t type_size = gguf_type_size(type);
+        GGML_ASSERT(data.size() % type_size == 0);
+        GGML_ASSERT(data.size() >= (i+1)*type_size);
+        return reinterpret_cast<const T *>(data.data())[i];
+    }
+
+    void cast(const enum gguf_type new_type) {
+        const size_t new_type_size = gguf_type_size(new_type);
+        GGML_ASSERT(data.size() % new_type_size == 0);
+        type = new_type;
+    }
+};
+
+struct gguf_tensor_info {
+    struct ggml_tensor t; // for holding the equivalent info
+    uint64_t offset;      // offset from start of `data`, must be a multiple of `ALIGNMENT`
+};
+
+struct gguf_context {
+    uint32_t version = GGUF_VERSION;
+
+    std::vector<struct gguf_kv> kv;
+    std::vector<struct gguf_tensor_info> info;
+
+    size_t alignment = GGUF_DEFAULT_ALIGNMENT;
+    size_t offset    = 0; // offset of `data` from beginning of file
+    size_t size      = 0; // size of `data` in bytes
+
+    void * data = nullptr;
+};
+
+struct gguf_reader {
+    FILE * file;
+
+    gguf_reader(FILE * file) : file(file) {}
+
+    template <typename T>
+    bool read(T & dst) const {
+        return fread(&dst, 1, sizeof(dst), file) == sizeof(dst);
+    }
+
+    template <typename T>
+    bool read(std::vector<T> & dst, const size_t n) const {
+        dst.resize(n);
+        for (size_t i = 0; i < dst.size(); ++i) {
+            if constexpr (std::is_same<T, bool>::value) {
+                bool tmp;
+                if (!read(tmp)) {
+                    return false;
+                }
+                dst[i] = tmp;
+            } else {
+                if (!read(dst[i])) {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+
+    bool read(bool & dst) const {
+        int8_t tmp = -1;
+        if (!read(tmp)) {
+            return false;
+        }
+        dst = tmp != 0;
+        return true;
+    }
+
+    bool read(enum ggml_type & dst) const {
+        int32_t tmp = -1;
+        if (!read(tmp)) {
+            return false;
+        }
+        dst = ggml_type(tmp);
+        return true;
+    }
+
+    bool read(enum gguf_type & dst) const {
+        int32_t tmp = -1;
+        if (!read(tmp)) {
+            return false;
+        }
+        dst = gguf_type(tmp);
+        return true;
+    }
+
+    bool read(std::string & dst) const {
+        uint64_t size = -1;
+        if (!read(size)) {
+            return false;
+        }
+        dst.resize(size);
+        return fread(dst.data(), 1, dst.length(), file) == dst.length();
+    }
+
+    bool read(void * dst, const size_t size) const {
+        return fread(dst, 1, size, file) == size;
+    }
+};
+
+struct gguf_context * gguf_init_empty(void) {
+    return new gguf_context;
+}
+
+template<typename T>
+bool gguf_read_emplace_helper(const struct gguf_reader & gr, std::vector<struct gguf_kv> & kv, const std::string & key, const bool is_array, const size_t n) {
+    if (is_array) {
+        std::vector<T> value;
+        try {
+            if (!gr.read(value, n)) {
+                return false;
+            }
+        } catch (std::length_error &) {
+            fprintf(stderr, "%s: encountered length_error while reading value for key '%s'\n", __func__, key.c_str());
+            return false;
+        } catch (std::bad_alloc &) {
+            fprintf(stderr, "%s: encountered bad_alloc error while reading value for key '%s'\n", __func__, key.c_str());
+            return false;
+        }
+        kv.emplace_back(key, value);
+    } else {
+        T value;
+        if (!gr.read(value)) {
+            return false;
+        }
+        kv.emplace_back(key, value);
+    }
+    return true;
+}
+
+struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
+    const struct gguf_reader gr(file);
+    struct gguf_context * ctx = new gguf_context;
+
+    bool ok = true;
+
+    // file magic
+    {
+        std::vector<char> magic;
+        ok = ok && gr.read(magic, 4);
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to read magic\n", __func__);
+            gguf_free(ctx);
+            return nullptr;
+        }
+
+        for (uint32_t i = 0; i < magic.size(); i++) {
+            if (magic[i] != GGUF_MAGIC[i]) {
+                fprintf(stderr, "%s: invalid magic characters: '%c%c%c%c', expected 'GGUF'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
+                gguf_free(ctx);
+                return nullptr;
+            }
+        }
+    }
+
+    // header
+    int64_t n_kv      = 0;
+    int64_t n_tensors = 0;
+
+    if (ok && gr.read(ctx->version)) {
+        if (ctx->version == 1) {
+            fprintf(stderr, "%s: GGUFv1 is no longer supported, please use a more up-to-date version\n", __func__);
+            ok = false;
+        }
+        if (ctx->version > GGUF_VERSION) {
+            fprintf(stderr, "%s: this GGUF file is version %" PRIu32 " but this software only supports up to version %d\n",
+                __func__, ctx->version, GGUF_VERSION);
+            ok = false;
+        }
+    } else {
+        ok = false;
+    }
+
+    if (ok && gr.read(n_tensors)) {
+        static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing");
+        if (n_tensors < 0 || n_tensors > int64_t(SIZE_MAX/sizeof(gguf_tensor_info))) {
+            fprintf(stderr, "%s: number of tensors is %" PRIi64 " but must be in [0, %zu]\n",
+                __func__, n_tensors, SIZE_MAX/sizeof(gguf_tensor_info));
+            ok = false;
+        }
+    } else {
+        ok = false;
+    }
+
+    if (ok && gr.read(n_kv)) {
+        static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing");
+        if (n_kv < 0 || n_kv > int64_t(SIZE_MAX/sizeof(gguf_kv))) {
+            fprintf(stderr, "%s: number of key value pairs is %" PRIi64 " but must be in [0, %zu]\n",
+                    __func__, n_kv, SIZE_MAX/sizeof(gguf_kv));
+            ok = false;
+        }
+    } else {
+        ok = false;
+    }
+
+    if (!ok) {
+        fprintf(stderr, "%s: failed to read header\n", __func__);
+        gguf_free(ctx);
+        return nullptr;
+    }
+
+    // KV pairs
+    {
+        for (int64_t i = 0; ok && i < n_kv; ++i) {
+            std::string key;
+            gguf_type   type     = gguf_type(-1);
+            bool        is_array = false;
+            uint64_t    n        = 1;
+
+            try {
+                ok = ok && gr.read(key);
+            } catch (std::length_error &) {
+                fprintf(stderr, "%s: encountered length_error while reading key %" PRIi64 "\n", __func__, i);
+                ok = false;
+            } catch (std::bad_alloc &) {
+                fprintf(stderr, "%s: encountered bad_alloc error while reading key %" PRIi64 "\n", __func__, i);
+                ok = false;
+            }
+            for (size_t j = 0; ok && j < ctx->kv.size(); ++j) {
+                if (key == ctx->kv[j].key) {
+                    fprintf(stderr, "%s: duplicate key '%s' for tensors %zu and %" PRIi64 " \n", __func__, key.c_str(), j, i);
+                    ok = false;
+                }
+            }
+            if (!ok) {
+                break;
+            }
+
+            ok = ok && gr.read(type);
+            if (type == GGUF_TYPE_ARRAY) {
+                is_array = true;
+                ok = ok && gr.read(type);
+                ok = ok && gr.read(n);
+            }
+            if (!ok) {
+                break;
+            }
+
+            switch (type) {
+                case GGUF_TYPE_UINT8:   ok = ok && gguf_read_emplace_helper<uint8_t>    (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_INT8:    ok = ok && gguf_read_emplace_helper<int8_t>     (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_UINT16:  ok = ok && gguf_read_emplace_helper<uint16_t>   (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_INT16:   ok = ok && gguf_read_emplace_helper<int16_t>    (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_UINT32:  ok = ok && gguf_read_emplace_helper<uint32_t>   (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_INT32:   ok = ok && gguf_read_emplace_helper<int32_t>    (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_FLOAT32: ok = ok && gguf_read_emplace_helper<float>      (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_BOOL:    ok = ok && gguf_read_emplace_helper<bool>       (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_STRING:  ok = ok && gguf_read_emplace_helper<std::string>(gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_UINT64:  ok = ok && gguf_read_emplace_helper<uint64_t>   (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_INT64:   ok = ok && gguf_read_emplace_helper<int64_t>    (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_FLOAT64: ok = ok && gguf_read_emplace_helper<double>     (gr, ctx->kv, key, is_array, n); break;
+                case GGUF_TYPE_ARRAY:
+                default:
+                    {
+                        fprintf(stderr, "%s: key '%s' has invalid GGUF type %d\n", __func__, key.c_str(), type);
+                        ok = false;
+                    } break;
+            }
+        }
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
+            gguf_free(ctx);
+            return nullptr;
+        }
+        GGML_ASSERT(int64_t(ctx->kv.size()) == n_kv);
+
+        const int alignment_idx = gguf_find_key(ctx, GGUF_KEY_GENERAL_ALIGNMENT);
+        ctx->alignment = alignment_idx == -1 ? GGUF_DEFAULT_ALIGNMENT : gguf_get_val_u32(ctx, alignment_idx);
+
+        if (ctx->alignment == 0 || (ctx->alignment & (ctx->alignment - 1)) != 0) {
+            fprintf(stderr, "%s: alignment %zu is not a power of 2\n", __func__, ctx->alignment);
+            gguf_free(ctx);
+            return nullptr;
+        }
+    }
+
+    // read the tensor info
+    for (int64_t i = 0; ok && i < n_tensors; ++i) {
+        struct gguf_tensor_info info;
+
+        // tensor name
+        {
+            std::string name;
+            try {
+                ok = ok && gr.read(name);
+            } catch (std::length_error &) {
+                fprintf(stderr, "%s: encountered length_error while reading tensor name %" PRIi64 "\n", __func__, i);
+                ok = false;
+            } catch (std::bad_alloc &) {
+                fprintf(stderr, "%s: encountered bad_alloc error while reading tensor name %" PRIi64 "\n", __func__, i);
+                ok = false;
+            }
+            if (name.length() >= GGML_MAX_NAME) {
+                fprintf(stderr, "%s: tensor name %" PRIi64 " is too long: %zu >= %d\n", __func__, i, name.length(), GGML_MAX_NAME);
+                ok = false;
+                break;
+            }
+            ggml_set_name(&info.t, name.c_str());
+
+            // make sure there are no duplicate tensor names
+            for (int64_t j = 0; ok && j < i; ++j) {
+                if (strcmp(info.t.name, ctx->info[j].t.name) == 0) {
+                    fprintf(stderr, "%s: duplicate tensor name '%s' for tensors %" PRIi64 " and %" PRIi64 "\n", __func__, info.t.name, j, i);
+                    ok = false;
+                    break;
+                }
+            }
+        }
+        if (!ok) {
+            break;
+        }
+
+        // tensor shape
+        {
+            uint32_t n_dims = -1;
+            ok = ok && gr.read(n_dims);
+            if (n_dims > GGML_MAX_DIMS) {
+                fprintf(stderr, "%s: tensor '%s' has invalid number of dimensions: %" PRIu32 " > %" PRIu32 "\n",
+                    __func__, info.t.name, n_dims, GGML_MAX_DIMS);
+                ok = false;
+                break;
+            }
+            for (uint32_t j = 0; ok && j < GGML_MAX_DIMS; ++j) {
+                info.t.ne[j] = 1;
+                if (j < n_dims) {
+                    ok = ok && gr.read(info.t.ne[j]);
+                }
+
+                // check that all ne are non-negative
+                if (info.t.ne[j] < 0) {
+                    fprintf(stderr, "%s: tensor '%s' dimension %" PRIu32 " has invalid number of elements: %" PRIi64 " < 0\n",
+                        __func__, info.t.name, j, info.t.ne[j]);
+                    ok = false;
+                    break;
+                }
+            }
+
+            // check that the total number of elements is representable
+            if (ok && ((INT64_MAX/info.t.ne[1] <= info.t.ne[0]) ||
+                       (INT64_MAX/info.t.ne[2] <= info.t.ne[0]*info.t.ne[1]) ||
+                       (INT64_MAX/info.t.ne[3] <= info.t.ne[0]*info.t.ne[1]*info.t.ne[2]))) {
+
+                fprintf(stderr, "%s: total number of elements in tensor '%s' with shape "
+                    "(%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") is >= %" PRIi64 "\n",
+                    __func__, info.t.name, info.t.ne[0], info.t.ne[1], info.t.ne[2], info.t.ne[3], INT64_MAX);
+                ok = false;
+                break;
+            }
+        }
+        if (!ok) {
+            break;
+        }
+
+        // tensor type
+        {
+            ok = ok && gr.read(info.t.type);
+
+            // check that tensor type is within defined range
+            if (info.t.type < 0 || info.t.type >= GGML_TYPE_COUNT) {
+                fprintf(stderr, "%s: tensor '%s' has invalid ggml type %d (%s)\n",
+                    __func__, info.t.name, info.t.type, ggml_type_name(info.t.type));
+                ok = false;
+                break;
+            }
+            const size_t  type_size = ggml_type_size(info.t.type);
+            const int64_t blck_size = ggml_blck_size(info.t.type);
+
+            // check that row size is divisible by block size
+            if (blck_size == 0 || info.t.ne[0] % blck_size != 0) {
+                fprintf(stderr, "%s: tensor '%s' of type %d (%s) has %" PRId64 " elements per row, "
+                    "not a multiple of block size (%" PRId64 ")\n",
+                    __func__, info.t.name, (int) info.t.type, ggml_type_name(info.t.type), info.t.ne[0], blck_size);
+                ok = false;
+                break;
+            }
+
+            // calculate byte offsets given the tensor shape and type
+            info.t.nb[0] = type_size;
+            info.t.nb[1] = info.t.nb[0]*(info.t.ne[0]/blck_size);
+            for (int j = 2; j < GGML_MAX_DIMS; ++j) {
+                info.t.nb[j] = info.t.nb[j - 1]*info.t.ne[j - 1];
+            }
+        }
+        if (!ok) {
+            break;
+        }
+
+        // tensor data offset within buffer
+        ok = ok && gr.read(info.offset);
+
+        ctx->info.push_back(info);
+    }
+
+    if (!ok) {
+        fprintf(stderr, "%s: failed to read tensor info\n", __func__);
+        gguf_free(ctx);
+        return nullptr;
+    }
+    GGML_ASSERT(int64_t(ctx->info.size()) == n_tensors);
+
+    // we require the data section to be aligned, so take into account any padding
+    if (fseek(file, GGML_PAD(ftell(file), ctx->alignment), SEEK_SET) != 0) {
+        fprintf(stderr, "%s: failed to seek to beginning of data section\n", __func__);
+        gguf_free(ctx);
+        return nullptr;
+    }
+
+    // store the current file offset - this is where the data section starts
+    ctx->offset = ftell(file);
+
+    // compute the total size of the data section, taking into account the alignment
+    {
+        ctx->size = 0;
+        for (size_t i = 0; i < ctx->info.size(); ++i) {
+            const gguf_tensor_info & ti = ctx->info[i];
+            if (ti.offset != ctx->size) {
+                fprintf(stderr, "%s: tensor '%s' has offset %" PRIu64 ", expected %zu\n",
+                    __func__, ti.t.name, ti.offset, ctx->size);
+                fprintf(stderr, "%s: failed to read tensor data\n", __func__);
+                gguf_free(ctx);
+                return nullptr;
+            }
+            ctx->size += GGML_PAD(ggml_nbytes(&ti.t), ctx->alignment);
+        }
+    }
+
+    // load the tensor data only if requested
+    if (params.ctx != nullptr) {
+        // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob
+        // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of
+        //   the ggml_tensor structs to the appropriate locations in the binary blob
+
+        // compute the exact size needed for the new ggml_context
+        const size_t mem_size =
+            params.no_alloc ?
+            (n_tensors    )*ggml_tensor_overhead() :
+            (n_tensors + 1)*ggml_tensor_overhead() + ctx->size;
+
+        struct ggml_init_params pdata = {
+            /*mem_size   =*/ mem_size,
+            /*mem_buffer =*/ nullptr,
+            /*no_alloc   =*/ params.no_alloc,
+        };
+
+        *params.ctx = ggml_init(pdata);
+        if (*params.ctx == nullptr) {
+            fprintf(stderr, "%s: failed to initialize ggml context for storing tensors\n", __func__);
+            gguf_free(ctx);
+            return nullptr;
+        }
+
+        struct ggml_context * ctx_data = *params.ctx;
+
+        struct ggml_tensor * data = nullptr;
+
+        if (!params.no_alloc) {
+            data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
+
+            ok = ok && data != nullptr;
+
+            // read the binary blob with the tensor data
+            ok = ok && gr.read(data->data, ctx->size);
+
+            if (!ok) {
+                fprintf(stderr, "%s: failed to read tensor data binary blob\n", __func__);
+                ggml_free(ctx_data);
+                *params.ctx = nullptr;
+                gguf_free(ctx);
+                return nullptr;
+            }
+
+            ctx->data = data->data;
+        }
+
+        ggml_set_no_alloc(ctx_data, true);
+
+        // create the tensors
+        for (size_t i = 0; i < ctx->info.size(); ++i) {
+            const struct gguf_tensor_info & info = ctx->info[i];
+
+            struct ggml_tensor * cur = ggml_new_tensor(ctx_data, info.t.type, GGML_MAX_DIMS, info.t.ne);
+
+            ok = ok && cur != nullptr;
+
+            if (!ok) {
+                break;
+            }
+
+            ggml_set_name(cur, info.t.name);
+
+            // point the data member to the appropriate location in the binary blob using the tensor info
+            if (!params.no_alloc) {
+                cur->data = (char *) data->data + info.offset;
+            }
+        }
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to create tensors\n", __func__);
+            ggml_free(ctx_data);
+            *params.ctx = nullptr;
+            gguf_free(ctx);
+            return nullptr;
+        }
+
+        ggml_set_no_alloc(ctx_data, params.no_alloc);
+    }
+
+    return ctx;
+}
+
+struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
+    FILE * file = ggml_fopen(fname, "rb");
+
+    if (!file) {
+        fprintf(stderr, "%s: failed to open GGUF file '%s'\n", __func__, fname);
+        return nullptr;
+    }
+
+    struct gguf_context * result = gguf_init_from_file_impl(file, params);
+    fclose(file);
+    return result;
+}
+
+void gguf_free(struct gguf_context * ctx) {
+    if (ctx == nullptr) {
+        return;
+    }
+    delete ctx;
+}
+
+const char * gguf_type_name(enum gguf_type type) {
+    auto it = GGUF_TYPE_NAME.find(type);
+    return it == GGUF_TYPE_NAME.end() ? nullptr : it->second;
+}
+
+uint32_t gguf_get_version(const struct gguf_context * ctx) {
+    return ctx->version;
+}
+
+size_t gguf_get_alignment(const struct gguf_context * ctx) {
+    return ctx->alignment;
+}
+
+size_t gguf_get_data_offset(const struct gguf_context * ctx) {
+    return ctx->offset;
+}
+
+int64_t gguf_get_n_kv(const struct gguf_context * ctx) {
+    return ctx->kv.size();
+}
+
+int64_t gguf_find_key(const struct gguf_context * ctx, const char * key) {
+    // return -1 if key not found
+    int64_t keyfound = -1;
+
+    const int64_t n_kv = gguf_get_n_kv(ctx);
+
+    for (int64_t i = 0; i < n_kv; ++i) {
+        if (strcmp(key, gguf_get_key(ctx, i)) == 0) {
+            keyfound = i;
+            break;
+        }
+    }
+
+    return keyfound;
+}
+
+const char * gguf_get_key(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    return ctx->kv[key_id].get_key().c_str();
+}
+
+enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    return ctx->kv[key_id].is_array ? GGUF_TYPE_ARRAY : ctx->kv[key_id].get_type();
+}
+
+enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].is_array);
+    return ctx->kv[key_id].get_type();
+}
+
+const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+    return ctx->kv[key_id].data.data();
+}
+
+const char * gguf_get_arr_str(const struct gguf_context * ctx, int64_t key_id, size_t i) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_type() == GGUF_TYPE_STRING);
+    return ctx->kv[key_id].data_string[i].c_str();
+}
+
+size_t gguf_get_arr_n(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+
+    if (ctx->kv[key_id].type == GGUF_TYPE_STRING) {
+        return ctx->kv[key_id].data_string.size();
+    }
+
+    const size_t type_size = gguf_type_size(ctx->kv[key_id].type);
+    GGML_ASSERT(ctx->kv[key_id].data.size() % type_size == 0);
+    return ctx->kv[key_id].data.size() / type_size;
+}
+
+uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<uint8_t>();
+}
+
+int8_t gguf_get_val_i8(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<int8_t>();
+}
+
+uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<uint16_t>();
+}
+
+int16_t gguf_get_val_i16(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<int16_t>();
+}
+
+uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<uint32_t>();
+}
+
+int32_t gguf_get_val_i32(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<int32_t>();
+}
+
+float gguf_get_val_f32(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<float>();
+}
+
+uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<uint64_t>();
+}
+
+int64_t gguf_get_val_i64(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<int64_t>();
+}
+
+double gguf_get_val_f64(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<double>();
+}
+
+bool gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<bool>();
+}
+
+const char * gguf_get_val_str(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    return ctx->kv[key_id].get_val<std::string>().c_str();
+}
+
+const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+    return ctx->kv[key_id].data.data();
+}
+
+int64_t gguf_get_n_tensors(const struct gguf_context * ctx) {
+    return ctx->info.size();
+}
+
+int64_t gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
+    // return -1 if tensor not found
+    int64_t tensor_id = -1;
+
+    const int64_t n_tensors = gguf_get_n_tensors(ctx);
+
+    for (int64_t i = 0; i < n_tensors; ++i) {
+        if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) {
+            tensor_id = i;
+            break;
+        }
+    }
+
+    return tensor_id;
+}
+
+size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id) {
+    GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+    return ctx->info[tensor_id].offset;
+}
+
+const char * gguf_get_tensor_name(const struct gguf_context * ctx, int64_t tensor_id) {
+    GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+    return ctx->info[tensor_id].t.name;
+}
+
+enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int64_t tensor_id) {
+    GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+    return ctx->info[tensor_id].t.type;
+}
+
+size_t gguf_get_tensor_size(const struct gguf_context * ctx, int64_t tensor_id) {
+    GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx));
+    return ggml_nbytes(&ctx->info[tensor_id].t);
+}
+
+int64_t gguf_remove_key(struct gguf_context * ctx, const char * key) {
+    const int64_t key_id = gguf_find_key(ctx, key);
+    if (key_id >= 0) {
+        ctx->kv.erase(ctx->kv.begin() + key_id);
+    }
+    return key_id;
+}
+
+template<typename T>
+static void gguf_check_reserved_keys(const std::string & key, const T val) {
+    if (key == GGUF_KEY_GENERAL_ALIGNMENT) {
+        if constexpr (std::is_same<T, uint32_t>::value) {
+            GGML_ASSERT(val > 0 && (val & (val - 1)) == 0 && GGUF_KEY_GENERAL_ALIGNMENT " must be power of 2");
+        } else {
+            GGML_ABORT(GGUF_KEY_GENERAL_ALIGNMENT " must be type u32");
+        }
+    }
+}
+
+void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, val);
+}
+
+void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) {
+    gguf_check_reserved_keys(key, val);
+    gguf_remove_key(ctx, key);
+    ctx->kv.emplace_back(key, std::string(val));
+}
+
+void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n) {
+    gguf_check_reserved_keys(key, data);
+    gguf_remove_key(ctx, key);
+
+    const size_t nbytes = n*gguf_type_size(type);
+    std::vector<int8_t> tmp(nbytes);
+    if (!tmp.empty()) {
+        memcpy(tmp.data(), data, nbytes);
+    }
+    ctx->kv.emplace_back(key, tmp);
+    ctx->kv.back().cast(type);
+}
+
+void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, size_t n) {
+    gguf_check_reserved_keys(key, data);
+    gguf_remove_key(ctx, key);
+
+    std::vector<std::string> tmp(n);
+    for (size_t i = 0; i < n; ++i) {
+        tmp[i] = data[i];
+    }
+    ctx->kv.emplace_back(key, tmp);
+}
+
+// set or add KV pairs from another context
+void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src) {
+    const int64_t n_kv = gguf_get_n_kv(src);
+    for (int64_t i = 0; i < n_kv; ++i) {
+        const struct gguf_kv & kv = src->kv[i];
+
+        if (!kv.is_array) {
+            switch (kv.get_type()) {
+                case GGUF_TYPE_UINT8:   gguf_set_val_u8  (ctx, kv.get_key().c_str(), kv.get_val<uint8_t>());             break;
+                case GGUF_TYPE_INT8:    gguf_set_val_i8  (ctx, kv.get_key().c_str(), kv.get_val<int8_t>());              break;
+                case GGUF_TYPE_UINT16:  gguf_set_val_u16 (ctx, kv.get_key().c_str(), kv.get_val<uint16_t>());            break;
+                case GGUF_TYPE_INT16:   gguf_set_val_i16 (ctx, kv.get_key().c_str(), kv.get_val<int16_t>());             break;
+                case GGUF_TYPE_UINT32:  gguf_set_val_u32 (ctx, kv.get_key().c_str(), kv.get_val<uint32_t>());            break;
+                case GGUF_TYPE_INT32:   gguf_set_val_i32 (ctx, kv.get_key().c_str(), kv.get_val<int32_t>());             break;
+                case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, kv.get_key().c_str(), kv.get_val<float>());               break;
+                case GGUF_TYPE_UINT64:  gguf_set_val_u64 (ctx, kv.get_key().c_str(), kv.get_val<uint64_t>());            break;
+                case GGUF_TYPE_INT64:   gguf_set_val_i64 (ctx, kv.get_key().c_str(), kv.get_val<int64_t>());             break;
+                case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, kv.get_key().c_str(), kv.get_val<double>());              break;
+                case GGUF_TYPE_BOOL:    gguf_set_val_bool(ctx, kv.get_key().c_str(), kv.get_val<bool>());                break;
+                case GGUF_TYPE_STRING:  gguf_set_val_str (ctx, kv.get_key().c_str(), kv.get_val<std::string>().c_str()); break;
+                case GGUF_TYPE_ARRAY:
+                default: GGML_ABORT("invalid type");
+            }
+            continue;
+        }
+
+        const size_t ne = kv.get_ne();
+
+        switch (kv.get_type()) {
+            case GGUF_TYPE_UINT8:
+            case GGUF_TYPE_INT8:
+            case GGUF_TYPE_UINT16:
+            case GGUF_TYPE_INT16:
+            case GGUF_TYPE_UINT32:
+            case GGUF_TYPE_INT32:
+            case GGUF_TYPE_FLOAT32:
+            case GGUF_TYPE_UINT64:
+            case GGUF_TYPE_INT64:
+            case GGUF_TYPE_FLOAT64:
+            case GGUF_TYPE_BOOL: {
+                gguf_set_arr_data(ctx, kv.get_key().c_str(), kv.get_type(), kv.data.data(), ne);
+            } break;
+            case GGUF_TYPE_STRING: {
+                std::vector<const char *> tmp(ne);
+                for (size_t j = 0; j < ne; ++j) {
+                    tmp[j] = kv.data_string[j].c_str();
+                }
+                gguf_set_arr_str(ctx, kv.get_key().c_str(), tmp.data(), ne);
+            } break;
+            case GGUF_TYPE_ARRAY:
+            default: GGML_ABORT("invalid type");
+        }
+    }
+}
+
+void gguf_add_tensor(
+             struct gguf_context * ctx,
+        const struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
+    if (gguf_find_tensor(ctx, tensor->name) != -1) {
+        GGML_ABORT("duplicate tensor name: %s", tensor->name);
+    }
+
+    struct gguf_tensor_info ti;
+    ti.t = *tensor;
+    ti.offset = ctx->info.empty() ? 0 :
+        ctx->info.back().offset + GGML_PAD(ggml_nbytes(&ctx->info.back().t), ctx->alignment);
+    ctx->info.push_back(ti);
+}
+
+void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
+    const int64_t tensor_id = gguf_find_tensor(ctx, name);
+    if (tensor_id < 0) {
+        GGML_ABORT("tensor not found: %s", name);
+    }
+    struct ggml_tensor * tensor = &ctx->info[tensor_id].t;
+    const size_t  type_size = ggml_type_size(type);
+    const int64_t blck_size = ggml_blck_size(type);
+
+    tensor->type = type;
+    GGML_ASSERT(tensor->ne[0] % blck_size == 0 && "tensor row size not divisible by block size of new type");
+
+    tensor->nb[0] = type_size;
+    tensor->nb[1] = tensor->nb[0]*(tensor->ne[0]/blck_size);
+    for (int i = 2; i < GGML_MAX_DIMS; i++) {
+        tensor->nb[i] = tensor->nb[i - 1]*tensor->ne[i - 1];
+    }
+
+    // update offsets
+    const int64_t n_tensors = gguf_get_n_tensors(ctx);
+    for (int64_t i = tensor_id + 1; i < n_tensors; ++i) {
+        ctx->info[i].offset = ctx->info[i - 1].offset + GGML_PAD(ggml_nbytes(&ctx->info[i - 1].t), ctx->alignment);
+    }
+}
+
+void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data) {
+    const int64_t tensor_id = gguf_find_tensor(ctx, name);
+    if (tensor_id < 0) {
+        GGML_ABORT("tensor not found: %s", name);
+    }
+
+    ctx->info[tensor_id].t.data = (void *)(uintptr_t)data; // double cast suppresses warning about casting away const
+}
+
+struct gguf_writer {
+    std::vector<int8_t> & buf;
+
+    gguf_writer(std::vector<int8_t> & buf) : buf(buf) {}
+
+    template <typename T>
+    void write(const T & val) const {
+        for (size_t i = 0; i < sizeof(val); ++i) {
+            buf.push_back(reinterpret_cast<const int8_t *>(&val)[i]);
+        }
+    }
+
+    void write(const std::vector<int8_t> & val) const {
+        buf.insert(buf.end(), val.begin(), val.end());
+    }
+
+    void write(const bool & val) const {
+        const int8_t val8 = val ? 1 : 0;
+        write(val8);
+    }
+
+    void write(const std::string & val) const {
+        {
+            const uint64_t n = val.length();
+            write(n);
+        }
+        for (size_t i = 0; i < val.length(); ++i) {
+            buf.push_back(reinterpret_cast<const int8_t *>(val.data())[i]);
+        }
+    }
+
+    void write(const char * val) const {
+        write(std::string(val));
+    }
+
+    void write(const enum ggml_type & val) const {
+        write(int32_t(val));
+    }
+
+    void write(const enum gguf_type & val) const {
+        write(int32_t(val));
+    }
+
+    void write(const struct gguf_kv & kv) const {
+        const uint64_t ne = kv.get_ne();
+
+        write(kv.get_key());
+
+        if (kv.is_array) {
+            write(GGUF_TYPE_ARRAY);
+            write(kv.get_type());
+            write(ne);
+        } else {
+            write(kv.get_type());
+        }
+
+        switch (kv.get_type()) {
+            case GGUF_TYPE_UINT8:
+            case GGUF_TYPE_INT8:
+            case GGUF_TYPE_UINT16:
+            case GGUF_TYPE_INT16:
+            case GGUF_TYPE_UINT32:
+            case GGUF_TYPE_INT32:
+            case GGUF_TYPE_FLOAT32:
+            case GGUF_TYPE_UINT64:
+            case GGUF_TYPE_INT64:
+            case GGUF_TYPE_FLOAT64: {
+                write(kv.data);
+            } break;
+            case GGUF_TYPE_BOOL: {
+                for (size_t i = 0; i < ne; ++i) {
+                    write(kv.get_val<bool>(i));
+                }
+            } break;
+            case GGUF_TYPE_STRING: {
+                for (size_t i = 0; i < ne; ++i) {
+                    write(kv.get_val<std::string>(i));
+                }
+            } break;
+            case GGUF_TYPE_ARRAY:
+            default: GGML_ABORT("invalid type");
+        }
+    }
+
+    void write_tensor_meta(const struct gguf_tensor_info & info) const {
+        write(info.t.name);
+
+        const uint32_t n_dims = ggml_n_dims(&info.t);
+        write(n_dims);
+
+        for (uint32_t j = 0; j < n_dims; ++j) {
+            write(info.t.ne[j]);
+        }
+        write(info.t.type);
+        write(info.offset);
+    }
+
+    void pad(const size_t alignment) const {
+        while (buf.size() % alignment != 0) {
+            const int8_t zero = 0;
+            write(zero);
+        }
+    }
+
+    void write_tensor_data(const struct gguf_tensor_info & info, const size_t offset_data, const size_t alignment) const {
+        GGML_ASSERT(buf.size() - offset_data == info.offset);
+
+        GGML_ASSERT(ggml_is_contiguous(&info.t));
+        const size_t offset = buf.size();
+        const size_t nbytes = ggml_nbytes(&info.t);
+
+        buf.resize(offset + nbytes);
+        if (info.t.buffer) {
+            ggml_backend_tensor_get(&info.t, buf.data() + offset, 0, nbytes);
+        } else {
+            GGML_ASSERT(info.t.data);
+            memcpy(buf.data() + offset, info.t.data, nbytes);
+        }
+
+        pad(alignment);
+    }
+};
+
+void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta) {
+    const struct gguf_writer gw(buf);
+
+    const int64_t n_kv      = gguf_get_n_kv(ctx);
+    const int64_t n_tensors = gguf_get_n_tensors(ctx);
+
+    // write header
+    gw.write(GGUF_MAGIC[0]);
+    gw.write(GGUF_MAGIC[1]);
+    gw.write(GGUF_MAGIC[2]);
+    gw.write(GGUF_MAGIC[3]);
+    gw.write(ctx->version);
+    gw.write(n_tensors);
+    gw.write(n_kv);
+
+    // write key-value pairs
+    for (int64_t i = 0; i < n_kv; ++i) {
+        gw.write(ctx->kv[i]);
+    }
+
+    // write tensor info
+    for (int64_t i = 0; i < n_tensors; ++i) {
+        gw.write_tensor_meta(ctx->info[i]);
+    }
+
+    // we require the data section to be aligned
+    gw.pad(ctx->alignment);
+
+    if (only_meta) {
+        return;
+    }
+
+    const size_t offset_data = gw.buf.size();
+
+    // write tensor data
+    for (int64_t i = 0; i < n_tensors; ++i) {
+        gw.write_tensor_data(ctx->info[i], offset_data, ctx->alignment);
+    }
+}
+
+bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
+    FILE * file = ggml_fopen(fname, "wb");
+
+    if (!file) {
+        fprintf(stderr, "%s: failed to open file '%s' for writing GGUF data\n", __func__, fname);
+        return false;
+    }
+
+    std::vector<int8_t> buf;
+    gguf_write_to_buf(ctx, buf, only_meta);
+    const bool ok = fwrite(buf.data(), 1, buf.size(), file) == buf.size();
+    fclose(file);
+    return ok;
+}
+
+size_t gguf_get_meta_size(const struct gguf_context * ctx) {
+    // only return size
+    std::vector<int8_t> buf;
+    gguf_write_to_buf(ctx, buf, /*only_meta =*/ true);
+    return buf.size();
+}
+
+void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
+    std::vector<int8_t> buf;
+    gguf_write_to_buf(ctx, buf, /*only_meta =*/ true);
+    memcpy(data, buf.data(), buf.size());
+}
diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp
index a05ba4f63..6ec709dd3 100644
--- a/src/llama-impl.cpp
+++ b/src/llama-impl.cpp
@@ -1,5 +1,6 @@
 #include "llama-impl.h"
 
+#include "gguf.h"
 #include "llama.h"
 
 #include <cinttypes>
@@ -138,7 +139,7 @@ std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
             {
                 const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
                 int arr_n = gguf_get_arr_n(ctx_gguf, i);
-                const void * data = gguf_get_arr_data(ctx_gguf, i);
+                const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i);
                 std::stringstream ss;
                 ss << "[";
                 for (int j = 0; j < arr_n; j++) {
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 7743b4652..1c4e30878 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -18,7 +18,7 @@ const char * llama_file_version_name(llama_fver version) {
 }
 
 namespace GGUFMeta {
-    template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
+    template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int64_t)>
     struct GKV_Base_Type {
         static constexpr gguf_type gt = gt_;
 
@@ -60,10 +60,11 @@ namespace GGUFMeta {
         public:
         static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
         static ArrayInfo getter(const gguf_context *ctx, const int k) {
+            const enum gguf_type arr_type = gguf_get_arr_type(ctx, k);
             return ArrayInfo {
-                gguf_get_arr_type(ctx, k),
+                arr_type,
                 size_t(gguf_get_arr_n(ctx, k)),
-                gguf_get_arr_data(ctx, k),
+                arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx, k),
             };
         }
     };
@@ -553,7 +554,7 @@ llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap,
             const enum gguf_type type   = gguf_get_kv_type(meta.get(), i);
             const std::string type_name =
                 type == GGUF_TYPE_ARRAY
-                ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i))
+                ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i))
                 : gguf_type_name(type);
 
             std::string value          = gguf_kv_to_str(meta.get(), i);
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 104f90343..038cf58dd 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -875,7 +875,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
         // update the gguf meta data as we go
         gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
-        gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data, new_size);
+        GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), name.c_str())) == new_size);
+        gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data);
 
         // write tensor data + padding
         fout.write((const char *) new_data, new_size);
diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp
index 1bb5fb47c..611957ac0 100644
--- a/tests/test-gguf.cpp
+++ b/tests/test-gguf.cpp
@@ -15,66 +15,71 @@ constexpr int offset_has_tensors = 2000;
 constexpr int offset_has_data    = 3000;
 
 enum handcrafted_file_type {
-    HANDCRAFTED_HEADER_BAD_MAGIC          =  10,
-    HANDCRAFTED_HEADER_BAD_VERSION_1      =  20,
-    HANDCRAFTED_HEADER_BAD_VERSION_FUTURE =  30,
-    HANDCRAFTED_HEADER_BAD_N_TENSORS      =  40,
-    HANDCRAFTED_HEADER_BAD_N_KV           =  50,
-    HANDCRAFTED_HEADER_EMPTY              = 800,
+    HANDCRAFTED_HEADER_BAD_MAGIC           =  10,
+    HANDCRAFTED_HEADER_BAD_VERSION_1       =  20,
+    HANDCRAFTED_HEADER_BAD_VERSION_FUTURE  =  30,
+    HANDCRAFTED_HEADER_BAD_N_TENSORS       =  40,
+    HANDCRAFTED_HEADER_BAD_N_KV            =  50,
+    HANDCRAFTED_HEADER_EMPTY               = 800,
 
-    HANDCRAFTED_KV_BAD_KEY_SIZE           =  10 + offset_has_kv,
-    HANDCRAFTED_KV_BAD_TYPE               =  20 + offset_has_kv,
-    HANDCRAFTED_KV_BAD_VALUE_SIZE         =  30 + offset_has_kv,
-    HANDCRAFTED_KV_DUPLICATE_KEY          =  40 + offset_has_kv,
-    HANDCRAFTED_KV_SUCCESS                = 800 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_KEY_SIZE            =  10 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_TYPE                =  20 + offset_has_kv,
+    // HANDCRAFTED_KV_BAD_VALUE_SIZE          =  30 + offset_has_kv, // removed because it can result in allocations > 1 TB (default sanitizer limit)
+    HANDCRAFTED_KV_DUPLICATE_KEY           =  40 + offset_has_kv,
+    HANDCRAFTED_KV_BAD_ALIGN               =  50 + offset_has_kv,
+    HANDCRAFTED_KV_SUCCESS                 = 800 + offset_has_kv,
 
-    HANDCRAFTED_TENSORS_BAD_NAME_SIZE     =  10 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_BAD_N_DIMS        =  20 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_BAD_SHAPE         =  30 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_NE_TOO_BIG        =  40 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_BAD_TYPE          =  50 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_BAD_OFFSET        =  60 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_DUPLICATE_NAME    =  70 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_BAD_ALIGNMENT     =  80 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_SUCCESS           = 800 + offset_has_tensors,
-    HANDCRAFTED_TENSORS_CUSTOM_ALIGN      = 810 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_NAME_SIZE      =  10 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_N_DIMS         =  20 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_SHAPE          =  30 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_NE_TOO_BIG         =  40 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_TYPE           =  50 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_OFFSET         =  60 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_DUPLICATE_NAME     =  70 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_BAD_ALIGN          =  75 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN =  80 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_SUCCESS            = 800 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_CUSTOM_ALIGN       = 810 + offset_has_tensors,
 
-    HANDCRAFTED_DATA_NOT_ENOUGH_DATA      =  10 + offset_has_data,
-    HANDCRAFTED_DATA_BAD_ALIGNMENT        =  20 + offset_has_data,
-    HANDCRAFTED_DATA_SUCCESS              = 800 + offset_has_data,
-    HANDCRAFTED_DATA_CUSTOM_ALIGN         = 810 + offset_has_data,
+    HANDCRAFTED_DATA_NOT_ENOUGH_DATA       =  10 + offset_has_data,
+    HANDCRAFTED_DATA_BAD_ALIGN             =  15 + offset_has_data,
+    HANDCRAFTED_DATA_INCONSISTENT_ALIGN    =  20 + offset_has_data,
+    HANDCRAFTED_DATA_SUCCESS               = 800 + offset_has_data,
+    HANDCRAFTED_DATA_CUSTOM_ALIGN          = 810 + offset_has_data,
 };
 
 std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
     switch (hft) {
-        case HANDCRAFTED_HEADER_BAD_MAGIC:          return "HEADER_BAD_MAGIC";
-        case HANDCRAFTED_HEADER_BAD_VERSION_1:      return "HEADER_BAD_VERSION_1";
-        case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE: return "HEADER_BAD_VERSION_FUTURE";
-        case HANDCRAFTED_HEADER_BAD_N_KV:           return "HEADER_BAD_N_KV";
-        case HANDCRAFTED_HEADER_BAD_N_TENSORS:      return "HEADER_BAD_N_TENSORS";
-        case HANDCRAFTED_HEADER_EMPTY:              return "HEADER_EMPTY";
+        case HANDCRAFTED_HEADER_BAD_MAGIC:           return "HEADER_BAD_MAGIC";
+        case HANDCRAFTED_HEADER_BAD_VERSION_1:       return "HEADER_BAD_VERSION_1";
+        case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE:  return "HEADER_BAD_VERSION_FUTURE";
+        case HANDCRAFTED_HEADER_BAD_N_KV:            return "HEADER_BAD_N_KV";
+        case HANDCRAFTED_HEADER_BAD_N_TENSORS:       return "HEADER_BAD_N_TENSORS";
+        case HANDCRAFTED_HEADER_EMPTY:               return "HEADER_EMPTY";
 
-        case HANDCRAFTED_KV_BAD_KEY_SIZE:           return "KV_BAD_KEY_SIZE";
-        case HANDCRAFTED_KV_BAD_TYPE:               return "KV_BAD_TYPE";
-        case HANDCRAFTED_KV_BAD_VALUE_SIZE:         return "KV_BAD_VALUE_SIZE";
-        case HANDCRAFTED_KV_DUPLICATE_KEY:          return "KV_DUPLICATE_KEY";
-        case HANDCRAFTED_KV_SUCCESS:                return "KV_RANDOM_KV";
+        case HANDCRAFTED_KV_BAD_KEY_SIZE:            return "KV_BAD_KEY_SIZE";
+        case HANDCRAFTED_KV_BAD_TYPE:                return "KV_BAD_TYPE";
+        case HANDCRAFTED_KV_DUPLICATE_KEY:           return "KV_DUPLICATE_KEY";
+        case HANDCRAFTED_KV_BAD_ALIGN:               return "KV_BAD_ALIGN";
+        case HANDCRAFTED_KV_SUCCESS:                 return "KV_RANDOM_KV";
 
-        case HANDCRAFTED_TENSORS_BAD_NAME_SIZE:     return "TENSORS_BAD_NAME_SIZE";
-        case HANDCRAFTED_TENSORS_BAD_N_DIMS:        return "TENSORS_BAD_N_DIMS";
-        case HANDCRAFTED_TENSORS_BAD_SHAPE:         return "TENSORS_BAD_SHAPE";
-        case HANDCRAFTED_TENSORS_NE_TOO_BIG:        return "TENSORS_NE_TOO_BIG";
-        case HANDCRAFTED_TENSORS_BAD_TYPE:          return "TENSORS_BAD_TYPE";
-        case HANDCRAFTED_TENSORS_BAD_OFFSET:        return "TENSORS_BAD_OFFSET";
-        case HANDCRAFTED_TENSORS_DUPLICATE_NAME:    return "TENSORS_DUPLICATE_NAME";
-        case HANDCRAFTED_TENSORS_BAD_ALIGNMENT:     return "TENSORS_BAD_ALIGNMENT";
-        case HANDCRAFTED_TENSORS_SUCCESS:           return "TENSORS_SUCCESS";
-        case HANDCRAFTED_TENSORS_CUSTOM_ALIGN:      return "TENSORS_CUSTOM_ALIGN";
+        case HANDCRAFTED_TENSORS_BAD_NAME_SIZE:      return "TENSORS_BAD_NAME_SIZE";
+        case HANDCRAFTED_TENSORS_BAD_N_DIMS:         return "TENSORS_BAD_N_DIMS";
+        case HANDCRAFTED_TENSORS_BAD_SHAPE:          return "TENSORS_BAD_SHAPE";
+        case HANDCRAFTED_TENSORS_NE_TOO_BIG:         return "TENSORS_NE_TOO_BIG";
+        case HANDCRAFTED_TENSORS_BAD_TYPE:           return "TENSORS_BAD_TYPE";
+        case HANDCRAFTED_TENSORS_BAD_OFFSET:         return "TENSORS_BAD_OFFSET";
+        case HANDCRAFTED_TENSORS_DUPLICATE_NAME:     return "TENSORS_DUPLICATE_NAME";
+        case HANDCRAFTED_TENSORS_BAD_ALIGN:          return "TENSORS_BAD_ALIGN";
+        case HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN: return "TENSORS_INCONSISTENT_ALIGN";
+        case HANDCRAFTED_TENSORS_SUCCESS:            return "TENSORS_SUCCESS";
+        case HANDCRAFTED_TENSORS_CUSTOM_ALIGN:       return "TENSORS_CUSTOM_ALIGN";
 
-        case HANDCRAFTED_DATA_NOT_ENOUGH_DATA:      return "DATA_NOT_ENOUGH_DATA";
-        case HANDCRAFTED_DATA_BAD_ALIGNMENT:        return "DATA_BAD_ALIGNMENT";
-        case HANDCRAFTED_DATA_SUCCESS:              return "DATA_SUCCESS";
-        case HANDCRAFTED_DATA_CUSTOM_ALIGN:         return "DATA_CUSTOM_ALIGN";
+        case HANDCRAFTED_DATA_NOT_ENOUGH_DATA:       return "DATA_NOT_ENOUGH_DATA";
+        case HANDCRAFTED_DATA_BAD_ALIGN:             return "DATA_BAD_ALIGN";
+        case HANDCRAFTED_DATA_INCONSISTENT_ALIGN:    return "DATA_INCONSISTENT_ALIGN";
+        case HANDCRAFTED_DATA_SUCCESS:               return "DATA_SUCCESS";
+        case HANDCRAFTED_DATA_CUSTOM_ALIGN:          return "DATA_CUSTOM_ALIGN";
     }
     GGML_ABORT("fatal error");
 }
@@ -140,31 +145,41 @@ std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937
     return kv_types;
 }
 
-static void helper_write(const void * data, const size_t nbytes, FILE * file) {
+template <typename T>
+static void helper_write(FILE * file, const T & val) {
+    GGML_ASSERT(fwrite(&val, 1, sizeof(val), file) == sizeof(val));
+}
+
+static void helper_write(FILE * file, const void * data, const size_t nbytes) {
     GGML_ASSERT(fwrite(data, 1, nbytes, file) == nbytes);
 }
 
 static FILE * get_handcrafted_file(const unsigned int seed, const enum handcrafted_file_type hft, const int extra_bytes = 0) {
     FILE * file = tmpfile();
 
+    if (!file) {
+        return file;
+    }
+
     std::mt19937 rng(seed);
+    uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
 
     if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) {
         const char bad_magic[4] = {'F', 'U', 'G', 'G'};
-        helper_write(bad_magic, sizeof(bad_magic), file);
+        helper_write(file, bad_magic, sizeof(bad_magic));
     } else {
-        helper_write(GGUF_MAGIC, 4, file);
+        helper_write(file, GGUF_MAGIC, 4);
     }
 
     if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) {
         const uint32_t version = 1;
-        helper_write(&version, sizeof(version), file);
+        helper_write(file, version);
     } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) {
         const uint32_t version = GGUF_VERSION + 1;
-        helper_write(&version, sizeof(version), file);
+        helper_write(file, version);
     } else {
         const uint32_t version = GGUF_VERSION;
-        helper_write(&version, sizeof(version), file);
+        helper_write(file, version);
     }
 
     std::vector<tensor_config_t> tensor_configs;
@@ -174,10 +189,10 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
 
     if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) {
         const uint64_t n_tensors = -1;
-        helper_write(&n_tensors, sizeof(n_tensors), file);
+        helper_write(file, n_tensors);
     } else {
         const uint64_t n_tensors = tensor_configs.size();
-        helper_write(&n_tensors, sizeof(n_tensors), file);
+        helper_write(file, n_tensors);
     }
 
     std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
@@ -186,41 +201,49 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
     }
     {
         uint64_t n_kv = kv_types.size();
-        if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+        if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
+            hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+            hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+
             n_kv += 1;
         } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
             n_kv = -1;
         }
-        helper_write(&n_kv, sizeof(n_kv), file);
+        helper_write(file, n_kv);
     }
 
     if (hft < offset_has_kv) {
+        while (ftell(file) % alignment != 0) {
+            const char pad = 0;
+            helper_write(file, pad);
+        }
+
         for (int i = 0; i < extra_bytes; ++i) {
             const char tmp = 0;
-            helper_write(&tmp, sizeof(tmp), file);
+            helper_write(file, tmp);
         }
         rewind(file);
         return file;
     }
 
     for (int i = 0; i < int(kv_types.size()); ++i) {
-        const enum gguf_type type     = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].first);
-        const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].second);
+        const enum gguf_type type     = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].first);
+        const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? GGUF_TYPE_COUNT : kv_types[i].second);
 
         const std::string key = "my_key_" + std::to_string((hft == HANDCRAFTED_KV_DUPLICATE_KEY ? i/2 : i));
 
         if (hft == HANDCRAFTED_KV_BAD_KEY_SIZE) {
             const uint64_t n = -1;
-            helper_write(&n, sizeof(n), file);
+            helper_write(file, n);
         } else {
             const uint64_t n = key.length();
-            helper_write(&n, sizeof(n), file);
+            helper_write(file, n);
         }
-        helper_write(key.data(), key.length(), file);
+        helper_write(file, key.data(), key.length());
 
         {
             const int32_t type32 = int32_t(type);
-            helper_write(&type32, sizeof(type32), file);
+            helper_write(file, type32);
         }
 
         uint32_t data[16];
@@ -233,69 +256,67 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
 
         if (type == GGUF_TYPE_STRING) {
             const uint64_t n = rng() % sizeof(data);
-            helper_write(&n,   sizeof(n), file);
-            helper_write(data,        n,  file);
+            helper_write(file, n);
+            helper_write(file, data, n);
             continue;
         }
 
         if (type == GGUF_TYPE_ARRAY) {
             {
                 const int32_t type32 = int32_t(type_arr);
-                helper_write(&type32, sizeof(type32), file);
+                helper_write(file, type32);
             }
             if (type_arr == GGUF_TYPE_STRING) {
                 const uint64_t nstr = rng() % (16 + 1);
-                helper_write(&nstr, sizeof(nstr), file);
+                helper_write(file, nstr);
                 for (uint64_t istr = 0; istr < nstr; ++istr) {
                     const uint64_t n = rng() % (sizeof(uint32_t) + 1);
-                    helper_write(&n,          sizeof(n), file);
-                    helper_write(&data[istr],        n,  file);
+                    helper_write(file, n);
+                    helper_write(file, &data[istr], n);
                 }
                 continue;
             }
             const size_t type_size = gguf_type_size(type_arr);
             const uint64_t n = (rng() % sizeof(data)) / type_size;
-            helper_write(&n,    sizeof(n),   file);
-            helper_write(&data, n*type_size, file);
+            helper_write(file, n);
+            helper_write(file, &data, n*type_size);
             continue;
         }
 
-        size_t type_size = hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type);
-        if (hft == HANDCRAFTED_KV_BAD_VALUE_SIZE) {
-            type_size += rng() % 3;
-        }
-        helper_write(data, type_size, file);
+        helper_write(file, data, hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type));
     }
 
-    if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
-        const std::string key = "general.alignment";
-        {
-            const uint64_t n = key.length();
-            helper_write(&n, sizeof(n), file);
-        }
-        helper_write(key.data(), key.length(), file);
+    if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
+        hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+        hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+
+        const uint64_t n = strlen(GGUF_KEY_GENERAL_ALIGNMENT);
+        helper_write(file, n);
+        helper_write(file, GGUF_KEY_GENERAL_ALIGNMENT, n);
 
         const int32_t type = gguf_type(GGUF_TYPE_UINT32);
-        helper_write(&type, sizeof(type), file);
+        helper_write(file, type);
 
-        const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT + 1;
-        helper_write(&alignment, sizeof(alignment), file);
+        alignment = expect_context_not_null(hft) ? 1 : 13;
+        helper_write(file, alignment);
     }
 
     if (hft < offset_has_tensors) {
+        while (ftell(file) % alignment != 0) {
+            const char pad = 0;
+            helper_write(file, pad);
+        }
+
         for (int i = 0; i < extra_bytes; ++i) {
             const char tmp = 0;
-            helper_write(&tmp, sizeof(tmp), file);
+            helper_write(file, tmp);
         }
         rewind(file);
         return file;
     }
 
-    uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
-    if (hft == HANDCRAFTED_TENSORS_BAD_ALIGNMENT || hft == HANDCRAFTED_DATA_BAD_ALIGNMENT) {
-        alignment -= 1;
-    } else if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
-        alignment += 1;
+    if (hft == HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN || hft == HANDCRAFTED_DATA_INCONSISTENT_ALIGN) {
+        alignment = 1;
     }
 
     uint64_t offset = 0;
@@ -313,9 +334,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         }
         {
             const uint64_t n = name.length();
-            helper_write(&n, sizeof(n), file);
+            helper_write(file, n);
         }
-        helper_write(name.data(), name.length(), file);
+        helper_write(file, name.data(), name.length());
 
         uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_TOO_BIG ? 2 : 1;
         for (int i = GGML_MAX_DIMS-1; i >= 1; --i) {
@@ -326,35 +347,35 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         }
         if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) {
             const uint32_t n_dims_bad = GGML_MAX_DIMS + 1;
-            helper_write(&n_dims_bad, sizeof(n_dims_bad), file);
+            helper_write(file, n_dims_bad);
         } else {
-            helper_write(&n_dims,     sizeof(n_dims),     file);
+            helper_write(file, n_dims);
         }
 
         if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) {
             for (uint32_t j = 0; j < n_dims; ++j) {
                 const int64_t bad_dim = -1;
-                helper_write(&bad_dim, sizeof(bad_dim), file);
+                helper_write(file, bad_dim);
             }
         } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
             for (uint32_t j = 0; j < n_dims; ++j) {
                 const int64_t big_dim = 4*int64_t(INT32_MAX);
-                helper_write(&big_dim, sizeof(big_dim), file);
+                helper_write(file, big_dim);
             }
         } else {
-            helper_write(shape.data(), n_dims*sizeof(int64_t), file);
+            helper_write(file, shape.data(), n_dims*sizeof(int64_t));
         }
 
         {
-            const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? -1 : int32_t(type);
-            helper_write(&type32, sizeof(type32), file);
+            const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? GGML_TYPE_COUNT : int32_t(type);
+            helper_write(file, type32);
         }
 
         if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) {
             const uint64_t bad_offset = -1;
-            helper_write(&bad_offset, sizeof(bad_offset), file);
+            helper_write(file, bad_offset);
         } else {
-            helper_write(&offset, sizeof(offset), file);
+            helper_write(file, offset);
         }
 
         int64_t ne = shape[0];
@@ -364,12 +385,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         offset += GGML_PAD(ggml_row_size(type, ne), alignment);
     }
 
-    const uint32_t alignment_overshoot = ftell(file) % alignment;
-    if (alignment_overshoot != 0) {
-        for (size_t i = alignment_overshoot; i < alignment; ++i) {
-            const char pad = 0;
-            helper_write(&pad, sizeof(pad), file);
-        }
+    while (ftell(file) % alignment != 0) {
+        const char pad = 0;
+        helper_write(file, pad);
     }
 
     if (hft >= offset_has_data) {
@@ -380,13 +398,13 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         }
         for (uint64_t i = 0; i < nbytes; ++i) {
             const uint8_t random_byte = i % 256;
-            helper_write(&random_byte, sizeof(random_byte), file);
+            helper_write(file, random_byte);
         }
     }
 
     for (int i = 0; i < extra_bytes; ++i) {
         const char tmp = 0;
-        helper_write(&tmp, sizeof(tmp), file);
+        helper_write(file, tmp);
     }
     rewind(file);
     return file;
@@ -505,6 +523,16 @@ static bool handcrafted_check_kv(const gguf_context * gguf_ctx, const unsigned i
             }
 
             const char * data_gguf = reinterpret_cast<const char *>(gguf_get_arr_data(gguf_ctx, id));
+
+            if (type_arr == GGUF_TYPE_BOOL) {
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    if (bool(data8[arr_i]) != bool(data_gguf[arr_i])) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
             if (!std::equal(data8, data8 + arr_n*type_size, data_gguf)) {
                 ok = false;
             }
@@ -512,12 +540,20 @@ static bool handcrafted_check_kv(const gguf_context * gguf_ctx, const unsigned i
         }
 
         const char * data_gguf = reinterpret_cast<const char *>(gguf_get_val_data(gguf_ctx, id));
+
+        if (type == GGUF_TYPE_BOOL) {
+            if (bool(*data8) != bool(*data_gguf)) {
+                ok = false;
+            }
+            continue;
+        }
+
         if (!std::equal(data8, data8 + gguf_type_size(type), data_gguf)) {
             ok = false;
         }
     }
 
-    const uint32_t expected_alignment = alignment_defined ? GGUF_DEFAULT_ALIGNMENT + 1 : GGUF_DEFAULT_ALIGNMENT;
+    const uint32_t expected_alignment = alignment_defined ? 1 : GGUF_DEFAULT_ALIGNMENT;
     if (gguf_get_alignment(gguf_ctx) != expected_alignment) {
         ok = false;
     }
@@ -539,7 +575,7 @@ static bool handcrafted_check_tensors(const gguf_context * gguf_ctx, const unsig
 
     bool ok = true;
 
-    const int id_alignment = gguf_find_key(gguf_ctx, "general.alignment");
+    const int id_alignment = gguf_find_key(gguf_ctx, GGUF_KEY_GENERAL_ALIGNMENT);
     const uint32_t alignment = id_alignment >= 0 ? gguf_get_val_u32(gguf_ctx, id_alignment) : GGUF_DEFAULT_ALIGNMENT;
 
     uint64_t expected_offset = 0;
@@ -607,7 +643,7 @@ static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const u
 
         std::vector<uint8_t> data(size);
         GGML_ASSERT(fseek(file, gguf_get_data_offset(gguf_ctx) + offset, SEEK_SET) == 0);
-        GGML_ASSERT(fread(data.data(), 1, size, file) == size);
+        GGML_ASSERT(fread(data.data(), 1, data.size(), file) == data.size());
 
         for (size_t j = 0; j < size; ++j) {
             const uint8_t expected_byte = (j + offset) % 256;
@@ -627,15 +663,15 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
     const std::vector<handcrafted_file_type> hfts = {
         HANDCRAFTED_HEADER_BAD_MAGIC,
         HANDCRAFTED_HEADER_BAD_VERSION_1,
-        // HANDCRAFTED_FILE_TYPE_BAD_VERSION_FUTURE, // FIXME
+        HANDCRAFTED_HEADER_BAD_VERSION_FUTURE,
         HANDCRAFTED_HEADER_BAD_N_KV,
         HANDCRAFTED_HEADER_BAD_N_TENSORS,
         HANDCRAFTED_HEADER_EMPTY,
 
         HANDCRAFTED_KV_BAD_KEY_SIZE,
         HANDCRAFTED_KV_BAD_TYPE,
-        // HANDCRAFTED_KV_BAD_VALUE_SIZE, // FIXME sanitizer limit
-        // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME
+        HANDCRAFTED_KV_DUPLICATE_KEY,
+        HANDCRAFTED_KV_BAD_ALIGN,
         HANDCRAFTED_KV_SUCCESS,
 
         HANDCRAFTED_TENSORS_BAD_NAME_SIZE,
@@ -643,14 +679,16 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
         HANDCRAFTED_TENSORS_BAD_SHAPE,
         HANDCRAFTED_TENSORS_NE_TOO_BIG,
         HANDCRAFTED_TENSORS_BAD_TYPE,
-        // HANDCRAFTED_TENSORS_BAD_OFFSET, // FIXME
+        HANDCRAFTED_TENSORS_BAD_OFFSET,
         HANDCRAFTED_TENSORS_DUPLICATE_NAME,
-        // HANDCRAFTED_TENSORS_BAD_ALIGNMENT, // FIXME
+        HANDCRAFTED_TENSORS_BAD_ALIGN,
+        HANDCRAFTED_TENSORS_INCONSISTENT_ALIGN,
         HANDCRAFTED_TENSORS_SUCCESS,
         HANDCRAFTED_TENSORS_CUSTOM_ALIGN,
 
         HANDCRAFTED_DATA_NOT_ENOUGH_DATA,
-        // HANDCRAFTED_DATA_BAD_ALIGNMENT, // FIXME
+        HANDCRAFTED_DATA_BAD_ALIGN,
+        HANDCRAFTED_DATA_INCONSISTENT_ALIGN,
         HANDCRAFTED_DATA_SUCCESS,
         HANDCRAFTED_DATA_CUSTOM_ALIGN,
     };
@@ -674,6 +712,7 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
             /*no_alloc =*/ false,
             /*ctx      =*/ hft >= offset_has_data ? &ctx : nullptr,
         };
+
         struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params);
 
         if (expect_context_not_null(hft)) {
@@ -689,7 +728,7 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
         }
         ntest++;
 
-        if (false && hft >= offset_has_data && !expect_context_not_null(hft)) { // FIXME
+        if (hft >= offset_has_data && !expect_context_not_null(hft)) {
             printf("%s:   - no_dangling_ggml_context_pointer: ", __func__);
             if (ctx) {
                 printf("\033[1;31mFAIL\033[0m\n");
@@ -700,23 +739,6 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
             ntest++;
         }
 
-        if (false && expect_context_not_null(hft)) { // FIXME
-            FILE * file_eb = get_handcrafted_file(seed, hft, /*extra_bytes =*/ 1);
-            struct gguf_context * gguf_ctx_eb = gguf_init_from_file_impl(file_eb, gguf_params);
-
-            printf("%s:   - context_null_with_extra_bytes: ", __func__);
-            if (gguf_ctx_eb) {
-                printf("\033[1;31mFAIL\033[0m\n");
-            } else {
-                printf("\033[1;32mOK\033[0m\n");
-                npass++;
-            }
-            ntest++;
-
-            gguf_free(gguf_ctx_eb);
-            fclose(file_eb);
-        }
-
         const bool alignment_defined = hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN;
 
         if (expect_context_not_null(hft)) {
@@ -763,14 +785,15 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
             ntest++;
         }
 
+        fclose(file);
         if (gguf_ctx) {
             ggml_free(ctx);
             gguf_free(gguf_ctx);
         }
-        fclose(file);
         printf("\n");
     }
 
+
     return std::make_pair(npass, ntest);
 }
 
@@ -789,10 +812,6 @@ static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t
         const std::string key = "my_key_" + std::to_string(rng() % 1024);
         const enum gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT);
 
-        if (type == GGUF_TYPE_STRING || type == GGUF_TYPE_ARRAY) {
-            continue; // FIXME memory leak
-        }
-
         switch (type) {
             case GGUF_TYPE_UINT8:   gguf_set_val_u8  (gguf_ctx, key.c_str(), rng() % (1 <<  7));             break;
             case GGUF_TYPE_INT8:    gguf_set_val_i8  (gguf_ctx, key.c_str(), rng() % (1 <<  7) - (1 <<  6)); break;
@@ -826,6 +845,9 @@ static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t
                         std::vector<uint32_t> random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t));
                         for (size_t j = 0; j < random_data.size(); ++j) {
                             random_data[j] = rng();
+                            if (type_arr == GGUF_TYPE_BOOL) {
+                                random_data[j] &= 0x01010101; // the sanitizer complains if booleans are not 0 or 1
+                            }
                         }
                         gguf_set_arr_data(gguf_ctx, key.c_str(), type_arr, random_data.data(), ne);
                     } break;
@@ -928,6 +950,17 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
                 continue;
             }
 
+            if (type_arr == GGUF_TYPE_BOOL) {
+                const int8_t * data       = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx,   id));
+                const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
+                for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
+                    if (bool(data[arr_i]) != bool(data_other[arr_i])) {
+                        ok = false;
+                    }
+                }
+                continue;
+            }
+
             if (type_arr == GGUF_TYPE_STRING) {
                 for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
                     const std::string str       = gguf_get_arr_str(ctx,   id,       arr_i);
@@ -939,8 +972,8 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
                 continue;
             }
 
-            const char * data       = reinterpret_cast<const char *>(gguf_get_arr_data(ctx,   id));
-            const char * data_other = reinterpret_cast<const char *>(gguf_get_arr_data(other, idx_other));
+            const int8_t * data       = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx,   id));
+            const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
             if (!std::equal(data, data + arr_n*gguf_type_size(type_arr), data_other)) {
                 ok = false;
             }
@@ -1028,21 +1061,6 @@ static bool same_tensor_data(const struct ggml_context * orig, const struct ggml
 }
 
 static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
-    FILE * file = tmpfile();
-#ifdef _WIN32
-    if (!file) {
-        printf("%s: failed to create tmpfile(), needs elevated privileges on Windows");
-        printf("%s: skipping tests");
-        return std::make_pair(0, 0);
-    }
-#else
-    GGML_ASSERT(file);
-#endif // _WIN32
-
-    if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
-        return std::make_pair(0, 0); // FIXME
-    }
-
     ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
     printf("%s: device=%s, backend=%s, only_meta=%s\n",
         __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend), only_meta ? "yes" : "no");
@@ -1060,10 +1078,24 @@ static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned
         bbuf       = result.buffer;
     }
 
-    struct gguf_buf gbuf = gguf_buf_init(16 * 1024);
-    gguf_write_to_buf(gguf_ctx_0, &gbuf, only_meta);
-    helper_write(gbuf.data, gbuf.offset, file);
-    rewind(file);
+    FILE * file = tmpfile();
+
+#ifdef _WIN32
+    if (!file) {
+        printf("%s: failed to create tmpfile(), needs elevated privileges on Windows");
+        printf("%s: skipping tests");
+        return std::make_pair(0, 0);
+    }
+#else
+    GGML_ASSERT(file);
+#endif // _WIN32
+
+    {
+        std::vector<int8_t> buf;
+        gguf_write_to_buf(gguf_ctx_0, buf, only_meta);
+        GGML_ASSERT(fwrite(buf.data(), 1, buf.size(), file) == buf.size());
+        rewind(file);
+    }
 
     struct ggml_context * ctx_1 = nullptr;
     struct gguf_init_params gguf_params = {
@@ -1151,9 +1183,8 @@ static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned
     ggml_free(ctx_1);
     gguf_free(gguf_ctx_0);
     gguf_free(gguf_ctx_1);
-    gguf_buf_free(gbuf);
     ggml_backend_free(backend);
-    GGML_ASSERT(fclose(file) == 0);
+    fclose(file);
 
     printf("\n");
     return std::make_pair(npass, ntest);

From bec2183f2c8d37cf1278c11d1adb9311e9eaa242 Mon Sep 17 00:00:00 2001
From: ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
Date: Wed, 8 Jan 2025 16:17:29 +0800
Subject: [PATCH 132/376] fix: Vulkan shader gen binary path when
 Cross-compiling (#11096)

* fix: Vulkan shader gen binary path when cross compiling
---
 ggml/src/ggml-vulkan/CMakeLists.txt | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt
index 9501de736..61de21d6a 100644
--- a/ggml/src/ggml-vulkan/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/CMakeLists.txt
@@ -69,11 +69,15 @@ if (Vulkan_FOUND)
 
     file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
 
+    if (NOT CMAKE_CROSSCOMPILING)
+        set(_ggml_vk_genshaders_cmd "$<TARGET_FILE_DIR:vulkan-shaders-gen>/${_ggml_vk_genshaders_cmd}")
+    endif ()
+
     add_custom_command(
         OUTPUT ${_ggml_vk_header}
                 ${_ggml_vk_source}
 
-        COMMAND "$<TARGET_FILE_DIR:vulkan-shaders-gen>/${_ggml_vk_genshaders_cmd}"
+        COMMAND ${_ggml_vk_genshaders_cmd}
             --glslc      ${Vulkan_GLSLC_EXECUTABLE}
             --input-dir  ${_ggml_vk_input_dir}
             --output-dir ${_ggml_vk_output_dir}

From 02f04301417e7fb44fa1025bc1b0aef866e2ca89 Mon Sep 17 00:00:00 2001
From: Mathieu Baudier <mbaudier@argeo.org>
Date: Wed, 8 Jan 2025 09:18:13 +0100
Subject: [PATCH 133/376] Disable GL_KHR_cooperative_matrix Vulkan extension if
 not available. (#11117)

* Disable GL_KHR_cooperative_matrix Vulkan extension if not available.

* Perform Vulkan extensions checks in a more sensible order

* Remove unnecessary #ifdef directive
---
 ggml/src/ggml-vulkan/CMakeLists.txt            | 14 ++++++++++++++
 ggml/src/ggml-vulkan/ggml-vulkan.cpp           | 18 +++++++++++++++---
 .../vulkan-shaders/test_coopmat_support.comp   |  7 +++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp      |  2 ++
 4 files changed, 38 insertions(+), 3 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/test_coopmat_support.comp

diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt
index 61de21d6a..c0ddaac82 100644
--- a/ggml/src/ggml-vulkan/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/CMakeLists.txt
@@ -8,6 +8,20 @@ if (Vulkan_FOUND)
                              ../../include/ggml-vulkan.h
                             )
 
+    # Compile a test shader to determine whether GL_KHR_cooperative_matrix is supported.
+    # If it's not, there will be an error to stderr.
+    # If it's supported, set a define to indicate that we should compile those shaders
+    execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_coopmat_support.comp"
+                    OUTPUT_VARIABLE glslc_output
+                    ERROR_VARIABLE glslc_error)
+
+    if (${glslc_error} MATCHES ".*extension not supported: GL_KHR_cooperative_matrix.*")
+        message(STATUS "GL_KHR_cooperative_matrix not supported by glslc")
+    else()
+        message(STATUS "GL_KHR_cooperative_matrix supported by glslc")
+        add_compile_definitions(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+    endif()
+
     # Compile a test shader to determine whether GL_NV_cooperative_matrix2 is supported.
     # If it's not, there will be an error to stderr.
     # If it's supported, set a define to indicate that we should compile those shaders
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index d75cd6d61..077452424 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1645,6 +1645,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
 #undef CREATE_MM2
     } else
 #endif  // defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     if (device->coopmat_support) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
@@ -1739,7 +1740,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
         }
 #undef CREATE_MM2
 #undef CREATE_MM
-    } else if (device->fp16) {
+    } else
+#endif  // defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+    if (device->fp16) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l) \
@@ -2242,6 +2245,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
             last_struct = (VkBaseOutStructure *)&subgroup_size_control_features;
         }
 
+#if defined(VK_KHR_cooperative_matrix)
         VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features;
         coopmat_features.pNext = nullptr;
         coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR;
@@ -2251,6 +2255,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
             last_struct->pNext = (VkBaseOutStructure *)&coopmat_features;
             last_struct = (VkBaseOutStructure *)&coopmat_features;
         }
+#endif
 
 #if defined(VK_NV_cooperative_matrix2)
         VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {};
@@ -2283,7 +2288,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device_extensions.push_back("VK_EXT_subgroup_size_control");
         }
 
+#if defined(VK_KHR_cooperative_matrix)
         device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix;
+#endif
 
         if (coopmat2_support) {
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
@@ -2376,6 +2383,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device_extensions.push_back("VK_KHR_shader_float16_int8");
         }
 
+#if defined(VK_KHR_cooperative_matrix)
         if (device->coopmat_support) {
             // Query supported shapes
             std::vector<VkCooperativeMatrixPropertiesKHR> cm_props;
@@ -2442,7 +2450,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         if (device->coopmat_support) {
             device_extensions.push_back("VK_KHR_cooperative_matrix");
         }
-
+#endif
         device->name = GGML_VK_NAME + std::to_string(idx);
 
         device_create_info = {
@@ -2553,9 +2561,11 @@ static void ggml_vk_print_gpu_info(size_t idx) {
             fp16_storage = true;
         } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
             fp16_compute = true;
-        } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 &&
+#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+       } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 &&
                    !getenv("GGML_VK_DISABLE_COOPMAT")) {
             coopmat_support = true;
+#endif
 #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
         } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 &&
                    !getenv("GGML_VK_DISABLE_COOPMAT2")) {
@@ -2593,6 +2603,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     // Pointer to the last chain element
     VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&vk12_features;
 
+#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features;
     coopmat_features.pNext = nullptr;
     coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR;
@@ -2608,6 +2619,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     fp16 = fp16 && vk12_features.shaderFloat16;
 
     coopmat_support = coopmat_support && coopmat_features.cooperativeMatrix;
+#endif
 
     std::string matrix_cores = coopmat2_support ? "NV_coopmat2" : coopmat_support ? "KHR_coopmat" : "none";
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/test_coopmat_support.comp b/ggml/src/ggml-vulkan/vulkan-shaders/test_coopmat_support.comp
new file mode 100644
index 000000000..8c5dd1bd1
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/test_coopmat_support.comp
@@ -0,0 +1,7 @@
+#version 460
+
+#extension GL_KHR_cooperative_matrix : require
+
+void main()
+{
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 8111c0638..7b5044798 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -342,9 +342,11 @@ void process_shaders() {
         matmul_shaders(true, matmul_id, false, false, false);
         matmul_shaders(true, matmul_id, false, false, true);
 
+#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
         // Coopmat, fp32acc and fp16acc
         matmul_shaders(true, matmul_id, true, false, false);
         matmul_shaders(true, matmul_id, true, false, true);
+#endif
 
 #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
         // Coopmat2, fp32acc and fp16acc

From 0d52a69e4bf0d6181beec7853307bdcdeec9905b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 8 Jan 2025 11:29:34 +0200
Subject: [PATCH 134/376] ci : fix cmake option (#11125)

---
 .github/workflows/build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 602cf5220..02a193b86 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -665,7 +665,7 @@ jobs:
           - build: 'llvm-arm64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
           - build: 'msvc-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=O'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
           - build: 'llvm-arm64-opencl-adreno'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
 

From 8cef75c743ba13ebbd6d380c531200c768a8b8aa Mon Sep 17 00:00:00 2001
From: amritahs-ibm <amritahs@linux.vnet.ibm.com>
Date: Wed, 8 Jan 2025 16:24:19 +0530
Subject: [PATCH 135/376] llamafile : ppc64le MMA INT8 implementation (#10912)

This change upstreams llamafile's cpu matrix
multiplication kernels for ppc64le using MMA
builtins for quantised int8 datatype.

This change results in 10% - 70% improvement
in total speed(ie all tokens/total time), across
various batch sizes.

The patch is tested with Meta-Lllama-3-8B,
Mistral-7B, Llama-2-7B-chat-hf models on a
IBM POWER10 machine.

Signed-off-by: Amrita H S <amritahs@linux.vnet.ibm.com>
---
 ggml/src/ggml-cpu/llamafile/sgemm.cpp | 836 ++++++++++++++++++++++++--
 1 file changed, 770 insertions(+), 66 deletions(-)

diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index 8fce576c3..c22a66287 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -54,6 +54,7 @@
 #include "ggml-quants.h"
 
 #include <atomic>
+#include <array>
 
 #ifdef _MSC_VER
 #define NOINLINE __declspec(noinline)
@@ -1051,6 +1052,704 @@ class tinyBLAS_Q0_AVX {
       } \
    } \
 
+template <typename TA, typename TB, typename TC>
+class tinyBLAS_Q0_PPC {
+  public:
+    tinyBLAS_Q0_PPC(int64_t k,
+                const TA *A, int64_t lda,
+                const TB *B, int64_t ldb,
+                TC *C, int64_t ldc,
+                int ith, int nth)
+        : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
+    }
+
+    void matmul(int64_t m, int64_t n) {
+        mnpack(0, m, 0, n);
+    }
+
+  private:
+
+    template<int RM, int RN>
+    inline void save_res(int ii, int jj, int idx, vector float* fin_res) {
+       for (int I = 0; I < RM; I++) {
+          for (int J = 0; J < RN; J++) {
+             *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&fin_res[idx+I]+J);
+          }
+       }
+    }
+
+    template<int size>
+    inline void compute(acc_t* ACC, int c_idx, int s_idx, std::array<int, size>& comparray, vector float* vs, vector float* fin_res) {
+       vector signed int vec_C[4];
+       vector float CA[4] = {0};
+       vector float res[4] = {0};
+       __builtin_mma_disassemble_acc(vec_C, ACC);
+       for (int i = 0; i < 4; i++) {
+          CA[i] = vec_splats((float)(((double)comparray[c_idx+i]) * -128.0));
+          res[i] = vec_add(vec_ctf(vec_C[i], 0), CA[i]);
+          fin_res[s_idx+i] = vec_madd(res[i], vs[s_idx+i], fin_res[s_idx+i]);
+       }
+    }
+
+    template<typename VA, typename VB>
+    void packNormal(const TA* a, int64_t lda, int rows, int cols, VA* vec, bool flip) {
+        int64_t i, j;
+        TA *aoffset = NULL;
+        VA *vecOffset = NULL;
+        TA *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL;
+        TA *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL;
+        __vector_pair C1, C2, C3, C4, C5, C6, C7, C8;
+        VB c1[2] = {0}, c2[2] = {0}, c3[2] = {0}, c4[2]={0};
+        VB c5[2] = {0}, c6[2] = {0}, c7[2] = {0}, c8[2]={0};
+        VB t1, t2, t3, t4, t5, t6, t7, t8;
+        vector unsigned char xor_vector;
+        uint8_t flip_vec = 0x80;
+        xor_vector = vec_splats(flip_vec);
+        vector unsigned char swiz1 = {0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23};
+        vector unsigned char swiz2 = {8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31};
+        vector unsigned char swiz3 = {0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27};
+        vector unsigned char swiz4 = {4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31};
+
+        aoffset = const_cast<TA*>(a);
+        vecOffset = vec;
+        j = (rows >> 3);
+        if (j > 0) {
+            do {
+            aoffset1 = aoffset;
+            aoffset2 = aoffset1 + lda;
+            aoffset3 = aoffset2 + lda;
+            aoffset4 = aoffset3 + lda;
+            aoffset5 = aoffset4 + lda;
+            aoffset6 = aoffset5 + lda;
+            aoffset7 = aoffset6 + lda;
+            aoffset8 = aoffset7 + lda;
+            aoffset += 8 * lda;
+
+            i = (cols >> 3);
+            if (i > 0) {
+               do {
+                    C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1->qs);
+                    C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2->qs);
+                    C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3->qs);
+                    C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4->qs);
+                    C5 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset5->qs);
+                    C6 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset6->qs);
+                    C7 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset7->qs);
+                    C8 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset8->qs);
+
+                    __builtin_vsx_disassemble_pair(c1, &C1);
+                    __builtin_vsx_disassemble_pair(c2, &C2);
+                    __builtin_vsx_disassemble_pair(c3, &C3);
+                    __builtin_vsx_disassemble_pair(c4, &C4);
+                    __builtin_vsx_disassemble_pair(c5, &C5);
+                    __builtin_vsx_disassemble_pair(c6, &C6);
+                    __builtin_vsx_disassemble_pair(c7, &C7);
+                    __builtin_vsx_disassemble_pair(c8, &C8);
+
+                    t1 = vec_perm(c1[0], c2[0], swiz1);
+                    t2 = vec_perm(c1[0], c2[0], swiz2);
+                    t3 = vec_perm(c3[0], c4[0], swiz1);
+                    t4 = vec_perm(c3[0], c4[0], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset);
+                    vec_xst(t6, 0, vecOffset+16);
+                    vec_xst(t7, 0, vecOffset+32);
+                    vec_xst(t8, 0, vecOffset+48);
+
+                    t1 = vec_perm(c1[1], c2[1], swiz1);
+                    t2 = vec_perm(c1[1], c2[1], swiz2);
+                    t3 = vec_perm(c3[1], c4[1], swiz1);
+                    t4 = vec_perm(c3[1], c4[1], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset+64);
+                    vec_xst(t6, 0, vecOffset+80);
+                    vec_xst(t7, 0, vecOffset+96);
+                    vec_xst(t8, 0, vecOffset+112);
+
+                    t1 = vec_perm(c5[0], c6[0], swiz1);
+                    t2 = vec_perm(c5[0], c6[0], swiz2);
+                    t3 = vec_perm(c7[0], c8[0], swiz1);
+                    t4 = vec_perm(c7[0], c8[0], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset+128);
+                    vec_xst(t6, 0, vecOffset+144);
+                    vec_xst(t7, 0, vecOffset+160);
+                    vec_xst(t8, 0, vecOffset+176);
+
+                    t1 = vec_perm(c5[1], c6[1], swiz1);
+                    t2 = vec_perm(c5[1], c6[1], swiz2);
+                    t3 = vec_perm(c7[1], c8[1], swiz1);
+                    t4 = vec_perm(c7[1], c8[1], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset+192);
+                    vec_xst(t6, 0, vecOffset+208);
+                    vec_xst(t7, 0, vecOffset+224);
+                    vec_xst(t8, 0, vecOffset+240);
+
+                    aoffset1 += lda;
+                    aoffset2 += lda;
+                    aoffset3 += lda;
+                    aoffset4 += lda;
+                    aoffset5 += lda;
+                    aoffset6 += lda;
+                    aoffset7 += lda;
+                    aoffset8 += lda;
+                    vecOffset += 256;
+                    i--;
+               } while(i > 0);
+            }
+            j--;
+        } while(j > 0);
+    }
+
+    if (rows & 4) {
+            aoffset1 = aoffset;
+            aoffset2 = aoffset1 + lda;
+            aoffset3 = aoffset2 + lda;
+            aoffset4 = aoffset3 + lda;
+            aoffset += 4 * lda;
+
+        i = (cols >> 3);
+            if (i > 0) {
+               do {
+                    C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1->qs);
+                    C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2->qs);
+                    C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3->qs);
+                    C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4->qs);
+
+                    __builtin_vsx_disassemble_pair(c1, &C1);
+                    __builtin_vsx_disassemble_pair(c2, &C2);
+                    __builtin_vsx_disassemble_pair(c3, &C3);
+                    __builtin_vsx_disassemble_pair(c4, &C4);
+
+                    t1 = vec_perm(c1[0], c2[0], swiz1);
+                    t2 = vec_perm(c1[0], c2[0], swiz2);
+                    t3 = vec_perm(c3[0], c4[0], swiz1);
+                    t4 = vec_perm(c3[0], c4[0], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset);
+                    vec_xst(t6, 0, vecOffset+16);
+                    vec_xst(t7, 0, vecOffset+32);
+                    vec_xst(t8, 0, vecOffset+48);
+
+                    t1 = vec_perm(c1[1], c2[1], swiz1);
+                    t2 = vec_perm(c1[1], c2[1], swiz2);
+                    t3 = vec_perm(c3[1], c4[1], swiz1);
+                    t4 = vec_perm(c3[1], c4[1], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset+64);
+                    vec_xst(t6, 0, vecOffset+80);
+                    vec_xst(t7, 0, vecOffset+96);
+                    vec_xst(t8, 0, vecOffset+112);
+
+                    aoffset1 += lda;
+                    aoffset2 += lda;
+                    aoffset3 += lda;
+                    aoffset4 += lda;
+                    vecOffset += 128;
+                    i--;
+               } while(i > 0);
+            }
+        }
+        if (rows & 3) {
+            aoffset1 = aoffset;
+            aoffset2 = aoffset1 + lda;
+            aoffset3 = aoffset2 + lda;
+            i = (cols >> 3);
+        if (i > 0) {
+                do {
+                    switch(rows) {
+                        case 3: C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3->qs);
+                                __builtin_vsx_disassemble_pair(c3, &C3);
+                        case 2: C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2->qs);
+                                __builtin_vsx_disassemble_pair(c2, &C2);
+                        case 1: C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1->qs);
+                                __builtin_vsx_disassemble_pair(c1, &C1);
+                                break;
+                    }
+                    t1 = vec_perm(c1[0], c2[0], swiz1);
+                    t2 = vec_perm(c1[0], c2[0], swiz2);
+                    t3 = vec_perm(c3[0], c4[0], swiz1);
+                    t4 = vec_perm(c3[0], c4[0], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset);
+                    vec_xst(t6, 0, vecOffset+16);
+                    vec_xst(t7, 0, vecOffset+32);
+                    vec_xst(t8, 0, vecOffset+48);
+
+                    t1 = vec_perm(c1[1], c2[1], swiz1);
+                    t2 = vec_perm(c1[1], c2[1], swiz2);
+                    t3 = vec_perm(c3[1], c4[1], swiz1);
+                    t4 = vec_perm(c3[1], c4[1], swiz2);
+                    t5 = vec_perm(t1, t3, swiz3);
+                    t6 = vec_perm(t1, t3, swiz4);
+                    t7 = vec_perm(t2, t4, swiz3);
+                    t8 = vec_perm(t2, t4, swiz4);
+                    if (flip == true) {
+                       t5 = vec_xor(t5, xor_vector);
+                       t6 = vec_xor(t6, xor_vector);
+                       t7 = vec_xor(t7, xor_vector);
+                       t8 = vec_xor(t8, xor_vector);
+                    }
+                    vec_xst(t5, 0, vecOffset+64);
+                    vec_xst(t6, 0, vecOffset+80);
+                    vec_xst(t7, 0, vecOffset+96);
+                    vec_xst(t8, 0, vecOffset+112);
+
+                    aoffset1 += lda;
+                    aoffset2 += lda;
+                    aoffset3 += lda;
+                    vecOffset += 128;
+                    i--;
+               } while(i > 0);
+            }
+        }
+    }
+
+    void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t mc, nc, mp, np;
+        int m_rem = MIN(m - m0, 8);
+        int n_rem = MIN(n - n0, 8);
+        // TO-DO: KERNEL_16x8 and KERNEL_8x16 are having some performance
+        // issues. After resolving them, below code will be enabled.
+        /*if (m_rem >= 16 && n_rem >= 8) {
+            mc = 16;
+            nc = 8;
+            gemm<16,8>(m0, m, n0, n);
+        } else if(m_rem >= 8 && n_rem >= 16) {
+            mc = 8;
+            nc = 16;
+            gemm<8,16>(m0, m, n0, n);
+        }*/
+        if (m_rem >= 8 && n_rem >= 8) {
+            mc = 8;
+            nc = 8;
+            gemm<8,8>(m0, m, n0, n);
+        } else if (m_rem >= 4 && n_rem >= 8) {
+            mc = 4;
+            nc = 8;
+            gemm<4,8>(m0, m, n0, n);
+        } else if (m_rem >= 8 && n_rem >= 4) {
+            mc = 8;
+            nc = 4;
+            gemm<8,4>(m0, m, n0, n);
+        } else if (m_rem >= 4 && n_rem >= 4) {
+            mc = 4;
+            nc = 4;
+            gemm_small<4, 4>(m0, m, n0, n);
+        } else if ((m_rem < 4) && (n_rem > 4)) {
+            nc = 4;
+            switch(m_rem) {
+                case 1:
+                    mc = 1;
+                    gemm_small<1, 4>(m0, m, n0, n);
+                    break;
+                case 2:
+                    mc = 2;
+                    gemm_small<2, 4>(m0, m, n0, n);
+                    break;
+                case 3:
+                    mc = 3;
+                    gemm_small<3, 4>(m0, m, n0, n);
+                    break;
+                default:
+                    return;
+            }
+        } else if ((m_rem > 4) && (n_rem < 4)) {
+            mc = 4;
+            switch(n_rem) {
+                case 1:
+                    nc = 1;
+                    gemm_small<4, 1>(m0, m, n0, n);
+                    break;
+                case 2:
+                    nc = 2;
+                    gemm_small<4, 2>(m0, m, n0, n);
+                    break;
+                case 3:
+                    nc = 3;
+                    gemm_small<4, 3>(m0, m, n0, n);
+                    break;
+                default:
+                    return;
+            }
+        } else {
+            switch((m_rem << 4) | n_rem) {
+                case 0x43:
+                    mc = 4;
+                    nc = 3;
+                    gemm_small<4, 3>(m0, m, n0, n);
+                    break;
+                case 0x42:
+                    mc = 4;
+                    nc = 2;
+                    gemm_small<4, 2>(m0, m, n0, n);
+                    break;
+                case 0x41:
+                    mc = 4;
+                    nc = 1;
+                    gemm_small<4, 1>(m0, m, n0, n);
+                    break;
+                case 0x34:
+                    mc = 3;
+                    nc = 4;
+                    gemm_small<3, 4>(m0, m, n0, n);
+                    break;
+                case 0x33:
+                    mc = 3;
+                    nc = 3;
+                    gemm_small<3, 3>(m0, m, n0, n);
+                    break;
+                case 0x32:
+                    mc = 3;
+                    nc = 2;
+                    gemm_small<3, 2>(m0, m, n0, n);
+                    break;
+                case 0x31:
+                    mc = 3;
+                    nc = 1;
+                    gemm_small<3, 1>(m0, m, n0, n);
+                    break;
+                case 0x24:
+                    mc = 2;
+                    nc = 4;
+                    gemm_small<2, 4>(m0, m, n0, n);
+                    break;
+                case 0x23:
+                    mc = 2;
+                    nc = 3;
+                    gemm_small<2, 3>(m0, m, n0, n);
+                    break;
+                case 0x22:
+                    mc = 2;
+                    nc = 2;
+                    gemm_small<2, 2>(m0, m, n0, n);
+                    break;
+                case 0x21:
+                    mc = 2;
+                    nc = 1;
+                    gemm_small<2, 1>(m0, m, n0, n);
+                    break;
+                case 0x14:
+                    mc = 1;
+                    nc = 4;
+                    gemm_small<1, 4>(m0, m, n0, n);
+                    break;
+                case 0x13:
+                    mc = 1;
+                    nc = 3;
+                    gemm_small<1, 3>(m0, m, n0, n);
+                    break;
+                case 0x12:
+                    mc = 1;
+                    nc = 2;
+                    gemm_small<1, 2>(m0, m, n0, n);
+                    break;
+                case 0x11:
+                    mc = 1;
+                    nc = 1;
+                    gemm_small<1, 1>(m0, m, n0, n);
+                    break;
+                default:
+                    return;
+            }
+        }
+        mp = m0 + (m - m0) / mc * mc;
+        np = n0 + (n - n0) / nc * nc;
+        mnpack(mp, m, n0, np);
+        mnpack(m0, m, np, n);
+    }
+
+    void KERNEL_4x8(int64_t ii, int64_t jj) {
+        vec_t vec_A[8], vec_B[16] = {0};
+        acc_t acc_0, acc_1;
+        std::array<int, 4> comparray;
+        vector float fin_res[8] = {0};
+        vector float vs[8] = {0};
+        for (int l = 0; l < k; l++) {
+            __builtin_mma_xxsetaccz(&acc_0);
+            __builtin_mma_xxsetaccz(&acc_1);
+            packNormal<int8_t, vector signed char>((A+(ii*lda)+l), lda, 4, 8, (int8_t*)vec_A, false);
+            packNormal<uint8_t, vector unsigned char>((B+(jj*ldb)+l), ldb, 8, 8, (uint8_t*)vec_B, true);
+            for(int x = 0; x < 8; x++) {
+                __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x], vec_B[x]);
+                __builtin_mma_xvi8ger4pp(&acc_1, vec_A[x], vec_B[x+8]);
+            }
+            for (int I = 0; I<4; I++) {
+                for (int J = 0; J<4; J++) {
+                    *((float*)&vs[I]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J)*ldb)+l)->d));
+                    *((float*)&vs[I+4]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J+4)*ldb)+l)->d));
+                }
+            }
+            auto aoffset = A+(ii*lda)+l;
+            for (int i = 0; i < 4; i++) {
+                comparray[i] = 0;
+                int ca = 0;
+                const int8_t *at = aoffset->qs;
+                for (int j = 0; j < 32; j++)
+                    ca += (int)*at++;
+                comparray[i] = ca;
+                aoffset += lda;
+            }
+            compute<4>(&acc_0, 0, 0, comparray, vs, fin_res);
+            compute<4>(&acc_1, 0, 4, comparray, vs, fin_res);
+        }
+        save_res<4, 4>(ii, jj, 0, fin_res);
+        save_res<4, 4>(ii, jj+4, 4, fin_res);
+    }
+
+    void KERNEL_8x4(int64_t ii, int64_t jj) {
+        vec_t vec_A[16], vec_B[8] = {0};
+        acc_t acc_0, acc_1;
+        std::array<int, 8> comparray;
+        vector float fin_res[8] = {0};
+        vector float vs[8] = {0};
+        for (int l = 0; l < k; l++) {
+            __builtin_mma_xxsetaccz(&acc_0);
+            __builtin_mma_xxsetaccz(&acc_1);
+            packNormal<int8_t, vector signed char>((A+(ii*lda)+l), lda, 8, 8, (int8_t*)vec_A, false);
+            packNormal<uint8_t, vector unsigned char>((B+(jj*ldb)+l), ldb, 4, 8, (uint8_t*)vec_B, true);
+            for(int x = 0; x < 8; x++) {
+                __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x], vec_B[x]);
+                __builtin_mma_xvi8ger4pp(&acc_1, vec_A[x+8], vec_B[x]);
+            }
+            for (int I = 0; I<8; I++) {
+                for (int J = 0; J<4; J++) {
+                    *((float*)&vs[I]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J)*ldb)+l)->d));
+                }
+            }
+            auto aoffset = A+(ii*lda)+l;
+            for (int i = 0; i < 8; i++) {
+                comparray[i] = 0;
+                int ca = 0;
+                const int8_t *at = aoffset->qs;
+                for (int j = 0; j < 32; j++)
+                    ca += (int)*at++;
+                comparray[i] = ca;
+                aoffset += lda;
+            }
+            compute<8>(&acc_0, 0, 0, comparray, vs, fin_res);
+            compute<8>(&acc_1, 4, 4, comparray, vs, fin_res);
+        }
+        save_res<4, 4>(ii, jj, 0, fin_res);
+        save_res<4, 4>(ii+4, jj, 4, fin_res);
+    }
+
+    void KERNEL_8x8(int64_t ii, int64_t jj) {
+        vec_t vec_A[16], vec_B[16] = {0};
+        acc_t acc_0, acc_1, acc_2, acc_3;
+        std::array<int, 8> comparray;
+        vector float fin_res[16] = {0};
+        vector float vs[16] = {0};
+        for (int l = 0; l < k; l++) {
+            __builtin_mma_xxsetaccz(&acc_0);
+            __builtin_mma_xxsetaccz(&acc_1);
+            __builtin_mma_xxsetaccz(&acc_2);
+            __builtin_mma_xxsetaccz(&acc_3);
+            packNormal<int8_t, vector signed char>((A+(ii*lda)+l), lda, 8, 8, (int8_t*)vec_A, false);
+            packNormal<uint8_t, vector unsigned char>((B+(jj*ldb)+l), ldb, 8, 8, (uint8_t*)vec_B, true);
+            for(int x = 0; x < 8; x++) {
+                __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x], vec_B[x]);
+                __builtin_mma_xvi8ger4pp(&acc_1, vec_A[x+8], vec_B[x]);
+                __builtin_mma_xvi8ger4pp(&acc_2, vec_A[x], vec_B[x+8]);
+                __builtin_mma_xvi8ger4pp(&acc_3, vec_A[x+8], vec_B[x+8]);
+            }
+            for (int I = 0; I<8; I++) {
+                for (int J = 0; J<4; J++) {
+                    *((float*)&vs[I]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J)*ldb)+l)->d));
+                    *((float*)&vs[I+8]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J+4)*ldb)+l)->d));
+                }
+            }
+            auto aoffset = A+(ii*lda)+l;
+            for (int i = 0; i < 8; i++) {
+                comparray[i] = 0;
+                int ca = 0;
+                const int8_t *at = aoffset->qs;
+                for (int j = 0; j < 32; j++)
+                    ca += (int)*at++;
+                comparray[i] = ca;
+                aoffset += lda;
+            }
+            compute<8>(&acc_0, 0, 0, comparray, vs, fin_res);
+            compute<8>(&acc_1, 4, 4, comparray, vs, fin_res);
+            compute<8>(&acc_2, 0, 8, comparray, vs, fin_res);
+            compute<8>(&acc_3, 4, 12, comparray, vs, fin_res);
+        }
+        save_res<4, 4>(ii, jj, 0, fin_res);
+        save_res<4, 4>(ii+4, jj, 4, fin_res);
+        save_res<4, 4>(ii, jj+4, 8, fin_res);
+        save_res<4, 4>(ii+4, jj+4, 12, fin_res);
+    }
+
+    template<int RM, int RN>
+    void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t ytiles = (m - m0) / RM;
+        int64_t xtiles = (n - n0) / RN;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        vec_t vec_A[8], vec_B[8] = {0};
+        vector signed int vec_C[4];
+        acc_t acc_0;
+
+        if (end > tiles)
+            end = tiles;
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = m0 + job / xtiles * RM;
+            int64_t jj = n0 + job % xtiles * RN;
+            std::array<int, RM> comparray;
+            vector float res[4] = {0};
+            vector float fin_res[4] = {0};
+            vector float vs[4] = {0};
+            vector float CA[4] = {0};
+            __builtin_prefetch((A+(ii*lda)+0)->qs, 0, 1); // prefetch first value
+            __builtin_prefetch((B+(jj*ldb)+0)->qs, 0, 1); // prefetch first value
+            for (int l = 0; l < k; l++) {
+                __builtin_prefetch((A+(ii*lda)+(l+1))->qs, 0, 1); // prefetch one loop ahead
+                __builtin_prefetch((B+(jj*ldb)+(l+1))->qs, 0, 1); // prefetch one loop ahead
+                __builtin_mma_xxsetaccz(&acc_0);
+                packNormal<int8_t, vector signed char>((A+(ii*lda)+l), lda, RM, 8, (int8_t*)vec_A, false);
+                packNormal<uint8_t, vector unsigned char>((B+(jj*ldb)+l), ldb, RN, 8, (uint8_t*)vec_B, true);
+                for(int x = 0; x < 8; x+=4) {
+                    __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x], vec_B[x]);
+                    __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x+1], vec_B[x+1]);
+                    __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x+2], vec_B[x+2]);
+                    __builtin_mma_xvi8ger4pp(&acc_0, vec_A[x+3], vec_B[x+3]);
+                }
+                for (int I = 0; I<RM; I++) {
+                    for (int J = 0; J<RN; J++) {
+                        *((float*)&vs[I]+J) = (unhalf((A+((ii+I)*lda)+l)->d) * unhalf((B+((jj+J)*ldb)+l)->d));
+                    }
+                }
+                __builtin_mma_disassemble_acc(vec_C, &acc_0);
+                auto aoffset = A+(ii*lda)+l;
+                for (int i = 0; i < RM; i++) {
+                    comparray[i] = 0;
+                    int ca = 0;
+                    const int8_t *at = aoffset->qs;
+                    for (int j = 0; j < 32; j++)
+                        ca += (int)*at++;
+                    comparray[i] = ca;
+                    aoffset += lda;
+                }
+
+                for (int i = 0; i < RM; i++) {
+                    CA[i] = vec_splats((float)(((double)comparray[i]) * -128.0));
+                    res[i] = vec_add(vec_ctf(vec_C[i], 0), CA[i]);
+                    fin_res[i] = vec_madd(res[i], vs[i], fin_res[i]);
+                }
+            }
+            save_res<RM, RN>(ii, jj, 0, fin_res);
+        }
+    }
+
+    template<int RM, int RN>
+    inline void kernel(int64_t ii, int64_t jj) {
+       if constexpr(RM == 4 && RN == 8) {
+          KERNEL_4x8(ii,jj);
+       } else if constexpr(RM == 8 && RN == 4) {
+          KERNEL_8x4(ii,jj);
+       } else if constexpr(RM == 8 && RN == 8) {
+          KERNEL_8x8(ii,jj);
+       } else {
+          static_assert(false, "RN/RM values not supported");
+       }
+    }
+
+    template <int RM, int RN>
+    NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
+        int64_t ytiles = (m - m0) / RM;
+        int64_t xtiles = (n - n0) / RN;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        if (end > tiles)
+            end = tiles;
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = m0 + job / xtiles * RM;
+            int64_t jj = n0 + job % xtiles * RN;
+            kernel<RM, RN>(ii, jj);
+        }
+    }
+
+    const TA *const A;
+    const TB *const B;
+    TC *C;
+    TA *At;
+    TB *Bt;
+    const int64_t k;
+    const int64_t lda;
+    const int64_t ldb;
+    const int64_t ldc;
+    const int ith;
+    const int nth;
+};
+
 template <typename TA, typename TB, typename TC>
 class tinyBLAS_PPC {
   public:
@@ -1070,13 +1769,17 @@ class tinyBLAS_PPC {
 
     void (tinyBLAS_PPC::*kernel)(int64_t, int64_t);
 
-    void READ_BLOCK(const float* a, int64_t lda, int rows, int cols, float* vec) {
+    template<typename VA>
+    void packTranspose(const TA* a, int64_t lda, int rows, int cols, TA* vec) {
         int64_t i, j;
-        float *aoffset = NULL, *boffset = NULL;
-        float *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL;
-        float *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL;
-
-        aoffset = const_cast<float*>(a);
+        TA *aoffset = NULL, *boffset = NULL;
+        TA *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL;
+        TA *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL;
+        __vector_pair C1, C2, C3, C4, C5, C6, C7, C8;
+        VA c1[2] = {0}, c2[2] = {0}, c3[2] = {0}, c4[2] = {0};
+        VA c5[2] = {0}, c6[2] = {0}, c7[2] = {0}, c8[2] = {0};
+        VA t1, t2, t3, t4, t5, t6, t7, t8;
+        aoffset = const_cast<TA*>(a);
         boffset = vec;
         j = (rows >> 3);
         if (j > 0) {
@@ -1092,9 +1795,6 @@ class tinyBLAS_PPC {
                 aoffset += 8 * lda;
                 i = (cols >> 3);
                 if (i > 0) {
-                    __vector_pair C1, C2, C3, C4, C5, C6, C7, C8;
-                    vector float c1[2], c2[2], c3[2], c4[2], c5[2], c6[2], c7[2], c8[2];
-                    vector float t1, t2, t3, t4, t5, t6, t7, t8;
                     do {
                         C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1);
                         C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2);
@@ -1174,21 +1874,19 @@ class tinyBLAS_PPC {
                     } while(i > 0);
                 }
                 if (cols & 4) {
-                    vector float c1, c2, c3, c4, c5, c6, c7, c8;
-                    vector float t1, t2, t3, t4, t5, t6, t7, t8;
-                    c1 = vec_xl(0, aoffset1);
-                    c2 = vec_xl(0, aoffset2);
-                    c3 = vec_xl(0, aoffset3);
-                    c4 = vec_xl(0, aoffset4);
-                    c5 = vec_xl(0, aoffset5);
-                    c6 = vec_xl(0, aoffset6);
-                    c7 = vec_xl(0, aoffset7);
-                    c8 = vec_xl(0, aoffset8);
+                    c1[0] = vec_xl(0, aoffset1);
+                    c2[0] = vec_xl(0, aoffset2);
+                    c3[0] = vec_xl(0, aoffset3);
+                    c4[0] = vec_xl(0, aoffset4);
+                    c5[0] = vec_xl(0, aoffset5);
+                    c6[0] = vec_xl(0, aoffset6);
+                    c7[0] = vec_xl(0, aoffset7);
+                    c8[0] = vec_xl(0, aoffset8);
 
-                    t1 = vec_mergeh(c1, c2);
-                    t2 = vec_mergeh(c3, c4);
-                    t3 = vec_mergeh(c5, c6);
-                    t4 = vec_mergeh(c7, c8);
+                    t1 = vec_mergeh(c1[0], c2[0]);
+                    t2 = vec_mergeh(c3[0], c4[0]);
+                    t3 = vec_mergeh(c5[0], c6[0]);
+                    t4 = vec_mergeh(c7[0], c8[0]);
                     t5 = vec_xxpermdi(t1, t2, 0);
                     t6 = vec_xxpermdi(t3, t4, 0);
                     t7 = vec_xxpermdi(t1, t2, 3);
@@ -1198,10 +1896,10 @@ class tinyBLAS_PPC {
                     vec_xst(t7, 0, boffset+8);
                     vec_xst(t8, 0, boffset+12);
 
-                    t1 = vec_mergel(c1, c2);
-                    t2 = vec_mergel(c3, c4);
-                    t3 = vec_mergel(c5, c6);
-                    t4 = vec_mergel(c7, c8);
+                    t1 = vec_mergel(c1[0], c2[0]);
+                    t2 = vec_mergel(c3[0], c4[0]);
+                    t3 = vec_mergel(c5[0], c6[0]);
+                    t4 = vec_mergel(c7[0], c8[0]);
                     t5 = vec_xxpermdi(t1, t2, 0);
                     t6 = vec_xxpermdi(t3, t4, 0);
                     t7 = vec_xxpermdi(t1, t2, 3);
@@ -1223,9 +1921,6 @@ class tinyBLAS_PPC {
             aoffset += 4 * lda;
             i = (cols >> 3);
             if (i > 0) {
-                __vector_pair C1, C2, C3, C4;
-                vector float c1[2], c2[2], c3[2], c4[2];
-                vector float t1, t2, t3, t4, t5, t6, t7, t8;
                 do {
                     C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1);
                     C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2);
@@ -1272,22 +1967,20 @@ class tinyBLAS_PPC {
             }
 
             if (cols & 4) {
-                vector float c1, c2, c3, c4;
-                vector float t1, t2, t3, t4;
-                c1 = vec_xl(0, aoffset1);
-                c2 = vec_xl(0, aoffset2);
-                c3 = vec_xl(0, aoffset3);
-                c4 = vec_xl(0, aoffset4);
+                c1[0] = vec_xl(0, aoffset1);
+                c2[0] = vec_xl(0, aoffset2);
+                c3[0] = vec_xl(0, aoffset3);
+                c4[0] = vec_xl(0, aoffset4);
 
-                t1 = vec_mergeh(c1, c2);
-                t2 = vec_mergeh(c3, c4);
+                t1 = vec_mergeh(c1[0], c2[0]);
+                t2 = vec_mergeh(c3[0], c4[0]);
                 t3 = vec_xxpermdi(t1, t2, 0);
                 t4 = vec_xxpermdi(t1, t2, 3);
                 vec_xst(t3, 0, boffset);
                 vec_xst(t4, 0, boffset+4);
 
-                t1 = vec_mergel(c1, c2);
-                t2 = vec_mergel(c3, c4);
+                t1 = vec_mergel(c1[0], c2[0]);
+                t2 = vec_mergel(c3[0], c4[0]);
                 t3 = vec_xxpermdi(t1, t2, 0);
                 t4 = vec_xxpermdi(t1, t2, 3);
                 vec_xst(t3, 0, boffset+8);
@@ -1299,21 +1992,19 @@ class tinyBLAS_PPC {
             aoffset2 = aoffset1 + lda;
             aoffset3 = aoffset2 + lda;
             if (cols & 4) {
-                vector float c1, c2, c3, c4 = {0};
-                vector float t1, t2, t3, t4;
-                c1 = vec_xl(0, aoffset1);
-                c2 = vec_xl(0, aoffset2);
-                c3 = vec_xl(0, aoffset3);
+                c1[0] = vec_xl(0, aoffset1);
+                c2[0] = vec_xl(0, aoffset2);
+                c3[0] = vec_xl(0, aoffset3);
 
-                t1 = vec_mergeh(c1, c2);
-                t2 = vec_mergeh(c3, c4);
+                t1 = vec_mergeh(c1[0], c2[0]);
+                t2 = vec_mergeh(c3[0], c4[0]);
                 t3 = vec_xxpermdi(t1, t2, 0);
                 t4 = vec_xxpermdi(t1, t2, 3);
                 vec_xst(t3, 0, boffset);
                 vec_xst(t4, 0, boffset+4);
 
-                t1 = vec_mergel(c1, c2);
-                t2 = vec_mergel(c3, c4);
+                t1 = vec_mergel(c1[0], c2[0]);
+                t2 = vec_mergel(c3[0], c4[0]);
                 t3 = vec_xxpermdi(t1, t2, 0);
                 t4 = vec_xxpermdi(t1, t2, 3);
                 vec_xst(t3, 0, boffset+8);
@@ -1321,14 +2012,13 @@ class tinyBLAS_PPC {
             }
         }
     }
-
     void KERNEL_4x4(int64_t ii, int64_t jj) {
         vec_t vec_A[4], vec_B[4], vec_C[4];
         acc_t acc_0;
         __builtin_mma_xxsetaccz(&acc_0);
         for (int l = 0; l < k; l+=4) {
-            READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
-            READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
+            packTranspose<vector float>(A+(ii*lda)+l, lda, 4, 4, (TA*)vec_A);
+            packTranspose<vector float>(B+(jj*ldb)+l, ldb, 4, 4, (TA*)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]);
@@ -1343,8 +2033,8 @@ class tinyBLAS_PPC {
         __builtin_mma_xxsetaccz(&acc_0);
         __builtin_mma_xxsetaccz(&acc_1);
         for (int64_t l = 0; l < k; l+=4) {
-            READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
-            READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 4, (float*)vec_B);
+            packTranspose<vector float>(A+(ii*lda)+l, lda, 4, 4, (TA*)vec_A);
+            packTranspose<vector float>(B+(jj*ldb)+l, ldb, 8, 4, (TA*)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], (vec_t)vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_1, vec_A[0], (vec_t)vec_B[1]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], (vec_t)vec_B[2]);
@@ -1364,8 +2054,8 @@ class tinyBLAS_PPC {
         __builtin_mma_xxsetaccz(&acc_0);
         __builtin_mma_xxsetaccz(&acc_1);
         for (int64_t l = 0; l < k; l+=4) {
-            READ_BLOCK(A+(ii*lda)+l, lda, 8, 4, (float*)vec_A);
-            READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
+            packTranspose<vector float>(A+(ii*lda)+l, lda, 8, 4, (TA*)vec_A);
+            packTranspose<vector float>(B+(jj*ldb)+l, ldb, 4, 4, (TA*)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[0], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[1], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[2], vec_B[1]);
@@ -1387,8 +2077,8 @@ class tinyBLAS_PPC {
         __builtin_mma_xxsetaccz(&acc_2);
         __builtin_mma_xxsetaccz(&acc_3);
         for (int l = 0; l < k; l+=8) {
-            READ_BLOCK(A+(ii*lda)+l, lda, 8, 8, (float*)vec_A);
-            READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 8, (float*)vec_B);
+            packTranspose<vector float>(A+(ii*lda)+l, lda, 8, 8, (TA*)vec_A);
+            packTranspose<vector float>(B+(jj*ldb)+l, ldb, 8, 8, (TA*)vec_B);
             for(int x = 0; x < 16; x+=2) {
                 __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[x], vec_B[x]);
                 __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x+1]);
@@ -1571,15 +2261,15 @@ class tinyBLAS_PPC {
             vec_t vec_A[4], vec_B[4];
             for (int l=0; l<k; l+=4) {
                 if (RN >= 4 && RM == 1) {
-                    float* a = const_cast<float*>(A+(ii)*lda+l);
-                    READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
+                    TA* a = const_cast<TA*>(A+(ii)*lda+l);
+                    packTranspose<vector float>(B+(jj*ldb)+l, ldb, 4, 4, (TA*)vec_B);
                     vec_A[0] = (vec_t)vec_xl(0,a);
-                    vec_A[1] = (vec_t)vec_splats(*((float*)&vec_A+1));
-                    vec_A[2] = (vec_t)vec_splats(*((float*)&vec_A+2));
-                    vec_A[3] = (vec_t)vec_splats(*((float*)&vec_A+3));
+                    vec_A[1] = (vec_t)vec_splats(*((TA*)&vec_A+1));
+                    vec_A[2] = (vec_t)vec_splats(*((TA*)&vec_A+2));
+                    vec_A[3] = (vec_t)vec_splats(*((TA*)&vec_A+3));
                 } else {
-                    READ_BLOCK(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A);
-                    READ_BLOCK(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B);
+                    packTranspose<vector float>(A+(ii*lda)+l, lda, RM, 4, (TA*)vec_A);
+                    packTranspose<vector float>(B+(jj*ldb)+l, ldb, RN, 4, (TA*)vec_B);
                 }
                 __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
                 __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
@@ -1589,7 +2279,7 @@ class tinyBLAS_PPC {
             __builtin_mma_disassemble_acc(vec_C, &acc_0);
             for (int I = 0; I < RM; I++) {
                 for (int J = 0; J < RN; J++) {
-                    *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J);
+                    *((TC*)(C+ii+((jj+J)*ldc)+I)) = *((TC*)&vec_C[I]+J);
                 }
             }
        }
@@ -1812,6 +2502,20 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
             params->ith, params->nth};
         tb.matmul(m, n);
         return true;
+
+#elif defined(__MMA__)
+        if (n < 8 && n != 4)
+           return false;
+        if (m < 8 && m != 4)
+           return false;
+        tinyBLAS_Q0_PPC<block_q8_0, block_q8_0, float> tb{
+            k, (const block_q8_0 *)A, lda,
+            (const block_q8_0 *)B, ldb,
+            (float *)C, ldc,
+            params->ith, params->nth};
+        tb.matmul(m, n);
+        return true;
+
 #else
         return false;
 #endif

From a3c1232c3f475f0a77b9cc5225516ac31c567a06 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 8 Jan 2025 12:55:36 +0200
Subject: [PATCH 136/376] arg : option to exclude arguments from specific
 examples (#11136)

* arg : option to exclude arguments from specific examples

ggml-ci

* readme : remove old args [no ci]
---
 common/arg.cpp            | 17 +++++++++++++----
 common/arg.h              |  3 +++
 examples/server/README.md |  3 ---
 3 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index c81b15217..27886b84e 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -22,6 +22,11 @@ common_arg & common_arg::set_examples(std::initializer_list<enum llama_example>
     return *this;
 }
 
+common_arg & common_arg::set_excludes(std::initializer_list<enum llama_example> excludes) {
+    this->excludes = std::move(excludes);
+    return *this;
+}
+
 common_arg & common_arg::set_env(const char * env) {
     help = help + "\n(env: " + env + ")";
     this->env = env;
@@ -37,6 +42,10 @@ bool common_arg::in_example(enum llama_example ex) {
     return examples.find(ex) != examples.end();
 }
 
+bool common_arg::is_exclude(enum llama_example ex) {
+    return excludes.find(ex) != excludes.end();
+}
+
 bool common_arg::get_value_from_env(std::string & output) {
     if (env == nullptr) return false;
     char * value = std::getenv(env);
@@ -420,7 +429,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
      * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
      */
     auto add_opt = [&](common_arg arg) {
-        if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
+        if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) {
             ctx_arg.options.push_back(std::move(arg));
         }
     };
@@ -649,7 +658,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.prompt = value;
         }
-    ));
+    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@@ -673,7 +682,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.prompt.pop_back();
             }
         }
-    ));
+    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
         "an input file (repeat to specify multiple files)",
@@ -700,7 +709,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.prompt = ss.str();
             fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
         }
-    ));
+    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-e", "--escape"},
         string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
diff --git a/common/arg.h b/common/arg.h
index a6700d323..49ab8667b 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -12,6 +12,7 @@
 
 struct common_arg {
     std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
+    std::set<enum llama_example> excludes = {};
     std::vector<const char *> args;
     const char * value_hint   = nullptr; // help text or example for arg value
     const char * value_hint_2 = nullptr; // for second arg value
@@ -53,9 +54,11 @@ struct common_arg {
     ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
 
     common_arg & set_examples(std::initializer_list<enum llama_example> examples);
+    common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
     common_arg & set_env(const char * env);
     common_arg & set_sparam();
     bool in_example(enum llama_example ex);
+    bool is_exclude(enum llama_example ex);
     bool get_value_from_env(std::string & output);
     bool has_value_from_env();
     std::string to_string();
diff --git a/examples/server/README.md b/examples/server/README.md
index 3ce16945a..1f0a27d96 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -45,10 +45,7 @@ The project is under active development, and we are [looking for feedback and co
 | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
 | `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
-| `-p, --prompt PROMPT` | prompt to start generation with |
 | `--no-perf` | disable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_NO_PERF) |
-| `-f, --file FNAME` | a file containing the prompt (default: none) |
-| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
 | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
 | `--no-escape` | do not process escape sequences |
 | `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model<br/>(env: LLAMA_ARG_ROPE_SCALING_TYPE) |

From 80ccf5d725571035b454659e3c1b4b2b07b65e71 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 8 Jan 2025 12:07:20 +0100
Subject: [PATCH 137/376] ci : pin dependency to specific version (#11137)

* ci : pin dependency to specific version

* will this fix ec?
---
 .github/workflows/docker.yml       | 2 +-
 .github/workflows/editorconfig.yml | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 41f1a89ee..f5af72d0b 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -100,7 +100,7 @@ jobs:
       # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
       - name: Free Disk Space (Ubuntu)
         if: ${{ matrix.config.free_disk_space == true }}
-        uses: jlumbroso/free-disk-space@main
+        uses: jlumbroso/free-disk-space@v1.3.1
         with:
           # this might remove tools that are actually needed,
           # if set to "true" but frees about 6 GB
diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml
index ae86e9927..f02b7c219 100644
--- a/.github/workflows/editorconfig.yml
+++ b/.github/workflows/editorconfig.yml
@@ -23,5 +23,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: editorconfig-checker/action-editorconfig-checker@main
+      - uses: editorconfig-checker/action-editorconfig-checker@v2
+        with:
+          version: v3.0.3
       - run: editorconfig-checker

From c792dcf4880461c2b5f3960584db241ac71a893a Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Sun, 5 Jan 2025 09:50:37 +0200
Subject: [PATCH 138/376] ggml : allow loading backend with env variable
 (ggml/1059)

ref: #1058
---
 ggml/src/ggml-backend-reg.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 7ddd178b5..955ed505f 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -574,4 +574,9 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
     ggml_backend_load_best("opencl", silent, dir_path);
     ggml_backend_load_best("musa", silent, dir_path);
     ggml_backend_load_best("cpu", silent, dir_path);
+    // check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
+    const char * backend_path = std::getenv("GGML_BACKEND_PATH");
+    if (backend_path) {
+        ggml_backend_load(backend_path);
+    }
 }

From 99a3755a3c518119d0156766122f7b4b796ea576 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 8 Jan 2025 13:40:30 +0200
Subject: [PATCH 139/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index b67445ecd..a0921f1a9 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-a2af72be7baf5b1f4a33d34e77e509e5e85b7cd7
+c8bd0fee71dc8328d93be301bbee06bc10d30429

From c07d437bbd417f42b122e767ad42b3298767dca0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 8 Jan 2025 16:19:36 +0200
Subject: [PATCH 140/376] llama : avoid hardcoded QK_K (#11061)

ggml-ci
---
 src/llama-quant.cpp | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 038cf58dd..466e7bc61 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -7,14 +7,12 @@
 #include <algorithm>
 #include <cmath>
 #include <cstring>
+#include <cinttypes>
 #include <fstream>
 #include <mutex>
 #include <thread>
 #include <unordered_map>
 
-// TODO: replace with ggml API call
-#define QK_K 256
-
 static void zeros(std::ofstream & file, size_t n) {
     char zero = 0;
     for (size_t i = 0; i < n; ++i) {
@@ -154,8 +152,10 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t
         if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
             new_type = qs.params->output_tensor_type;
         } else {
-            int nx = tensor->ne[0];
-            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+            const int64_t nx = tensor->ne[0];
+            const int64_t qk_k = ggml_blck_size(new_type);
+
+            if (arch == LLM_ARCH_FALCON || nx % qk_k != 0) {
                 new_type = GGML_TYPE_Q8_0;
             }
             else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
@@ -367,20 +367,19 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t
     //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
     //}
     bool convert_incompatible_tensor = false;
-    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
-        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K    || new_type == GGML_TYPE_IQ4_XS ||
-        new_type == GGML_TYPE_IQ2_XS  || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S  ||
-        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S   || new_type == GGML_TYPE_IQ3_S  ||
-        new_type == GGML_TYPE_IQ1_M) {
-        int nx = tensor->ne[0];
-        int ny = tensor->ne[1];
-        if (nx % QK_K != 0) {
-            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
+    {
+        const int64_t nx = tensor->ne[0];
+        const int64_t ny = tensor->ne[1];
+        const int64_t qk_k = ggml_blck_size(new_type);
+
+        if (nx % qk_k != 0) {
+            LLAMA_LOG_WARN("\n\n%s : tensor cols %" PRId64 " x %" PRId64 " are not divisible by %" PRId64 ", required for %s", __func__, nx, ny, qk_k, ggml_type_name(new_type));
             convert_incompatible_tensor = true;
         } else {
             ++qs.n_k_quantized;
         }
     }
+
     if (convert_incompatible_tensor) {
         switch (new_type) {
             case GGML_TYPE_TQ1_0:

From 4d2b3d88041705b20c30b3219838aa435e7ffbde Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 8 Jan 2025 15:59:53 +0100
Subject: [PATCH 141/376] lora : improve compat with `mergekit-extract-lora`
 (#11131)

* (wip) support mergekit-extracted lora

* support mergekit-extract-lora

* use lora->get_scale

* correct comment

* correct norm name & condition

* add some hints
---
 convert_lora_to_gguf.py | 34 +++++++++++++++++++++++++++++++---
 src/llama-adapter.cpp   | 24 ++++++++++++++++++------
 src/llama-adapter.h     |  7 +++++++
 src/llama.cpp           | 21 ++++++++++++++++++---
 4 files changed, 74 insertions(+), 12 deletions(-)

diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index ed1014cae..6dea14a23 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -226,6 +226,9 @@ def get_base_tensor_name(lora_tensor_name: str) -> str:
     base_name = lora_tensor_name.replace("base_model.model.", "")
     base_name = base_name.replace(".lora_A.weight", ".weight")
     base_name = base_name.replace(".lora_B.weight", ".weight")
+    # models produced by mergekit-extract-lora have token embeddings in the adapter
+    base_name = base_name.replace(".lora_embedding_A", ".weight")
+    base_name = base_name.replace(".lora_embedding_B", ".weight")
     return base_name
 
 
@@ -260,6 +263,10 @@ def parse_args() -> argparse.Namespace:
         "--base", type=Path,
         help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
     )
+    parser.add_argument(
+        "--base-model-id", type=str,
+        help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')",
+    )
     parser.add_argument(
         "lora_path", type=Path,
         help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
@@ -290,6 +297,7 @@ if __name__ == '__main__':
 
     dir_base_model: Path | None = args.base
     dir_lora: Path = args.lora_path
+    base_model_id: str | None = args.base_model_id
     lora_config = dir_lora / "adapter_config.json"
     input_model = dir_lora / "adapter_model.safetensors"
 
@@ -313,7 +321,10 @@ if __name__ == '__main__':
         lparams: dict[str, Any] = json.load(f)
 
     # load base model
-    if dir_base_model is None:
+    if base_model_id is not None:
+        logger.info(f"Loading base model from Hugging Face: {base_model_id}")
+        hparams = load_hparams_from_hf(base_model_id)
+    elif dir_base_model is None:
         if "base_model_name_or_path" in lparams:
             model_id = lparams["base_model_name_or_path"]
             logger.info(f"Loading base model from Hugging Face: {model_id}")
@@ -371,11 +382,16 @@ if __name__ == '__main__':
                     if self.lazy:
                         tensor = LazyTorchTensor.from_eager(tensor)
                     base_name = get_base_tensor_name(name)
-                    is_lora_a = ".lora_A.weight" in name
-                    is_lora_b = ".lora_B.weight" in name
+                    # note: mergekit-extract-lora also adds token embeddings to the adapter
+                    is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
+                    is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name
                     if not is_lora_a and not is_lora_b:
                         if ".base_layer.weight" in name:
                             continue
+                        # mergekit-extract-lora add these layernorm to the adapter, we need to keep them
+                        if "_layernorm" in name or ".norm" in name:
+                            yield (base_name, tensor)
+                            continue
                         logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
                         if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
                             logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
@@ -407,9 +423,21 @@ if __name__ == '__main__':
                 if name == "lm_head.weight" and len(dest) == 0:
                     raise ValueError("lm_head is present in adapter, but is ignored in base model")
                 for dest_name, dest_data in dest:
+                    # mergekit-extract-lora add these layernorm to the adapter
+                    if "_norm" in dest_name:
+                        assert dest_data.dim() == 1
+                        yield (dest_name, dest_data)
+                        continue
+
+                    # otherwise, we must get the lora_A and lora_B tensors
                     assert isinstance(dest_data, LoraTorchTensor)
                     lora_a, lora_b = dest_data.get_lora_A_B()
 
+                    # note: mergekit-extract-lora flip and transpose A and B
+                    # here we only need to transpose token_embd.lora_a, see llm_build_inp_embd()
+                    if "token_embd.weight" in dest_name:
+                        lora_a = lora_a.T
+
                     yield (dest_name + ".lora_a", lora_a)
                     yield (dest_name + ".lora_b", lora_b)
 
diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp
index 9fd7edea3..d4879b778 100644
--- a/src/llama-adapter.cpp
+++ b/src/llama-adapter.cpp
@@ -242,6 +242,10 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
             } else {
                 ab_map[name].b = cur;
             }
+        } else if (str_endswith(name, "_norm.weight")) {
+            // TODO: add support for norm vector
+            // for now, we don't really care because most adapters still work fine without it
+            continue;
         } else {
             throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
         }
@@ -251,6 +255,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
     for (auto & it : ab_map) {
         const std::string & name = it.first;
         llama_lora_weight & w = it.second;
+        bool is_token_embd = str_endswith(name, "token_embd.weight");
 
         if (!w.a || !w.b) {
             throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
@@ -259,16 +264,23 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
         // device buft and device ctx
         auto * model_tensor = llama_model_get_tensor(model, name.c_str());
         if (!model_tensor) {
-            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
+            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
         }
 
         struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
         // validate tensor shape
-        if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
-            throw std::runtime_error("tensor '" + name + "' has incorrect shape");
-        }
-        if (w.a->ne[1] != w.b->ne[0]) {
-            throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+        if (is_token_embd) {
+            // expect B to be non-transposed, A and B are flipped; see llm_build_inp_embd()
+            if (model_tensor->ne[0] != w.b->ne[1] || model_tensor->ne[1] != w.a->ne[1]) {
+                throw std::runtime_error("tensor '" + name + "' has incorrect shape (hint: maybe wrong base model?)");
+            }
+        } else {
+            if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
+                throw std::runtime_error("tensor '" + name + "' has incorrect shape (hint: maybe wrong base model?)");
+            }
+            if (w.a->ne[1] != w.b->ne[0]) {
+                throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+            }
         }
 
         // save tensor to adapter
diff --git a/src/llama-adapter.h b/src/llama-adapter.h
index 5f1870cc8..3448656b1 100644
--- a/src/llama-adapter.h
+++ b/src/llama-adapter.h
@@ -45,6 +45,13 @@ struct llama_lora_weight {
     struct ggml_tensor * a = nullptr;
     struct ggml_tensor * b = nullptr;
 
+    // get actual scale based on rank and alpha
+    float get_scale(float alpha, float adapter_scale) {
+        const float rank  = (float) b->ne[0];
+        const float scale = alpha ? adapter_scale * alpha / rank : adapter_scale;
+        return scale;
+    }
+
     llama_lora_weight() = default;
     llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
 };
diff --git a/src/llama.cpp b/src/llama.cpp
index 8ea6686c9..97e716cd6 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2545,6 +2545,21 @@ static struct ggml_tensor * llm_build_inp_embd(
         ggml_set_input(lctx.inp_tokens);
 
         inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
+
+        // apply lora for embedding tokens if needed
+        for (auto & it : lctx.lora_adapters) {
+            struct llama_lora_weight * lora = it.first->get_weight(tok_embd);
+            if (lora == nullptr) {
+                continue;
+            }
+            const float adapter_scale = it.second;
+            const float scale = lora->get_scale(it.first->alpha, adapter_scale);
+            struct ggml_tensor * inpL_delta = ggml_scale(ctx, ggml_mul_mat(
+                ctx, lora->b, // non-transposed lora_b
+                ggml_get_rows(ctx, lora->a, lctx.inp_tokens)
+            ), scale);
+            inpL = ggml_add(ctx, inpL, inpL_delta);
+        }
     } else {
         lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
         inpL = lctx.inp_embd;
@@ -2617,9 +2632,8 @@ static struct ggml_tensor * llm_build_lora_mm(
         if (lora == nullptr) {
             continue;
         }
-        const float alpha = it.first->alpha;
-        const float rank  = (float) lora->b->ne[0];
-        const float scale = alpha ? it.second * alpha / rank : it.second;
+        const float adapter_scale = it.second;
+        const float scale = lora->get_scale(it.first->alpha, adapter_scale);
         struct ggml_tensor * ab_cur = ggml_mul_mat(
             ctx0, lora->b,
             ggml_mul_mat(ctx0, lora->a, cur)
@@ -3967,6 +3981,7 @@ struct llm_build_context {
 
             // feed-forward network
             if (model.layers[il].ffn_gate_inp == nullptr) {
+
                 cur = llm_build_norm(ctx0, ffn_inp, hparams,
                         model.layers[il].ffn_norm, NULL,
                         LLM_NORM_RMS, cb, il);

From f7cd13301c2a88f97073fd119072b4cc92c08df1 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 8 Jan 2025 16:09:20 +0100
Subject: [PATCH 142/376] ci : use actions from ggml-org (#11140)

---
 .github/workflows/build.yml  | 2 +-
 .github/workflows/docker.yml | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 02a193b86..c85999b89 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1237,7 +1237,7 @@ jobs:
 
       - name: Create release
         id: create_release
-        uses: anzz1/action-create-release@v1
+        uses: ggml-org/action-create-release@v1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index f5af72d0b..d71f1eb38 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -97,10 +97,9 @@ jobs:
           GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
           GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
 
-      # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
       - name: Free Disk Space (Ubuntu)
         if: ${{ matrix.config.free_disk_space == true }}
-        uses: jlumbroso/free-disk-space@v1.3.1
+        uses: ggml-org/free-disk-space@v1.3.1
         with:
           # this might remove tools that are actually needed,
           # if set to "true" but frees about 6 GB

From 1bf839b1e8b9d043306c65eddd9021fe4337733e Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Wed, 8 Jan 2025 18:47:05 +0000
Subject: [PATCH 143/376] Enhance user input handling for llama-run (#11138)

The main motivation for this change is it was not handing
ctrl-c/ctrl-d correctly. Modify `read_user_input` to handle EOF,
"/bye" command, and empty input cases. Introduce `get_user_input`
function to manage user input loop and handle different return
cases.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 63 +++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 60 insertions(+), 3 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 2888fcfed..61420e441 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -11,6 +11,8 @@
 #    include <curl/curl.h>
 #endif
 
+#include <signal.h>
+
 #include <climits>
 #include <cstdarg>
 #include <cstdio>
@@ -25,6 +27,13 @@
 #include "json.hpp"
 #include "llama-cpp.h"
 
+#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
+[[noreturn]] static void sigint_handler(int) {
+    printf("\n");
+    exit(0);  // not ideal, but it's the only way to guarantee exit in all cases
+}
+#endif
+
 GGML_ATTRIBUTE_FORMAT(1, 2)
 static std::string fmt(const char * fmt, ...) {
     va_list ap;
@@ -801,7 +810,20 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
 
 static int read_user_input(std::string & user) {
     std::getline(std::cin, user);
-    return user.empty();  // Should have data in happy path
+    if (std::cin.eof()) {
+        printf("\n");
+        return 1;
+    }
+
+    if (user == "/bye") {
+        return 1;
+    }
+
+    if (user.empty()) {
+        return 2;
+    }
+
+    return 0;  // Should have data in happy path
 }
 
 // Function to generate a response based on the prompt
@@ -868,7 +890,25 @@ static bool is_stdout_a_terminal() {
 #endif
 }
 
-// Function to tokenize the prompt
+// Function to handle user input
+static int get_user_input(std::string & user_input, const std::string & user) {
+    while (true) {
+        const int ret = handle_user_input(user_input, user);
+        if (ret == 1) {
+            return 1;
+        }
+
+        if (ret == 2) {
+            continue;
+        }
+
+        break;
+    }
+
+    return 0;
+}
+
+// Main chat loop function
 static int chat_loop(LlamaData & llama_data, const std::string & user) {
     int prev_len = 0;
     llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
@@ -876,7 +916,8 @@ static int chat_loop(LlamaData & llama_data, const std::string & user) {
     while (true) {
         // Get user input
         std::string user_input;
-        while (handle_user_input(user_input, user)) {
+        if (get_user_input(user_input, user) == 1) {
+            return 0;
         }
 
         add_message("user", user.empty() ? user_input : user, llama_data);
@@ -917,7 +958,23 @@ static std::string read_pipe_data() {
     return result.str();
 }
 
+static void ctrl_c_handling() {
+#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
+    struct sigaction sigint_action;
+    sigint_action.sa_handler = sigint_handler;
+    sigemptyset(&sigint_action.sa_mask);
+    sigint_action.sa_flags = 0;
+    sigaction(SIGINT, &sigint_action, NULL);
+#elif defined(_WIN32)
+    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+        return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+    };
+    SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+}
+
 int main(int argc, const char ** argv) {
+    ctrl_c_handling();
     Opt       opt;
     const int ret = opt.init(argc, argv);
     if (ret == 2) {

From 8a1d9c25fafbaf4182dd0b785dd6303ee40d55bc Mon Sep 17 00:00:00 2001
From: Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com>
Date: Wed, 8 Jan 2025 12:54:58 -0600
Subject: [PATCH 144/376] gguf-py : move scripts directory (#11116)

* Moved scripts dir and fixed pyproject.toml

* updated readme

* fixed README urls

* bump pypi gguf to v0.14.0

* retrigger ci

* empty commit - trigger ci
---
 gguf-py/README.md                                 |  8 ++++----
 gguf-py/{ => gguf}/scripts/__init__.py            |  0
 gguf-py/{ => gguf}/scripts/gguf_convert_endian.py |  0
 gguf-py/{ => gguf}/scripts/gguf_dump.py           |  0
 gguf-py/{ => gguf}/scripts/gguf_hash.py           |  0
 gguf-py/{ => gguf}/scripts/gguf_new_metadata.py   |  0
 gguf-py/{ => gguf}/scripts/gguf_set_metadata.py   |  0
 gguf-py/pyproject.toml                            | 11 +++++------
 8 files changed, 9 insertions(+), 10 deletions(-)
 rename gguf-py/{ => gguf}/scripts/__init__.py (100%)
 rename gguf-py/{ => gguf}/scripts/gguf_convert_endian.py (100%)
 rename gguf-py/{ => gguf}/scripts/gguf_dump.py (100%)
 rename gguf-py/{ => gguf}/scripts/gguf_hash.py (100%)
 rename gguf-py/{ => gguf}/scripts/gguf_new_metadata.py (100%)
 rename gguf-py/{ => gguf}/scripts/gguf_set_metadata.py (100%)

diff --git a/gguf-py/README.md b/gguf-py/README.md
index 24af96a17..37a75923b 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -15,13 +15,13 @@ pip install gguf
 
 [examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
 
-[scripts/gguf_dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console.
+[gguf/scripts/gguf_dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console.
 
-[scripts/gguf_set_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key.
+[gguf/scripts/gguf_set_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key.
 
-[scripts/gguf_convert_endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_convert_endian.py) — Allows converting the endianness of GGUF files.
+[gguf/scripts/gguf_convert_endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_convert_endian.py) — Allows converting the endianness of GGUF files.
 
-[scripts/gguf_new_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_new_metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
+[gguf/scripts/gguf_new_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_new_metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
 
 ## Development
 Maintainers who participate in development of this package are advised to install it in editable mode:
diff --git a/gguf-py/scripts/__init__.py b/gguf-py/gguf/scripts/__init__.py
similarity index 100%
rename from gguf-py/scripts/__init__.py
rename to gguf-py/gguf/scripts/__init__.py
diff --git a/gguf-py/scripts/gguf_convert_endian.py b/gguf-py/gguf/scripts/gguf_convert_endian.py
similarity index 100%
rename from gguf-py/scripts/gguf_convert_endian.py
rename to gguf-py/gguf/scripts/gguf_convert_endian.py
diff --git a/gguf-py/scripts/gguf_dump.py b/gguf-py/gguf/scripts/gguf_dump.py
similarity index 100%
rename from gguf-py/scripts/gguf_dump.py
rename to gguf-py/gguf/scripts/gguf_dump.py
diff --git a/gguf-py/scripts/gguf_hash.py b/gguf-py/gguf/scripts/gguf_hash.py
similarity index 100%
rename from gguf-py/scripts/gguf_hash.py
rename to gguf-py/gguf/scripts/gguf_hash.py
diff --git a/gguf-py/scripts/gguf_new_metadata.py b/gguf-py/gguf/scripts/gguf_new_metadata.py
similarity index 100%
rename from gguf-py/scripts/gguf_new_metadata.py
rename to gguf-py/gguf/scripts/gguf_new_metadata.py
diff --git a/gguf-py/scripts/gguf_set_metadata.py b/gguf-py/gguf/scripts/gguf_set_metadata.py
similarity index 100%
rename from gguf-py/scripts/gguf_set_metadata.py
rename to gguf-py/gguf/scripts/gguf_set_metadata.py
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 9c3956256..92d7f22ec 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,12 +1,11 @@
 [tool.poetry]
 name = "gguf"
-version = "0.13.0"
+version = "0.14.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
     {include = "gguf"},
     {include = "gguf/py.typed"},
-    {include = "scripts"},
 ]
 readme = "README.md"
 homepage = "https://ggml.ai"
@@ -33,7 +32,7 @@ requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
-gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint"
-gguf-dump = "scripts:gguf_dump_entrypoint"
-gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint"
-gguf-new-metadata = "scripts:gguf_new_metadata_entrypoint"
+gguf-convert-endian = "gguf.scripts:gguf_convert_endian_entrypoint"
+gguf-dump = "gguf.scripts:gguf_dump_entrypoint"
+gguf-set-metadata = "gguf.scripts:gguf_set_metadata_entrypoint"
+gguf-new-metadata = "gguf.scripts:gguf_new_metadata_entrypoint"

From 8d59d911711b8f1ba9ec57c4b192ccd2628af033 Mon Sep 17 00:00:00 2001
From: hydai <z54981220@gmail.com>
Date: Thu, 9 Jan 2025 04:03:28 +0800
Subject: [PATCH 145/376] fix: add missing msg in static_assert (#11143)

Signed-off-by: hydai <z54981220@gmail.com>
---
 ggml/src/ggml-cuda/concat.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu
index 2f42b8a95..aafbaf803 100644
--- a/ggml/src/ggml-cuda/concat.cu
+++ b/ggml/src/ggml-cuda/concat.cu
@@ -124,7 +124,7 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
           uint64_t   nb1,
           uint64_t   nb2,
           uint64_t   nb3){
-    static_assert(dim >= 0 && dim <= 3);
+    static_assert(dim >= 0 && dim <= 3, "dim must be in [0, 3]");
 
     const int64_t i3 = blockIdx.z;
     const int64_t i2 = blockIdx.y;

From d9feae1c06321aac9662fd4b4249452dccaec553 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 9 Jan 2025 10:07:33 +0100
Subject: [PATCH 146/376] llama-chat : add phi 4 template (#11148)

---
 src/llama-chat.cpp           | 13 ++++++++++++-
 src/llama-chat.h             |  1 +
 tests/test-chat-template.cpp |  6 +++++-
 3 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 44670d3d8..1347ec156 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -35,6 +35,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
+    { "phi4",              LLM_CHAT_TEMPLATE_PHI_4             },
     { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
     { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
     { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
@@ -73,7 +74,9 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return tmpl.find(haystack) != std::string::npos;
     };
     if (tmpl_contains("<|im_start|>")) {
-        return LLM_CHAT_TEMPLATE_CHATML;
+        return tmpl_contains("<|im_sep|>")
+            ? LLM_CHAT_TEMPLATE_PHI_4
+            : LLM_CHAT_TEMPLATE_CHATML;
     } else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
         if (tmpl_contains("[SYSTEM_PROMPT]")) {
             return LLM_CHAT_TEMPLATE_MISTRAL_V7;
@@ -269,6 +272,14 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_PHI_4) {
+        // chatml template
+        for (auto message : chat) {
+            ss << "<|im_start|>" << message->role << "<|im_sep|>" << message->content << "<|im_end|>";
+        }
+        if (add_ass) {
+            ss << "<|im_start|>assistant<|im_sep|>";
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
         // Falcon 3
         for (auto message : chat) {
diff --git a/src/llama-chat.h b/src/llama-chat.h
index b8e94d9ef..3a4d07ce3 100644
--- a/src/llama-chat.h
+++ b/src/llama-chat.h
@@ -15,6 +15,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
     LLM_CHAT_TEMPLATE_PHI_3,
+    LLM_CHAT_TEMPLATE_PHI_4,
     LLM_CHAT_TEMPLATE_FALCON_3,
     LLM_CHAT_TEMPLATE_ZEPHYR,
     LLM_CHAT_TEMPLATE_MONARCH,
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 51bfb155b..f1f9aec4d 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -78,7 +78,9 @@ int main(void) {
         // ai-sage/GigaChat-20B-A3B-instruct
         "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
         // Infinigence/Megrez-3B-Instruct
-        u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}"
+        u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}",
+        // phi-4
+        "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|><|im_start|>assistant<|im_sep|>'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -137,6 +139,8 @@ int main(void) {
         "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
         // Infinigence/Megrez-3B-Instruct
         "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|>   I am an assistant   <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
+        // phi-4
+        "<|im_start|>system<|im_sep|>You are a helpful assistant<|im_end|><|im_start|>user<|im_sep|>Hello<|im_end|><|im_start|>assistant<|im_sep|>Hi there<|im_end|><|im_start|>user<|im_sep|>Who are you<|im_end|><|im_start|>assistant<|im_sep|>   I am an assistant   <|im_end|><|im_start|>user<|im_sep|>Another question<|im_end|><|im_start|>assistant<|im_sep|>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;

From be0e950c91cde2d8488ae32162b549d7023482f0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 9 Jan 2025 11:15:15 +0200
Subject: [PATCH 147/376] media : remove old img [no ci]

---
 media/llama-leader.jpeg | Bin 199945 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 media/llama-leader.jpeg

diff --git a/media/llama-leader.jpeg b/media/llama-leader.jpeg
deleted file mode 100644
index 0b4e6e1cfbd442f1d945f90d5d668e19252ccffd..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 199945
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PtmFAW6rj{ueP{pKfXbNy


From f8feb4b01af374ad2fce302fd5790529c615710b Mon Sep 17 00:00:00 2001
From: Pierrick Hymbert <pierrick.hymbert@gmail.com>
Date: Thu, 9 Jan 2025 11:21:41 +0100
Subject: [PATCH 148/376] model: Add support for PhiMoE arch (#11003)

* model: support phimoe

* python linter

* doc: minor

Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com>

* doc: minor

Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com>

* doc: add phimoe as supported model

ggml-ci

---------

Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com>
---
 README.md                           |  1 +
 convert_hf_to_gguf.py               | 57 +++++++++++++++++++++
 docs/development/HOWTO-add-model.md | 10 ++--
 gguf-py/gguf/constants.py           | 20 ++++++++
 gguf-py/gguf/tensor_mapping.py      | 37 +++++++-------
 src/llama-arch.cpp                  | 22 ++++++++
 src/llama-arch.h                    |  1 +
 src/llama-model.cpp                 | 11 ++++
 src/llama-model.h                   |  1 +
 src/llama.cpp                       | 79 +++++++++++++++++++++++++----
 10 files changed, 208 insertions(+), 31 deletions(-)

diff --git a/README.md b/README.md
index 0126da89c..a71015256 100644
--- a/README.md
+++ b/README.md
@@ -69,6 +69,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
 - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
 - [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
+- [x] [PhiMoE](https://github.com/ggerganov/llama.cpp/pull/11003)
 - [x] [GPT-2](https://huggingface.co/gpt2)
 - [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
 - [x] [InternLM2](https://huggingface.co/models?search=internlm2)
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 01b58f976..5562499aa 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2562,6 +2562,63 @@ class Phi3MiniModel(Model):
         yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
 
 
+@Model.register("PhiMoEForCausalLM")
+class PhiMoeModel(Phi3MiniModel):
+    model_arch = gguf.MODEL_ARCH.PHIMOE
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
+        self.gguf_writer.add_expert_count(self.hparams["num_local_experts"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
     model_arch = gguf.MODEL_ARCH.PLAMO
diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md
index 04c5ccbbe..8fcd70811 100644
--- a/docs/development/HOWTO-add-model.md
+++ b/docs/development/HOWTO-add-model.md
@@ -28,7 +28,7 @@ The required steps to implement for an HF model are:
 ```python
 @Model.register("MyModelForCausalLM")
 class MyModel(Model):
-    model_arch = gguf.MODEL_ARCH.GROK
+    model_arch = gguf.MODEL_ARCH.MYMODEL
 ```
 
 2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
@@ -79,14 +79,14 @@ Depending on the model configuration, tokenizer, code and tensors layout, you wi
 - `Model#set_vocab`
 - `Model#write_tensors`
 
-NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
+NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.
 
 ### 2. Define the model architecture in `llama.cpp`
 
 The model params and tensors layout must be defined in `llama.cpp`:
 1. Define a new `llm_arch`
 2. Define the tensors layout in `LLM_TENSOR_NAMES`
-3. Add any non standard metadata in `llm_load_hparams`
+3. Add any non-standard metadata in `llm_load_hparams`
 4. Create the tensors for inference in `llm_load_tensors`
 5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
 
@@ -96,9 +96,9 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc
 
 This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
 
-Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
+Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`.
 
-When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
+Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR.
 
 Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 9d0e7489f..cf05bf47e 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -244,6 +244,7 @@ class MODEL_ARCH(IntEnum):
     QWEN2VL          = auto()
     PHI2             = auto()
     PHI3             = auto()
+    PHIMOE           = auto()
     PLAMO            = auto()
     CODESHELL        = auto()
     ORION            = auto()
@@ -428,6 +429,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN2VL:          "qwen2vl",
     MODEL_ARCH.PHI2:             "phi2",
     MODEL_ARCH.PHI3:             "phi3",
+    MODEL_ARCH.PHIMOE:           "phimoe",
     MODEL_ARCH.PLAMO:            "plamo",
     MODEL_ARCH.CODESHELL:        "codeshell",
     MODEL_ARCH.ORION:            "orion",
@@ -940,6 +942,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.PHIMOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FACTORS_LONG,
+        MODEL_TENSOR.ROPE_FACTORS_SHORT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     MODEL_ARCH.CODESHELL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.POS_EMBD,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index efe2a4aa4..7616c468a 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -55,7 +55,7 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo2
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo2 phimoe
             "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
@@ -68,7 +68,7 @@ class TensorNameMap:
         MODEL_TENSOR.OUTPUT_NORM: (
             "gpt_neox.final_layer_norm",               # gptneox
             "transformer.ln_f",                        # gpt2 gpt-j falcon jais exaone
-            "model.norm",                              # llama-hf baichuan internlm2 olmoe olmo2
+            "model.norm",                              # llama-hf baichuan internlm2 olmoe olmo2 phimoe
             "norm",                                    # llama-pth
             "transformer.norm_f",                      # mpt dbrx
             "ln_f",                                    # refact bloom qwen gpt2
@@ -108,7 +108,7 @@ class TensorNameMap:
             "transformer.h.{bid}.input_layernorm",                  # falcon7b
             "h.{bid}.input_layernorm",                              # bloom
             "transformer.h.{bid}.ln_mlp",                           # falcon40b
-            "model.layers.{bid}.input_layernorm",                   # llama-hf nemotron olmoe
+            "model.layers.{bid}.input_layernorm",                   # llama-hf nemotron olmoe phimoe
             "layers.{bid}.attention_norm",                          # llama-pth
             "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
@@ -152,7 +152,7 @@ class TensorNameMap:
 
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
-            "model.layers.{bid}.self_attn.q_proj",                       # llama-hf nemotron olmoe olmo2
+            "model.layers.{bid}.self_attn.q_proj",                       # llama-hf nemotron olmoe olmo2 phimoe
             "model.layers.{bid}.self_attn.q_proj_no_perm",               # llama-custom
             "layers.{bid}.attention.wq",                                 # llama-pth
             "encoder.layer.{bid}.attention.self.query",                  # bert
@@ -165,7 +165,7 @@ class TensorNameMap:
 
         # Attention key
         MODEL_TENSOR.ATTN_K: (
-            "model.layers.{bid}.self_attn.k_proj",                     # llama-hf nemotron olmoe olmo2
+            "model.layers.{bid}.self_attn.k_proj",                     # llama-hf nemotron olmoe olmo2 phimoe
             "model.layers.{bid}.self_attn.k_proj_no_perm",             # llama-custom
             "layers.{bid}.attention.wk",                               # llama-pth
             "encoder.layer.{bid}.attention.self.key",                  # bert
@@ -179,7 +179,7 @@ class TensorNameMap:
 
         # Attention value
         MODEL_TENSOR.ATTN_V: (
-            "model.layers.{bid}.self_attn.v_proj",                       # llama-hf nemotron olmoe olmo2
+            "model.layers.{bid}.self_attn.v_proj",                       # llama-hf nemotron olmoe olmo2 phimoe
             "layers.{bid}.attention.wv",                                 # llama-pth
             "encoder.layer.{bid}.attention.self.value",                  # bert
             "transformer.h.{bid}.attn.v_proj",                           # gpt-j
@@ -197,7 +197,7 @@ class TensorNameMap:
             "transformer.blocks.{bid}.attn.out_proj",                       # mpt
             "transformer.h.{bid}.self_attention.dense",                     # falcon
             "h.{bid}.self_attention.dense",                                 # bloom
-            "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2
+            "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2 phimoe
             "model.layers.{bid}.self_attn.linear_attn",                     # deci
             "layers.{bid}.attention.wo",                                    # llama-pth
             "encoder.layer.{bid}.attention.output.dense",                   # bert
@@ -242,7 +242,7 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen jais exaone
             "h.{bid}.post_attention_layernorm",                              # bloom
             "transformer.blocks.{bid}.norm_2",                               # mpt
-            "model.layers.{bid}.post_attention_layernorm",                   # llama-hf nemotron olmoe
+            "model.layers.{bid}.post_attention_layernorm",                   # llama-hf nemotron olmoe phimoe
             "layers.{bid}.ffn_norm",                                         # llama-pth
             "language_model.encoder.layers.{bid}.post_attention_layernorm",  # persimmon
             "model.layers.{bid}.ln2",                                        # yi
@@ -265,7 +265,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_GATE_INP: (
             "layers.{bid}.feed_forward.gate",                   # mixtral
-            "model.layers.{bid}.block_sparse_moe.gate",         # mixtral
+            "model.layers.{bid}.block_sparse_moe.gate",         # mixtral phimoe
             "model.layers.{bid}.mlp.gate",                      # qwen2moe olmoe
             "transformer.decoder_layer.{bid}.router",           # Grok
             "transformer.blocks.{bid}.ffn.router.layer",        # dbrx
@@ -310,10 +310,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
-            "layers.{bid}.feed_forward.experts.w3",          # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear_v",  # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.v1",   # dbrx
-            "model.layers.{bid}.mlp.experts.up_proj",        # qwen2moe olmoe (merged)
+            "layers.{bid}.feed_forward.experts.w3",           # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_v",   # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.v1",    # dbrx
+            "model.layers.{bid}.mlp.experts.up_proj",         # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged)
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -342,10 +343,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.w1",         # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear",   # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.w1",  # dbrx
-            "model.layers.{bid}.mlp.experts.gate_proj",     # qwen2moe olmoe (merged)
+            "layers.{bid}.feed_forward.experts.w1",           # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear",     # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w1",    # dbrx
+            "model.layers.{bid}.mlp.experts.gate_proj",       # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
@@ -387,6 +389,7 @@ class TensorNameMap:
             "transformer.blocks.{bid}.ffn.experts.mlp.w2",       # dbrx
             "model.layers.{bid}.mlp.experts.down_proj",          # qwen2moe olmoe (merged)
             "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
+            "model.layers.{bid}.block_sparse_moe.experts.w2",    # phimoe (merged)
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 007d79f82..eef66ed31 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -27,6 +27,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
     { LLM_ARCH_PHI2,             "phi2"             },
     { LLM_ARCH_PHI3,             "phi3"             },
+    { LLM_ARCH_PHIMOE,           "phimoe"           },
     { LLM_ARCH_PLAMO,            "plamo"            },
     { LLM_ARCH_CODESHELL,        "codeshell"        },
     { LLM_ARCH_ORION,            "orion"            },
@@ -584,6 +585,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_PHIMOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+        },
+    },
     {
         LLM_ARCH_PLAMO,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 45e458bb9..2e5f97b77 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -31,6 +31,7 @@ enum llm_arch {
     LLM_ARCH_QWEN2VL,
     LLM_ARCH_PHI2,
     LLM_ARCH_PHI3,
+    LLM_ARCH_PHIMOE,
     LLM_ARCH_PLAMO,
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 7deb3683b..7260cb155 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -76,6 +76,7 @@ const char * llm_type_name(llm_type type) {
         case MODEL_8x7B:          return "8x7B";
         case MODEL_8x22B:         return "8x22B";
         case MODEL_16x12B:        return "16x12B";
+        case MODEL_16x3_8B:       return "16x3.8B";
         case MODEL_10B_128x3_66B: return "10B+128x3.66B";
         case MODEL_57B_A14B:      return "57B.A14B";
         case MODEL_27B:           return "27B";
@@ -661,6 +662,15 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                     throw std::runtime_error("invalid value for sliding_window");
                 }
             } break;
+        case LLM_ARCH_PHIMOE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_16x3_8B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_PLAMO:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -2094,6 +2104,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_OLMOE:
         case LLM_ARCH_PHI2:
         case LLM_ARCH_PHI3:
+        case LLM_ARCH_PHIMOE:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_GEMMA2:
         case LLM_ARCH_STARCODER2:
diff --git a/src/llama-model.h b/src/llama-model.h
index ce038932d..424cb0f52 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -73,6 +73,7 @@ enum llm_type {
     MODEL_8x7B,
     MODEL_8x22B,
     MODEL_16x12B,
+    MODEL_16x3_8B,
     MODEL_10B_128x3_66B,
     MODEL_57B_A14B,
     MODEL_27B,
diff --git a/src/llama.cpp b/src/llama.cpp
index 97e716cd6..ae375bcd3 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1212,6 +1212,50 @@ static bool llm_load_tensors(
                         layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
                         layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
 
+                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                    }
+                } break;
+            case LLM_ARCH_PHIMOE:
+                {
+                    const int64_t n_embd_head = n_embd / n_head;
+
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
+                    model.output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        if (layer.wqkv == nullptr) {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias",   i), {n_embd}, 0);
+
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
+
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
+                        }
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+
                         layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                         layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                     }
@@ -6266,7 +6310,7 @@ struct llm_build_context {
 
                 struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm,
-                    NULL,
+                    model.layers[il].attn_norm_b,
                     LLM_NORM_RMS, cb, il);
                 cb(attn_norm_output, "attn_norm", il);
 
@@ -6281,8 +6325,7 @@ struct llm_build_context {
                     Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
                     Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
                     Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
-                }
-                else {
+                } else {
                     Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                     Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                     Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
@@ -6326,14 +6369,12 @@ struct llm_build_context {
             residual = cur;
 
             cur = llm_build_norm(ctx0, cur, hparams,
-                model.layers[il].ffn_norm, NULL,
+                model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
                 LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            // FF
-            // special-case: the up and gate tensors are merged into a single tensor
-            // TOOD: support into llm_build_ffn
-            {
+            // feed-forward network
+            if (model.layers[il].ffn_gate_inp == nullptr) {
                 cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         NULL,                      NULL, NULL,
@@ -6341,6 +6382,20 @@ struct llm_build_context {
                         NULL,
                         LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = llm_build_moe_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        cb, il);
+                cb(cur, "ffn_moe_out", il);
             }
 
             cur = ggml_add(ctx0, residual, cur);
@@ -6353,11 +6408,16 @@ struct llm_build_context {
 
         cur = llm_build_norm(ctx0, inpL, hparams,
             model.output_norm,
-            NULL,
+            model.output_norm_b,
             LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
         cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        if (model.output_b != nullptr) {
+            cb(cur, "result_output_no_bias", -1);
+            cur = ggml_add(ctx0, cur, model.output_b);
+        }
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10536,6 +10596,7 @@ static struct ggml_cgraph * llama_build_graph(
                 result = llm.build_phi2();
             } break;
         case LLM_ARCH_PHI3:
+        case LLM_ARCH_PHIMOE:
             {
                 result = llm.build_phi3();
             } break;

From 8eceb888d7b7f5e93d20a4f85ca6511022b87040 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 9 Jan 2025 11:28:29 +0100
Subject: [PATCH 149/376] server : add tooltips to settings and themes btn
 (#11154)

* server : add tooltips to settings and themes btn

This commit adds tooltips to the settings and themes buttons in the
webui. The tooltip will be displayed below the actual buttons when
hovered over.

The motivation for this change is to clarify the purpose of the themes
button.

* squash! server : add tooltips to settings and themes btn

This commit adds a tooltip to the '...' button when a chat has been
started. The tooltip is "Chat options" which think could be a good
description as the dropdown contains options to delete or download the
current chat.

* rm tooltip for 3 dots button

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/public/index.html.gz | Bin 1205858 -> 1206458 bytes
 examples/server/webui/index.html     |  72 ++++++++++++++-------------
 2 files changed, 38 insertions(+), 34 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 36f9c9fe9a68d6843be38c7b798e29a950ebcee5..3640a7a6cfa76764d93684e9051a32c263932c8a 100644
GIT binary patch
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diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index dcdd41079..86a79b77f 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -62,53 +62,57 @@
           <!-- action buttons (top right) -->
           <div class="flex items-center">
             <div v-if="messages.length > 0" class="dropdown dropdown-end">
-              <!-- "more" button -->
+              <!-- "..." button -->
               <button tabindex="0" role="button" class="btn m-1" :disabled="isGenerating">
                 <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-three-dots-vertical" viewBox="0 0 16 16">
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                 </svg>
               </button>
-              <!-- "more" dropdown menu -->
+              <!-- "delete" dropdown menu -->
               <ul tabindex="0" class="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
                 <li @click="downloadConv(viewingConvId)"><a>Download</a></li>
                 <li class="text-error" @click="deleteConv(viewingConvId)"><a>Delete</a></li>
               </ul>
             </div>
-            <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
-              <!-- settings button -->
-              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
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-            </button>
+            <div class="tooltip tooltip-bottom" data-tip="Settings">
+              <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
+                <!-- settings button -->
+                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
+                  <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
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+                </svg>
+              </button>
+            </div>
 
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+            <div class="tooltip tooltip-bottom" data-tip="Themes">
+              <div class="dropdown dropdown-end dropdown-bottom">
+                <div tabindex="0" role="button" class="btn m-1">
+                  <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-palette2" viewBox="0 0 16 16">
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+                  </svg>
+                </div>
+                <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
+                  <li>
+                    <button
+                      class="btn btn-sm btn-block btn-ghost justify-start"
+                      :class="{ 'btn-active': selectedTheme === 'auto' }"
+                      @click="setSelectedTheme('auto')">
+                      auto
+                    </button>
+                  </li>
+                  <li v-for="theme in themes">
+                    <input
+                      type="radio"
+                      name="theme-dropdown"
+                      class="theme-controller btn btn-sm btn-block btn-ghost justify-start"
+                      :aria-label="theme"
+                      :value="theme"
+                      :checked="selectedTheme === theme"
+                      @click="setSelectedTheme(theme)" />
+                  </li>
+                </ul>
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-              <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
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-                    class="btn btn-sm btn-block btn-ghost justify-start"
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From 1204f9727005974587d6fc1dcd4d4f0ead87c856 Mon Sep 17 00:00:00 2001
From: Tei Home <taiteitonghome@proton.me>
Date: Thu, 9 Jan 2025 19:32:06 +0800
Subject: [PATCH 150/376] doc: add cuda guide for fedora (#11135)

Since NVIDIA does not release CUDA for in-maintenance versions of Fedora, the process of setting up the CUDA toolkit on Fedora has become quite involved. This guide should help mere mortals install CUDA for development in a Fedora 39 toolbox environment, without affecting the host system.
---
 docs/build.md       |   2 +
 docs/cuda-fedora.md | 317 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 319 insertions(+)
 create mode 100644 docs/cuda-fedora.md

diff --git a/docs/build.md b/docs/build.md
index 84019b204..3b0d2211d 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -127,6 +127,8 @@ For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
 
 This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
 
+If you are using Fedora (using Fedora Workstation, or an 'Atomic' variant such as Silverblue), or would like to set up CUDA in a toolbox, please consider our [Fedora CUDA guide](./cuda-fedora.md). Unfortunately, the process is not as simple as one might expect.
+
 - Using `CMake`:
 
   ```bash
diff --git a/docs/cuda-fedora.md b/docs/cuda-fedora.md
new file mode 100644
index 000000000..b993386c8
--- /dev/null
+++ b/docs/cuda-fedora.md
@@ -0,0 +1,317 @@
+# Setting Up CUDA on Fedora
+
+In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
+- [Fedora Workstation](https://fedoraproject.org/workstation/)
+- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
+- [Fedora Spins](https://fedoraproject.org/spins)
+- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.`, `Arch Linux`, and `Ubuntu`.
+
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [Monitoring NVIDIA CUDA Repositories](#monitoring-nvidia-cuda-repositories)
+- [Using the Fedora 39 CUDA Repository](#using-the-fedora-39-cuda-repository)
+- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
+- [Installing Essential Development Tools](#installing-essential-development-tools)
+- [Adding the CUDA Repository](#adding-the-cuda-repository)
+- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs)
+- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts)
+- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs)
+- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package)
+- [Configuring the Environment](#configuring-the-environment)
+- [Verifying the Installation](#verifying-the-installation)
+- [Conclusion](#conclusion)
+- [Troubleshooting](#troubleshooting)
+- [Additional Notes](#additional-notes)
+- [References](#references)
+
+## Prerequisites
+
+- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
+- **NVIDIA Drivers and Graphics Card installed on Host System (optional)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
+- **Internet connectivity** to download packages.
+
+### Monitoring NVIDIA CUDA Repositories
+
+Before proceeding, it is advisable to check if NVIDIA has updated their CUDA repositories for your Fedora version. NVIDIA's repositories can be found at:
+
+- [Fedora 40 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora40/x86_64/)
+- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
+
+As of the latest update, these repositories do not contain the `cuda` meta-package or are missing essential components.
+
+### Using the Fedora 39 CUDA Repository
+
+Since the newer repositories are incomplete, we'll use the Fedora 39 repository:
+
+- [Fedora 39 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/)
+
+**Note:** Fedora 39 is no longer maintained, so we recommend using a toolbox environment to prevent system conflicts.
+
+## Creating a Fedora Toolbox Environment
+
+This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using a Fedora 39 toolbox allows us to install the necessary packages without affecting the host system.
+
+**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
+
+We do not recommend installing on the host system, as Fedora 39 is out-of-maintenance, and instead you should upgrade to a maintained version of Fedora for your host.
+
+1. **Create a Fedora 39 Toolbox:**
+
+   ```bash
+   toolbox create --image registry.fedoraproject.org/fedora-toolbox:39 --container fedora-toolbox-39-cuda
+   ```
+
+2. **Enter the Toolbox:**
+
+   ```bash
+   toolbox enter --container fedora-toolbox-39-cuda
+   ```
+
+   Inside the toolbox, you have root privileges and can install packages without affecting the host system.
+
+## Installing Essential Development Tools
+
+1. **Synchronize the DNF Package Manager:**
+
+   ```bash
+   sudo dnf distro-sync
+   ```
+
+2. **Install the Default Text Editor (Optional):**
+
+   ```bash
+   sudo dnf install vim-default-editor --allowerasing
+   ```
+
+   The `--allowerasing` flag resolves any package conflicts.
+
+3. **Install Development Tools and Libraries:**
+
+   ```bash
+   sudo dnf install @c-development @development-tools cmake
+   ```
+
+   This installs essential packages for compiling software, including `gcc`, `make`, and other development headers.
+
+## Adding the CUDA Repository
+
+Add the NVIDIA CUDA repository to your DNF configuration:
+
+```bash
+sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo
+```
+
+After adding the repository, synchronize the package manager again:
+
+```bash
+sudo dnf distro-sync
+```
+
+## Installing `nvidia-driver-libs`
+
+Attempt to install `nvidia-driver-libs`:
+
+```bash
+sudo dnf install nvidia-driver-libs
+```
+
+**Explanation:**
+
+- `nvidia-driver-libs` contains necessary NVIDIA driver libraries required by CUDA.
+- This step might fail due to conflicts with existing NVIDIA drivers on the host system.
+
+## Manually Resolving Package Conflicts
+
+If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
+
+### 1. Download the `nvidia-driver-libs` RPM
+
+```bash
+sudo dnf download --arch x86_64 nvidia-driver-libs
+```
+
+You should see a file similar to:
+
+```
+nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
+```
+
+### 2. Attempt to Install the RPM
+
+```bash
+sudo dnf install nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
+```
+
+**Expected Error:**
+
+Installation may fail with errors pointing to conflicts with `egl-gbm` and `egl-wayland`.
+
+**Note: It is important to carefully read the error messages to identify the exact paths that need to be excluded.**
+
+### 3. Download Dependencies
+
+```bash
+sudo dnf download --arch x86_64 egl-gbm egl-wayland
+```
+
+### 4. Install `egl-gbm` with Excluded Paths
+
+Exclude conflicting files during installation:
+
+```bash
+sudo rpm --install --verbose --hash \
+  --excludepath=/usr/lib64/libnvidia-egl-gbm.so.1.1.2 \
+  --excludepath=/usr/share/egl/egl_external_platform.d/15_nvidia_gbm.json \
+  egl-gbm-1.1.2^20240919gitb24587d-3.fc39.x86_64.rpm
+```
+
+**Explanation:**
+
+- The `--excludepath` option skips installing files that conflict with existing files.
+- Adjust the paths based on the error messages you receive.
+
+### 5. Install `egl-wayland` with Excluded Paths
+
+```bash
+sudo rpm --install --verbose --hash \
+  --excludepath=/usr/share/egl/egl_external_platform.d/10_nvidia_wayland.json \
+  egl-wayland-1.1.17^20241118giteeb29e1-5.fc39.x86_64.rpm
+```
+
+### 6. Install `nvidia-driver-libs` with Excluded Paths
+
+```bash
+sudo rpm --install --verbose --hash \
+  --excludepath=/usr/share/glvnd/egl_vendor.d/10_nvidia.json \
+  --excludepath=/usr/share/nvidia/nvoptix.bin \
+  nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
+```
+
+**Note:**
+
+- Replace the paths with the ones causing conflicts in your installation if they differ.
+- The `--verbose` and `--hash` options provide detailed output during installation.
+
+## Finalizing the Installation of `nvidia-driver-libs`
+
+After manually installing the dependencies, run:
+
+```bash
+sudo dnf install nvidia-driver-libs
+```
+
+You should receive a message indicating the package is already installed:
+
+```
+Package nvidia-driver-libs-3:560.35.05-1.fc39.x86_64 is already installed.
+Dependencies resolved.
+Nothing to do.
+Complete!
+```
+
+## Installing the CUDA Meta-Package
+
+Now that the driver libraries are installed, proceed to install CUDA:
+
+```bash
+sudo dnf install cuda
+```
+
+This installs the CUDA toolkit and associated packages.
+
+## Configuring the Environment
+
+To use CUDA, add its binary directory to your system's `PATH`.
+
+1. **Create a Profile Script:**
+
+   ```bash
+   sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh'
+   ```
+
+   **Explanation:**
+
+   - We add to  `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
+   - The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
+
+2. **Make the Script Executable:**
+
+   ```bash
+   sudo chmod +x /etc/profile.d/cuda.sh
+   ```
+
+3. **Source the Script to Update Your Environment:**
+
+   ```bash
+   source /etc/profile.d/cuda.sh
+   ```
+
+   **Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions.
+
+## Verifying the Installation
+
+To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`):
+
+```bash
+nvcc --version
+```
+
+You should see output similar to:
+
+```
+nvcc: NVIDIA (R) Cuda compiler driver
+Copyright (c) 2005-2024 NVIDIA Corporation
+Built on Tue_Oct_29_23:50:19_PDT_2024
+Cuda compilation tools, release 12.6, V12.6.85
+Build cuda_12.6.r12.6/compiler.35059454_0
+```
+
+This output confirms that the CUDA compiler is accessible and indicates the installed version.
+
+## Conclusion
+
+You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 39 CUDA repository. By manually resolving package conflicts and configuring the environment, you can develop CUDA applications without affecting your host system.
+
+## Troubleshooting
+
+- **Installation Failures:**
+  - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
+  - Use the `--excludepath` option with `rpm` to exclude conflicting files during manual installations.
+
+- **Driver Conflicts:**
+  - Since the host system may already have NVIDIA drivers installed, conflicts can arise. Using the toolbox environment helps isolate these issues.
+
+- **Environment Variables Not Set:**
+  - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
+  - Run `echo $PATH` to check if the path is included.
+  - Re-source the profile script or open a new terminal session.
+
+## Additional Notes
+
+- **Updating CUDA in the Future:**
+  - Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
+  - When an updated repository becomes available, adjust your `dnf` configuration accordingly.
+
+- **Building `llama.cpp`:**
+  - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
+  - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.
+
+- **Using the Toolbox Environment:**
+  - The toolbox environment is isolated from your host system, which helps prevent conflicts.
+  - Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox.
+
+---
+
+**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox.
+
+**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide.
+
+## References
+
+- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/)
+- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
+- [Podman Documentation](https://podman.io/get-started)
+
+---

From c6860cc7346c90219475e4467bb8a288e0df975c Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Fri, 10 Jan 2025 05:43:03 +0530
Subject: [PATCH 151/376] SYCL: Refactor ggml_sycl_compute_forward (#11121)

* SYCL: refactor ggml_sycl_compute_forward

* SYCL: add back GGML_USED(dst) to ggml_sycl_cpy

* SYCL: add function name to noop debug

* SYCL: Some device info print refactoring and add details of XMX availability
---
 ggml/src/ggml-sycl/common.cpp       |   4 +
 ggml/src/ggml-sycl/common.hpp       |   1 +
 ggml/src/ggml-sycl/concat.cpp       |   5 +-
 ggml/src/ggml-sycl/concat.hpp       |   3 +-
 ggml/src/ggml-sycl/conv.cpp         |   5 +-
 ggml/src/ggml-sycl/conv.hpp         |   3 +-
 ggml/src/ggml-sycl/element_wise.cpp |  96 +++++-----
 ggml/src/ggml-sycl/element_wise.hpp |  48 ++---
 ggml/src/ggml-sycl/ggml-sycl.cpp    | 262 +++++++++++++---------------
 ggml/src/ggml-sycl/outprod.cpp      |   6 +-
 ggml/src/ggml-sycl/outprod.hpp      |   3 +-
 ggml/src/ggml-sycl/tsembd.cpp       |   5 +-
 ggml/src/ggml-sycl/tsembd.hpp       |   3 +-
 ggml/src/ggml-sycl/wkv6.cpp         |   6 +-
 ggml/src/ggml-sycl/wkv6.hpp         |   3 +-
 15 files changed, 223 insertions(+), 230 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
index 88314a5cd..022e7b763 100644
--- a/ggml/src/ggml-sycl/common.cpp
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -51,6 +51,10 @@ void ggml_sycl_host_free(void* ptr) try {
   std::exit(1);
 }
 
+bool gpu_has_xmx(sycl::device &dev) {
+    return dev.has(sycl::aspect::ext_intel_matrix);
+}
+
 int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size) {
   const int64_t max_range = std::numeric_limits<int>::max();
   int64_t sycl_down_blk_size = block_size;
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 62b4cea3a..e9500f3a1 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -662,6 +662,7 @@ inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_t
     }
 }
 
+bool gpu_has_xmx(sycl::device &dev);
 
 void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                  const ggml_tensor *src1, ggml_tensor *dst,
diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp
index a240968ad..d41cfd3a6 100644
--- a/ggml/src/ggml-sycl/concat.cpp
+++ b/ggml/src/ggml-sycl/concat.cpp
@@ -158,8 +158,9 @@ static void concat_f32_sycl_non_cont(
       });
 }
 
-void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst) {
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
+  const ggml_tensor *src0 = dst->src[0];
+  const ggml_tensor *src1 = dst->src[1];
   queue_ptr stream = ctx.stream();
 
   const int32_t dim = ((int32_t *)dst->op_params)[0];
diff --git a/ggml/src/ggml-sycl/concat.hpp b/ggml/src/ggml-sycl/concat.hpp
index 5a04feaab..e5cb7314c 100644
--- a/ggml/src/ggml-sycl/concat.hpp
+++ b/ggml/src/ggml-sycl/concat.hpp
@@ -15,7 +15,6 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst);
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst);
 
 #endif // GGML_SYCL_CONCAT_HPP
diff --git a/ggml/src/ggml-sycl/conv.cpp b/ggml/src/ggml-sycl/conv.cpp
index bc4ab1ddb..ddba601e1 100644
--- a/ggml/src/ggml-sycl/conv.cpp
+++ b/ggml/src/ggml-sycl/conv.cpp
@@ -71,8 +71,9 @@ static void conv_transpose_1d_f32_f32_sycl(
         });
 }
 
-void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-    const ggml_tensor *src1, ggml_tensor *dst) {
+void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
     const float * src0_d = (const float *)src0->data;
     const float * src1_d = (const float *)src1->data;
 
diff --git a/ggml/src/ggml-sycl/conv.hpp b/ggml/src/ggml-sycl/conv.hpp
index eb20730f9..f9e60dc75 100644
--- a/ggml/src/ggml-sycl/conv.hpp
+++ b/ggml/src/ggml-sycl/conv.hpp
@@ -15,7 +15,6 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-  const ggml_tensor *src1, ggml_tensor *dst);
+void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, ggml_tensor *dst);
 
 #endif // GGML_SYCL_CONV_HPP
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index d05a51f80..4bcd74376 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -882,149 +882,149 @@ inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor
 }
 
 
-void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqrt);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sqrt);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_sin(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sin);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sin);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_cos(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_cos);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_cos);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_acc(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_acc);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_gelu);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_silu);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_gelu_quick);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_tanh);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_relu);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sigmoid);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sigmoid);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_hardsigmoid);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_hardswish);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 
-void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_exp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_exp);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_exp);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_log(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_log);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_log);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_neg);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_neg);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_step(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_step);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_step);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_leaky_relu);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sqr);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_upscale);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_pad);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
 
 
-void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_add(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_add);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sub);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sub);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_mul);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+void ggml_sycl_div(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_div);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp
index 8152edf58..464432645 100644
--- a/ggml/src/ggml-sycl/element_wise.hpp
+++ b/ggml/src/ggml-sycl/element_wise.hpp
@@ -25,52 +25,52 @@ static __dpct_inline__ float op_div(const float a, const float b) {
 }
 
 
-void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_sin(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_cos(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_acc(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_exp(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_log(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_step(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_add(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
-void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+void ggml_sycl_div(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 #endif // GGML_SYCL_ELEMENTWISE_HPP
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 312ccfeb8..037c8093e 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -54,18 +54,12 @@ static ggml_sycl_device_info ggml_sycl_init() {
     GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES);
 
     int64_t total_vram = 0;
-#if defined(GGML_SYCL_FORCE_MMQ)
-    GGML_LOG_INFO("%s: GGML_SYCL_FORCE_MMQ:   yes\n", __func__);
-#else
-    GGML_LOG_INFO("%s: GGML_SYCL_FORCE_MMQ:   no\n", __func__);
-#endif
-#if defined(SYCL_USE_XMX)
-    GGML_LOG_INFO("%s: SYCL_USE_XMX: yes\n", __func__);
-#else
-    GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__);
-#endif
-    GGML_LOG_INFO("%s: found %d %s devices:\n", __func__, info.device_count, GGML_SYCL_NAME);
-
+/* This is a bit misleading;  reserved for later */
+// #if defined(SYCL_USE_XMX)
+//     GGML_LOG_INFO("%s: SYCL_USE_XMX: yes\n", __func__);
+// #else
+//     GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__);
+// #endif
     for (int i = 0; i < info.device_count; ++i) {
         info.devices[i].vmm = 0;
         dpct::device_info prop;
@@ -109,11 +103,11 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
     name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
 
     auto global_mem_size = prop.get_global_mem_size()/1000000;
-
-    GGML_LOG_INFO("|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
+    std::string xmx = gpu_has_xmx(device) ? "yes" : "no";
+    GGML_LOG_INFO("|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|%14s|\n", id, device_type.c_str(),
             name.c_str(), version.c_str(), prop.get_max_compute_units(),
             prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
-            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str(), xmx.c_str());
 }
 
 void ggml_backend_sycl_print_sycl_devices() {
@@ -124,16 +118,16 @@ void ggml_backend_sycl_print_sycl_devices() {
 
     GGML_LOG_INFO(
         "|  |                   |                                       |      "
-        " |Max    |        |Max  |Global |                     |\n");
+        " |Max    |        |Max  |Global |                     |         XMX  |\n");
     GGML_LOG_INFO(
         "|  |                   |                                       |      "
-        " |compute|Max work|sub  |mem    |                     |\n");
+        " |compute|Max work|sub  |mem    |                     |          or  |\n");
     GGML_LOG_INFO(
         "|ID|        Device Type|                                   "
-        "Name|Version|units  |group   |group|size   |       Driver version|\n");
+        "Name|Version|units  |group   |group|size   |       Driver version| Tensor Cores |\n");
     GGML_LOG_INFO(
         "|--|-------------------|---------------------------------------|------"
-        "-|-------|--------|-----|-------|---------------------|\n");
+        "-|-------|--------|-----|-------|---------------------|--------------|\n");
 
     for (int id = 0; id < device_count; ++id) {
       sycl::device device = dpct::dev_mgr::instance().get_device(id);
@@ -164,14 +158,18 @@ static void ggml_check_sycl() try {
     static bool initialized = false;
 
     if (!initialized) {
-        GGML_LOG_INFO("[SYCL] call ggml_check_sycl\n");
+        GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n");
         g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
-        GGML_LOG_INFO("%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
-
-#if defined(GGML_SYCL_F16)
-        GGML_LOG_INFO("%s: GGML_SYCL_F16: yes\n", __func__);
+        GGML_LOG_INFO("GGML_SYCL_DEBUG: %d\n", g_ggml_sycl_debug);
+#if defined(GGML_SYCL_FORCE_MMQ)
+        GGML_LOG_INFO("GGML_SYCL_FORCE_MMQ:   yes\n");
 #else
-        GGML_LOG_INFO("%s: GGML_SYCL_F16: no\n", __func__);
+        GGML_LOG_INFO("GGML_SYCL_FORCE_MMQ:   no\n");
+#endif
+#if defined(GGML_SYCL_F16)
+        GGML_LOG_INFO("GGML_SYCL_F16: yes\n");
+#else
+        GGML_LOG_INFO("GGML_SYCL_F16: no\n");
 #endif
 
 /* NOT REMOVE, keep it for next optimize for XMX.
@@ -1189,7 +1187,6 @@ std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_device(q
 /// kernels
 
 typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
-typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
 typedef void (*ggml_sycl_op_mul_mat_t)(
     ggml_backend_sycl_context & ctx,
     const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
@@ -3171,33 +3168,33 @@ catch (sycl::exception const &exc) {
 }
 
 
-static void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_repeat);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_repeat);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_get_rows);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_get_rows);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_norm);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_rms_norm);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm);
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_group_norm);
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
@@ -3572,9 +3569,10 @@ __dpct_inline__ static void k_copy_dst_from_contiguous(
     }
 }
 
-static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                 const ggml_tensor *src1,
+static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
                                  ggml_tensor *dst) try {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
     GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers");
 
     const ggml_tensor *ids = dst->src[2];
@@ -3740,12 +3738,12 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_scale);
+static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_scale);
 }
 
-static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_clamp);
+static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_clamp);
 }
 
 static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@@ -3787,7 +3785,6 @@ static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
                 ggml_type_name(src0->type), ggml_type_name(src1->type));
         GGML_ABORT("fatal error");
     }
-
     GGML_UNUSED(dst);
 }
 catch (sycl::exception const &exc) {
@@ -3796,59 +3793,52 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     // TODO: why do we pass dst as src1 here?
-    ggml_sycl_cpy(ctx, src0, dst, nullptr);
-    GGML_UNUSED(src1);
+    ggml_sycl_cpy(ctx, dst->src[0], dst, nullptr);
 }
 
-static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_diag_mask_inf);
+static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_diag_mask_inf);
 }
 
-static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_soft_max);
+static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_soft_max);
 }
 
-static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rope);
+static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst->src[0])); // TODO: this restriction is temporary until non-cont support is implemented
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_rope);
 }
 
-static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pool2d);
+static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_pool2d);
 }
 
-static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col);
+static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_im2col);
 }
 
-static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum);
+static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sum);
 }
 
-static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows);
+static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_sum_rows);
 }
 
-static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort);
+static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_argsort);
 }
 
-static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argmax);
+static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
+    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_argmax);
 }
 
-static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_UNUSED(src0);
-    GGML_UNUSED(src1);
-    GGML_UNUSED(dst);
-    GGML_UNUSED(ctx);
-}
 
 void ggml_sycl_set_main_device(const int main_device) try {
     if (dpct::get_current_device_id() == static_cast<unsigned int> (main_device)) {
@@ -3871,191 +3861,189 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tensor * tensor) {
+bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tensor * dst) {
     if (!g_sycl_loaded) return false;
 
-    ggml_sycl_func_t func;
+    if (dst->src[0] != nullptr && ggml_backend_buffer_is_sycl_split(dst->src[0]->buffer)) {
+        ggml_sycl_set_peer_access(dst->src[1]->ne[1], ctx.device);
+    }
 
-    switch (tensor->op) {
+    switch (dst->op) {
         case GGML_OP_ARGMAX:
-            func = ggml_sycl_argmax;
+            ggml_sycl_argmax(ctx, dst);
             break;
         case GGML_OP_CONV_TRANSPOSE_1D:
-            func = ggml_sycl_op_conv_transpose_1d;
+            ggml_sycl_op_conv_transpose_1d(ctx, dst);
             break;
         case GGML_OP_REPEAT:
-            func = ggml_sycl_repeat;
+            ggml_sycl_repeat(ctx, dst);
             break;
         case GGML_OP_GET_ROWS:
-            func = ggml_sycl_get_rows;
+            ggml_sycl_get_rows(ctx, dst);
             break;
         case GGML_OP_DUP:
-            func = ggml_sycl_dup;
+            ggml_sycl_dup(ctx, dst);
             break;
         case GGML_OP_ADD:
         case GGML_OP_ADD1: // TODO: more efficient implementation
-            func = ggml_sycl_add;
+            ggml_sycl_add(ctx, dst);
             break;
         case GGML_OP_SUB:
-            func = ggml_sycl_sub;
+            ggml_sycl_sub(ctx, dst);
             break;
         case GGML_OP_ACC:
-            func = ggml_sycl_acc;
+            ggml_sycl_acc(ctx, dst);
             break;
         case GGML_OP_MUL:
-            func = ggml_sycl_mul;
+            ggml_sycl_mul(ctx, dst);
             break;
         case GGML_OP_LOG:
-            func = ggml_sycl_log;
+            ggml_sycl_log(ctx, dst);
             break;
         case GGML_OP_DIV:
-            func = ggml_sycl_div;
+            ggml_sycl_div(ctx, dst);
             break;
         case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(tensor)) {
+            switch (ggml_get_unary_op(dst)) {
                 case GGML_UNARY_OP_NEG:
-                    func = ggml_sycl_neg;
+                    ggml_sycl_neg(ctx, dst);
                     break;
                 case GGML_UNARY_OP_STEP:
-                    func = ggml_sycl_step;
+                    ggml_sycl_step(ctx, dst);
                     break;
                 case GGML_UNARY_OP_GELU:
-                    func = ggml_sycl_gelu;
+                    ggml_sycl_gelu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_SILU:
-                    func = ggml_sycl_silu;
+                    ggml_sycl_silu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_GELU_QUICK:
-                    func = ggml_sycl_gelu_quick;
+                    ggml_sycl_gelu_quick(ctx, dst);
                     break;
                 case GGML_UNARY_OP_TANH:
-                    func = ggml_sycl_tanh;
+                    ggml_sycl_tanh(ctx, dst);
                     break;
                 case GGML_UNARY_OP_RELU:
-                    func = ggml_sycl_relu;
+                    ggml_sycl_relu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_SIGMOID:
-                    func = ggml_sycl_sigmoid;
+                    ggml_sycl_sigmoid(ctx, dst);
                     break;
                 case GGML_UNARY_OP_HARDSIGMOID:
-                    func = ggml_sycl_hardsigmoid;
+                    ggml_sycl_hardsigmoid(ctx, dst);
                     break;
                 case GGML_UNARY_OP_HARDSWISH:
-                    func = ggml_sycl_hardswish;
+                    ggml_sycl_hardswish(ctx, dst);
                     break;
                 case GGML_UNARY_OP_EXP:
-                    func = ggml_sycl_exp;
+                    ggml_sycl_exp(ctx, dst);
                     break;
                 default:
                     return false;
             }
             break;
         case GGML_OP_NORM:
-            func = ggml_sycl_norm;
+            ggml_sycl_norm(ctx, dst);
             break;
         case GGML_OP_GROUP_NORM:
-            func = ggml_sycl_group_norm;
+            ggml_sycl_group_norm(ctx, dst);
             break;
         case GGML_OP_CONCAT:
-            func = ggml_sycl_op_concat;
+            ggml_sycl_op_concat(ctx, dst);
             break;
         case GGML_OP_UPSCALE:
-            func = ggml_sycl_upscale;
+            ggml_sycl_upscale(ctx, dst);
             break;
         case GGML_OP_PAD:
-            func = ggml_sycl_pad;
+            ggml_sycl_pad(ctx, dst);
             break;
         case GGML_OP_LEAKY_RELU:
-            func = ggml_sycl_leaky_relu;
+            ggml_sycl_leaky_relu(ctx, dst);
             break;
         case GGML_OP_RMS_NORM:
-            func = ggml_sycl_rms_norm;
+            ggml_sycl_rms_norm(ctx, dst);
             break;
         case GGML_OP_MUL_MAT:
-            if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
+            if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
                 return false;
             }
-            func = ggml_sycl_mul_mat;
+            /* ggml_sycl_mul_mat_id is dependent on ggml_sycl_mul_mat */
+            ggml_sycl_mul_mat(ctx, dst->src[0], dst->src[1], dst);
             break;
         case GGML_OP_MUL_MAT_ID:
-            if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
+            if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
                 return false;
             }
-            func = ggml_sycl_mul_mat_id;
+            ggml_sycl_mul_mat_id(ctx, dst);
             break;
         case GGML_OP_OUT_PROD:
-            func = ggml_sycl_op_out_prod;
+            ggml_sycl_op_out_prod(ctx, dst);
             break;
         case GGML_OP_SCALE:
-            func = ggml_sycl_scale;
+            ggml_sycl_scale(ctx, dst);
             break;
         case GGML_OP_SQR:
-            func = ggml_sycl_sqr;
+            ggml_sycl_sqr(ctx, dst);
             break;
         case GGML_OP_SQRT:
-            func = ggml_sycl_sqrt;
+            ggml_sycl_sqrt(ctx, dst);
             break;
         case GGML_OP_SIN:
-            func = ggml_sycl_sin;
+            ggml_sycl_sin(ctx, dst);
             break;
         case GGML_OP_COS:
-            func = ggml_sycl_cos;
+            ggml_sycl_cos(ctx, dst);
             break;
         case GGML_OP_CLAMP:
-            func = ggml_sycl_clamp;
+            ggml_sycl_clamp(ctx, dst);
             break;
         case GGML_OP_CPY:
-            func = ggml_sycl_cpy;
+            ggml_sycl_cpy(ctx, dst->src[0], dst->src[1], dst);
             break;
         case GGML_OP_CONT:
-            func = ggml_sycl_dup;
+            ggml_sycl_dup(ctx, dst);
             break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-            func = ggml_sycl_nop;
+            GGML_SYCL_DEBUG("%s: Tensor NO-OP\n", __func__);
             break;
         case GGML_OP_DIAG_MASK_INF:
-            func = ggml_sycl_diag_mask_inf;
+            ggml_sycl_diag_mask_inf(ctx, dst);
             break;
         case GGML_OP_SOFT_MAX:
-            func = ggml_sycl_soft_max;
+            ggml_sycl_soft_max(ctx, dst);
             break;
         case GGML_OP_ROPE:
-            func = ggml_sycl_rope;
+            ggml_sycl_rope(ctx, dst);
             break;
         case GGML_OP_IM2COL:
-            func = ggml_sycl_im2col;
+            ggml_sycl_im2col(ctx, dst);
             break;
         case GGML_OP_POOL_2D:
-            func = ggml_sycl_pool2d;
+            ggml_sycl_pool2d(ctx, dst);
             break;
         case GGML_OP_SUM:
-            func = ggml_sycl_sum;
+            ggml_sycl_sum(ctx, dst);
             break;
         case GGML_OP_SUM_ROWS:
-            func = ggml_sycl_sum_rows;
+            ggml_sycl_sum_rows(ctx, dst);
             break;
         case GGML_OP_ARGSORT:
-            func = ggml_sycl_argsort;
+            ggml_sycl_argsort(ctx, dst);
             break;
         case GGML_OP_TIMESTEP_EMBEDDING:
-            func = ggml_sycl_op_timestep_embedding;
+            ggml_sycl_op_timestep_embedding(ctx, dst);
             break;
         case GGML_OP_RWKV_WKV6:
-            func = ggml_sycl_op_rwkv_wkv6;
+            ggml_sycl_op_rwkv_wkv6(ctx, dst);
             break;
         default:
             return false;
     }
 
-    if (tensor->src[0] != nullptr && ggml_backend_buffer_is_sycl_split(tensor->src[0]->buffer)) {
-        ggml_sycl_set_peer_access(tensor->src[1]->ne[1], ctx.device);
-    }
-
-    func(ctx, tensor->src[0], tensor->src[1], tensor);
     return true;
 }
 
diff --git a/ggml/src/ggml-sycl/outprod.cpp b/ggml/src/ggml-sycl/outprod.cpp
index ef9af0b76..8e8347ff4 100644
--- a/ggml/src/ggml-sycl/outprod.cpp
+++ b/ggml/src/ggml-sycl/outprod.cpp
@@ -3,9 +3,9 @@
 #include "outprod.hpp"
 
 
-void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
-    const ggml_tensor* src1, ggml_tensor* dst) {
-
+void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
diff --git a/ggml/src/ggml-sycl/outprod.hpp b/ggml/src/ggml-sycl/outprod.hpp
index 9c042738a..f50413d3f 100644
--- a/ggml/src/ggml-sycl/outprod.hpp
+++ b/ggml/src/ggml-sycl/outprod.hpp
@@ -3,8 +3,7 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
-    const ggml_tensor* src1, ggml_tensor* dst);
+void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst);
 
 
 #endif // GGML_SYCL_OUTPROD_HPP
diff --git a/ggml/src/ggml-sycl/tsembd.cpp b/ggml/src/ggml-sycl/tsembd.cpp
index 2ffe3cca9..b877d18c1 100644
--- a/ggml/src/ggml-sycl/tsembd.cpp
+++ b/ggml/src/ggml-sycl/tsembd.cpp
@@ -55,8 +55,9 @@ static void timestep_embedding_f32_sycl(
         });
 }
 
-void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-    const ggml_tensor *src1, ggml_tensor * dst) {
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
     const float * src0_d = (const float *)src0->data;
     float * dst_d = (float *)dst->data;
     dpct::queue_ptr stream = ctx.stream();
diff --git a/ggml/src/ggml-sycl/tsembd.hpp b/ggml/src/ggml-sycl/tsembd.hpp
index ff854c337..4c18748bb 100644
--- a/ggml/src/ggml-sycl/tsembd.hpp
+++ b/ggml/src/ggml-sycl/tsembd.hpp
@@ -15,7 +15,6 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-    const ggml_tensor *src1, ggml_tensor * dst);
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 #endif // GGML_SYCL_TSEMBD_HPP
diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp
index 105db6f03..4fed18c2a 100644
--- a/ggml/src/ggml-sycl/wkv6.cpp
+++ b/ggml/src/ggml-sycl/wkv6.cpp
@@ -95,8 +95,10 @@ static void rwkv_wkv_f32_kernel(
     }
 }
 
-void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
-    const ggml_tensor* src1, ggml_tensor* dst) {
+void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
 
     const float* k_d = (const float*)dst->src[0]->data;
     const float* v_d = (const float*)dst->src[1]->data;
diff --git a/ggml/src/ggml-sycl/wkv6.hpp b/ggml/src/ggml-sycl/wkv6.hpp
index ddfa3377b..8c596a997 100644
--- a/ggml/src/ggml-sycl/wkv6.hpp
+++ b/ggml/src/ggml-sycl/wkv6.hpp
@@ -3,8 +3,7 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-    const ggml_tensor *src1, ggml_tensor * dst);
+void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 
 #endif // GGML_SYCL_WKV6_HPP

From ee7136c6d1e0ba7633294dad137b1573048031ec Mon Sep 17 00:00:00 2001
From: Molly Sophia <mollysophia379@gmail.com>
Date: Fri, 10 Jan 2025 09:58:08 +0800
Subject: [PATCH 152/376] llama: add support for QRWKV6 model architecture
 (#11001)

llama: add support for QRWKV6 model architecture (#11001)

* WIP: Add support for RWKV6Qwen2

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* RWKV: Some graph simplification

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Add support for RWKV6Qwen2 with cpu and cuda GLA

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* RWKV6[QWEN2]: Concat lerp weights together to reduce cpu overhead

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix some typos

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* code format changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix wkv test & add gla test

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix cuda warning

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update README.md

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update ggml/src/ggml-cuda/gla.cu

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Fix fused lerp weights loading with RWKV6

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* better sanity check skipping for QRWKV6 in llama-quant

thanks @compilade

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: compilade <git@compilade.net>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: compilade <git@compilade.net>
---
 README.md                            |   1 +
 convert_hf_to_gguf.py                |  81 ++++++-
 ggml/include/ggml.h                  |  10 +
 ggml/src/ggml-cpu/ggml-cpu.c         | 200 +++++++++++++++-
 ggml/src/ggml-cuda/ggml-cuda.cu      |   5 +
 ggml/src/ggml-cuda/gla.cu            |  93 +++++++
 ggml/src/ggml-cuda/gla.cuh           |   3 +
 ggml/src/ggml-cuda/wkv6.cu           |   4 +-
 ggml/src/ggml-sycl/wkv6.cpp          |   4 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp |   4 +-
 ggml/src/ggml.c                      |  61 ++++-
 gguf-py/gguf/constants.py            |  35 +++
 gguf-py/gguf/gguf_writer.py          |   3 +
 gguf-py/gguf/tensor_mapping.py       |  26 +-
 src/llama-arch.cpp                   |  30 +++
 src/llama-arch.h                     |   3 +
 src/llama-hparams.cpp                |   2 +-
 src/llama-hparams.h                  |   1 +
 src/llama-model.cpp                  |   8 +-
 src/llama-model.h                    |  20 +-
 src/llama-quant.cpp                  |   4 +-
 src/llama.cpp                        | 346 ++++++++++++++++++++-------
 tests/test-backend-ops.cpp           |  42 +++-
 23 files changed, 862 insertions(+), 124 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/gla.cu
 create mode 100644 ggml/src/ggml-cuda/gla.cuh

diff --git a/README.md b/README.md
index a71015256..6302ac977 100644
--- a/README.md
+++ b/README.md
@@ -99,6 +99,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
+- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
 - [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 
 #### Multimodal
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 5562499aa..cf317eeae 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -326,6 +326,7 @@ class Model:
                             gguf.MODEL_TENSOR.TIME_MIX_W2,
                             gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
                             gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
+                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
                             gguf.MODEL_TENSOR.POSNET_NORM1,
                             gguf.MODEL_TENSOR.POSNET_NORM2,
                         )
@@ -3316,6 +3317,8 @@ class Rwkv6Model(Model):
         # required by llama.cpp, unused
         self.gguf_writer.add_head_count(0)
 
+    lerp_weights: dict[int, dict[str, Tensor]] = {}
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         new_name = self.map_tensor_name(name)
 
@@ -3331,14 +3334,84 @@ class Rwkv6Model(Model):
         if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
             data_torch = data_torch.squeeze()
 
-        rescale_every_n_layers = self.hparams["rescale_every"]
-        if rescale_every_n_layers > 0:
-            if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
-                data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
+        try:
+            rescale_every_n_layers = self.hparams["rescale_every"]
+            if rescale_every_n_layers > 0:
+                if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
+                    data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
+        except KeyError:
+            pass
+
+        # concat time_mix_lerp weights to reduce some cpu overhead
+        # also reduces the number of tensors in the model
+        if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name:
+            try:
+                self.lerp_weights[bid][new_name] = data_torch
+            except KeyError:
+                self.lerp_weights[bid] = {new_name: data_torch}
+            if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]):
+                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
+                data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1)
+                yield (new_name, data)
+            return
 
         yield (new_name, data_torch)
 
 
+@Model.register("RWKV6Qwen2ForCausalLM")
+class RWKV6Qwen2Model(Rwkv6Model):
+    model_arch = gguf.MODEL_ARCH.RWKV6QWEN2
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        num_attention_heads = self.hparams["num_attention_heads"]
+        num_key_value_heads = self.hparams["num_key_value_heads"]
+        hidden_size = self.hparams["hidden_size"]
+        head_size = hidden_size // num_attention_heads
+        rms_norm_eps = self.hparams["rms_norm_eps"]
+        intermediate_size = self.hparams["intermediate_size"]
+        time_mix_extra_dim = 64 if hidden_size >= 4096 else 32
+        time_decay_extra_dim = 128 if hidden_size >= 4096 else 64
+
+        # RWKV isn't context limited
+        self.gguf_writer.add_context_length(1048576)
+        self.gguf_writer.add_embedding_length(hidden_size)
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_wkv_head_size(head_size)
+        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
+        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
+        self.gguf_writer.add_feed_forward_length(intermediate_size)
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # special parameters for time_mixing in RWKV6QWEN2
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_token_shift_count(1)
+        # RWKV6QWEN2 use grouped key/value like GQA
+        self.gguf_writer.add_head_count_kv(num_key_value_heads)
+
+        # required by llama.cpp, unused
+        self.gguf_writer.add_head_count(0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        for new_name, data in super().modify_tensors(data_torch, name, bid):
+            if "time_mix_w1" in new_name or "time_mix_w2" in new_name:
+                data = data.view(5, -1, data.shape[-1])
+                # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg
+                # permute them here to avoid code changes
+                data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1])
+                if "w2" in new_name:
+                    data = data.view(5, -1, data.shape[-1])
+                yield (new_name, data)
+                continue
+            yield (new_name, data)
+
+
 @Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
 class MambaModel(Model):
     model_arch = gguf.MODEL_ARCH.MAMBA
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 8630d92c5..8f8cb9e1a 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -501,6 +501,7 @@ extern "C" {
         GGML_OP_GET_REL_POS,
         GGML_OP_ADD_REL_POS,
         GGML_OP_RWKV_WKV6,
+        GGML_OP_GATED_LINEAR_ATTN,
 
         GGML_OP_UNARY,
 
@@ -1859,6 +1860,15 @@ extern "C" {
             struct ggml_tensor  * td,
             struct ggml_tensor  * state);
 
+    GGML_API struct ggml_tensor * ggml_gated_linear_attn(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * g,
+            struct ggml_tensor  * state,
+            float scale);
+
     // custom operators
 
     typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index b7fefb9dd..2966ff768 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -11803,9 +11803,9 @@ static void ggml_compute_forward_add_rel_pos(
 static void ggml_compute_forward_rwkv_wkv6_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
-    const int64_t T = dst->src[1]->ne[3];
+    const int64_t T = dst->src[1]->ne[2];
     const int64_t C = dst->ne[0];
-    const int64_t HEADS = dst->src[1]->ne[2];
+    const int64_t HEADS = dst->src[1]->ne[1];
     const int64_t n_seqs = dst->src[5]->ne[1];
     const int64_t head_size = C / HEADS;
 
@@ -12000,6 +12000,197 @@ static void ggml_compute_forward_rwkv_wkv6(
     }
 }
 
+// ggml_compute_forward_gla
+
+static void ggml_compute_forward_gla_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    const int64_t T = dst->src[1]->ne[2];
+    const int64_t C = dst->ne[0];
+    const int64_t HEADS = dst->src[1]->ne[1];
+    const int64_t n_seqs = dst->src[4]->ne[1];
+    const int64_t head_size = C / HEADS;
+    const float scale = ggml_get_op_params_f32(dst, 0);
+
+    float * dst_data = (float *) dst->data;
+    float * state = ((float *) dst->data) + C * T;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    if (ith >= HEADS) {
+        return;
+    }
+
+    const int h_start = (HEADS * ith) / nth;
+    const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ?
+                (HEADS * (ith + 1)) / nth : HEADS;
+
+    float * k = (float *) dst->src[0]->data;
+    float * v = (float *) dst->src[1]->data;
+    float * q = (float *) dst->src[2]->data;
+    float * g = (float *) dst->src[3]->data;
+
+    size_t t_stride = HEADS * head_size; // Same to C
+
+    size_t h_stride = C / HEADS;
+    GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS
+    size_t h_stride_2d = head_size * head_size;
+
+    if (ith == 0) {
+        memset(dst_data, 0, T * C * sizeof(float));
+    }
+    ggml_barrier(params->threadpool);
+
+
+    #if defined(__AVX__) && !defined(__AVX512F__)
+        #define GGML_F32X GGML_F32x8
+        #define GGML_F32X_SET1 GGML_F32x8_SET1
+        #define GGML_F32X_LOAD GGML_F32x8_LOAD
+        #define GGML_F32X_STORE GGML_F32x8_STORE
+        #define GGML_F32X_MUL GGML_F32x8_MUL
+        #define GGML_F32X_FMA GGML_F32x8_FMA
+        #define GLA_VECTOR_SIZE 8
+    #elif defined(__AVX512F__)
+        #define GGML_F32X GGML_F32x16
+        #define GGML_F32X_SET1 GGML_F32x16_SET1
+        #define GGML_F32X_LOAD GGML_F32x16_LOAD
+        #define GGML_F32X_STORE GGML_F32x16_STORE
+        #define GGML_F32X_MUL GGML_F32x16_MUL
+        #define GGML_F32X_FMA GGML_F32x16_FMA
+        #define GLA_VECTOR_SIZE 16
+    #elif defined(__ARM_NEON) && defined(__aarch64__)
+        #define GGML_F32X GGML_F32x4
+        #define GGML_F32X_SET1 GGML_F32x4_SET1
+        #define GGML_F32X_LOAD GGML_F32x4_LOAD
+        #define GGML_F32X_STORE GGML_F32x4_STORE
+        #define GGML_F32X_MUL GGML_F32x4_MUL
+        #define GGML_F32X_FMA GGML_F32x4_FMA
+        #define GLA_VECTOR_SIZE 4
+    #endif
+
+    #ifdef GLA_VECTOR_SIZE
+        const int64_t vec_count = head_size / GLA_VECTOR_SIZE;
+
+        for (int64_t t = 0; t < T; t++) {
+            size_t t_offset = t * t_stride;
+            size_t state_offset = head_size * C * (t / (T / n_seqs));
+            float * state_cur = state + state_offset;
+            float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset;
+
+            for (int64_t h = h_start; h < h_end; h++) {
+                size_t h_offset = h * h_stride;
+                size_t t_h_offset = t_offset + h_offset;
+                size_t h_2d_offset = h * h_stride_2d;
+
+                for (int64_t i = 0; i < head_size; i++) {
+                    size_t t_h_i_offset = t_h_offset + i;
+                    size_t h_2d_i_offset = h_2d_offset + i * h_stride;
+
+                    float k_val = k[t_h_i_offset];
+                    float q_val = q[t_h_i_offset] * scale;
+                    float g_val = g[t_h_i_offset];
+
+                    // Broadcast scalar values to vectors
+                    GGML_F32X k_vec = GGML_F32X_SET1(k_val);
+                    GGML_F32X q_vec = GGML_F32X_SET1(q_val);
+                    GGML_F32X g_vec = GGML_F32X_SET1(g_val);
+
+                    for (int64_t j = 0; j < vec_count; j++) {
+                        size_t base_j = j * GLA_VECTOR_SIZE;
+                        size_t t_h_j_offset = t_h_offset + base_j;
+                        size_t h_2d_i_j_offset = h_2d_i_offset + base_j;
+
+                        // Load x elements at once
+                        GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]);
+                        GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]);
+                        GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]);
+
+                        // Compute kv = v * k
+                        GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec);
+
+                        // Compute temp = prev_state * g + kv
+                        GGML_F32X temp_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, g_vec);
+
+                        // Update dst: dst += temp * q
+                        dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, q_vec);
+                        GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec);
+
+                        // Update state
+                        GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], temp_vec);
+                    }
+
+                    // Handle remaining elements, this will not be used.
+                    for (int64_t j = vec_count * GLA_VECTOR_SIZE; j < head_size; j++) {
+                        size_t t_h_j_offset = t_h_offset + j;
+                        size_t h_2d_i_j_offset = h_2d_i_offset + j;
+                        float v_val = v[t_h_j_offset];
+                        float kv_val = v_val * k_val;
+                        float prev_state_val = state_prev[h_2d_i_j_offset];
+                        float temp_val = kv_val + prev_state_val * g_val;
+                        dst_data[t_h_j_offset] += temp_val * q_val;
+                        state_cur[h_2d_i_j_offset] = temp_val;
+                    }
+                }
+            }
+        }
+
+    #else
+        for (int64_t t = 0; t < T; t++) {
+            size_t t_offset = t * t_stride;
+            size_t state_offset = head_size * C * (t / (T / n_seqs));
+            float * state_cur = state + state_offset;
+            float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[4]->data + state_offset;
+
+            for (int64_t h = h_start; h < h_end; h++) {
+                size_t h_offset = h * h_stride;
+                size_t t_h_offset = t_offset + h_offset;
+                size_t h_2d_offset = h * h_stride_2d;
+
+                for (int64_t i = 0; i < head_size; i++) {
+                    size_t t_h_i_offset = t_h_offset + i;
+                    size_t h_2d_i_offset = h_2d_offset + i * h_stride;
+
+                    float k_val = k[t_h_i_offset];
+                    float q_val = q[t_h_i_offset] * scale;
+                    float g_val = g[t_h_i_offset];
+
+                    for (int64_t j = 0; j < head_size; j++) {
+                        size_t t_h_j_offset = t_h_offset + j;
+                        size_t h_2d_i_j_offset = h_2d_i_offset + j;
+
+                        float v_val = v[t_h_j_offset];
+                        float kv_val = v_val * k_val;
+                        float prev_state_val = state_prev[h_2d_i_j_offset];
+                        float temp_val = prev_state_val * g_val + kv_val;
+                        dst_data[t_h_j_offset] += temp_val * q_val;
+                        state_cur[h_2d_i_j_offset] = temp_val;
+                    }
+                }
+            }
+        }
+    #endif
+}
+
+
+static void ggml_compute_forward_gla(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_gla_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_map_unary
 
 static void ggml_compute_forward_map_unary_f32(
@@ -12749,6 +12940,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_rwkv_wkv6(params, tensor);
             } break;
+        case GGML_OP_GATED_LINEAR_ATTN:
+            {
+                ggml_compute_forward_gla(params, tensor);
+            } break;
         case GGML_OP_MAP_UNARY:
             {
                 ggml_unary_op_f32_t fun;
@@ -13047,6 +13242,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_WIN_UNPART:
         case GGML_OP_GET_REL_POS:
         case GGML_OP_RWKV_WKV6:
+        case GGML_OP_GATED_LINEAR_ATTN:
         case GGML_OP_MAP_UNARY:
         case GGML_OP_MAP_BINARY:
         case GGML_OP_MAP_CUSTOM1_F32:
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 0b06be729..8476ee1bc 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -37,6 +37,7 @@
 #include "ggml-cuda/unary.cuh"
 #include "ggml-cuda/upscale.cuh"
 #include "ggml-cuda/wkv6.cuh"
+#include "ggml-cuda/gla.cuh"
 
 #include <algorithm>
 #include <array>
@@ -2167,6 +2168,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_RWKV_WKV6:
             ggml_cuda_op_rwkv_wkv6(ctx, dst);
             break;
+        case GGML_OP_GATED_LINEAR_ATTN:
+            ggml_cuda_op_gated_linear_attn(ctx, dst);
+            break;
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             ggml_cuda_cross_entropy_loss_back(ctx, dst);
             break;
@@ -3011,6 +3015,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_RWKV_WKV6:
+        case GGML_OP_GATED_LINEAR_ATTN:
             return true;
         case GGML_OP_FLASH_ATTN_EXT: {
 #ifndef FLASH_ATTN_AVAILABLE
diff --git a/ggml/src/ggml-cuda/gla.cu b/ggml/src/ggml-cuda/gla.cu
new file mode 100644
index 000000000..f7d615a82
--- /dev/null
+++ b/ggml/src/ggml-cuda/gla.cu
@@ -0,0 +1,93 @@
+#include "common.cuh"
+#include "gla.cuh"
+
+template<int HEAD_SIZE>
+static __global__ void gated_linear_attn_f32(const int B, const int T, const int C, const int H, const float scale,
+     const float * k, const float * v, const float * r, const float * td, const float * s, float * dst) {
+    const int tid = threadIdx.x;
+    const int bid = blockIdx.x;
+
+    const int head_size = HEAD_SIZE;
+    const int batch_i = bid / H;
+    const int head_i = bid % H;
+    const int state_size = C * head_size;
+    const int n_seq_tokens = T / B;
+
+    float state[head_size];
+    __shared__ float _k[head_size], _r[head_size], _td[head_size];
+
+    #pragma unroll
+    for (int i = 0; i < head_size; i++) {
+        state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
+    }
+
+    for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
+        __syncthreads();
+        _k[tid] = k[t];
+        _r[tid] = r[t];
+        _td[tid] = td[t];
+        __syncthreads();
+
+        const float _v = v[t];
+        float y = 0;
+        for (int j = 0; j < head_size; j += 4) {
+            const float4 & k = (float4 &)(_k[j]);
+            const float4 & r = (float4 &)(_r[j]);
+            const float4 & td = (float4 &)(_td[j]);
+            float4 & s = (float4 &)(state[j]);
+            float4 kv;
+
+            kv.x = k.x * _v;
+            kv.y = k.y * _v;
+            kv.z = k.z * _v;
+            kv.w = k.w * _v;
+
+            s.x = s.x * td.x + kv.x;
+            s.y = s.y * td.y + kv.y;
+            s.z = s.z * td.z + kv.z;
+            s.w = s.w * td.w + kv.w;
+
+            y += r.x * s.x;
+            y += r.y * s.y;
+            y += r.z * s.z;
+            y += r.w * s.w;
+        }
+        dst[t] = y * scale;
+    }
+
+    #pragma unroll
+    for (int i = 0; i < head_size; i++) {
+        dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
+    }
+}
+
+void ggml_cuda_op_gated_linear_attn(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const float * k_d  = (const float *)dst->src[0]->data;
+    const float * v_d  = (const float *)dst->src[1]->data;
+    const float * r_d  = (const float *)dst->src[2]->data;
+    const float * td_d = (const float *)dst->src[3]->data;
+    const float * s_d  = (const float *)dst->src[4]->data;
+
+    const int64_t B = dst->src[4]->ne[1];
+    const int64_t T = dst->src[0]->ne[2];
+    const int64_t C = dst->ne[0];
+    const int64_t H = dst->src[0]->ne[1];
+
+    float scale;
+    memcpy(&scale, (float*)dst->op_params, sizeof(float));
+
+    float * dst_d = (float *)dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(dst->src[4]->type == GGML_TYPE_F32);
+    GGML_ASSERT(C % H == 0);
+    GGML_ASSERT(C / H == 64 || C / H == 128);
+
+
+    if (C / H == 64) {
+        gated_linear_attn_f32<64><<<B * H, C / H, 0, stream>>>(B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+    } else {
+        gated_linear_attn_f32<128><<<B * H, C / H, 0, stream>>>(B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+    }
+}
diff --git a/ggml/src/ggml-cuda/gla.cuh b/ggml/src/ggml-cuda/gla.cuh
new file mode 100644
index 000000000..2c82ad7dd
--- /dev/null
+++ b/ggml/src/ggml-cuda/gla.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_gated_linear_attn(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/wkv6.cu b/ggml/src/ggml-cuda/wkv6.cu
index 42578341a..bbdafbee5 100644
--- a/ggml/src/ggml-cuda/wkv6.cu
+++ b/ggml/src/ggml-cuda/wkv6.cu
@@ -73,9 +73,9 @@ void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     const float * s_d  = (const float *)dst->src[5]->data;
 
     const int64_t B = dst->src[5]->ne[1];
-    const int64_t T = dst->src[0]->ne[3];
+    const int64_t T = dst->src[0]->ne[2];
     const int64_t C = dst->ne[0];
-    const int64_t H = dst->src[0]->ne[2];
+    const int64_t H = dst->src[0]->ne[1];
 
     float * dst_d = (float *)dst->data;
 
diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp
index 4fed18c2a..b54c20964 100644
--- a/ggml/src/ggml-sycl/wkv6.cpp
+++ b/ggml/src/ggml-sycl/wkv6.cpp
@@ -109,9 +109,9 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     float* dst_d = (float*)dst->data;
 
     const int64_t B = dst->src[5]->ne[1];
-    const int64_t T = dst->src[0]->ne[3];
+    const int64_t T = dst->src[0]->ne[2];
     const int64_t C = dst->ne[0];
-    const int64_t H = dst->src[0]->ne[2];
+    const int64_t H = dst->src[0]->ne[1];
 
     GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
     GGML_ASSERT(C % H == 0);
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 077452424..1b9174682 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5633,9 +5633,9 @@ static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subc
 }
 
 static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) {
-    const size_t seq_length = dst->src[0]->ne[3];
+    const size_t seq_length = dst->src[0]->ne[2];
     const size_t n_embed = dst->ne[0];
-    const size_t n_heads = dst->src[0]->ne[2];
+    const size_t n_heads = dst->src[0]->ne[1];
     const size_t n_seqs = dst->src[5]->ne[1];
 
     ggml_vk_op_f32_rwkv6(
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 90abc6ad4..da5b817e1 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -968,6 +968,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GET_REL_POS",
     "ADD_REL_POS",
     "RWKV_WKV6",
+    "GATED_LINEAR_ATTN",
 
     "UNARY",
 
@@ -987,7 +988,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "OPT_STEP_ADAMW",
 };
 
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1064,6 +1065,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "get_rel_pos(x)",
     "add_rel_pos(x)",
     "rwkv_wkv6(k, v, r, tf, td, s)",
+    "gated_linear_attn(k, v, q, gate, s)",
 
     "unary(x)",
 
@@ -1083,7 +1085,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "adamw(x)",
 };
 
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4629,15 +4631,13 @@ struct ggml_tensor * ggml_rwkv_wkv6(
     GGML_ASSERT(ggml_is_contiguous(state));
 
     const int64_t S = k->ne[0];
-    const int64_t H = k->ne[2];
-    const int64_t n_tokens = k->ne[3];
+    const int64_t H = k->ne[1];
+    const int64_t n_tokens = k->ne[2];
     const int64_t n_seqs = state->ne[1];
     {
-        GGML_ASSERT(k->ne[1] == 1);
-        GGML_ASSERT(v->ne[0] == 1 && v->ne[1] == S && v->ne[2] == H && v->ne[3] == n_tokens);
-        GGML_ASSERT(r->ne[0] == 1 && r->ne[1] == S && r->ne[2] == H && r->ne[3] == n_tokens);
-        // TODO: RWKV v4 and v5
-        GGML_ASSERT(td->ne[0] == 1 && td->ne[1] == S && td->ne[2] == H && td->ne[3] == n_tokens);
+        GGML_ASSERT(v->ne[0] == S && v->ne[1] == H && v->ne[2] == n_tokens);
+        GGML_ASSERT(r->ne[0] == S && r->ne[1] == H && r->ne[2] == n_tokens);
+        GGML_ASSERT(td->ne[0] == S && td->ne[1] == H && td->ne[2] == n_tokens);
         GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs);
     }
 
@@ -4656,6 +4656,49 @@ struct ggml_tensor * ggml_rwkv_wkv6(
     return result;
 }
 
+// ggml_gated_linear_attn
+
+struct ggml_tensor * ggml_gated_linear_attn(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * k,
+        struct ggml_tensor  * v,
+        struct ggml_tensor  * q,
+        struct ggml_tensor  * g,
+        struct ggml_tensor  * state,
+        float scale) {
+    GGML_ASSERT(ggml_is_contiguous(k));
+    GGML_ASSERT(ggml_is_contiguous(v));
+    GGML_ASSERT(ggml_is_contiguous(q));
+    GGML_ASSERT(ggml_is_contiguous(g));
+    GGML_ASSERT(ggml_is_contiguous(state));
+
+    const int64_t S = k->ne[0];
+    const int64_t H = k->ne[1];
+    const int64_t n_tokens = k->ne[2];
+    const int64_t n_seqs = state->ne[1];
+    {
+        GGML_ASSERT(v->ne[0] == S && v->ne[1] == H && v->ne[2] == n_tokens);
+        GGML_ASSERT(q->ne[0] == S && q->ne[1] == H && q->ne[2] == n_tokens);
+        GGML_ASSERT(g->ne[0] == S && g->ne[1] == H && g->ne[2] == n_tokens);
+        GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs);
+    }
+
+    // concat output and new_state
+    const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    ggml_set_op_params_f32(result, 0, scale);
+
+    result->op     = GGML_OP_GATED_LINEAR_ATTN;
+    result->src[0] = k;
+    result->src[1] = v;
+    result->src[2] = q;
+    result->src[3] = g;
+    result->src[4] = state;
+
+    return result;
+}
+
 // ggml_unary
 
 static struct ggml_tensor * ggml_unary_impl(
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index cf05bf47e..56aa9288d 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -115,6 +115,7 @@ class Keys:
         TIME_DECAY_EXTRA_DIM              = "{arch}.time_decay_extra_dim"
         RESIDUAL_SCALE                    = "{arch}.residual_scale"
         EMBEDDING_SCALE                   = "{arch}.embedding_scale"
+        TOKEN_SHIFT_COUNT                 = "{arch}.token_shift_count"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -255,6 +256,7 @@ class MODEL_ARCH(IntEnum):
     GEMMA2           = auto()
     STARCODER2       = auto()
     RWKV6            = auto()
+    RWKV6QWEN2       = auto()
     MAMBA            = auto()
     XVERSE           = auto()
     COMMAND_R        = auto()
@@ -334,6 +336,7 @@ class MODEL_TENSOR(IntEnum):
     TIME_MIX_LERP_V      = auto()
     TIME_MIX_LERP_R      = auto()
     TIME_MIX_LERP_G      = auto()
+    TIME_MIX_LERP_FUSED  = auto()
     TIME_MIX_LERP_W      = auto()
     TIME_MIX_FIRST       = auto()
     TIME_MIX_DECAY       = auto()
@@ -440,6 +443,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.GEMMA2:           "gemma2",
     MODEL_ARCH.STARCODER2:       "starcoder2",
     MODEL_ARCH.RWKV6:            "rwkv6",
+    MODEL_ARCH.RWKV6QWEN2:       "rwkv6qwen2",
     MODEL_ARCH.MAMBA:            "mamba",
     MODEL_ARCH.XVERSE:           "xverse",
     MODEL_ARCH.COMMAND_R:        "command-r",
@@ -519,6 +523,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.TIME_MIX_LERP_V:           "blk.{bid}.time_mix_lerp_v",
     MODEL_TENSOR.TIME_MIX_LERP_R:           "blk.{bid}.time_mix_lerp_r",
     MODEL_TENSOR.TIME_MIX_LERP_G:           "blk.{bid}.time_mix_lerp_g",
+    MODEL_TENSOR.TIME_MIX_LERP_FUSED:       "blk.{bid}.time_mix_lerp_fused",
     MODEL_TENSOR.TIME_MIX_LERP_W:           "blk.{bid}.time_mix_lerp_w",
     MODEL_TENSOR.TIME_MIX_FIRST:            "blk.{bid}.time_mix_first",
     MODEL_TENSOR.TIME_MIX_DECAY:            "blk.{bid}.time_mix_decay",
@@ -1103,6 +1108,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.TIME_MIX_LERP_R,
         MODEL_TENSOR.TIME_MIX_LERP_G,
         MODEL_TENSOR.TIME_MIX_LERP_W,
+        MODEL_TENSOR.TIME_MIX_LERP_FUSED,
         MODEL_TENSOR.TIME_MIX_FIRST,
         MODEL_TENSOR.TIME_MIX_DECAY,
         MODEL_TENSOR.TIME_MIX_DECAY_W1,
@@ -1119,6 +1125,35 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE,
         MODEL_TENSOR.CHANNEL_MIX_VALUE,
     ],
+    MODEL_ARCH.RWKV6QWEN2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.TIME_MIX_W1,
+        MODEL_TENSOR.TIME_MIX_W2,
+        MODEL_TENSOR.TIME_MIX_LERP_X,
+        MODEL_TENSOR.TIME_MIX_LERP_K,
+        MODEL_TENSOR.TIME_MIX_LERP_V,
+        MODEL_TENSOR.TIME_MIX_LERP_R,
+        MODEL_TENSOR.TIME_MIX_LERP_G,
+        MODEL_TENSOR.TIME_MIX_LERP_W,
+        MODEL_TENSOR.TIME_MIX_LERP_FUSED,
+        MODEL_TENSOR.TIME_MIX_FIRST,
+        MODEL_TENSOR.TIME_MIX_DECAY,
+        MODEL_TENSOR.TIME_MIX_DECAY_W1,
+        MODEL_TENSOR.TIME_MIX_DECAY_W2,
+        MODEL_TENSOR.TIME_MIX_KEY,
+        MODEL_TENSOR.TIME_MIX_VALUE,
+        MODEL_TENSOR.TIME_MIX_RECEPTANCE,
+        MODEL_TENSOR.TIME_MIX_GATE,
+        MODEL_TENSOR.TIME_MIX_LN,
+        MODEL_TENSOR.TIME_MIX_OUTPUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.MAMBA: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 4a0a65e3c..bf851c92c 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -743,6 +743,9 @@ class GGUFWriter:
     def add_wkv_head_size(self, size: int) -> None:
         self.add_uint32(Keys.WKV.HEAD_SIZE.format(arch=self.arch), size)
 
+    def add_token_shift_count(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count)
+
     def add_layer_norm_eps(self, value: float) -> None:
         self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 7616c468a..617791e24 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -13,7 +13,7 @@ class TensorNameMap:
             "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx jais exaone
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
-            "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2
+            "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2 rwkv6qwen2
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert nomic-bert
             "language_model.embedding.word_embeddings",  # persimmon
@@ -464,34 +464,42 @@ class TensorNameMap:
 
         MODEL_TENSOR.TIME_MIX_W1: (
             "rwkv.blocks.{bid}.attention.time_maa_w1",  # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_w1", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_W2: (
             "rwkv.blocks.{bid}.attention.time_maa_w2",  # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_w2", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_X: (
             "rwkv.blocks.{bid}.attention.time_maa_x",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_x",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_K: (
             "rwkv.blocks.{bid}.attention.time_maa_k",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_k",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_V: (
             "rwkv.blocks.{bid}.attention.time_maa_v",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_v",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_R: (
             "rwkv.blocks.{bid}.attention.time_maa_r",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_r",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_G: (
             "rwkv.blocks.{bid}.attention.time_maa_g",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_g",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LERP_W: (
             "rwkv.blocks.{bid}.attention.time_maa_w",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_maa_w",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_FIRST: (
@@ -500,30 +508,37 @@ class TensorNameMap:
 
         MODEL_TENSOR.TIME_MIX_DECAY: (
             "rwkv.blocks.{bid}.attention.time_decay",   # rwkv v6
+            "model.layers.{bid}.self_attn.time_decay",  # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_DECAY_W1: (
             "rwkv.blocks.{bid}.attention.time_decay_w1",  # rwkv v6
+            "model.layers.{bid}.self_attn.time_decay_w1", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_DECAY_W2: (
             "rwkv.blocks.{bid}.attention.time_decay_w2",  # rwkv v6
+            "model.layers.{bid}.self_attn.time_decay_w2", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_KEY: (
-            "rwkv.blocks.{bid}.attention.key", # rwkv
+            "rwkv.blocks.{bid}.attention.key",     # rwkv
+            "model.layers.{bid}.self_attn.k_proj", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_VALUE: (
-            "rwkv.blocks.{bid}.attention.value", # rwkv
+            "rwkv.blocks.{bid}.attention.value",   # rwkv
+            "model.layers.{bid}.self_attn.v_proj", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_RECEPTANCE: (
             "rwkv.blocks.{bid}.attention.receptance", # rwkv
+            "model.layers.{bid}.self_attn.q_proj",    # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_GATE: (
-            "rwkv.blocks.{bid}.attention.gate", # rwkv
+            "rwkv.blocks.{bid}.attention.gate",  # rwkv
+            "model.layers.{bid}.self_attn.gate", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.TIME_MIX_LN: (
@@ -531,7 +546,8 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.TIME_MIX_OUTPUT: (
-            "rwkv.blocks.{bid}.attention.output", # rwkv
+            "rwkv.blocks.{bid}.attention.output",  # rwkv
+            "model.layers.{bid}.self_attn.o_proj", # rwkv6qwen2
         ),
 
         MODEL_TENSOR.CHANNEL_MIX_LERP_K: (
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index eef66ed31..7300bd26a 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -57,6 +57,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_NEMOTRON,         "nemotron"         },
     { LLM_ARCH_EXAONE,           "exaone"           },
     { LLM_ARCH_RWKV6,            "rwkv6"            },
+    { LLM_ARCH_RWKV6QWEN2,       "rwkv6qwen2"       },
     { LLM_ARCH_GRANITE,          "granite"          },
     { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
     { LLM_ARCH_CHAMELEON,        "chameleon"        },
@@ -106,6 +107,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
     { LLM_KV_RESIDUAL_SCALE,                    "%s.residual_scale"                    },
     { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
+    { LLM_KV_TOKEN_SHIFT_COUNT,                 "%s.token_shift_count"                 },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"             },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"          },
@@ -1166,6 +1168,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_TIME_MIX_LERP_V,           "blk.%d.time_mix_lerp_v" },
             { LLM_TENSOR_TIME_MIX_LERP_R,           "blk.%d.time_mix_lerp_r" },
             { LLM_TENSOR_TIME_MIX_LERP_G,           "blk.%d.time_mix_lerp_g" },
+            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
             { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
             { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
             { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
@@ -1183,6 +1186,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,    "blk.%d.channel_mix_receptance" },
         },
     },
+    {
+        LLM_ARCH_RWKV6QWEN2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
+            { LLM_TENSOR_OUTPUT,                    "output" },
+            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
+            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
+            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
+            { LLM_TENSOR_TIME_MIX_LERP_X,           "blk.%d.time_mix_lerp_x" },
+            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
+            { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
+            { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
+            { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
+            { LLM_TENSOR_TIME_MIX_DECAY_W2,         "blk.%d.time_mix_decay_w2" },
+            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
+            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
+            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
+            { LLM_TENSOR_TIME_MIX_GATE,             "blk.%d.time_mix_gate" },
+            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
+            { LLM_TENSOR_FFN_NORM,                  "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,                  "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,                  "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,                    "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_GRANITE,
         {
@@ -1365,6 +1394,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_TIME_MIX_LERP_V,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     {LLM_TENSOR_TIME_MIX_LERP_R,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     {LLM_TENSOR_TIME_MIX_LERP_G,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_TIME_MIX_LERP_FUSED,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     {LLM_TENSOR_TIME_MIX_DECAY,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     {LLM_TENSOR_TIME_MIX_FIRST,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV6}},
     {LLM_TENSOR_ATTN_NORM,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 2e5f97b77..79909f03f 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -61,6 +61,7 @@ enum llm_arch {
     LLM_ARCH_NEMOTRON,
     LLM_ARCH_EXAONE,
     LLM_ARCH_RWKV6,
+    LLM_ARCH_RWKV6QWEN2,
     LLM_ARCH_GRANITE,
     LLM_ARCH_GRANITE_MOE,
     LLM_ARCH_CHAMELEON,
@@ -110,6 +111,7 @@ enum llm_kv {
     LLM_KV_TIME_DECAY_EXTRA_DIM,
     LLM_KV_RESIDUAL_SCALE,
     LLM_KV_EMBEDDING_SCALE,
+    LLM_KV_TOKEN_SHIFT_COUNT,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -253,6 +255,7 @@ enum llm_tensor {
     LLM_TENSOR_TIME_MIX_LERP_V,
     LLM_TENSOR_TIME_MIX_LERP_R,
     LLM_TENSOR_TIME_MIX_LERP_G,
+    LLM_TENSOR_TIME_MIX_LERP_FUSED,
     LLM_TENSOR_TIME_MIX_FIRST,
     LLM_TENSOR_TIME_MIX_DECAY,
     LLM_TENSOR_TIME_MIX_DECAY_W1,
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index c40534696..ea87b2953 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -52,7 +52,7 @@ uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
 uint32_t llama_hparams::n_embd_k_s() const {
     if (wkv_head_size != 0) {
         // for RWKV models
-        return 2 * n_embd;
+        return token_shift_count * n_embd;
     }
 
     // TODO: maybe support other convolution strides than 1
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index a29f20ec4..3542bef49 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -76,6 +76,7 @@ struct llama_hparams {
     uint32_t time_mix_extra_dim     = 0;
     uint32_t time_decay_extra_dim   = 0;
     uint32_t wkv_head_size          = 0;
+    uint32_t token_shift_count      = 2;
 
     float    rope_attn_factor = 1.0f;
     float    rope_freq_base_train;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 7260cb155..c056204b0 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1054,12 +1054,15 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 }
             } break;
         case LLM_ARCH_RWKV6:
+        case LLM_ARCH_RWKV6QWEN2:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
                 ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
                 ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
                 ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
                 ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
+                ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count, false);
 
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1_6B; break;
@@ -1070,6 +1073,7 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 61: model.type = e_model::MODEL_14B; break;
+                    case 64: model.type = e_model::MODEL_32B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -2064,6 +2068,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_T5ENCODER:
         case LLM_ARCH_JAIS:
         case LLM_ARCH_RWKV6:
+        case LLM_ARCH_RWKV6QWEN2:
         case LLM_ARCH_WAVTOKENIZER_DEC:
             return LLAMA_ROPE_TYPE_NONE;
 
@@ -2208,6 +2213,7 @@ bool llama_model_is_recurrent(const struct llama_model * model) {
     switch (model->arch) {
         case LLM_ARCH_MAMBA:  return true;
         case LLM_ARCH_RWKV6:  return true;
+        case LLM_ARCH_RWKV6QWEN2: return true;
         default:              return false;
     }
 }
diff --git a/src/llama-model.h b/src/llama-model.h
index 424cb0f52..565d2dbdf 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -241,15 +241,19 @@ struct llama_layer {
     struct ggml_tensor * time_mix_lerp_v     = nullptr;
     struct ggml_tensor * time_mix_lerp_r     = nullptr;
     struct ggml_tensor * time_mix_lerp_g     = nullptr;
+    struct ggml_tensor * time_mix_lerp_fused = nullptr;
 
-    struct ggml_tensor * time_mix_first      = nullptr;
-    struct ggml_tensor * time_mix_decay      = nullptr;
-    struct ggml_tensor * time_mix_decay_w1   = nullptr;
-    struct ggml_tensor * time_mix_decay_w2   = nullptr;
-    struct ggml_tensor * time_mix_key        = nullptr;
-    struct ggml_tensor * time_mix_value      = nullptr;
-    struct ggml_tensor * time_mix_receptance = nullptr;
-    struct ggml_tensor * time_mix_gate       = nullptr;
+    struct ggml_tensor * time_mix_first        = nullptr;
+    struct ggml_tensor * time_mix_decay        = nullptr;
+    struct ggml_tensor * time_mix_decay_w1     = nullptr;
+    struct ggml_tensor * time_mix_decay_w2     = nullptr;
+    struct ggml_tensor * time_mix_key          = nullptr;
+    struct ggml_tensor * time_mix_key_b        = nullptr;
+    struct ggml_tensor * time_mix_value        = nullptr;
+    struct ggml_tensor * time_mix_value_b      = nullptr;
+    struct ggml_tensor * time_mix_receptance   = nullptr;
+    struct ggml_tensor * time_mix_receptance_b = nullptr;
+    struct ggml_tensor * time_mix_gate         = nullptr;
 
     struct ggml_tensor * time_mix_ln     = nullptr;
     struct ggml_tensor * time_mix_ln_b   = nullptr;
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 466e7bc61..a45044f30 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -620,7 +620,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
     qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
 
-    // sanity checks
+    // sanity checks for models that have attention layers
+    if (qs.n_attention_wv != 0)
     {
         const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
         // attention layers have a non-zero number of kv heads
@@ -758,6 +759,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         quantize &= name.find("time_mix_w2.weight") == std::string::npos;
         quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
         quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
+        quantize &= name.find("time_mix_lerp_fused.weight") == std::string::npos;
 
         // do not quantize relative position bias (T5)
         quantize &= name.find("attn_rel_b.weight") == std::string::npos;
diff --git a/src/llama.cpp b/src/llama.cpp
index ae375bcd3..a364861d3 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -134,11 +134,11 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
                 const int64_t H = 123;
                 const int64_t n_tokens = 123;
                 const int64_t n_seqs = 123;
-                ggml_tensor  * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens);
-                ggml_tensor  * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
-                ggml_tensor  * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
+                ggml_tensor  * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
                 ggml_tensor  * tf = w;
-                ggml_tensor  * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
+                ggml_tensor  * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
                 ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
                 op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
             } break;
@@ -2186,11 +2186,13 @@ static bool llm_load_tensors(
                         layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
 
                         layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, 0);
+                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
 
                         layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
                         layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
@@ -2214,6 +2216,59 @@ static bool llm_load_tensors(
                     }
 
                 } break;
+            case LLM_ARCH_RWKV6QWEN2:
+                {
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
+                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
+                    const int head_size = hparams.wkv_head_size;
+                    const int attn_hidden_size = n_embd;
+                    const int n_head_kv = hparams.n_head_kv();
+                    int attn_key_value_size;
+                    if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
+                        attn_key_value_size = attn_hidden_size;
+                    } else {
+                        attn_key_value_size = n_head_kv * head_size;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
+                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
+
+                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
+                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
+
+                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
+                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
+                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
+                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
+                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
+                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        // optional bias tensors
+                        layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
             case LLM_ARCH_CHAMELEON:
                 {
                     model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -3337,16 +3392,20 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
         const struct llama_layer * layer,
         struct ggml_tensor * cur,
         struct ggml_tensor * x_prev,
-        struct ggml_tensor ** wkv_state) {
+        struct ggml_tensor ** wkv_state,
+        size_t wkv_head_size,
+        size_t head_count_kv) {
     size_t n_embd       = cur->ne[0];
     size_t n_seq_tokens = cur->ne[1];
     size_t n_seqs       = cur->ne[2];
 
-    size_t head_size  = layer->time_mix_first->ne[0];
-    size_t head_count = layer->time_mix_first->ne[1];
+    size_t head_size  = wkv_head_size;
+    size_t head_count = n_embd / head_size;
 
     size_t n_tokens = n_seqs * n_seq_tokens;
 
+    bool is_qrwkv = layer->time_mix_first == nullptr;
+
     struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
 
     sx  = ggml_reshape_2d(ctx, sx,  n_embd, n_tokens);
@@ -3375,69 +3434,64 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
         xxx
     );
 
-    struct ggml_tensor *mw = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], 0);
-    struct ggml_tensor *mk = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
-    struct ggml_tensor *mv = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
-    struct ggml_tensor *mr = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
-    struct ggml_tensor *mg = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
+    struct ggml_tensor *xw, *xk, *xv, *xr, *xg;
+    if (layer->time_mix_lerp_fused) {
+        // fusing these weights makes some performance improvement
+        sx  = ggml_reshape_3d(ctx, sx,  n_embd, 1, n_tokens);
+        cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
+        xxx = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xxx, layer->time_mix_lerp_fused), sx), cur);
+        xw = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], 0);
+        xk = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
+        xv = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
+        xr = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
+        xg = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
+    } else {
+        // for backward compatibility
+        xw = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], 0);
+        xk = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
+        xv = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
+        xr = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
+        xg = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
 
-    struct ggml_tensor * xw = ggml_add(
-        ctx,
-        ggml_mul(
-            ctx,
-            ggml_add(ctx, mw, layer->time_mix_lerp_w),
-            sx
-        ),
-        cur
-    );
+        xw = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xw, layer->time_mix_lerp_w), sx), cur);
+        xk = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xk, layer->time_mix_lerp_k), sx), cur);
+        xv = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xv, layer->time_mix_lerp_v), sx), cur);
+        xr = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xr, layer->time_mix_lerp_r), sx), cur);
+        xg = ggml_add(ctx, ggml_mul(ctx, ggml_add(ctx, xg, layer->time_mix_lerp_g), sx), cur);
+    }
 
-    struct ggml_tensor * xk = ggml_add(
-        ctx,
-        ggml_mul(
-            ctx,
-            ggml_add(ctx, mk, layer->time_mix_lerp_k),
-            sx
-        ),
-        cur
-    );
+    struct ggml_tensor * r = llm_build_lora_mm(lctx, ctx, layer->time_mix_receptance, xr);
+    struct ggml_tensor * k = llm_build_lora_mm(lctx, ctx, layer->time_mix_key,        xk);
+    struct ggml_tensor * v = llm_build_lora_mm(lctx, ctx, layer->time_mix_value,      xv);
+    if (layer->time_mix_receptance_b) {
+        r = ggml_add(ctx, r, layer->time_mix_receptance_b);
+    }
+    if (layer->time_mix_key_b) {
+        k = ggml_add(ctx, k, layer->time_mix_key_b);
+    }
+    if (layer->time_mix_value_b) {
+        v = ggml_add(ctx, v, layer->time_mix_value_b);
+    }
 
-    struct ggml_tensor * xv = ggml_add(
-        ctx,
-        ggml_mul(
-            ctx,
-            ggml_add(ctx, mv, layer->time_mix_lerp_v),
-            sx
-        ),
-        cur
-    );
+    struct ggml_tensor * g = llm_build_lora_mm(lctx, ctx, layer->time_mix_gate, xg);
+    if (is_qrwkv) {
+        g = ggml_sigmoid(ctx, g);
+    } else {
+        g = ggml_silu(ctx, g);
+    }
 
-    struct ggml_tensor * xr = ggml_add(
-        ctx,
-        ggml_mul(
-            ctx,
-            ggml_add(ctx, mr, layer->time_mix_lerp_r),
-            sx
-        ),
-        cur
-    );
+    if (head_count_kv != head_count) {
+        GGML_ASSERT(head_count % head_count_kv == 0);
+        k = ggml_reshape_4d(ctx, k, head_size, 1, head_count_kv, n_tokens);
+        v = ggml_reshape_4d(ctx, v, head_size, 1, head_count_kv, n_tokens);
+        struct ggml_tensor * tmp = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, head_size, head_count / head_count_kv, head_count_kv, n_tokens);
+        k = ggml_repeat(ctx, k, tmp);
+        v = ggml_repeat(ctx, v, tmp);
+    }
 
-    struct ggml_tensor * xg = ggml_add(
-        ctx,
-        ggml_mul(
-            ctx,
-            ggml_add(ctx, mg, layer->time_mix_lerp_g),
-            sx
-        ),
-        cur
-    );
-
-    struct ggml_tensor * r = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_receptance, xr), head_size, 1,         head_count, n_tokens);
-    struct ggml_tensor * k = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_key,        xk), 1,         head_size, head_count, n_tokens);
-    struct ggml_tensor * v = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_value,      xv), head_size, 1,         head_count, n_tokens);
-    struct ggml_tensor * g = ggml_silu(
-        ctx,
-        llm_build_lora_mm(lctx, ctx, layer->time_mix_gate, xg)
-    );
+    k = ggml_reshape_3d(ctx, k, head_size, head_count, n_tokens);
+    v = ggml_reshape_3d(ctx, v, head_size, head_count, n_tokens);
+    r = ggml_reshape_3d(ctx, r, head_size, head_count, n_tokens);
 
     struct ggml_tensor * w = ggml_mul_mat(
         ctx,
@@ -3448,25 +3502,35 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
         )
     );
 
-    w = ggml_add(ctx, w, ggml_reshape_1d(ctx, layer->time_mix_decay, n_embd));
+    w = ggml_add(ctx, w, layer->time_mix_decay);
     w = ggml_exp(ctx, ggml_neg(ctx, ggml_exp(ctx, w)));
-    w = ggml_reshape_4d(ctx, w, 1, head_size, head_count, n_tokens);
+    w = ggml_reshape_3d(ctx, w, head_size, head_count, n_tokens);
 
-    k = ggml_transpose(ctx, k);
-    v = ggml_transpose(ctx, v);
-    r = ggml_transpose(ctx, r);
+    if (is_qrwkv) {
+        // k = k * (1 - w)
+        k = ggml_sub(ctx, k, ggml_mul(ctx, k, w));
+    }
 
-    struct ggml_tensor * wkv_output = ggml_rwkv_wkv6(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
+    struct ggml_tensor * wkv_output;
+    if (!layer->time_mix_first) {
+        wkv_output = ggml_gated_linear_attn(ctx, k, v, r, w, *wkv_state, pow(head_size, -0.5f));
+    } else {
+        wkv_output = ggml_rwkv_wkv6(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
+    }
     cur = ggml_view_1d(ctx, wkv_output, n_embd * n_tokens, 0);
     *wkv_state = ggml_view_1d(ctx, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
 
-    // group norm with head_count groups
-    cur = ggml_reshape_3d(ctx, cur, n_embd / head_count, head_count, n_tokens);
-    cur = ggml_norm(ctx, cur, 64e-5f);
+    if (!is_qrwkv) {
+        // group norm with head_count groups
+        cur = ggml_reshape_3d(ctx, cur, n_embd / head_count, head_count, n_tokens);
+        cur = ggml_norm(ctx, cur, 64e-5f);
 
-    // Convert back to regular vectors.
-    cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
-    cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);
+        // Convert back to regular vectors.
+        cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
+        cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);
+    } else {
+        cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
+    }
 
     cur = ggml_mul(ctx, cur, g);
     cur = llm_build_lora_mm(lctx, ctx, layer->time_mix_output, cur);
@@ -10048,7 +10112,7 @@ struct llm_build_context {
                 1
             );
 
-            cur = ggml_add(ctx0, cur, llm_build_rwkv6_time_mix(lctx, ctx0, layer, x_norm_att, x_prev, &wkv_states));
+            cur = ggml_add(ctx0, cur, llm_build_rwkv6_time_mix(lctx, ctx0, layer, x_norm_att, x_prev, &wkv_states, hparams.wkv_head_size, n_embd / hparams.wkv_head_size));
             ggml_build_forward_expand(gf, cur);
             ggml_build_forward_expand(
                 gf,
@@ -10115,6 +10179,118 @@ struct llm_build_context {
         return gf;
     }
 
+    // ref: https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1/blob/main/modeling_rwkv6qwen2.py
+    ggml_cgraph * build_rwkv6qwen2() {
+        ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        GGML_ASSERT(n_embd == hparams.n_embd_k_s());
+
+        const int64_t n_seqs = ubatch.n_seqs;
+        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+        const int64_t n_tokens = ubatch.n_tokens;
+        GGML_ASSERT(n_seqs != 0);
+        GGML_ASSERT(ubatch.equal_seqs);
+        GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+        struct ggml_tensor * state_copy = build_inp_s_copy();
+        struct ggml_tensor * state_mask = build_inp_s_mask();
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+        for (int il = 0; il < n_layer; ++il) {
+            const llama_layer * layer = &model.layers[il];
+
+            // (ab)using the KV cache to store the states
+            struct ggml_tensor * token_shift = llm_build_copy_mask_state(ctx0,
+                    gf, kv_self.k_l[il], state_copy, state_mask,
+                    hparams.n_embd_k_s(), kv_self.size, kv_head, n_kv, n_seqs);
+            struct ggml_tensor * wkv_states = llm_build_copy_mask_state(ctx0,
+                    gf, kv_self.v_l[il], state_copy, state_mask,
+                    hparams.n_embd_v_s(), kv_self.size, kv_head, n_kv, n_seqs);
+
+            cur = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
+            token_shift = ggml_reshape_3d(ctx0, token_shift, n_embd, 1, n_seqs);
+
+            struct ggml_tensor * x_norm_att = llm_build_norm(ctx0, cur, hparams, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, cb, il);
+            struct ggml_tensor * x_prev = ggml_concat(
+                ctx0,
+                token_shift,
+                ggml_view_3d(ctx0, x_norm_att, n_embd, n_seq_tokens - 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], 0),
+                1
+            );
+
+            ggml_build_forward_expand(
+                gf,
+                ggml_cpy(
+                    ctx0,
+                    wkv_states,
+                    ggml_view_1d(
+                        ctx0,
+                        kv_self.v_l[il],
+                        hparams.n_embd_v_s() * n_seqs,
+                        hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
+                    )
+                )
+            );
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, llm_build_rwkv6_time_mix(lctx, ctx0, layer, x_norm_att, x_prev, &wkv_states, hparams.wkv_head_size, hparams.n_head_kv()));
+            ggml_build_forward_expand(gf, ffn_inp);
+            ggml_build_forward_expand(
+                gf,
+                ggml_cpy(
+                    ctx0,
+                    wkv_states,
+                    ggml_view_1d(
+                        ctx0,
+                        kv_self.v_l[il],
+                        hparams.n_embd_v_s() * n_seqs,
+                        hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
+                    )
+                )
+            );
+
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+
+        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     // ref: https://github.com/facebookresearch/chameleon
     // based on the original build_llama() function, changes:
     //   * qk-norm
@@ -10724,6 +10900,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_rwkv6();
             } break;
+        case LLM_ARCH_RWKV6QWEN2:
+            {
+                result = llm.build_rwkv6qwen2();
+            } break;
         case LLM_ARCH_CHAMELEON:
             {
                 result = llm.build_chameleon();
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 1e892f663..3834e0f84 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1659,17 +1659,46 @@ struct test_rwkv_wkv6 : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         const int64_t n_tokens = n_seq_tokens * n_seqs;
-        ggml_tensor * r   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
-        ggml_tensor * k   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ head_size, 1, head_count, n_tokens }.data());
-        ggml_tensor * v   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
+        ggml_tensor * r   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * k   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * v   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
         ggml_tensor * tf  = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size, head_count }.data());
-        ggml_tensor * td  = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
+        ggml_tensor * td  = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
         ggml_tensor * s   = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size * head_size * head_count, n_seqs }.data());
         ggml_tensor * out = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, s);
         return out;
     }
 };
 
+// GGML_OP_GATED_LINEAR_ATTN
+struct test_gla : public test_case {
+    const ggml_type type;
+
+    const int64_t head_count;
+    const int64_t head_size;
+    const int64_t n_seq_tokens;
+    const int64_t n_seqs;
+
+    std::string vars() override {
+        return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs);
+    }
+
+    test_gla(ggml_type type = GGML_TYPE_F32,
+            int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32)
+        : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        const int64_t n_tokens = n_seq_tokens * n_seqs;
+        ggml_tensor * q   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * k   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * v   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * g   = ggml_new_tensor(ctx, type, 3, std::vector<int64_t>{ head_size, head_count, n_tokens }.data());
+        ggml_tensor * s   = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size * head_size * head_count, n_seqs }.data());
+        ggml_tensor * out = ggml_gated_linear_attn(ctx, k, v, q, g, s, pow(head_size, -0.5));
+        return out;
+    }
+};
+
 // GGML_OP_MUL_MAT
 struct test_mul_mat : public test_case {
     const ggml_type type_a;
@@ -3626,6 +3655,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 4));
     test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 128, 4));
 
+    test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 1, 1));
+    test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 1));
+    test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 4));
+    test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 128, 4));
+
     for (int i = 1; i < 9; ++i) {
         test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16,    GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
         test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));

From c3f9d25706ac84297067aeaa662c1f1af42ed443 Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Fri, 10 Jan 2025 06:39:33 +0100
Subject: [PATCH 153/376] Vulkan: Fix float16 use on devices without float16
 support + fix subgroup_size_control validation error (#11161)

* Vulkan: Remove float16 use in shaders

* Fix validation error about subgroup_size_control extension
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  2 +-
 .../vulkan-shaders/mul_mat_vec.comp           |  9 +++----
 .../vulkan-shaders/mul_mat_vec_q2_k.comp      | 24 +++++++++----------
 .../vulkan-shaders/mul_mat_vec_q3_k.comp      | 18 +++++++-------
 .../vulkan-shaders/mul_mat_vec_q4_k.comp      | 12 +++++-----
 .../vulkan-shaders/mul_mat_vec_q5_k.comp      | 20 ++++++++--------
 .../vulkan-shaders/mul_mat_vec_q6_k.comp      | 10 ++++----
 .../ggml-vulkan/vulkan-shaders/soft_max.comp  |  1 -
 .../src/ggml-vulkan/vulkan-shaders/types.comp |  5 +++-
 9 files changed, 50 insertions(+), 51 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 1b9174682..649146d7b 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2277,6 +2277,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         if (device->subgroup_size_control) {
             device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
             device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
+            device_extensions.push_back("VK_EXT_subgroup_size_control");
         }
 
         device->subgroup_size_control = device->subgroup_size_control &&
@@ -2285,7 +2286,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         if (device->subgroup_size_control) {
             device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;
-            device_extensions.push_back("VK_EXT_subgroup_size_control");
         }
 
 #if defined(VK_KHR_cooperative_matrix)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
index 24875cdcf..53902858d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
@@ -1,9 +1,6 @@
 #version 450
 
-#ifdef FLOAT16
-#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
-#endif
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -27,8 +24,8 @@ void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const
 
 #if K_PER_ITER == 8
 #if QUANT_R == 2
-        const B_TYPE_VEC4 bv02 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4];
-        const B_TYPE_VEC4 bv13 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs + y_offset) / 4];
+        const vec4 bv02 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]);
+        const vec4 bv13 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs + y_offset) / 4]);
         const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y);
         const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w);
 #else
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
index 934213446..6a9b9b2d1 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -1,5 +1,5 @@
 #version 450
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -40,9 +40,9 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            f16vec2 d = data_a[ib0 + i].d;
-            const FLOAT_TYPE dall = d.x;
-            const FLOAT_TYPE dmin = d.y;
+            vec2 d = vec2(data_a[ib0 + i].d);
+            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
 
             uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0];
             uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1];
@@ -63,14 +63,14 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             uvec2 qs16 = uvec2(unpack8(qs16_u16));
 
             [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0];
-                B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8];
-                B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16];
-                B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24];
-                B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32];
-                B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40];
-                B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48];
-                B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56];
+                vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+                vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+                vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+                vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+                vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+                vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+                vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+                vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
 
                 FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
                 FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
index 86b0159d9..96ef50fdd 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -1,5 +1,5 @@
 #version 450
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -60,14 +60,14 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 
             [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
 
-                B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0];
-                B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8];
-                B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16];
-                B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24];
-                B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32];
-                B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40];
-                B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48];
-                B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56];
+                vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+                vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+                vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+                vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+                vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+                vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+                vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+                vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
 
                 FLOAT_TYPE sum = FLOAT_TYPE(0.0);
                 [[unroll]] for (int l = 0; l < 2; ++l) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
index cd1dd8e89..f97eb8744 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -1,6 +1,6 @@
 #version 450
 
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -45,7 +45,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            f16vec2 d = data_a[ib0 + i].d;
+            vec2 d = vec2(data_a[ib0 + i].d);
             const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
             const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
 
@@ -96,10 +96,10 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             const uint32_t q4_15 = qs64_hi4.w;
 
             [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                B_TYPE_VEC4 by10 =  data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4];
-                B_TYPE_VEC4 by132 = data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8];
-                B_TYPE_VEC4 by20 =  data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4];
-                B_TYPE_VEC4 by232 = data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8];
+                vec4 by10 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4    ]);
+                vec4 by132 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8]);
+                vec4 by20 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4    ]);
+                vec4 by232 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8]);
 
                 const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
                 const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
index 0a68891c3..79d7db0e3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -1,6 +1,6 @@
 #version 450
 
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -42,7 +42,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
             const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            f16vec2 d = data_a[ib0 + i].d;
+            vec2 d = vec2(data_a[ib0 + i].d);
             const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
             const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
 
@@ -105,14 +105,14 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             const uint32_t q4_15 = qs64_80_hi4.w;
 
             [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                B_TYPE_VEC2 by10 =  data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2];
-                B_TYPE_VEC2 by116 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 8];
-                B_TYPE_VEC2 by132 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16];
-                B_TYPE_VEC2 by148 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24];
-                B_TYPE_VEC2 by20 =  data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2];
-                B_TYPE_VEC2 by216 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 8];
-                B_TYPE_VEC2 by232 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16];
-                B_TYPE_VEC2 by248 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24];
+                vec2 by10 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2     ]);
+                vec2 by116 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 +  8]);
+                vec2 by132 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16]);
+                vec2 by148 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24]);
+                vec2 by20 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2     ]);
+                vec2 by216 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 +  8]);
+                vec2 by232 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16]);
+                vec2 by248 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24]);
 
                 const FLOAT_TYPE sx =
                   fma(FLOAT_TYPE(by10.x), q4_0,
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
index 70e13a56b..041fd27c1 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -1,6 +1,6 @@
 #version 450
 
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.comp"
 
@@ -77,10 +77,10 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             uvec4 q3 = uvec4(unpack8(q3_u32));
 
             [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                B_TYPE_VEC4 by0  = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4];
-                B_TYPE_VEC4 by32 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 8];
-                B_TYPE_VEC4 by64 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16];
-                B_TYPE_VEC4 by96 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24];
+                vec4 by0  = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4     ]);
+                vec4 by32 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 +  8]);
+                vec4 by64 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16]);
+                vec4 by96 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24]);
 
                 FLOAT_TYPE sum = FLOAT_TYPE(0.0);
                 [[unroll]] for (int l = 0; l < 4; ++l) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
index a25808e16..51fc2dc7e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
@@ -1,6 +1,5 @@
 #version 450
 
-#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
 #extension GL_EXT_control_flow_attributes : enable
 
 layout (push_constant) uniform parameter
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
index eecc47f3a..f12e61bbe 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
@@ -2,7 +2,10 @@
 #if !defined(GGML_TYPES_COMP)
 #define GGML_TYPES_COMP
 
-#extension GL_EXT_shader_explicit_arithmetic_types : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+#extension GL_EXT_shader_16bit_storage : require
 
 #if defined(DATA_A_F32)
 #define QUANT_K 1

From ff3fcabc727b2dd0c477d23a258217b27cc639fb Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 10 Jan 2025 11:30:53 +0100
Subject: [PATCH 154/376] convert : add --print-supported-models option
 (#11172)

* convert : add --print-supported-models option

This commit adds a new option to the convert_hf_to_gguf.py script to
print the supported models.

The motivation for this is that it can be useful to know which models
are supported by the script without having to look at the code.

Example usage:
```console
$ ./convert_hf_to_gguf.py --print-supported-models
Supported models:
- GPTNeoXForCausalLM
- BloomForCausalLM
- BloomModel
- MPTForCausalLM
- OrionForCausalLM
- BaichuanForCausalLM
- BaiChuanForCausalLM
- XverseForCausalLM
- FalconForCausalLM
- RWForCausalLM
- GPTBigCodeForCausalLM
- GPTRefactForCausalLM
- StableLmForCausalLM
- StableLMEpochForCausalLM
- LlavaStableLMEpochForCausalLM
- LLaMAForCausalLM
- LlamaForCausalLM
- MistralForCausalLM
- MixtralForCausalLM
- DeciLMForCausalLM
- BitnetForCausalLM
- GrokForCausalLM
- DbrxForCausalLM
- MiniCPMForCausalLM
- MiniCPM3ForCausalLM
- QWenLMHeadModel
- Qwen2ForCausalLM
- Qwen2VLForConditionalGeneration
- WavTokenizerDec
- Qwen2MoeForCausalLM
- GPT2LMHeadModel
- PhiForCausalLM
- Phi3ForCausalLM
- PhiMoEForCausalLM
- PlamoForCausalLM
- CodeShellForCausalLM
- InternLM2ForCausalLM
- BertModel
- BertForMaskedLM
- CamembertModel
- RobertaModel
- NomicBertModel
- XLMRobertaModel
- XLMRobertaForSequenceClassification
- GemmaForCausalLM
- Gemma2ForCausalLM
- Starcoder2ForCausalLM
- Rwkv6ForCausalLM
- RWKV6Qwen2ForCausalLM
- MambaForCausalLM
- MambaLMHeadModel
- FalconMambaForCausalLM
- CohereForCausalLM
- Cohere2ForCausalLM
- OLMoForCausalLM
- OlmoForCausalLM
- Olmo2ForCausalLM
- OlmoeForCausalLM
- JinaBertModel
- JinaBertForMaskedLM
- OpenELMForCausalLM
- ArcticForCausalLM
- DeepseekForCausalLM
- DeepseekV3ForCausalLM
- DeepseekV2ForCausalLM
- UMT5ForConditionalGeneration
- MT5ForConditionalGeneration
- T5ForConditionalGeneration
- T5WithLMHeadModel
- T5EncoderModel
- JAISLMHeadModel
- ChatGLMModel
- ChatGLMForConditionalGeneration
- NemotronForCausalLM
- ExaoneForCausalLM
- GraniteForCausalLM
- GraniteMoeForCausalLM
- ChameleonForCausalLM
- ChameleonForConditionalGeneration
```

* squash! convert : add --print-supported-models option

Fix flake8 error.
---
 convert_hf_to_gguf.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index cf317eeae..81f19bf37 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -478,6 +478,11 @@ class Model:
             return modelcls
         return func
 
+    @classmethod
+    def print_registered_models(cls):
+        for name in cls._model_classes.keys():
+            logger.error(f"- {name}")
+
     @classmethod
     def from_model_architecture(cls, arch: str) -> type[Model]:
         try:
@@ -4929,6 +4934,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "model", type=Path,
         help="directory containing model file",
+        nargs="?",
     )
     parser.add_argument(
         "--use-temp-file", action="store_true",
@@ -4966,8 +4972,15 @@ def parse_args() -> argparse.Namespace:
         "--metadata", type=Path,
         help="Specify the path for an authorship metadata override file"
     )
+    parser.add_argument(
+        "--print-supported-models", action="store_true",
+        help="Print the supported models"
+    )
 
-    return parser.parse_args()
+    args = parser.parse_args()
+    if not args.print_supported_models and args.model is None:
+        parser.error("the following arguments are required: model")
+    return args
 
 
 def split_str_to_n_bytes(split_str: str) -> int:
@@ -4991,6 +5004,11 @@ def split_str_to_n_bytes(split_str: str) -> int:
 def main() -> None:
     args = parse_args()
 
+    if args.print_supported_models:
+        logger.error("Supported models:")
+        Model.print_registered_models()
+        sys.exit(0)
+
     if args.verbose:
         logging.basicConfig(level=logging.DEBUG)
     else:

From ba8a1f9c5b675459c55a83e3f97f10df3a66c788 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 10 Jan 2025 13:16:16 +0100
Subject: [PATCH 155/376] examples : add README.md to tts example [no ci]
 (#11155)

* examples : add README.md to tts example [no ci]

* squash! examples : add README.md to tts example [no ci]

Fix heading to be consistent with other examples, and add a quickstart
section to README.md.

* squash! examples : add README.md to tts example [no ci]

Fix spelling mistake.
---
 examples/tts/README.md | 80 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 examples/tts/README.md

diff --git a/examples/tts/README.md b/examples/tts/README.md
new file mode 100644
index 000000000..b0d20111a
--- /dev/null
+++ b/examples/tts/README.md
@@ -0,0 +1,80 @@
+# llama.cpp/example/tts
+This example demonstrates the Text To Speech feature. It uses a
+[model](https://www.outeai.com/blog/outetts-0.2-500m) from
+[outeai](https://www.outeai.com/).
+
+## Quickstart
+If you have built llama.cpp with `-DLLAMA_CURL=ON` you can simply run the
+following command and the required models will be downloaded automatically:
+```console
+$ build/bin/llama-tts --tts-oute-default -p "Hello world" && aplay output.wav
+```
+For details about the models and how to convert them to the required format
+see the following sections.
+
+### Model conversion
+Checkout or download the model that contains the LLM model:
+```console
+$ pushd models
+$ git clone --branch main --single-branch --depth 1 https://huggingface.co/OuteAI/OuteTTS-0.2-500M
+$ cd OuteTTS-0.2-500M && git lfs install && git lfs pull
+$ popd
+```
+Convert the model to .gguf format:
+```console
+(venv) python convert_hf_to_gguf.py models/OuteTTS-0.2-500M \
+    --outfile models/outetts-0.2-0.5B-f16.gguf --outtype f16
+```
+The generated model will be `models/outetts-0.2-0.5B-f16.gguf`.
+
+We can optionally quantize this to Q8_0 using the following command:
+```console
+$ build/bin/llama-quantize models/outetts-0.2-0.5B-f16.gguf \
+    models/outetts-0.2-0.5B-q8_0.gguf q8_0
+```
+The quantized model will be `models/outetts-0.2-0.5B-q8_0.gguf`.
+
+Next we do something simlar for the audio decoder. First download or checkout
+the model for the voice decoder:
+```console
+$ pushd models
+$ git clone --branch main --single-branch --depth 1 https://huggingface.co/novateur/WavTokenizer-large-speech-75token
+$ cd WavTokenizer-large-speech-75token && git lfs install && git lfs pull
+$ popd
+```
+This model file is PyTorch checkpoint (.ckpt) and we first need to convert it to
+huggingface format:
+```console
+(venv) python examples/tts/convert_pt_to_hf.py \
+    models/WavTokenizer-large-speech-75token/wavtokenizer_large_speech_320_24k.ckpt
+...
+Model has been successfully converted and saved to models/WavTokenizer-large-speech-75token/model.safetensors
+Metadata has been saved to models/WavTokenizer-large-speech-75token/index.json
+Config has been saved to models/WavTokenizer-large-speech-75tokenconfig.json
+```
+Then we can convert the huggingface format to gguf:
+```console
+(venv) python convert_hf_to_gguf.py models/WavTokenizer-large-speech-75token \
+    --outfile models/wavtokenizer-large-75-f16.gguf --outtype f16
+...
+INFO:hf-to-gguf:Model successfully exported to models/wavtokenizer-large-75-f16.gguf
+```
+
+### Running the example
+
+With both of the models generated, the LLM model and the voice decoder model,
+we can run the example:
+```console
+$ build/bin/llama-tts -m  ./models/outetts-0.2-0.5B-q8_0.gguf \
+    -mv ./models/wavtokenizer-large-75-f16.gguf \
+    -p "Hello world"
+...
+main: audio written to file 'output.wav'
+```
+The output.wav file will contain the audio of the prompt. This can be heard
+by playing the file with a media player. On Linux the following command will
+play the audio:
+```console
+$ aplay output.wav
+```
+

From 2739a71e4b88474833b64aa974ca4515574fd3c4 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sat, 11 Jan 2025 05:50:33 +0100
Subject: [PATCH 156/376] convert : sort print supported models [no ci]
 (#11179)

This commit sorts the list of supported models when printing them out.

The motivation for this change is to make it easier to find a specific
model in the list of supported models. For example:
```console
$ ./convert_hf_to_gguf.py --print-supported-models
Supported models:
- ArcticForCausalLM
- BaiChuanForCausalLM
- BaichuanForCausalLM
- BertForMaskedLM
- BertModel
- BitnetForCausalLM
- BloomForCausalLM
- BloomModel
- CamembertModel
- ChameleonForCausalLM
- ChameleonForConditionalGeneration
- ChatGLMForConditionalGeneration
- ChatGLMModel
- CodeShellForCausalLM
- Cohere2ForCausalLM
- CohereForCausalLM
- DbrxForCausalLM
- DeciLMForCausalLM
- DeepseekForCausalLM
- DeepseekV2ForCausalLM
- DeepseekV3ForCausalLM
- ExaoneForCausalLM
- FalconForCausalLM
- FalconMambaForCausalLM
- GPT2LMHeadModel
- GPTBigCodeForCausalLM
- GPTNeoXForCausalLM
- GPTRefactForCausalLM
- Gemma2ForCausalLM
- GemmaForCausalLM
- GraniteForCausalLM
- GraniteMoeForCausalLM
- GrokForCausalLM
- InternLM2ForCausalLM
- JAISLMHeadModel
- JinaBertForMaskedLM
- JinaBertModel
- LLaMAForCausalLM
- LlamaForCausalLM
- LlavaStableLMEpochForCausalLM
- MPTForCausalLM
- MT5ForConditionalGeneration
- MambaForCausalLM
- MambaLMHeadModel
- MiniCPM3ForCausalLM
- MiniCPMForCausalLM
- MistralForCausalLM
- MixtralForCausalLM
- NemotronForCausalLM
- NomicBertModel
- OLMoForCausalLM
- Olmo2ForCausalLM
- OlmoForCausalLM
- OlmoeForCausalLM
- OpenELMForCausalLM
- OrionForCausalLM
- Phi3ForCausalLM
- PhiForCausalLM
- PhiMoEForCausalLM
- PlamoForCausalLM
- QWenLMHeadModel
- Qwen2ForCausalLM
- Qwen2MoeForCausalLM
- Qwen2VLForConditionalGeneration
- RWForCausalLM
- RWKV6Qwen2ForCausalLM
- RobertaModel
- Rwkv6ForCausalLM
- StableLMEpochForCausalLM
- StableLmForCausalLM
- Starcoder2ForCausalLM
- T5EncoderModel
- T5ForConditionalGeneration
- T5WithLMHeadModel
- UMT5ForConditionalGeneration
- WavTokenizerDec
- XLMRobertaForSequenceClassification
- XLMRobertaModel
- XverseForCausalLM
```
---
 convert_hf_to_gguf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 81f19bf37..4dc9837ab 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -480,7 +480,7 @@ class Model:
 
     @classmethod
     def print_registered_models(cls):
-        for name in cls._model_classes.keys():
+        for name in sorted(cls._model_classes.keys()):
             logger.error(f"- {name}")
 
     @classmethod

From c05e8c9934f94fde49bc1bc9dc51eed282605150 Mon Sep 17 00:00:00 2001
From: Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com>
Date: Sat, 11 Jan 2025 03:42:31 -0600
Subject: [PATCH 157/376] gguf-py: fixed local detection of gguf package
 (#11180)

* updated path to gguf package for non-installed setups

* added reader.py to readme

* Bumped gguf version to 0.15.0
---
 gguf-py/README.md                           | 2 ++
 gguf-py/gguf/scripts/gguf_convert_endian.py | 4 ++--
 gguf-py/gguf/scripts/gguf_dump.py           | 4 ++--
 gguf-py/gguf/scripts/gguf_hash.py           | 4 ++--
 gguf-py/gguf/scripts/gguf_new_metadata.py   | 4 ++--
 gguf-py/gguf/scripts/gguf_set_metadata.py   | 4 ++--
 gguf-py/pyproject.toml                      | 2 +-
 7 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/gguf-py/README.md b/gguf-py/README.md
index 37a75923b..2e513633d 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -15,6 +15,8 @@ pip install gguf
 
 [examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
 
+[examples/reader.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/reader.py) — Extracts and displays key-value pairs and tensor details from a GGUF file in a readable format.
+
 [gguf/scripts/gguf_dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console.
 
 [gguf/scripts/gguf_set_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key.
diff --git a/gguf-py/gguf/scripts/gguf_convert_endian.py b/gguf-py/gguf/scripts/gguf_convert_endian.py
index b698af0fe..f97e91bd4 100755
--- a/gguf-py/gguf/scripts/gguf_convert_endian.py
+++ b/gguf-py/gguf/scripts/gguf_convert_endian.py
@@ -11,8 +11,8 @@ from pathlib import Path
 import numpy as np
 
 # Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 import gguf
 
diff --git a/gguf-py/gguf/scripts/gguf_dump.py b/gguf-py/gguf/scripts/gguf_dump.py
index 1b6546541..f95b4fd48 100755
--- a/gguf-py/gguf/scripts/gguf_dump.py
+++ b/gguf-py/gguf/scripts/gguf_dump.py
@@ -12,8 +12,8 @@ from typing import Any
 import numpy as np
 
 # Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 from gguf import GGUFReader, GGUFValueType, ReaderTensor  # noqa: E402
 
diff --git a/gguf-py/gguf/scripts/gguf_hash.py b/gguf-py/gguf/scripts/gguf_hash.py
index ee34d09bf..3ef989921 100755
--- a/gguf-py/gguf/scripts/gguf_hash.py
+++ b/gguf-py/gguf/scripts/gguf_hash.py
@@ -13,8 +13,8 @@ from pathlib import Path
 from tqdm import tqdm
 
 # Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 from gguf import GGUFReader  # noqa: E402
 
diff --git a/gguf-py/gguf/scripts/gguf_new_metadata.py b/gguf-py/gguf/scripts/gguf_new_metadata.py
index fce52a8c1..a8cfc9d58 100755
--- a/gguf-py/gguf/scripts/gguf_new_metadata.py
+++ b/gguf-py/gguf/scripts/gguf_new_metadata.py
@@ -13,8 +13,8 @@ from tqdm import tqdm
 from typing import Any, Sequence, NamedTuple
 
 # Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 import gguf
 
diff --git a/gguf-py/gguf/scripts/gguf_set_metadata.py b/gguf-py/gguf/scripts/gguf_set_metadata.py
index e35b651b8..f5809c35c 100755
--- a/gguf-py/gguf/scripts/gguf_set_metadata.py
+++ b/gguf-py/gguf/scripts/gguf_set_metadata.py
@@ -6,8 +6,8 @@ import sys
 from pathlib import Path
 
 # Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 from gguf import GGUFReader  # noqa: E402
 
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 92d7f22ec..78c6baa64 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.14.0"
+version = "0.15.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

From afa8a9ec9b520137bbd1ca6838cda93ee39baf20 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 12 Jan 2025 11:32:42 +0200
Subject: [PATCH 158/376] llama : add `llama_vocab`, functions -> methods,
 naming (#11110)

* llama : functions -> methods (#11110)

* llama : add struct llama_vocab to the API (#11156)

ggml-ci

* hparams : move vocab params to llama_vocab (#11159)

ggml-ci

* vocab : more pimpl (#11165)

ggml-ci

* vocab : minor tokenization optimizations (#11160)

ggml-ci

Co-authored-by: Diego Devesa <slarengh@gmail.com>

* lora : update API names (#11167)

ggml-ci

* llama : update API names to use correct prefix (#11174)

* llama : update API names to use correct prefix

ggml-ci

* cont

ggml-ci

* cont

ggml-ci

* minor [no ci]

* vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174)

ggml-ci

* vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174)

ggml-ci

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 common/common.cpp                             |  105 +-
 common/common.h                               |   26 +-
 common/sampling.cpp                           |   17 +-
 common/speculative.cpp                        |   33 +-
 examples/batched-bench/batched-bench.cpp      |    2 +-
 examples/batched.swift/Sources/main.swift     |    6 +-
 examples/batched/batched.cpp                  |   10 +-
 .../convert-llama2c-to-ggml.cpp               |    2 +-
 .../cvector-generator/cvector-generator.cpp   |    8 +-
 examples/embedding/embedding.cpp              |    8 +-
 examples/eval-callback/eval-callback.cpp      |    5 +-
 examples/export-lora/export-lora.cpp          |    3 +-
 examples/gritlm/gritlm.cpp                    |   19 +-
 examples/imatrix/imatrix.cpp                  |   16 +-
 examples/infill/infill.cpp                    |   40 +-
 examples/llama-bench/llama-bench.cpp          |   12 +-
 .../llama/src/main/cpp/llama-android.cpp      |    7 +-
 .../llama.cpp.swift/LibLlama.swift            |    6 +-
 examples/llava/llava-cli.cpp                  |    9 +-
 examples/llava/llava.cpp                      |    4 +-
 examples/llava/minicpmv-cli.cpp               |    8 +-
 examples/llava/qwen2vl-cli.cpp                |   13 +-
 examples/lookahead/lookahead.cpp              |    6 +-
 examples/lookup/lookup.cpp                    |    4 +-
 examples/main/main.cpp                        |   23 +-
 examples/parallel/parallel.cpp                |    4 +-
 examples/passkey/passkey.cpp                  |    8 +-
 examples/perplexity/perplexity.cpp            |   52 +-
 examples/quantize-stats/quantize-stats.cpp    |    2 +-
 examples/retrieval/retrieval.cpp              |   10 +-
 examples/run/run.cpp                          |   24 +-
 examples/save-load-state/save-load-state.cpp  |    4 +-
 examples/server/server.cpp                    |   74 +-
 examples/server/utils.hpp                     |   83 +-
 examples/simple-chat/simple-chat.cpp          |   20 +-
 examples/simple/simple.cpp                    |   13 +-
 .../speculative-simple/speculative-simple.cpp |    4 +-
 examples/speculative/speculative.cpp          |   29 +-
 examples/tokenize/tokenize.cpp                |    8 +-
 examples/tts/tts.cpp                          |   26 +-
 include/llama-cpp.h                           |    6 +-
 include/llama.h                               |  172 +-
 src/llama-adapter.cpp                         |   77 +-
 src/llama-adapter.h                           |   59 +-
 src/llama-arch.cpp                            |    1 +
 src/llama-arch.h                              |    1 +
 src/llama-context.cpp                         |   14 +-
 src/llama-context.h                           |   10 +-
 src/llama-grammar.cpp                         |    8 +-
 src/llama-hparams.h                           |    2 -
 src/llama-kv-cache.cpp                        |    2 +-
 src/llama-mmap.cpp                            |    2 +-
 src/llama-model-loader.cpp                    |   61 +
 src/llama-model-loader.h                      |    4 +
 src/llama-model.cpp                           | 4153 ++++++++++++-----
 src/llama-model.h                             |  232 +-
 src/llama-quant.cpp                           |   14 +-
 src/llama-sampling.cpp                        |   58 +-
 src/llama-sampling.h                          |   22 +-
 src/llama-vocab.cpp                           | 2369 +++++++---
 src/llama-vocab.h                             |  274 +-
 src/llama.cpp                                 | 2932 +-----------
 tests/test-autorelease.cpp                    |    2 +-
 tests/test-chat-template.cpp                  |    3 +-
 tests/test-tokenizer-0.cpp                    |    2 +-
 tests/test-tokenizer-1-bpe.cpp                |   10 +-
 tests/test-tokenizer-1-spm.cpp                |    8 +-
 tests/test-tokenizer-random.py                |    4 +-
 68 files changed, 5855 insertions(+), 5400 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 86e4e1e24..39bfb0c2e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -857,21 +857,23 @@ struct common_init_result common_init_from_params(common_params & params) {
         return iparams;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     if (params.reranking) {
         bool ok = true;
 
-        if (llama_token_bos(model) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: model does not have a  BOS token, reranking will not work\n", __func__);
+        if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
             ok = false;
         }
 
-        if (llama_token_eos(model) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__);
+        if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
             ok = false;
         }
 
-        if (llama_token_sep(model) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: model does not have a  SEP token, reranking will not work\n", __func__);
+        if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have a  SEP token, reranking will not work\n", __func__);
             ok = false;
         }
 
@@ -884,7 +886,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     auto cparams = common_context_params_to_llama(params);
 
-    llama_context * lctx = llama_new_context_with_model(model, cparams);
+    llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
         llama_model_free(model);
@@ -898,7 +900,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     if (!params.control_vectors.empty()) {
         if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
-        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_model_n_layer(model);
 
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
@@ -908,12 +910,13 @@ struct common_init_result common_init_from_params(common_params & params) {
             return iparams;
         }
 
-        int err = llama_control_vector_apply(lctx,
-                                             cvec.data.data(),
-                                             cvec.data.size(),
-                                             cvec.n_embd,
-                                             params.control_vector_layer_start,
-                                             params.control_vector_layer_end);
+        int err = llama_apply_adapter_cvec(
+                lctx,
+                cvec.data.data(),
+                cvec.data.size(),
+                cvec.n_embd,
+                params.control_vector_layer_start,
+                params.control_vector_layer_end);
         if (err) {
             llama_free(lctx);
             llama_model_free(model);
@@ -924,8 +927,8 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     // load and optionally apply lora adapters
     for (auto & la : params.lora_adapters) {
-        llama_lora_adapter_ptr lora;
-        lora.reset(llama_lora_adapter_init(model, la.path.c_str()));
+        llama_adapter_lora_ptr lora;
+        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
         if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
             llama_free(lctx);
@@ -938,17 +941,17 @@ struct common_init_result common_init_from_params(common_params & params) {
     }
 
     if (!params.lora_init_without_apply) {
-        common_lora_adapters_apply(lctx, params.lora_adapters);
+        common_set_adapter_lora(lctx, params.lora_adapters);
     }
 
-    if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
+    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
         params.sampling.ignore_eos = false;
     }
 
     if (params.sampling.ignore_eos) {
-        for (llama_token i = 0; i < llama_n_vocab(model); i++) {
-            if (llama_token_is_eog(model, i)) {
+        for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
+            if (llama_vocab_is_eog(vocab, i)) {
                 LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
                 params.sampling.logit_bias.push_back({i, -INFINITY});
             }
@@ -969,8 +972,9 @@ struct common_init_result common_init_from_params(common_params & params) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
         std::vector<llama_token> tmp;
-        llama_token bos = llama_token_bos(model);
-        llama_token eos = llama_token_eos(model);
+        llama_token bos = llama_vocab_bos(vocab);
+        llama_token eos = llama_vocab_eos(vocab);
+
         // some models (e.g. T5) don't have a BOS token
         if (bos != LLAMA_TOKEN_NULL) {
             tmp.push_back(bos);
@@ -1005,11 +1009,11 @@ struct common_init_result common_init_from_params(common_params & params) {
     return iparams;
 }
 
-void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora) {
-    llama_lora_adapter_clear(ctx);
+void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
+    llama_clear_adapter_lora(ctx);
     for (auto & la : lora) {
         if (la.scale != 0.0f) {
-            llama_lora_adapter_set(ctx, la.ptr, la.scale);
+            llama_set_adapter_lora(ctx, la.ptr, la.scale);
         }
     }
 }
@@ -1559,21 +1563,23 @@ std::vector<llama_token> common_tokenize(
            const std::string & text,
                         bool   add_special,
                         bool   parse_special) {
-    return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    return common_tokenize(vocab, text, add_special, parse_special);
 }
 
 std::vector<llama_token> common_tokenize(
-    const struct llama_model * model,
+    const struct llama_vocab * vocab,
            const std::string & text,
                         bool   add_special,
                         bool   parse_special) {
     // upper limit for the number of tokens
     int n_tokens = text.length() + 2 * add_special;
     std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -1582,12 +1588,18 @@ std::vector<llama_token> common_tokenize(
 }
 
 std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    return common_token_to_piece(vocab, token, special);
+}
+
+std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
     std::string piece;
     piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
-    const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
+    const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
     if (n_chars < 0) {
         piece.resize(-n_chars);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
+        int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
         GGML_ASSERT(check == -n_chars);
     }
     else {
@@ -1597,13 +1609,19 @@ std::string common_token_to_piece(const struct llama_context * ctx, llama_token
     return piece;
 }
 
-std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
+std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    return common_detokenize(vocab, tokens, special);
+}
+
+std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
     std::string text;
     text.resize(std::max(text.capacity(), tokens.size()));
-    int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+    int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
     if (n_chars < 0) {
         text.resize(-n_chars);
-        n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+        n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
         GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
     }
 
@@ -1631,7 +1649,7 @@ std::string common_get_builtin_chat_template(const struct llama_model * model) {
 
 bool common_chat_verify_template(const std::string & tmpl) {
     llama_chat_message chat[] = {{"user", "test"}};
-    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
     return res >= 0;
 }
 
@@ -1642,16 +1660,16 @@ std::string common_chat_apply_template(const struct llama_model * model,
     int alloc_size = 0;
     bool fallback = false; // indicate if we must fallback to default chatml
     std::vector<llama_chat_message> chat;
-    for (auto & msg : msgs) {
+    for (const auto & msg : msgs) {
         chat.push_back({msg.role.c_str(), msg.content.c_str()});
         alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
     }
 
-    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    const char * ptr_tmpl = tmpl.empty() ? llama_model_chat_template(model) : tmpl.c_str();
     std::vector<char> buf(alloc_size);
 
     // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+    int32_t res = llama_chat_apply_template(ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
 
     // error: chat template is not supported
     if (res < 0) {
@@ -1659,18 +1677,17 @@ std::string common_chat_apply_template(const struct llama_model * model,
             // if the custom "tmpl" is not supported, we throw an error
             // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
             throw std::runtime_error("this custom template is not supported");
-        } else {
-            // If the built-in template is not supported, we default to chatml
-            res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
-            fallback = true;
         }
+
+        // If the built-in template is not supported, we default to chatml
+        res = llama_chat_apply_template("chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+        fallback = true;
     }
 
     // if it turns out that our buffer is too small, we resize it
     if ((size_t) res > buf.size()) {
         buf.resize(res);
         res = llama_chat_apply_template(
-            fallback ? nullptr : model,
             fallback ? "chatml" : ptr_tmpl,
             chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     }
diff --git a/common/common.h b/common/common.h
index 0d452cf0f..d523948b0 100644
--- a/common/common.h
+++ b/common/common.h
@@ -24,11 +24,11 @@
 
 #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
 
-struct common_lora_adapter_info {
+struct common_adapter_lora_info {
     std::string path;
     float scale;
 
-    struct llama_lora_adapter * ptr;
+    struct llama_adapter_lora * ptr;
 };
 
 using llama_tokens = std::vector<llama_token>;
@@ -246,8 +246,8 @@ struct common_params {
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
 
-    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
-    std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
+    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
+    std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale
 
     std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
 
@@ -481,7 +481,7 @@ struct common_init_result {
     llama_model_ptr   model;
     llama_context_ptr context;
 
-    std::vector<llama_lora_adapter_ptr> lora;
+    std::vector<llama_adapter_lora_ptr> lora;
 };
 
 struct common_init_result     common_init_from_params(common_params & params);
@@ -503,7 +503,7 @@ struct llama_model * common_load_model_from_hf(
     const struct llama_model_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
-void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_info> & lora);
+void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
 
 //
 // Batch utils
@@ -541,7 +541,7 @@ std::vector<llama_token> common_tokenize(
                         bool   parse_special = false);
 
 std::vector<llama_token> common_tokenize(
-    const struct llama_model * model,
+    const struct llama_vocab * vocab,
            const std::string & text,
                         bool   add_special,
                         bool   parse_special = false);
@@ -553,11 +553,21 @@ std::string common_token_to_piece(
                        llama_token   token,
                        bool          special = true);
 
+std::string common_token_to_piece(
+          const struct llama_vocab * vocab,
+                       llama_token   token,
+                       bool          special = true);
+
 // detokenizes a vector of tokens into a string
 // should work similar to Python's `tokenizer.decode`
 // optionally renders special/control tokens
 std::string common_detokenize(
-                         llama_context * ctx,
+            const struct llama_context * ctx,
+        const std::vector<llama_token> & tokens,
+                                  bool   special = true);
+
+std::string common_detokenize(
+              const struct llama_vocab * vocab,
         const std::vector<llama_token> & tokens,
                                   bool   special = true);
 
diff --git a/common/sampling.cpp b/common/sampling.cpp
index e83a971c7..7241ac321 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -113,7 +113,10 @@ struct common_sampler {
     void set_logits(struct llama_context * ctx, int idx) {
         const auto * logits = llama_get_logits_ith(ctx, idx);
 
-        const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+        const llama_model * model = llama_get_model(ctx);
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+
+        const int n_vocab = llama_vocab_n_tokens(vocab);
 
         cur.resize(n_vocab);
 
@@ -142,13 +145,15 @@ std::string common_params_sampling::print() const {
 }
 
 struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
     lparams.no_perf = params.no_perf;
 
     auto * result = new common_sampler {
         /* .params = */ params,
-        /* .grmr   = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
+        /* .grmr   = */ llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
         /* .chain  = */ llama_sampler_chain_init(lparams),
         /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur    = */ {},
@@ -157,7 +162,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     llama_sampler_chain_add(result->chain,
             llama_sampler_init_logit_bias(
-                llama_n_vocab(model),
+                llama_vocab_n_tokens(vocab),
                 params.logit_bias.size(),
                 params.logit_bias.data()));
 
@@ -172,7 +177,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                             c_breakers.push_back(str.c_str());
                         }
 
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
@@ -194,7 +199,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
@@ -206,7 +211,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
     } else if (params.mirostat == 1) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
+        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
     } else if (params.mirostat == 2) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
         llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 3fcbb0020..318e96ea3 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -79,10 +79,13 @@ bool common_speculative_are_compatible(
     const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
     const struct llama_model * model_dft = llama_get_model(ctx_dft);
 
-    const bool vocab_type_tgt = llama_vocab_type(model_tgt);
+    const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
+    const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
+
+    const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
     LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
 
-    const bool vocab_type_dft = llama_vocab_type(model_dft);
+    const bool vocab_type_dft = llama_vocab_type(vocab_dft);
     LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
@@ -91,34 +94,34 @@ bool common_speculative_are_compatible(
         return false;
     }
 
-    if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
-        llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
-        llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
-        llama_token_eos(model_tgt) != llama_token_eos(model_dft)) {
-        LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
-        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_tgt), llama_add_bos_token(model_tgt), llama_token_eos(model_tgt), llama_add_eos_token(model_tgt));
-        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_dft), llama_add_bos_token(model_dft), llama_token_eos(model_dft), llama_add_eos_token(model_dft));
+    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
+        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
+        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
+        LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
+        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
+        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
         return false;
     }
 
     {
-        const int n_vocab_tgt = llama_n_vocab(model_tgt);
-        const int n_vocab_dft = llama_n_vocab(model_dft);
+        const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
+        const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
 
         const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
 
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
             LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
                          "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    __func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+                    __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return false;
         }
 
         for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
-            const char * token_text_tgt = llama_token_get_text(model_tgt, i);
-            const char * token_text_dft = llama_token_get_text(model_dft, i);
+            const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
+            const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_ERR("%s: draft model vocab must match target model to use speculation but "
+                LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
                              "token %d content differs - target '%s', draft '%s'\n", __func__, i,
                         common_token_to_piece(ctx_tgt, i).c_str(),
                         common_token_to_piece(ctx_dft, i).c_str());
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index dd75ff9f1..0659ab6f1 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -50,7 +50,7 @@ int main(int argc, char ** argv) {
     // ensure enough sequences are available
     ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
 
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     if (ctx == NULL) {
         fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index 10f2e7fd1..371917b2e 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -23,12 +23,12 @@ defer {
 }
 
 let model_params = llama_model_default_params()
-guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {
+guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else {
     print("Failed to load model")
     exit(1)
 }
 defer {
-    llama_free_model(model)
+    llama_model_free(model)
 }
 
 var tokens = tokenize(text: prompt, add_bos: true)
@@ -141,7 +141,7 @@ while n_cur <= n_len {
         let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
 
         // is it an end of stream? -> mark the stream as finished
-        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
+        if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index d34b03099..21b95ef5e 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -48,10 +48,12 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // tokenize the prompt
 
     std::vector<llama_token> tokens_list;
-    tokens_list = common_tokenize(model, params.prompt, true);
+    tokens_list = common_tokenize(vocab, params.prompt, true);
 
     const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
 
@@ -62,7 +64,7 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();
     sparams.no_perf = false;
@@ -121,7 +123,7 @@ int main(int argc, char ** argv) {
 
         llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
         if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
-            decoder_start_token_id = llama_token_bos(model);
+            decoder_start_token_id = llama_vocab_bos(vocab);
         }
 
         common_batch_clear(batch);
@@ -174,7 +176,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
-            if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
+            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
                 i_batch[i] = -1;
                 LOG("\n");
                 if (n_parallel > 1) {
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 1256abb17..bdf0eed2a 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -911,7 +911,7 @@ int main(int argc, char ** argv) {
     load_vocab(params.fn_vocab_model, &config, &vocab);
 
     struct my_llama_model model;
-    model.hparams.n_vocab   = config.vocab_size; //llama_n_vocab(lctx);
+    model.hparams.n_vocab   = config.vocab_size; //llama_vocab_n_vocab(lctx);
     model.hparams.n_ctx     = params.n_ctx;
     model.hparams.n_embd    = config.dim; //params.n_embd;
     model.hparams.n_ff      = config.hidden_dim;
diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp
index e899c1078..413b71d34 100644
--- a/examples/cvector-generator/cvector-generator.cpp
+++ b/examples/cvector-generator/cvector-generator.cpp
@@ -273,7 +273,9 @@ struct tokenized_prompt {
     size_t max_seq_len;
 
     tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
-        const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+        const llama_model * model = llama_get_model(ctx);
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+        const bool add_bos = llama_vocab_get_add_bos(vocab);
         tokens_pos = common_tokenize(ctx, pos, add_bos, true);
         tokens_neg = common_tokenize(ctx, neg, add_bos, true);
         max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
@@ -421,8 +423,8 @@ int main(int argc, char ** argv) {
     llama_context * ctx = llama_init.context.get();
 
     // int n_ctx = llama_n_ctx(ctx);
-    int n_layers = llama_n_layer(model);
-    int n_embd = llama_n_embd(model);
+    int n_layers = llama_model_n_layer(model);
+    int n_embd = llama_model_n_embd(model);
 
     // get model hint param (a.k.a model arch name)
     char model_hint[128];
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 27f75cb77..38d22c90f 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -105,7 +105,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const int n_ctx_train = llama_model_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
@@ -148,7 +150,7 @@ int main(int argc, char ** argv) {
     // check if the last token is SEP
     // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
     for (auto & inp : inputs) {
-        if (inp.empty() || inp.back() != llama_token_sep(model)) {
+        if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) {
             LOG_WRN("%s: last token in the prompt is not SEP\n", __func__);
             LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
         }
@@ -181,7 +183,7 @@ int main(int argc, char ** argv) {
     }
 
     // allocate output
-    const int n_embd = llama_n_embd(model);
+    const int n_embd = llama_model_n_embd(model);
     std::vector<float> embeddings(n_embd_count * n_embd, 0);
     float * emb = embeddings.data();
 
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 2111c3cda..fb188f5a9 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -127,7 +127,10 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
 }
 
 static bool run(llama_context * ctx, const common_params & params) {
-    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
 
     std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
 
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index d5dcd20a0..99063b5d5 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -8,7 +8,6 @@
 #include <map>
 #include <vector>
 #include <string>
-#include <thread>
 #include <fstream>
 
 static bool g_verbose = false;
@@ -130,7 +129,7 @@ struct lora_merge_ctx {
 
     lora_merge_ctx(
             std::string & base_fname,
-            std::vector<common_lora_adapter_info> & lora_files,
+            std::vector<common_adapter_lora_info> & lora_files,
             std::string & outfile,
             int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
         fout.exceptions(std::ofstream::failbit); // fail fast on write errors
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
index 4d2db5624..72eb46257 100644
--- a/examples/gritlm/gritlm.cpp
+++ b/examples/gritlm/gritlm.cpp
@@ -11,6 +11,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
     std::vector<std::vector<float>> result;
 
     const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
 
@@ -19,16 +20,16 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
 
         const std::string input_string = instruction + sentences[i];
 
-        std::vector<llama_token> inputs = common_tokenize(model, input_string, true, false);
+        std::vector<llama_token> inputs = common_tokenize(vocab, input_string, true, false);
 
         const int32_t n_toks = inputs.size();
 
         // GritLM seems to have EOS = ""
         // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
-        // inputs.push_back(llama_token_eos(model));
+        // inputs.push_back(llama_vocab_eos(vocab));
 
         // we want to ignore instruction tokens for mean pooling
-        const int32_t n_inst = common_tokenize(model, instruction, true, false).size();
+        const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size();
 
 #ifdef GRIT_DEBUG
         // debug tokens - should be matching as referenced in the GritLM sample
@@ -52,7 +53,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
         llama_decode(ctx, batch);
 
         // get embedding dimensions
-        uint64_t n_embd = llama_n_embd(model);
+        uint64_t n_embd = llama_model_n_embd(model);
 
         // allocate embedding output
         std::vector<float> emb_unorm(n_embd, 0.0f);
@@ -97,7 +98,9 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
     std::string result;
 
     const llama_model * model = llama_get_model(ctx);
-    llama_token eos_token = llama_token_eos(model);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    llama_token eos_token = llama_vocab_eos(vocab);
 
     llama_kv_cache_clear(ctx);
     llama_set_embeddings(ctx, false);
@@ -105,7 +108,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
 
     llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
 
-    std::vector<llama_token> inputs = common_tokenize(model, prompt, false, true);
+    std::vector<llama_token> inputs = common_tokenize(vocab, prompt, false, true);
     int32_t i_current_token = 0;
 
     while (true) {
@@ -168,7 +171,7 @@ int main(int argc, char * argv[]) {
     llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams);
 
     // create generation context
-    llama_context * ctx = llama_new_context_with_model(model, cparams);
+    llama_context * ctx = llama_init_from_model(model, cparams);
 
     auto sparams = llama_sampler_chain_default_params();
 
@@ -197,7 +200,7 @@ int main(int argc, char * argv[]) {
         const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
         const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
 
-        const int n_embd = llama_n_embd(model);
+        const int n_embd = llama_model_n_embd(model);
 
         const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
         const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
index 588114ecd..b5f3feb9f 100644
--- a/examples/imatrix/imatrix.cpp
+++ b/examples/imatrix/imatrix.cpp
@@ -7,7 +7,6 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <sstream>
 #include <thread>
 #include <mutex>
 #include <vector>
@@ -40,7 +39,7 @@ public:
     void set_params(common_params params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
     void save_imatrix(int ncall = -1) const;
-    bool load_imatrix(const char * file_name);
+    bool load_imatrix(const char * fname);
 private:
     std::unordered_map<std::string, Stats> m_stats;
     common_params                          m_params;
@@ -429,10 +428,13 @@ static void process_logits(
 }
 
 static bool compute_imatrix(llama_context * ctx, const common_params & params) {
-    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
     const int n_ctx = llama_n_ctx(ctx);
 
-    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
+    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
     auto tim1 = std::chrono::high_resolution_clock::now();
     LOG_INF("%s: tokenizing the input ..\n", __func__);
@@ -468,7 +470,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
     const int n_chunk_max = tokens.size() / n_ctx;
 
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
     const int n_batch = params.n_batch;
 
     int count = 0;
@@ -508,7 +510,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+                tokens[batch_start] = llama_vocab_bos(vocab);
             }
 
             common_batch_clear(batch);
@@ -627,7 +629,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx_train = llama_model_n_ctx_train(model);
     if (params.n_ctx > n_ctx_train) {
         LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, params.n_ctx);
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index d460be314..489a208b6 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -139,7 +139,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const int n_ctx_train = llama_model_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
     LOG_DBG("n_ctx: %d\n", n_ctx);
 
@@ -152,28 +154,28 @@ int main(int argc, char ** argv) {
         LOG_INF("\n");
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
-    const bool add_bos = llama_add_bos_token(model);
-    GGML_ASSERT(!llama_add_eos_token(model));
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
     std::vector<llama_token> embd_inp;
     std::vector<llama_token> embd_end;
     std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
     std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
 
-    GGML_ASSERT(llama_token_fim_pre(model) >= 0);
-    GGML_ASSERT(llama_token_fim_suf(model) >= 0);
+    GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0);
+    GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0);
 
-    inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
-    inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
+    inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
+    inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
 
     embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
     embd_end = params.spm_infill ? inp_pfx : inp_sfx;
     if (add_bos) {
-        embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+        embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
     }
     embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
 
-    const llama_token middle_token = llama_token_fim_mid(model);
+    const llama_token middle_token = llama_vocab_fim_mid(vocab);
     if (middle_token >= 0) {
         embd_inp.push_back(middle_token);
     }
@@ -185,7 +187,7 @@ int main(int argc, char ** argv) {
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(model));
+        embd_inp.push_back(llama_vocab_bos(vocab));
         LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
     }
 
@@ -420,10 +422,10 @@ int main(int argc, char ** argv) {
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
             // deal with eot token in infill mode
-            if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
+            if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){
                 if (is_interacting && !params.interactive_first) {
                     // print an eot token
-                    LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
+                    LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
                 }
                 LOG("\n");
                 console::set_display(console::user_input);
@@ -463,13 +465,13 @@ int main(int argc, char ** argv) {
                 std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
                 std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
 
-                inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
-                inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
+                inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
+                inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
 
                 embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
                 embd_end = params.spm_infill ? inp_pfx : inp_sfx;
                 if (add_bos) {
-                    embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+                    embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
                 }
                 embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
 
@@ -484,7 +486,7 @@ int main(int argc, char ** argv) {
                 is_interacting = false;
             }
             // deal with end of generation tokens in interactive mode
-            else if (llama_token_is_eog(model, common_sampler_last(smpl))) {
+            else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
                 LOG_DBG("found EOS token\n");
 
                 if (params.interactive) {
@@ -500,7 +502,7 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG_DBG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(model));
+                    embd_inp.push_back(llama_vocab_bos(vocab));
                 }
 
                 std::string buffer;
@@ -563,7 +565,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of generation
-        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
+        if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) {
             break;
         }
 
@@ -575,7 +577,7 @@ int main(int argc, char ** argv) {
         }
     }
     if (!params.interactive && n_remain <= 0) {
-        LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
+        LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
     }
 
     LOG("\n");
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 2a0916766..a3b4c5ac8 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -1401,7 +1401,8 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th
     llama_set_n_threads(ctx, n_threads, n_threads);
 
     const llama_model * model   = llama_get_model(ctx);
-    const int32_t       n_vocab = llama_n_vocab(model);
+    const llama_vocab * vocab   = llama_model_get_vocab(model);
+    const int32_t       n_vocab = llama_vocab_n_tokens(vocab);
 
     std::vector<llama_token> tokens(n_batch);
 
@@ -1409,7 +1410,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th
 
     while (n_processed < n_prompt) {
         int n_tokens = std::min(n_prompt - n_processed, n_batch);
-        tokens[0]    = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+        tokens[0]    = n_processed == 0 && llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
         for (int i = 1; i < n_tokens; i++) {
             tokens[i] = std::rand() % n_vocab;
         }
@@ -1424,9 +1425,10 @@ static void test_gen(llama_context * ctx, int n_gen, int n_threads) {
     llama_set_n_threads(ctx, n_threads, n_threads);
 
     const llama_model * model   = llama_get_model(ctx);
-    const int32_t       n_vocab = llama_n_vocab(model);
+    const llama_vocab * vocab   = llama_model_get_vocab(model);
+    const int32_t       n_vocab = llama_vocab_n_tokens(vocab);
 
-    llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+    llama_token token = llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
 
     for (int i = 0; i < n_gen; i++) {
         llama_decode(ctx, llama_batch_get_one(&token, 1));
@@ -1537,7 +1539,7 @@ int main(int argc, char ** argv) {
             prev_inst = &inst;
         }
 
-        llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams());
+        llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams());
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
             llama_model_free(lmodel);
diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
index 66ec2aeeb..99b14961d 100644
--- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -87,7 +87,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi
     auto path_to_model = env->GetStringUTFChars(filename, 0);
     LOGi("Loading model from %s", path_to_model);
 
-    auto model = llama_load_model_from_file(path_to_model, model_params);
+    auto model = llama_model_load_from_file(path_to_model, model_params);
     env->ReleaseStringUTFChars(filename, path_to_model);
 
     if (!model) {
@@ -102,7 +102,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi
 extern "C"
 JNIEXPORT void JNICALL
 Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
-    llama_free_model(reinterpret_cast<llama_model *>(model));
+    llama_model_free(reinterpret_cast<llama_model *>(model));
 }
 
 extern "C"
@@ -405,6 +405,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
     const auto batch   = reinterpret_cast<llama_batch   *>(batch_pointer);
     const auto sampler = reinterpret_cast<llama_sampler *>(sampler_pointer);
     const auto model = llama_get_model(context);
+    const auto vocab = llama_model_get_vocab(model);
 
     if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
     if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
@@ -414,7 +415,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
     const auto new_token_id = llama_sampler_sample(sampler, context, -1);
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
-    if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
+    if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
         return nullptr;
     }
 
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 998c673d5..477c3e6f2 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -52,8 +52,8 @@ actor LlamaContext {
     deinit {
         llama_sampler_free(sampling)
         llama_batch_free(batch)
+        llama_model_free(model)
         llama_free(context)
-        llama_free_model(model)
         llama_backend_free()
     }
 
@@ -65,7 +65,7 @@ actor LlamaContext {
         model_params.n_gpu_layers = 0
         print("Running on simulator, force use n_gpu_layers = 0")
 #endif
-        let model = llama_load_model_from_file(path, model_params)
+        let model = llama_model_load_from_file(path, model_params)
         guard let model else {
             print("Could not load model at \(path)")
             throw LlamaError.couldNotInitializeContext
@@ -151,7 +151,7 @@ actor LlamaContext {
 
         new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1)
 
-        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
+        if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
             print("\n")
             is_done = true
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index 27215a42e..40aa0876f 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -47,8 +47,12 @@ static const char * sample(struct common_sampler * smpl,
                            int * n_past) {
     const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
     common_sampler_accept(smpl, id, true);
+
+    const llama_model * model = llama_get_model(ctx_llama);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     static std::string ret;
-    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+    if (llama_vocab_is_eog(vocab, id)) {
         ret = "</s>";
     } else {
         ret = common_token_to_piece(ctx_llama, id);
@@ -239,11 +243,10 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
 
     auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
 
-
     llama_context_params ctx_params = common_context_params_to_llama(*params);
     ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
 
-    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
         LOG_ERR("%s: failed to create the llama_context\n" , __func__);
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 16f30c56c..c598caf3d 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -384,7 +384,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
 bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
         // make sure that the correct mmproj was used, i.e., compare apples to apples
-    int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
+    int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama));
     auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
     if (n_image_embd != n_llama_embd) {
         LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
@@ -456,7 +456,7 @@ struct llava_embd_batch {
 };
 
 bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
-    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
 
     for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
         int n_eval = image_embed->n_image_pos - i;
diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp
index 2342bdd09..38c44e130 100644
--- a/examples/llava/minicpmv-cli.cpp
+++ b/examples/llava/minicpmv-cli.cpp
@@ -54,7 +54,7 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
         ctx_params.n_ctx = params->n_ctx;
     }
 
-    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
         LOG_ERR("%s: failed to create the llama_context\n" , __func__);
@@ -167,8 +167,12 @@ static const char * sample(struct common_sampler * smpl,
                            int * n_past) {
     const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
     common_sampler_accept(smpl, id, true);
+
+    const llama_model * model = llama_get_model(ctx_llama);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     static std::string ret;
-    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+    if (llama_vocab_is_eog(vocab, id)) {
         ret = "</s>";
     } else {
         ret = common_token_to_piece(ctx_llama, id);
diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp
index f3e5d66e2..132a7da54 100644
--- a/examples/llava/qwen2vl-cli.cpp
+++ b/examples/llava/qwen2vl-cli.cpp
@@ -27,7 +27,7 @@
 
 static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
                                      int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
-    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
     const int patch_size = 14 * 2;
     const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
     const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
@@ -132,8 +132,12 @@ static const char * sample(struct common_sampler * smpl,
                            int * n_past, int * st_pos_id) {
     const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
     common_sampler_accept(smpl, id, true);
+
+    const llama_model * model = llama_get_model(ctx_llama);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     static std::string ret;
-    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+    if (llama_vocab_is_eog(vocab, id)) {
         ret = "</s>";
     } else {
         ret = common_token_to_piece(ctx_llama, id);
@@ -328,11 +332,10 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
 
     auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
 
-
     llama_context_params ctx_params = common_context_params_to_llama(*params);
     ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
 
-    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
         LOG_ERR("%s: failed to create the llama_context\n" , __func__);
@@ -481,7 +484,7 @@ static void debug_test_mrope_2d() {
 }
 
 static void debug_dump_img_embed(struct llava_context * ctx_llava) {
-    int n_embd  = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
+    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
     int ne = n_embd * 4;
     float vals[56 * 56 * 3];
     // float embd[ne];
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index e016618e3..2f0898e62 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -61,6 +61,8 @@ int main(int argc, char ** argv) {
     llama_model * model = llama_init.model.get();
     llama_context * ctx = llama_init.context.get();
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // Tokenize the prompt
     std::vector<llama_token> inp;
     std::vector<llama_token> all;
@@ -147,7 +149,7 @@ int main(int argc, char ** argv) {
     }
 
     // here we keep adding new n-grams as we go
-    ngram_container ngrams_observed(llama_n_vocab(model), N, G);
+    ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G);
 
     // debug
     struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
@@ -297,7 +299,7 @@ int main(int argc, char ** argv) {
                 }
                 fflush(stdout);
 
-                if (llama_token_is_eog(model, id)) {
+                if (llama_vocab_is_eog(vocab, id)) {
                     has_eos = true;
                 }
 
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 0d68b80b9..dbd0444ec 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -36,6 +36,8 @@ int main(int argc, char ** argv){
     llama_model * model = llama_init.model.get();
     llama_context * ctx = llama_init.context.get();
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // tokenize the prompt
     std::vector<llama_token> inp;
     inp = common_tokenize(ctx, params.prompt, true, true);
@@ -136,7 +138,7 @@ int main(int argc, char ** argv){
                 LOG("%s", token_str.c_str());
             }
 
-            if (llama_token_is_eog(model, id)) {
+            if (llama_vocab_is_eog(vocab, id)) {
                 has_eos = true;
             }
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index aaee47e32..640b35c1d 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -5,7 +5,6 @@
 #include "sampling.h"
 #include "llama.h"
 
-#include <cassert>
 #include <cstdio>
 #include <cstring>
 #include <ctime>
@@ -163,6 +162,8 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
 
     auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
@@ -196,7 +197,7 @@ int main(int argc, char ** argv) {
 
     llama_attach_threadpool(ctx, threadpool, threadpool_batch);
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx_train = llama_model_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
     if (n_ctx > n_ctx_train) {
@@ -241,9 +242,9 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const bool add_bos = llama_add_bos_token(model);
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
     if (!llama_model_has_encoder(model)) {
-        GGML_ASSERT(!llama_add_eos_token(model));
+        GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
     }
 
     LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos);
@@ -269,7 +270,7 @@ int main(int argc, char ** argv) {
     // Should not run without any tokens
     if (embd_inp.empty()) {
         if (add_bos) {
-            embd_inp.push_back(llama_token_bos(model));
+            embd_inp.push_back(llama_vocab_bos(vocab));
             LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
         } else {
             LOG_ERR("input is empty\n");
@@ -495,7 +496,7 @@ int main(int argc, char ** argv) {
 
         llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
         if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
-            decoder_start_token_id = llama_token_bos(model);
+            decoder_start_token_id = llama_vocab_bos(vocab);
         }
 
         embd_inp.clear();
@@ -742,7 +743,7 @@ int main(int argc, char ** argv) {
             }
 
             // deal with end of generation tokens in interactive mode
-            if (llama_token_is_eog(model, common_sampler_last(smpl))) {
+            if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
                 LOG_DBG("found an EOG token\n");
 
                 if (params.interactive) {
@@ -776,7 +777,7 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG_DBG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(model));
+                    embd_inp.push_back(llama_vocab_bos(vocab));
                 }
 
                 std::string buffer;
@@ -830,8 +831,8 @@ int main(int argc, char ** argv) {
 
                     // if user stop generation mid-way, we must add EOT to finish model's last response
                     if (need_insert_eot && format_chat) {
-                        llama_token eot = llama_token_eot(model);
-                        embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_token_eos(model) : eot);
+                        llama_token eot = llama_vocab_eot(vocab);
+                        embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot);
                         need_insert_eot = false;
                     }
 
@@ -866,7 +867,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of generation
-        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) {
+        if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) {
             LOG(" [end of text]\n");
             break;
         }
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index d48f51975..7ef43d5e1 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -135,6 +135,8 @@ int main(int argc, char ** argv) {
     llama_model * model = llama_init.model.get();
     llama_context * ctx = llama_init.context.get();
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // load the prompts from an external file if there are any
     if (params.prompt.empty()) {
         LOG_INF("\033[32mNo new questions so proceed with build-in defaults.\033[0m\n");
@@ -358,7 +360,7 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (llama_token_is_eog(model, id) ||
+                        (llama_vocab_is_eog(vocab, id) ||
                          (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                          client.response.find("User:") != std::string::npos ||
                          client.response.find('\n') != std::string::npos)) {
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index ea91f376c..5953928d4 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -70,15 +70,17 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // initialize the context
 
     llama_context_params ctx_params = common_context_params_to_llama(params);
 
-    ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
+    ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep;
 
     GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
 
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
     if (ctx == NULL) {
         LOG_ERR("%s: failed to create the llama_context\n" , __func__);
         return 1;
@@ -223,7 +225,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
 
             // is it an end of generation?
-            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
+            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
                 LOG("\n");
 
                 break;
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 6bdc57f8e..9bf6c5743 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -296,8 +296,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
     LOG_INF("%s: tokenizing the input ..\n", __func__);
 
@@ -338,7 +341,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
     const int n_batch = params.n_batch;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     int count = 0;
     double nll = 0.0;
@@ -382,7 +385,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+                tokens[batch_start] = llama_vocab_bos(vocab);
             }
 
             const auto * batch_logits = llama_get_logits(ctx);
@@ -444,8 +447,11 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
     std::ofstream logits_stream;
     if (!params.logits_file.empty()) {
@@ -485,7 +491,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
     const int n_batch = params.n_batch;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     int count = 0;
     double nll = 0.0;
@@ -557,7 +563,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
 
                 // add BOS token for the first batch of each chunk
                 if (add_bos && j == 0) {
-                    tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
+                    tokens[seq_start] = llama_vocab_bos(vocab);
                 }
 
                 for (int k = 0; k < batch_size; ++k) {
@@ -732,6 +738,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
 }
 
 static void hellaswag_score(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // Calculates hellaswag score (acc_norm) from prompt
     //
     // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
@@ -765,7 +774,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
     size_t hs_task_count = prompt_lines.size()/6;
     LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
 
-    const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
+    const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM;
     LOG_INF("================================= is_spm = %d\n", is_spm);
 
     // The tasks should be randomized so the score stabilizes quickly.
@@ -848,7 +857,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
     const int n_ctx   = llama_n_ctx(ctx);
     const int n_batch = params.n_batch;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     const int max_tasks_per_batch = 32;
     const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@@ -1072,6 +1081,8 @@ static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string
  *
  */
 static void winogrande_score(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
 
     constexpr int k_min_trailing_ctx = 3;
 
@@ -1130,7 +1141,7 @@ static void winogrande_score(llama_context * ctx, const common_params & params)
     const int n_ctx   = llama_n_ctx(ctx);
     const int n_batch = params.n_batch;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     const int max_tasks_per_batch = 128;
     const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@@ -1374,6 +1385,8 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
 //     https://huggingface.co/datasets/truthful_qa
 //
 static void multiple_choice_score(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
 
     std::istringstream strstream(params.prompt);
     uint32_t n_task;
@@ -1482,7 +1495,7 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
     const int n_ctx   = llama_n_ctx(ctx);
     const int n_batch = params.n_batch;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     const int max_tasks_per_batch = 32;
     const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@@ -1655,6 +1668,9 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
 }
 
 static void kl_divergence(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     if (params.logits_file.empty()) {
         LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
         return;
@@ -1688,8 +1704,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
         LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
         return;
     }
-    if (n_vocab != llama_n_vocab(llama_get_model(ctx))) {
-        LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
+    if (n_vocab != llama_vocab_n_tokens(vocab)) {
+        LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab));
     }
 
     std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk);
@@ -1701,8 +1717,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
     const int n_batch = params.n_batch;
     const int num_batches = (n_ctx + n_batch - 1)/n_batch;
     const int nv = 2*((n_vocab + 1)/2) + 4;
-    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
     std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
     std::vector<float>    kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
@@ -1761,7 +1777,7 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+                tokens[batch_start] = llama_vocab_bos(vocab);
             }
 
             common_batch_clear(batch);
@@ -1995,7 +2011,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx_train = llama_model_n_ctx_train(model);
 
     if (params.n_ctx > n_ctx_train) {
         LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 9bfbb8862..bd2f73467 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -319,7 +319,7 @@ int main(int argc, char ** argv) {
         auto cparams = llama_context_default_params();
         cparams.n_ctx = 256;
 
-        ctx = llama_new_context_with_model(model, cparams);
+        ctx = llama_init_from_model(model, cparams);
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index f534b5eff..2439022a2 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -159,7 +159,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx_train = llama_n_ctx_train(model);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const int n_ctx_train = llama_model_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
@@ -192,8 +194,8 @@ int main(int argc, char ** argv) {
             return 1;
         }
         // add eos if not present
-        if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
-            inp.push_back(llama_token_eos(model));
+        if (llama_vocab_eos(vocab) >= 0 && (inp.empty() || inp.back() != llama_vocab_eos(vocab))) {
+            inp.push_back(llama_vocab_eos(vocab));
         }
         chunk.tokens = inp;
     }
@@ -215,7 +217,7 @@ int main(int argc, char ** argv) {
     struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
 
     // allocate output
-    const int n_embd = llama_n_embd(model);
+    const int n_embd = llama_model_n_embd(model);
     std::vector<float> embeddings(n_chunks * n_embd, 0);
     float * emb = embeddings.data();
 
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 61420e441..bfa8378bb 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -685,7 +685,7 @@ class LlamaData {
 
     // Initializes the context with the specified parameters
     llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) {
-        llama_context_ptr context(llama_new_context_with_model(model.get(), opt.ctx_params));
+        llama_context_ptr context(llama_init_from_model(model.get(), opt.ctx_params));
         if (!context) {
             printe("%s: error: failed to create the llama_context\n", __func__);
         }
@@ -713,11 +713,11 @@ static void add_message(const char * role, const std::string & text, LlamaData &
 // Function to apply the chat template and resize `formatted` if needed
 static int apply_chat_template(LlamaData & llama_data, const bool append) {
     int result = llama_chat_apply_template(
-        llama_data.model.get(), nullptr, llama_data.messages.data(), llama_data.messages.size(), append,
+        llama_model_chat_template(llama_data.model.get()), llama_data.messages.data(), llama_data.messages.size(), append,
         append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
     if (append && result > static_cast<int>(llama_data.fmtted.size())) {
         llama_data.fmtted.resize(result);
-        result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
+        result = llama_chat_apply_template(llama_model_chat_template(llama_data.model.get()), llama_data.messages.data(),
                                            llama_data.messages.size(), append, llama_data.fmtted.data(),
                                            llama_data.fmtted.size());
     }
@@ -726,11 +726,11 @@ static int apply_chat_template(LlamaData & llama_data, const bool append) {
 }
 
 // Function to tokenize the prompt
-static int tokenize_prompt(const llama_model_ptr & model, const std::string & prompt,
+static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
                            std::vector<llama_token> & prompt_tokens) {
-    const int n_prompt_tokens = -llama_tokenize(model.get(), prompt.c_str(), prompt.size(), NULL, 0, true, true);
+    const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
     prompt_tokens.resize(n_prompt_tokens);
-    if (llama_tokenize(model.get(), prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
+    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
                        true) < 0) {
         printe("failed to tokenize the prompt\n");
         return -1;
@@ -753,9 +753,9 @@ static int check_context_size(const llama_context_ptr & ctx, const llama_batch &
 }
 
 // convert the token to a string
-static int convert_token_to_string(const llama_model_ptr & model, const llama_token token_id, std::string & piece) {
+static int convert_token_to_string(const llama_vocab * vocab, const llama_token token_id, std::string & piece) {
     char buf[256];
-    int  n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
+    int  n = llama_token_to_piece(vocab, token_id, buf, sizeof(buf), 0, true);
     if (n < 0) {
         printe("failed to convert token to piece\n");
         return 1;
@@ -773,8 +773,10 @@ static void print_word_and_concatenate_to_response(const std::string & piece, st
 
 // helper function to evaluate a prompt and generate a response
 static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) {
+    const llama_vocab * vocab = llama_model_get_vocab(llama_data.model.get());
+
     std::vector<llama_token> tokens;
-    if (tokenize_prompt(llama_data.model, prompt, tokens) < 0) {
+    if (tokenize_prompt(vocab, prompt, tokens) < 0) {
         return 1;
     }
 
@@ -790,12 +792,12 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
 
         // sample the next token, check is it an end of generation?
         new_token_id = llama_sampler_sample(llama_data.sampler.get(), llama_data.context.get(), -1);
-        if (llama_token_is_eog(llama_data.model.get(), new_token_id)) {
+        if (llama_vocab_is_eog(vocab, new_token_id)) {
             break;
         }
 
         std::string piece;
-        if (convert_token_to_string(llama_data.model, new_token_id, piece)) {
+        if (convert_token_to_string(vocab, new_token_id, piece)) {
             return 1;
         }
 
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index cd03661cf..cf7cbd815 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -97,7 +97,7 @@ int main(int argc, char ** argv) {
     printf("\n\n");
 
     // make new context
-    llama_context * ctx2 = llama_new_context_with_model(model, common_context_params_to_llama(params));
+    llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params));
 
     llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
 
@@ -154,7 +154,7 @@ int main(int argc, char ** argv) {
     }
 
     // make new context
-    llama_context * ctx3 = llama_new_context_with_model(model, common_context_params_to_llama(params));
+    llama_context * ctx3 = llama_init_from_model(model, common_context_params_to_llama(params));
 
     llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 127323e77..64c0c4ef6 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -98,7 +98,7 @@ struct slot_params {
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
-    std::vector<common_lora_adapter_info> lora;
+    std::vector<common_adapter_lora_info> lora;
 
     std::vector<std::string> antiprompt;
     std::vector<std::string> response_fields;
@@ -198,15 +198,17 @@ struct server_task {
     bool metrics_reset_bucket = false;
 
     // used by SERVER_TASK_TYPE_SET_LORA
-    std::vector<common_lora_adapter_info> set_lora;
+    std::vector<common_adapter_lora_info> set_lora;
 
     server_task(server_task_type type) : type(type) {}
 
     static slot_params params_from_json_cmpl(
-            const llama_model * model,
             const llama_context * ctx,
             const common_params & params_base,
             const json & data) {
+        const llama_model * model = llama_get_model(ctx);
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+
         slot_params params;
 
         // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
@@ -329,7 +331,7 @@ struct server_task {
 
             const auto & logit_bias = data.find("logit_bias");
             if (logit_bias != data.end() && logit_bias->is_array()) {
-                const int n_vocab = llama_n_vocab(model);
+                const int n_vocab = llama_vocab_n_tokens(vocab);
                 for (const auto & el : *logit_bias) {
                     // TODO: we may want to throw errors here, in case "el" is incorrect
                     if (el.is_array() && el.size() == 2) {
@@ -348,7 +350,7 @@ struct server_task {
                                 params.sampling.logit_bias.push_back({tok, bias});
                             }
                         } else if (el[0].is_string()) {
-                            auto toks = common_tokenize(model, el[0].get<std::string>(), false);
+                            auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
                             for (auto tok : toks) {
                                 params.sampling.logit_bias.push_back({tok, bias});
                             }
@@ -1131,7 +1133,7 @@ struct server_slot {
 
     common_speculative * spec = nullptr;
 
-    std::vector<common_lora_adapter_info> lora;
+    std::vector<common_adapter_lora_info> lora;
 
     // the index relative to completion multi-task request
     size_t index = 0;
@@ -1633,6 +1635,8 @@ struct server_context {
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
 
+    const llama_vocab * vocab = nullptr;
+
     llama_model * model_dft = nullptr;
 
     llama_context_params cparams_dft;
@@ -1690,10 +1694,12 @@ struct server_context {
             return false;
         }
 
+        vocab = llama_model_get_vocab(model);
+
         n_ctx = llama_n_ctx(ctx);
 
-        add_bos_token = llama_add_bos_token(model);
-        has_eos_token = llama_token_eos(model) != LLAMA_TOKEN_NULL;
+        add_bos_token = llama_vocab_get_add_bos(vocab);
+        has_eos_token = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
 
         if (!params_base.speculative.model.empty()) {
             SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str());
@@ -1736,7 +1742,8 @@ struct server_context {
 
     bool validate_builtin_chat_template() const {
         llama_chat_message chat[] = {{"user", "test"}};
-        int32_t chat_res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
+        const char * tmpl = llama_model_chat_template(model);
+        const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0);
         return chat_res > 0;
     }
 
@@ -1756,7 +1763,7 @@ struct server_context {
             if (model_dft) {
                 slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1);
 
-                slot.ctx_dft = llama_new_context_with_model(model_dft, cparams_dft);
+                slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft);
                 if (slot.ctx_dft == nullptr) {
                     SRV_ERR("%s", "failed to create draft context\n");
                     return;
@@ -1891,7 +1898,7 @@ struct server_context {
         }
 
         if (slot.params.ignore_eos && has_eos_token) {
-            slot.params.sampling.logit_bias.push_back({llama_token_eos(model), -INFINITY});
+            slot.params.sampling.logit_bias.push_back({llama_vocab_eos(vocab), -INFINITY});
         }
 
         {
@@ -2047,14 +2054,14 @@ struct server_context {
                     slot.n_decoded, slot.n_prompt_tokens, slot.n_past, slot.n_ctx);
         }
 
-        if (llama_token_is_eog(model, result.tok)) {
+        if (llama_vocab_is_eog(vocab, result.tok)) {
             slot.stop           = STOP_TYPE_EOS;
             slot.has_next_token = false;
 
             SLT_DBG(slot, "%s", "stopped by EOS\n");
         }
 
-        const auto n_ctx_train = llama_n_ctx_train(model);
+        const auto n_ctx_train = llama_model_n_ctx_train(model);
 
         if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
             slot.truncated      = true;
@@ -2074,7 +2081,7 @@ struct server_context {
 
     void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) {
         size_t n_probs = slot.params.sampling.n_probs;
-        size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
+        size_t n_vocab = llama_vocab_n_tokens(vocab);
         if (post_sampling) {
             const auto * cur_p = common_sampler_get_candidates(slot.smpl);
             const size_t max_probs = cur_p->size;
@@ -2225,7 +2232,7 @@ struct server_context {
         res->n_tokens  = slot.n_prompt_tokens;
         res->oaicompat = slot.params.oaicompat;
 
-        const int n_embd = llama_n_embd(model);
+        const int n_embd = llama_model_n_embd(model);
 
         std::vector<float> embd_res(n_embd, 0.0f);
 
@@ -2927,7 +2934,7 @@ struct server_context {
             // make sure we're in the right embedding mode
             llama_set_embeddings(ctx, slot_batched->is_non_causal());
             // apply lora, only need to do it once per batch
-            common_lora_adapters_apply(ctx, slot_batched->lora);
+            common_set_adapter_lora(ctx, slot_batched->lora);
         }
 
         // process the created batch of tokens
@@ -3129,12 +3136,12 @@ struct server_context {
 
     json model_meta() const {
         return json {
-            {"vocab_type",  llama_vocab_type    (model)},
-            {"n_vocab",     llama_n_vocab       (model)},
-            {"n_ctx_train", llama_n_ctx_train   (model)},
-            {"n_embd",      llama_n_embd        (model)},
-            {"n_params",    llama_model_n_params(model)},
-            {"size",        llama_model_size    (model)},
+            {"vocab_type",  llama_vocab_type       (vocab)},
+            {"n_vocab",     llama_vocab_n_tokens   (vocab)},
+            {"n_ctx_train", llama_model_n_ctx_train(model)},
+            {"n_embd",      llama_model_n_embd     (model)},
+            {"n_params",    llama_model_n_params   (model)},
+            {"size",        llama_model_size       (model)},
         };
     }
 };
@@ -3639,7 +3646,7 @@ int main(int argc, char ** argv) {
         std::vector<server_task> tasks;
 
         try {
-            std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, data.at("prompt"), true, true);
+            std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, data.at("prompt"), true, true);
             tasks.reserve(tokenized_prompts.size());
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {
                 server_task task = server_task(type);
@@ -3649,7 +3656,6 @@ int main(int argc, char ** argv) {
 
                 task.prompt_tokens    = std::move(tokenized_prompts[i]);
                 task.params           = server_task::params_from_json_cmpl(
-                                            ctx_server.model,
                                             ctx_server.ctx,
                                             ctx_server.params_base,
                                             data);
@@ -3745,13 +3751,13 @@ int main(int argc, char ** argv) {
     const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         // check model compatibility
         std::string err;
-        if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) {
+        if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
             err += "prefix token is missing. ";
         }
-        if (llama_token_fim_suf(ctx_server.model) == LLAMA_TOKEN_NULL) {
+        if (llama_vocab_fim_suf(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
             err += "suffix token is missing. ";
         }
-        if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) {
+        if (llama_vocab_fim_mid(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
             err += "middle token is missing. ";
         }
         if (!err.empty()) {
@@ -3797,10 +3803,10 @@ int main(int argc, char ** argv) {
         data["input_extra"] = input_extra; // default to empty array if it's not exist
 
         std::string prompt = json_value(data, "prompt", std::string());
-        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, false, true);
+        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, false, true);
         SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
         data["prompt"] = format_infill(
-            ctx_server.ctx,
+            ctx_server.vocab,
             data.at("input_prefix"),
             data.at("input_suffix"),
             data.at("input_extra"),
@@ -3857,7 +3863,7 @@ int main(int argc, char ** argv) {
             const bool add_special = json_value(body, "add_special", false);
             const bool with_pieces = json_value(body, "with_pieces", false);
 
-            llama_tokens tokens = tokenize_mixed(ctx_server.ctx, body.at("content"), add_special, true);
+            llama_tokens tokens = tokenize_mixed(ctx_server.vocab, body.at("content"), add_special, true);
 
             if (with_pieces) {
                 for (const auto& token : tokens) {
@@ -3933,7 +3939,7 @@ int main(int argc, char ** argv) {
             }
         }
 
-        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
+        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
         for (const auto & tokens : tokenized_prompts) {
             // this check is necessary for models that do not add BOS token to the input
             if (tokens.empty()) {
@@ -4033,20 +4039,20 @@ int main(int argc, char ** argv) {
             return;
         }
 
-        llama_tokens tokenized_query = tokenize_input_prompts(ctx_server.ctx, query, /* add_special */ false, true)[0];
+        llama_tokens tokenized_query = tokenize_input_prompts(ctx_server.vocab, query, /* add_special */ false, true)[0];
 
         // create and queue the task
         json responses = json::array();
         bool error = false;
         {
             std::vector<server_task> tasks;
-            std::vector<llama_tokens> tokenized_docs = tokenize_input_prompts(ctx_server.ctx, documents, /* add_special */ false, true);
+            std::vector<llama_tokens> tokenized_docs = tokenize_input_prompts(ctx_server.vocab, documents, /* add_special */ false, true);
             tasks.reserve(tokenized_docs.size());
             for (size_t i = 0; i < tokenized_docs.size(); i++) {
                 server_task task   = server_task(SERVER_TASK_TYPE_RERANK);
                 task.id            = ctx_server.queue_tasks.get_new_id();
                 task.index         = i;
-                task.prompt_tokens = format_rerank(ctx_server.model, tokenized_query, tokenized_docs[i]);
+                task.prompt_tokens = format_rerank(ctx_server.vocab, tokenized_query, tokenized_docs[i]);
                 tasks.push_back(task);
             }
 
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index ad130d490..699480f90 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -118,7 +118,7 @@ static json json_get_nested_values(const std::vector<std::string> & paths, const
  * - only string, example: "string"
  * - mixed string and tokens, example: [12, 34, "string", 56, 78]
  */
-static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
+static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) {
     // If `add_bos` is true, we only add BOS, when json_prompt is a string,
     // or the first element of the json_prompt array is a string.
     llama_tokens prompt_tokens;
@@ -131,10 +131,10 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
 
                 llama_tokens p;
                 if (first) {
-                    p = common_tokenize(ctx, s, add_special, parse_special);
+                    p = common_tokenize(vocab, s, add_special, parse_special);
                     first = false;
                 } else {
-                    p = common_tokenize(ctx, s, false, parse_special);
+                    p = common_tokenize(vocab, s, false, parse_special);
                 }
 
                 prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
@@ -148,7 +148,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
         }
     } else {
         auto s = json_prompt.template get<std::string>();
-        prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
+        prompt_tokens = common_tokenize(vocab, s, add_special, parse_special);
     }
 
     return prompt_tokens;
@@ -166,11 +166,11 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
  * - "prompt": [[12, 34, 56], [78, 90, 12]]
  * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
  */
-static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
+static std::vector<llama_tokens> tokenize_input_prompts(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) {
     std::vector<llama_tokens> result;
     if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
         // string or mixed
-        result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special));
+        result.push_back(tokenize_mixed(vocab, json_prompt, add_special, parse_special));
     } else if (json_is_array_of_numbers(json_prompt)) {
         // array of tokens
         result.push_back(json_prompt.get<llama_tokens>());
@@ -179,7 +179,7 @@ static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, con
         result.reserve(json_prompt.size());
         for (const auto & p : json_prompt) {
             if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) {
-                result.push_back(tokenize_mixed(ctx, p, add_special, parse_special));
+                result.push_back(tokenize_mixed(vocab, p, add_special, parse_special));
             } else if (json_is_array_of_numbers(p)) {
                 // array of tokens
                 result.push_back(p.get<llama_tokens>());
@@ -231,21 +231,23 @@ static size_t validate_utf8(const std::string& text) {
 //
 
 // format rerank task: [BOS]query[EOS][SEP]doc[EOS]
-static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) {
+static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_tokens & query, const llama_tokens & doc) {
     llama_tokens result;
+
     result.reserve(doc.size() + query.size() + 4);
-    result.push_back(llama_token_bos(model));
+    result.push_back(llama_vocab_bos(vocab));
     result.insert(result.end(), query.begin(), query.end());
-    result.push_back(llama_token_eos(model));
-    result.push_back(llama_token_sep(model));
+    result.push_back(llama_vocab_eos(vocab));
+    result.push_back(llama_vocab_sep(vocab));
     result.insert(result.end(), doc.begin(), doc.end());
-    result.push_back(llama_token_eos(model));
+    result.push_back(llama_vocab_eos(vocab));
+
     return result;
 }
 
 // format infill task
 static llama_tokens format_infill(
-        const llama_context * ctx,
+        const llama_vocab * vocab,
         const json & input_prefix,
         const json & input_suffix,
         const json & input_extra,
@@ -272,15 +274,14 @@ static llama_tokens format_infill(
     llama_tokens extra_tokens;
     extra_tokens.reserve(n_ctx);
 
-    auto model = llama_get_model(ctx);
-    auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false);
-    auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false);
+    auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false);
+    auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false);
 
-    if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
+    if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) {
         // TODO: make project name an input
-        static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false);
+        static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false);
 
-        extra_tokens.push_back(llama_token_fim_rep(model));
+        extra_tokens.push_back(llama_vocab_fim_rep(vocab));
         extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
     }
     for (const auto & chunk : input_extra) {
@@ -288,28 +289,28 @@ static llama_tokens format_infill(
         const std::string text     = json_value(chunk, "text",     std::string());
         const std::string filename = json_value(chunk, "filename", std::string("tmp"));
 
-        if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
-            const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false);
+        if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) {
+            const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false);
 
-            extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
+            extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab));
             extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
         } else {
             // chunk separator in binary form to avoid confusing the AI
             static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
-            static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false);
+            static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false);
 
             extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
         }
 
-        const auto chunk_tokens = common_tokenize(ctx, text, false, false);
+        const auto chunk_tokens = common_tokenize(vocab, text, false, false);
         extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
     }
 
-    if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
+    if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) {
         // TODO: current filename
-        static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false);
+        static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false);
 
-        extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
+        extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab));
         extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
     }
 
@@ -325,15 +326,15 @@ static llama_tokens format_infill(
     tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
     tokens_suffix.resize(n_suffix_take);
 
-    tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
+    tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab));
     tokens_prefix.insert(tokens_prefix.end(),   tokens_prompt.begin(), tokens_prompt.end());
-    tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
+    tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab));
 
     auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix;
     auto embd_end = spm_infill ? tokens_prefix : tokens_suffix;
 
-    if (llama_add_bos_token(model)) {
-        embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+    if (llama_vocab_get_add_bos(vocab)) {
+        embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
     }
 
     SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size());
@@ -342,7 +343,7 @@ static llama_tokens format_infill(
     embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end());
 
     embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
-    embd_inp.push_back(llama_token_fim_mid(model));
+    embd_inp.push_back(llama_vocab_fim_mid(vocab));
 
     return embd_inp;
 }
@@ -764,14 +765,18 @@ static json format_logit_bias(const std::vector<llama_logit_bias> & logit_bias)
     return data;
 }
 
-static std::string safe_json_to_str(json data) {
+static std::string safe_json_to_str(const json & data) {
     return data.dump(-1, ' ', false, json::error_handler_t::replace);
 }
 
 static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx) {
     std::vector<llama_token_data> cur;
     const auto * logits = llama_get_logits_ith(ctx, idx);
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     cur.resize(n_vocab);
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
@@ -799,8 +804,8 @@ static std::vector<llama_token_data> get_token_probabilities(llama_context * ctx
 }
 
 static bool are_lora_equal(
-        const std::vector<common_lora_adapter_info> & l1,
-        const std::vector<common_lora_adapter_info> & l2) {
+        const std::vector<common_adapter_lora_info> & l1,
+        const std::vector<common_adapter_lora_info> & l2) {
     if (l1.size() != l2.size()) {
         return false;
     }
@@ -814,10 +819,10 @@ static bool are_lora_equal(
 }
 
 // parse lora config from JSON request, returned a copy of lora_base with updated scale
-static std::vector<common_lora_adapter_info> parse_lora_request(
-        const std::vector<common_lora_adapter_info> & lora_base,
+static std::vector<common_adapter_lora_info> parse_lora_request(
+        const std::vector<common_adapter_lora_info> & lora_base,
         const json & data) {
-    std::vector<common_lora_adapter_info> lora(lora_base);
+    std::vector<common_adapter_lora_info> lora(lora_base);
     int max_idx = lora.size();
 
     // clear existing value
diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index d72f5bcdd..e8eda9c22 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -75,12 +75,14 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     // initialize the context
     llama_context_params ctx_params = llama_context_default_params();
     ctx_params.n_ctx = n_ctx;
     ctx_params.n_batch = n_ctx;
 
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
     if (!ctx) {
         fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
         return 1;
@@ -97,9 +99,9 @@ int main(int argc, char ** argv) {
         std::string response;
 
         // tokenize the prompt
-        const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+        const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
         std::vector<llama_token> prompt_tokens(n_prompt_tokens);
-        if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) {
+        if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) {
             GGML_ABORT("failed to tokenize the prompt\n");
         }
 
@@ -124,13 +126,13 @@ int main(int argc, char ** argv) {
             new_token_id = llama_sampler_sample(smpl, ctx, -1);
 
             // is it an end of generation?
-            if (llama_token_is_eog(model, new_token_id)) {
+            if (llama_vocab_is_eog(vocab, new_token_id)) {
                 break;
             }
 
             // convert the token to a string, print it and add it to the response
             char buf[256];
-            int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
+            int n = llama_token_to_piece(vocab, new_token_id, buf, sizeof(buf), 0, true);
             if (n < 0) {
                 GGML_ABORT("failed to convert token to piece\n");
             }
@@ -159,12 +161,14 @@ int main(int argc, char ** argv) {
             break;
         }
 
+        const char * tmpl = llama_model_chat_template(model);
+
         // add the user input to the message list and format it
         messages.push_back({"user", strdup(user.c_str())});
-        int new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
+        int new_len = llama_chat_apply_template(tmpl, messages.data(), messages.size(), true, formatted.data(), formatted.size());
         if (new_len > (int)formatted.size()) {
             formatted.resize(new_len);
-            new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
+            new_len = llama_chat_apply_template(tmpl, messages.data(), messages.size(), true, formatted.data(), formatted.size());
         }
         if (new_len < 0) {
             fprintf(stderr, "failed to apply the chat template\n");
@@ -181,7 +185,7 @@ int main(int argc, char ** argv) {
 
         // add the response to the messages
         messages.push_back({"assistant", strdup(response.c_str())});
-        prev_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), false, nullptr, 0);
+        prev_len = llama_chat_apply_template(tmpl, messages.data(), messages.size(), false, nullptr, 0);
         if (prev_len < 0) {
             fprintf(stderr, "failed to apply the chat template\n");
             return 1;
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index f69117890..10e79a0a6 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -84,6 +84,7 @@ int main(int argc, char ** argv) {
     model_params.n_gpu_layers = ngl;
 
     llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@@ -93,11 +94,11 @@ int main(int argc, char ** argv) {
     // tokenize the prompt
 
     // find the number of tokens in the prompt
-    const int n_prompt = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+    const int n_prompt = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
 
     // allocate space for the tokens and tokenize the prompt
     std::vector<llama_token> prompt_tokens(n_prompt);
-    if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
+    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
         fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
         return 1;
     }
@@ -112,7 +113,7 @@ int main(int argc, char ** argv) {
     // enable performance counters
     ctx_params.no_perf = false;
 
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     if (ctx == NULL) {
         fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
@@ -131,7 +132,7 @@ int main(int argc, char ** argv) {
 
     for (auto id : prompt_tokens) {
         char buf[128];
-        int n = llama_token_to_piece(model, id, buf, sizeof(buf), 0, true);
+        int n = llama_token_to_piece(vocab, id, buf, sizeof(buf), 0, true);
         if (n < 0) {
             fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
             return 1;
@@ -164,12 +165,12 @@ int main(int argc, char ** argv) {
             new_token_id = llama_sampler_sample(smpl, ctx, -1);
 
             // is it an end of generation?
-            if (llama_token_is_eog(model, new_token_id)) {
+            if (llama_vocab_is_eog(vocab, new_token_id)) {
                 break;
             }
 
             char buf[128];
-            int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
+            int n = llama_token_to_piece(vocab, new_token_id, buf, sizeof(buf), 0, true);
             if (n < 0) {
                 fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
                 return 1;
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index 9070c3512..403ba2dd2 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -45,6 +45,8 @@ int main(int argc, char ** argv) {
     model_tgt = llama_init_tgt.model.get();
     ctx_tgt   = llama_init_tgt.context.get();
 
+    const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
+
     // load the draft model
     params.devices      = params.speculative.devices;
     params.model        = params.speculative.model;
@@ -196,7 +198,7 @@ int main(int argc, char ** argv) {
 
             id_last = ids[i];
 
-            if (llama_token_is_eog(model_tgt, id_last)) {
+            if (llama_vocab_is_eog(vocab, id_last)) {
                 has_eos = true;
                 break;
             }
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index bc0b6813b..c7ccea50d 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -90,10 +90,13 @@ int main(int argc, char ** argv) {
     model_dft = llama_init_dft.model.get();
     ctx_dft   = llama_init_dft.context.get();
 
-    const bool vocab_type_tgt = llama_vocab_type(model_tgt);
+    const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
+    const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
+
+    const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
     LOG_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
 
-    const bool vocab_type_dft = llama_vocab_type(model_dft);
+    const bool vocab_type_dft = llama_vocab_type(vocab_dft);
     LOG_DBG("vocab_type dft: %d\n", vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
@@ -103,18 +106,18 @@ int main(int argc, char ** argv) {
     }
 
     if (
-        llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
-        llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
-        llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
-        llama_token_eos(model_tgt) != llama_token_eos(model_dft)
+        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
+        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
+        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
     ) {
         LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
         return 1;
     }
 
     {
-        const int n_vocab_tgt = llama_n_vocab(model_tgt);
-        const int n_vocab_dft = llama_n_vocab(model_dft);
+        const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
+        const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
         const int vocab_diff  = n_vocab_tgt > n_vocab_dft
             ? n_vocab_tgt - n_vocab_dft
             : n_vocab_dft - n_vocab_tgt;
@@ -122,13 +125,13 @@ int main(int argc, char ** argv) {
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
             LOG_ERR("%s: draft model vocab must closely match target model to use speculation but ", __func__);
             LOG_ERR("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+                    n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return 1;
         }
 
         for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
-            const char * token_text_tgt = llama_token_get_text(model_tgt, i);
-            const char * token_text_dft = llama_token_get_text(model_dft, i);
+            const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
+            const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
                 LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
                 LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
@@ -170,7 +173,7 @@ int main(int argc, char ** argv) {
     const auto t_enc_end = ggml_time_us();
 
     // the 2 models should have the same vocab
-    //GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
+    //GGML_ASSERT(n_vocab == llama_vocab_n_tokens(model_dft));
 
     // how many tokens to draft each time
     int n_draft = params.speculative.n_max;
@@ -386,7 +389,7 @@ int main(int argc, char ** argv) {
                     }
                 }
 
-                if (llama_token_is_eog(model_tgt, token_id)) {
+                if (llama_vocab_is_eog(vocab_tgt, token_id)) {
                     has_eos = true;
                 }
                 ++n_predict;
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 684ca054a..7375759eb 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -344,8 +344,10 @@ int main(int raw_argc, char ** raw_argv) {
         return 1;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     llama_context_params ctx_params = llama_context_default_params();
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
     if (!ctx) {
         fprintf(stderr, "Error: could not create context.\n");
         return 1;
@@ -365,7 +367,7 @@ int main(int raw_argc, char ** raw_argv) {
         prompt = stdin_buffer.str();
     }
 
-    const bool model_wants_add_bos = llama_add_bos_token(model);
+    const bool model_wants_add_bos = llama_vocab_get_add_bos(vocab);
     const bool add_bos = model_wants_add_bos && !no_bos;
     const bool parse_special = !no_parse_special;
     const bool escape = !no_escape;
@@ -375,7 +377,7 @@ int main(int raw_argc, char ** raw_argv) {
     }
 
     std::vector<llama_token> tokens;
-    tokens = common_tokenize(model, prompt, add_bos, parse_special);
+    tokens = common_tokenize(vocab, prompt, add_bos, parse_special);
 
     if (printing_ids) {
         printf("[");
diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp
index 522f5e881..5a9161181 100644
--- a/examples/tts/tts.cpp
+++ b/examples/tts/tts.cpp
@@ -414,15 +414,15 @@ static void prompt_add(llama_tokens & prompt, const llama_tokens & tokens) {
     prompt.insert(prompt.end(), tokens.begin(), tokens.end());
 }
 
-static void prompt_add(llama_tokens & prompt, const llama_model * model, const std::string & txt, bool add_special, bool parse_special) {
-    auto tmp = common_tokenize(model, txt, add_special, parse_special);
+static void prompt_add(llama_tokens & prompt, const llama_vocab * vocab, const std::string & txt, bool add_special, bool parse_special) {
+    auto tmp = common_tokenize(vocab, txt, add_special, parse_special);
     prompt_add(prompt, tmp);
 }
 
-static void prompt_init(llama_tokens & prompt, const llama_model * model) {
+static void prompt_init(llama_tokens & prompt, const llama_vocab * vocab) {
     prompt.clear();
 
-    prompt_add(prompt, model, "<|im_start|>\n", true, true);
+    prompt_add(prompt, vocab, "<|im_start|>\n", true, true);
 }
 
 int main(int argc, char ** argv) {
@@ -462,6 +462,8 @@ int main(int argc, char ** argv) {
     model_ttc = llama_init_ttc.model.get();
     ctx_ttc   = llama_init_ttc.context.get();
 
+    const llama_vocab * vocab = llama_model_get_vocab(model_ttc);
+
     // TODO: refactor in a common struct
     params.model     = params.vocoder.model;
     params.model_url = params.vocoder.model_url;
@@ -499,9 +501,9 @@ int main(int argc, char ** argv) {
 
         std::vector<llama_token> prompt_inp;
 
-        prompt_init(prompt_inp, model_ttc);
+        prompt_init(prompt_inp, vocab);
 
-        prompt_add(prompt_inp, model_ttc, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true);
+        prompt_add(prompt_inp, vocab, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true);
 
         // convert the input text into the necessary format expected by OuteTTS
         {
@@ -509,10 +511,10 @@ int main(int argc, char ** argv) {
 
             LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
 
-            prompt_add(prompt_inp, model_ttc, prompt_clean, false, true);
+            prompt_add(prompt_inp, vocab, prompt_clean, false, true);
         }
 
-        prompt_add(prompt_inp, model_ttc, "<|text_end|>\n", false, true);
+        prompt_add(prompt_inp, vocab, "<|text_end|>\n", false, true);
 
         // disabled to save time on tokenizing each time
         // TODO: load voices from the json files
@@ -549,7 +551,7 @@ it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><
 looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
 lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>)";
 
-        auto tmp = common_tokenize(model_ttc, voice_data, false, true);
+        auto tmp = common_tokenize(vocab, voice_data, false, true);
         printf("\n\n");
         for (int i = 0; i < tmp.size(); ++i) {
             printf("%d, ", tmp[i]);
@@ -735,9 +737,9 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
                 const auto * cands = common_sampler_get_candidates(smpl[i]);
 
                 // is it an end of generation? -> mark the stream as finished
-                if (llama_token_is_eog(model_ttc, new_token_id) || n_decode == n_predict) {
+                if (llama_vocab_is_eog(vocab, new_token_id) || n_decode == n_predict) {
                     std::string reason;
-                    if (llama_token_is_eog(model_ttc, new_token_id)) {
+                    if (llama_vocab_is_eog(vocab, new_token_id)) {
                         reason = "eos";
                     } else {
                         reason = "n_predict";
@@ -873,7 +875,7 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
 
 #if 1
     // spectral operations
-    const int n_embd = llama_n_embd(model_cts);
+    const int n_embd = llama_model_n_embd(model_cts);
     const float * embd = llama_get_embeddings(ctx_cts);
 
     auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads);
diff --git a/include/llama-cpp.h b/include/llama-cpp.h
index 11306b17f..8f6368177 100644
--- a/include/llama-cpp.h
+++ b/include/llama-cpp.h
@@ -20,11 +20,11 @@ struct llama_sampler_deleter {
     void operator()(llama_sampler * sampler) { llama_sampler_free(sampler); }
 };
 
-struct llama_lora_adapter_deleter {
-    void operator()(llama_lora_adapter * lora_adapter) { llama_lora_adapter_free(lora_adapter); }
+struct llama_adapter_lora_deleter {
+    void operator()(llama_adapter_lora * adapter) { llama_adapter_lora_free(adapter); }
 };
 
 typedef std::unique_ptr<llama_model, llama_model_deleter> llama_model_ptr;
 typedef std::unique_ptr<llama_context, llama_context_deleter> llama_context_ptr;
 typedef std::unique_ptr<llama_sampler, llama_sampler_deleter> llama_sampler_ptr;
-typedef std::unique_ptr<llama_lora_adapter, llama_lora_adapter_deleter> llama_lora_adapter_ptr;
+typedef std::unique_ptr<llama_adapter_lora, llama_adapter_lora_deleter> llama_adapter_lora_ptr;
diff --git a/include/llama.h b/include/llama.h
index 0295a51fb..9f04bc622 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -56,7 +56,7 @@ extern "C" {
     // TODO: show sample usage
     //
 
-    // struct llama_vocab; // TODO: add in the future
+    struct llama_vocab;
     struct llama_model;
     struct llama_context;
     struct llama_sampler;
@@ -385,8 +385,7 @@ extern "C" {
     } llama_chat_message;
 
     // lora adapter
-    // TODO: rename to llama_adapter_lora
-    struct llama_lora_adapter;
+    struct llama_adapter_lora;
 
     // Helpers for getting default parameters
     // TODO: update API to start accepting pointers to params structs (https://github.com/ggerganov/llama.cpp/discussions/9172)
@@ -400,18 +399,19 @@ extern "C" {
     // Call once at the start of the program
     LLAMA_API void llama_backend_init(void);
 
+    // Call once at the end of the program - currently only used for MPI
+    LLAMA_API void llama_backend_free(void);
+
     //optional:
     LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
 
     // Optional: an auto threadpool gets created in ggml if not passed explicitly
     LLAMA_API void llama_attach_threadpool(
-               struct   llama_context * ctx,
-            ggml_threadpool_t   threadpool,
-            ggml_threadpool_t   threadpool_batch);
-    LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
+            struct llama_context * ctx,
+               ggml_threadpool_t   threadpool,
+               ggml_threadpool_t   threadpool_batch);
 
-    // Call once at the end of the program - currently only used for MPI
-    LLAMA_API void llama_backend_free(void);
+    LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
 
     DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
                              const char * path_model,
@@ -427,11 +427,15 @@ extern "C" {
 
     LLAMA_API void llama_model_free(struct llama_model * model);
 
-    // TODO: rename to llama_init_from_model
-    LLAMA_API struct llama_context * llama_new_context_with_model(
+    LLAMA_API struct llama_context * llama_init_from_model(
                      struct llama_model * model,
             struct llama_context_params   params);
 
+    DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model(
+                     struct llama_model * model,
+            struct llama_context_params   params),
+            "use llama_init_from_model instead");
+
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
@@ -449,20 +453,30 @@ extern "C" {
     LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 
-    LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
-    LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
-    LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
-    LLAMA_API int32_t llama_n_layer    (const struct llama_model * model);
-    LLAMA_API int32_t llama_n_head     (const struct llama_model * model);
+    DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead");
+    DEPRECATED(LLAMA_API int32_t llama_n_embd     (const struct llama_model * model), "use llama_model_n_embd instead");
+    DEPRECATED(LLAMA_API int32_t llama_n_layer    (const struct llama_model * model), "use llama_model_n_layer instead");
+    DEPRECATED(LLAMA_API int32_t llama_n_head     (const struct llama_model * model), "use llama_model_n_head instead");
 
-    LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
+    DEPRECATED(LLAMA_API int32_t llama_n_vocab    (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead");
 
-    LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
-    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
-    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model * model);
+    LLAMA_API const struct llama_model * llama_get_model   (const struct llama_context * ctx);
+    LLAMA_API enum llama_pooling_type    llama_pooling_type(const struct llama_context * ctx);
+
+    LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
+    LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
+
+    LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
-    LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
+    LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
+
+    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_vocab * vocab);
+
+    LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab);
 
     // Functions to access the model's GGUF metadata scalar values
     // - The functions return the length of the string on success, or -1 on failure
@@ -488,6 +502,9 @@ extern "C" {
     // Returns the total size of all the tensors in the model in bytes
     LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
 
+    // Get the default chat template. Returns nullptr if not available
+    LLAMA_API const char * llama_model_chat_template(const struct llama_model * model);
+
     // Returns the total number of parameters in the model
     LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
 
@@ -515,34 +532,31 @@ extern "C" {
     //
 
     // Load a LoRA adapter from file
-    // TODO: rename to llama_adapter_lora_init
-    LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
+    LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init(
             struct llama_model * model,
             const char * path_lora);
 
+    // Manually free a LoRA adapter
+    // Note: loaded adapters will be free when the associated model is deleted
+    LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
+
+    // The following functions operate on a llama_context, hence the naming: llama_verb_...
+
     // Add a loaded LoRA adapter to given context
     // This will not modify model's weight
-    // TODO: rename to llama_set_adapter_lora
-    LLAMA_API int32_t llama_lora_adapter_set(
+    LLAMA_API int32_t llama_set_adapter_lora(
             struct llama_context * ctx,
-            struct llama_lora_adapter * adapter,
+            struct llama_adapter_lora * adapter,
             float scale);
 
     // Remove a specific LoRA adapter from given context
     // Return -1 if the adapter is not present in the context
-    // TODO: rename to llama_rm_adapter_lora
-    LLAMA_API int32_t llama_lora_adapter_remove(
+    LLAMA_API int32_t llama_rm_adapter_lora(
             struct llama_context * ctx,
-            struct llama_lora_adapter * adapter);
+            struct llama_adapter_lora * adapter);
 
     // Remove all LoRA adapters from given context
-    // TODO: rename to llama_clear_adapter_lora
-    LLAMA_API void llama_lora_adapter_clear(struct llama_context * ctx);
-
-    // Manually free a LoRA adapter
-    // Note: loaded adapters will be free when the associated model is deleted
-    // TODO: rename to llama_adapter_lora_free
-    LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
+    LLAMA_API void llama_clear_adapter_lora(struct llama_context * ctx);
 
     // Apply a loaded control vector to a llama_context, or if data is NULL, clear
     // the currently loaded vector.
@@ -550,9 +564,8 @@ extern "C" {
     // to an n_embd x n_layers buffer starting from layer 1.
     // il_start and il_end are the layer range the vector should apply to (both inclusive)
     // See llama_control_vector_load in common to load a control vector.
-    // TODO: rename to llama_adapter_cvec_apply
-    LLAMA_API int32_t llama_control_vector_apply(
-            struct llama_context * lctx,
+    LLAMA_API int32_t llama_apply_adapter_cvec(
+            struct llama_context * ctx,
                      const float * data,
                           size_t   len,
                          int32_t   n_embd,
@@ -908,41 +921,57 @@ extern "C" {
     // Vocab
     //
 
-    LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
+    LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token);
 
-    LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
+    LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token);
 
-    LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
+    LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token);
 
     // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
-    LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
+    LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token);
 
     // Identify if Token Id is a control token or a render-able token
-    LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
+    LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token);
 
     // Special tokens
-    LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
-    LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
-    LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn
-    LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
-    LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
-    LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
-    LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
+    LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence
+    LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence
+    LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn
+    LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab); // classification
+    LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator
+    LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line
+    LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding
 
-    LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
-    LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
+    LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab);
+    LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
 
-    // infill tokens
-    DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead");
-    DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead");
-    DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead");
+    LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
+    LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
+    LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
+    LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab);
+    LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab);
+    LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab);
 
-    LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model);
-    LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model);
-    LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model);
-    LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model);
-    LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model);
-    LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model);
+    DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocabable_get_text instead");
+    DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead");
+    DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead");
+    DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead");
+    DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead");
+    DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead");
+    DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead");
 
     //
     // Tokenization
@@ -958,7 +987,7 @@ extern "C" {
     /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
     ///                      as plaintext. Does not insert a leading space.
     LLAMA_API int32_t llama_tokenize(
-        const struct llama_model * model,
+        const struct llama_vocab * vocab,
                       const char * text,
                          int32_t   text_len,
                      llama_token * tokens,
@@ -972,7 +1001,7 @@ extern "C" {
     // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
     // @param special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_token_to_piece(
-              const struct llama_model * model,
+              const struct llama_vocab * vocab,
                            llama_token   token,
                                   char * buf,
                                int32_t   length,
@@ -986,7 +1015,7 @@ extern "C" {
     /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
     /// @param unparse_special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_detokenize(
-        const struct llama_model * model,
+        const struct llama_vocab * vocab,
                const llama_token * tokens,
                          int32_t   n_tokens,
                             char * text,
@@ -1009,7 +1038,6 @@ extern "C" {
     /// @param length The size of the allocated buffer
     /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
     LLAMA_API int32_t llama_chat_apply_template(
-              const struct llama_model * model,
                             const char * tmpl,
        const struct llama_chat_message * chat,
                                 size_t   n_msg,
@@ -1057,7 +1085,6 @@ extern "C" {
     //    llama_sampler_free(smpl);
     //
     // TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU).
-    // TODO: in the future, the entire sampling API that uses llama_model should start using llama_vocab
     //
 
     typedef void * llama_sampler_context_t;
@@ -1157,7 +1184,7 @@ extern "C" {
                                float   eta);
 
     LLAMA_API struct llama_sampler * llama_sampler_init_grammar(
-            const struct llama_model * model,
+            const struct llama_vocab * vocab,
                           const char * grammar_str,
                           const char * grammar_root);
 
@@ -1169,8 +1196,9 @@ extern "C" {
                                float   penalty_present); // 0.0 = disabled
 
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
-    LLAMA_API struct llama_sampler *    llama_sampler_init_dry(
-            const struct llama_model *  model,
+    LLAMA_API struct llama_sampler * llama_sampler_init_dry(
+            const struct llama_vocab *  vocab,
+                             int32_t    n_ctx_train,
                                float    dry_multiplier,
                                float    dry_base,
                              int32_t    dry_allowed_length,
@@ -1204,7 +1232,7 @@ extern "C" {
     // 3. discard non-EOG tokens with low prob
     // 4. if no tokens are left -> pick EOT
     //
-    LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model);
+    LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab);
 
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp
index d4879b778..8a0800463 100644
--- a/src/llama-adapter.cpp
+++ b/src/llama-adapter.cpp
@@ -1,5 +1,7 @@
 #include "llama-adapter.h"
 
+#include "llama-impl.h"
+#include "llama-mmap.h"
 #include "llama-model.h"
 
 #include <algorithm>
@@ -9,7 +11,7 @@
 
 // vec
 
-struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
+struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
     if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
         return nullptr;
     }
@@ -17,7 +19,7 @@ struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
     return tensors[il];
 }
 
-struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
+struct ggml_tensor * llama_adapter_cvec::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
     ggml_tensor * layer_dir = tensor_for(il);
     if (layer_dir != nullptr) {
         cur = ggml_add(ctx, cur, layer_dir);
@@ -26,12 +28,12 @@ struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, s
     return cur;
 }
 
-static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
+bool llama_adapter_cvec::init(const llama_model & model) {
     const auto & hparams = model.hparams;
 
-    GGML_ASSERT(cvec.tensors.empty());
-    GGML_ASSERT(cvec.ctxs.empty());
-    GGML_ASSERT(cvec.bufs.empty());
+    GGML_ASSERT(tensors.empty());
+    GGML_ASSERT(ctxs.empty());
+    GGML_ASSERT(bufs.empty());
 
     // create a context for each buffer type
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
@@ -50,7 +52,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
             }
 
             ctx_map[buft] = ctx;
-            cvec.ctxs.emplace_back(ctx);
+            ctxs.emplace_back(ctx);
 
             return ctx;
         }
@@ -59,21 +61,21 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
     };
 
     // make tensors
-    cvec.tensors.reserve(hparams.n_layer);
-    cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
+    tensors.reserve(hparams.n_layer);
+    tensors.push_back(nullptr); // there's never a tensor for layer 0
     for (size_t il = 1; il < hparams.n_layer; il++) {
-        ggml_backend_buffer_type_t buft = llama_model_select_buft(model, il);
+        ggml_backend_buffer_type_t buft = model.select_buft(il);
         ggml_context * ctx = ctx_for_buft(buft);
         if (!ctx) {
             LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
             return false;
         }
         ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
-        cvec.tensors.push_back(tensor);
+        tensors.push_back(tensor);
     }
 
     // allocate tensors / buffers and zero
-    cvec.bufs.reserve(ctx_map.size());
+    bufs.reserve(ctx_map.size());
     for (auto it : ctx_map) {
         ggml_backend_buffer_type_t buft = it.first;
         ggml_context * ctx = it.second;
@@ -83,14 +85,13 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
             return false;
         }
         ggml_backend_buffer_clear(buf, 0);
-        cvec.bufs.emplace_back(buf);
+        bufs.emplace_back(buf);
     }
 
     return true;
 }
 
-int32_t llama_control_vector_apply(
-        struct llama_control_vector & cvec,
+int32_t llama_adapter_cvec::apply(
         const llama_model & model,
         const float * data,
         size_t len,
@@ -101,8 +102,8 @@ int32_t llama_control_vector_apply(
 
     if (data == nullptr) {
         // disable the current control vector (but leave allocated for later)
-        cvec.layer_start = -1;
-        cvec.layer_end   = -1;
+        layer_start = -1;
+        layer_end   = -1;
         return 0;
     }
 
@@ -111,21 +112,21 @@ int32_t llama_control_vector_apply(
         return 1;
     }
 
-    if (cvec.tensors.empty()) {
-        if (!llama_control_vector_init(cvec, model)) {
+    if (tensors.empty()) {
+        if (!init(model)) {
             return 1;
         }
     }
 
-    cvec.layer_start = il_start;
-    cvec.layer_end   = il_end;
+    layer_start = il_start;
+    layer_end   = il_end;
 
     for (size_t il = 1; il < hparams.n_layer; il++) {
-        assert(cvec.tensors[il] != nullptr);
+        assert(tensors[il] != nullptr);
 
         const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
         if (off + n_embd <= len) {
-            ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
+            ggml_backend_tensor_set(tensors[il], data + off, 0, n_embd * ggml_element_size(tensors[il]));
         }
     }
 
@@ -134,7 +135,7 @@ int32_t llama_control_vector_apply(
 
 // lora
 
-llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
+llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor * w) {
     const std::string name(w->name);
 
     const auto pos = ab_map.find(name);
@@ -145,11 +146,7 @@ llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
     return nullptr;
 }
 
-void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
-    delete adapter;
-}
-
-static void llama_lora_adapter_init_impl(struct llama_model & model, const char * path_lora, struct llama_lora_adapter & adapter) {
+static void llama_adapter_lora_init_impl(struct llama_model & model, const char * path_lora, struct llama_adapter_lora & adapter) {
     LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
 
     ggml_context * ctx_init;
@@ -221,7 +218,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
     };
 
     // bundle lora_a and lora_b into pairs
-    std::map<std::string, llama_lora_weight> ab_map;
+    std::map<std::string, llama_adapter_lora_weight> ab_map;
     auto str_endswith = [](const std::string & str, const std::string & suffix) {
         return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
     };
@@ -231,14 +228,14 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
         if (str_endswith(name, ".lora_a")) {
             replace_all(name, ".lora_a", "");
             if (ab_map.find(name) == ab_map.end()) {
-                ab_map[name] = llama_lora_weight(cur, nullptr);
+                ab_map[name] = llama_adapter_lora_weight(cur, nullptr);
             } else {
                 ab_map[name].a = cur;
             }
         } else if (str_endswith(name, ".lora_b")) {
             replace_all(name, ".lora_b", "");
             if (ab_map.find(name) == ab_map.end()) {
-                ab_map[name] = llama_lora_weight(nullptr, cur);
+                ab_map[name] = llama_adapter_lora_weight(nullptr, cur);
             } else {
                 ab_map[name].b = cur;
             }
@@ -254,7 +251,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
     // add tensors
     for (auto & it : ab_map) {
         const std::string & name = it.first;
-        llama_lora_weight & w = it.second;
+        llama_adapter_lora_weight & w = it.second;
         bool is_token_embd = str_endswith(name, "token_embd.weight");
 
         if (!w.a || !w.b) {
@@ -262,7 +259,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
         }
 
         // device buft and device ctx
-        auto * model_tensor = llama_model_get_tensor(model, name.c_str());
+        const auto * model_tensor = model.get_tensor(name.c_str());
         if (!model_tensor) {
             throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
         }
@@ -288,7 +285,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
         struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
         ggml_set_name(tensor_a, w.a->name);
         ggml_set_name(tensor_b, w.b->name);
-        adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
+        adapter.ab_map[name] = llama_adapter_lora_weight(tensor_a, tensor_b);
     }
 
     // allocate tensors / buffers and zero
@@ -330,11 +327,11 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
     LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
 }
 
-struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
-    struct llama_lora_adapter * adapter = new llama_lora_adapter();
+struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model, const char * path_lora) {
+    struct llama_adapter_lora * adapter = new llama_adapter_lora();
 
     try {
-        llama_lora_adapter_init_impl(*model, path_lora, *adapter);
+        llama_adapter_lora_init_impl(*model, path_lora, *adapter);
         return adapter;
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
@@ -344,3 +341,7 @@ struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model,
 
     return nullptr;
 }
+
+void llama_adapter_lora_free(struct llama_adapter_lora * adapter) {
+    delete adapter;
+}
diff --git a/src/llama-adapter.h b/src/llama-adapter.h
index 3448656b1..603fa08f6 100644
--- a/src/llama-adapter.h
+++ b/src/llama-adapter.h
@@ -1,73 +1,74 @@
 #pragma once
 
-#include "llama-impl.h"
-#include "llama-hparams.h"
+#include "llama.h"
 
 #include "ggml-cpp.h"
 
+#include <string>
 #include <unordered_map>
 #include <vector>
 
+// TODO: pimpl
+
 //
 // llama_adapter_cvec
 //
 
-// TODO: rename to llama_adapter_cvec
-struct llama_control_vector {
-    std::vector<ggml_context_ptr> ctxs;
-    std::vector<ggml_backend_buffer_ptr> bufs;
+struct llama_adapter_cvec {
+    struct ggml_tensor * tensor_for(int il) const;
 
-    std::vector<struct ggml_tensor *> tensors; // per layer
+    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const;
+
+    int32_t apply(
+            const llama_model & model,
+            const float * data,
+            size_t len,
+            int32_t n_embd,
+            int32_t il_start,
+            int32_t il_end);
+
+private:
+    bool init(const llama_model & model);
 
     int32_t layer_start = -1;
     int32_t layer_end   = -1;
 
-    struct ggml_tensor * tensor_for(int il) const;
+    std::vector<ggml_context_ptr> ctxs;
+    std::vector<ggml_backend_buffer_ptr> bufs;
 
-    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const;
+    std::vector<struct ggml_tensor *> tensors; // per layer
 };
 
-int32_t llama_control_vector_apply(
-        struct llama_control_vector & cvec,
-        const llama_model & model,
-        const float * data,
-        size_t len,
-        int32_t n_embd,
-        int32_t il_start,
-        int32_t il_end);
-
 //
 // llama_adapter_lora
 //
 
-// TODO: rename to llama_adapter_lora_weight
-struct llama_lora_weight {
+struct llama_adapter_lora_weight {
     struct ggml_tensor * a = nullptr;
     struct ggml_tensor * b = nullptr;
 
     // get actual scale based on rank and alpha
-    float get_scale(float alpha, float adapter_scale) {
+    float get_scale(float alpha, float adapter_scale) const {
         const float rank  = (float) b->ne[0];
         const float scale = alpha ? adapter_scale * alpha / rank : adapter_scale;
         return scale;
     }
 
-    llama_lora_weight() = default;
-    llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
+    llama_adapter_lora_weight() = default;
+    llama_adapter_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
 };
 
-// TODO: rename to llama_adapter_lora
-struct llama_lora_adapter {
+struct llama_adapter_lora {
     // map tensor name to lora_a_b
-    std::unordered_map<std::string, struct llama_lora_weight> ab_map;
+    std::unordered_map<std::string, struct llama_adapter_lora_weight> ab_map;
 
     std::vector<ggml_context_ptr> ctxs;
     std::vector<ggml_backend_buffer_ptr> bufs;
 
     float alpha;
 
-    llama_lora_adapter() = default;
-    ~llama_lora_adapter() = default;
+    llama_adapter_lora() = default;
+    ~llama_adapter_lora() = default;
 
-    llama_lora_weight * get_weight(struct ggml_tensor * w);
+    llama_adapter_lora_weight * get_weight(struct ggml_tensor * w);
 };
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 7300bd26a..5c1f14cfd 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -178,6 +178,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
     { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
     { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat.template"                 },
     { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
     { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
     { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 79909f03f..349844790 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -176,6 +176,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
+    LLM_KV_TOKENIZER_CHAT_TEMPLATE,
     LLM_KV_TOKENIZER_FIM_PRE_ID,
     LLM_KV_TOKENIZER_FIM_SUF_ID,
     LLM_KV_TOKENIZER_FIM_MID_ID,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 38a55fb2c..671d2a81a 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1,5 +1,8 @@
 #include "llama-context.h"
 
+#include "llama-impl.h"
+#include "llama-mmap.h"
+
 #include <cassert>
 #include <cmath>
 #include <cstring>
@@ -467,11 +470,12 @@ void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
 size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
     const auto & cparams = lctx.cparams;
     const auto & hparams = lctx.model.hparams;
+    const auto & vocab   = lctx.model.vocab;
 
     const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
 
     const auto n_batch = cparams.n_batch;
-    const auto n_vocab = hparams.n_vocab;
+    const auto n_vocab = vocab.n_tokens();
     const auto n_embd  = hparams.n_embd;
 
     // TODO: use a per-batch flag for logits presence instead
@@ -504,7 +508,7 @@ size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
 
         auto * buft = ggml_backend_cpu_buffer_type();
         // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
-        auto * output_dev = lctx.model.dev_output.dev;
+        auto * output_dev = lctx.model.dev_output();
         auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
         if (output_dev_host_buft) {
             buft = output_dev_host_buft;
@@ -538,7 +542,7 @@ size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
 void llama_output_reorder(struct llama_context & ctx) {
     std::vector<size_t> & out_ids = ctx.sbatch.out_ids;
     if (!out_ids.empty()) {
-        const uint32_t n_vocab = ctx.model.hparams.n_vocab;
+        const uint32_t n_vocab = ctx.model.vocab.n_tokens();
         const uint32_t n_embd  = ctx.model.hparams.n_embd;
 
         const int32_t n_outputs = ctx.n_outputs;
@@ -722,7 +726,7 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
             throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
         }
 
-        return ctx->logits + j*ctx->model.hparams.n_vocab;
+        return ctx->logits + j*ctx->model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -882,7 +886,7 @@ struct llama_data_write {
     }
 
     void write_logits(const struct llama_context * ctx) {
-        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_vocab);
+        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.vocab.n_tokens());
 
         write(&logits_size, sizeof(logits_size));
 
diff --git a/src/llama-context.h b/src/llama-context.h
index 0d163c470..a9268b292 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -22,12 +22,12 @@ struct llama_context {
 
     const struct llama_model & model;
 
-    struct llama_cparams        cparams;
-    struct llama_sbatch         sbatch;  // TODO: revisit if needed
-    struct llama_kv_cache       kv_self;
-    struct llama_control_vector cvec;
+    struct llama_cparams      cparams;
+    struct llama_sbatch       sbatch;  // TODO: revisit if needed
+    struct llama_kv_cache     kv_self;
+    struct llama_adapter_cvec cvec;
 
-    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
+    std::unordered_map<struct llama_adapter_lora *, float> lora;
 
     std::vector<ggml_backend_ptr> backends;
     std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index 186dc9a25..bebe4e9a3 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -1092,9 +1092,9 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_
 
     for (size_t i = 0; i < cur_p->size; ++i) {
         const llama_token id      = cur_p->data[i].id;
-        const std::string & piece = grammar.vocab->cache_token_to_piece.at(id);
+        const std::string & piece = grammar.vocab->token_to_piece(id);
 
-        if (llama_token_is_eog_impl(*grammar.vocab, id)) {
+        if (grammar.vocab->is_eog(id)) {
             if (!allow_eog) {
                 cur_p->data[i].logit = -INFINITY;
             }
@@ -1115,7 +1115,7 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_
 void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token) {
     GGML_ASSERT(grammar.vocab != nullptr);
 
-    if (llama_token_is_eog_impl(*grammar.vocab, token)) {
+    if (grammar.vocab->is_eog(token)) {
         for (const auto & stack : grammar.stacks) {
             if (stack.empty()) {
                 return;
@@ -1124,7 +1124,7 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
         GGML_ABORT("fatal error");
     }
 
-    const std::string & piece = grammar.vocab->cache_token_to_piece.at(token);
+    const std::string & piece = grammar.vocab->token_to_piece(token);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar.partial_utf8);
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 3542bef49..1fe454103 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -30,7 +30,6 @@ struct llama_hparams {
     bool use_par_res;
     bool swin_norm;
 
-    uint32_t n_vocab = 0;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
     uint32_t n_embd_features = 0;
@@ -41,7 +40,6 @@ struct llama_hparams {
     uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
-    uint32_t n_vocab_type = 0; // for BERT-style token types
     uint32_t n_rel_attn_bkts = 0;
 
     // for WavTokenizer
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 90b6c56ed..feffdf0de 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -79,7 +79,7 @@ bool llama_kv_cache_init(
 
         ggml_backend_buffer_type_t buft;
         if (offload) {
-            auto * dev = model.dev_layer.at(i).dev;
+            auto * dev = model.dev_layer(i);
             buft = ggml_backend_dev_buffer_type(dev);
         } else {
             buft = ggml_backend_cpu_buffer_type();
diff --git a/src/llama-mmap.cpp b/src/llama-mmap.cpp
index a8cb9439b..57c6e4f51 100644
--- a/src/llama-mmap.cpp
+++ b/src/llama-mmap.cpp
@@ -35,7 +35,7 @@
 
 // TODO: consider moving to llama-impl.h if needed in more places
 #if defined(_WIN32)
-std::string llama_format_win_err(DWORD err) {
+static std::string llama_format_win_err(DWORD err) {
     LPSTR buf;
     size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
                                  NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 1c4e30878..53175f0e0 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -7,6 +7,10 @@
 #include <cstring>
 #include <future>
 
+static const size_t kiB = 1024;
+static const size_t MiB = 1024*kiB;
+static const size_t GiB = 1024*MiB;
+
 const char * llama_file_version_name(llama_fver version) {
     switch (version) {
         case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
@@ -17,6 +21,49 @@ const char * llama_file_version_name(llama_fver version) {
     return "unknown";
 }
 
+static std::string llama_model_ftype_name(llama_ftype ftype) {
+    if (ftype & LLAMA_FTYPE_GUESSED) {
+        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
+    }
+
+    switch (ftype) {
+        case LLAMA_FTYPE_ALL_F32:         return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
+        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
+        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
+        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
+        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
+        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
+        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
+        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
+
+        default: return "unknown, may not work";
+    }
+}
+
 namespace GGUFMeta {
     template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int64_t)>
     struct GKV_Base_Type {
@@ -1009,3 +1056,17 @@ bool llama_model_loader::load_all_data(
 
     return true;
 }
+
+std::string llama_model_loader::ftype_name() const {
+    return llama_model_ftype_name(ftype);
+}
+
+void llama_model_loader::print_info() const {
+    LLAMA_LOG_INFO("%s: file format = %s\n", __func__, llama_file_version_name(fver));
+    LLAMA_LOG_INFO("%s: file type   = %s\n", __func__, llama_model_ftype_name(ftype).c_str());
+    if (n_bytes < GiB) {
+        LLAMA_LOG_INFO("%s: file size   = %.2f MiB (%.2f BPW) \n", __func__, n_bytes/1024.0/1024.0,        n_bytes*8.0/n_elements);
+    } else {
+        LLAMA_LOG_INFO("%s: file size   = %.2f GiB (%.2f BPW) \n", __func__, n_bytes/1024.0/1024.0/1024.0, n_bytes*8.0/n_elements);
+    }
+}
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index 1ec478195..b63d158d9 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -155,4 +155,8 @@ struct llama_model_loader {
             llama_mlocks * lmlocks,
             llama_progress_callback progress_callback,
             void * progress_callback_user_data);
+
+    std::string ftype_name() const;
+
+    void print_info() const;
 };
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c056204b0..f90f5e746 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1,129 +1,85 @@
 #include "llama-model.h"
 
 #include "llama-impl.h"
+#include "llama-mmap.h"
 #include "llama-model-loader.h"
 
-#include "unicode.h" // TODO: remove
+#include "ggml-cpp.h"
 
 #include <algorithm>
 #include <cassert>
+#include <cstring>
 #include <functional>
+#include <map>
 #include <sstream>
 #include <stdexcept>
 
-static const size_t kiB = 1024;
-static const size_t MiB = 1024*kiB;
-static const size_t GiB = 1024*MiB;
-
 const char * llm_type_name(llm_type type) {
     switch (type) {
-        case MODEL_14M:           return "14M";
-        case MODEL_17M:           return "17M";
-        case MODEL_22M:           return "22M";
-        case MODEL_33M:           return "33M";
-        case MODEL_60M:           return "60M";
-        case MODEL_70M:           return "70M";
-        case MODEL_80M:           return "80M";
-        case MODEL_109M:          return "109M";
-        case MODEL_137M:          return "137M";
-        case MODEL_160M:          return "160M";
-        case MODEL_220M:          return "220M";
-        case MODEL_250M:          return "250M";
-        case MODEL_270M:          return "270M";
-        case MODEL_335M:          return "335M";
-        case MODEL_410M:          return "410M";
-        case MODEL_450M:          return "450M";
-        case MODEL_770M:          return "770M";
-        case MODEL_780M:          return "780M";
-        case MODEL_0_5B:          return "0.5B";
-        case MODEL_1B:            return "1B";
-        case MODEL_1_3B:          return "1.3B";
-        case MODEL_1_4B:          return "1.4B";
-        case MODEL_1_5B:          return "1.5B";
-        case MODEL_1_6B:          return "1.6B";
-        case MODEL_2B:            return "2B";
-        case MODEL_2_8B:          return "2.8B";
-        case MODEL_3B:            return "3B";
-        case MODEL_4B:            return "4B";
-        case MODEL_6B:            return "6B";
-        case MODEL_6_9B:          return "6.9B";
-        case MODEL_7B:            return "7B";
-        case MODEL_8B:            return "8B";
-        case MODEL_9B:            return "9B";
-        case MODEL_11B:           return "11B";
-        case MODEL_12B:           return "12B";
-        case MODEL_13B:           return "13B";
-        case MODEL_14B:           return "14B";
-        case MODEL_15B:           return "15B";
-        case MODEL_16B:           return "16B";
-        case MODEL_20B:           return "20B";
-        case MODEL_30B:           return "30B";
-        case MODEL_32B:           return "32B";
-        case MODEL_34B:           return "34B";
-        case MODEL_35B:           return "35B";
-        case MODEL_40B:           return "40B";
-        case MODEL_65B:           return "65B";
-        case MODEL_70B:           return "70B";
-        case MODEL_236B:          return "236B";
-        case MODEL_314B:          return "314B";
-        case MODEL_671B:          return "671B";
-        case MODEL_SMALL:         return "0.1B";
-        case MODEL_MEDIUM:        return "0.4B";
-        case MODEL_LARGE:         return "0.8B";
-        case MODEL_XL:            return "1.5B";
-        case MODEL_A1_7B:         return "A1.7B";
-        case MODEL_A2_7B:         return "A2.7B";
-        case MODEL_8x7B:          return "8x7B";
-        case MODEL_8x22B:         return "8x22B";
-        case MODEL_16x12B:        return "16x12B";
-        case MODEL_16x3_8B:       return "16x3.8B";
-        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
-        case MODEL_57B_A14B:      return "57B.A14B";
-        case MODEL_27B:           return "27B";
-        default:                  return "?B";
-    }
-}
-
-static std::string llama_model_ftype_name(llama_ftype ftype) {
-    if (ftype & LLAMA_FTYPE_GUESSED) {
-        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
-    }
-
-    switch (ftype) {
-        case LLAMA_FTYPE_ALL_F32:         return "all F32";
-        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
-        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
-        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
-        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
-        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
-        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
-        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
-
-        default: return "unknown, may not work";
+        case LLM_TYPE_14M:           return "14M";
+        case LLM_TYPE_17M:           return "17M";
+        case LLM_TYPE_22M:           return "22M";
+        case LLM_TYPE_33M:           return "33M";
+        case LLM_TYPE_60M:           return "60M";
+        case LLM_TYPE_70M:           return "70M";
+        case LLM_TYPE_80M:           return "80M";
+        case LLM_TYPE_109M:          return "109M";
+        case LLM_TYPE_137M:          return "137M";
+        case LLM_TYPE_160M:          return "160M";
+        case LLM_TYPE_220M:          return "220M";
+        case LLM_TYPE_250M:          return "250M";
+        case LLM_TYPE_270M:          return "270M";
+        case LLM_TYPE_335M:          return "335M";
+        case LLM_TYPE_410M:          return "410M";
+        case LLM_TYPE_450M:          return "450M";
+        case LLM_TYPE_770M:          return "770M";
+        case LLM_TYPE_780M:          return "780M";
+        case LLM_TYPE_0_5B:          return "0.5B";
+        case LLM_TYPE_1B:            return "1B";
+        case LLM_TYPE_1_3B:          return "1.3B";
+        case LLM_TYPE_1_4B:          return "1.4B";
+        case LLM_TYPE_1_5B:          return "1.5B";
+        case LLM_TYPE_1_6B:          return "1.6B";
+        case LLM_TYPE_2B:            return "2B";
+        case LLM_TYPE_2_8B:          return "2.8B";
+        case LLM_TYPE_3B:            return "3B";
+        case LLM_TYPE_4B:            return "4B";
+        case LLM_TYPE_6B:            return "6B";
+        case LLM_TYPE_6_9B:          return "6.9B";
+        case LLM_TYPE_7B:            return "7B";
+        case LLM_TYPE_8B:            return "8B";
+        case LLM_TYPE_9B:            return "9B";
+        case LLM_TYPE_11B:           return "11B";
+        case LLM_TYPE_12B:           return "12B";
+        case LLM_TYPE_13B:           return "13B";
+        case LLM_TYPE_14B:           return "14B";
+        case LLM_TYPE_15B:           return "15B";
+        case LLM_TYPE_16B:           return "16B";
+        case LLM_TYPE_20B:           return "20B";
+        case LLM_TYPE_30B:           return "30B";
+        case LLM_TYPE_32B:           return "32B";
+        case LLM_TYPE_34B:           return "34B";
+        case LLM_TYPE_35B:           return "35B";
+        case LLM_TYPE_40B:           return "40B";
+        case LLM_TYPE_65B:           return "65B";
+        case LLM_TYPE_70B:           return "70B";
+        case LLM_TYPE_236B:          return "236B";
+        case LLM_TYPE_314B:          return "314B";
+        case LLM_TYPE_671B:          return "671B";
+        case LLM_TYPE_SMALL:         return "0.1B";
+        case LLM_TYPE_MEDIUM:        return "0.4B";
+        case LLM_TYPE_LARGE:         return "0.8B";
+        case LLM_TYPE_XL:            return "1.5B";
+        case LLM_TYPE_A1_7B:         return "A1.7B";
+        case LLM_TYPE_A2_7B:         return "A2.7B";
+        case LLM_TYPE_8x7B:          return "8x7B";
+        case LLM_TYPE_8x22B:         return "8x22B";
+        case LLM_TYPE_16x12B:        return "16x12B";
+        case LLM_TYPE_16x3_8B:       return "16x3.8B";
+        case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
+        case LLM_TYPE_57B_A14B:      return "57B.A14B";
+        case LLM_TYPE_27B:           return "27B";
+        default:                     return "?B";
     }
 }
 
@@ -135,84 +91,6 @@ static const char * llama_expert_gating_func_name(llama_expert_gating_func_type
     }
 }
 
-std::string llama_model_arch_name (const llama_model & model) {
-    return llm_arch_name(model.arch);
-}
-
-std::string llama_model_type_name (const llama_model & model) {
-    return llm_type_name(model.type);
-}
-
-std::string llama_model_ftype_name(const llama_model & model) {
-    return llama_model_ftype_name(model.ftype);
-}
-
-template<typename F>
-static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*8,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context_ptr ctx { ggml_init(params) };
-    if (!ctx) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-
-    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
-    ggml_tensor * op_tensor = fn(ctx.get());
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op_tensor->src[i] != nullptr) {
-            assert(op_tensor->src[i]->buffer == nullptr);
-            op_tensor->src[i]->buffer = buf.get();
-        }
-    }
-
-    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
-
-    return op_supported;
-}
-
-template<typename F>
-static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
-    for (const auto & cur : buft_list) {
-        ggml_backend_dev_t cur_dev = cur.first;
-        ggml_backend_buffer_type_t cur_buft = cur.second;
-        if (buft_supported(cur_buft, cur_dev, fn)) {
-            return cur_buft;
-        }
-    }
-
-    throw std::runtime_error(format("no suitable buffer type found"));
-}
-
-ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il) {
-    return select_buft(
-            *model.dev_layer.at(il).buft_list,
-            [&](ggml_context * ctx) {
-                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
-                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
-                return ggml_add(ctx, cur, layer_dir);
-            });
-}
-
-struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name) {
-    auto it = std::find_if(model.tensors_by_name.begin(), model.tensors_by_name.end(),
-            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
-                return it.first == name;
-            });
-    if (it == model.tensors_by_name.end()) {
-        return nullptr;
-    }
-
-    return it->second;
-}
-
-size_t llama_model_max_nodes(const llama_model & model) {
-    return std::max<size_t>(8192, model.tensors_by_name.size()*5);
-}
-
 static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
     { LLAMA_ROPE_SCALING_TYPE_NONE,       "none"       },
     { LLAMA_ROPE_SCALING_TYPE_LINEAR,     "linear"     },
@@ -230,37 +108,284 @@ static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::st
     return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
 }
 
-// NOTE: avoid ever using this except for building the token_to_piece caches
-static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
-    std::string piece;
-    piece.resize(piece.capacity());  // using string internal cache
-    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
-    if (n_chars < 0) {
-        piece.resize(-n_chars);
-        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
-        GGML_ASSERT(check == -n_chars);
-    }
-    else {
-        piece.resize(n_chars);
+// checks if the weight tensor can be used with the specified buffer type and device
+static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) {
+    GGML_ASSERT(w != nullptr);
+
+    if (op == GGML_OP_NONE) {
+        return true;
     }
 
-    return piece;
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*8,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    if (!ctx_ptr) {
+        throw std::runtime_error(format("failed to create ggml context"));
+    }
+    ggml_context * ctx = ctx_ptr.get();
+
+    ggml_tensor * op_tensor = nullptr;
+
+    switch (op) {
+        case GGML_OP_GET_ROWS:
+            {
+                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
+                op_tensor = ggml_get_rows(ctx, w, b);
+            } break;
+        case GGML_OP_MUL_MAT:
+            {
+                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
+                op_tensor = ggml_mul_mat(ctx, w, b);
+            } break;
+        case GGML_OP_MUL_MAT_ID:
+            {
+                int n_expert_used = hparams.n_expert_used;
+                ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
+                ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
+                op_tensor = ggml_mul_mat_id(ctx, w, b, ids);
+            } break;
+        case GGML_OP_ADD:
+            {
+                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
+                op_tensor = ggml_add(ctx, a, w);
+            } break;
+        case GGML_OP_MUL:
+            {
+                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
+                op_tensor = ggml_mul(ctx, a, w);
+            } break;
+        case GGML_OP_DIV:
+            {
+                ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]);
+                op_tensor = ggml_div(ctx, a, w);
+            } break;
+        case GGML_OP_ROPE:
+            {
+                int n_embd_head = hparams.n_embd_head_v;
+                int n_head = hparams.n_head();
+                ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
+                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
+                op_tensor = ggml_rope_ext(
+                    ctx, a, b, w,
+                    0, 0, 0, 0, 0,
+                    0, 0, 0, 0
+                );
+
+            } break;
+        case GGML_OP_SSM_CONV:
+            {
+                // FIXME
+                ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
+                op_tensor = ggml_ssm_conv(ctx, conv_x, w);
+            } break;
+        case GGML_OP_SSM_SCAN:
+            {
+                // FIXME
+                const int64_t d_state      = w->ne[0];
+                const int64_t d_inner      = w->ne[1];
+                const int64_t n_seq_tokens = 512;
+                const int64_t n_seqs       = 1;
+                ggml_tensor * s  = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
+                ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
+                ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
+                ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
+                ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
+                op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
+            } break;
+        case GGML_OP_RWKV_WKV6:
+            {
+                // FIXME
+                const int64_t S = 123;
+                const int64_t H = 123;
+                const int64_t n_tokens = 123;
+                const int64_t n_seqs = 123;
+                ggml_tensor  * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * tf = w;
+                ggml_tensor  * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
+                ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
+                op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
+            } break;
+        case GGML_OP_IM2COL:
+            {
+                const int n_embd = hparams.n_embd;
+                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
+                op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
+            } break;
+        default:
+            GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
+    }
+
+    // create a temporary dummy buffer for the weight so that supports_op can check the buffer type
+    GGML_ASSERT(w->buffer == nullptr);
+    w->buffer = ggml_backend_buft_alloc_buffer(buft, 0);
+    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
+    ggml_backend_buffer_free(w->buffer);
+    w->buffer = nullptr;
+
+    return op_supported;
 }
 
-void llm_load_stats(llama_model_loader & ml, llama_model & model) {
-    model.n_elements = ml.n_elements;
-    model.n_bytes = ml.n_bytes;
+// lists of buffer types used for each layer
+using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
+
+// find the first buffer type in the list that can use the tensor
+static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hparams, ggml_tensor * tensor, ggml_op op, const buft_list_t & buft_list) {
+    GGML_ASSERT(!buft_list.empty());
+    for (const auto & cur : buft_list) {
+        ggml_backend_dev_t cur_dev = cur.first;
+        ggml_backend_buffer_type_t cur_buft = cur.second;
+        if (weight_buft_supported(hparams, tensor, op, cur_buft, cur_dev)) {
+            return cur_buft;
+        }
+    }
+    return nullptr;
 }
 
-void llm_load_arch(llama_model_loader & ml, llama_model & model) {
-    model.arch = ml.get_arch();
-    if (model.arch == LLM_ARCH_UNKNOWN) {
+// CPU: ACCEL -> CPU extra -> GPU host -> CPU
+static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices) {
+    buft_list_t buft_list;
+
+    // add ACCEL buffer types
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
+            auto * buft = ggml_backend_dev_buffer_type(dev);
+            // skip
+            if (buft != ggml_backend_cpu_buffer_type()) {
+                buft_list.emplace_back(dev, buft);
+            }
+        }
+    }
+
+    // add extra buffer types
+    auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
+    auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
+        ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
+    if (ggml_backend_dev_get_extra_bufts_fn) {
+        ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
+        while (extra_bufts && *extra_bufts) {
+            buft_list.emplace_back(cpu_dev, *extra_bufts);
+            ++extra_bufts;
+        }
+    }
+
+    // add a host buffer type
+    // storing the tensors in a host buffer is useful when the processing of large batches
+    // is offloaded to a GPU device, since it reduces the time spent on data transfers
+    // generally, this will be done using the first device in the list
+    // a better approach would be to handle this on a weight-by-weight basis using the offload_op
+    // function of the device to determine if it would benefit from being stored in a host buffer
+    for (auto * dev : devices) {
+        ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
+        if (buft) {
+            buft_list.emplace_back(dev, buft);
+            break;
+        }
+    }
+
+    // add the CPU buffer type
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+            buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
+        }
+    }
+
+    return buft_list;
+}
+
+// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
+static buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) {
+    buft_list_t buft_list;
+
+    // add the device split buffer type if requested and available
+    if (split_mode == LLAMA_SPLIT_MODE_ROW) {
+        ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+        auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
+            ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
+        if (ggml_backend_split_buffer_type_fn) {
+            size_t dev_index = [&]() {
+                auto * reg = ggml_backend_dev_backend_reg(dev);
+                for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
+                    if (ggml_backend_reg_dev_get(reg, i) == dev) {
+                        return i;
+                    }
+                }
+                throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
+            }();
+            auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
+            if (buft != nullptr) {
+                buft_list.emplace_back(dev, buft);
+            }
+        }
+    }
+
+    // add the device default buffer type
+    buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
+
+    return buft_list;
+}
+
+struct llama_model::impl {
+    impl() {}
+    ~impl() {}
+
+    uint64_t n_elements = 0;
+
+    size_t n_bytes = 0;
+
+    std::string desc_str;
+
+    // model memory mapped files
+    llama_mmaps mappings;
+
+    // objects representing data potentially being locked in memory
+    llama_mlocks mlock_bufs;
+    llama_mlocks mlock_mmaps;
+
+    // contexts where the model tensors metadata is stored
+    std::vector<ggml_context_ptr> ctxs;
+
+    // the model memory buffers for the tensor data
+    std::vector<ggml_backend_buffer_ptr> bufs;
+
+    buft_list_t cpu_buft_list;
+    std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
+
+    struct layer_dev {
+        ggml_backend_dev_t dev;
+        buft_list_t * buft_list;
+    };
+
+    layer_dev dev_input = {};
+    layer_dev dev_output = {};
+    std::vector<layer_dev> dev_layer;
+};
+
+llama_model::llama_model(const struct llama_model_params & params) : params(params), pimpl(std::make_unique<impl>()) {
+}
+
+llama_model::~llama_model() {}
+
+void llama_model::load_stats(llama_model_loader & ml) {
+    pimpl->n_elements = ml.n_elements;
+    pimpl->n_bytes = ml.n_bytes;
+}
+
+void llama_model::load_arch(llama_model_loader & ml) {
+    arch = ml.get_arch();
+    if (arch == LLM_ARCH_UNKNOWN) {
         throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
     }
 }
 
-void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
-    auto & hparams = model.hparams;
+void llama_model::load_hparams(llama_model_loader & ml) {
     const gguf_context * ctx = ml.meta.get();
 
     // get metadata as string
@@ -271,14 +396,11 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
         }
         const char * name = gguf_get_key(ctx, i);
         const std::string value = gguf_kv_to_str(ctx, i);
-        model.gguf_kv.emplace(name, value);
+        gguf_kv.emplace(name, value);
     }
 
     // get general kv
-    ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
-
-    // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false);
+    ml.get_key(LLM_KV_GENERAL_NAME, name, false);
 
     // everything past this point is not vocab-related
     if (hparams.vocab_only) {
@@ -291,7 +413,7 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
     ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
     ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
 
-    if (model.arch == LLM_ARCH_WAVTOKENIZER_DEC) {
+    if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
         ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
 
         ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
@@ -364,7 +486,7 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
-        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
+        if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON) {
             if (hparams.n_rot != hparams.n_embd_head_k) {
                 throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
             }
@@ -375,34 +497,36 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
         hparams.n_embd_head_v = 0;
     }
 
-    using e_model = llm_type; // TMP
+    // for differentiating model types
+    uint32_t n_vocab = 0;
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
 
     // arch-specific KVs
-    switch (model.arch) {
+    switch (arch) {
         case LLM_ARCH_LLAMA:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 if (hparams.n_expert == 8) {
                     switch (hparams.n_layer) {
-                        case 32: model.type = e_model::MODEL_8x7B; break;
-                        case 56: model.type = e_model::MODEL_8x22B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
+                        case 32: type = LLM_TYPE_8x7B; break;
+                        case 56: type = LLM_TYPE_8x22B; break;
+                        default: type = LLM_TYPE_UNKNOWN;
                     }
                 } else {
                     switch (hparams.n_layer) {
-                        case 16: model.type = e_model::MODEL_1B; break; // Llama 3.2 1B
-                        case 22: model.type = e_model::MODEL_1B; break;
-                        case 26: model.type = e_model::MODEL_3B; break;
-                        case 28: model.type = e_model::MODEL_3B; break; // Llama 3.2 3B
+                        case 16: type = LLM_TYPE_1B; break; // Llama 3.2 1B
+                        case 22: type = LLM_TYPE_1B; break;
+                        case 26: type = LLM_TYPE_3B; break;
+                        case 28: type = LLM_TYPE_3B; break; // Llama 3.2 3B
                         // granite uses a vocab with len 49152
-                        case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
-                        case 36: model.type = e_model::MODEL_8B; break; // granite
-                        case 40: model.type = e_model::MODEL_13B; break;
-                        case 48: model.type = e_model::MODEL_34B; break;
-                        case 60: model.type = e_model::MODEL_30B; break;
-                        case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
+                        case 32: type = n_vocab == 49152 ? LLM_TYPE_3B : (n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
+                        case 36: type = LLM_TYPE_8B; break; // granite
+                        case 40: type = LLM_TYPE_13B; break;
+                        case 48: type = LLM_TYPE_34B; break;
+                        case 60: type = LLM_TYPE_30B; break;
+                        case 80: type = hparams.n_head() == hparams.n_head_kv() ? LLM_TYPE_65B : LLM_TYPE_70B; break;
+                        default: type = LLM_TYPE_UNKNOWN;
                     }
                 }
             } break;
@@ -410,33 +534,33 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 80: type = LLM_TYPE_70B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_MINICPM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale);
+                ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale);
+                ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
 
                 switch (hparams.n_layer) {
-                    case 52: model.type = e_model::MODEL_1B; break;
-                    case 40: model.type = e_model::MODEL_2B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 52: type = LLM_TYPE_1B; break;
+                    case 40: type = LLM_TYPE_2B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_MINICPM3:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
 
                 switch (hparams.n_layer) {
-                    case 62: model.type = e_model::MODEL_4B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 62: type = LLM_TYPE_4B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_GROK:
@@ -444,8 +568,8 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 64: model.type = e_model::MODEL_314B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 64: type = LLM_TYPE_314B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_FALCON:
@@ -453,21 +577,21 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 60: model.type = e_model::MODEL_40B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 60: type = LLM_TYPE_40B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_BAICHUAN:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_13B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
 
-                if (model.type == e_model::MODEL_13B) {
+                if (type == LLM_TYPE_13B) {
                     // TODO: become GGUF KV parameter
                     hparams.f_max_alibi_bias = 8.0f;
                 }
@@ -476,19 +600,19 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 36: model.type = e_model::MODEL_3B; break;
-                    case 42: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_15B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = LLM_TYPE_1B; break;
+                    case 36: type = LLM_TYPE_3B; break;
+                    case 42: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_15B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_REFACT:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_1B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_1B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
 
                 // TODO: become GGUF KV parameter
@@ -498,48 +622,45 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
 
                 switch (hparams.n_layer) {
                     case 3:
-                        model.type = e_model::MODEL_17M; break; // bge-micro
+                        type = LLM_TYPE_17M; break; // bge-micro
                     case 6:
-                        model.type = e_model::MODEL_22M; break; // MiniLM-L6
+                        type = LLM_TYPE_22M; break; // MiniLM-L6
                     case 12:
                         switch (hparams.n_embd) {
-                            case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
-                            case 768: model.type = e_model::MODEL_109M; break; // bge-base
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 384: type = LLM_TYPE_33M; break; // MiniLM-L12, bge-small
+                            case 768: type = LLM_TYPE_109M; break; // bge-base
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 24:
-                        model.type = e_model::MODEL_335M; break; // bge-large
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                        type = LLM_TYPE_335M; break; // bge-large
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_JINA_BERT_V2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
                 hparams.f_max_alibi_bias = 8.0f;
 
                 switch (hparams.n_layer) {
-                    case 4:  model.type = e_model::MODEL_33M;  break; // jina-embeddings-small
-                    case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 4:  type = LLM_TYPE_33M;  break; // jina-embeddings-small
+                    case 12: type = LLM_TYPE_137M; break; // jina-embeddings-base
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_NOMIC_BERT:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
-                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
 
                 if (hparams.n_layer == 12 && hparams.n_embd == 768) {
-                    model.type = e_model::MODEL_137M;
+                    type = LLM_TYPE_137M;
                 }
             } break;
         case LLM_ARCH_BLOOM:
@@ -547,14 +668,14 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 24: type = LLM_TYPE_1B; break;
                     case 30:
                         switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            case 4096: model.type = e_model::MODEL_7B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 2560: type = LLM_TYPE_3B; break;
+                            case 4096: type = LLM_TYPE_7B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
 
                 // TODO: become GGUF KV parameter
@@ -567,9 +688,9 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_30B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 48: type = LLM_TYPE_30B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_STABLELM:
@@ -577,10 +698,10 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_12B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_3B; break;
+                    case 40: type = LLM_TYPE_12B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_QWEN:
@@ -588,9 +709,9 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_13B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_QWEN2VL:
@@ -602,27 +723,27 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
-                    case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 36: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
-                    case 48: model.type = e_model::MODEL_14B; break;
-                    case 64: model.type = e_model::MODEL_32B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
+                    case 28: type = hparams.n_embd == 1536 ? LLM_TYPE_1_5B : LLM_TYPE_7B; break;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 36: type = LLM_TYPE_3B; break;
+                    case 40: type = hparams.n_head() == 20 ? LLM_TYPE_4B : LLM_TYPE_13B; break;
+                    case 48: type = LLM_TYPE_14B; break;
+                    case 64: type = LLM_TYPE_32B; break;
+                    case 80: type = LLM_TYPE_70B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_QWEN2MOE:
             {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
                 ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
 
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_A2_7B; break;
-                    case 28: model.type = e_model::MODEL_57B_A14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = LLM_TYPE_A2_7B; break;
+                    case 28: type = LLM_TYPE_57B_A14B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_PHI2:
@@ -630,9 +751,9 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_PHI3:
@@ -640,10 +761,10 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 24: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_3B; break;
+                    case 40: type = LLM_TYPE_14B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
 
                 // for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
@@ -667,8 +788,8 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_16x3_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_16x3_8B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_PLAMO:
@@ -676,27 +797,27 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 40: type = LLM_TYPE_13B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_GPT2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 12: model.type = e_model::MODEL_SMALL; break;
-                    case 24: model.type = e_model::MODEL_MEDIUM; break;
-                    case 36: model.type = e_model::MODEL_LARGE; break;
-                    case 48: model.type = e_model::MODEL_XL; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 12: type = LLM_TYPE_SMALL; break;
+                    case 24: type = LLM_TYPE_MEDIUM; break;
+                    case 36: type = LLM_TYPE_LARGE; break;
+                    case 48: type = LLM_TYPE_XL; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_CODESHELL:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 42: model.type = e_model::MODEL_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 42: type = LLM_TYPE_7B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_ORION:
@@ -704,17 +825,17 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_14B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 40: type = LLM_TYPE_14B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_INTERNLM2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_20B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 48: type = LLM_TYPE_20B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_GEMMA:
@@ -722,37 +843,37 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 18: model.type = e_model::MODEL_2B; break;
-                    case 28: model.type = e_model::MODEL_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 18: type = LLM_TYPE_2B; break;
+                    case 28: type = LLM_TYPE_7B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_GEMMA2:
             {
                 hparams.n_swa = 4096; // default value of gemma 2
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
+                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,      hparams.f_attn_logit_softcapping, false);
+                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
                 hparams.attn_soft_cap = true;
 
                 switch (hparams.n_layer) {
-                    case 26: model.type = e_model::MODEL_2B; break;
-                    case 42: model.type = e_model::MODEL_9B; break;
-                    case 46: model.type = e_model::MODEL_27B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 26: type = LLM_TYPE_2B; break;
+                    case 42: type = LLM_TYPE_9B; break;
+                    case 46: type = LLM_TYPE_27B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_STARCODER2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 30: model.type = e_model::MODEL_3B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_15B; break;
-                    case 52: model.type = e_model::MODEL_20B; break; // granite
-                    case 88: model.type = e_model::MODEL_34B; break; // granite
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 30: type = LLM_TYPE_3B; break;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_15B; break;
+                    case 52: type = LLM_TYPE_20B; break; // granite
+                    case 88: type = LLM_TYPE_34B; break; // granite
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_MAMBA:
@@ -768,51 +889,51 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 switch (hparams.n_layer) {
                     case 24:
                         switch (hparams.n_embd) {
-                            case 768: model.type = e_model::MODEL_SMALL; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 768: type = LLM_TYPE_SMALL; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 48:
                         switch (hparams.n_embd) {
-                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
-                            case 1536: model.type = e_model::MODEL_LARGE; break;
-                            case 2048: model.type = e_model::MODEL_XL; break;
-                            default:   model.type = e_model::MODEL_UNKNOWN;
+                            case 1024: type = LLM_TYPE_MEDIUM; break;
+                            case 1536: type = LLM_TYPE_LARGE; break;
+                            case 2048: type = LLM_TYPE_XL; break;
+                            default:   type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 64:
                         switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 2560: type = LLM_TYPE_3B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_XVERSE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    case 80: model.type = e_model::MODEL_65B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_13B; break;
+                    case 80: type = LLM_TYPE_65B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_COMMAND_R:
             {
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_LOGIT_SCALE,             hparams.f_logit_scale);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 40: model.type = e_model::MODEL_35B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 40: type = LLM_TYPE_35B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_COHERE2:
             {
                 ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_LOGIT_SCALE,              hparams.f_logit_scale);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_8B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_DBRX:
@@ -821,8 +942,8 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
             ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv);
 
             switch (hparams.n_layer) {
-                case 40: model.type = e_model::MODEL_16x12B; break;
-                default: model.type = e_model::MODEL_UNKNOWN;
+                case 40: type = LLM_TYPE_16x12B; break;
+                default: type = LLM_TYPE_UNKNOWN;
             }
         } break;
         case LLM_ARCH_OLMO:
@@ -831,10 +952,10 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
 
                 switch (hparams.n_layer) {
-                    case 22: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 80: model.type = e_model::MODEL_70B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 22: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 80: type = LLM_TYPE_70B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_OLMO2:
@@ -842,18 +963,18 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 16: model.type = e_model::MODEL_1B; break;
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 16: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 40: type = LLM_TYPE_13B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_OLMOE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 16: model.type = e_model::MODEL_A1_7B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 16: type = LLM_TYPE_A1_7B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_OPENELM:
@@ -861,57 +982,57 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                case 16: model.type = e_model::MODEL_270M; break;
-                case 20: model.type = e_model::MODEL_450M; break;
-                case 28: model.type = e_model::MODEL_1B; break;
-                case 36: model.type = e_model::MODEL_3B; break;
-                default: model.type = e_model::MODEL_UNKNOWN;
+                case 16: type = LLM_TYPE_270M; break;
+                case 20: type = LLM_TYPE_450M; break;
+                case 28: type = LLM_TYPE_1B; break;
+                case 36: type = LLM_TYPE_3B; break;
+                default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_GPTNEOX:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL,   hparams.use_par_res);
                 switch (hparams.n_layer) {
                     case 6:
                         switch (hparams.n_ff()) {
-                            case 512: model.type = e_model::MODEL_14M; break;
-                            case 2048: model.type = e_model::MODEL_70M; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 512:  type = LLM_TYPE_14M; break;
+                            case 2048: type = LLM_TYPE_70M; break;
+                            default:   type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 12:
                         switch (hparams.n_ff()) {
-                            case 3072: model.type = e_model::MODEL_160M; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 3072: type = LLM_TYPE_160M; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 16:
                         switch (hparams.n_ff()) {
-                            case 8192: model.type = e_model::MODEL_1B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 8192: type = LLM_TYPE_1B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 24:
                         switch (hparams.n_ff()) {
-                            case 4096: model.type = e_model::MODEL_410M; break;
-                            case 8192: model.type = e_model::MODEL_1_4B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 4096: type = LLM_TYPE_410M; break;
+                            case 8192: type = LLM_TYPE_1_4B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 32:
                         switch (hparams.n_ff()) {
-                            case 10240: model.type = e_model::MODEL_2_8B; break;
-                            case 16384: model.type = e_model::MODEL_6_9B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 10240: type = LLM_TYPE_2_8B; break;
+                            case 16384: type = LLM_TYPE_6_9B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 36:
                         switch (hparams.n_ff()) {
-                            case 20480: model.type = e_model::MODEL_12B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 20480: type = LLM_TYPE_12B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 44:
                         switch (hparams.n_ff()) {
-                            case 24576: model.type = e_model::MODEL_20B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 24576: type = LLM_TYPE_20B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_ARCTIC:
@@ -920,40 +1041,40 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
 
                 if (hparams.n_expert == 128) {
                     switch (hparams.n_layer) {
-                        case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
-                        default: model.type = e_model::MODEL_UNKNOWN;
+                        case 35: type = LLM_TYPE_10B_128x3_66B; break;
+                        default: type = LLM_TYPE_UNKNOWN;
                     }
                 } else {
-                    model.type = e_model::MODEL_UNKNOWN;
+                    type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_DEEPSEEK:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale);
 
                 switch (hparams.n_layer) {
-                    case 28: model.type = e_model::MODEL_20B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 28: type = LLM_TYPE_20B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_DEEPSEEK2:
             {
                 bool is_lite = (hparams.n_layer == 27);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
                 if (!is_lite) {
                     ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
                 }
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
                 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,       hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,        hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,         hparams.expert_gating_func, false);
                 if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
                     // for compatibility with existing DeepSeek V2 and V2.5 GGUFs
                     // that have no expert_gating_func model parameter set
@@ -962,19 +1083,19 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
 
                 switch (hparams.n_layer) {
-                    case 27: model.type = e_model::MODEL_16B; break;
-                    case 60: model.type = e_model::MODEL_236B; break;
-                    case 61: model.type = e_model::MODEL_671B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 27: type = LLM_TYPE_16B; break;
+                    case 60: type = LLM_TYPE_236B; break;
+                    case 61: type = LLM_TYPE_671B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_CHATGLM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 28: model.type = e_model::MODEL_6B; break;
-                    case 40: model.type = e_model::MODEL_9B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 28: type = LLM_TYPE_6B; break;
+                    case 40: type = LLM_TYPE_9B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_BITNET:
@@ -982,13 +1103,13 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 26: model.type = e_model::MODEL_3B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 26: type = LLM_TYPE_3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_T5:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      hparams.f_norm_rms_eps);
                 ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
 
                 uint32_t dec_start_token_id;
@@ -997,32 +1118,32 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 }
 
                 switch (hparams.n_layer) {
-                    case 6:  model.type = e_model::MODEL_60M;  break; // t5-small
-                    case 8:  model.type = e_model::MODEL_80M;  break; // flan-t5-small
+                    case 6:  type = LLM_TYPE_60M;  break; // t5-small
+                    case 8:  type = LLM_TYPE_80M;  break; // flan-t5-small
                     case 12:
                         switch (hparams.n_ff()) {
-                            case 3072: model.type = e_model::MODEL_220M; break; // t5-base
-                            case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 3072: type = LLM_TYPE_220M; break; // t5-base
+                            case 2048: type = LLM_TYPE_250M; break; // flan-t5-base
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
                     case 24:
                         switch (hparams.n_ff()) {
-                            case 4096:  model.type = e_model::MODEL_770M; break; // t5-large
-                            case 2816:  model.type = e_model::MODEL_780M; break; // flan-t5-large
-                            case 16384: model.type = e_model::MODEL_3B;   break; // t5-3b
-                            case 5120:  model.type = e_model::MODEL_3B;   break; // flan-t5-xl
-                            case 65536: model.type = e_model::MODEL_11B;  break; // t5-11b
-                            case 10240: model.type = e_model::MODEL_11B;  break; // flan-t5-xxl
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 4096:  type = LLM_TYPE_770M; break; // t5-large
+                            case 2816:  type = LLM_TYPE_780M; break; // flan-t5-large
+                            case 16384: type = LLM_TYPE_3B;   break; // t5-3b
+                            case 5120:  type = LLM_TYPE_3B;   break; // flan-t5-xl
+                            case 65536: type = LLM_TYPE_11B;  break; // t5-11b
+                            case 10240: type = LLM_TYPE_11B;  break; // flan-t5-xxl
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_T5ENCODER:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
-                model.type = e_model::MODEL_UNKNOWN;
+                type = LLM_TYPE_UNKNOWN;
             } break;
         case LLM_ARCH_JAIS:
             {
@@ -1030,18 +1151,18 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1_3B; break;
-                    case 40: model.type = e_model::MODEL_13B; break;
+                    case 24: type = LLM_TYPE_1_3B; break;
+                    case 40: type = LLM_TYPE_13B; break;
                     /* TODO: add variants */
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_NEMOTRON:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_4B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_4B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_EXAONE:
@@ -1049,48 +1170,48 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_8B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_RWKV6:
         case LLM_ARCH_RWKV6QWEN2:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps, false);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
-                ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
-                ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
-                ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
-                ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
-                ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count, false);
+                ml.get_key(LLM_KV_WKV_HEAD_SIZE,               hparams.wkv_head_size);
+                ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM,          hparams.time_mix_extra_dim);
+                ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM,        hparams.time_decay_extra_dim);
+                ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS,      hparams.rescale_every_n_layers, false);
+                ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,           hparams.token_shift_count, false);
 
                 switch (hparams.n_layer) {
-                    case 24: model.type = e_model::MODEL_1_6B; break;
+                    case 24: type = LLM_TYPE_1_6B; break;
                     case 32:
                         switch (hparams.n_embd) {
-                            case 2560: model.type = e_model::MODEL_3B; break;
-                            case 4096: model.type = e_model::MODEL_7B; break;
-                            default: model.type = e_model::MODEL_UNKNOWN;
+                            case 2560: type = LLM_TYPE_3B; break;
+                            case 4096: type = LLM_TYPE_7B; break;
+                            default: type = LLM_TYPE_UNKNOWN;
                         } break;
-                    case 61: model.type = e_model::MODEL_14B; break;
-                    case 64: model.type = e_model::MODEL_32B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 61: type = LLM_TYPE_14B; break;
+                    case 64: type = LLM_TYPE_32B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_GRANITE:
         case LLM_ARCH_GRANITE_MOE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
-                ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
+                ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+                ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale);
+                ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale);
+                ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_3B; break;
-                    case 40: model.type = e_model::MODEL_3B; break;
+                    case 32: type = LLM_TYPE_3B; break;
+                    case 40: type = LLM_TYPE_3B; break;
                     // Add additional layer/vocab/etc checks here for other model sizes
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_CHAMELEON:
@@ -1100,9 +1221,9 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                 ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
 
                 switch (hparams.n_layer) {
-                    case 32: model.type = e_model::MODEL_7B; break;
-                    case 48: model.type = e_model::MODEL_34B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
+                    case 32: type = LLM_TYPE_7B; break;
+                    case 48: type = LLM_TYPE_34B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
         case LLM_ARCH_WAVTOKENIZER_DEC:
@@ -1115,732 +1236,2288 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
         default: throw std::runtime_error("unsupported model architecture");
     }
 
-    model.ftype = ml.ftype;
+    pimpl->n_bytes = ml.n_bytes;
+
+    pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
 
     if (hparams.f_max_alibi_bias > 0.0f) {
         hparams.use_alibi = true;
     }
 
-    hparams.rope_type = llama_rope_type(&model);
+    hparams.rope_type = llama_model_rope_type(this);
 }
 
-void llm_load_vocab(llama_model_loader & ml, llama_model & model) {
-    auto & vocab = model.vocab;
+void llama_model::load_vocab(llama_model_loader & ml) {
+    const auto kv = LLM_KV(arch);
 
-    struct gguf_context * ctx = ml.meta.get();
+    vocab.load(ml, kv);
+}
 
-    const auto kv = LLM_KV(model.arch);
+bool llama_model::load_tensors(llama_model_loader & ml) {
+    const auto & split_mode   = params.split_mode;
+    const auto & n_gpu_layers = params.n_gpu_layers;
+    const auto & use_mlock    = params.use_mlock;
+    const auto & tensor_split = params.tensor_split;
 
-    // determine vocab type
-    {
-        std::string tokenizer_model;
-        std::string tokenizer_pre;
+    const int n_layer = hparams.n_layer;
 
-        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
-        ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
+    const bool use_mmap_buffer = true;
 
-        if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
-            vocab.type = LLAMA_VOCAB_TYPE_NONE;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-            vocab.linefeed_id     = LLAMA_TOKEN_NULL;
-
-            // read vocab size from metadata
-            if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) {
-                vocab.n_vocab = 0;
-                LLAMA_LOG_WARN("%s: there is no vocab_size in metadata, vocab.n_vocab will be set to %u\n", __func__, vocab.n_vocab);
-            }
-            return;
-        }
-
-        if (tokenizer_model == "llama") {
-            vocab.type = LLAMA_VOCAB_TYPE_SPM;
-
-            // default special tokens
-            vocab.special_bos_id  = 1;
-            vocab.special_eos_id  = 2;
-            vocab.special_unk_id  = 0;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-        } else if (tokenizer_model == "bert") {
-            vocab.type = LLAMA_VOCAB_TYPE_WPM;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id  = 100;
-            vocab.special_sep_id  = 102;
-            vocab.special_pad_id  = 0;
-            vocab.special_cls_id  = 101;
-            vocab.special_mask_id = 103;
-        } else if (tokenizer_model == "gpt2") {
-            vocab.type = LLAMA_VOCAB_TYPE_BPE;
-
-            // read bpe merges and populate bpe ranks
-            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
-            if (merges_keyidx == -1) {
-                throw std::runtime_error("cannot find tokenizer merges in model file\n");
-            }
-
-            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
-            for (int i = 0; i < n_merges; i++) {
-                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
-                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
-
-                std::string first;
-                std::string second;
-
-                const size_t pos = word.find(' ', 1);
-
-                if (pos != std::string::npos) {
-                    first  = word.substr(0, pos);
-                    second = word.substr(pos + 1);
-                }
-
-                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
-            }
-
-            // default special tokens
-            vocab.special_bos_id  = 11;
-            vocab.special_eos_id  = 11;
-            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-        } else if (tokenizer_model == "t5") {
-            vocab.type = LLAMA_VOCAB_TYPE_UGM;
-
-            // default special tokens
-            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id  = 1;
-            vocab.special_unk_id  = 2;
-            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id  = 0;
-            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
-            vocab.special_mask_id = LLAMA_TOKEN_NULL;
-
-            const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
-            if (precompiled_charsmap_keyidx != -1) {
-                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
-                const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
-                vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
-#ifdef IS_BIG_ENDIAN
-                // correct endiannes of data in precompiled_charsmap binary blob
-                uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
-                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
-                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
-                size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
-                uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
-                for (size_t i = 0; i < xcda_array_size; ++i) {
-                    xcda_array[i] = __builtin_bswap32(xcda_array[i]);
-                }
-#endif
-            }
-        } else if (tokenizer_model == "rwkv") {
-            vocab.type = LLAMA_VOCAB_TYPE_RWKV;
-
-            // default special tokens
-            vocab.special_bos_id = LLAMA_TOKEN_NULL;
-            vocab.special_eos_id = LLAMA_TOKEN_NULL;
-            vocab.special_unk_id = LLAMA_TOKEN_NULL;
-            vocab.special_sep_id = LLAMA_TOKEN_NULL;
-            vocab.special_pad_id = LLAMA_TOKEN_NULL;
-        } else {
-            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
-        }
-
-        // for now, only BPE models have pre-tokenizers
-        if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = true;
-            if (tokenizer_pre.empty()) {
-                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
-                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (tokenizer_pre == "default") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (
-                    tokenizer_pre == "llama3"   ||
-                    tokenizer_pre == "llama-v3" ||
-                    tokenizer_pre == "llama-bpe"||
-                    tokenizer_pre == "falcon3") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
-            } else if (
-                    tokenizer_pre == "deepseek-llm") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                    tokenizer_pre == "deepseek-coder") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                    tokenizer_pre == "deepseek-v3") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                    tokenizer_pre == "falcon") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
-            } else if (
-                    tokenizer_pre == "mpt") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
-            } else if (
-                    tokenizer_pre == "starcoder") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
-            } else if (
-                    tokenizer_pre == "gpt-2"   ||
-                    tokenizer_pre == "phi-2"   ||
-                    tokenizer_pre == "jina-es" ||
-                    tokenizer_pre == "jina-de" ||
-                    tokenizer_pre == "gigachat"   ||
-                    tokenizer_pre == "jina-v1-en" ||
-                    tokenizer_pre == "jina-v2-es" ||
-                    tokenizer_pre == "jina-v2-de" ||
-                    tokenizer_pre == "jina-v2-code" ||
-                    tokenizer_pre == "roberta-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
-            } else if (
-                    tokenizer_pre == "refact") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
-            } else if (
-                tokenizer_pre == "command-r") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "qwen2") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "stablelm2") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
-            } else if (
-                tokenizer_pre == "olmo") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
-            } else if (
-                tokenizer_pre == "dbrx") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
-            } else if (
-                tokenizer_pre == "smaug-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
-            } else if (
-                tokenizer_pre == "poro-chat") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "chatglm-bpe") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
-                vocab.special_bos_id = LLAMA_TOKEN_NULL;
-            } else if (
-                tokenizer_pre == "viking") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "jais") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
-            } else if (
-                tokenizer_pre == "tekken") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
-                vocab.tokenizer_clean_spaces = false;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
-            } else if (
-                tokenizer_pre == "smollm") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "codeshell") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
-            } else if (
-                tokenizer_pre == "bloom") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_BLOOM;
-            } else if (
-                tokenizer_pre == "gpt3-finnish") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
-            } else if (
-                tokenizer_pre == "exaone") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
-            } else if (
-                tokenizer_pre == "chameleon") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
-                vocab.tokenizer_add_bos = true;
-                vocab.tokenizer_clean_spaces = false;
-            } else if (
-                tokenizer_pre == "minerva-7b") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA;
-            } else if (
-                tokenizer_pre == "megrez") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
-            } else {
-                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
-            }
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = true;
-            vocab.tokenizer_clean_spaces = false;
-            vocab.tokenizer_add_bos = true;
-            vocab.tokenizer_add_eos = false;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = true;
-            vocab.tokenizer_add_bos = true;
-            vocab.tokenizer_add_eos = false;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_bos = false;
-            vocab.tokenizer_add_eos = true;
-        } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            vocab.tokenizer_add_space_prefix = false;
-            vocab.tokenizer_clean_spaces = false;
-            vocab.tokenizer_add_bos = false;
-            vocab.tokenizer_add_eos = false;
-        } else {
-            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-        }
-
-        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      vocab.tokenizer_add_space_prefix,         false);
-        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
+    // build a list of buffer types for the CPU and GPU devices
+    pimpl->cpu_buft_list = make_cpu_buft_list(devices);
+    for (auto * dev : devices) {
+        buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
+        // add CPU buffer types as a fallback
+        buft_list.insert(buft_list.end(), pimpl->cpu_buft_list.begin(), pimpl->cpu_buft_list.end());
+        pimpl->gpu_buft_list.emplace(dev, std::move(buft_list));
     }
 
-    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
-    if (token_idx == -1) {
-        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
-    }
-
-    const float * scores = nullptr;
-    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
-    if (score_idx != -1) {
-        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
-    }
-
-    const int * toktypes = nullptr;
-    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
-    if (toktype_idx != -1) {
-        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
-    }
-
-    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
-
-    vocab.n_vocab = n_vocab;
-    vocab.id_to_token.resize(n_vocab);
-
-    for (uint32_t i = 0; i < n_vocab; i++) {
-        std::string word = gguf_get_arr_str(ctx, token_idx, i);
-        if (word.empty()) {
-            LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
-            word = "[EMPTY_" + std::to_string(i) + "]";
+    // calculate the split points
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + n_devices(), [](float x) { return x == 0.0f; });
+    std::vector<float> splits(n_devices());
+    if (all_zero) {
+        // default split, by free memory
+        for (size_t i = 0; i < n_devices(); ++i) {
+            ggml_backend_dev_t dev = devices[i];
+            size_t total;
+            size_t free;
+            ggml_backend_dev_memory(dev, &free, &total);
+            splits[i] = free;
         }
-
-        vocab.token_to_id[word] = i;
-        vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
-
-        auto & token_data = vocab.id_to_token[i];
-        token_data.text  = std::move(word);
-        token_data.score = scores ? scores[i] : 0.0f;
-        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
-
-        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
-            switch(toktypes[i]) {
-                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
-                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
-                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
-                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
-                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
-                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
-                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
-                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
-            }
-        }
-    }
-    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
-
-    vocab.init_tokenizer();
-
-    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
-    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-        try {
-            vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
-        } catch (const std::exception & e) {
-            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
-            vocab.linefeed_id = vocab.special_pad_id;
-        }
-    } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
-        vocab.linefeed_id = vocab.special_pad_id;
-    } else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\n", false);
-        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
-        vocab.linefeed_id = ids[0];
     } else {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
-
-        //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
-        if (ids.empty()) {
-            LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
-            vocab.linefeed_id = vocab.special_pad_id;
-        } else {
-            vocab.linefeed_id = ids[0];
-        }
+        std::copy(tensor_split, tensor_split + n_devices(), splits.begin());
     }
 
-    // special tokens
-    {
-        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
-            { LLM_KV_TOKENIZER_BOS_ID,     vocab.special_bos_id     },
-            { LLM_KV_TOKENIZER_EOS_ID,     vocab.special_eos_id     },
-            { LLM_KV_TOKENIZER_EOT_ID,     vocab.special_eot_id     },
-            { LLM_KV_TOKENIZER_EOM_ID,     vocab.special_eom_id     },
-            { LLM_KV_TOKENIZER_UNK_ID,     vocab.special_unk_id     },
-            { LLM_KV_TOKENIZER_SEP_ID,     vocab.special_sep_id     },
-            { LLM_KV_TOKENIZER_PAD_ID,     vocab.special_pad_id     },
-            { LLM_KV_TOKENIZER_CLS_ID,     vocab.special_cls_id     },
-            { LLM_KV_TOKENIZER_MASK_ID,    vocab.special_mask_id    },
-            { LLM_KV_TOKENIZER_FIM_PRE_ID, vocab.special_fim_pre_id },
-            { LLM_KV_TOKENIZER_FIM_SUF_ID, vocab.special_fim_suf_id },
-            { LLM_KV_TOKENIZER_FIM_MID_ID, vocab.special_fim_mid_id },
-            { LLM_KV_TOKENIZER_FIM_PAD_ID, vocab.special_fim_pad_id },
-            { LLM_KV_TOKENIZER_FIM_REP_ID, vocab.special_fim_rep_id },
-            { LLM_KV_TOKENIZER_FIM_SEP_ID, vocab.special_fim_sep_id },
+    // sum and normalize the splits to get the split points
+    float split_sum = 0.0f;
+    for (size_t i = 0; i < n_devices(); ++i) {
+        split_sum += splits[i];
+        splits[i] = split_sum;
+    }
+    for (size_t i = 0; i < n_devices(); ++i) {
+        splits[i] /= split_sum;
+    }
 
-            // deprecated
-            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_fim_pre_id },
-            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_fim_suf_id },
-            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_fim_mid_id },
+    ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
+    const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
+    auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
+        if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
+            return {cpu_dev, &pimpl->cpu_buft_list};
+        }
+        const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
+        auto * dev = devices.at(layer_gpu);
+        return {dev, &pimpl->gpu_buft_list.at(dev)};
+    };
+
+    // assign the input layer
+    // there is very little benefit to offloading the input layer, so always keep it on the CPU
+    pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
+
+    // assign the repeating layers to the devices according to the splits
+    pimpl->dev_layer.resize(n_layer);
+    for (int il = 0; il < n_layer; ++il) {
+        pimpl->dev_layer[il] = get_layer_buft_list(il);
+    }
+
+    // assign the output layer
+    pimpl->dev_output = get_layer_buft_list(n_layer);
+
+    // one ggml context per buffer type
+    int max_n_tensors = ml.n_tensors;
+    max_n_tensors += 1;         // duplicated output tensor
+    max_n_tensors += n_layer*2; // duplicated rope freq tensors
+    const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors;
+
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            ggml_init_params params = {
+                /*.mem_size   =*/ ctx_size,
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+
+            ggml_context * ctx = ggml_init(params);
+            if (!ctx) {
+                throw std::runtime_error(format("failed to create ggml context"));
+            }
+
+            ctx_map[buft] = ctx;
+            pimpl->ctxs.emplace_back(ctx);
+
+            return ctx;
+        }
+        return it->second;
+    };
+
+    const auto TENSOR_DUPLICATED   = llama_model_loader::TENSOR_DUPLICATED;
+    const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
+
+    // create tensors for the weights
+    {
+        // note: cast to int64_t since we will use these for the tensor dimensions
+        const int64_t n_head        = hparams.n_head();
+        const int64_t n_head_kv     = hparams.n_head_kv();
+        const int64_t n_embd        = hparams.n_embd;
+        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
+        const int64_t n_embd_head_v = hparams.n_embd_head_v;
+        const int64_t n_ff          = hparams.n_ff();
+        const int64_t n_embd_gqa    = n_embd_v_gqa;
+        const int64_t n_vocab       = vocab.n_tokens();
+        const int64_t n_token_types = vocab.n_token_types();
+        const int64_t n_rot         = hparams.n_rot;
+        const int64_t n_expert      = hparams.n_expert;
+        const int64_t n_expert_used = hparams.n_expert_used;
+        const int64_t n_ctx_train   = hparams.n_ctx_train;
+
+        if (n_expert > 0 && hparams.n_expert_used == 0) {
+            throw std::runtime_error("model has expert layers but no expert layers are used");
+        }
+
+        int n_moved_tensors = 0;
+        ggml_tensor * first_moved_tensor = nullptr;
+        ggml_backend_buffer_type_t first_moved_from_buft = nullptr;
+        ggml_backend_buffer_type_t first_moved_to_buft = nullptr;
+
+        auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
+            ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str());
+
+            if (!t_meta) {
+                if (flags & TENSOR_NOT_REQUIRED) {
+                    return nullptr;
+                }
+                throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str()));
+            }
+
+            // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops
+            // the tensor is duplicated
+            // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor
+            llm_tensor tn_tensor = tn.tensor;
+            if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & TENSOR_DUPLICATED) {
+                tn_tensor = LLM_TENSOR_OUTPUT;
+            }
+
+            llm_tensor_info info;
+            try {
+                info = llm_tensor_info_for(tn_tensor);
+            } catch (const std::out_of_range & e) {
+                throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str()));
+            }
+
+            // tensors with "bias" suffix are always used with GGML_OP_ADD
+            ggml_op op;
+            bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
+            if (bias) {
+                op = GGML_OP_ADD;
+            } else {
+                op = info.op;
+            }
+
+            // sanity checks
+            if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) {
+                if (tn.bid != -1) {
+                    GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str());
+                }
+            } else {
+                if (tn.bid == -1) {
+                    GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str());
+                }
+            }
+
+            // select the buffer type for this tensor
+            buft_list_t * buft_list;
+            switch (info.layer) {
+                case LLM_TENSOR_LAYER_INPUT:
+                    buft_list = pimpl->dev_input.buft_list;
+                    break;
+                case LLM_TENSOR_LAYER_OUTPUT:
+                    buft_list = pimpl->dev_output.buft_list;
+                    break;
+                case LLM_TENSOR_LAYER_REPEATING:
+                    buft_list = pimpl->dev_layer.at(tn.bid).buft_list;
+                    break;
+                default:
+                    GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str());
+            }
+
+            ggml_backend_buffer_type_t buft = select_weight_buft(hparams, t_meta, op, *buft_list);
+            if (!buft) {
+                throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str()));
+            }
+
+            // avoid using a host buffer when using mmap
+            auto * buft_dev = ggml_backend_buft_get_device(buft);
+            if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) {
+                auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+                buft = ggml_backend_dev_buffer_type(cpu_dev);
+            }
+
+            if (buft != buft_list->front().second) {
+                n_moved_tensors++;
+                if (!first_moved_tensor) {
+                    first_moved_tensor = t_meta;
+                    first_moved_from_buft = buft_list->front().second;
+                    first_moved_to_buft   = buft;
+                }
+            }
+
+            ggml_context * ctx = ctx_for_buft(buft);
+
+            // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
+            if (flags & TENSOR_DUPLICATED) {
+                ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str());
+                if (t) {
+                    return t;
+                }
+            }
+            return ml.create_tensor(ctx, tn, ne, flags);
         };
 
-        for (const auto & it : special_token_types) {
-            const std::string & key = kv(std::get<0>(it));
-            int32_t & id = std::get<1>(it);
+        layers.resize(n_layer);
 
-            uint32_t new_id;
-            if (!ml.get_key(std::get<0>(it), new_id, false)) {
-                continue;
-            }
-            if (new_id >= vocab.id_to_token.size()) {
-                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
-                    __func__, key.c_str(), new_id, id);
-            } else {
-                id = new_id;
-            }
-        }
+        // TODO: move to a separate function
+        const auto tn = LLM_TN(arch);
+        switch (arch) {
+            case LLM_ARCH_LLAMA:
+            case LLM_ARCH_REFACT:
+            case LLM_ARCH_MINICPM:
+            case LLM_ARCH_GRANITE:
+            case LLM_ARCH_GRANITE_MOE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
-        // Handle add_bos_token and add_eos_token
-        {
-            bool temp = true;
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
 
-            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
-                vocab.tokenizer_add_bos = temp;
-            }
-            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
-                vocab.tokenizer_add_eos = temp;
-            }
-        }
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
 
-        // auto-detect special tokens by text
-        // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
-        //       for now, we apply this workaround to find the tokens based on their text
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
 
-        for (const auto & t : vocab.token_to_id) {
-            // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
-            if (vocab.special_eot_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|eot_id|>"
-                        || t.first == "<|im_end|>"
-                        || t.first == "<|end|>"
-                        || t.first == "<end_of_turn>"
-                        || t.first == "<|endoftext|>"
-                        || t.first == "<EOT>"
-                        || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
-                   ) {
-                    vocab.special_eot_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+                        else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        if (n_expert == 0) {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                            // optional MLP bias
+                            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                        } else {
+                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_DECI:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+                        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+                        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+                        const int64_t n_embd_gqa    = hparams.n_embd_v_gqa(i);
+                        const int64_t n_ff          = hparams.n_ff(i);
+                        const int64_t n_head        = hparams.n_head(i);
+                        const int64_t n_head_kv     = hparams.n_head_kv(i);
+
+                        if (n_head_kv == 0 && n_head > 0) {
+                            // linear attention for DeciLMCausalModel
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        }
+                        else if (n_head_kv > 0) {
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+                        }
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+                        else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        // optional MLP bias
+                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
+            case LLM_ARCH_MINICPM3:
+                {
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+
+                    const int64_t q_lora_rank  = hparams.n_lora_q;
+                    const int64_t kv_lora_rank = hparams.n_lora_kv;
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+
+                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+                        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
+
+                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
+                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
+                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                    }
+                } break;
+            case LLM_ARCH_GROK:
+                {
+                    if (n_expert == 0) {
+                        throw std::runtime_error("Grok model cannot have zero experts");
+                    }
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_DBRX:
+                {
+                    if (n_expert == 0) {
+                        throw std::runtime_error("DBRX model cannot have zero experts");
+                    }
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                    }
+                } break;
+            case LLM_ARCH_BAICHUAN:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    {
+                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_FALCON:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    {
+                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+
+                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                        if (!output) {
+                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_STARCODER:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
+
+                    // output
+                    {
+                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                        output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                        if (!output) {
+                            // needs to be on GPU
+                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                        }
+
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
+                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_BERT:
+            case LLM_ARCH_NOMIC_BERT:
+                {
+                    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+                    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0);
+
+                    if (arch == LLM_ARCH_BERT) {
+                        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
+
+                        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+                        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+                        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
+                        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         TENSOR_NOT_REQUIRED);
+                    }
+
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        if (arch == LLM_ARCH_BERT) {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd}, 0);
+
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa}, 0);
+
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa}, 0);
+                        } else {
+                            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        }
+
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd}, 0);
+
+                        if (arch == LLM_ARCH_BERT) {
+                            layer.bo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, 0);
+                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
+                        } else {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+                        }
+
+                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_JINA_BERT_V2:
+                {
+                    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
+                    type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); // token_type_embeddings
+
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0); //LayerNorm bias
+
+                    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
+                    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         TENSOR_NOT_REQUIRED);
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i]; // JinaBertLayer
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);
+
+                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
+
+                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
+
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0); //output_dens
+
+                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
+                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd}, 0);
+
+                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
+
+                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_BLOOM:
+                {
+                    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias",   i), {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_MPT:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, TENSOR_NOT_REQUIRED);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    if (!output) {
+                        output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+
+                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        // AWQ ScaleActivation layer
+                        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
+            case LLM_ARCH_STABLELM:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // optional bias tensors, present in Stable LM 2 1.6B
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+
+                        // optional q and k layernorms, present in StableLM 2 12B
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
+
+                        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd*3}, 0);
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2}, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN2:
+            case LLM_ARCH_QWEN2VL:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN2MOE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                        if (n_expert == 0) {
+                            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
+                        }
+                        if (n_expert_used == 0) {
+                            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
+                        }
+
+                        // MoE branch
+                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                        // Shared expert branch
+                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
+                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);
+                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
+                    }
+                } break;
+            case LLM_ARCH_PHI2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+                    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+
+                        if (layer.wqkv == nullptr) {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);
+
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa}, 0);
+
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa}, 0);
+                        }
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_PHI3:
+                {
+                    const int64_t n_embd_head = n_embd / n_head;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, TENSOR_NOT_REQUIRED);
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
+
+                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                    }
+                } break;
+            case LLM_ARCH_PLAMO:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_GPT2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_CODESHELL:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_ORION:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_INTERNLM2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_GEMMA:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_GEMMA2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_STARCODER2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        // optional bias tensors
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_MAMBA:
+                {
+                    const int64_t d_conv  = hparams.ssm_d_conv;
+                    const int64_t d_inner = hparams.ssm_d_inner;
+                    const int64_t d_state = hparams.ssm_d_state;
+                    const int64_t dt_rank = hparams.ssm_dt_rank;
+
+                    // only an expansion factor of 2 is supported for now
+                    if (2 * n_embd != d_inner) {
+                        throw std::runtime_error("only an expansion factor of 2 is supported for now");
+                    }
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed, duplicated to allow offloading
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        // norm
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
+
+                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
+                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
+
+                        layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
+
+                        layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
+                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
+
+                        // no "weight" suffix for these
+                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
+                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
+
+                        // out_proj
+                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_XVERSE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_COMMAND_R:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    // init output from the input tok embed
+                    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (n_layer >= 64){
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
+                        }
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_COHERE2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    // init output from the input tok embed
+                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
+                                                      TENSOR_DUPLICATED);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
                     }
                 }
-            }
+                break;
+            case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
-            // find EOM token: "<|eom_id|>"
-            if (vocab.special_eom_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|eom_id|>"
-                        ) {
-                    vocab.special_eom_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    // output
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                     }
-                }
-            }
 
-            // find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
-            if (vocab.special_fim_pre_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_prefix|>"  // Qwen
-                        || t.first == "<fim-prefix>"
-                        || t.first == "<｜fim▁begin｜>" // DeepSeek
-                        || t.first == "<PRE>"
-                        ) {
-                    vocab.special_fim_pre_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
 
-            // find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
-            if (vocab.special_fim_suf_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_suffix|>" // Qwen
-                        || t.first == "<fim-suffix>"
-                        || t.first == "<｜fim▁hole｜>" // DeepSeek
-                        || t.first == "<SUF>"
-                        ) {
-                    vocab.special_fim_suf_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
 
-            // find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
-            if (vocab.special_fim_mid_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_middle|>" // Qwen
-                        || t.first == "<fim-middle>"
-                        || t.first == "<｜fim▁end｜>"  // DeepSeek
-                        || t.first == "<MID>"
-                        ) {
-                    vocab.special_fim_mid_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
-                }
-            }
+                } break;
+            case LLM_ARCH_OLMO2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
-            // find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
-            if (vocab.special_fim_pad_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_pad|>" // Qwen
-                        || t.first == "<fim-pad>"
-                        || t.first == "<PAD>"
-                        ) {
-                    vocab.special_fim_pad_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
 
-            // find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
-            if (vocab.special_fim_rep_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|fim_repo|>"  // Qwen
-                        || t.first == "<|repo_name|>"
-                        || t.first == "<fim-repo>"
-                        || t.first == "<REPO>"
-                        ) {
-                    vocab.special_fim_rep_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                    }
-                }
-            }
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
 
-            // find FIM_SEP token: "<|file_sep|>"
-            if (vocab.special_fim_sep_id == LLAMA_TOKEN_NULL) {
-                if (false
-                        || t.first == "<|file_sep|>" // Qwen
-                        ) {
-                    vocab.special_fim_sep_id = t.second;
-                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.second, t.first.c_str());
-                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                     }
-                }
-            }
+                } break;
+            case LLM_ARCH_OLMOE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                        if (n_expert == 0) {
+                            throw std::runtime_error("n_expert must be > 0");
+                        }
+                        if (n_expert_used == 0) {
+                            throw std::runtime_error("n_expert_used must be > 0");
+                        }
+
+                        // MoE branch
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                    }
+                } break;
+            case LLM_ARCH_OPENELM:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    // init output from the input tok embed
+                    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        const int64_t n_head      =   hparams.n_head(i);
+                        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
+                        const int64_t n_ff        =   hparams.n_ff(i);
+
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_GPTNEOX:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_ARCTIC:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+                    }
+                } break;
+            case LLM_ARCH_DEEPSEEK:
+                {
+
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        } else {
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+                            if (n_expert == 0) {
+                                throw std::runtime_error("n_expert must be > 0");
+                            }
+                            if (n_expert_used == 0) {
+                                throw std::runtime_error("n_expert_used must be > 0");
+                            }
+
+                            // MoE branch
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_DEEPSEEK2:
+                {
+                    const bool is_lite = (hparams.n_layer == 27);
+
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+
+                    const int64_t q_lora_rank  = hparams.n_lora_q;
+                    const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        if (!is_lite) {
+                            layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+                        }
+
+                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+                        if (!is_lite) {
+                            layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+                            layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
+                        } else {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        }
+
+                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
+                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
+                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        } else {
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                            if (n_expert == 0) {
+                                throw std::runtime_error("n_expert must be > 0");
+                            }
+                            if (n_expert_used == 0) {
+                                throw std::runtime_error("n_expert_used must be > 0");
+                            }
+
+                            // MoE branch
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_BITNET:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
+                        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);
+                        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);
+
+                        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_scale   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
+            case LLM_ARCH_T5:
+                {
+                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+                        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+                        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);
+                        // this tensor seems to be unused in HF transformers implementation
+                        layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+                        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_T5ENCODER:
+                {
+                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_JAIS:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_CHATGLM:
+                {
+                    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
+
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                    }
+                } break;
+            case LLM_ARCH_NEMOTRON:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
+
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                        // optional MLP bias
+                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
+            case LLM_ARCH_EXAONE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM,   "weight", i), {n_embd}, 0);
+                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN,   "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,     "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_RWKV6:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // Block 0, LN0
+                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
+                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
+                    const int head_size = hparams.wkv_head_size;
+                    const int attn_hidden_size = n_embd;
+                    const int ffn_size = hparams.n_ff_arr[0];
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
+
+                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
+                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
+
+                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
+                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
+
+                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
+                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
+                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
+                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
+                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
+
+                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
+                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
+                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+                        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
+                        layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
+
+                        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
+                        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
+                        layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
+                    }
+
+                } break;
+            case LLM_ARCH_RWKV6QWEN2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
+                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
+                    const int head_size = hparams.wkv_head_size;
+                    const int attn_hidden_size = n_embd;
+                    const int n_head_kv = hparams.n_head_kv();
+                    int attn_key_value_size;
+                    if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
+                        attn_key_value_size = attn_hidden_size;
+                    } else {
+                        attn_key_value_size = n_head_kv * head_size;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
+                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
+
+                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
+                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
+
+                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
+                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
+                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
+                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
+                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
+                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
+                        // optional bias tensors
+                        layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_CHAMELEON:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
+                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                    }
+                } break;
+            case LLM_ARCH_WAVTOKENIZER_DEC:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
+
+                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
+                    conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
+
+                    // posnet
+                    {
+                        const int64_t n_embd = hparams.posnet.n_embd;
+
+                        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
+                            auto & layer = layers[i].posnet;
+
+                            // posnet:
+                            //
+                            //  - resnet
+                            //  - resnet
+                            //  - attn
+                            //  - resnet
+                            //  - resnet
+                            //  - norm
+                            //
+                            switch (i) {
+                                case 0:
+                                case 1:
+                                case 3:
+                                case 4:
+                                    {
+                                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
+                                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
+                                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
+                                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
+                                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                case 2:
+                                    {
+                                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);
+
+                                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
+                                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                case 5:
+                                    {
+                                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+                                    } break;
+                                default: GGML_ABORT("unknown posnet layer");
+                            };
+                        }
+                    }
+
+                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
+
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {hparams.posnet.n_embd}, 0);
+
+                    // convnext
+                    {
+                        const int64_t n_embd = hparams.convnext.n_embd;
+
+                        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
+                            auto & layer = layers[i].convnext;
+
+                            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
+                            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);
+
+                            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
+                            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);
+
+                            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
+                            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);
+
+                            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
+                            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);
+
+                            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
+                        }
+
+                        // output
+                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    }
+
+                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
+                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
+                } break;
+            default:
+                throw std::runtime_error("unknown architecture");
         }
 
-        // maintain a list of tokens that cause end-of-generation
-        // this is currently determined based on the token text, which is obviously not ideal
-        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
-        vocab.special_eog_ids.clear();
-
-        if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_pad_id);
-        }
-
-        if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_rep_id);
-        }
-
-        if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_fim_sep_id);
-        }
-
-        for (const auto & t : vocab.token_to_id) {
-            if (false
-                    || t.first == "<|eot_id|>"
-                    || t.first == "<|im_end|>"
-                    || t.first == "<|end|>"
-                    || t.first == "<end_of_turn>"
-                    || t.first == "<|endoftext|>"
-                    || t.first == "<|eom_id|>"
-                    || t.first == "<EOT>"
-               ) {
-                vocab.special_eog_ids.insert(t.second);
-                if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                    LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                            __func__, t.second, t.first.c_str());
-                    vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
-                }
-            } else {
-                // token is control, but not marked as EOG -> print a debug log
-                if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) {
-                    LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
-                            __func__, t.second, t.first.c_str());
-                }
-            }
-        }
-
-        // sanity checks
-        if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eos_id);
-            LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eot_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eot_id);
-            LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
-        }
-
-        if (vocab.special_eom_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
-            vocab.special_eog_ids.insert(vocab.special_eom_id);
-            LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        if (n_moved_tensors > 0) {
+            LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n",
+                __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1,
+                ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft));
         }
     }
 
-    // build special tokens cache
-    {
-        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
-            if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
-                vocab.cache_special_tokens.push_back(id);
-            }
+    ml.done_getting_tensors();
+
+    ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
+    pimpl->mappings.reserve(ml.mappings.size());
+
+    // create the backend buffers
+    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
+    ctx_bufs.reserve(ctx_map.size());
+
+    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+    const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
+    pimpl->bufs.reserve(n_max_backend_buffer);
+
+    for (auto & it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx              = it.second;
+
+        // skip contexts without tensors
+        if (ggml_get_first_tensor(ctx) == nullptr) {
+            continue;
         }
 
-        std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
-            [&] (const llama_vocab::id a, const llama_vocab::id b) {
-                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
-            }
-        );
+        llama_buf_map buf_map;
+        buf_map.reserve(n_max_backend_buffer);
 
-        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
-    }
-
-    // build token to piece cache
-    {
-        size_t size_cache = 0;
-
-        std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
-
-        for (uint32_t id = 0; id < n_vocab; ++id) {
-            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
-
-            size_cache += cache_token_to_piece[id].size();
+        // check if it is possible to use buffer_from_host_ptr with this buffer type
+        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
+        if (!dev) {
+            // FIXME: workaround for CPU backend buft having a NULL device
+            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
         }
+        ggml_backend_dev_props props;
+        ggml_backend_dev_get_props(dev, &props);
+        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
+        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
 
-        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
-
-        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
-    }
-
-    // Handle per token attributes
-    //NOTE: Each model customizes per token attributes.
-    //NOTE: Per token attributes are missing from the GGUF file.
-    //TODO: Extract attributes from GGUF file.
-    {
-        auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
-            for (auto substr : substrs) {
-                if (str.find(substr) < std::string::npos) {
-                    return true;
+        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                // only the mmap region containing the tensors in the model is mapped to the backend buffer
+                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
+                void * addr = nullptr;
+                size_t first, last; // NOLINT
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
                 }
+                const size_t max_size = ggml_get_max_tensor_size(ctx);
+                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
+                if (buf == nullptr) {
+                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
+                }
+                pimpl->bufs.emplace_back(buf);
+                buf_map.emplace(idx, buf);
             }
+        }
+        else {
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+            if (buf == nullptr) {
+                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
+            }
+            pimpl->bufs.emplace_back(buf);
+            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
+                pimpl->mlock_bufs.emplace_back(new llama_mlock);
+                auto & mlock_buf = pimpl->mlock_bufs.back();
+                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
+                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
+            }
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                buf_map.emplace(idx, buf);
+            }
+        }
+
+        if (pimpl->bufs.empty()) {
+            throw std::runtime_error("failed to allocate buffer");
+        }
+
+        for (auto & buf : buf_map) {
+            // indicate that this buffer contains weights
+            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
+            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
+
+        ctx_bufs.emplace_back(ctx, buf_map);
+    }
+
+    if (llama_supports_gpu_offload()) {
+        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
+
+        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
+        if (n_gpu_layers > (int) hparams.n_layer) {
+            LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
+        }
+
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers       = hparams.n_layer + 1;
+
+        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+    }
+
+    // print memory requirements per buffer type
+    for (auto & buf : pimpl->bufs) {
+        LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
+    }
+
+    // populate tensors_by_name
+    for (auto & ctx : pimpl->ctxs) {
+        for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) {
+            tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+        }
+    }
+
+    // load tensor data
+    for (auto & it : ctx_bufs) {
+        ggml_context * ctx = it.first;
+        auto & bufs = it.second;
+        if (!ml.load_all_data(ctx, bufs, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
             return false;
-        };
-
-        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
-            uint32_t current = vocab.id_to_token.at(id).attr;
-            current = value ? (current | attr) : (current & ~attr);
-            vocab.id_to_token[id].attr = (llama_token_attr) current;
-        };
-
-        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
-            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
-        };
-
-        std::string model_name;
-        std::string tokenizer_pre;
-
-        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
-        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
-
-        // model name to lowercase
-        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
-            [] (const std::string::value_type x) {
-                return std::tolower(x);
-            }
-        );
-
-        // set attributes by model/tokenizer name
-        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
-            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
-        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
-            for (auto id : vocab.cache_special_tokens) {
-                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
-            }
-            for (auto token : {"</s>"}) {
-                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
-            }
-            for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
-                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
-            }
         }
     }
+
+    if (use_mmap_buffer) {
+        for (auto & mapping : ml.mappings) {
+            pimpl->mappings.emplace_back(std::move(mapping));
+        }
+    }
+
+    return true;
 }
 
-void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
-    const auto & hparams = model.hparams;
-    const auto & vocab   = model.vocab;
+std::string llama_model::arch_name() const {
+    return llm_arch_name(arch);
+}
 
+std::string llama_model::type_name() const {
+    return llm_type_name(type);
+}
+
+std::string llama_model::desc() const {
+    return pimpl->desc_str;
+}
+
+size_t llama_model::size() const {
+    return pimpl->n_bytes;
+}
+
+size_t llama_model::max_nodes() const {
+    return std::max<size_t>(8192, tensors_by_name.size()*5);
+}
+
+size_t llama_model::n_devices() const {
+    return devices.size();
+}
+
+uint64_t llama_model::n_elements() const {
+    return pimpl->n_elements;
+}
+
+void llama_model::print_info() const {
     const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
     auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
@@ -1873,11 +3550,7 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     };
 
     // hparams
-    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
-    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, llm_arch_name(model.arch));
-    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
-    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
-    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, arch_name().c_str());
     LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
 
     if (!hparams.vocab_only) {
@@ -1916,60 +3589,28 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
         LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms   = %d\n",     __func__, hparams.ssm_dt_b_c_rms);
     }
 
-    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model).c_str());
-    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model).c_str());
-    if (ml.n_elements >= 1e12) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
-    } else if (ml.n_elements >= 1e9) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
-    } else if (ml.n_elements >= 1e6) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
+    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, type_name().c_str());
+    if (pimpl->n_elements >= 1e12) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, pimpl->n_elements*1e-12);
+    } else if (pimpl->n_elements >= 1e9) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, pimpl->n_elements*1e-9);
+    } else if (pimpl->n_elements >= 1e6) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, pimpl->n_elements*1e-6);
     } else {
-        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
-    }
-    if (ml.n_bytes < GiB) {
-        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
-    } else {
-        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, pimpl->n_elements*1e-3);
     }
 
     // general kv
-    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
+    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, name.c_str());
 
-    // special tokens
-    if (vocab.special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
-    if (vocab.special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
-    if (vocab.special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
-    if (vocab.special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
-    if (vocab.special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
-    if (vocab.special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
-    if (vocab.special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
-    if (vocab.special_cls_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
-    if (vocab.special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
-
-    if (vocab.linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
-
-    if (vocab.special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
-    if (vocab.special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
-    if (vocab.special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
-    if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
-    if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
-    if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
-
-    for (const auto & id : vocab.special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
-    }
-
-    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
-
-    if (model.arch == LLM_ARCH_DEEPSEEK) {
+    if (arch == LLM_ARCH_DEEPSEEK) {
         LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
         LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
         LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
         LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
     }
 
-    if (model.arch == LLM_ARCH_DEEPSEEK2) {
+    if (arch == LLM_ARCH_DEEPSEEK2) {
         LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
         LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
         LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
@@ -1981,16 +3622,88 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
         LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
     }
 
-    if (model.arch == LLM_ARCH_QWEN2MOE) {
+    if (arch == LLM_ARCH_QWEN2MOE) {
         LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
         LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
     }
 
-    if (model.arch == LLM_ARCH_MINICPM || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
+    if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) {
         LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
         LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
         LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
     }
+
+    vocab.print_info();
+}
+
+ggml_backend_dev_t llama_model::dev_layer(int il) const {
+    return pimpl->dev_layer.at(il).dev;
+}
+
+ggml_backend_dev_t llama_model::dev_output() const {
+    return pimpl->dev_output.dev;
+}
+
+template<typename F>
+static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*8,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context_ptr ctx { ggml_init(params) };
+    if (!ctx) {
+        throw std::runtime_error(format("failed to create ggml context"));
+    }
+
+    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
+    ggml_tensor * op_tensor = fn(ctx.get());
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op_tensor->src[i] != nullptr) {
+            assert(op_tensor->src[i]->buffer == nullptr);
+            op_tensor->src[i]->buffer = buf.get();
+        }
+    }
+
+    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
+
+    return op_supported;
+}
+
+template<typename F>
+static ggml_backend_buffer_type_t select_buft(const buft_list_t & buft_list, const F & fn) {
+    for (const auto & cur : buft_list) {
+        ggml_backend_dev_t cur_dev = cur.first;
+        ggml_backend_buffer_type_t cur_buft = cur.second;
+        if (buft_supported(cur_buft, cur_dev, fn)) {
+            return cur_buft;
+        }
+    }
+
+    throw std::runtime_error(format("no suitable buffer type found"));
+}
+
+ggml_backend_buffer_type_t llama_model::select_buft(int il) const {
+    return ::select_buft(
+            *pimpl->dev_layer.at(il).buft_list,
+            [&](ggml_context * ctx) {
+                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
+                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
+                return ggml_add(ctx, cur, layer_dir);
+            });
+}
+
+const struct ggml_tensor * llama_model::get_tensor(const char * name) const {
+    auto it = std::find_if(tensors_by_name.begin(), tensors_by_name.end(),
+            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
+                return it.first == name;
+            });
+    if (it == tensors_by_name.end()) {
+        return nullptr;
+    }
+
+    return it->second;
 }
 
 //
@@ -2022,6 +3735,10 @@ struct llama_model_params llama_model_default_params() {
     return result;
 }
 
+const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model) {
+    return &model->vocab;
+}
+
 void llama_free_model(struct llama_model * model) {
     llama_model_free(model);
 }
@@ -2030,31 +3747,43 @@ void llama_model_free(struct llama_model * model) {
     delete model;
 }
 
-enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
-    return model->vocab.type;
-}
-
-int32_t llama_n_vocab(const struct llama_model * model) {
-    return model->hparams.n_vocab;
-}
-
-int32_t llama_n_ctx_train(const struct llama_model * model) {
+int32_t llama_model_n_ctx_train(const struct llama_model * model) {
     return model->hparams.n_ctx_train;
 }
 
-int32_t llama_n_embd(const struct llama_model * model) {
+int32_t llama_model_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
-int32_t llama_n_layer(const struct llama_model * model) {
+int32_t llama_model_n_layer(const struct llama_model * model) {
     return model->hparams.n_layer;
 }
 
-int32_t llama_n_head(const struct llama_model * model) {
+int32_t llama_model_n_head(const struct llama_model * model) {
     return model->hparams.n_head();
 }
 
-enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+// deprecated
+int32_t llama_n_ctx_train(const struct llama_model * model) {
+    return llama_model_n_ctx_train(model);
+}
+
+// deprecated
+int32_t llama_n_embd(const struct llama_model * model) {
+    return llama_model_n_embd(model);
+}
+
+// deprecated
+int32_t llama_n_layer(const struct llama_model * model) {
+    return llama_model_n_layer(model);
+}
+
+// deprecated
+int32_t llama_n_head(const struct llama_model * model) {
+    return llama_model_n_head(model);
+}
+
+enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
     switch (model->arch) {
         // these models do not use RoPE
         case LLM_ARCH_GPT2:
@@ -2132,7 +3861,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
     return LLAMA_ROPE_TYPE_NONE;
 }
 
-float llama_rope_freq_scale_train(const struct llama_model * model) {
+float llama_model_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
 
@@ -2176,18 +3905,24 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
 }
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
-    return snprintf(buf, buf_size, "%s %s %s",
-            llama_model_arch_name (*model).c_str(),
-            llama_model_type_name (*model).c_str(),
-            llama_model_ftype_name(*model).c_str());
+    return snprintf(buf, buf_size, "%s", model->desc().c_str());
 }
 
 uint64_t llama_model_size(const struct llama_model * model) {
-    return model->n_bytes;
+    return model->size();
+}
+
+const char * llama_model_chat_template(const struct llama_model * model) {
+    const auto & it = model->gguf_kv.find(LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE));
+    if (it == model->gguf_kv.end()) {
+        return nullptr;
+    }
+
+    return it->second.c_str();
 }
 
 uint64_t llama_model_n_params(const struct llama_model * model) {
-    return model->n_elements;
+    return model->n_elements();
 }
 
 bool llama_model_has_encoder(const struct llama_model * model) {
diff --git a/src/llama-model.h b/src/llama-model.h
index 565d2dbdf..4cc8abb75 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -4,79 +4,80 @@
 #include "llama-arch.h"
 #include "llama-hparams.h"
 #include "llama-vocab.h"
-#include "llama-mmap.h"
-
-#include "ggml-cpp.h"
 
+#include <memory>
+#include <string>
+#include <unordered_map>
 #include <vector>
 
+struct llama_model_loader;
+
 // available models
-// TODO: this enum does not follow the enum naming convention
 enum llm_type {
-    MODEL_UNKNOWN,
-    MODEL_14M,
-    MODEL_17M,
-    MODEL_22M,
-    MODEL_33M,
-    MODEL_60M,
-    MODEL_70M,
-    MODEL_80M,
-    MODEL_109M,
-    MODEL_137M,
-    MODEL_160M,
-    MODEL_220M,
-    MODEL_250M,
-    MODEL_270M,
-    MODEL_335M,
-    MODEL_410M,
-    MODEL_450M,
-    MODEL_770M,
-    MODEL_780M,
-    MODEL_0_5B,
-    MODEL_1B,
-    MODEL_1_3B,
-    MODEL_1_4B,
-    MODEL_1_5B,
-    MODEL_1_6B,
-    MODEL_2B,
-    MODEL_2_8B,
-    MODEL_3B,
-    MODEL_4B,
-    MODEL_6B,
-    MODEL_6_9B,
-    MODEL_7B,
-    MODEL_8B,
-    MODEL_9B,
-    MODEL_11B,
-    MODEL_12B,
-    MODEL_13B,
-    MODEL_14B,
-    MODEL_15B,
-    MODEL_16B,
-    MODEL_20B,
-    MODEL_30B,
-    MODEL_32B,
-    MODEL_34B,
-    MODEL_35B,
-    MODEL_40B,
-    MODEL_65B,
-    MODEL_70B,
-    MODEL_236B,
-    MODEL_314B,
-    MODEL_671B,
-    MODEL_SMALL,
-    MODEL_MEDIUM,
-    MODEL_LARGE,
-    MODEL_XL,
-    MODEL_A1_7B,
-    MODEL_A2_7B,
-    MODEL_8x7B,
-    MODEL_8x22B,
-    MODEL_16x12B,
-    MODEL_16x3_8B,
-    MODEL_10B_128x3_66B,
-    MODEL_57B_A14B,
-    MODEL_27B,
+    LLM_TYPE_UNKNOWN,
+    LLM_TYPE_14M,
+    LLM_TYPE_17M,
+    LLM_TYPE_22M,
+    LLM_TYPE_33M,
+    LLM_TYPE_60M,
+    LLM_TYPE_70M,
+    LLM_TYPE_80M,
+    LLM_TYPE_109M,
+    LLM_TYPE_137M,
+    LLM_TYPE_160M,
+    LLM_TYPE_220M,
+    LLM_TYPE_250M,
+    LLM_TYPE_270M,
+    LLM_TYPE_335M,
+    LLM_TYPE_410M,
+    LLM_TYPE_450M,
+    LLM_TYPE_770M,
+    LLM_TYPE_780M,
+    LLM_TYPE_0_5B,
+    LLM_TYPE_1B,
+    LLM_TYPE_1_3B,
+    LLM_TYPE_1_4B,
+    LLM_TYPE_1_5B,
+    LLM_TYPE_1_6B,
+    LLM_TYPE_2B,
+    LLM_TYPE_2_8B,
+    LLM_TYPE_3B,
+    LLM_TYPE_4B,
+    LLM_TYPE_6B,
+    LLM_TYPE_6_9B,
+    LLM_TYPE_7B,
+    LLM_TYPE_8B,
+    LLM_TYPE_9B,
+    LLM_TYPE_11B,
+    LLM_TYPE_12B,
+    LLM_TYPE_13B,
+    LLM_TYPE_14B,
+    LLM_TYPE_15B,
+    LLM_TYPE_16B,
+    LLM_TYPE_20B,
+    LLM_TYPE_30B,
+    LLM_TYPE_32B,
+    LLM_TYPE_34B,
+    LLM_TYPE_35B,
+    LLM_TYPE_40B,
+    LLM_TYPE_65B,
+    LLM_TYPE_70B,
+    LLM_TYPE_236B,
+    LLM_TYPE_314B,
+    LLM_TYPE_671B,
+    LLM_TYPE_SMALL,
+    LLM_TYPE_MEDIUM,
+    LLM_TYPE_LARGE,
+    LLM_TYPE_XL,
+    LLM_TYPE_A1_7B,
+    LLM_TYPE_A2_7B,
+    LLM_TYPE_8x7B,
+    LLM_TYPE_8x22B,
+    LLM_TYPE_16x12B,
+    LLM_TYPE_16x3_8B,
+    LLM_TYPE_10B_128x3_66B,
+    LLM_TYPE_57B_A14B,
+    LLM_TYPE_27B,
 };
 
 struct llama_layer_posnet {
@@ -286,11 +287,9 @@ struct llama_layer {
 };
 
 struct llama_model {
-    llm_type type = MODEL_UNKNOWN;
+    llm_type type = LLM_TYPE_UNKNOWN;
     llm_arch arch = LLM_ARCH_UNKNOWN;
 
-    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
     std::string name = "n/a";
 
     llama_hparams hparams = {};
@@ -319,78 +318,55 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
+    llama_model_params params;
+
     // gguf metadata
     std::unordered_map<std::string, std::string> gguf_kv;
 
-    llama_split_mode split_mode;
-    int main_gpu;
-    int n_gpu_layers;
-
     std::vector<std::string> rpc_servers;
 
     // list of devices used in this model
     std::vector<ggml_backend_dev_t> devices;
 
-
-    // lists of buffer types used for each layer
-    using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
-    buft_list_t cpu_buft_list;
-    std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
-
-    struct layer_dev {
-        ggml_backend_dev_t dev;
-        buft_list_t * buft_list;
-    };
-
-    layer_dev dev_input = {};
-    layer_dev dev_output = {};
-    std::vector<layer_dev> dev_layer;
-
-    // contexts where the model tensors metadata is stored
-    std::vector<ggml_context_ptr> ctxs;
-
-    // the model memory buffers for the tensor data
-    std::vector<ggml_backend_buffer_ptr> bufs;
-
-    // model memory mapped files
-    llama_mmaps mappings;
-
-    // objects representing data potentially being locked in memory
-    llama_mlocks mlock_bufs;
-    llama_mlocks mlock_mmaps;
-
     // for quantize-stats only
     std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
 
     int64_t t_load_us  = 0;
     int64_t t_start_us = 0;
 
-    // total number of parameters in the model
-    uint64_t n_elements = 0;
+    explicit llama_model(const struct llama_model_params & params);
+    ~llama_model();
 
-    // total size of all the tensors in the model in bytes
-    size_t  n_bytes     = 0;
+    void load_stats  (llama_model_loader & ml);
+    void load_arch   (llama_model_loader & ml);
+    void load_hparams(llama_model_loader & ml);
+    void load_vocab  (llama_model_loader & ml);
+    bool load_tensors(llama_model_loader & ml); // returns false if cancelled by progress_callback
+
+    std::string arch_name() const;
+    std::string type_name() const;
+
+    std::string desc() const;
+
+    size_t size() const;
+    size_t max_nodes() const;
+    size_t n_devices() const;
+
+    // total number of parameters in the model
+    uint64_t n_elements() const;
+
+    void print_info() const;
+
+    ggml_backend_dev_t dev_layer(int il) const;
+    ggml_backend_dev_t dev_output() const;
+
+    ggml_backend_buffer_type_t select_buft(int il) const;
+
+    const struct ggml_tensor * get_tensor(const char * name) const;
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };
 
 const char * llm_type_name(llm_type type);
-
-std::string llama_model_arch_name (const llama_model & model);
-std::string llama_model_type_name (const llama_model & model);
-std::string llama_model_ftype_name(const llama_model & model);
-
-// used by llama_adapter_cvec
-ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il);
-
-// used by llama_adapter_lora
-struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name);
-
-size_t llama_model_max_nodes(const llama_model & model);
-
-struct llama_model_loader;
-
-// TODO: become llama_model methods
-void llm_load_stats     (llama_model_loader & ml, llama_model & model);
-void llm_load_arch      (llama_model_loader & ml, llama_model & model);
-void llm_load_hparams   (llama_model_loader & ml, llama_model & model);
-void llm_load_vocab     (llama_model_loader & ml, llama_model & model);
-void llm_load_print_meta(llama_model_loader & ml, llama_model & model);
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index a45044f30..d4947a780 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -235,7 +235,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
-        if (qs.model.type == MODEL_70B) {
+        if (qs.model.type == LLM_TYPE_70B) {
             // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
             // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
             // nearly negligible increase in model size by quantizing this tensor with more bits:
@@ -525,18 +525,20 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
         kv_overrides = v->data();
     }
+
     llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
     ml.init_mappings(false); // no prefetching
 
-    llama_model model;
-    llm_load_arch   (ml, model);
-    llm_load_hparams(ml, model);
-    llm_load_stats  (ml, model);
+    llama_model model(llama_model_default_params());
+
+    model.load_arch   (ml);
+    model.load_hparams(ml);
+    model.load_stats  (ml);
 
     struct quantize_state_impl qs(model, params);
 
     if (params->only_copy) {
-        ftype = model.ftype;
+        ftype = ml.ftype;
     }
     const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
     if (params->imatrix) {
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index ef5a576cc..b3a12386e 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -371,7 +371,10 @@ void llama_sampler_free(struct llama_sampler * smpl) {
 llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
     const auto * logits = llama_get_logits_ith(ctx, idx);
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     // TODO: do not allocate each time
     std::vector<llama_token_data> cur;
@@ -1445,7 +1448,7 @@ static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {
 static struct llama_sampler * llama_sampler_grammar_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_grammar *) smpl->ctx;
 
-    auto * result = llama_sampler_init_grammar_impl(*ctx->vocab, nullptr, nullptr);
+    auto * result = llama_sampler_init_grammar(ctx->vocab, nullptr, nullptr);
 
     // copy the state
     {
@@ -1481,19 +1484,19 @@ static struct llama_sampler_i llama_sampler_grammar_i = {
     /* .free   = */ llama_sampler_grammar_free,
 };
 
-struct llama_sampler * llama_sampler_init_grammar_impl(const struct llama_vocab & vocab, const char * grammar_str, const char * grammar_root) {
+struct llama_sampler * llama_sampler_init_grammar(const struct llama_vocab * vocab, const char * grammar_str, const char * grammar_root) {
     auto * ctx = new llama_sampler_grammar;
 
     if (grammar_str != nullptr && grammar_str[0] != '\0') {
         *ctx = {
-            /* .vocab        = */ &vocab,
+            /* .vocab        = */ vocab,
             /* .grammar_str  = */ grammar_str,
             /* .grammar_root = */ grammar_root,
-            /* .grammar      = */ llama_grammar_init_impl(&vocab, grammar_str, grammar_root),
+            /* .grammar      = */ llama_grammar_init_impl(vocab, grammar_str, grammar_root),
         };
     } else {
         *ctx = {
-            /* .vocab        = */ &vocab,
+            /* .vocab        = */ vocab,
             /* .grammar_str  = */ {},
             /* .grammar_root = */ {},
             /* .grammar      = */ nullptr,
@@ -1663,8 +1666,8 @@ struct llama_sampler_dry {
 
 // Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am)
 static void get_overlapping_token_sequences(const llama_vocab & vocab, const std::string& str, std::unordered_multimap<llama_token, std::vector<llama_token>>& token_sequences, int max_tail_len = -1) {
-    for (llama_token token_id = 0; token_id < (llama_token)vocab.n_vocab; token_id++) {
-        std::string word = llama_detokenize(vocab, {token_id}, true);
+    for (llama_token token_id = 0; token_id < (llama_token) vocab.n_tokens(); token_id++) {
+        std::string word = vocab.detokenize({token_id}, true);
         if (word.find(str) != std::string::npos) {
             token_sequences.emplace(token_id, std::vector<llama_token>());
         } else {
@@ -1681,7 +1684,7 @@ static void get_overlapping_token_sequences(const llama_vocab & vocab, const std
                     }
                 }
                 if (match) {
-                    std::vector<llama_token> tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false);
+                    std::vector<llama_token> tokenization = vocab.tokenize(str.substr(i), false, false);
                     if (max_tail_len >= 0 && tokenization.size() > (size_t)max_tail_len) {
                         tokenization.resize(max_tail_len);
                     }
@@ -1937,7 +1940,7 @@ static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler
     llama_vocab dummy_vocab;
 
     // dummy vocab is passed because it is only needed for raw sequence breaker processing, which we have already done and will simply be copying
-    auto * result = llama_sampler_init_dry_impl(dummy_vocab, ctx->total_context_size, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0);
+    auto * result = llama_sampler_init_dry(&dummy_vocab, ctx->total_context_size, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0);
 
     // Copy the state, including the processed breakers
     {
@@ -1964,7 +1967,7 @@ static struct llama_sampler_i llama_sampler_dry_i = {
     /* .free   = */ llama_sampler_dry_free,
 };
 
-struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
+struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
     int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? context_size : std::max(dry_penalty_last_n, 0);
     std::unordered_multimap<llama_token, std::vector<llama_token>> processed_breakers;
     const int MAX_CHAR_LEN = 40;
@@ -1991,7 +1994,7 @@ struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vo
                 sequence_break.resize(MAX_CHAR_LEN);
             }
 
-            get_overlapping_token_sequences(vocab, sequence_break, processed_breakers, MAX_SEQ_LEN);
+            get_overlapping_token_sequences(*vocab, sequence_break, processed_breakers, MAX_SEQ_LEN);
         }
     }
 
@@ -2014,7 +2017,7 @@ struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vo
 // wrapper for test-sampling.cpp
 struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers) {
     llama_vocab dummy_vocab;
-    auto * result = llama_sampler_init_dry_impl(dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0);
+    auto * result = llama_sampler_init_dry(&dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0);
     auto * ctx = (llama_sampler_dry *) result->ctx;
 
     // Process the token-based sequence breakers
@@ -2153,7 +2156,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
     float p_eog_sum = 0.0f;
 
     for (size_t i = 0; i < cur_p->size; ++i) {
-        if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) {
+        if (ctx->vocab->is_eog(cur_p->data[i].id)) {
             p_eog_sum += cur_p->data[i].p;
         } else {
             p_txt_sum += cur_p->data[i].p;
@@ -2175,7 +2178,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
         float p_sum = 0.0f;
 
         for (size_t i = 0; i < size_org; ++i) {
-            if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) {
+            if (ctx->vocab->is_eog(cur_p->data[i].id)) {
                 p_sum += cur_p->data[i].p;
 
                 cur_p->data[cur_p->size++] = cur_p->data[i];
@@ -2203,17 +2206,17 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
                 continue;
             }
 
-            int len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false);
+            int len0 = ctx->vocab->token_to_piece(cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false);
             if (len0 < 0) {
                 ctx->buf0.resize(len0);
-                len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false);
+                len0 = ctx->vocab->token_to_piece(cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false);
                 assert(len0 > 0);
             }
 
-            int len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false);
+            int len1 = ctx->vocab->token_to_piece(cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false);
             if (len1 < 0) {
                 ctx->buf1.resize(len1);
-                len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false);
+                len1 = ctx->vocab->token_to_piece(cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false);
                 assert(len1 > 0);
             }
 
@@ -2248,7 +2251,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
     LOG_DBG_CUR("%s: n_combined = %zu, applying thold = %.3f\n", __func__, n_combined, thold);
 
     for (size_t i = 0; i < size_org; ++i) {
-        const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id);
+        const bool is_eog = ctx->vocab->is_eog(cur_p->data[i].id);
 
         if (cur_p->data[i].p < thold && !is_eog) {
             continue;
@@ -2269,7 +2272,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
     // if no non-EOG tokens are left -> reduce cur_p to single EOT token
     if (n_non_eog == 0) {
         cur_p->size = 1;
-        cur_p->data[0].id = llama_token_eot_impl(*ctx->vocab);
+        cur_p->data[0].id = ctx->vocab->token_eot();
         cur_p->data[0].logit = 1.0f;
 
         return;
@@ -2291,7 +2294,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
     LOG_DBG_CUR("%s: applying thold = %.3f\n", __func__, thold);
 
     for (size_t i = 0; i < size_org; ++i) {
-        const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id);
+        const bool is_eog = ctx->vocab->is_eog(cur_p->data[i].id);
 
         if (cur_p->data[i].p < thold && !is_eog) {
             continue;
@@ -2314,7 +2317,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
 
 static struct llama_sampler * llama_sampler_infill_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_infill *) smpl->ctx;
-    return llama_sampler_init_infill_impl(*ctx->vocab);
+    return llama_sampler_init_infill(ctx->vocab);
 }
 
 static void llama_sampler_infill_free(struct llama_sampler * smpl) {
@@ -2330,14 +2333,13 @@ static struct llama_sampler_i llama_sampler_infill_i = {
     /* .free   = */ llama_sampler_infill_free,
 };
 
-struct llama_sampler * llama_sampler_init_infill_impl(
-        const struct llama_vocab & vocab) {
+struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
     return new llama_sampler {
         /* .iface = */ &llama_sampler_infill_i,
         /* .ctx   = */ new llama_sampler_infill {
-            /* .vocab = */ &vocab,
-            /* .buf0 = */ std::vector<char>(512),
-            /* .buf1 = */ std::vector<char>(512),
+            /* .vocab = */ vocab,
+            /* .buf0  = */ std::vector<char>(512),
+            /* .buf1  = */ std::vector<char>(512),
         },
     };
 }
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index 919f6fdfc..759dd7dcb 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -2,7 +2,9 @@
 
 // TODO: rename llama-sampling.h/.cpp to llama-sampler.h/.cpp ?
 
-#include "llama-grammar.h"
+#include "llama.h"
+
+#include <vector>
 
 struct llama_vocab;
 struct llama_grammar;
@@ -21,24 +23,6 @@ struct llama_sampler_chain {
     mutable int32_t n_sample;
 };
 
-struct llama_sampler * llama_sampler_init_grammar_impl(
-        const struct llama_vocab & vocab,
-                      const char * grammar_str,
-                      const char * grammar_root);
-
-struct llama_sampler * llama_sampler_init_infill_impl(
-        const struct llama_vocab & vocab);
-
-struct llama_sampler * llama_sampler_init_dry_impl(
-        const struct llama_vocab &  vocab,
-                         int32_t    context_size,
-                           float    dry_multiplier,
-                           float    dry_base,
-                         int32_t    dry_allowed_length,
-                         int32_t    dry_penalty_last_n,
-                      const char ** seq_breakers,
-                          size_t    num_breakers);
-
 struct llama_sampler * llama_sampler_init_dry_testing(
                          int32_t   context_size,
                            float   dry_multiplier,
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index a4c015484..ed8751737 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1,6 +1,7 @@
 #include "llama-vocab.h"
 
 #include "llama-impl.h"
+#include "llama-model-loader.h"
 
 #include "unicode.h"
 
@@ -11,8 +12,10 @@
 #include <cstdarg>
 #include <cstring>
 #include <forward_list>
+#include <map>
 #include <queue>
-#include <sstream>
+#include <set>
+#include <unordered_map>
 
 //
 // helpers
@@ -62,96 +65,14 @@ struct naive_trie {
 };
 
 //
-// impl
+// tokenizers
 //
 
 struct llm_tokenizer {
-   llm_tokenizer() {}
-   virtual ~llm_tokenizer() = default;
+    llm_tokenizer() {}
+    virtual ~llm_tokenizer() = default;
 };
 
-llama_vocab::~llama_vocab() {
-    delete tokenizer;
-}
-
-int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
-    GGML_ASSERT(token_left.find(' ')   == std::string::npos);
-    GGML_ASSERT(token_left.find('\n')  == std::string::npos);
-    GGML_ASSERT(token_right.find(' ')  == std::string::npos);
-    GGML_ASSERT(token_right.find('\n') == std::string::npos);
-
-    auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
-    if (it == bpe_ranks.end()) {
-        return -1;
-    }
-
-    return it->second;
-}
-
-static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
-    return vocab.type;
-}
-
-static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
-}
-
-static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
-}
-
-static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
-}
-
-static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
-}
-
-static bool llama_is_user_defined_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
-}
-
-static bool llama_is_unused_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
-}
-
-static uint8_t llama_token_to_byte(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    GGML_ASSERT(llama_is_byte_token(vocab, id));
-    const auto & token_data = vocab.id_to_token.at(id);
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM:
-        case LLAMA_VOCAB_TYPE_UGM: {
-            auto buf = token_data.text.substr(3, 2);
-            return strtol(buf.c_str(), NULL, 16);
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            GGML_ABORT("fatal error");
-            //return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
-        }
-        case LLAMA_VOCAB_TYPE_WPM: {
-            GGML_ABORT("fatal error");
-        }
-        default:
-            GGML_ABORT("fatal error");
-    }
-}
-
-static void llama_escape_whitespace(std::string & text) {
-    replace_all(text, " ", "\xe2\x96\x81");
-}
-
-static void llama_unescape_whitespace(std::string & word) {
-    replace_all(word, "\xe2\x96\x81", " ");
-}
-
 struct llm_symbol {
     using index = int;
     index prev;
@@ -183,14 +104,13 @@ struct llm_bigram_spm {
 };
 
 struct llm_tokenizer_spm : llm_tokenizer {
-    llm_tokenizer_spm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
+    llm_tokenizer_spm(const llama_vocab & /*vocab*/) {}
 };
 
 struct llm_tokenizer_spm_session {
     llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {}
 
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-
+    void tokenize(const std::string & text, std::vector<llama_token> & output) {
         // split string into utf8 chars
         int index = 0;
         size_t offs = 0;
@@ -249,13 +169,13 @@ struct llm_tokenizer_spm_session {
     }
 
 private:
-    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
+    void resegment(llm_symbol & symbol, std::vector<llama_token> & output) {
         auto text = std::string(symbol.text, symbol.n);
-        auto token = vocab.token_to_id.find(text);
+        auto token = vocab.text_to_token(text);
 
         // Do we need to support is_unused?
-        if (token != vocab.token_to_id.end()) {
-            output.push_back((*token).second);
+        if (token != LLAMA_TOKEN_NULL) {
+            output.push_back(token);
             return;
         }
 
@@ -265,8 +185,8 @@ private:
             // output any symbols that did not form tokens as bytes.
             output.reserve(output.size() + symbol.n);
             for (int j = 0; j < (int)symbol.n; ++j) {
-                llama_vocab::id token_id = llama_byte_to_token_impl(vocab, symbol.text[j]);
-                output.push_back(token_id);
+                llama_token id = vocab.byte_to_token(symbol.text[j]);
+                output.push_back(id);
             }
             return;
         }
@@ -280,17 +200,17 @@ private:
             return;
         }
         const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
-        auto token = vocab.token_to_id.find(text);
+        auto token = vocab.text_to_token(text);
 
-        if (token == vocab.token_to_id.end()) {
+        if (token == LLAMA_TOKEN_NULL) {
             return;
         }
 
-        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
+        if (static_cast<uint32_t>(token) >= vocab.n_tokens()) {
             return;
         }
 
-        const auto & tok_data = vocab.id_to_token[(*token).second];
+        const auto & tok_data = vocab.get_token_data(token);
 
         llm_bigram_spm bigram;
         bigram.left  = left;
@@ -353,9 +273,9 @@ struct llm_bigram_bpe {
 };
 
 struct llm_tokenizer_bpe : llm_tokenizer {
-    llm_tokenizer_bpe(const llama_vocab & vocab) : llm_tokenizer() {
-        GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
-        switch (vocab.type_pre) {
+    llm_tokenizer_bpe(const llama_vocab & vocab) {
+        GGML_ASSERT(vocab.get_type() == LLAMA_VOCAB_TYPE_BPE);
+        switch (vocab.get_pre_type()) {
             case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
                 regex_exprs = {
                     // original regex from tokenizer.json
@@ -488,39 +408,38 @@ struct llm_tokenizer_bpe : llm_tokenizer {
 };
 
 struct llm_tokenizer_bpe_session {
-    llm_tokenizer_bpe_session(const llama_vocab & vocab) : vocab(vocab),
-        bpe_tokenizer(static_cast<const llm_tokenizer_bpe *>(vocab.tokenizer)) {}
+    llm_tokenizer_bpe_session(const llama_vocab & vocab, const llm_tokenizer_bpe & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
 
-    static void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output)  {
+    static void append(const llama_token token_id, std::vector<llama_token> & output)  {
         output.push_back(token_id);
     }
 
-    bool append_bos(std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_bos) {
-            GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
-            output.push_back(vocab.special_bos_id);
+    bool append_bos(std::vector<llama_token> & output) const {
+        if (vocab.get_add_bos()) {
+            GGML_ASSERT(vocab.token_bos() != LLAMA_TOKEN_NULL);
+            output.push_back(vocab.token_bos());
             return true;
         }
         return false;
     }
 
-    bool append_eos(std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_eos) {
-            GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
-            output.push_back(vocab.special_eos_id);
+    bool append_eos(std::vector<llama_token> & output) const {
+        if (vocab.get_add_eos()) {
+            GGML_ASSERT(vocab.token_eos() != LLAMA_TOKEN_NULL);
+            output.push_back(vocab.token_eos());
             return true;
         }
         return false;
     }
 
-    void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+    void check_double_bos_eos(const std::vector<llama_token> & output) const {
+        if (vocab.get_add_bos() && output.size() >= 2 && output[1] == vocab.token_bos()) {
             LLAMA_LOG_WARN(
                 "%s: Added a BOS token to the prompt as specified by the model but the prompt "
                 "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
                 "Are you sure this is what you want?\n", __FUNCTION__);
         }
-        if (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
+        if (vocab.get_add_bos() && output.size() >= 2 && *(output.end()-2) == vocab.token_eos()) {
             LLAMA_LOG_WARN(
                 "%s: Added a EOS token to the prompt as specified by the model but the prompt "
                 "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
@@ -528,9 +447,9 @@ struct llm_tokenizer_bpe_session {
         }
     }
 
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+    void tokenize(const std::string & text, std::vector<llama_token> & output) {
         int final_prev_index = -1;
-        const auto word_collection = unicode_regex_split(text, bpe_tokenizer->regex_exprs);
+        const auto word_collection = unicode_regex_split(text, tokenizer.regex_exprs);
 
         symbols_final.clear();
 
@@ -541,7 +460,8 @@ struct llm_tokenizer_bpe_session {
             int index = 0;
             size_t offset = 0;
 
-            if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+            //if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+            if (vocab.get_ignore_merges() && vocab.text_to_token(word) != LLAMA_TOKEN_NULL) {
                 symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
                 offset = word.size();
             }
@@ -615,18 +535,18 @@ struct llm_tokenizer_bpe_session {
                 }
 
                 const std::string str = std::string(symbol.text, symbol.n);
-                const auto token = vocab.token_to_id.find(str);
+                const auto token = vocab.text_to_token(str);
 
-                if (token == vocab.token_to_id.end()) {
+                if (token == LLAMA_TOKEN_NULL) {
                     for (auto j = str.begin(); j != str.end(); ++j) {
                         std::string byte_str(1, *j);
-                        auto token_multibyte = vocab.token_to_id.find(byte_str);
-                        if (token_multibyte != vocab.token_to_id.end()) {
-                            output.push_back(token_multibyte->second);
+                        auto token_multibyte = vocab.text_to_token(byte_str);
+                        if (token_multibyte != LLAMA_TOKEN_NULL) {
+                            output.push_back(token_multibyte);
                         }
                     }
                 } else {
-                    output.push_back((*token).second);
+                    output.push_back(token);
                 }
             }
         }
@@ -660,7 +580,7 @@ private:
     }
 
     const llama_vocab & vocab;
-    const llm_tokenizer_bpe * bpe_tokenizer;
+    const llm_tokenizer_bpe & tokenizer;
 
     std::vector<llm_symbol> symbols;
     std::vector<llm_symbol> symbols_final;
@@ -672,14 +592,13 @@ private:
 //
 
 struct llm_tokenizer_wpm : llm_tokenizer {
-    llm_tokenizer_wpm(const llama_vocab & /*vocab*/) : llm_tokenizer() {}
+    llm_tokenizer_wpm(const llama_vocab & /*vocab*/) {}
 };
 
 struct llm_tokenizer_wpm_session {
     llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {}
 
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        const auto & token_map = vocab.token_to_id;
+    void tokenize(const std::string & text, std::vector<llama_token> & output) {
         // normalize and split by whitespace
         std::vector<std::string> words = preprocess(text);
         // bos token prepended already
@@ -702,10 +621,10 @@ struct llm_tokenizer_wpm_session {
             for (int i = 0; i < n; ++i) {
                 // loop through possible match length
                 bool match = false;
-                for (int j = std::min(n, i + vocab.max_token_len + 1); j > i; j--) {
-                    auto it = token_map.find(word1.substr(i, j - i));
-                    if (it != token_map.end()) {
-                        output.push_back(it->second);
+                for (int j = std::min(n, i + vocab.max_token_len() + 1); j > i; j--) {
+                    auto id = vocab.text_to_token(word1.substr(i, j - i));
+                    if (id != LLAMA_TOKEN_NULL) {
+                        output.push_back(id);
                         match = true;
                         i = j - 1;
                         break;
@@ -720,7 +639,7 @@ struct llm_tokenizer_wpm_session {
 
             // we didn't find any matches for this word
             if (current_tokens == output.size()) {
-                output.push_back(vocab.special_unk_id);
+                output.push_back(vocab.token_unk());
             }
         }
     }
@@ -789,45 +708,45 @@ private:
 //
 
 struct llm_tokenizer_ugm : llm_tokenizer {
-    llm_tokenizer_ugm(const llama_vocab & vocab) : llm_tokenizer() {
-        if (vocab.precompiled_charsmap.size() > 0) {
+    llm_tokenizer_ugm(const llama_vocab & vocab, const std::vector<char> & precompiled_charsmap) {
+        if (precompiled_charsmap.size() > 0) {
             size_t charsmap_offset = 0;
 
             // First four bytes of precompiled_charsmap contains length of binary
             // blob containing XOR-compressed compact double array (XCDA) entries
-            uint32_t xcda_blob_size = *(const uint32_t *) &vocab.precompiled_charsmap[0];
+            uint32_t xcda_blob_size = *(const uint32_t *) &precompiled_charsmap[0];
             charsmap_offset += sizeof(xcda_blob_size);
-            if (xcda_blob_size + charsmap_offset >= vocab.precompiled_charsmap.size()) {
+            if (xcda_blob_size + charsmap_offset >= precompiled_charsmap.size()) {
                 throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
             }
 
             // Next xcda_blob_size bytes contain entries of XOR-compressed compact
             // double array (XCDA). Each entry is bit-packed into a 32-bit integer.
-            xcda_array = (const uint32_t *) &vocab.precompiled_charsmap[charsmap_offset];
+            xcda_array = (const uint32_t *) &precompiled_charsmap[charsmap_offset];
             xcda_array_size = xcda_blob_size / sizeof(uint32_t);
             charsmap_offset += xcda_blob_size;
 
             // Remaining bytes of precompiled charsmap contain null-terminated
             // replacement strings for prefixes matched by the XCDA.
-            prefix_replacements = &vocab.precompiled_charsmap[charsmap_offset];
-            prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
+            prefix_replacements = &precompiled_charsmap[charsmap_offset];
+            prefix_replacements_size = precompiled_charsmap.size() - charsmap_offset;
         }
 
-        for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
-            const auto &token_data = vocab.id_to_token[id];
+        for (uint32_t id = 0; id < vocab.n_tokens(); ++id) {
+            const auto & token_data = vocab.get_token_data(id);
 
-            if (llama_is_normal_token(vocab, id)) {
+            if (vocab.is_normal(id)) {
                 min_score = std::min<float>(min_score, token_data.score);
                 max_score = std::max<float>(max_score, token_data.score);
             }
 
-            if (llama_is_normal_token(vocab, id) ||
-                llama_is_user_defined_token(vocab, id) ||
-                llama_is_unused_token(vocab, id)) {
+            if (vocab.is_normal(id) ||
+                vocab.is_user_defined(id) ||
+                vocab.is_unused(id)) {
                 token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
             }
 
-            if (llama_is_user_defined_token(vocab, id)) {
+            if (vocab.is_user_defined(id)) {
                 user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
             }
         }
@@ -856,8 +775,7 @@ struct llm_tokenizer_ugm : llm_tokenizer {
 };
 
 struct llm_tokenizer_ugm_session {
-    llm_tokenizer_ugm_session(const llama_vocab & vocab) : vocab(vocab),
-        ugm_tokenizer(static_cast<const llm_tokenizer_ugm *>(vocab.tokenizer)) {}
+    llm_tokenizer_ugm_session(const llama_vocab & vocab, const llm_tokenizer_ugm & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
 
     /* This implementation is based on SentencePiece optimized Viterbi algorithm for
      * unigram language models. The general idea is to:
@@ -872,7 +790,7 @@ struct llm_tokenizer_ugm_session {
      * After processing the whole sequence we backtrack from the end to get
      * the best tokenization.
     */
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+    void tokenize(const std::string & text, std::vector<llama_token> & output) {
         // get current size of output (for reversal later)
         size_t output_size = output.size();
 
@@ -885,9 +803,9 @@ struct llm_tokenizer_ugm_session {
         }
 
         // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
-        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX});
+        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.token_unk(), 0, -FLT_MAX});
         // at the beginning tokenization score is zero
-        tokenization_results[0] = { vocab.special_unk_id, 0, 0 };
+        tokenization_results[0] = { vocab.token_unk(), 0, 0 };
 
         for (size_t input_offset = 0; input_offset < input_len;) {
             size_t prefix_offset = input_offset;
@@ -897,7 +815,7 @@ struct llm_tokenizer_ugm_session {
             // traverse the token matcher trie to find a matching token
             bool single_codepoint_token_found = false;
             const struct best_tokenization & current_best = tokenization_results[input_offset];
-            const struct naive_trie * node = ugm_tokenizer->token_matcher.traverse(normalized[prefix_offset++]);
+            const struct naive_trie * node = tokenizer.token_matcher.traverse(normalized[prefix_offset++]);
 
             while (prefix_offset <= input_len && node != NULL) {
                 // check if we found valid token in prefix
@@ -907,13 +825,13 @@ struct llm_tokenizer_ugm_session {
                         single_codepoint_token_found = true;
                     }
                     llama_token token_id = node->value;
-                    const auto & token_data = vocab.id_to_token[token_id];
+                    const auto & token_data = vocab.get_token_data(token_id);
 
                     // we set the user-defined token scores to 0 to make them more likely to be selected
                     // (normal token scores are log probabilities, so they are negative)
                     // score type is double here to make tokenization results exactly
                     // the same as in the HF tokenizer using SentencePiece
-                    const double token_score = llama_is_user_defined_token(vocab, token_id) ? 0.0 : token_data.score;
+                    const double token_score = vocab.is_user_defined(token_id) ? 0.0 : token_data.score;
                     const double challenger_score = current_best.score_sum + token_score;
                     struct best_tokenization & current_champ = tokenization_results[prefix_offset];
                     if (challenger_score > current_champ.score_sum) {
@@ -927,11 +845,11 @@ struct llm_tokenizer_ugm_session {
             // if we didn't find a valid token corresponding to the whole UTF code point
             // then use unknown token as the tokenization of this UTF code point
             if (!single_codepoint_token_found) {
-                const double challenger_score = current_best.score_sum + ugm_tokenizer->unknown_token_score;
+                const double challenger_score = current_best.score_sum + tokenizer.unknown_token_score;
                 prefix_offset = input_offset + n_utf8_code_units;
                 struct best_tokenization & current_champ = tokenization_results[prefix_offset];
                 if (challenger_score > current_champ.score_sum) {
-                    struct best_tokenization challenger = { vocab.special_unk_id, input_offset, (float) challenger_score };
+                    struct best_tokenization challenger = { vocab.token_unk(), input_offset, (float) challenger_score };
                     current_champ = challenger;
                 }
             }
@@ -944,7 +862,7 @@ struct llm_tokenizer_ugm_session {
         // merge sequences of consecutive unknown tokens into single unknown tokens
         bool is_prev_unknown = false;
         for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
-            bool is_unknown = tokenization.token_id == vocab.special_unk_id;
+            bool is_unknown = tokenization.token_id == vocab.token_unk();
             if (!(is_prev_unknown && is_unknown)) {
                 output.push_back(tokenization.token_id);
             }
@@ -971,11 +889,11 @@ private:
         normalized->clear();
         normalized->reserve(input.size() * 3);
 
-        const std::string space = vocab.tokenizer_escape_whitespaces ? ugm_tokenizer->escaped_space : " ";
+        const std::string space = vocab.get_escape_whitespaces() ? tokenizer.escaped_space : " ";
 
-        bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
-        bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
-        bool shall_merge_spaces = vocab.tokenizer_remove_extra_whitespaces;
+        const bool shall_prepend_space = !vocab.get_treat_whitespace_as_suffix() && vocab.get_add_space_prefix();
+        const bool shall_append_space  =  vocab.get_treat_whitespace_as_suffix() && vocab.get_add_space_prefix();
+        const bool shall_merge_spaces  =  vocab.get_remove_extra_whitespaces();
 
         bool is_space_prepended = false;
         bool processing_non_ws = false;
@@ -1067,7 +985,7 @@ private:
 
         // if input prefix matches some user-defined token return this token as normalization result
         auto user_defined_token_match =
-           ugm_tokenizer->user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
+           tokenizer.user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
         if (user_defined_token_match.second > 0) {
             return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
         }
@@ -1075,8 +993,8 @@ private:
         size_t longest_prefix_length = 0;
         size_t longest_prefix_offset = 0;
 
-        if (ugm_tokenizer->xcda_array_size > 0) {
-            struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_tokenizer->xcda_array_size);
+        if (tokenizer.xcda_array_size > 0) {
+            struct xcda_array_view xcda_view(tokenizer.xcda_array, tokenizer.xcda_array_size);
 
             // Find the longest normalized sequence matching the input prefix by walking
             // the XOR-compressed compact double array (XCDA) starting from the root node
@@ -1112,10 +1030,10 @@ private:
 
         if (longest_prefix_length > 0) {
             // we have a match, so return the replacement sequence
-            if (longest_prefix_offset >= ugm_tokenizer->prefix_replacements_size) {
+            if (longest_prefix_offset >= tokenizer.prefix_replacements_size) {
                 throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
             }
-            const char * prefix_replacement = &(ugm_tokenizer->prefix_replacements)[longest_prefix_offset];
+            const char * prefix_replacement = &(tokenizer.prefix_replacements)[longest_prefix_offset];
             return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
         }
 
@@ -1132,7 +1050,7 @@ private:
     }
 
     const llama_vocab & vocab;
-    const llm_tokenizer_ugm * ugm_tokenizer;
+    const llm_tokenizer_ugm & tokenizer;
 };
 
 //
@@ -1194,15 +1112,15 @@ static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escape
 }
 
 struct llm_tokenizer_rwkv : llm_tokenizer {
-    llm_tokenizer_rwkv(const llama_vocab & vocab) : llm_tokenizer() {
+    llm_tokenizer_rwkv(const llama_vocab & vocab) {
         // RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
         // For now, we decode the vocab here into the lookup we'll use for tokenization.
 
         // build trie
-        for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
-            const auto & token = vocab.id_to_token[id];
-            const auto data = llama_unescape_rwkv_token(token.text);
-            token_matcher.insert((const char *) data.data(), data.size(), id);
+        for (uint32_t id = 0; id < vocab.n_tokens(); ++id) {
+            const auto & data = vocab.get_token_data(id);
+            const auto text = llama_unescape_rwkv_token(data.text);
+            token_matcher.insert((const char *) text.data(), text.size(), id);
         }
     }
 
@@ -1210,16 +1128,15 @@ struct llm_tokenizer_rwkv : llm_tokenizer {
 };
 
 struct llm_tokenizer_rwkv_session {
-    llm_tokenizer_rwkv_session(const llama_vocab & vocab) : vocab(vocab),
-        rwkv_tokenizer(static_cast<const llm_tokenizer_rwkv &>(*vocab.tokenizer)) {}
+    llm_tokenizer_rwkv_session(const llama_vocab & vocab, const llm_tokenizer_rwkv & tokenizer) : vocab(vocab), tokenizer(tokenizer) {}
 
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+    void tokenize(const std::string & text, std::vector<llama_token> & output) {
         uint32_t position = 0;
         while (position < text.size()) {
-            const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]);
+            const struct naive_trie * node = tokenizer.token_matcher.traverse(text[position]);
             if (node == NULL) {
                 // no matching token found, add unknown token
-                output.push_back(vocab.special_unk_id);
+                output.push_back(vocab.token_unk());
                 position += 1;
                 continue;
             }
@@ -1243,33 +1160,11 @@ struct llm_tokenizer_rwkv_session {
 
 private:
     const llama_vocab & vocab;
-    const llm_tokenizer_rwkv & rwkv_tokenizer;
+    const llm_tokenizer_rwkv & tokenizer;
 };
 
-void llama_vocab::init_tokenizer() {
-    switch (type) {
-        case LLAMA_VOCAB_TYPE_SPM:
-            tokenizer = new llm_tokenizer_spm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_BPE:
-            tokenizer = new llm_tokenizer_bpe(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_WPM:
-            tokenizer = new llm_tokenizer_wpm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_UGM:
-            tokenizer = new llm_tokenizer_ugm(*this);
-            break;
-        case LLAMA_VOCAB_TYPE_RWKV:
-            tokenizer = new llm_tokenizer_rwkv(*this);
-            break;
-        default:
-            GGML_ABORT("unsupported vocab type");
-    }
-}
-
 //
-// (de-) tokenize
+// impl
 //
 
 typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
@@ -1278,7 +1173,7 @@ typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
 } FRAGMENT_BUFFER_VARIANT_TYPE;
 
 struct fragment_buffer_variant {
-    fragment_buffer_variant(llama_vocab::id _token)
+    fragment_buffer_variant(llama_token _token)
     :
         type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
         token(_token),
@@ -1289,7 +1184,7 @@ struct fragment_buffer_variant {
     fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
     :
         type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
-        token((llama_vocab::id) - 1),
+        token((llama_token) - 1),
         raw_text(_raw_text),
         offset(_offset),
         length(_length){
@@ -1299,20 +1194,963 @@ struct fragment_buffer_variant {
         }
 
     const FRAGMENT_BUFFER_VARIANT_TYPE type;
-    const llama_vocab::id token;
+    const llama_token token;
     const std::string _dummy;
     const std::string & raw_text;
     const uint64_t offset;
     const uint64_t length;
 };
 
+struct llama_vocab::impl {
+    uint32_t n_token_types = 0; // for BERT-style token types
+
+    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
+    enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+
+    int max_token_len = 0; // used for optimizing longest token search
+
+    // default LLaMA special tokens
+    // TODO: should we set all of these to LLAMA_TOKEN_NULL?
+    llama_token special_bos_id  = 1;
+    llama_token special_eos_id  = 2;
+    llama_token special_eot_id  = LLAMA_TOKEN_NULL;
+    llama_token special_eom_id  = LLAMA_TOKEN_NULL;
+    llama_token special_unk_id  = 0;
+    llama_token special_sep_id  = LLAMA_TOKEN_NULL;
+    llama_token special_pad_id  = LLAMA_TOKEN_NULL;
+    llama_token special_cls_id  = LLAMA_TOKEN_NULL; // TODO: revisit if this is really needed https://github.com/ggerganov/llama.cpp/pull/10930
+    llama_token special_mask_id = LLAMA_TOKEN_NULL;
+
+    llama_token linefeed_id = 13;
+
+    // fim tokens
+    llama_token special_fim_pre_id = LLAMA_TOKEN_NULL;
+    llama_token special_fim_suf_id = LLAMA_TOKEN_NULL;
+    llama_token special_fim_mid_id = LLAMA_TOKEN_NULL;
+    llama_token special_fim_pad_id = LLAMA_TOKEN_NULL;
+    llama_token special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
+    llama_token special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
+
+    // tokenizer flags
+    bool add_space_prefix           = false;
+    bool add_bos                    = false;
+    bool add_eos                    = false;
+    bool ignore_merges              = false;
+    bool clean_spaces               = false;  // clean_up_tokenization_spaces
+    bool remove_extra_whitespaces   = false;
+    bool escape_whitespaces         = true;
+    bool treat_whitespace_as_suffix = false;
+
+    std::unordered_map<std::string, llama_token> token_to_id;
+    std::vector<token_data>                      id_to_token;
+
+    std::vector<llama_token> cache_special_tokens;
+    std::vector<std::string> cache_token_to_piece; // llama_token_to_piece(special = true);
+
+    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
+
+    // set of all tokens that cause "end of generation"
+    std::set<llama_token> special_eog_ids;
+
+    std::unique_ptr<llm_tokenizer> tokenizer;
+
+    std::vector<char> precompiled_charsmap;
+
+    impl(const llama_vocab & vocab) : vocab(vocab) {
+    }
+
+    ~impl() = default;
+
+    void load(llama_model_loader & ml, const LLM_KV & kv);
+
+    enum llama_vocab_type get_type() const;
+
+    std::string type_name() const;
+
+    bool is_normal      (llama_token id) const;
+    bool is_unknown     (llama_token id) const;
+    bool is_control     (llama_token id) const;
+    bool is_byte        (llama_token id) const;
+    bool is_user_defined(llama_token id) const;
+    bool is_unused      (llama_token id) const;
+    bool is_eog         (llama_token id) const;
+
+    uint8_t token_to_byte(llama_token id) const;
+
+    llama_token_attr token_get_attr(llama_token id) const;
+
+    void init_tokenizer(enum llama_vocab_type type);
+
+    void tokenizer_st_partition(std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) const;
+
+    std::string token_to_piece_for_cache(
+                  llama_token   token,
+                         bool   special) const;
+
+
+    std::vector<llama_token> tokenize(
+            const std::string & raw_text,
+                         bool   add_special,
+                         bool   parse_special = false) const;
+
+    int32_t tokenize(
+                   const char * text,
+                      int32_t   text_len,
+                  llama_token * tokens,
+                      int32_t   n_tokens_max,
+                         bool   add_special,
+                         bool   parse_special) const;
+
+    // does not write null-terminator to buf
+    int32_t token_to_piece(
+                  llama_token   token,
+                         char * buf,
+                      int32_t   length,
+                      int32_t   lstrip,
+                         bool   special) const;
+
+    // use cached data
+    const std::string & token_to_piece(llama_token token) const;
+
+    int32_t detokenize(
+            const llama_token * tokens,
+                      int32_t   n_tokens,
+                         char * text,
+                      int32_t   text_len_max,
+                         bool   remove_special,
+                         bool   unparse_special) const;
+
+    std::string detokenize(
+            const std::vector<llama_token> & tokens,
+                                      bool   special) const;
+
+    void print_info() const;
+
+private:
+    const llama_vocab & vocab;
+};
+
+void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
+    struct gguf_context * ctx = ml.meta.get();
+
+    // determine vocab type
+    {
+        std::string tokenizer_model;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
+        ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
+
+        ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, n_token_types, false);
+
+        if (tokenizer_model == "no_vocab" || tokenizer_model == "none") {
+            type = LLAMA_VOCAB_TYPE_NONE;
+
+            // default special tokens
+            special_bos_id  = LLAMA_TOKEN_NULL;
+            special_eos_id  = LLAMA_TOKEN_NULL;
+            special_unk_id  = LLAMA_TOKEN_NULL;
+            special_sep_id  = LLAMA_TOKEN_NULL;
+            special_pad_id  = LLAMA_TOKEN_NULL;
+            special_cls_id  = LLAMA_TOKEN_NULL;
+            special_mask_id = LLAMA_TOKEN_NULL;
+            linefeed_id     = LLAMA_TOKEN_NULL;
+
+            // read vocab size from metadata
+            uint32_t n_tokens = 0;
+            if (!ml.get_key(LLM_KV_VOCAB_SIZE, n_tokens, false)) {
+                LLAMA_LOG_WARN("%s: there is no vocab_size in metadata\n", __func__);
+            }
+
+            return;
+        }
+
+        if (tokenizer_model == "llama") {
+            type = LLAMA_VOCAB_TYPE_SPM;
+
+            // default special tokens
+            special_bos_id  = 1;
+            special_eos_id  = 2;
+            special_unk_id  = 0;
+            special_sep_id  = LLAMA_TOKEN_NULL;
+            special_pad_id  = LLAMA_TOKEN_NULL;
+            special_cls_id  = LLAMA_TOKEN_NULL;
+            special_mask_id = LLAMA_TOKEN_NULL;
+        } else if (tokenizer_model == "bert") {
+            type = LLAMA_VOCAB_TYPE_WPM;
+
+            // default special tokens
+            special_bos_id  = LLAMA_TOKEN_NULL;
+            special_eos_id  = LLAMA_TOKEN_NULL;
+            special_unk_id  = 100;
+            special_sep_id  = 102;
+            special_pad_id  = 0;
+            special_cls_id  = 101;
+            special_mask_id = 103;
+        } else if (tokenizer_model == "gpt2") {
+            type = LLAMA_VOCAB_TYPE_BPE;
+
+            // read bpe merges and populate bpe ranks
+            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+
+            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+            for (int i = 0; i < n_merges; i++) {
+                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+                //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
+
+                std::string first;
+                std::string second;
+
+                const size_t pos = word.find(' ', 1);
+
+                if (pos != std::string::npos) {
+                    first  = word.substr(0, pos);
+                    second = word.substr(pos + 1);
+                }
+
+                bpe_ranks.emplace(std::make_pair(first, second), i);
+            }
+
+            // default special tokens
+            special_bos_id  = 11;
+            special_eos_id  = 11;
+            special_unk_id  = LLAMA_TOKEN_NULL;
+            special_sep_id  = LLAMA_TOKEN_NULL;
+            special_pad_id  = LLAMA_TOKEN_NULL;
+            special_cls_id  = LLAMA_TOKEN_NULL;
+            special_mask_id = LLAMA_TOKEN_NULL;
+        } else if (tokenizer_model == "t5") {
+            type = LLAMA_VOCAB_TYPE_UGM;
+
+            // default special tokens
+            special_bos_id  = LLAMA_TOKEN_NULL;
+            special_eos_id  = 1;
+            special_unk_id  = 2;
+            special_sep_id  = LLAMA_TOKEN_NULL;
+            special_pad_id  = 0;
+            special_cls_id  = LLAMA_TOKEN_NULL;
+            special_mask_id = LLAMA_TOKEN_NULL;
+
+            const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
+            if (precompiled_charsmap_keyidx != -1) {
+                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+                const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
+                precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
+#ifdef IS_BIG_ENDIAN
+                // correct endiannes of data in precompiled_charsmap binary blob
+                uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
+                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
+                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
+                size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
+                uint32_t * xcda_array = (uint32_t *) &precompiled_charsmap[sizeof(uint32_t)];
+                for (size_t i = 0; i < xcda_array_size; ++i) {
+                    xcda_array[i] = __builtin_bswap32(xcda_array[i]);
+                }
+#endif
+            }
+        } else if (tokenizer_model == "rwkv") {
+            type = LLAMA_VOCAB_TYPE_RWKV;
+
+            // default special tokens
+            special_bos_id = LLAMA_TOKEN_NULL;
+            special_eos_id = LLAMA_TOKEN_NULL;
+            special_unk_id = LLAMA_TOKEN_NULL;
+            special_sep_id = LLAMA_TOKEN_NULL;
+            special_pad_id = LLAMA_TOKEN_NULL;
+        } else {
+            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
+        }
+
+        // for now, only BPE models have pre-tokenizers
+        if (type == LLAMA_VOCAB_TYPE_BPE) {
+            add_space_prefix = false;
+            clean_spaces = true;
+            if (tokenizer_pre.empty()) {
+                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
+                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
+                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
+                LLAMA_LOG_WARN("%s:                                             \n", __func__);
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (tokenizer_pre == "default") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            } else if (
+                    tokenizer_pre == "llama3"   ||
+                    tokenizer_pre == "llama-v3" ||
+                    tokenizer_pre == "llama-bpe"||
+                    tokenizer_pre == "falcon3") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+                ignore_merges = true;
+                add_bos = true;
+            } else if (
+                    tokenizer_pre == "deepseek-llm") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
+                clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "deepseek-coder") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
+                clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "deepseek-v3") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
+                clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "falcon") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_FALCON;
+            } else if (
+                    tokenizer_pre == "mpt") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_MPT;
+            } else if (
+                    tokenizer_pre == "starcoder") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_STARCODER;
+            } else if (
+                    tokenizer_pre == "gpt-2"   ||
+                    tokenizer_pre == "phi-2"   ||
+                    tokenizer_pre == "jina-es" ||
+                    tokenizer_pre == "jina-de" ||
+                    tokenizer_pre == "gigachat"   ||
+                    tokenizer_pre == "jina-v1-en" ||
+                    tokenizer_pre == "jina-v2-es" ||
+                    tokenizer_pre == "jina-v2-de" ||
+                    tokenizer_pre == "jina-v2-code" ||
+                    tokenizer_pre == "roberta-bpe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
+            } else if (
+                    tokenizer_pre == "refact") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT;
+            } else if (
+                tokenizer_pre == "command-r") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "qwen2") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "stablelm2") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
+            } else if (
+                tokenizer_pre == "olmo") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_OLMO;
+            } else if (
+                tokenizer_pre == "dbrx") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_DBRX;
+            } else if (
+                tokenizer_pre == "smaug-bpe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+            } else if (
+                tokenizer_pre == "poro-chat") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_PORO;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "chatglm-bpe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
+                special_bos_id = LLAMA_TOKEN_NULL;
+            } else if (
+                tokenizer_pre == "viking") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_VIKING;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "jais") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_JAIS;
+            } else if (
+                tokenizer_pre == "tekken") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
+                clean_spaces = false;
+                ignore_merges = true;
+                add_bos = true;
+            } else if (
+                tokenizer_pre == "smollm") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "codeshell") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
+            } else if (
+                tokenizer_pre == "bloom") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_BLOOM;
+            } else if (
+                tokenizer_pre == "gpt3-finnish") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
+            } else if (
+                tokenizer_pre == "exaone") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_EXAONE;
+            } else if (
+                tokenizer_pre == "chameleon") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
+                add_bos = true;
+                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "minerva-7b") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_MINERVA;
+            } else if (
+                tokenizer_pre == "megrez") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+            } else {
+                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+            }
+        } else if (type == LLAMA_VOCAB_TYPE_SPM) {
+            pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            add_space_prefix = true;
+            clean_spaces = false;
+            add_bos = true;
+            add_eos = false;
+        } else if (type == LLAMA_VOCAB_TYPE_WPM) {
+            pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            add_space_prefix = false;
+            clean_spaces = true;
+            add_bos = true;
+            add_eos = false;
+        } else if (type == LLAMA_VOCAB_TYPE_UGM) {
+            pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            add_bos = false;
+            add_eos = true;
+        } else if (type == LLAMA_VOCAB_TYPE_RWKV) {
+            pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            add_space_prefix = false;
+            clean_spaces = false;
+            add_bos = false;
+            add_eos = false;
+        } else {
+            pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+        }
+
+        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      add_space_prefix,         false);
+        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, remove_extra_whitespaces, false);
+    }
+
+    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
+    if (token_idx == -1) {
+        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+    }
+
+    const float * scores = nullptr;
+    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
+    if (score_idx != -1) {
+        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+    }
+
+    const int * toktypes = nullptr;
+    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
+    if (toktype_idx != -1) {
+        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+    }
+
+    uint32_t n_tokens = gguf_get_arr_n(ctx, token_idx);
+    id_to_token.resize(n_tokens);
+
+    for (uint32_t i = 0; i < n_tokens; i++) {
+        std::string word = gguf_get_arr_str(ctx, token_idx, i);
+        if (word.empty()) {
+            LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i);
+            word = "[EMPTY_" + std::to_string(i) + "]";
+        }
+
+        token_to_id[word] = i;
+        max_token_len = std::max(max_token_len, (int) word.size());
+
+        auto & token_data = id_to_token[i];
+        token_data.text  = std::move(word);
+        token_data.score = scores ? scores[i] : 0.0f;
+        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
+
+        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
+            switch(toktypes[i]) {
+                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
+                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
+                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
+                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
+                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
+                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+            }
+        }
+    }
+    GGML_ASSERT(id_to_token.size() == token_to_id.size());
+
+    init_tokenizer(type);
+
+    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
+    if (type == LLAMA_VOCAB_TYPE_SPM) {
+        try {
+            linefeed_id = vocab.byte_to_token('\n');
+        } catch (const std::exception & e) {
+            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
+            linefeed_id = special_pad_id;
+        }
+    } else if (type == LLAMA_VOCAB_TYPE_WPM) {
+        linefeed_id = special_pad_id;
+    } else if (type == LLAMA_VOCAB_TYPE_RWKV) {
+        const std::vector<int> ids = tokenize("\n", false);
+        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+        linefeed_id = ids[0];
+    } else {
+        const std::vector<int> ids = tokenize("\xC4\x8A", false); // U+010A
+
+        //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+        if (ids.empty()) {
+            LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__);
+            linefeed_id = special_pad_id;
+        } else {
+            linefeed_id = ids[0];
+        }
+    }
+
+    // special tokens
+    {
+        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
+            { LLM_KV_TOKENIZER_BOS_ID,     special_bos_id     },
+            { LLM_KV_TOKENIZER_EOS_ID,     special_eos_id     },
+            { LLM_KV_TOKENIZER_EOT_ID,     special_eot_id     },
+            { LLM_KV_TOKENIZER_EOM_ID,     special_eom_id     },
+            { LLM_KV_TOKENIZER_UNK_ID,     special_unk_id     },
+            { LLM_KV_TOKENIZER_SEP_ID,     special_sep_id     },
+            { LLM_KV_TOKENIZER_PAD_ID,     special_pad_id     },
+            { LLM_KV_TOKENIZER_CLS_ID,     special_cls_id     },
+            { LLM_KV_TOKENIZER_MASK_ID,    special_mask_id    },
+            { LLM_KV_TOKENIZER_FIM_PRE_ID, special_fim_pre_id },
+            { LLM_KV_TOKENIZER_FIM_SUF_ID, special_fim_suf_id },
+            { LLM_KV_TOKENIZER_FIM_MID_ID, special_fim_mid_id },
+            { LLM_KV_TOKENIZER_FIM_PAD_ID, special_fim_pad_id },
+            { LLM_KV_TOKENIZER_FIM_REP_ID, special_fim_rep_id },
+            { LLM_KV_TOKENIZER_FIM_SEP_ID, special_fim_sep_id },
+
+            // deprecated
+            { LLM_KV_TOKENIZER_PREFIX_ID, special_fim_pre_id },
+            { LLM_KV_TOKENIZER_SUFFIX_ID, special_fim_suf_id },
+            { LLM_KV_TOKENIZER_MIDDLE_ID, special_fim_mid_id },
+        };
+
+        for (const auto & it : special_token_types) {
+            const std::string & key = kv(std::get<0>(it));
+            int32_t & id = std::get<1>(it);
+
+            uint32_t new_id;
+            if (!ml.get_key(std::get<0>(it), new_id, false)) {
+                continue;
+            }
+            if (new_id >= id_to_token.size()) {
+                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
+                    __func__, key.c_str(), new_id, id);
+            } else {
+                id = new_id;
+            }
+        }
+
+        // Handle add_bos and add_eos
+        {
+            bool temp = true;
+
+            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
+                add_bos = temp;
+            }
+            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
+                add_eos = temp;
+            }
+        }
+
+        // auto-detect special tokens by text
+        // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
+        //       for now, we apply this workaround to find the tokens based on their text
+
+        for (const auto & t : token_to_id) {
+            // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
+            if (special_eot_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|eot_id|>"
+                        || t.first == "<|im_end|>"
+                        || t.first == "<|end|>"
+                        || t.first == "<end_of_turn>"
+                        || t.first == "<|endoftext|>"
+                        || t.first == "<EOT>"
+                        || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
+                   ) {
+                    special_eot_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find EOM token: "<|eom_id|>"
+            if (special_eom_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|eom_id|>"
+                        ) {
+                    special_eom_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
+            if (special_fim_pre_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_prefix|>"  // Qwen
+                        || t.first == "<fim-prefix>"
+                        || t.first == "<｜fim▁begin｜>" // DeepSeek
+                        || t.first == "<PRE>"
+                        ) {
+                    special_fim_pre_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
+            if (special_fim_suf_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_suffix|>" // Qwen
+                        || t.first == "<fim-suffix>"
+                        || t.first == "<｜fim▁hole｜>" // DeepSeek
+                        || t.first == "<SUF>"
+                        ) {
+                    special_fim_suf_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
+            if (special_fim_mid_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_middle|>" // Qwen
+                        || t.first == "<fim-middle>"
+                        || t.first == "<｜fim▁end｜>"  // DeepSeek
+                        || t.first == "<MID>"
+                        ) {
+                    special_fim_mid_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
+            if (special_fim_pad_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_pad|>" // Qwen
+                        || t.first == "<fim-pad>"
+                        || t.first == "<PAD>"
+                        ) {
+                    special_fim_pad_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
+            if (special_fim_rep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_repo|>"  // Qwen
+                        || t.first == "<|repo_name|>"
+                        || t.first == "<fim-repo>"
+                        || t.first == "<REPO>"
+                        ) {
+                    special_fim_rep_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SEP token: "<|file_sep|>"
+            if (special_fim_sep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|file_sep|>" // Qwen
+                        ) {
+                    special_fim_sep_id = t.second;
+                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+        }
+
+        // maintain a list of tokens that cause end-of-generation
+        // this is currently determined based on the token text, which is obviously not ideal
+        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
+        special_eog_ids.clear();
+
+        if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) {
+            special_eog_ids.insert(special_fim_pad_id);
+        }
+
+        if (special_fim_rep_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_rep_id) == 0) {
+            special_eog_ids.insert(special_fim_rep_id);
+        }
+
+        if (special_fim_sep_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_sep_id) == 0) {
+            special_eog_ids.insert(special_fim_sep_id);
+        }
+
+        for (const auto & t : token_to_id) {
+            if (false
+                    || t.first == "<|eot_id|>"
+                    || t.first == "<|im_end|>"
+                    || t.first == "<|end|>"
+                    || t.first == "<end_of_turn>"
+                    || t.first == "<|endoftext|>"
+                    || t.first == "<|eom_id|>"
+                    || t.first == "<EOT>"
+               ) {
+                special_eog_ids.insert(t.second);
+                if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                            __func__, t.second, t.first.c_str());
+                    id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                }
+            } else {
+                // token is control, but not marked as EOG -> print a debug log
+                if (id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && special_eog_ids.count(t.second) == 0) {
+                    LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
+                            __func__, t.second, t.first.c_str());
+                }
+            }
+        }
+
+        // sanity checks
+        if (special_eos_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eos_id) == 0) {
+            special_eog_ids.insert(special_eos_id);
+            LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+
+        if (special_eot_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eot_id) == 0) {
+            special_eog_ids.insert(special_eot_id);
+            LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+
+        if (special_eom_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_eom_id) == 0) {
+            special_eog_ids.insert(special_eom_id);
+            LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
+        }
+    }
+
+    // build special tokens cache
+    {
+        for (llama_token id = 0; id < (llama_token) n_tokens; ++id) {
+            if (id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
+                cache_special_tokens.push_back(id);
+            }
+        }
+
+        std::sort(cache_special_tokens.begin(), cache_special_tokens.end(),
+            [&] (const llama_token a, const llama_token b) {
+                return id_to_token[a].text.size() > id_to_token[b].text.size();
+            }
+        );
+
+        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t) cache_special_tokens.size());
+    }
+
+    // build token to piece cache
+    {
+        size_t size_cache = 0;
+
+        std::vector<std::string> cache(n_tokens);
+
+        for (uint32_t id = 0; id < n_tokens; ++id) {
+            cache[id] = token_to_piece_for_cache(id, true);
+
+            size_cache += cache[id].size();
+        }
+
+        std::swap(cache_token_to_piece, cache);
+
+        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+    }
+
+    // Handle per token attributes
+    //NOTE: Each model customizes per token attributes.
+    //NOTE: Per token attributes are missing from the GGUF file.
+    //TODO: Extract attributes from GGUF file.
+    {
+        auto _contains_any = [] (const std::string & str, const std::vector<std::string> & substrs) -> bool {
+            for (const auto & substr : substrs) {
+                if (str.find(substr) < std::string::npos) {
+                    return true;
+                }
+            }
+            return false;
+        };
+
+        auto _set_tokenid_attr = [&] (const llama_token id, llama_token_attr attr, bool value) {
+            uint32_t current = id_to_token.at(id).attr;
+            current = value ? (current | attr) : (current & ~attr);
+            id_to_token[id].attr = (llama_token_attr) current;
+        };
+
+        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+            _set_tokenid_attr(token_to_id.at(token), attr, value);
+        };
+
+        std::string model_name;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_GENERAL_NAME,  model_name,    false);
+        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+        // model name to lowercase
+        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+            [] (const std::string::value_type x) {
+                return std::tolower(x);
+            }
+        );
+
+        // set attributes by model/tokenizer name
+        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
+            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+            for (auto id : cache_special_tokens) {
+                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (const auto * token : {"</s>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (const auto * token : {"<unk>", "<s>", "<|endoftext|>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+            }
+        }
+    }
+}
+
+enum llama_vocab_type llama_vocab::impl::get_type() const {
+    return type;
+}
+
+std::string llama_vocab::impl::type_name() const{
+    switch (type) {
+        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
+        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
+        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
+        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
+        case LLAMA_VOCAB_TYPE_UGM:  return "UGM";
+        case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
+        default:                    return "unknown";
+    }
+}
+
+bool llama_vocab::impl::is_normal(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
+}
+
+bool llama_vocab::impl::is_unknown(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
+}
+
+bool llama_vocab::impl::is_control(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
+}
+
+bool llama_vocab::impl::is_byte(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
+}
+
+bool llama_vocab::impl::is_user_defined(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
+}
+
+bool llama_vocab::impl::is_unused(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
+}
+
+bool llama_vocab::impl::is_eog(llama_token id) const {
+    return id != LLAMA_TOKEN_NULL && special_eog_ids.count(id) > 0;
+}
+
+uint8_t llama_vocab::impl::token_to_byte(llama_token id) const {
+    GGML_ASSERT(get_type() != LLAMA_VOCAB_TYPE_NONE);
+    GGML_ASSERT(is_byte(id));
+    const auto & token_data = id_to_token.at(id);
+    switch (get_type()) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            auto buf = token_data.text.substr(3, 2);
+            return strtol(buf.c_str(), NULL, 16);
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            GGML_ABORT("fatal error");
+        }
+        case LLAMA_VOCAB_TYPE_WPM: {
+            GGML_ABORT("fatal error");
+        }
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+llama_token_attr llama_vocab::impl::token_get_attr(llama_token id) const {
+    GGML_ASSERT(type != LLAMA_VOCAB_TYPE_NONE);
+    return id_to_token.at(id).attr;
+}
+
+void llama_vocab::impl::init_tokenizer(enum llama_vocab_type type) {
+    LLAMA_LOG_DEBUG("%s: initializing tokenizer for type %d\n", __func__, type);
+
+    switch (type) {
+        case LLAMA_VOCAB_TYPE_SPM:
+            tokenizer = std::make_unique<llm_tokenizer_spm>(vocab);
+            break;
+        case LLAMA_VOCAB_TYPE_BPE:
+            tokenizer = std::make_unique<llm_tokenizer_bpe>(vocab);
+            break;
+        case LLAMA_VOCAB_TYPE_WPM:
+            tokenizer = std::make_unique<llm_tokenizer_wpm>(vocab);
+            break;
+        case LLAMA_VOCAB_TYPE_UGM:
+            tokenizer = std::make_unique<llm_tokenizer_ugm>(vocab, precompiled_charsmap);
+            break;
+        case LLAMA_VOCAB_TYPE_RWKV:
+            tokenizer = std::make_unique<llm_tokenizer_rwkv>(vocab);
+            break;
+        default:
+            GGML_ABORT("unsupported vocab type");
+    }
+}
+
+//
+// (de-) tokenize
+//
+
 // #define PRETOKENIZERDEBUG
 
-static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
+void llama_vocab::impl::tokenizer_st_partition(std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) const {
     // for each special token
-    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
-        const auto & data = vocab.id_to_token[special_id];
-        const auto & special_token = data.text;
+    for (const llama_token special_id : cache_special_tokens) {
+        const auto & data = vocab.get_token_data(special_id);
+        const auto & text = data.text;
 
         if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
             // Ignore control and unknown tokens when parse_special == false
@@ -1339,13 +2177,13 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     // find the first occurrence of a given special token in this fragment
                     //  passing offset argument only limit the "search area" but match coordinates
                     //  are still relative to the source full raw_text
-                    auto match = raw_text.find(special_token, raw_text_base_offset);
+                    auto match = raw_text.find(text, raw_text_base_offset);
 
                     // no occurrences found, stop processing this fragment for a given special token
                     if (match == std::string::npos) break;
 
                     // check if match is within bounds of offset <-> length
-                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
+                    if (match + text.length() > raw_text_base_offset + raw_text_base_length) break;
 
 #ifdef PRETOKENIZERDEBUG
                     LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
@@ -1380,9 +2218,9 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     it++;
 
                     // right
-                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
-                        int64_t right_reminder_offset = match + special_token.length();
-                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+                    if (match + text.length() < raw_text_base_offset + raw_text_base_length) {
+                        int64_t right_reminder_offset = match + text.length();
+                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + text.length());
 
                         if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
                             while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
@@ -1428,322 +2266,29 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
     }
 }
 
-std::vector<llama_vocab::id> llama_tokenize_internal(
-        const llama_vocab & vocab,
-        std::string raw_text,
-        bool add_special,
-        bool parse_special) {
-    GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
-
-    std::vector<llama_vocab::id> output;
-    std::forward_list<fragment_buffer_variant> fragment_buffer;
-
-    if (!raw_text.empty()) {
-        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
-        tokenizer_st_partition(vocab, fragment_buffer, parse_special);
+// NOTE: avoid ever using this except for building the token_to_piece caches
+std::string llama_vocab::impl::token_to_piece_for_cache(llama_token token, bool special) const {
+    std::string piece;
+    piece.resize(piece.capacity());  // using string internal cache
+    const int n_chars = vocab.token_to_piece(token, &piece[0], piece.size(), 0, special);
+    if (n_chars < 0) {
+        piece.resize(-n_chars);
+        int check = vocab.token_to_piece(token, &piece[0], piece.size(), 0, special);
+        GGML_ASSERT(check == -n_chars);
+    }
+    else {
+        piece.resize(n_chars);
     }
 
-    switch (vocab.type) {
-        case LLAMA_VOCAB_TYPE_SPM:
-            {
-                // OG tokenizer behavior:
-                //
-                // tokenizer.encode('', add_special_tokens=True)  returns [1]
-                // tokenizer.encode('', add_special_tokens=False) returns []
-
-                bool is_prev_special = true;  // prefix with space if first token
-
-                if (add_special && vocab.tokenizer_add_bos) {
-                    GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_bos_id);
-                    is_prev_special = true;
-                }
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-                        // prefix with space if previous is special
-                        if (vocab.tokenizer_add_space_prefix && is_prev_special) {
-                            raw_text = " " + raw_text;
-                        }
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        llama_escape_whitespace(raw_text);
-                        llm_tokenizer_spm_session session(vocab);
-                        session.tokenize(raw_text, output);
-                        is_prev_special = false;
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                        is_prev_special = true;
-                    }
-                }
-
-                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
-
-                if (add_special && vocab.tokenizer_add_eos) {
-                    GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_BPE:
-            {
-                llm_tokenizer_bpe_session session(vocab);
-                // it calls some other methods that are not exist in llm_tokenizer,
-                // here just cast it to bpe tokenizer object
-                if (add_special) {
-                    session.append_bos(output);
-                }
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        session.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        session.append(fragment.token, output);
-                    }
-                }
-
-                if (add_special) {
-                    session.append_eos(output);
-                    session.check_double_bos_eos(output);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_WPM:
-            {
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_cls_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_cls_id);
-                }
-
-                llm_tokenizer_wpm_session session(vocab);
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        session.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
-
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_sep_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_sep_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_UGM:
-            {
-                if (add_special && vocab.tokenizer_add_bos) {
-                    GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_bos_id);
-                }
-                llm_tokenizer_ugm_session session(vocab);
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        session.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
-
-                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
-
-                if (add_special && vocab.tokenizer_add_eos) {
-                    GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_RWKV:
-            {
-                llm_tokenizer_rwkv_session session(vocab);
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-
-                        session.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_NONE:
-            GGML_ABORT("fatal error");
-    }
-
-    return output;
+    return piece;
 }
 
-llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    static const char * hex = "0123456789ABCDEF";
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM:
-        case LLAMA_VOCAB_TYPE_UGM: {
-            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
-            auto token = vocab.token_to_id.find(buf);
-            if (token != vocab.token_to_id.end()) {
-                return (*token).second;
-            }
-            // Try to fall back to just the byte as a string
-            const char buf2[2] = { (char)ch, 0 };
-            return vocab.token_to_id.at(buf2);
-        }
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_BPE: {
-            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
-        }
-        default:
-            GGML_ABORT("fatal error");
-    }
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
 }
 
-const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[token].text.c_str();
-}
-
-float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[token].score;
-}
-
-llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[token].attr;
-}
-
-bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
-    return token != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(token) > 0;
-}
-
-bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
-    return llama_is_control_token(vocab, token);
-}
-
-llama_token llama_token_bos_impl(const struct llama_vocab & vocab) {
-    return vocab.type != LLAMA_VOCAB_TYPE_WPM ? vocab.special_bos_id : vocab.special_cls_id;
-}
-
-llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
-    return vocab.special_eos_id;
-}
-
-llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
-    return vocab.special_eot_id;
-}
-
-llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
-    return vocab.special_eom_id;
-}
-
-llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
-    return vocab.special_cls_id;
-}
-
-llama_token llama_token_sep_impl(const struct llama_vocab & vocab) {
-    return vocab.special_sep_id;
-}
-
-llama_token llama_token_nl_impl(const struct llama_vocab & vocab) {
-    return vocab.linefeed_id;
-}
-
-llama_token llama_token_pad_impl(const struct llama_vocab & vocab) {
-    return vocab.special_pad_id;
-}
-
-bool llama_add_bos_token_impl(const struct llama_vocab & vocab) {
-    return vocab.tokenizer_add_bos;
-}
-
-bool llama_add_eos_token_impl(const struct llama_vocab & vocab) {
-    return vocab.tokenizer_add_eos;
-}
-
-llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_pre_id;
-}
-
-llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_mid_id;
-}
-
-llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_suf_id;
-}
-
-llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_pre_id;
-}
-
-llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_suf_id;
-}
-
-llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_mid_id;
-}
-
-llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_pad_id;
-}
-
-llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_rep_id;
-}
-
-llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab) {
-    return vocab.special_fim_sep_id;
-}
-
-int32_t llama_tokenize_impl(
-        const struct llama_vocab & vocab,
-                      const char * text,
-                         int32_t   text_len,
-                     llama_token * tokens,
-                         int32_t   n_tokens_max,
-                            bool   add_special,
-                            bool   parse_special) {
-    auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special);
-    if (n_tokens_max < (int) res.size()) {
-        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
-        return -((int) res.size());
-    }
-
-    for (size_t i = 0; i < res.size(); i++) {
-        tokens[i] = res[i];
-    }
-
-    return res.size();
+static void llama_unescape_whitespace(std::string & word) {
+    replace_all(word, "\xe2\x96\x81", " ");
 }
 
 static std::string llama_decode_text(const std::string & text) {
@@ -1766,11 +2311,185 @@ static std::string llama_decode_text(const std::string & text) {
     return decoded_text;
 }
 
-// does not write null-terminator to buf
-int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) {
+std::vector<llama_token> llama_vocab::impl::tokenize(
+        const std::string & raw_text,
+        bool add_special,
+        bool parse_special) const {
+    GGML_ASSERT(tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
+
+    std::vector<llama_token> output;
+    std::forward_list<fragment_buffer_variant> fragment_buffer;
+
+    if (!raw_text.empty()) {
+        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
+        tokenizer_st_partition(fragment_buffer, parse_special);
+    }
+
+    switch (get_type()) {
+        case LLAMA_VOCAB_TYPE_SPM:
+            {
+                // OG tokenizer behavior:
+                //
+                // tokenizer.encode('', add_special_tokens=True)  returns [1]
+                // tokenizer.encode('', add_special_tokens=False) returns []
+
+                bool is_prev_special = true;  // prefix with space if first token
+
+                if (add_special && add_bos) {
+                    GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_bos_id);
+                    is_prev_special = true;
+                }
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        std::string text;
+
+                        // prefix with space if previous is special
+                        if (add_space_prefix && is_prev_special) {
+                            text = ' ';
+                        }
+
+                        text += fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
+#endif
+                        llama_escape_whitespace(text);
+                        llm_tokenizer_spm_session session(vocab);
+                        session.tokenize(text, output);
+                        is_prev_special = false;
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                        is_prev_special = true;
+                    }
+                }
+
+                if (add_special && add_bos && output.size() >= 2 && output[1] == special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && add_eos) {
+                    GGML_ASSERT(special_eos_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_BPE:
+            {
+                llm_tokenizer_bpe_session session(vocab, *static_cast<const llm_tokenizer_bpe *>(tokenizer.get()));
+                // it calls some other methods that are not exist in llm_tokenizer,
+                // here just cast it to bpe tokenizer object
+                if (add_special) {
+                    session.append_bos(output);
+                }
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
+#endif
+                        session.tokenize(text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        session.append(fragment.token, output);
+                    }
+                }
+
+                if (add_special) {
+                    session.append_eos(output);
+                    session.check_double_bos_eos(output);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_WPM:
+            {
+                if (add_special) {
+                    GGML_ASSERT(special_cls_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_cls_id);
+                }
+
+                llm_tokenizer_wpm_session session(vocab);
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
+#endif
+                        session.tokenize(text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special) {
+                    GGML_ASSERT(special_sep_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_sep_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_UGM:
+            {
+                if (add_special && add_bos) {
+                    GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_bos_id);
+                }
+                llm_tokenizer_ugm_session session(vocab, *static_cast<const llm_tokenizer_ugm *>(tokenizer.get()));
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
+#endif
+                        session.tokenize(text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special && add_bos && output.size() >= 2 && output[1] == special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && add_eos) {
+                    GGML_ASSERT(special_eos_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_RWKV:
+            {
+                llm_tokenizer_rwkv_session session(vocab, *static_cast<const llm_tokenizer_rwkv *>(tokenizer.get()));
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
+#endif
+
+                        session.tokenize(text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_NONE:
+            GGML_ABORT("fatal error");
+    }
+
+    return output;
+}
+
+int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const {
     // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
     static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
-    const llama_token_attr attr = llama_token_get_attr_impl(vocab, token);
+    const llama_token_attr attr = token_get_attr(token);
     if (!special && (attr & attr_special)) {
         return 0;
     }
@@ -1791,7 +2510,7 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
 
     // if we have a cache - use it
     {
-        const auto & cache = vocab.cache_token_to_piece;
+        const auto & cache = cache_token_to_piece;
 
         if (!cache.empty()) {
             const auto & result = cache.at(token);
@@ -1799,9 +2518,9 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
         }
     }
 
-    if (0 <= token && token < (int32_t) vocab.id_to_token.size()) {
-        const std::string & token_text = vocab.id_to_token[token].text;
-        switch (llama_vocab_get_type(vocab)) {
+    if (0 <= token && token < (int32_t) id_to_token.size()) {
+        const std::string & token_text = id_to_token[token].text;
+        switch (get_type()) {
             case LLAMA_VOCAB_TYPE_WPM:
             case LLAMA_VOCAB_TYPE_SPM:
             case LLAMA_VOCAB_TYPE_UGM: {
@@ -1816,7 +2535,7 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
                     return _try_copy(result.data(), result.size());
                 }
                 if (attr & LLAMA_TOKEN_ATTR_BYTE) {
-                    char byte = (char) llama_token_to_byte(vocab, token);
+                    char byte = (char) token_to_byte(token);
                     return _try_copy((char*) &byte, 1);
                 }
                 break;
@@ -1852,43 +2571,46 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
     return 0;
 }
 
-int32_t llama_detokenize_impl(
-        const struct llama_vocab & vocab,
+const std::string & llama_vocab::impl::token_to_piece(llama_token token) const {
+    return cache_token_to_piece.at(token);
+}
+
+int32_t llama_vocab::impl::detokenize(
                const llama_token * tokens,
                          int32_t   n_tokens,
                             char * text,
                          int32_t   text_len_max,
                             bool   remove_special,
-                            bool   unparse_special) {
-    if (vocab.type == LLAMA_VOCAB_TYPE_NONE) {
+                            bool   unparse_special) const {
+    if (type == LLAMA_VOCAB_TYPE_NONE) {
         return 0;
     }
 
-    GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
+    GGML_ASSERT(tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
 
     int32_t avail = text_len_max;
     int32_t total = 0;
 
     // remove the leading space
-    bool remove_space = vocab.tokenizer_add_space_prefix;
+    bool remove_space = add_space_prefix;
 
-    if (remove_special && vocab.tokenizer_add_bos) {
-        if (n_tokens > 0 && tokens[0] == vocab.special_bos_id) {
+    if (remove_special && add_bos) {
+        if (n_tokens > 0 && tokens[0] == special_bos_id) {
             remove_space = false;
             n_tokens--;
             tokens++;
         }
     }
 
-    if (remove_special && vocab.tokenizer_add_eos) {
-        if (n_tokens > 0 && tokens[n_tokens - 1] == vocab.special_eos_id) {
+    if (remove_special && add_eos) {
+        if (n_tokens > 0 && tokens[n_tokens - 1] == special_eos_id) {
             n_tokens--;
         }
     }
 
     for (int32_t i = 0; i < n_tokens; ++i) {
         GGML_ASSERT(avail >= 0);
-        int32_t n_chars = llama_token_to_piece_impl(vocab, tokens[i], text, avail, remove_space, unparse_special);
+        int32_t n_chars = token_to_piece(tokens[i], text, avail, remove_space, unparse_special);
         remove_space = false;
         if (n_chars < 0) {
             avail = 0;
@@ -1904,7 +2626,7 @@ int32_t llama_detokenize_impl(
         return -total;
     }
 
-    if (vocab.tokenizer_clean_spaces) {
+    if (clean_spaces) {
         text -= total;  // restart text
 
         // first pass: characters ?!.,  //TODO: where do these characters come from?
@@ -1965,13 +2687,326 @@ int32_t llama_detokenize_impl(
     return total <= text_len_max ? total : -total;
 }
 
-std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector<llama_token> & tokens, bool special) {
+void llama_vocab::impl::print_info() const {
+    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, type_name().c_str());
+    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, vocab.n_tokens());
+    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (uint32_t) bpe_ranks.size());
+
+    // special tokens
+    if (special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, special_bos_id,     id_to_token[special_bos_id].text.c_str() );  }
+    if (special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, special_eos_id,     id_to_token[special_eos_id].text.c_str() );  }
+    if (special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, special_eot_id,     id_to_token[special_eot_id].text.c_str() );  }
+    if (special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, special_eom_id,     id_to_token[special_eom_id].text.c_str() );  }
+    if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, special_unk_id,     id_to_token[special_unk_id].text.c_str() );  }
+    if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, special_sep_id,     id_to_token[special_sep_id].text.c_str() );  }
+    if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, special_pad_id,     id_to_token[special_pad_id].text.c_str() );  }
+    if (special_cls_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, special_cls_id,     id_to_token[special_cls_id].text.c_str() );  }
+    if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, special_mask_id,    id_to_token[special_mask_id].text.c_str() ); }
+
+    if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, linefeed_id,        id_to_token[linefeed_id].text.c_str() ); }
+
+    if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, special_fim_pre_id, id_to_token[special_fim_pre_id].text.c_str() ); }
+    if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, special_fim_suf_id, id_to_token[special_fim_suf_id].text.c_str() ); }
+    if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, special_fim_mid_id, id_to_token[special_fim_mid_id].text.c_str() ); }
+    if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, special_fim_pad_id, id_to_token[special_fim_pad_id].text.c_str() ); }
+    if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, special_fim_rep_id, id_to_token[special_fim_rep_id].text.c_str() ); }
+    if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, special_fim_sep_id, id_to_token[special_fim_sep_id].text.c_str() ); }
+
+    for (const auto & id : special_eog_ids) {
+        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token[id].text.c_str() );
+    }
+
+    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, max_token_len);
+}
+
+llama_vocab::llama_vocab() : pimpl(new impl(*this)) {
+}
+
+llama_vocab::~llama_vocab() {
+}
+
+void llama_vocab::load(llama_model_loader & ml, const LLM_KV & kv) {
+    pimpl->load(ml, kv);
+}
+
+enum llama_vocab_type llama_vocab::get_type() const {
+    return pimpl->type;
+}
+
+enum llama_vocab_pre_type llama_vocab::get_pre_type() const {
+    return pimpl->pre_type;
+}
+
+uint32_t llama_vocab::n_tokens() const {
+    return (uint32_t) pimpl->id_to_token.size();
+}
+
+uint32_t llama_vocab::n_token_types() const {
+    return (uint32_t) pimpl->n_token_types;
+}
+
+std::string llama_vocab::type_name() const{
+    return pimpl->type_name();
+}
+
+bool llama_vocab::is_normal(llama_token id) const {
+    return pimpl->is_normal(id);
+}
+
+bool llama_vocab::is_unknown(llama_token id) const {
+    return pimpl->is_unknown(id);
+}
+
+bool llama_vocab::is_control(llama_token id) const {
+    return pimpl->is_control(id);
+}
+
+bool llama_vocab::is_byte(llama_token id) const {
+    return pimpl->is_byte(id);
+}
+
+bool llama_vocab::is_user_defined(llama_token id) const {
+    return pimpl->is_user_defined(id);
+}
+
+bool llama_vocab::is_unused(llama_token id) const {
+    return pimpl->is_unused(id);
+}
+
+bool llama_vocab::is_eog(llama_token id) const {
+    return pimpl->is_eog(id);
+}
+
+uint8_t llama_vocab::token_to_byte(llama_token id) const {
+    return pimpl->token_to_byte(id);
+}
+
+llama_token llama_vocab::byte_to_token(uint8_t ch) const {
+    GGML_ASSERT(get_type() != LLAMA_VOCAB_TYPE_NONE);
+    static const char * hex = "0123456789ABCDEF";
+    switch (get_type()) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
+            auto token = pimpl->token_to_id.find(buf);
+            if (token != pimpl->token_to_id.end()) {
+                return (*token).second;
+            }
+            // Try to fall back to just the byte as a string
+            const char buf2[2] = { (char)ch, 0 };
+            return pimpl->token_to_id.at(buf2);
+        }
+        case LLAMA_VOCAB_TYPE_WPM:
+        case LLAMA_VOCAB_TYPE_BPE: {
+            return pimpl->token_to_id.at(unicode_byte_to_utf8(ch));
+        }
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+llama_token llama_vocab::text_to_token(const std::string & text) const {
+    GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
+    auto it = pimpl->token_to_id.find(text);
+    if (it != pimpl->token_to_id.end()) {
+        return (*it).second;
+    }
+    return LLAMA_TOKEN_NULL;
+}
+
+const llama_vocab::token_data & llama_vocab::get_token_data(llama_token id) const {
+    GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
+    return pimpl->id_to_token.at(id);
+}
+
+const char * llama_vocab::token_get_text(llama_token id) const {
+    GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
+    return pimpl->id_to_token.at(id).text.c_str();
+}
+
+float llama_vocab::token_get_score(llama_token id) const {
+    GGML_ASSERT(pimpl->type != LLAMA_VOCAB_TYPE_NONE);
+    return pimpl->id_to_token.at(id).score;
+}
+
+llama_token_attr llama_vocab::token_get_attr(llama_token id) const {
+    return pimpl->token_get_attr(id);
+}
+
+llama_token llama_vocab::token_bos() const {
+    return pimpl->type != LLAMA_VOCAB_TYPE_WPM ? pimpl->special_bos_id : pimpl->special_cls_id;
+}
+
+llama_token llama_vocab::token_eos() const {
+    return pimpl->special_eos_id;
+}
+
+llama_token llama_vocab::token_eot() const {
+    return pimpl->special_eot_id;
+}
+
+llama_token llama_vocab::token_eom() const {
+    return pimpl->special_eom_id;
+}
+
+llama_token llama_vocab::token_unk() const {
+    return pimpl->special_unk_id;
+}
+
+llama_token llama_vocab::token_cls() const {
+    return pimpl->special_cls_id;
+}
+
+llama_token llama_vocab::token_sep() const {
+    return pimpl->special_sep_id;
+}
+
+llama_token llama_vocab::token_nl() const {
+    return pimpl->linefeed_id;
+}
+
+llama_token llama_vocab::token_pad() const {
+    return pimpl->special_pad_id;
+}
+
+llama_token llama_vocab::token_prefix() const {
+    return pimpl->special_fim_pre_id;
+}
+
+llama_token llama_vocab::token_middle() const {
+    return pimpl->special_fim_mid_id;
+}
+
+llama_token llama_vocab::token_suffix() const {
+    return pimpl->special_fim_suf_id;
+}
+
+llama_token llama_vocab::token_fim_pre() const {
+    return pimpl->special_fim_pre_id;
+}
+
+llama_token llama_vocab::token_fim_suf() const {
+    return pimpl->special_fim_suf_id;
+}
+
+llama_token llama_vocab::token_fim_mid() const {
+    return pimpl->special_fim_mid_id;
+}
+
+llama_token llama_vocab::token_fim_pad() const {
+    return pimpl->special_fim_pad_id;
+}
+
+llama_token llama_vocab::token_fim_rep() const {
+    return pimpl->special_fim_rep_id;
+}
+
+llama_token llama_vocab::token_fim_sep() const {
+    return pimpl->special_fim_sep_id;
+}
+
+bool llama_vocab::get_add_space_prefix() const {
+    return pimpl->add_space_prefix;
+}
+
+bool llama_vocab::get_add_bos() const {
+    return pimpl->add_bos;
+}
+
+bool llama_vocab::get_add_eos() const {
+    return pimpl->add_eos;
+}
+
+bool llama_vocab::get_ignore_merges() const {
+    return pimpl->ignore_merges;
+}
+
+bool llama_vocab::get_clean_spaces() const {
+    return pimpl->clean_spaces;
+}
+
+bool llama_vocab::get_remove_extra_whitespaces() const {
+    return pimpl->remove_extra_whitespaces;
+}
+
+bool llama_vocab::get_escape_whitespaces() const {
+    return pimpl->escape_whitespaces;
+}
+
+bool llama_vocab::get_treat_whitespace_as_suffix() const {
+    return pimpl->treat_whitespace_as_suffix;
+}
+
+int llama_vocab::max_token_len() const {
+    return pimpl->max_token_len;
+}
+
+int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
+    GGML_ASSERT(token_left.find(' ')   == std::string::npos);
+    GGML_ASSERT(token_left.find('\n')  == std::string::npos);
+    GGML_ASSERT(token_right.find(' ')  == std::string::npos);
+    GGML_ASSERT(token_right.find('\n') == std::string::npos);
+
+    auto it = pimpl->bpe_ranks.find(std::make_pair(token_left, token_right));
+    if (it == pimpl->bpe_ranks.end()) {
+        return -1;
+    }
+
+    return it->second;
+}
+
+int32_t llama_vocab::tokenize(
+                  const char * text,
+                     int32_t   text_len,
+                 llama_token * tokens,
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) const {
+    auto res = tokenize(std::string(text, text_len), add_special, parse_special);
+    if (n_tokens_max < (int) res.size()) {
+        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
+        return -((int) res.size());
+    }
+
+    for (size_t i = 0; i < res.size(); i++) {
+        tokens[i] = res[i];
+    }
+
+    return res.size();
+}
+
+std::vector<llama_token> llama_vocab::tokenize(
+        const std::string & raw_text,
+        bool add_special,
+        bool parse_special) const {
+    return pimpl->tokenize(raw_text, add_special, parse_special);
+}
+
+const std::string & llama_vocab::token_to_piece(llama_token token) const {
+    return pimpl->token_to_piece(token);
+}
+
+int32_t llama_vocab::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const {
+    return pimpl->token_to_piece(token, buf, length, lstrip, special);
+}
+
+int32_t llama_vocab::detokenize(
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special) const {
+    return pimpl->detokenize(tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
+}
+
+std::string llama_vocab::detokenize(const std::vector<llama_token> & tokens, bool special) const {
     std::string text;
     text.resize(std::max(text.capacity(), tokens.size()));
-    int32_t n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+    int32_t n_chars = detokenize(tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
     if (n_chars < 0) {
         text.resize(-n_chars);
-        n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+        n_chars = detokenize(tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
         GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
     }
 
@@ -1980,3 +3015,241 @@ std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector
     // NOTE: the original tokenizer decodes bytes after collecting the pieces.
     return text;
 }
+
+void llama_vocab::print_info() const {
+    pimpl->print_info();
+}
+
+//
+// interface implementation
+//
+
+int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab) {
+    return vocab->n_tokens();
+}
+
+// deprecated
+int32_t llama_n_vocab(const struct llama_vocab * vocab) {
+    return llama_vocab_n_tokens(vocab);
+}
+
+enum llama_vocab_type llama_vocab_type(const struct llama_vocab * vocab) {
+    return vocab->get_type();
+}
+
+const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token) {
+    return vocab->token_get_text(token);
+}
+
+float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token) {
+    return vocab->token_get_score(token);
+}
+
+enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token) {
+    return vocab->token_get_attr(token);
+}
+
+bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token) {
+    return vocab->is_eog(token);
+}
+
+bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token) {
+    return vocab->is_control(token);
+}
+
+llama_token llama_vocab_bos(const struct llama_vocab * vocab) {
+    return vocab->token_bos();
+}
+
+llama_token llama_vocab_eos(const struct llama_vocab * vocab) {
+    return vocab->token_eos();
+}
+
+llama_token llama_vocab_eot(const struct llama_vocab * vocab) {
+    return vocab->token_eot();
+}
+
+llama_token llama_vocab_cls(const struct llama_vocab * vocab) {
+    return vocab->token_cls();
+}
+
+llama_token llama_vocab_sep(const struct llama_vocab * vocab) {
+    return vocab->token_sep();
+}
+
+llama_token llama_vocab_nl (const struct llama_vocab * vocab) {
+    return vocab->token_nl();
+}
+
+llama_token llama_vocab_pad(const struct llama_vocab * vocab) {
+    return vocab->token_pad();
+}
+
+bool llama_vocab_get_add_bos(const struct llama_vocab * vocab) {
+    return vocab->get_add_bos();
+}
+
+bool llama_vocab_get_add_eos(const struct llama_vocab * vocab) {
+    return vocab->get_add_eos();
+}
+
+llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) {
+    return vocab->token_fim_pre();
+}
+
+llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab) {
+    return vocab->token_fim_suf();
+}
+
+llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab) {
+    return vocab->token_fim_mid();
+}
+
+llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab) {
+    return vocab->token_fim_pad();
+}
+
+llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab) {
+    return vocab->token_fim_rep();
+}
+
+llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab) {
+    return vocab->token_fim_sep();
+}
+
+// deprecated
+const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token) {
+    return llama_vocab_get_text(vocab, token);
+}
+
+// deprecated
+float llama_token_get_score(const struct llama_vocab * vocab, llama_token token) {
+    return llama_vocab_get_score(vocab, token);
+}
+
+// deprecated
+enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token) {
+    return llama_vocab_get_attr(vocab, token);
+}
+
+// deprecated
+bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token) {
+    return llama_vocab_is_eog(vocab, token);
+}
+
+// deprecated
+bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token) {
+    return llama_vocab_is_control(vocab, token);
+}
+
+// deprecated
+llama_token llama_token_bos(const struct llama_vocab * vocab) {
+    return llama_vocab_bos(vocab);
+}
+
+// deprecated
+llama_token llama_token_eos(const struct llama_vocab * vocab) {
+    return llama_vocab_eos(vocab);
+}
+
+// deprecated
+llama_token llama_token_eot(const struct llama_vocab * vocab) {
+    return llama_vocab_eot(vocab);
+}
+
+// deprecated
+llama_token llama_token_cls(const struct llama_vocab * vocab) {
+    return llama_vocab_cls(vocab);
+}
+
+// deprecated
+llama_token llama_token_sep(const struct llama_vocab * vocab) {
+    return llama_vocab_sep(vocab);
+}
+
+// deprecated
+llama_token llama_token_nl (const struct llama_vocab * vocab) {
+    return llama_vocab_nl(vocab);
+}
+
+// deprecated
+llama_token llama_token_pad(const struct llama_vocab * vocab) {
+    return llama_vocab_pad(vocab);
+}
+
+// deprecated
+bool llama_add_bos_token(const struct llama_vocab * vocab) {
+    return llama_vocab_get_add_bos(vocab);
+}
+
+// deprecated
+bool llama_add_eos_token(const struct llama_vocab * vocab) {
+    return llama_vocab_get_add_eos(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_pre(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_pre(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_suf(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_suf(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_mid(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_mid(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_pad(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_pad(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_rep(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_rep(vocab);
+}
+
+// deprecated
+llama_token llama_token_fim_sep(const struct llama_vocab * vocab) {
+    return llama_vocab_fim_sep(vocab);
+}
+
+//
+// tokenization
+//
+
+int32_t llama_tokenize(
+    const struct llama_vocab * vocab,
+                  const char * text,
+                     int32_t   text_len,
+                 llama_token * tokens,
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) {
+    return vocab->tokenize(text, text_len, tokens, n_tokens_max, add_special, parse_special);
+}
+
+int32_t llama_token_to_piece(
+    const struct llama_vocab * vocab,
+                 llama_token   token,
+                        char * buf,
+                     int32_t   length,
+                     int32_t   lstrip,
+                        bool   special) {
+    return vocab->token_to_piece(token, buf, length, lstrip, special);
+}
+
+int32_t llama_detokenize(
+    const struct llama_vocab * vocab,
+           const llama_token * tokens,
+                     int32_t   n_tokens,
+                        char * text,
+                     int32_t   text_len_max,
+                        bool   remove_special,
+                        bool   unparse_special) {
+    return vocab->detokenize(tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
+}
+
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 0d00086da..020f2b533 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -4,179 +4,123 @@
 
 #include <string>
 #include <vector>
-#include <unordered_map>
-#include <map>
-#include <set>
+#include <memory>
 
-static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
-    switch (type) {
-        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
-        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
-        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
-        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
-        case LLAMA_VOCAB_TYPE_UGM:  return "UGM";
-        case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
-        default:                    return "unknown";
-    }
-}
-
-struct llm_tokenizer;
+struct LLM_KV;
+struct llama_model_loader;
 
 struct llama_vocab {
-    using id    = llama_token;
-    using token = std::string;
-    using tattr = llama_token_attr;
-
     struct token_data {
-        token text;
-        float score;
-        tattr attr;
+        std::string      text;
+        float            score;
+        llama_token_attr attr;
     };
 
-    uint32_t n_vocab = 0; // TODO: not great because has to keep in sync with hparams.n_vocab
-
-    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
-    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-
-    int max_token_len = 0; // used for optimizing longest token search
-
-    std::unordered_map<token, id> token_to_id;
-    std::vector<token_data>       id_to_token;
-
-    std::vector<id>    cache_special_tokens;
-    std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
-
-    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
-
-    // default LLaMA special tokens
-    // TODO: should we set all of these to LLAMA_TOKEN_NULL?
-    id special_bos_id  = 1;
-    id special_eos_id  = 2;
-    id special_eot_id  = LLAMA_TOKEN_NULL;
-    id special_eom_id  = LLAMA_TOKEN_NULL;
-    id special_unk_id  = 0;
-    id special_sep_id  = LLAMA_TOKEN_NULL;
-    id special_pad_id  = LLAMA_TOKEN_NULL;
-    id special_cls_id  = LLAMA_TOKEN_NULL; // TODO: revisit if this is really needed https://github.com/ggerganov/llama.cpp/pull/10930
-    id special_mask_id = LLAMA_TOKEN_NULL;
-
-    id linefeed_id = 13;
-
-    // fim tokens
-    id special_fim_pre_id = LLAMA_TOKEN_NULL;
-    id special_fim_suf_id = LLAMA_TOKEN_NULL;
-    id special_fim_mid_id = LLAMA_TOKEN_NULL;
-    id special_fim_pad_id = LLAMA_TOKEN_NULL;
-    id special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
-    id special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
-
-    // set of all tokens that cause "end of generation"
-    std::set<id> special_eog_ids;
-
-    // tokenizer flags
-    bool tokenizer_add_space_prefix           = false;
-    bool tokenizer_add_bos                    = false;
-    bool tokenizer_add_eos                    = false;
-    bool tokenizer_ignore_merges              = false;
-    bool tokenizer_clean_spaces               = false;  // clean_up_tokenization_spaces
-    bool tokenizer_remove_extra_whitespaces   = false;
-    bool tokenizer_escape_whitespaces         = true;
-    bool tokenizer_treat_whitespace_as_suffix = false;
-
-    std::vector<char> precompiled_charsmap;
-
-    llm_tokenizer * tokenizer = nullptr;
-
-    llama_vocab() = default;
+    llama_vocab();
     ~llama_vocab();
 
+    void load(llama_model_loader & ml, const LLM_KV & kv);
+
+    enum llama_vocab_type     get_type()     const;
+    enum llama_vocab_pre_type get_pre_type() const;
+
+    uint32_t n_tokens() const;
+    uint32_t n_token_types() const;
+
+    std::string type_name() const;
+
+    bool is_normal      (llama_token id) const;
+    bool is_unknown     (llama_token id) const;
+    bool is_control     (llama_token id) const;
+    bool is_byte        (llama_token id) const;
+    bool is_user_defined(llama_token id) const;
+    bool is_unused      (llama_token id) const;
+    bool is_eog         (llama_token id) const;
+
+    uint8_t     token_to_byte(llama_token id) const;
+    llama_token byte_to_token(uint8_t ch)     const;
+
+    llama_token text_to_token(const std::string & text) const;
+
+    const token_data & get_token_data(llama_token id) const;
+
+    const char *     token_get_text (llama_token id) const;
+    float            token_get_score(llama_token id) const;
+    llama_token_attr token_get_attr (llama_token id) const;
+
+    llama_token token_bos() const;
+    llama_token token_eos() const;
+    llama_token token_eot() const;
+    llama_token token_eom() const;
+    llama_token token_unk() const;
+    llama_token token_cls() const;
+    llama_token token_sep() const;
+    llama_token token_nl () const;
+    llama_token token_pad() const;
+
+    llama_token token_prefix() const;
+    llama_token token_middle() const;
+    llama_token token_suffix() const;
+
+    llama_token token_fim_pre() const;
+    llama_token token_fim_suf() const;
+    llama_token token_fim_mid() const;
+    llama_token token_fim_pad() const;
+    llama_token token_fim_rep() const;
+    llama_token token_fim_sep() const;
+
+    bool get_add_space_prefix          () const;
+    bool get_add_bos                   () const;
+    bool get_add_eos                   () const;
+    bool get_ignore_merges             () const;
+    bool get_clean_spaces              () const;
+    bool get_remove_extra_whitespaces  () const;
+    bool get_escape_whitespaces        () const;
+    bool get_treat_whitespace_as_suffix() const;
+
+    int max_token_len() const;
+
     int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
 
-    void init_tokenizer();
+    int32_t tokenize(
+                   const char * text,
+                      int32_t   text_len,
+                  llama_token * tokens,
+                      int32_t   n_tokens_max,
+                         bool   add_special,
+                         bool   parse_special) const;
+
+    std::vector<llama_token> tokenize(
+            const std::string & raw_text,
+                         bool   add_special,
+                         bool   parse_special = false) const;
+
+    // does not write null-terminator to buf
+    int32_t token_to_piece(
+                  llama_token   token,
+                         char * buf,
+                      int32_t   length,
+                      int32_t   lstrip,
+                         bool   special) const;
+
+    // use cached data
+    const std::string & token_to_piece(llama_token token) const;
+
+    int32_t detokenize(
+            const llama_token * tokens,
+                      int32_t   n_tokens,
+                         char * text,
+                      int32_t   text_len_max,
+                         bool   remove_special,
+                         bool   unparse_special) const;
+
+    std::string detokenize(
+            const std::vector<llama_token> & tokens,
+                                      bool   special) const;
+
+    void print_info() const;
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };
-
-//
-// internal API
-//
-
-// TODO: rename to llama_tokenize_impl
-// TODO: This should probably be in llama.h
-std::vector<llama_vocab::id> llama_tokenize_internal(
-        const llama_vocab & vocab,
-        std::string raw_text,
-        bool add_special,
-        bool parse_special = false);
-
-// TODO: move the API below as member functions of llama_vocab
-llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch);
-
-const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token);
-
-float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token);
-
-llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token);
-
-bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token);
-
-bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token);
-
-llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
-llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
-llama_token llama_token_eot_impl(const struct llama_vocab & vocab);
-llama_token llama_token_eom_impl(const struct llama_vocab & vocab);
-llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
-llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
-llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
-llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
-
-llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
-llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
-llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
-
-llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab);
-llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab);
-llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab);
-llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab);
-llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab);
-llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab);
-
-bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
-bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
-
-int32_t llama_tokenize_impl(
-        const struct llama_vocab & vocab,
-                      const char * text,
-                         int32_t   text_len,
-                     llama_token * tokens,
-                         int32_t   n_tokens_max,
-                            bool   add_special,
-                            bool   parse_special);
-
-// does not write null-terminator to buf
-int32_t llama_token_to_piece_impl(
-        const struct llama_vocab & vocab,
-                     llama_token   token,
-                            char * buf,
-                         int32_t   length,
-                         int32_t   lstrip,
-                            bool   special);
-
-// check if token0 is contained as a prefix in token1
-bool llama_token_is_prefix_impl(
-        const struct llama_vocab & vocab,
-                     llama_token   token0,
-                     llama_token   token1);
-
-int32_t llama_detokenize_impl(
-        const struct llama_vocab & vocab,
-               const llama_token * tokens,
-                         int32_t   n_tokens,
-                            char * text,
-                         int32_t   text_len_max,
-                            bool   remove_special,
-                            bool   unparse_special);
-
-std::string llama_detokenize(
-        const struct llama_vocab & vocab,
-  const std::vector<llama_token> & tokens,
-                            bool   special);
diff --git a/src/llama.cpp b/src/llama.cpp
index a364861d3..daf1b7c97 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -25,2569 +25,52 @@
 #include <cstring>
 #include <ctime>
 #include <functional>
-#include <initializer_list>
-#include <map>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-//
-// tensor loading (TODO: add llama_tesor_loader?)
-//
-
-static int llama_get_device_count(const llama_model & model) {
-    return (int) model.devices.size();
-}
-
-// checks if the weight tensor can be used with the specified buffer type and device
-static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) {
-    GGML_ASSERT(w != nullptr);
-
-    if (op == GGML_OP_NONE) {
-        return true;
-    }
-
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*8,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-    ggml_context_ptr ctx_ptr { ggml_init(params) };
-    if (!ctx_ptr) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-    ggml_context * ctx = ctx_ptr.get();
-
-    ggml_tensor * op_tensor = nullptr;
-
-    switch (op) {
-        case GGML_OP_GET_ROWS:
-            {
-                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
-                op_tensor = ggml_get_rows(ctx, w, b);
-            } break;
-        case GGML_OP_MUL_MAT:
-            {
-                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
-                op_tensor = ggml_mul_mat(ctx, w, b);
-            } break;
-        case GGML_OP_MUL_MAT_ID:
-            {
-                int n_expert_used = hparams.n_expert_used;
-                ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
-                ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
-                op_tensor = ggml_mul_mat_id(ctx, w, b, ids);
-            } break;
-        case GGML_OP_ADD:
-            {
-                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
-                op_tensor = ggml_add(ctx, a, w);
-            } break;
-        case GGML_OP_MUL:
-            {
-                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
-                op_tensor = ggml_mul(ctx, a, w);
-            } break;
-        case GGML_OP_DIV:
-            {
-                ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]);
-                op_tensor = ggml_div(ctx, a, w);
-            } break;
-        case GGML_OP_ROPE:
-            {
-                int n_embd_head = hparams.n_embd_head_v;
-                int n_head = hparams.n_head();
-                ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
-                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
-                op_tensor = ggml_rope_ext(
-                    ctx, a, b, w,
-                    0, 0, 0, 0, 0,
-                    0, 0, 0, 0
-                );
-
-            } break;
-        case GGML_OP_SSM_CONV:
-            {
-                // FIXME
-                ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
-                op_tensor = ggml_ssm_conv(ctx, conv_x, w);
-            } break;
-        case GGML_OP_SSM_SCAN:
-            {
-                // FIXME
-                const int64_t d_state      = w->ne[0];
-                const int64_t d_inner      = w->ne[1];
-                const int64_t n_seq_tokens = 512;
-                const int64_t n_seqs       = 1;
-                ggml_tensor * s  = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
-                ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
-                ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
-                ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
-                ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
-                op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
-            } break;
-        case GGML_OP_RWKV_WKV6:
-            {
-                // FIXME
-                const int64_t S = 123;
-                const int64_t H = 123;
-                const int64_t n_tokens = 123;
-                const int64_t n_seqs = 123;
-                ggml_tensor  * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
-                ggml_tensor  * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
-                ggml_tensor  * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
-                ggml_tensor  * tf = w;
-                ggml_tensor  * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
-                ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
-                op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
-            } break;
-        case GGML_OP_IM2COL:
-            {
-                const int n_embd = hparams.n_embd;
-                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
-                op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
-            } break;
-        default:
-            GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
-    }
-
-    // create a temporary dummy buffer for the weight so that supports_op can check the buffer type
-    GGML_ASSERT(w->buffer == nullptr);
-    w->buffer = ggml_backend_buft_alloc_buffer(buft, 0);
-    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
-    ggml_backend_buffer_free(w->buffer);
-    w->buffer = nullptr;
-
-    return op_supported;
-}
-
-// find the first buffer type in the list that can use the tensor
-static ggml_backend_buffer_type_t select_weight_buft(const llama_model & model, ggml_tensor * tensor, ggml_op op, const llama_model::buft_list_t & buft_list) {
-    GGML_ASSERT(!buft_list.empty());
-    for (const auto & cur : buft_list) {
-        ggml_backend_dev_t cur_dev = cur.first;
-        ggml_backend_buffer_type_t cur_buft = cur.second;
-        if (weight_buft_supported(model.hparams, tensor, op, cur_buft, cur_dev)) {
-            return cur_buft;
-        }
-    }
-    return nullptr;
-}
-
-// CPU: ACCEL -> CPU extra -> GPU host -> CPU
-static llama_model::buft_list_t make_cpu_buft_list(llama_model & model) {
-    llama_model::buft_list_t buft_list;
-
-    // add ACCEL buffer types
-    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
-        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
-        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
-            auto * buft = ggml_backend_dev_buffer_type(dev);
-            // skip
-            if (buft != ggml_backend_cpu_buffer_type()) {
-                buft_list.emplace_back(dev, buft);
-            }
-        }
-    }
-
-    // add extra buffer types
-    auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-    auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
-    auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
-        ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
-    if (ggml_backend_dev_get_extra_bufts_fn) {
-        ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
-        while (extra_bufts && *extra_bufts) {
-            buft_list.emplace_back(cpu_dev, *extra_bufts);
-            ++extra_bufts;
-        }
-    }
-
-    // add a host buffer type
-    // storing the tensors in a host buffer is useful when the processing of large batches
-    // is offloaded to a GPU device, since it reduces the time spent on data transfers
-    // generally, this will be done using the first device in the list
-    // a better approach would be to handle this on a weight-by-weight basis using the offload_op
-    // function of the device to determine if it would benefit from being stored in a host buffer
-    for (auto * dev : model.devices) {
-        ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
-        if (buft) {
-            buft_list.emplace_back(dev, buft);
-            break;
-        }
-    }
-
-    // add the CPU buffer type
-    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
-        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
-        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
-            buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
-        }
-    }
-
-    return buft_list;
-}
-
-// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
-static llama_model::buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) {
-    llama_model::buft_list_t buft_list;
-
-    // add the device split buffer type if requested and available
-    if (split_mode == LLAMA_SPLIT_MODE_ROW) {
-        ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
-        auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
-            ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
-        if (ggml_backend_split_buffer_type_fn) {
-            size_t dev_index = [&]() {
-                auto * reg = ggml_backend_dev_backend_reg(dev);
-                for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
-                    if (ggml_backend_reg_dev_get(reg, i) == dev) {
-                        return i;
-                    }
-                }
-                throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
-            }();
-            auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
-            if (buft != nullptr) {
-                buft_list.emplace_back(dev, buft);
-            }
-        }
-    }
-
-    // add the device default buffer type
-    buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
-
-    return buft_list;
-}
-
-// Returns false if cancelled by progress_callback
-static bool llm_load_tensors(
-        llama_model_loader & ml,
-        llama_model & model,
-        int n_gpu_layers,
-        enum llama_split_mode split_mode,
-        int main_gpu,
-        const float * tensor_split,
-        bool use_mlock,
-        llama_progress_callback progress_callback,
-        void * progress_callback_user_data) {
-    auto & hparams = model.hparams;
-
-    model.split_mode   = split_mode;
-    model.main_gpu     = main_gpu;
-    model.n_gpu_layers = n_gpu_layers;
-
-    const int n_layer = hparams.n_layer;
-
-    bool use_mmap_buffer = true;
-
-    // build a list of buffer types for the CPU and GPU devices
-    model.cpu_buft_list = make_cpu_buft_list(model);
-    for (auto * dev : model.devices) {
-        llama_model::buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
-        // add CPU buffer types as a fallback
-        buft_list.insert(buft_list.end(), model.cpu_buft_list.begin(), model.cpu_buft_list.end());
-        model.gpu_buft_list.emplace(dev, std::move(buft_list));
-    }
-
-    // calculate the split points
-    int device_count = llama_get_device_count(model);
-    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
-    std::vector<float> splits(device_count);
-    if (all_zero) {
-        // default split, by free memory
-        for (int i = 0; i < device_count; ++i) {
-            ggml_backend_dev_t dev = model.devices[i];
-            size_t total;
-            size_t free;
-            ggml_backend_dev_memory(dev, &free, &total);
-            splits[i] = free;
-        }
-    } else {
-        std::copy(tensor_split, tensor_split + device_count, splits.begin());
-    }
-
-    // sum and normalize the splits to get the split points
-    float split_sum = 0.0f;
-    for (int i = 0; i < device_count; ++i) {
-        split_sum += splits[i];
-        splits[i] = split_sum;
-    }
-    for (int i = 0; i < device_count; ++i) {
-        splits[i] /= split_sum;
-    }
-
-    ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-    const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
-    const int act_gpu_layers = model.devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
-    auto get_layer_buft_list = [&](int il) -> llama_model::layer_dev {
-        if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
-            return {cpu_dev, &model.cpu_buft_list};
-        }
-        int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
-        auto * dev = model.devices.at(layer_gpu);
-        return {dev, &model.gpu_buft_list.at(dev)};
-    };
-
-    // assign the input layer
-    // there is very little benefit to offloading the input layer, so always keep it on the CPU
-    model.dev_input = { cpu_dev, &model.cpu_buft_list };
-
-    // assign the repeating layers to the devices according to the splits
-    model.dev_layer.resize(n_layer);
-    for (int il = 0; il < n_layer; ++il) {
-        model.dev_layer[il] = get_layer_buft_list(il);
-    }
-    // assign the output layer
-    model.dev_output = get_layer_buft_list(n_layer);
-
-    // one ggml context per buffer type
-    int max_n_tensors = ml.n_tensors;
-    max_n_tensors += 1;         // duplicated output tensor
-    max_n_tensors += n_layer*2; // duplicated rope freq tensors
-    const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors;
-
-    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
-    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
-        auto it = ctx_map.find(buft);
-        if (it == ctx_map.end()) {
-            ggml_init_params params = {
-                /*.mem_size   =*/ ctx_size,
-                /*.mem_buffer =*/ NULL,
-                /*.no_alloc   =*/ true,
-            };
-            ggml_context * ctx = ggml_init(params);
-            if (!ctx) {
-                throw std::runtime_error(format("failed to create ggml context"));
-            }
-            ctx_map[buft] = ctx;
-            model.ctxs.emplace_back(ctx);
-            return ctx;
-        }
-        return it->second;
-    };
-
-    // create tensors for the weights
-    {
-        // note: cast to int64_t since we will use these for the tensor dimensions
-        const int64_t n_head        = hparams.n_head();
-        const int64_t n_head_kv     = hparams.n_head_kv();
-        const int64_t n_embd        = hparams.n_embd;
-        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
-        const int64_t n_embd_head_k = hparams.n_embd_head_k;
-        const int64_t n_embd_head_v = hparams.n_embd_head_v;
-        const int64_t n_ff          = hparams.n_ff();
-        const int64_t n_embd_gqa    = n_embd_v_gqa;
-        const int64_t n_vocab       = hparams.n_vocab;
-        const int64_t n_vocab_type  = hparams.n_vocab_type;
-        const int64_t n_rot         = hparams.n_rot;
-        const int64_t n_expert      = hparams.n_expert;
-        const int64_t n_expert_used = hparams.n_expert_used;
-        const int64_t n_ctx_train   = hparams.n_ctx_train;
-
-        if (n_expert > 0 && hparams.n_expert_used == 0) {
-            throw std::runtime_error("model has expert layers but no expert layers are used");
-        }
-
-        int n_moved_tensors = 0;
-        ggml_tensor * first_moved_tensor = nullptr;
-        ggml_backend_buffer_type_t first_moved_from_buft = nullptr;
-        ggml_backend_buffer_type_t first_moved_to_buft = nullptr;
-
-        auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
-            ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str());
-
-            if (!t_meta) {
-                if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) {
-                    return nullptr;
-                }
-                throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str()));
-            }
-
-            // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops
-            // the tensor is duplicated
-            // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor
-            llm_tensor tn_tensor = tn.tensor;
-            if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & llama_model_loader::TENSOR_DUPLICATED) {
-                tn_tensor = LLM_TENSOR_OUTPUT;
-            }
-
-            llm_tensor_info info;
-            try {
-                info = llm_tensor_info_for(tn_tensor);
-            } catch (const std::out_of_range & e) {
-                throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str()));
-            }
-
-            // tensors with "bias" suffix are always used with GGML_OP_ADD
-            ggml_op op;
-            bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
-            if (bias) {
-                op = GGML_OP_ADD;
-            } else {
-                op = info.op;
-            }
-
-            // sanity checks
-            if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) {
-                if (tn.bid != -1) {
-                    GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str());
-                }
-            } else {
-                if (tn.bid == -1) {
-                    GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str());
-                }
-            }
-
-            // select the buffer type for this tensor
-            llama_model::buft_list_t * buft_list;
-            switch (info.layer) {
-                case LLM_TENSOR_LAYER_INPUT:
-                    buft_list = model.dev_input.buft_list;
-                    break;
-                case LLM_TENSOR_LAYER_OUTPUT:
-                    buft_list = model.dev_output.buft_list;
-                    break;
-                case LLM_TENSOR_LAYER_REPEATING:
-                    buft_list = model.dev_layer.at(tn.bid).buft_list;
-                    break;
-                default:
-                    GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str());
-            }
-
-            ggml_backend_buffer_type_t buft = select_weight_buft(model, t_meta, op, *buft_list);
-            if (!buft) {
-                throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str()));
-            }
-
-            // avoid using a host buffer when using mmap
-            auto * buft_dev = ggml_backend_buft_get_device(buft);
-            if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) {
-                auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-                buft = ggml_backend_dev_buffer_type(cpu_dev);
-            }
-
-            if (buft != buft_list->front().second) {
-                n_moved_tensors++;
-                if (!first_moved_tensor) {
-                    first_moved_tensor = t_meta;
-                    first_moved_from_buft = buft_list->front().second;
-                    first_moved_to_buft   = buft;
-                }
-            }
-
-            ggml_context * ctx = ctx_for_buft(buft);
-
-            // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
-            if (flags & llama_model_loader::TENSOR_DUPLICATED) {
-                ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str());
-                if (t) {
-                    return t;
-                }
-            }
-            return ml.create_tensor(ctx, tn, ne, flags);
-        };
-
-        model.layers.resize(n_layer);
-
-        // TODO: move to a separate function
-        const auto tn = LLM_TN(model.arch);
-        switch (model.arch) {
-            case LLM_ARCH_LLAMA:
-            case LLM_ARCH_REFACT:
-            case LLM_ARCH_MINICPM:
-            case LLM_ARCH_GRANITE:
-            case LLM_ARCH_GRANITE_MOE:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        }
-                        else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        }
-
-                        if (n_expert == 0) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                            // optional MLP bias
-                            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        } else {
-                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DECI:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-                        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
-                        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
-                        const int64_t n_embd_gqa    = hparams.n_embd_v_gqa(i);
-                        const int64_t n_ff          = hparams.n_ff(i);
-                        const int64_t n_head        = hparams.n_head(i);
-                        const int64_t n_head_kv     = hparams.n_head_kv(i);
-
-                        if (n_head_kv == 0 && n_head > 0) {
-                            // linear attention for DeciLMCausalModel
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        }
-                        else if (n_head_kv > 0) {
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-                        }
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        }
-                        else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        }
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        // optional MLP bias
-                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_MINICPM3:
-                {
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
-
-                    const int64_t q_lora_rank  = hparams.n_lora_q;
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
-
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-
-                        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
-                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
-
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
-                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
-                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                    }
-                } break;
-            case LLM_ARCH_GROK:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error("Grok model cannot have zero experts");
-                    }
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-
-                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_DBRX:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error("DBRX model cannot have zero experts");
-                    }
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BAICHUAN:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    {
-                        model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_FALCON:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-
-                        model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        if (!model.output) {
-                            model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_STARCODER:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
-
-                    // output
-                    {
-                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                        model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        if (!model.output) {
-                            // needs to be on GPU
-                            model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                        }
-
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
-                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BERT:
-            case LLM_ARCH_NOMIC_BERT:
-                {
-                    model.tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
-                    model.type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0);
-
-                    if (model.arch == LLM_ARCH_BERT) {
-                        model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
-
-                        model.cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        model.cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        model.cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
-                    }
-
-                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        if (model.arch == LLM_ARCH_BERT) {
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd}, 0);
-
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa}, 0);
-
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa}, 0);
-                        } else {
-                            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        }
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd}, 0);
-
-                        if (model.arch == LLM_ARCH_BERT) {
-                            layer.bo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, 0);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
-                        } else {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        }
-
-                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JINA_BERT_V2:
-                {
-                    model.tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
-                    model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); // token_type_embeddings
-
-                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm
-                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0); //LayerNorm bias
-
-                    model.cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i]; // JinaBertLayer
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
-
-                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0); //output_dens
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
-                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
-
-                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BLOOM:
-                {
-                    model.tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias",   i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MPT:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    if (!model.output) {
-                        model.output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        // AWQ ScaleActivation layer
-                        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_STABLELM:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors, present in Stable LM 2 1.6B
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        // optional q and k layernorms, present in StableLM 2 12B
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd*3}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN2:
-            case LLM_ARCH_QWEN2VL:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN2MOE:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
-                        }
-
-                        // MoE branch
-                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                        // Shared expert branch
-                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
-
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
-                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);
-                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PHI2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-                    model.output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        if (layer.wqkv == nullptr) {
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
-                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);
-
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa}, 0);
-
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa}, 0);
-                        }
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PHI3:
-                {
-                    const int64_t n_embd_head = n_embd / n_head;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                    }
-                } break;
-            case LLM_ARCH_PHIMOE:
-                {
-                    const int64_t n_embd_head = n_embd / n_head;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
-                    model.output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        if (layer.wqkv == nullptr) {
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
-                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias",   i), {n_embd}, 0);
-
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
-
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
-                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
-                        }
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                    }
-                } break;
-            case LLM_ARCH_PLAMO:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GPT2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_CODESHELL:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_ORION:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_INTERNLM2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_STARCODER2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        // optional bias tensors
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MAMBA:
-                {
-                    const int64_t d_conv  = hparams.ssm_d_conv;
-                    const int64_t d_inner = hparams.ssm_d_inner;
-                    const int64_t d_state = hparams.ssm_d_state;
-                    const int64_t dt_rank = hparams.ssm_dt_rank;
-
-                    // only an expansion factor of 2 is supported for now
-                    if (2 * n_embd != d_inner) {
-                        throw std::runtime_error("only an expansion factor of 2 is supported for now");
-                    }
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        // norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
-
-                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
-                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
-
-                        layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
-
-                        layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
-                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
-
-                        // no "weight" suffix for these
-                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
-                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
-
-                        // out_proj
-                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_XVERSE:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_COMMAND_R:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // init output from the input tok embed
-                    model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (n_layer >= 64){
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
-                        }
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_COHERE2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    // init output from the input tok embed
-                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
-                                                      llama_model_loader::TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
-                    }
-                }
-                break;
-            case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OLMO2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OLMOE:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0");
-                        }
-
-                        // MoE branch
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OPENELM:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // init output from the input tok embed
-                    model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        const int64_t n_head      =   hparams.n_head(i);
-                        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
-                        const int64_t n_ff        =   hparams.n_ff(i);
-
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GPTNEOX:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_ARCTIC:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_DEEPSEEK:
-                {
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DEEPSEEK2:
-                {
-                    const bool is_lite = (hparams.n_layer == 27);
-
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
-
-                    const int64_t q_lora_rank  = hparams.n_lora_q;
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        if (!is_lite) {
-                            layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
-                        }
-
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-
-                        if (!is_lite) {
-                            layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
-                            layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
-                        } else {
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        }
-
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
-                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
-                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_BITNET:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
-                        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);
-                        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);
-
-                        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_scale   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_T5:
-                {
-                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        // this tensor seems to be unused in HF transformers implementation
-                        layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_T5ENCODER:
-                {
-                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JAIS:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_CHATGLM:
-                {
-                    model.tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_NEMOTRON:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
-                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        // optional MLP bias
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_EXAONE:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
-                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN,   "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,     "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_RWKV6:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // Block 0, LN0
-                    model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
-                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
-                    const int head_size = hparams.wkv_head_size;
-                    const int attn_hidden_size = n_embd;
-                    const int ffn_size = hparams.n_ff_arr[0];
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
-
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
-
-                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
-
-                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
-                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
-                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
-                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
-
-                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
-                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
-
-                        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
-                        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
-                        layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
-                    }
-
-                } break;
-            case LLM_ARCH_RWKV6QWEN2:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
-                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
-                    const int head_size = hparams.wkv_head_size;
-                    const int attn_hidden_size = n_embd;
-                    const int n_head_kv = hparams.n_head_kv();
-                    int attn_key_value_size;
-                    if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
-                        attn_key_value_size = attn_hidden_size;
-                    } else {
-                        attn_key_value_size = n_head_kv * head_size;
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
-
-                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
-
-                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
-                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
-                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        // optional bias tensors
-                        layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_CHAMELEON:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (model.output == NULL) {
-                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_WAVTOKENIZER_DEC:
-                {
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
-
-                    model.conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
-                    model.conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
-
-                    // posnet
-                    {
-                        const int64_t n_embd = hparams.posnet.n_embd;
-
-                        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
-                            auto & layer = model.layers[i].posnet;
-
-                            // posnet:
-                            //
-                            //  - resnet
-                            //  - resnet
-                            //  - attn
-                            //  - resnet
-                            //  - resnet
-                            //  - norm
-                            //
-                            switch (i) {
-                                case 0:
-                                case 1:
-                                case 3:
-                                case 4:
-                                    {
-                                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
-                                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
-                                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
-                                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
-                                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                case 2:
-                                    {
-                                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
-                                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                case 5:
-                                    {
-                                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
-                                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                default: GGML_ABORT("unknown posnet layer");
-                            };
-                        }
-                    }
-
-                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
-
-                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
-                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {hparams.posnet.n_embd}, 0);
-
-                    // convnext
-                    {
-                        const int64_t n_embd = hparams.convnext.n_embd;
-
-                        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
-                            auto & layer = model.layers[i].convnext;
-
-                            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
-                            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);
-
-                            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
-                            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);
-
-                            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
-                            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);
-
-                            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
-                            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);
-
-                            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
-                        }
-
-                        // output
-                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    }
-
-                    model.output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
-                    model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
-                } break;
-            default:
-                throw std::runtime_error("unknown architecture");
-        }
-
-        if (n_moved_tensors > 0) {
-            LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n",
-                __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1,
-                ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft));
-        }
-    }
-
-    ml.done_getting_tensors();
-
-    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
-    model.mappings.reserve(ml.mappings.size());
-
-    // create the backend buffers
-    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
-    ctx_bufs.reserve(ctx_map.size());
-
-    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
-    const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
-    model.bufs.reserve(n_max_backend_buffer);
-
-    for (auto & it : ctx_map) {
-        ggml_backend_buffer_type_t buft = it.first;
-        ggml_context * ctx              = it.second;
-
-        // skip contexts without tensors
-        if (ggml_get_first_tensor(ctx) == nullptr) {
-            continue;
-        }
-
-        llama_buf_map bufs;
-        bufs.reserve(n_max_backend_buffer);
-
-        // check if it is possible to use buffer_from_host_ptr with this buffer type
-        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
-        if (!dev) {
-            // FIXME: workaround for CPU backend buft having a NULL device
-            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-        }
-        ggml_backend_dev_props props;
-        ggml_backend_dev_get_props(dev, &props);
-        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
-        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
-
-        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                // only the mmap region containing the tensors in the model is mapped to the backend buffer
-                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
-                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
-                void * addr = nullptr;
-                size_t first, last; // NOLINT
-                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
-                if (first >= last) {
-                    continue;
-                }
-                const size_t max_size = ggml_get_max_tensor_size(ctx);
-                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
-                if (buf == nullptr) {
-                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
-                }
-                model.bufs.emplace_back(buf);
-                bufs.emplace(idx, buf);
-            }
-        }
-        else {
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
-            if (buf == nullptr) {
-                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
-            }
-            model.bufs.emplace_back(buf);
-            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
-                model.mlock_bufs.emplace_back(new llama_mlock);
-                auto & mlock_buf = model.mlock_bufs.back();
-                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
-                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
-            }
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                bufs.emplace(idx, buf);
-            }
-        }
-
-        if (bufs.empty()) {
-            throw std::runtime_error("failed to allocate buffer");
-        }
-
-        for (auto & buf : bufs) {
-            // indicate that this buffer contains weights
-            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
-            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-        }
-
-        ctx_bufs.emplace_back(ctx, bufs);
-    }
-
-    if (llama_supports_gpu_offload()) {
-        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
-
-        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
-        if (n_gpu_layers > (int) hparams.n_layer) {
-            LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
-        }
-
-        const int max_backend_supported_layers = hparams.n_layer + 1;
-        const int max_offloadable_layers       = hparams.n_layer + 1;
-
-        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
-    }
-
-    // print memory requirements per buffer type
-    for (auto & buf : model.bufs) {
-        LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
-    }
-
-    // populate tensors_by_name
-    for (auto & ctx : model.ctxs) {
-        for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) {
-            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
-        }
-    }
-
-    // load tensor data
-    for (auto & it : ctx_bufs) {
-        ggml_context * ctx = it.first;
-        auto & bufs = it.second;
-        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
-            return false;
-        }
-    }
-
-    if (use_mmap_buffer) {
-        for (auto & mapping : ml.mappings) {
-            model.mappings.emplace_back(std::move(mapping));
-        }
-    }
-
-    return true;
-}
-
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
-    model.t_start_us = ggml_time_us();
+    // loading time will be recalculated after the first eval, so
+    // we take page faults deferred by mmap() into consideration
+    model.t_load_us = 0;
+    time_meas tm(model.t_load_us);
+
+    model.t_start_us = tm.t_start_us;
 
     try {
         llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
 
+        ml.print_info();
+
         model.hparams.vocab_only = params.vocab_only;
 
         try {
-            llm_load_arch(ml, model);
+            model.load_arch(ml);
         } catch(const std::exception & e) {
             throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
         }
         try {
-            llm_load_hparams(ml, model);
+            model.load_hparams(ml);
         } catch(const std::exception & e) {
             throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
         }
         try {
-            llm_load_vocab(ml, model);
+            model.load_vocab(ml);
         } catch(const std::exception & e) {
             throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
         }
 
-        llm_load_stats(ml, model);
-        llm_load_print_meta(ml, model);
-
-        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
-            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
-            throw std::runtime_error("vocab size mismatch");
-        }
+        model.load_stats(ml);
+        model.print_info();
 
         if (params.vocab_only) {
             LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
             return 0;
         }
 
-        if (!llm_load_tensors(
-            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
-            params.progress_callback, params.progress_callback_user_data
-        )) {
+        if (!model.load_tensors(ml)) {
             return -2;
         }
     } catch (const std::exception & err) {
@@ -2595,10 +78,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         return -1;
     }
 
-    // loading time will be recalculate after the first eval, so
-    // we take page faults deferred by mmap() into consideration
-    model.t_load_us = ggml_time_us() - model.t_start_us;
-
     return 0;
 }
 
@@ -2646,16 +125,16 @@ static struct ggml_tensor * llm_build_inp_embd(
         inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
 
         // apply lora for embedding tokens if needed
-        for (auto & it : lctx.lora_adapters) {
-            struct llama_lora_weight * lora = it.first->get_weight(tok_embd);
-            if (lora == nullptr) {
+        for (auto & it : lctx.lora) {
+            struct llama_adapter_lora_weight * lw = it.first->get_weight(tok_embd);
+            if (lw == nullptr) {
                 continue;
             }
             const float adapter_scale = it.second;
-            const float scale = lora->get_scale(it.first->alpha, adapter_scale);
+            const float scale = lw->get_scale(it.first->alpha, adapter_scale);
             struct ggml_tensor * inpL_delta = ggml_scale(ctx, ggml_mul_mat(
-                ctx, lora->b, // non-transposed lora_b
-                ggml_get_rows(ctx, lora->a, lctx.inp_tokens)
+                ctx, lw->b, // non-transposed lora_b
+                ggml_get_rows(ctx, lw->a, lctx.inp_tokens)
             ), scale);
             inpL = ggml_add(ctx, inpL, inpL_delta);
         }
@@ -2726,16 +205,16 @@ static struct ggml_tensor * llm_build_lora_mm(
           struct ggml_tensor * w,
           struct ggml_tensor * cur) {
     struct ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
-    for (auto & it : lctx.lora_adapters) {
-        struct llama_lora_weight * lora = it.first->get_weight(w);
-        if (lora == nullptr) {
+    for (auto & it : lctx.lora) {
+        struct llama_adapter_lora_weight * lw = it.first->get_weight(w);
+        if (lw == nullptr) {
             continue;
         }
         const float adapter_scale = it.second;
-        const float scale = lora->get_scale(it.first->alpha, adapter_scale);
+        const float scale = lw->get_scale(it.first->alpha, adapter_scale);
         struct ggml_tensor * ab_cur = ggml_mul_mat(
-            ctx0, lora->b,
-            ggml_mul_mat(ctx0, lora->a, cur)
+            ctx0, lw->b,
+            ggml_mul_mat(ctx0, lw->a, cur)
         );
         ab_cur = ggml_scale(ctx0, ab_cur, scale);
         res = ggml_add(ctx0, res, ab_cur);
@@ -2751,17 +230,17 @@ static struct ggml_tensor * llm_build_lora_mm_id(
           struct ggml_tensor * cur, // struct ggml_tensor * b
           struct ggml_tensor * ids) {
     struct ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
-    for (auto & it : lctx.lora_adapters) {
-        struct llama_lora_weight * lora = it.first->get_weight(w);
-        if (lora == nullptr) {
+    for (auto & it : lctx.lora) {
+        struct llama_adapter_lora_weight * lw = it.first->get_weight(w);
+        if (lw == nullptr) {
             continue;
         }
         const float alpha = it.first->alpha;
-        const float rank  = (float) lora->b->ne[0];
+        const float rank  = (float) lw->b->ne[0];
         const float scale = alpha ? it.second * alpha / rank : it.second;
         struct ggml_tensor * ab_cur = ggml_mul_mat_id(
-            ctx0, lora->b,
-            ggml_mul_mat_id(ctx0, lora->a, cur, ids),
+            ctx0, lw->b,
+            ggml_mul_mat_id(ctx0, lw->a, cur, ids),
             ids
         );
         ab_cur = ggml_scale(ctx0, ab_cur, scale);
@@ -3686,7 +1165,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_k_shift() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         GGML_ASSERT(kv_self.size == n_ctx);
 
@@ -3736,7 +1215,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         for (uint32_t i = 0; i < ids.size(); ++i) {
             const uint32_t id = ids[i];
@@ -3995,7 +1474,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_llama() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -4161,7 +1640,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_deci() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -4322,7 +1801,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_baichuan() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -4334,7 +1813,7 @@ struct llm_build_context {
         inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
+        struct ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
         struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@@ -4359,7 +1838,7 @@ struct llm_build_context {
                 cb(Vcur, "Vcur", il);
 
                 switch (model.type) {
-                    case MODEL_7B:
+                    case LLM_TYPE_7B:
                         Qcur = ggml_rope_ext(
                             ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                             n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -4371,7 +1850,7 @@ struct llm_build_context {
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         break;
-                    case MODEL_13B:
+                    case LLM_TYPE_13B:
                         Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
                         Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
                         break;
@@ -4437,7 +1916,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_xverse() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -4540,7 +2019,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_falcon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -4660,7 +2139,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_grok() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -4819,7 +2298,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_dbrx() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -4947,7 +2426,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_starcoder() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -5051,7 +2530,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_refact() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -5145,7 +2624,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_bert() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -5339,7 +2818,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_bloom() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -5440,7 +2919,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_mpt() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -5730,7 +3209,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -5842,7 +3321,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -5954,7 +3433,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen2vl() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
         GGML_ASSERT(n_embd_head == hparams.n_rot);
@@ -6072,7 +3551,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen2moe() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -6220,7 +3699,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_phi2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -6341,7 +3820,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_phi3() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
@@ -6595,7 +4074,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gpt2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -6700,7 +4179,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_codeshell() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -6811,7 +4290,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_orion() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -6929,7 +4408,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_internlm2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -7047,7 +4526,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_minicpm3() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         //TODO: if the model varies, these parameters need to be read from the model
         const int64_t n_embd_base = 256;
@@ -7256,7 +4735,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gemma() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
@@ -7364,7 +4843,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gemma2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
@@ -7414,9 +4893,9 @@ struct llm_build_context {
 
                 // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
                 switch (model.type) {
-                    case llm_type::MODEL_2B:
-                    case llm_type::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
-                    case llm_type::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+                    case LLM_TYPE_2B:
+                    case LLM_TYPE_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
+                    case LLM_TYPE_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
                     default: GGML_ABORT("fatal error");
                 };
                 cb(Qcur, "Qcur_scaled", il);
@@ -7500,7 +4979,7 @@ struct llm_build_context {
 
 
     struct ggml_cgraph * build_starcoder2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -7619,7 +5098,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_mamba() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -7674,7 +5153,7 @@ struct llm_build_context {
 
     struct ggml_cgraph * build_command_r() {
 
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -7822,7 +5301,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_cohere2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -7959,7 +5438,7 @@ struct llm_build_context {
     //   * removed bias
     //   * removed MoE
     struct ggml_cgraph * build_olmo() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -8083,7 +5562,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_olmo2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -8211,7 +5690,7 @@ struct llm_build_context {
     //   * removed bias
     //   * added q, k norm
     struct ggml_cgraph * build_olmoe() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -8337,7 +5816,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_openelm() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -8462,7 +5941,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gptneox() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -8604,7 +6083,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_arctic() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -8738,7 +6217,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_deepseek() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -8895,7 +6374,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_deepseek2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -9125,7 +6604,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_bitnet() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -9276,7 +6755,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_t5_enc() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -9408,7 +6887,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_t5_dec() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -9613,7 +7092,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_jais() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -9705,7 +7184,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_chatglm() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -9819,7 +7298,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_nemotron() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -9940,7 +7419,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_exaone() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -10067,7 +7546,7 @@ struct llm_build_context {
     }
 
     ggml_cgraph * build_rwkv6() {
-        ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // Token shift state dimensions should be 2 * n_emb
         GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2);
@@ -10181,7 +7660,7 @@ struct llm_build_context {
 
     // ref: https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1/blob/main/modeling_rwkv6qwen2.py
     ggml_cgraph * build_rwkv6qwen2() {
-        ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         GGML_ASSERT(n_embd == hparams.n_embd_k_s());
 
@@ -10298,7 +7777,7 @@ struct llm_build_context {
     //   * removed bias
     //   * removed MoE
     struct ggml_cgraph * build_chameleon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -10470,7 +7949,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_wavtokenizer_dec() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -10679,12 +8158,12 @@ static struct ggml_cgraph * llama_build_graph(
 
         // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
         // FIXME: fix in ggml_backend_sched
-        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
+        const bool full_offload = lctx.model.params.n_gpu_layers > (int) lctx.model.hparams.n_layer;
         if (ubatch.n_tokens < 32 || full_offload) {
             if (il != -1 && strcmp(name, "norm") == 0) {
-                const auto & dev_layer = lctx.model.dev_layer.at(il);
+                const auto & dev_layer = lctx.model.dev_layer(il);
                 for (auto & backend : lctx.backends) {
-                    if (ggml_backend_get_device(backend.get()) == dev_layer.dev) {
+                    if (ggml_backend_get_device(backend.get()) == dev_layer) {
                         if (ggml_backend_supports_op(backend.get(), cur)) {
                             ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, backend.get());
                         }
@@ -10983,6 +8462,7 @@ static int llama_decode_impl(
     const uint32_t n_tokens_all = batch.n_tokens;
 
     const auto & model   = lctx.model;
+    const auto & vocab   = model.vocab;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
@@ -10990,7 +8470,7 @@ static int llama_decode_impl(
 
     if (batch.token) {
         for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
+            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
                 LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
                 return -1;
             }
@@ -11010,7 +8490,7 @@ static int llama_decode_impl(
     llama_kv_slot_restorer kv_slot_restorer(kv_self);
 
     const int64_t n_embd  = hparams.n_embd;
-    const int64_t n_vocab = hparams.n_vocab;
+    const int64_t n_vocab = vocab.n_tokens();
 
     uint32_t n_outputs = 0;
     uint32_t n_outputs_prev = 0;
@@ -11325,7 +8805,7 @@ static int llama_encode_impl(
 
     if (batch.token) {
         for (uint32_t i = 0; i < n_tokens; ++i) {
-            if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
+            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
                 LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
                 return -1;
             }
@@ -11502,9 +8982,9 @@ static void llama_kv_cache_defrag_impl(struct llama_context & lctx) {
     // each move requires 6*n_layer tensors (see build_defrag)
     //   - source view, destination view, copy operation
     //   - x2 for keys and values
-    //const uint32_t max_moves = llama_model_max_nodes(model)/(6*n_layer);
+    //const uint32_t max_moves = model.max_nodes()/(6*n_layer);
     // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
-    const uint32_t max_moves = (llama_model_max_nodes(lctx.model) - 2*n_layer)/(6*n_layer);
+    const uint32_t max_moves = (lctx.model.max_nodes() - 2*n_layer)/(6*n_layer);
 
     // determine which KV cells to move where
     //
@@ -11751,7 +9231,7 @@ static void llama_kv_cache_update_impl(struct llama_context & lctx) {
         // build worst-case graph
         uint32_t n_seqs = 1; // TODO: worst-case number of sequences
         uint32_t n_tokens = std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
-        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+        llama_token token = lctx.model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
         llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
         ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true);
 
@@ -11763,39 +9243,38 @@ static void llama_kv_cache_update_impl(struct llama_context & lctx) {
     }
 }
 
-int32_t llama_lora_adapter_set(
+int32_t llama_set_adapter_lora(
             struct llama_context * ctx,
-            struct llama_lora_adapter * adapter,
+            struct llama_adapter_lora * adapter,
             float scale) {
-    ctx->lora_adapters[adapter] = scale;
+    ctx->lora[adapter] = scale;
     return 0;
 }
 
-int32_t llama_lora_adapter_remove(
+int32_t llama_rm_adapter_lora(
             struct llama_context * ctx,
-            struct llama_lora_adapter * adapter) {
-    auto pos = ctx->lora_adapters.find(adapter);
-    if (pos != ctx->lora_adapters.end()) {
-        ctx->lora_adapters.erase(pos);
+            struct llama_adapter_lora * adapter) {
+    auto pos = ctx->lora.find(adapter);
+    if (pos != ctx->lora.end()) {
+        ctx->lora.erase(pos);
         return 0;
     }
 
     return -1;
 }
 
-void llama_lora_adapter_clear(struct llama_context * ctx) {
-    ctx->lora_adapters.clear();
+void llama_clear_adapter_lora(struct llama_context * ctx) {
+    ctx->lora.clear();
 }
 
-// TODO: tmp
-int32_t llama_control_vector_apply(
-        struct llama_context * lctx,
+int32_t llama_apply_adapter_cvec(
+        struct llama_context * ctx,
                  const float * data,
                       size_t   len,
                      int32_t   n_embd,
                      int32_t   il_start,
                      int32_t   il_end) {
-    return llama_control_vector_apply(lctx->cvec, lctx->model, data, len, n_embd, il_start, il_end);
+    return ctx->cvec.apply(ctx->model, data, len, n_embd, il_start, il_end);
 }
 
 //
@@ -11906,7 +9385,7 @@ struct llama_model * llama_model_load_from_file(
         struct llama_model_params params) {
     ggml_time_init();
 
-    llama_model * model = new llama_model;
+    llama_model * model = new llama_model(params);
 
     unsigned cur_percentage = 0;
     if (params.progress_callback == NULL) {
@@ -12006,7 +9485,7 @@ struct llama_model * llama_model_load_from_file(
         LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024);
     }
 
-    int status = llama_model_load(path_model, *model, params);
+    const int status = llama_model_load(path_model, *model, params);
     GGML_ASSERT(status <= 0);
     if (status < 0) {
         if (status == -1) {
@@ -12022,7 +9501,7 @@ struct llama_model * llama_model_load_from_file(
     return model;
 }
 
-struct llama_context * llama_new_context_with_model(
+struct llama_context * llama_init_from_model(
                  struct llama_model * model,
         struct llama_context_params   params) {
 
@@ -12280,7 +9759,7 @@ struct llama_context * llama_new_context_with_model(
                 backend_ptrs.push_back(backend.get());
             }
 
-            const size_t max_nodes = llama_model_max_nodes(*model);
+            const size_t max_nodes = model->max_nodes();
 
             // buffer used to store the computation graph and the tensor meta data
             ctx->buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
@@ -12288,9 +9767,9 @@ struct llama_context * llama_new_context_with_model(
             // TODO: move these checks to ggml_backend_sched
             // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
             bool pipeline_parallel =
-                llama_get_device_count(*model) > 1 &&
-                model->n_gpu_layers > (int)model->hparams.n_layer &&
-                model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
+                model->n_devices() > 1 &&
+                model->params.n_gpu_layers > (int)model->hparams.n_layer &&
+                model->params.split_mode == LLAMA_SPLIT_MODE_LAYER &&
                 params.offload_kqv;
 
             // pipeline parallelism requires support for async compute and events in all devices
@@ -12321,7 +9800,7 @@ struct llama_context * llama_new_context_with_model(
             // initialize scheduler with the worst-case graph
             uint32_t n_seqs = 1; // TODO: worst-case number of sequences
             uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-            llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+            llama_token token = ctx->model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
 
             llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
             ggml_cgraph * gf_pp = llama_build_graph(*ctx, ubatch_pp, true);
@@ -12373,6 +9852,12 @@ struct llama_context * llama_new_context_with_model(
     return ctx;
 }
 
+struct llama_context * llama_new_context_with_model(
+                 struct llama_model * model,
+        struct llama_context_params   params) {
+    return llama_init_from_model(model, params);
+}
+
 //
 // kv cache
 //
@@ -12470,166 +9955,18 @@ int32_t llama_decode(
     return ret;
 }
 
-//
-// vocab
-//
-
-// TODO: tmp bridges below until `struct llama_vocab` is exposed through the public API
-
-const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
-    return llama_token_get_text_impl(model->vocab, token);
-}
-
-float llama_token_get_score(const struct llama_model * model, llama_token token) {
-    return llama_token_get_score_impl(model->vocab, token);
-}
-
-enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
-    return llama_token_get_attr_impl(model->vocab, token);
-}
-
-bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
-    return llama_token_is_eog_impl(model->vocab, token);
-}
-
-bool llama_token_is_control(const struct llama_model * model, llama_token token) {
-    return llama_token_is_control_impl(model->vocab, token);
-}
-
-llama_token llama_token_bos(const struct llama_model * model) {
-    return llama_token_bos_impl(model->vocab);
-}
-
-llama_token llama_token_eos(const struct llama_model * model) {
-    return llama_token_eos_impl(model->vocab);
-}
-
-llama_token llama_token_eot(const struct llama_model * model) {
-    return llama_token_eot_impl(model->vocab);
-}
-
-llama_token llama_token_cls(const struct llama_model * model) {
-    return llama_token_cls_impl(model->vocab);
-}
-
-llama_token llama_token_sep(const struct llama_model * model) {
-    return llama_token_sep_impl(model->vocab);
-}
-
-llama_token llama_token_nl (const struct llama_model * model) {
-    return llama_token_nl_impl(model->vocab);
-}
-
-llama_token llama_token_pad(const struct llama_model * model) {
-    return llama_token_pad_impl(model->vocab);
-}
-
-bool llama_add_bos_token(const struct llama_model * model) {
-    return llama_add_bos_token_impl(model->vocab);
-}
-
-bool llama_add_eos_token(const struct llama_model * model) {
-    return llama_add_eos_token_impl(model->vocab);
-}
-
-llama_token llama_token_prefix(const struct llama_model * model) {
-    return llama_token_prefix_impl(model->vocab);
-}
-
-llama_token llama_token_middle(const struct llama_model * model) {
-    return llama_token_middle_impl(model->vocab);
-}
-
-llama_token llama_token_suffix(const struct llama_model * model) {
-    return llama_token_suffix_impl(model->vocab);
-}
-
-llama_token llama_token_fim_pre(const struct llama_model * model) {
-    return llama_token_fim_pre_impl(model->vocab);
-}
-
-llama_token llama_token_fim_suf(const struct llama_model * model) {
-    return llama_token_fim_suf_impl(model->vocab);
-}
-
-llama_token llama_token_fim_mid(const struct llama_model * model) {
-    return llama_token_fim_mid_impl(model->vocab);
-}
-
-llama_token llama_token_fim_pad(const struct llama_model * model) {
-    return llama_token_fim_pad_impl(model->vocab);
-}
-
-llama_token llama_token_fim_rep(const struct llama_model * model) {
-    return llama_token_fim_rep_impl(model->vocab);
-}
-
-llama_token llama_token_fim_sep(const struct llama_model * model) {
-    return llama_token_fim_sep_impl(model->vocab);
-}
-
-//
-// tokenization
-//
-
-int32_t llama_tokenize(
-    const struct llama_model * model,
-                  const char * text,
-                     int32_t   text_len,
-                 llama_token * tokens,
-                     int32_t   n_tokens_max,
-                        bool   add_special,
-                        bool   parse_special) {
-    return llama_tokenize_impl(model->vocab, text, text_len, tokens, n_tokens_max, add_special, parse_special);
-}
-
-int32_t llama_token_to_piece(
-    const struct llama_model * model,
-                 llama_token   token,
-                        char * buf,
-                     int32_t   length,
-                     int32_t   lstrip,
-                        bool   special) {
-    return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
-}
-
-int32_t llama_detokenize(
-    const struct llama_model * model,
-           const llama_token * tokens,
-                     int32_t   n_tokens,
-                        char * text,
-                     int32_t   text_len_max,
-                        bool   remove_special,
-                        bool   unparse_special) {
-    return llama_detokenize_impl(model->vocab, tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
-}
-
 //
 // chat templates
 //
 
 int32_t llama_chat_apply_template(
-                const struct llama_model * model,
                               const char * tmpl,
          const struct llama_chat_message * chat,
                                   size_t   n_msg,
                                     bool   add_ass,
                                     char * buf,
                                  int32_t   length) {
-    std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
-    if (tmpl == nullptr) {
-        GGML_ASSERT(model != nullptr);
-
-        // load template from model, if available
-        const auto & it = model->gguf_kv.find("tokenizer.chat_template");
-        if (it != model->gguf_kv.end() && it->second.size() > 0) {
-            curr_tmpl = it->second;
-        }
-        else {
-            // worst case: there is no information about template, we will use chatml by default
-            curr_tmpl = "chatml";  // see llm_chat_apply_template
-        }
-    }
+    const std::string curr_tmpl(tmpl == nullptr ? "chatml" : tmpl);
 
     // format the chat to string
     std::vector<const llama_chat_message *> chat_vec;
@@ -12653,23 +9990,6 @@ int32_t llama_chat_apply_template(
     return res;
 }
 
-//
-// sampling
-//
-
-// TODO: remove indirection when vocab becomes accesible in llama-sampling.cpp
-struct llama_sampler * llama_sampler_init_grammar(const struct llama_model * model, const char * grammar_str, const char * grammar_root) {
-    return llama_sampler_init_grammar_impl(model->vocab, grammar_str, grammar_root);
-}
-
-struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model) {
-    return llama_sampler_init_infill_impl(model->vocab);
-}
-
-struct llama_sampler * llama_sampler_init_dry(const struct llama_model * model, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
-    return llama_sampler_init_dry_impl(model->vocab, llama_n_ctx_train(model), dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers, num_breakers);
-}
-
 //
 // model split
 //
diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp
index ba084a91a..35b09aaea 100644
--- a/tests/test-autorelease.cpp
+++ b/tests/test-autorelease.cpp
@@ -14,7 +14,7 @@ int main(int argc, char ** argv) {
     std::thread([&model_path]() {
         llama_backend_init();
         auto * model = llama_model_load_from_file(model_path, llama_model_default_params());
-        auto * ctx = llama_new_context_with_model(model, llama_context_default_params());
+        auto * ctx = llama_init_from_model(model, llama_context_default_params());
         llama_free(ctx);
         llama_model_free(model);
         llama_backend_free();
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index f1f9aec4d..77d386954 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -157,7 +157,7 @@ int main(void) {
     }
 
     // test invalid chat template
-    res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
+    res = llama_chat_apply_template("INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
     assert(res < 0);
 
     for (size_t i = 0; i < templates.size(); i++) {
@@ -165,7 +165,6 @@ int main(void) {
         std::string expected = expected_output[i];
         formatted_chat.resize(1024);
         res = llama_chat_apply_template(
-            nullptr,
             custom_template.c_str(),
             conversation,
             message_count,
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
index 121c2c60c..59dda4877 100644
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -161,7 +161,7 @@ int main(int argc, char **argv) {
 
         auto cparams = llama_context_default_params();
 
-        ctx = llama_new_context_with_model(model, cparams);
+        ctx = llama_init_from_model(model, cparams);
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 5718fab04..55425d88a 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -55,7 +55,7 @@ int main(int argc, char **argv) {
 
         auto cparams = llama_context_default_params();
 
-        ctx = llama_new_context_with_model(model, cparams);
+        ctx = llama_init_from_model(model, cparams);
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
@@ -64,8 +64,10 @@ int main(int argc, char **argv) {
         }
     }
 
-    //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE);
-    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) {
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    //GGML_ASSERT(llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_BPE);
+    if (llama_vocab_type(vocab) != LLAMA_VOCAB_TYPE_BPE) {
         return 99;
     }
 
@@ -75,7 +77,7 @@ int main(int argc, char **argv) {
     atexit([]() { console::cleanup(); });
 #endif
 
-    const int n_vocab = llama_n_vocab(model);
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     for (int i = 0; i < n_vocab; ++i) {
         std::string str = common_detokenize(ctx, std::vector<int>(1, i));
diff --git a/tests/test-tokenizer-1-spm.cpp b/tests/test-tokenizer-1-spm.cpp
index ac05387c9..9e7b77f31 100644
--- a/tests/test-tokenizer-1-spm.cpp
+++ b/tests/test-tokenizer-1-spm.cpp
@@ -43,7 +43,7 @@ int main(int argc, char ** argv) {
 
         auto cparams = llama_context_default_params();
 
-        ctx = llama_new_context_with_model(model, cparams);
+        ctx = llama_init_from_model(model, cparams);
 
         if (ctx == NULL) {
             fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
@@ -52,8 +52,10 @@ int main(int argc, char ** argv) {
         }
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
-    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_SPM) {
+    if (llama_vocab_type(vocab) != LLAMA_VOCAB_TYPE_SPM) {
         return 99;
     }
 
@@ -63,7 +65,7 @@ int main(int argc, char ** argv) {
     atexit([]() { console::cleanup(); });
 #endif
 
-    const int n_vocab = llama_n_vocab(model);
+    const int n_vocab = llama_vocab_n_tokens(vocab);
 
     for (int i = 0; i < n_vocab; ++i) {
         std::string str = common_detokenize(ctx, std::vector<int>(1, i), true);
diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
index 9ebe6c891..c6cdcb554 100644
--- a/tests/test-tokenizer-random.py
+++ b/tests/test-tokenizer-random.py
@@ -76,7 +76,7 @@ class LibLlamaModel:
         self.ffi = libllama.ffi
         if isinstance(mparams, dict):
             mparams = libllama.model_default_params(**mparams)
-        self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
+        self.model = self.lib.llama_model_load_from_file(path_model.encode(), mparams)
         if not self.model:
             raise RuntimeError("error: failed to load model '%s'" % path_model)
         if isinstance(cparams, dict):
@@ -92,7 +92,7 @@ class LibLlamaModel:
         if self.ctx:
             self.lib.llama_free(self.ctx)
         if self.model:
-            self.lib.llama_free_model(self.model)
+            self.lib.llama_model_free(self.model)
         self.ctx = None
         self.model = None
         self.lib = None

From 08f10f69c38288e9e8bb1f933af63a3fc9013d40 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 12 Jan 2025 12:15:53 +0200
Subject: [PATCH 159/376] llama : remove notion of CLS token (#11064)

ggml-ci
---
 gguf-py/gguf/constants.py   |  2 --
 gguf-py/gguf/gguf_writer.py |  3 ---
 include/llama.h             |  5 ++++-
 src/llama-vocab.cpp         | 26 ++++++++------------------
 src/llama-vocab.h           |  1 -
 5 files changed, 12 insertions(+), 25 deletions(-)

diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 56aa9288d..8fe84df21 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -184,7 +184,6 @@ class Keys:
         UNK_ID               = "tokenizer.ggml.unknown_token_id"
         SEP_ID               = "tokenizer.ggml.seperator_token_id"
         PAD_ID               = "tokenizer.ggml.padding_token_id"
-        CLS_ID               = "tokenizer.ggml.cls_token_id"
         MASK_ID              = "tokenizer.ggml.mask_token_id"
         ADD_BOS              = "tokenizer.ggml.add_bos_token"
         ADD_EOS              = "tokenizer.ggml.add_eos_token"
@@ -1837,7 +1836,6 @@ KEY_TOKENIZER_EOM_ID     = Keys.Tokenizer.EOM_ID
 KEY_TOKENIZER_UNK_ID     = Keys.Tokenizer.UNK_ID
 KEY_TOKENIZER_SEP_ID     = Keys.Tokenizer.SEP_ID
 KEY_TOKENIZER_PAD_ID     = Keys.Tokenizer.PAD_ID
-KEY_TOKENIZER_CLS_ID     = Keys.Tokenizer.CLS_ID
 KEY_TOKENIZER_MASK_ID    = Keys.Tokenizer.MASK_ID
 KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
 KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index bf851c92c..080d2b9dc 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -857,9 +857,6 @@ class GGUFWriter:
     def add_pad_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.PAD_ID, id)
 
-    def add_cls_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.CLS_ID, id)
-
     def add_mask_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.MASK_ID, id)
 
diff --git a/include/llama.h b/include/llama.h
index 9f04bc622..a184884c7 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -937,7 +937,6 @@ extern "C" {
     LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence
     LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence
     LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn
-    LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab); // classification
     LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator
     LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line
     LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding
@@ -973,6 +972,10 @@ extern "C" {
     DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead");
     DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead");
 
+    // CLS is equivalent to BOS
+    DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification
+            "use llama_vocab_bos instead");
+
     //
     // Tokenization
     //
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index ed8751737..d0fb85cea 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1218,7 +1218,6 @@ struct llama_vocab::impl {
     llama_token special_unk_id  = 0;
     llama_token special_sep_id  = LLAMA_TOKEN_NULL;
     llama_token special_pad_id  = LLAMA_TOKEN_NULL;
-    llama_token special_cls_id  = LLAMA_TOKEN_NULL; // TODO: revisit if this is really needed https://github.com/ggerganov/llama.cpp/pull/10930
     llama_token special_mask_id = LLAMA_TOKEN_NULL;
 
     llama_token linefeed_id = 13;
@@ -1352,7 +1351,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_unk_id  = LLAMA_TOKEN_NULL;
             special_sep_id  = LLAMA_TOKEN_NULL;
             special_pad_id  = LLAMA_TOKEN_NULL;
-            special_cls_id  = LLAMA_TOKEN_NULL;
             special_mask_id = LLAMA_TOKEN_NULL;
             linefeed_id     = LLAMA_TOKEN_NULL;
 
@@ -1374,18 +1372,16 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_unk_id  = 0;
             special_sep_id  = LLAMA_TOKEN_NULL;
             special_pad_id  = LLAMA_TOKEN_NULL;
-            special_cls_id  = LLAMA_TOKEN_NULL;
             special_mask_id = LLAMA_TOKEN_NULL;
         } else if (tokenizer_model == "bert") {
             type = LLAMA_VOCAB_TYPE_WPM;
 
             // default special tokens
-            special_bos_id  = LLAMA_TOKEN_NULL;
+            special_bos_id  = 101;
             special_eos_id  = LLAMA_TOKEN_NULL;
             special_unk_id  = 100;
             special_sep_id  = 102;
             special_pad_id  = 0;
-            special_cls_id  = 101;
             special_mask_id = 103;
         } else if (tokenizer_model == "gpt2") {
             type = LLAMA_VOCAB_TYPE_BPE;
@@ -1420,7 +1416,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_unk_id  = LLAMA_TOKEN_NULL;
             special_sep_id  = LLAMA_TOKEN_NULL;
             special_pad_id  = LLAMA_TOKEN_NULL;
-            special_cls_id  = LLAMA_TOKEN_NULL;
             special_mask_id = LLAMA_TOKEN_NULL;
         } else if (tokenizer_model == "t5") {
             type = LLAMA_VOCAB_TYPE_UGM;
@@ -1431,7 +1426,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_unk_id  = 2;
             special_sep_id  = LLAMA_TOKEN_NULL;
             special_pad_id  = 0;
-            special_cls_id  = LLAMA_TOKEN_NULL;
             special_mask_id = LLAMA_TOKEN_NULL;
 
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
@@ -1712,7 +1706,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             { LLM_KV_TOKENIZER_UNK_ID,     special_unk_id     },
             { LLM_KV_TOKENIZER_SEP_ID,     special_sep_id     },
             { LLM_KV_TOKENIZER_PAD_ID,     special_pad_id     },
-            { LLM_KV_TOKENIZER_CLS_ID,     special_cls_id     },
             { LLM_KV_TOKENIZER_MASK_ID,    special_mask_id    },
             { LLM_KV_TOKENIZER_FIM_PRE_ID, special_fim_pre_id },
             { LLM_KV_TOKENIZER_FIM_SUF_ID, special_fim_suf_id },
@@ -2406,8 +2399,8 @@ std::vector<llama_token> llama_vocab::impl::tokenize(
         case LLAMA_VOCAB_TYPE_WPM:
             {
                 if (add_special) {
-                    GGML_ASSERT(special_cls_id != LLAMA_TOKEN_NULL);
-                    output.push_back(special_cls_id);
+                    GGML_ASSERT(special_bos_id != LLAMA_TOKEN_NULL);
+                    output.push_back(special_bos_id);
                 }
 
                 llm_tokenizer_wpm_session session(vocab);
@@ -2700,7 +2693,6 @@ void llama_vocab::impl::print_info() const {
     if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, special_unk_id,     id_to_token[special_unk_id].text.c_str() );  }
     if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, special_sep_id,     id_to_token[special_sep_id].text.c_str() );  }
     if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, special_pad_id,     id_to_token[special_pad_id].text.c_str() );  }
-    if (special_cls_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, special_cls_id,     id_to_token[special_cls_id].text.c_str() );  }
     if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, special_mask_id,    id_to_token[special_mask_id].text.c_str() ); }
 
     if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, linefeed_id,        id_to_token[linefeed_id].text.c_str() ); }
@@ -2834,7 +2826,7 @@ llama_token_attr llama_vocab::token_get_attr(llama_token id) const {
 }
 
 llama_token llama_vocab::token_bos() const {
-    return pimpl->type != LLAMA_VOCAB_TYPE_WPM ? pimpl->special_bos_id : pimpl->special_cls_id;
+    return pimpl->special_bos_id;
 }
 
 llama_token llama_vocab::token_eos() const {
@@ -2853,10 +2845,6 @@ llama_token llama_vocab::token_unk() const {
     return pimpl->special_unk_id;
 }
 
-llama_token llama_vocab::token_cls() const {
-    return pimpl->special_cls_id;
-}
-
 llama_token llama_vocab::token_sep() const {
     return pimpl->special_sep_id;
 }
@@ -3069,8 +3057,9 @@ llama_token llama_vocab_eot(const struct llama_vocab * vocab) {
     return vocab->token_eot();
 }
 
+// deprecated
 llama_token llama_vocab_cls(const struct llama_vocab * vocab) {
-    return vocab->token_cls();
+    return vocab->token_bos();
 }
 
 llama_token llama_vocab_sep(const struct llama_vocab * vocab) {
@@ -3159,7 +3148,8 @@ llama_token llama_token_eot(const struct llama_vocab * vocab) {
 
 // deprecated
 llama_token llama_token_cls(const struct llama_vocab * vocab) {
-    return llama_vocab_cls(vocab);
+    //return llama_vocab_cls(vocab);
+    return llama_vocab_bos(vocab); // avoid deprecation warning
 }
 
 // deprecated
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 020f2b533..5ce355214 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -53,7 +53,6 @@ struct llama_vocab {
     llama_token token_eot() const;
     llama_token token_eom() const;
     llama_token token_unk() const;
-    llama_token token_cls() const;
     llama_token token_sep() const;
     llama_token token_nl () const;
     llama_token token_pad() const;

From 9a483999a6fda350772aaf7bc541f1cb246f8a29 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Sun, 12 Jan 2025 13:45:14 +0100
Subject: [PATCH 160/376] llama : fix chat template gguf key (#11201)

---
 common/common.cpp  | 11 ++---------
 src/llama-arch.cpp |  2 +-
 2 files changed, 3 insertions(+), 10 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 39bfb0c2e..1a2e15247 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1636,15 +1636,8 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
 //
 
 std::string common_get_builtin_chat_template(const struct llama_model * model) {
-    static const char * template_key = "tokenizer.chat_template";
-    // call with NULL buffer to get the total size of the string
-    int32_t res = llama_model_meta_val_str(model, template_key, NULL, 0);
-    if (res > 0) {
-        std::vector<char> model_template(res + 1, 0);
-        llama_model_meta_val_str(model, template_key, model_template.data(), model_template.size());
-        return std::string(model_template.data(), model_template.size() - 1);
-    }
-    return "";
+    const char * ptr_tmpl = llama_model_chat_template(model);
+    return ptr_tmpl == nullptr ? "" : ptr_tmpl;
 }
 
 bool common_chat_verify_template(const std::string & tmpl) {
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 5c1f14cfd..d7d277e72 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -178,7 +178,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
     { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
     { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
-    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat.template"                 },
+    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat_template"                 },
     { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
     { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
     { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },

From 924518e2e5726e81f3aeb2518fb85963a500e93a Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Sun, 12 Jan 2025 18:23:10 +0000
Subject: [PATCH 161/376] Reset color before we exit (#11205)

We don't want colors to leak post termination of llama-run.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index bfa8378bb..0ad8bb15b 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -29,7 +29,7 @@
 
 #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
 [[noreturn]] static void sigint_handler(int) {
-    printf("\n");
+    printf("\n\033[0m");
     exit(0);  // not ideal, but it's the only way to guarantee exit in all cases
 }
 #endif

From 1244cdcf14900dd199907b13f25d9c91a507f578 Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Mon, 13 Jan 2025 13:31:41 +0200
Subject: [PATCH 162/376] ggml : do not define GGML_USE_CUDA when building with
 GGML_BACKEND_DL (#11211)

Build fails when using HIP and GGML_BACKEND_DL:
```
/usr/bin/ld: ../ggml/src/libggml.so: undefined reference to `ggml_backend_cuda_reg'
collect2: error: ld returned 1 exit status
```
This patch fixes this.
---
 ggml/src/ggml-hip/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index b15fbd24d..d090ba9bd 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -70,7 +70,9 @@ ggml_add_backend_library(ggml-hip
                         )
 
 # TODO: do not use CUDA definitions for HIP
-target_compile_definitions(ggml PUBLIC GGML_USE_CUDA)
+if (NOT GGML_BACKEND_DL)
+    target_compile_definitions(ggml PUBLIC GGML_USE_CUDA)
+endif()
 
 add_compile_definitions(GGML_USE_HIP)
 

From 8f70fc3d1b1d3c17b61842330dd106d391cc1227 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 13 Jan 2025 13:38:20 +0100
Subject: [PATCH 163/376] llama : remove 'd' from bad special token log
 (#11212)

This commit removes the 'd' from the log message in llama-vocab.cpp
when logging a bad special token.

The motivation for this is that currently the output can look something
like the following:
```console
load: bad special token:
    'tokenizer.ggml.image_token_id' = 128256d, using default id -1
```
---
 src/llama-vocab.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index d0fb85cea..96b74e93a 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1729,7 +1729,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 continue;
             }
             if (new_id >= id_to_token.size()) {
-                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
+                LLAMA_LOG_WARN("%s: bad special token: '%s' = %u, using default id %d\n",
                     __func__, key.c_str(), new_id, id);
             } else {
                 id = new_id;

From 7426a26b2492fc546a4db6991e871ee605714093 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 13 Jan 2025 14:46:36 +0200
Subject: [PATCH 164/376] contrib : add naming guidelines (#11177)

* contrib : add naming guidelines

* contrib : expand naming guidelines [no ci]

* contrib : cont [no ci]

* contrib : add `_t` suffix guideline [no ci]

* contrib : cont [no ci]

* minor [no ci]

* contrib : move coding guidelines to correct section [no ci]

* contrib : minor reword coding guidelines [no ci]

* contrib : add TODO for preprocessor directives [no ci]

* contrib : expand [no ci]

* minor [no ci]

* contrib : clarify `_context` suffix usage [no ci]

* contrib : filename guidelines [no ci]

* contrib : fix notes [no ci]
---
 CONTRIBUTING.md | 102 +++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 96 insertions(+), 6 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 5a85ec5d2..a86f00ac6 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,10 +1,10 @@
 # Pull requests (for contributors)
 
 - Test your changes:
-  - Execute [the full CI locally on your machine](ci/README.md) before publishing
-  - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
-  - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
-  - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
+    - Execute [the full CI locally on your machine](ci/README.md) before publishing
+    - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
+    - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
+    - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
 - If your PR becomes stale, don't hesitate to ping the maintainers in the comments
 
@@ -20,14 +20,104 @@
 - Avoid adding third-party dependencies, extra files, extra headers, etc.
 - Always consider cross-compatibility with other operating systems and architectures
 - Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
-- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
+- Vertical alignment makes things more readable and easier to batch edit
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
+- Use sized integer types in the public API
+- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
+    - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
+    ```cpp
+    // OK
+    llama_context * ctx;
+    const llama_rope_type rope_type;
+
+    // not OK
+    struct llama_context * ctx;
+    const enum llama_rope_type rope_type;
+    ```
+    
+    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
+  
+- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code
+- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
 - Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
 
 ![matmul](media/matmul.png)
 
+# Naming guidelines
+
+- Use `snake_case` for function, variable and type names
+- Naming usually optimizes for longest common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
+
+    ```cpp
+    // not OK
+    int small_number;
+    int big_number;
+
+    // OK
+    int number_small;
+    int number_big;
+    ```
+
+- Enum values are always in upper case and prefixed with the enum name
+
+    ```cpp
+    enum llama_vocab_type {
+        LLAMA_VOCAB_TYPE_NONE = 0,
+        LLAMA_VOCAB_TYPE_SPM  = 1,
+        LLAMA_VOCAB_TYPE_BPE  = 2,
+        LLAMA_VOCAB_TYPE_WPM  = 3,
+        LLAMA_VOCAB_TYPE_UGM  = 4,
+        LLAMA_VOCAB_TYPE_RWKV = 5,
+    };
+    ```
+
+- The general naming pattern is `<class>_<method>`, with `<method>` being `<action>_<noun>`
+
+    ```cpp
+    llama_model_init();           // class: "llama_model",         method: "init"
+    llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
+    llama_sampler_get_seed();     // class: "llama_sampler",       method: "get_seed"
+    llama_set_embeddings();       // class: "llama_context",       method: "set_embeddings"
+    llama_n_threads();            // class: "llama_context",       method: "n_threads"
+    llama_adapter_lora_free();    // class: "llama_adapter_lora",  method: "free"
+    ```
+
+    - The `get` `<action>` can be omitted
+    - The `<noun>` can be omitted if not necessary
+    - The `_context` suffix of the `<class>` is optional. Use it to disambiguate symbols when needed
+    - Use `init`/`free` for constructor/destructor `<action>`
+
+- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
+
+    ```cpp
+    typedef struct llama_context * llama_context_t;
+
+    enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
+    ```
+
+    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_
+
+- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension
+- Python filenames are all lowercase with underscores
+
+- _(TODO: abbreviations usage)_
+
+# Preprocessor directives
+
+- (TODO: add guidelines with examples and apply them to the codebase)
+
+    ```cpp
+    #ifdef FOO
+    #endif // FOO
+    ```
+
+# Documentation
+
+- Documentation is a community effort
+- When you need to look into the source code to figure out implementation details to figure out how to use an API consider adding a short summary to the header file for future reference
+- When you notice incorrect or outdated documentation, please update it
+
 # Resources
 
 The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:

From 00b4c3da6202e855087a4986bf19bb41b959e333 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 13 Jan 2025 13:56:23 +0100
Subject: [PATCH 165/376] common : support tag-based --hf-repo like on ollama
 (#11195)

* common : support tag-based hf_repo like on ollama

* fix build

* various fixes

* small fixes

* fix style

* fix windows build?

* move common_get_hf_file to common.cpp

* fix complain with noreturn
---
 common/arg.cpp    |  33 ++++++++++-----
 common/common.cpp | 106 +++++++++++++++++++++++++++++++++++++++++++---
 common/common.h   |   8 ++++
 3 files changed, 130 insertions(+), 17 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 27886b84e..1457a360f 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -130,17 +130,26 @@ std::string common_arg::to_string() {
 
 static void common_params_handle_model_default(
         std::string & model,
-        std::string & model_url,
+        const std::string & model_url,
         std::string & hf_repo,
-        std::string & hf_file) {
+        std::string & hf_file,
+        const std::string & hf_token) {
     if (!hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
         if (hf_file.empty()) {
             if (model.empty()) {
-                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
+                auto auto_detected = common_get_hf_file(hf_repo, hf_token);
+                if (auto_detected.first.empty() || auto_detected.second.empty()) {
+                    exit(1); // built without CURL, error message already printed
+                }
+                hf_repo = auto_detected.first;
+                hf_file = auto_detected.second;
+            } else {
+                hf_file = model;
             }
-            hf_file = model;
-        } else if (model.empty()) {
+        }
+        // make sure model path is present (for caching purposes)
+        if (model.empty()) {
             // this is to avoid different repo having same file name, or same file name in different subdirs
             std::string filename = hf_repo + "_" + hf_file;
             // to make sure we don't have any slashes in the filename
@@ -290,8 +299,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     }
 
     // TODO: refactor model params in a common struct
-    common_params_handle_model_default(params.model,         params.model_url,         params.hf_repo,         params.hf_file);
-    common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file);
+    common_params_handle_model_default(params.model,         params.model_url,         params.hf_repo,         params.hf_file,         params.hf_token);
+    common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file, params.hf_token);
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -1583,21 +1592,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_MODEL_URL"));
     add_opt(common_arg(
-        {"-hfr", "--hf-repo"}, "REPO",
-        "Hugging Face model repository (default: unused)",
+        {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
+        "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
+        "example: unsloth/phi-4-GGUF:q4_k_m\n"
+        "(default: unused)",
         [](common_params & params, const std::string & value) {
             params.hf_repo = value;
         }
     ).set_env("LLAMA_ARG_HF_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
-        "Hugging Face model file (default: unused)",
+        "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
         [](common_params & params, const std::string & value) {
             params.hf_file = value;
         }
     ).set_env("LLAMA_ARG_HF_FILE"));
     add_opt(common_arg(
-        {"-hfrv", "--hf-repo-v"}, "REPO",
+        {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
         "Hugging Face model repository for the vocoder model (default: unused)",
         [](common_params & params, const std::string & value) {
             params.vocoder.hf_repo = value;
diff --git a/common/common.cpp b/common/common.cpp
index 1a2e15247..a6f9252b2 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -73,6 +73,22 @@
 #include <sys/syslimits.h>
 #endif
 #define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
+
+//
+// CURL utils
+//
+
+using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
+
+// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
+struct curl_slist_ptr {
+    struct curl_slist * ptr = nullptr;
+    ~curl_slist_ptr() {
+        if (ptr) {
+            curl_slist_free_all(ptr);
+        }
+    }
+};
 #endif // LLAMA_USE_CURL
 
 using json = nlohmann::ordered_json;
@@ -1130,7 +1146,8 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
 
 static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
     // Initialize libcurl
-    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_slist_ptr http_headers;
     if (!curl) {
         LOG_ERR("%s: error initializing libcurl\n", __func__);
         return false;
@@ -1144,11 +1161,9 @@ static bool common_download_file(const std::string & url, const std::string & pa
 
     // Check if hf-token or bearer-token was specified
     if (!hf_token.empty()) {
-      std::string auth_header = "Authorization: Bearer ";
-      auth_header += hf_token.c_str();
-      struct curl_slist *http_headers = NULL;
-      http_headers = curl_slist_append(http_headers, auth_header.c_str());
-      curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
+        std::string auth_header = "Authorization: Bearer " + hf_token;
+        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
+        curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
     }
 
 #if defined(_WIN32)
@@ -1444,6 +1459,80 @@ struct llama_model * common_load_model_from_hf(
     return common_load_model_from_url(model_url, local_path, hf_token, params);
 }
 
+/**
+ * Allow getting the HF file from the HF repo with tag (like ollama), for example:
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
+ * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
+ *
+ * Return pair of <repo, file> (with "repo" already having tag removed)
+ *
+ * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
+ */
+std::pair<std::string, std::string> common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) {
+    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
+    std::string tag = parts.size() > 1 ? parts.back() : "latest";
+    std::string hf_repo = parts[0];
+    if (string_split<std::string>(hf_repo, '/').size() != 2) {
+        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
+    }
+
+    // fetch model info from Hugging Face Hub API
+    json model_info;
+    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_slist_ptr http_headers;
+    std::string res_str;
+    std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag;
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
+    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
+    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
+        static_cast<std::string *>(data)->append((char * ) ptr, size * nmemb);
+        return size * nmemb;
+    };
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str);
+#if defined(_WIN32)
+    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+    if (!hf_token.empty()) {
+        std::string auth_header = "Authorization: Bearer " + hf_token;
+        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
+    }
+    // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json");
+    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+
+    CURLcode res = curl_easy_perform(curl.get());
+
+    if (res != CURLE_OK) {
+        throw std::runtime_error("error: cannot make GET request to HF API");
+    }
+
+    long res_code;
+    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
+    if (res_code == 200) {
+        model_info = json::parse(res_str);
+    } else if (res_code == 401) {
+        throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
+    } else {
+        throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
+    }
+
+    // check response
+    if (!model_info.contains("ggufFile")) {
+        throw std::runtime_error("error: model does not have ggufFile");
+    }
+    json & gguf_file = model_info.at("ggufFile");
+    if (!gguf_file.contains("rfilename")) {
+        throw std::runtime_error("error: ggufFile does not have rfilename");
+    }
+
+    return std::make_pair(hf_repo, gguf_file.at("rfilename"));
+}
+
 #else
 
 struct llama_model * common_load_model_from_url(
@@ -1465,6 +1554,11 @@ struct llama_model * common_load_model_from_hf(
     return nullptr;
 }
 
+std::pair<std::string, std::string> common_get_hf_file(const std::string &, const std::string &) {
+    LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
+    return std::make_pair("", "");
+}
+
 #endif // LLAMA_USE_CURL
 
 //
diff --git a/common/common.h b/common/common.h
index d523948b0..c86a4ef39 100644
--- a/common/common.h
+++ b/common/common.h
@@ -454,6 +454,11 @@ static bool string_starts_with(const std::string & str,
     return str.rfind(prefix, 0) == 0;
 }
 
+static bool string_ends_with(const std::string & str,
+                               const std::string & suffix) {  // While we wait for C++20's std::string::ends_with...
+    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+}
+
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
 
@@ -501,6 +506,9 @@ struct llama_model * common_load_model_from_hf(
     const std::string & local_path,
     const std::string & hf_token,
     const struct llama_model_params & params);
+std::pair<std::string, std::string> common_get_hf_file(
+    const std::string & hf_repo_with_tag,
+    const std::string & hf_token);
 
 // clear LoRA adapters from context, then apply new list of adapters
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);

From ca001f6656c1c3d29ef479b3aa5d669453e63be5 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 13 Jan 2025 15:08:44 +0200
Subject: [PATCH 166/376] contrib : add naming guidelines (cont) (#11177)

---
 CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index a86f00ac6..dc58dbd51 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -22,7 +22,7 @@
 - Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
 - Vertical alignment makes things more readable and easier to batch edit
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- Use sized integer types in the public API
+- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets
 - Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
     - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
     ```cpp
@@ -115,7 +115,7 @@
 # Documentation
 
 - Documentation is a community effort
-- When you need to look into the source code to figure out implementation details to figure out how to use an API consider adding a short summary to the header file for future reference
+- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
 - When you notice incorrect or outdated documentation, please update it
 
 # Resources

From 437e05f714cdd67757405221578d3f95f8228b63 Mon Sep 17 00:00:00 2001
From: ebraminio <ebrahim@gnu.org>
Date: Mon, 13 Jan 2025 17:16:39 +0330
Subject: [PATCH 167/376] server : (UI) Support for RTL text as models input or
 output (#11208)

---
 examples/server/public/index.html.gz | Bin 1206458 -> 1206472 bytes
 examples/server/webui/index.html     |   5 +++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 3640a7a6cfa76764d93684e9051a32c263932c8a..489bff84b98f7bd7ebcdabeb7d0f9b31093eae58 100644
GIT binary patch
delta 8441
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N{{t=SD#I=KA^_ZbfVThu

diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index 86a79b77f..a76d831a9 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -37,7 +37,7 @@
           <div v-for="conv in conversations" :class="{
             'btn btn-ghost justify-start font-normal': true,
             'btn-active': conv.id === viewingConvId,
-          }" @click="setViewingConv(conv.id)">
+          }" @click="setViewingConv(conv.id)" dir="auto">
             <span class="truncate">{{ conv.messages[0].content }}</span>
           </div>
           <div class="text-center text-xs opacity-40 mt-auto mx-4">
@@ -156,6 +156,7 @@
             @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
             :disabled="isGenerating"
             id="msg-input"
+            dir="auto"
           ></textarea>
           <button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
           <button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
@@ -244,7 +245,7 @@
       <div :class="{
         'chat-bubble markdown': true,
         'chat-bubble-base-300': msg.role !== 'user',
-      }">
+      }" dir="auto">
         <!-- textarea for editing message -->
         <template v-if="editingContent !== null">
           <textarea

From a29f0870d4846f52eda14ae28cea612ab66d903c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 13 Jan 2025 15:59:26 +0200
Subject: [PATCH 168/376] contrib : add naming guidelines (cont) (#11177)

---
 CONTRIBUTING.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index dc58dbd51..8d411982b 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -34,9 +34,9 @@
     struct llama_context * ctx;
     const enum llama_rope_type rope_type;
     ```
-    
+
     _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
-  
+
 - Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code
 - For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
@@ -105,7 +105,7 @@
 
 # Preprocessor directives
 
-- (TODO: add guidelines with examples and apply them to the codebase)
+- _(TODO: add guidelines with examples and apply them to the codebase)_
 
     ```cpp
     #ifdef FOO

From 39509fb082895d1eae2486f8ad2cbf0e905346c4 Mon Sep 17 00:00:00 2001
From: Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
Date: Mon, 13 Jan 2025 16:45:53 +0100
Subject: [PATCH 169/376] cuda : CUDA Graph Compute Function Refactor
 (precursor for performance improvements) (#11042)

* Refactor: Moves cuda graph executable update step to separate function.

* Refactor: Moves cuda graph update check to separate function.

* Refactor: Moves cuda graph maintenance (update or adjusting copy parameters) to separate function for improved readability.

* Fix: Adds missing reference to maintain_cuda_graph() definition.

* Refactor: Improves structure and abstractions by moving CUDA graph evaluation and capture to its own function.

* Refactor: Moves node graph checks and copy ops into individual function for improved readability.

* Refactor: Removes code permanently excluded from compilation to increase readability.

* Style: Adds missing newline

* Style: Consolidates several neighboring '#ifdef USE_CUDA_GRAPH' into a single one

* Refactor: Makes 'cuda_graph_update_required' a local variable

* remove double lines between functions

---------

Co-authored-by: slaren <slarengh@gmail.com>
---
 ggml/src/ggml-cuda/ggml-cuda.cu | 426 +++++++++++++++++---------------
 1 file changed, 225 insertions(+), 201 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 8476ee1bc..1dac397c4 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2289,6 +2289,66 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
 }
 
 #ifdef USE_CUDA_GRAPH
+static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
+    std::vector<void *> & ggml_cuda_cpy_fn_ptrs, bool use_cuda_graph) {
+
+    // Loop over nodes in GGML graph to obtain info needed for CUDA graph
+    cuda_ctx->cuda_graph->updated_kernel_arg.clear();
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+            continue;
+        }
+
+        if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) {
+            use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
+#ifndef NDEBUG
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__);
+#endif
+        }
+
+        if (node->op == GGML_OP_MUL_MAT_ID) {
+            use_cuda_graph = false; // This node type is not supported by CUDA graph capture
+#ifndef NDEBUG
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
+#endif
+        }
+
+        if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
+            // disable CUDA graphs for batch size > 1 for now.
+            // Changes in batch size or context size can cause changes to the grid size of some kernels.
+            use_cuda_graph = false;
+#ifndef NDEBUG
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
+#endif
+        }
+
+        if (node->op == GGML_OP_CPY) {
+            // store the copy op parameter which changes with each token.
+            cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
+            // store a pointer to each copy op CUDA kernel to identify it later
+            void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
+            if (!ptr) {
+                use_cuda_graph = false;
+#ifndef NDEBUG
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
+#endif
+            } else {
+                if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
+                    ggml_cuda_cpy_fn_ptrs.push_back(ptr);
+                }
+            }
+        }
+
+        if (!use_cuda_graph) {
+            break;
+        }
+    }
+
+    return use_cuda_graph;
+}
+
 static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
     graph_node_properties->node_address = node->data;
     graph_node_properties->node_op = node->op;
@@ -2339,149 +2399,105 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra
 
     return true;
 }
-#endif
 
-static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+static void maintain_cuda_graph(ggml_backend_cuda_context * cuda_ctx, std::vector<void *> & ggml_cuda_cpy_fn_ptrs, bool cuda_graph_update_required) {
 
-    ggml_cuda_set_device(cuda_ctx->device);
+    if (cuda_graph_update_required) {
+        // Extract nodes from graph
+        // First call with null argument gets number of nodes in graph
+        CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
+        // Subsequent call with non-null argument gets nodes
+        cuda_ctx->cuda_graph->nodes.clear();
+        cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
+        cuda_ctx->cuda_graph->params.clear();
+        cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
+        if (cuda_ctx->cuda_graph->num_nodes > 0) {
+            CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
 
-#ifdef USE_CUDA_GRAPH
-    static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
-
-    // Objects required for CUDA Graph
-    if (cuda_ctx->cuda_graph == nullptr) {
-        cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
-    }
-
-    bool use_cuda_graph = true;
-    bool cuda_graph_update_required = false;
-    // vector of pointers to CUDA cpy kernels, which are required to identify
-    // kernel parameters which need updated in the graph for each token
-    std::vector<void *> ggml_cuda_cpy_fn_ptrs;
-
-    if (cuda_ctx->cuda_graph->graph == nullptr) {
-        if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
-            cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
-#ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
-#endif
-        }
-    }
-
-    // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
-    // or previous graph capture failure.
-    // Also disable for multi-gpu for now. TO DO investigate
-    if (disable_cuda_graphs_due_to_env
-        || cuda_ctx->cuda_graph->disable_due_to_gpu_arch
-        || cuda_ctx->cuda_graph->disable_due_to_too_many_updates
-        || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
-        use_cuda_graph = false;
-    }
-
-    if (use_cuda_graph) {
-        if (cuda_ctx->cuda_graph->instance == nullptr) {
-            cuda_graph_update_required = true;
-        }
-
-        // Check if the graph size has changed
-        if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
-            cuda_graph_update_required = true;
-            cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
-        }
-
-        // Loop over nodes in GGML graph to determine if CUDA graph update is required
-        // and store properties to allow this comparison for the next token
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            bool has_matching_properties = true;
-            if (!cuda_graph_update_required) {
-                has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
-            }
-            if (!has_matching_properties) {
-                cuda_graph_update_required = true;
-            }
-            set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
-        }
-
-        // Loop over nodes in GGML graph to obtain info needed for CUDA graph
-        cuda_ctx->cuda_graph->updated_kernel_arg.clear();
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            ggml_tensor * node = cgraph->nodes[i];
-
-            if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
-                continue;
-            }
-
-            if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) {
-                use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
-#ifndef NDEBUG
-                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__);
-#endif
-            }
-
-            if (node->op == GGML_OP_MUL_MAT_ID) {
-                use_cuda_graph = false; // This node type is not supported by CUDA graph capture
-#ifndef NDEBUG
-                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
-#endif
-            }
-
-            if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
-                // disable CUDA graphs for batch size > 1 for now.
-                // Changes in batch size or context size can cause changes to the grid size of some kernels.
-                use_cuda_graph = false;
-#ifndef NDEBUG
-                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
-#endif
-            }
-
-            if (node->op == GGML_OP_CPY) {
-                // store the copy op parameter which changes with each token.
-                cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
-                // store a pointer to each copy op CUDA kernel to identify it later
-                void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
-                if (!ptr) {
-                    use_cuda_graph = false;
-#ifndef NDEBUG
-                    GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
-#endif
-                } else {
-                    if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
-                        ggml_cuda_cpy_fn_ptrs.push_back(ptr);
+            // Loop over nodes, and extract kernel parameters from each node
+            for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
+                cudaGraphNodeType node_type;
+                CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
+                if (node_type == cudaGraphNodeTypeKernel) {
+                    cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
+                    if (stat == cudaErrorInvalidDeviceFunction) {
+                        // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
+                        // We don't need to update blas nodes, so clear error and move on.
+                        cudaGetLastError();
+                    } else {
+                        GGML_ASSERT(stat == cudaSuccess);
                     }
                 }
             }
-
-            if (!use_cuda_graph) {
-                break;
+        }
+    } else {
+        // One of the arguments to the copy kernel is updated for each token, hence we need to
+        // replace that argument with the updated value in the CUDA graph
+        // on update steps, the live parameters will already be captured
+        int k = 0;
+        for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
+            if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
+                char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
+                cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
+                CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
             }
         }
-
-        // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
-        if (use_cuda_graph && cuda_graph_update_required) {
-            cuda_ctx->cuda_graph->number_consecutive_updates++;
-        } else {
-            cuda_ctx->cuda_graph->number_consecutive_updates = 0;
-        }
-
-        if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
-            cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
-#ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
-#endif
-        }
     }
+}
 
-    if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
-        CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
-    }
+static bool is_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) {
 
-#else
-    bool use_cuda_graph = false;
     bool cuda_graph_update_required = false;
-#endif // USE_CUDA_GRAPH
 
-    bool graph_evaluated_or_captured = false;
+    if (cuda_ctx->cuda_graph->instance == nullptr) {
+        cuda_graph_update_required = true;
+    }
+
+    // Check if the graph size has changed
+    if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+        cuda_graph_update_required = true;
+        cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+    }
+
+    // Loop over nodes in GGML graph to determine if CUDA graph update is required
+    // and store properties to allow this comparison for the next token
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        bool has_matching_properties = true;
+        if (!cuda_graph_update_required) {
+            has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+        }
+        if (!has_matching_properties) {
+            cuda_graph_update_required = true;
+        }
+        set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+    }
+
+    return cuda_graph_update_required;
+}
+
+static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
+
+    cudaGraphExecUpdateResultInfo result_info;
+    cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
+    if (stat == cudaErrorGraphExecUpdateFailure) {
+#ifndef NDEBUG
+        GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
+#endif
+        // The pre-existing graph exec cannot be updated due to violated constraints
+        // so instead clear error and re-instantiate
+        cudaGetLastError();
+        CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
+        cuda_ctx->cuda_graph->instance = nullptr;
+        CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
+    } else {
+        GGML_ASSERT(stat == cudaSuccess);
+    }
+}
+#endif
+
+static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
+   [[maybe_unused]] std::vector<void *> & ggml_cuda_cpy_fn_ptrs,  bool & graph_evaluated_or_captured, bool & use_cuda_graph,
+    bool & cuda_graph_update_required) {
 
     while (!graph_evaluated_or_captured) {
         // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
@@ -2519,19 +2535,8 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
                 CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
                 cuda_ctx->cuda_graph->graph = nullptr;
             }
-            CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
 
-#if 0
-            if (disable_cuda_graphs_due_to_failed_capture) {
-                use_cuda_graph = false;
-                cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
-#ifndef NDEBUG
-                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
-#endif
-            } else {
-                graph_evaluated_or_captured = true; // CUDA graph has been captured
-            }
-#endif
+            CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
             graph_evaluated_or_captured = true; // CUDA graph has been captured
         } else {
             graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
@@ -2544,72 +2549,91 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
         }
 
         // Perform update to graph (if required for this token), and change copy parameter (required for every token)
-
-        if (cuda_graph_update_required) {
-            // Extract nodes from graph
-            // First call with null argument gets number of nodes in graph
-            CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
-            // Subsequent call with non-null argument gets nodes
-            cuda_ctx->cuda_graph->nodes.clear();
-            cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
-            cuda_ctx->cuda_graph->params.clear();
-            cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
-            if (cuda_ctx->cuda_graph->num_nodes > 0) {
-                CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
-
-                // Loop over nodes, and extract kernel parameters from each node
-                for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
-                    cudaGraphNodeType node_type;
-                    CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
-                    if (node_type == cudaGraphNodeTypeKernel) {
-                        cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
-                        if (stat == cudaErrorInvalidDeviceFunction) {
-                            // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
-                            // We don't need to update blas nodes, so clear error and move on.
-                            cudaGetLastError();
-                        } else {
-                            GGML_ASSERT(stat == cudaSuccess);
-                        }
-                    }
-                }
-            }
-        }
-
-        // One of the arguments to the copy kernel is updated for each token, hence we need to
-        // replace that argument with the updated value in the CUDA graph
-        if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
-            int k = 0;
-            for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
-                if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
-                    char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
-                    cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
-                    CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
-                }
-            }
-        }
+        maintain_cuda_graph(cuda_ctx, ggml_cuda_cpy_fn_ptrs, cuda_graph_update_required);
 
         // Update graph executable
-        cudaGraphExecUpdateResultInfo result_info;
-        cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
-        if (stat == cudaErrorGraphExecUpdateFailure) {
-#ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
-#endif
-            // The pre-existing graph exec cannot be updated due to violated constraints
-            // so instead clear error and re-instantiate
-            cudaGetLastError();
-            CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
-            cuda_ctx->cuda_graph->instance = nullptr;
-            CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
-        } else {
-            GGML_ASSERT(stat == cudaSuccess);
-        }
+        update_cuda_graph_executable(cuda_ctx);
+
         // Launch graph
         CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
 #else
         graph_evaluated_or_captured = true;
-#endif // USE_CUDA_GRAPH
+#endif  // USE_CUDA_GRAPH
     }
+}
+
+static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    ggml_cuda_set_device(cuda_ctx->device);
+
+    // vector of pointers to CUDA cpy kernels, which are required to identify
+    // kernel parameters which need updated in the graph for each token
+    std::vector<void *> ggml_cuda_cpy_fn_ptrs;
+
+#ifdef USE_CUDA_GRAPH
+    static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
+
+    // Objects required for CUDA Graph
+    if (cuda_ctx->cuda_graph == nullptr) {
+        cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
+    }
+
+    bool use_cuda_graph = true;
+    bool cuda_graph_update_required = false;
+
+    if (cuda_ctx->cuda_graph->graph == nullptr) {
+        if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
+            cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
+#ifndef NDEBUG
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
+#endif
+        }
+    }
+
+    // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
+    // or previous graph capture failure.
+    // Also disable for multi-gpu for now. TO DO investigate
+    if (disable_cuda_graphs_due_to_env
+        || cuda_ctx->cuda_graph->disable_due_to_gpu_arch
+        || cuda_ctx->cuda_graph->disable_due_to_too_many_updates
+        || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
+        use_cuda_graph = false;
+    }
+
+    if (use_cuda_graph) {
+        cuda_graph_update_required = is_cuda_graph_update_required(cuda_ctx, cgraph);
+
+        use_cuda_graph = check_node_graph_compatibility_and_refresh_copy_ops(cuda_ctx, cgraph,
+                             ggml_cuda_cpy_fn_ptrs, use_cuda_graph);
+
+        // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
+        if (use_cuda_graph && cuda_graph_update_required) {
+            cuda_ctx->cuda_graph->number_consecutive_updates++;
+        } else {
+            cuda_ctx->cuda_graph->number_consecutive_updates = 0;
+        }
+
+        if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
+            cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
+#ifndef NDEBUG
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
+#endif
+        }
+    }
+
+    if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
+        CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
+    }
+
+#else
+    bool use_cuda_graph = false;
+    bool cuda_graph_update_required = false;
+#endif // USE_CUDA_GRAPH
+
+    bool graph_evaluated_or_captured = false;
+
+    evaluate_and_capture_cuda_graph(cuda_ctx, cgraph, ggml_cuda_cpy_fn_ptrs, graph_evaluated_or_captured, use_cuda_graph, cuda_graph_update_required);
 
     return GGML_STATUS_SUCCESS;
 }

From 84a44815f704aaed8e8edec7a39e846a975c7ba9 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 13 Jan 2025 20:18:12 +0100
Subject: [PATCH 170/376] cli : auto activate conversation mode if chat
 template is available (#11214)

* cli : auto activate conversation mode if chat template is detected

* add warn on bad template

* update readme (writing with the help of chatgpt)

* update readme (2)

* do not activate -cnv for non-instruct models
---
 README.md              | 38 ++++++++++++++++++++----------------
 common/arg.cpp         | 20 +++++++++++--------
 common/common.h        |  9 ++++++++-
 examples/main/main.cpp | 44 ++++++++++++++++++++++++++++++++----------
 4 files changed, 75 insertions(+), 36 deletions(-)

diff --git a/README.md b/README.md
index 6302ac977..413a16422 100644
--- a/README.md
+++ b/README.md
@@ -245,6 +245,8 @@ The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](htt
 - [Trending](https://huggingface.co/models?library=gguf&sort=trending)
 - [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
 
+You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf <user>/<model>[:quant]`
+
 After downloading a model, use the CLI tools to run it locally - see below.
 
 `llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
@@ -263,21 +265,12 @@ To learn more about model quantization, [read this documentation](examples/quant
 #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
 
 - <details open>
-    <summary>Run simple text completion</summary>
-
-    ```bash
-    llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128
-
-    # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
-    ```
-
-    </details>
-
-- <details>
     <summary>Run in conversation mode</summary>
 
+    Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
+
     ```bash
-    llama-cli -m model.gguf -p "You are a helpful assistant" -cnv
+    llama-cli -m model.gguf
 
     # > hi, who are you?
     # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
@@ -289,17 +282,28 @@ To learn more about model quantization, [read this documentation](examples/quant
     </details>
 
 - <details>
-    <summary>Run with custom chat template</summary>
+    <summary>Run in conversation mode with custom chat template</summary>
 
     ```bash
-    # use the "chatml" template
-    llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
+    # use the "chatml" template (use -h to see the list of supported templates)
+    llama-cli -m model.gguf -cnv --chat-template chatml
 
     # use a custom template
-    llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
+    llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
     ```
 
-    [Supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
+    </details>
+
+- <details>
+    <summary>Run simple text completion</summary>
+
+    To disable conversation mode explicitly, use `-no-cnv`
+
+    ```bash
+    llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv
+
+    # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
+    ```
 
     </details>
 
diff --git a/common/arg.cpp b/common/arg.cpp
index 1457a360f..dd10b6352 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -777,15 +777,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cnv", "--conversation"},
-        string_format(
-            "run in conversation mode:\n"
-            "- does not print special tokens and suffix/prefix\n"
-            "- interactive mode is also enabled\n"
-            "(default: %s)",
-            params.conversation ? "true" : "false"
-        ),
+        "run in conversation mode:\n"
+        "- does not print special tokens and suffix/prefix\n"
+        "- interactive mode is also enabled\n"
+        "(default: auto enabled if chat template is available)",
         [](common_params & params) {
-            params.conversation = true;
+            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(common_arg(
+        {"-no-cnv", "--no-conversation"},
+        "force disable conversation mode (default: false)",
+        [](common_params & params) {
+            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
diff --git a/common/common.h b/common/common.h
index c86a4ef39..4fab1319a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -103,6 +103,12 @@ enum dimre_method {
     DIMRE_METHOD_MEAN,
 };
 
+enum common_conversation_mode {
+    COMMON_CONVERSATION_MODE_DISABLED = 0,
+    COMMON_CONVERSATION_MODE_ENABLED  = 1,
+    COMMON_CONVERSATION_MODE_AUTO     = 2,
+};
+
 // sampling parameters
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@@ -275,7 +281,6 @@ struct common_params {
     bool special           = false; // enable special token output
     bool interactive       = false; // interactive mode
     bool interactive_first = false; // wait for user input immediately
-    bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
@@ -301,6 +306,8 @@ struct common_params {
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
 
+    common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
+
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
     std::vector<std::string> image; // path to image file(s)
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 640b35c1d..39666a0e8 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -30,6 +30,8 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+static const char * DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant";
+
 static llama_context           ** g_ctx;
 static llama_model             ** g_model;
 static common_sampler          ** g_smpl;
@@ -204,8 +206,24 @@ int main(int argc, char ** argv) {
         LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx);
     }
 
+    // auto enable conversation mode if chat template is available
+    const bool has_chat_template = !common_get_builtin_chat_template(model).empty() || !params.chat_template.empty();
+    if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
+        if (has_chat_template) {
+            LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
+            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
+        } else {
+            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
+        }
+    }
+
+    // in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning
+    if (params.conversation_mode && !has_chat_template) {
+        LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__);
+    }
+
     // print chat template example in conversation mode
-    if (params.conversation) {
+    if (params.conversation_mode) {
         if (params.enable_chat_template) {
             LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str());
         } else {
@@ -252,8 +270,10 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd_inp;
 
     {
-        auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty())
-            ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
+        auto prompt = (params.conversation_mode && params.enable_chat_template)
+            // format the system prompt in conversation mode (fallback to default if empty)
+            ? chat_add_and_format(model, chat_msgs, "system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt)
+            // otherwise use the prompt as is
             : params.prompt;
         if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
             LOG_DBG("tokenize the prompt\n");
@@ -327,7 +347,7 @@ int main(int argc, char ** argv) {
         params.n_keep += add_bos; // always keep the BOS token
     }
 
-    if (params.conversation) {
+    if (params.conversation_mode) {
         params.interactive_first = true;
     }
 
@@ -451,7 +471,11 @@ int main(int argc, char ** argv) {
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
         LOG_INF(       " - Press Ctrl+C to interject at any time.\n");
 #endif
-        LOG_INF(       "%s\n", control_message);
+        LOG_INF(       "%s", control_message);
+        if (params.conversation_mode && params.enable_chat_template && params.prompt.empty()) {
+            LOG_INF(   " - Using default system message. To change it, set a different value via -p PROMPT or -f FILE argument.\n");
+        }
+        LOG_INF("\n");
 
         is_interacting = params.interactive_first;
     }
@@ -763,7 +787,7 @@ int main(int argc, char ** argv) {
             }
 
             // if current token is not EOG, we add it to current assistant message
-            if (params.conversation) {
+            if (params.conversation_mode) {
                 const auto id = common_sampler_last(smpl);
                 assistant_ss << common_token_to_piece(ctx, id, false);
             }
@@ -771,7 +795,7 @@ int main(int argc, char ** argv) {
             if (n_past > 0 && is_interacting) {
                 LOG_DBG("waiting for user input\n");
 
-                if (params.conversation) {
+                if (params.conversation_mode) {
                     LOG("\n> ");
                 }
 
@@ -781,7 +805,7 @@ int main(int argc, char ** argv) {
                 }
 
                 std::string buffer;
-                if (!params.input_prefix.empty() && !params.conversation) {
+                if (!params.input_prefix.empty() && !params.conversation_mode) {
                     LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str());
                     LOG("%s", params.input_prefix.c_str());
                 }
@@ -805,7 +829,7 @@ int main(int argc, char ** argv) {
                 // Entering a empty line lets the user pass control back
                 if (buffer.length() > 1) {
                     // append input suffix if any
-                    if (!params.input_suffix.empty() && !params.conversation) {
+                    if (!params.input_suffix.empty() && !params.conversation_mode) {
                         LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str());
                         LOG("%s", params.input_suffix.c_str());
                     }
@@ -818,7 +842,7 @@ int main(int argc, char ** argv) {
                         string_process_escapes(buffer);
                     }
 
-                    bool format_chat = params.conversation && params.enable_chat_template;
+                    bool format_chat = params.conversation_mode && params.enable_chat_template;
                     std::string user_inp = format_chat
                         ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
                         : std::move(buffer);

From 504af20ee4eae72080a56d59d744f6774f7901ce Mon Sep 17 00:00:00 2001
From: ebraminio <ebrahim@gnu.org>
Date: Mon, 13 Jan 2025 22:53:31 +0330
Subject: [PATCH 171/376] server : (UI) Improve messages bubble shape in RTL
 (#11220)

I simply have overlooked message bubble's tail placement for RTL
text as I use the dark mode and that isn't visible there and this
fixes it.
---
 examples/server/public/index.html.gz | Bin 1206472 -> 1206483 bytes
 examples/server/webui/index.html     |   7 +++++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 489bff84b98f7bd7ebcdabeb7d0f9b31093eae58..18f7ecac9ef63eb46dcb3b84b1e10e2b79f22c44 100644
GIT binary patch
delta 7093
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zdqo_3mM5FcBZ6$es$RE`B@H6>Xc#OJiCI*APclLK1k*ZUb7%PxQWE$ua%Gj&|MVxc
zXO+~kULHQ~<ZZG%$#9wkY%#~*xkRFXg|C_4<WZH>WWlCDN{By$E#N{b*edY@*o}Xm
zlI%TSbjdXN5;?vr{#ZRhA3q{G$p_A6_nwUb|NVef1@Uau>NreS8Y%e0<#$;UrC{cl
zO>cxBjJVnwJ%A?fV@Ls)&t?)Ef1Ql%SvfYNI#)It-@k)Qp+#-@oJA9&8uNa6xm#Ru
zKq9WUZlRGlcOEe-XBj{}Q!hHI($<e=sJ`#^vGOBG@+%O%{-nSJ0xBJlDDLNVFy8Iz
zxjw8>XpEyE4jA_0q(F^5lSduL4oF7$*Z=k3{_X$#cl5#!=(Fx)Qe3@ze~t+3>r&P3
zuzodWM&Yo-ag?u44(ZbW{onudpV96}MPIjmH>tfOedM$6LBjrze?eDC`q(LEW#x5W
z*t@)4Jnll4e7Ce~dD|b8Dwn##Lvit11^B&NYr$);Fa!l^DWgJ+VCANrp242j*Cb3U
zTS{WY6baLR!VcJ>T8O{*f4vKx6ROmBsv4dRYf-7Er-J1_{S(?nJ|Ra~M5cXn*g!tk
zA(I+UST5p=wQwScb#xpjqW0W-HweMXPK2XYeNRlqD7+*YL0gE)Wm`A%%SA6g0G_&R
zQ;R0ZTL&Qo>59YOrzj_6Rvi$}sq5X9F+mn~_;o{ahQt%Y)>U<?Yg?0oGCMlM$l={t
z^7`1;V@Ckb^&>{x{Tr;uLaer_F?e&?5qM*9Qjv-C16)h!&y4GZ^baHo=<(4e^<7G4
vyBbHQ=pWdj5Sd3sWJY6m?ywdj5PbA}!rzesFHM2>w><lQyOEeATKFOWeGI2S

diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index a76d831a9..2180ef4ad 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -245,10 +245,11 @@
       <div :class="{
         'chat-bubble markdown': true,
         'chat-bubble-base-300': msg.role !== 'user',
-      }" dir="auto">
+      }">
         <!-- textarea for editing message -->
         <template v-if="editingContent !== null">
           <textarea
+            dir="auto"
             class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
             v-model="editingContent"></textarea>
           <br/>
@@ -259,7 +260,9 @@
           <!-- show loading dots for pending message -->
           <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
           <!-- render message as markdown -->
-          <vue-markdown v-else :source="msg.content"></vue-markdown>
+          <div v-else dir="auto">
+            <vue-markdown :source="msg.content"></vue-markdown>
+          </div>
           <!-- render timings if enabled -->
           <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
             <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>

From d00a80e89dc6632dde7391e1e1ecbbbac088dd25 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 14 Jan 2025 09:19:58 +0200
Subject: [PATCH 172/376] scripts : sync opencl

---
 scripts/sync-ggml-am.sh | 4 +++-
 scripts/sync-ggml.sh    | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 8cf25b77f..d4d6c734c 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -81,6 +81,7 @@ while read c; do
         src/ggml-kompute/* \
         src/ggml-metal/* \
         src/ggml-musa/* \
+        src/ggml-opencl/* \
         src/ggml-rpc/* \
         src/ggml-sycl/* \
         src/ggml-vulkan/* \
@@ -131,6 +132,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-kompute/*   -> ggml/src/ggml-kompute/*
     # src/ggml-metal/*     -> ggml/src/ggml-metal/*
     # src/ggml-musa/*      -> ggml/src/ggml-musa/*
+    # src/ggml-opencl/*    -> ggml/src/ggml-opencl/*
     # src/ggml-rpc/*       -> ggml/src/ggml-rpc/*
     # src/ggml-sycl/*      -> ggml/src/ggml-sycl/*
     # src/ggml-vulkan/*    -> ggml/src/ggml-vulkan/*
@@ -156,7 +158,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/([[:space:]]|[ab]\/)src\/ggml-hip\//\1ggml\/src\/ggml-hip\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\//\1ggml\/src\/ggml-kompute\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\//\1ggml\/src\/ggml-metal\//g' \
-        -e 's/([[:space:]]|[ab]\/)src\/ggml-musa\//\1ggml\/src\/ggml-musa\//g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-opencl\//\1ggml\/src\/ggml-opencl\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\//\1ggml\/src\/ggml-rpc\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\//\1ggml\/src\/ggml-vulkan\//g' \
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index f81615bb6..8f9af13f2 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -15,6 +15,7 @@ cp -rpv ../ggml/src/ggml-hip/*     ./ggml/src/ggml-hip/
 cp -rpv ../ggml/src/ggml-kompute/* ./ggml/src/ggml-kompute/
 cp -rpv ../ggml/src/ggml-metal/*   ./ggml/src/ggml-metal/
 cp -rpv ../ggml/src/ggml-musa/*    ./ggml/src/ggml-musa/
+cp -rpv ../ggml/src/ggml-opencl/*  ./ggml/src/ggml-opencl/
 cp -rpv ../ggml/src/ggml-rpc/*     ./ggml/src/ggml-rpc/
 cp -rpv ../ggml/src/ggml-sycl/*    ./ggml/src/ggml-sycl/
 cp -rpv ../ggml/src/ggml-vulkan/*  ./ggml/src/ggml-vulkan/

From 48e1ae0e61b04c6a458ac207e84e86f5ce0abccd Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 14 Jan 2025 09:36:58 +0200
Subject: [PATCH 173/376] scripts : sync gguf

---
 scripts/sync-ggml.sh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index 8f9af13f2..e83d415c0 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -7,6 +7,7 @@ cp -rpv ../ggml/cmake/FindSIMD.cmake ./ggml/cmake/FindSIMD.cmake
 cp -rpv ../ggml/src/ggml*.c        ./ggml/src/
 cp -rpv ../ggml/src/ggml*.cpp      ./ggml/src/
 cp -rpv ../ggml/src/ggml*.h        ./ggml/src/
+cp -rpv ../ggml/src/gguf*.cpp      ./ggml/src/
 cp -rpv ../ggml/src/ggml-blas/*    ./ggml/src/ggml-blas/
 cp -rpv ../ggml/src/ggml-cann/*    ./ggml/src/ggml-cann/
 cp -rpv ../ggml/src/ggml-cpu/*     ./ggml/src/ggml-cpu/
@@ -21,6 +22,7 @@ cp -rpv ../ggml/src/ggml-sycl/*    ./ggml/src/ggml-sycl/
 cp -rpv ../ggml/src/ggml-vulkan/*  ./ggml/src/ggml-vulkan/
 
 cp -rpv ../ggml/include/ggml*.h ./ggml/include/
+cp -rpv ../ggml/include/gguf*.h ./ggml/include/
 
 cp -rpv ../ggml/tests/test-opt.cpp           ./tests/test-opt.cpp
 cp -rpv ../ggml/tests/test-quantize-fns.cpp  ./tests/test-quantize-fns.cpp

From a4f3f5d8e64b10fcf59913d487c1782dd0bc23e0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 14 Jan 2025 09:40:15 +0200
Subject: [PATCH 174/376] scripts : sync gguf (cont)

---
 scripts/sync-ggml-am.sh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index d4d6c734c..ec4f4b0a2 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -73,6 +73,7 @@ while read c; do
         src/ggml*.h \
         src/ggml*.c \
         src/ggml*.cpp \
+        src/gguf*.cpp \
         src/ggml-blas/* \
         src/ggml-cann/* \
         src/ggml-cpu/* \
@@ -86,6 +87,7 @@ while read c; do
         src/ggml-sycl/* \
         src/ggml-vulkan/* \
         include/ggml*.h \
+        include/gguf*.h \
         tests/test-opt.cpp \
         tests/test-quantize-fns.cpp \
         tests/test-quantize-perf.cpp \
@@ -124,6 +126,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml*.c          -> ggml/src/ggml*.c
     # src/ggml*.cpp        -> ggml/src/ggml*.cpp
     # src/ggml*.h          -> ggml/src/ggml*.h
+    # src/gguf*.cpp        -> ggml/src/gguf*.cpp
     # src/ggml-blas/*      -> ggml/src/ggml-blas/*
     # src/ggml-cann/*      -> ggml/src/ggml-cann/*
     # src/ggml-cpu/*       -> ggml/src/ggml-cpu/*
@@ -138,6 +141,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-vulkan/*    -> ggml/src/ggml-vulkan/*
     #
     # include/ggml*.h -> ggml/include/ggml*.h
+    # include/gguf*.h -> ggml/include/gguf*.h
     #
     # tests/test*.cpp -> tests/
     #
@@ -151,6 +155,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.c/\1ggml\/src\/ggml\2.c/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.cpp/\1ggml\/src\/ggml\2.cpp/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.h/\1ggml\/src\/ggml\2.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/gguf(.*)\.cpp/\1ggml\/src\/gguf\2.cpp/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-blas\//\1ggml\/src\/ggml-blas\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-cpu\//\1ggml\/src\/ggml-cpu\//g' \
@@ -163,6 +168,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\//\1ggml\/src\/ggml-vulkan\//g' \
         -e 's/([[:space:]]|[ab]\/)include\/ggml(.*)\.h/\1ggml\/include\/ggml\2.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/gguf(.*)\.h/\1ggml\/include\/gguf\2.h/g' \
         -e 's/([[:space:]]|[ab]\/)tests\/(.*)\.cpp/\1tests\/\2.cpp/g' \
         -e 's/([[:space:]]|[ab]\/)LICENSE/\1LICENSE/g' \
         -e 's/([[:space:]]|[ab]\/)scripts\/gen-authors\.sh/\1scripts\/gen-authors.sh/g' \

From 44d1e796d08641e7083fcbf37b33c79842a2f01e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 14 Jan 2025 10:39:42 +0200
Subject: [PATCH 175/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index a0921f1a9..cfba59d32 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-c8bd0fee71dc8328d93be301bbee06bc10d30429
+d92321c0d151fe73a47d89738c7c3091ac904297

From 091592d758cb55af7bfadd6c397f61db387aa8f3 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Tue, 14 Jan 2025 10:16:41 +0000
Subject: [PATCH 176/376] Refactor test-chat-template.cpp (#11224)

* Refactor test-chat-template

* Update test-chat-template.cpp
---
 tests/test-chat-template.cpp | 294 +++++++++++++++++++----------------
 1 file changed, 162 insertions(+), 132 deletions(-)

diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 77d386954..5ec318bd4 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -9,7 +9,7 @@
 #include "common.h"
 
 int main(void) {
-    llama_chat_message conversation[] = {
+    std::vector<llama_chat_message> conversation {
         {"system", "You are a helpful assistant"},
         {"user", "Hello"},
         {"assistant", "Hi there"},
@@ -17,130 +17,161 @@ int main(void) {
         {"assistant", "   I am an assistant   "},
         {"user", "Another question"},
     };
-    size_t message_count = 6;
-    std::vector<std::string> templates = {
-        // teknium/OpenHermes-2.5-Mistral-7B
-        "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
-        // mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt)
-        "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
-        // TheBloke/FusionNet_34Bx2_MoE-AWQ
-        "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
-        // bofenghuang/vigogne-2-70b-chat
-        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
-        // mlabonne/AlphaMonarch-7B
-        "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
-        // google/gemma-7b-it
-        "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
-        // OrionStarAI/Orion-14B-Chat
-        "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
-        // openchat/openchat-3.5-0106
-        // The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
-        // So we match against the included template but implement the suggested version.
-        "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
-        // deepseek-ai/deepseek-coder-33b-instruct
-        "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
-        // eachadea/vicuna-13b-1.1
-        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
-        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
-        // Orca-Vicuna
-        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
-        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
-        // CohereForAI/c4ai-command-r-plus
-        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
-        // Llama-3
-        "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
-        //Phi-3-mini
-        "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
-        //Phi-3-small
-        "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
-        //Phi-3-medium
-        "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
-        //Phi-3-vision
-        "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
-        // ChatGLM3
-        "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
-        // ChatGLM4
-        u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
-        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
-        u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
-        // DeepSeek-V2
-        "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
-        // ibm-granite/granite-3.0-8b-instruct
-        "{%- if tools %}\n    {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n    {%- for tool in tools %}\n    {{- tool | tojson(indent=4) }}\n    {%- if not loop.last %}\n        {{- '\n\n' }}\n    {%- endif %}\n    {%- endfor %}\n    {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n    {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'user' %}\n    {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>'  + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant_tool_call' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'tool_response' %}\n    {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- endif %}\n    {%- if loop.last and add_generation_prompt %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n    {%- endif %}\n{%- endfor %}",
-        // mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt)
-        "{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif %}\n    {%- if message['role'] == 'user' %}\n        {%- if loop.first and system_message is defined %}\n            {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n        {%- else %}\n            {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n        {%- endif %}\n    {%- elif message['role'] == 'assistant' %}\n        {{- ' ' + message['content'] + eos_token}}\n    {%- else %}\n        {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n    {%- endif %}\n{%- endfor %}\n",
-        // Mistral-Large-Instruct-2407 (mistralai 'v3' template)
-        "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS] [\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n        {{- \"[TOOL_CALLS] [\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- \" \" + message[\"content\"]|trim + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
-        // Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template)
-        "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS][\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n        {{- \"[TOOL_CALLS][\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
-        // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
-        "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
-        // ai-sage/GigaChat-20B-A3B-instruct
-        "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
-        // Infinigence/Megrez-3B-Instruct
-        u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}",
-        // phi-4
-        "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|><|im_start|>assistant<|im_sep|>'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}",
+    struct TestCase {
+        std::string name;
+        std::string template_str;
+        std::string expected_output;
     };
-    std::vector<std::string> expected_output = {
-        // teknium/OpenHermes-2.5-Mistral-7B
-        "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
-        // mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt)
-        "[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
-        // TheBloke/FusionNet_34Bx2_MoE-AWQ
-        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s><s>[INST] Who are you [/INST]   I am an assistant   </s><s>[INST] Another question [/INST]",
-        // bofenghuang/vigogne-2-70b-chat
-        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s>[INST] Who are you [/INST]I am an assistant</s>[INST] Another question [/INST]",
-        // mlabonne/AlphaMonarch-7B
-        "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
-        // google/gemma-7b-it
-        "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
-        // OrionStarAI/Orion-14B-Chat
-        "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
-        // openchat/openchat-3.5-0106
-        "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
-        // deepseek-ai/deepseek-coder-33b-instruct
-        "You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n   I am an assistant   \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
-        // eachadea/vicuna-13b-1.1
-        "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
-        // Orca-Vicuna
-        "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
-        // CohereForAI/c4ai-command-r-plus
-        "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
-        // Llama 3
-        "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-        //Phi-3-mini
-        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
-        //Phi-3-small
-        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
-        //Phi-3-medium
-        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
-        //Phi-3-vision
-        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
-        // ChatGLM3
-        "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
-        // ChatGLM4
-        "[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
-        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
-        u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
-        // DeepSeek-V2
-        u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant:    I am an assistant   <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
-        // ibm-granite/granite-3.0-8b-instruct
-        "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>   I am an assistant   <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n",
-        // mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt)
-        " [INST] You are a helpful assistant\n\nHello [/INST] Hi there</s> [INST] Who are you [/INST]    I am an assistant   </s> [INST] Another question [/INST]",
-        // Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start)
-        "[INST] You are a helpful assistant\n\nHello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant</s>[INST] Another question[/INST]",
-        // Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start)
-        "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
-        // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
-        "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
-        // ai-sage/GigaChat-20B-A3B-instruct
-        "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
-        // Infinigence/Megrez-3B-Instruct
-        "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|>   I am an assistant   <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
-        // phi-4
-        "<|im_start|>system<|im_sep|>You are a helpful assistant<|im_end|><|im_start|>user<|im_sep|>Hello<|im_end|><|im_start|>assistant<|im_sep|>Hi there<|im_end|><|im_start|>user<|im_sep|>Who are you<|im_end|><|im_start|>assistant<|im_sep|>   I am an assistant   <|im_end|><|im_start|>user<|im_sep|>Another question<|im_end|><|im_start|>assistant<|im_sep|>",
+    std::vector<TestCase> test_cases {
+        {
+            /* .name= */ "teknium/OpenHermes-2.5-Mistral-7B",
+            /* .template_str= */ "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
+            /* .expected_output= */ "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
+        },
+        {
+            /* .name= */ "mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt)",
+            /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+            /* .expected_output= */ "[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
+        },
+        {
+            /* .name= */ "TheBloke/FusionNet_34Bx2_MoE-AWQ",
+            /* .template_str= */ "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
+            /* .expected_output= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s><s>[INST] Who are you [/INST]   I am an assistant   </s><s>[INST] Another question [/INST]",
+        },
+        {
+            /* .name= */ "bofenghuang/vigogne-2-70b-chat",
+            /* .template_str= */ "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+            /* .expected_output= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s>[INST] Who are you [/INST]I am an assistant</s>[INST] Another question [/INST]",
+        },
+        {
+            /* .name= */ "mlabonne/AlphaMonarch-7B",
+            /* .template_str= */ "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
+            /* .expected_output= */ "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+        },
+        {
+            /* .name= */ "google/gemma-7b-it",
+            /* .template_str= */ "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
+            /* .expected_output= */ "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+        },
+        {
+            /* .name= */ "OrionStarAI/Orion-14B-Chat",
+            /* .template_str= */ "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
+            /* .expected_output= */ "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+        },
+        {
+            /* .name= */ "openchat/openchat-3.5-0106",
+            // The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
+            // So we match against the included template but implement the suggested version.
+            /* .template_str= */ "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
+            /* .expected_output= */ "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
+        },
+        {
+            /* .name= */ "deepseek-ai/deepseek-coder-33b-instruct",
+            /* .template_str= */ "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
+            /* .expected_output= */ "You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n   I am an assistant   \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
+        },
+        {
+            /* .name= */ "eachadea/vicuna-13b-1.1",
+            // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+            /* .template_str= */ "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+            /* .expected_output= */ "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+        },
+        {
+            /* .name= */ "Orca-Vicuna",
+            // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+            /* .template_str= */ "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+            /* .expected_output= */ "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+        },
+        {
+            /* .name= */ "CohereForAI/c4ai-command-r-plus",
+            /* .template_str= */ "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
+            /* .expected_output= */ "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
+        },
+        {
+            /* .name= */ "Llama-3",
+            /* .template_str= */ "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
+            /* .expected_output= */ "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+        },
+        {
+            /* .name= */ "Phi-3-mini",
+            /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
+            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        },
+        {
+            /* .name= */ "Phi-3-small",
+            /* .template_str= */ "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
+            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        },
+        {
+            /* .name= */ "Phi-3-medium",
+            /* .template_str= */ "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
+            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        },
+        {
+            /* .name= */ "Phi-3-vision",
+            /* .template_str= */ "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
+            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        },
+        {
+            /* .name= */ "ChatGLM3",
+            /* .template_str= */ "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+            /* .expected_output= */ "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
+        },
+        {
+            /* .name= */ "ChatGLM4",
+            /* .template_str= */ u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+            /* .expected_output= */ "[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
+        },
+        {
+            /* .name= */ "MiniCPM-3B-OpenHermes-2.5-v2-GGUF",
+            /* .template_str= */ u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
+            /* .expected_output= */ u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
+        },
+        {
+            /* .name= */ "DeepSeek-V2",
+            /* .template_str= */ "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
+            /* .expected_output= */ u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant:    I am an assistant   <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
+        },
+        {
+            /* .name= */ "ibm-granite/granite-3.0-8b-instruct",
+            /* .template_str= */ "{%- if tools %}\n    {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n    {%- for tool in tools %}\n    {{- tool | tojson(indent=4) }}\n    {%- if not loop.last %}\n        {{- '\n\n' }}\n    {%- endif %}\n    {%- endfor %}\n    {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n    {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'user' %}\n    {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>'  + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant_tool_call' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'tool_response' %}\n    {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- endif %}\n    {%- if loop.last and add_generation_prompt %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n    {%- endif %}\n{%- endfor %}",
+            /* .expected_output= */ "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>   I am an assistant   <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n",
+        },
+        {
+            /* .name= */ "mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt)",
+            /* .template_str= */ "{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif %}\n    {%- if message['role'] == 'user' %}\n        {%- if loop.first and system_message is defined %}\n            {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n        {%- else %}\n            {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n        {%- endif %}\n    {%- elif message['role'] == 'assistant' %}\n        {{- ' ' + message['content'] + eos_token}}\n    {%- else %}\n        {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n    {%- endif %}\n{%- endfor %}\n",
+            /* .expected_output= */ " [INST] You are a helpful assistant\n\nHello [/INST] Hi there</s> [INST] Who are you [/INST]    I am an assistant   </s> [INST] Another question [/INST]",
+        },
+        {
+            /* .name= */ "Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start)",
+            /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS] [\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n        {{- \"[TOOL_CALLS] [\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- \" \" + message[\"content\"]|trim + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
+            /* .expected_output= */ "[INST] You are a helpful assistant\n\nHello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant</s>[INST] Another question[/INST]",
+        },
+        {
+            /* .name= */ "Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start)",
+            /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS][\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n        {{- \"[TOOL_CALLS][\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
+            /* .expected_output= */ "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
+        },
+        {
+            /* .name= */ "mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)",
+            /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
+            /* .expected_output= */ "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
+        },
+        {
+            /* .name= */ "ai-sage/GigaChat-20B-A3B-instruct",
+            /* .template_str= */ "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
+            /* .expected_output= */ "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
+        },
+        {
+            /* .name= */ "Infinigence/Megrez-3B-Instruct",
+            /* .template_str= */ u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}",
+            /* .expected_output= */ "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|>   I am an assistant   <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
+        },
+        {
+            /* .name= */ "phi-4",
+            /* .template_str= */ "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|><|im_start|>assistant<|im_sep|>'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}",
+            /* .expected_output= */ "<|im_start|>system<|im_sep|>You are a helpful assistant<|im_end|><|im_start|>user<|im_sep|>Hello<|im_end|><|im_start|>assistant<|im_sep|>Hi there<|im_end|><|im_start|>user<|im_sep|>Who are you<|im_end|><|im_start|>assistant<|im_sep|>   I am an assistant   <|im_end|><|im_start|>user<|im_sep|>Another question<|im_end|><|im_start|>assistant<|im_sep|>",
+        },
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
@@ -157,17 +188,16 @@ int main(void) {
     }
 
     // test invalid chat template
-    res = llama_chat_apply_template("INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
+    res = llama_chat_apply_template("INVALID TEMPLATE", conversation.data(), conversation.size(), true, formatted_chat.data(), formatted_chat.size());
     assert(res < 0);
 
-    for (size_t i = 0; i < templates.size(); i++) {
-        std::string custom_template = templates[i];
-        std::string expected = expected_output[i];
+    for (const auto & test_case : test_cases) {
+        printf("\n\n=== %s ===\n\n", test_case.name.c_str());
         formatted_chat.resize(1024);
         res = llama_chat_apply_template(
-            custom_template.c_str(),
-            conversation,
-            message_count,
+            test_case.template_str.c_str(),
+            conversation.data(),
+            conversation.size(),
             true,
             formatted_chat.data(),
             formatted_chat.size()
@@ -176,7 +206,7 @@ int main(void) {
         std::string output(formatted_chat.data(), formatted_chat.size());
         printf("%s\n", output.c_str());
         printf("-------------------------\n");
-        assert(output == expected);
+        assert(output == test_case.expected_output);
     }
 
 

From c5bf0d1bd7eb8762edca47e0a95f64e6fbfaf5b1 Mon Sep 17 00:00:00 2001
From: ebraminio <ebrahim@gnu.org>
Date: Tue, 14 Jan 2025 14:09:33 +0330
Subject: [PATCH 177/376] server : Improve code snippets direction between RTL
 text (#11221)

---
 examples/server/public/index.html.gz | Bin 1206483 -> 1206492 bytes
 examples/server/webui/src/main.js    |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 18f7ecac9ef63eb46dcb3b84b1e10e2b79f22c44..26f3583bdf844ccdb67fb197a56991a25d4b2ecc 100644
GIT binary patch
delta 756171
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diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 358a40628..feb741a4e 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -111,12 +111,12 @@ const VueMarkdown = defineComponent(
       highlight: function (str, lang) { // Add highlight.js
         if (lang && hljs.getLanguage(lang)) {
           try {
-            return '<pre><code class="hljs">' +
+            return '<pre dir="auto"><code class="hljs">' +
                    hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
                    '</code></pre>';
           } catch (__) {}
         }
-        return '<pre><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
+        return '<pre dir="auto"><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
       }
     }));
     // support latex with double dollar sign and square brackets

From bbf3e55e352d309573bdafee01a014b0a2492155 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 14 Jan 2025 12:54:58 +0200
Subject: [PATCH 178/376] vocab : add dummy tokens for "no_vocab" type (#11231)

* vocab : add dummy tokens for "no_vocab" type

ggml-ci

* vocab : minor [no ci]
---
 src/llama-vocab.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 96b74e93a..4969d2628 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1356,8 +1356,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             // read vocab size from metadata
             uint32_t n_tokens = 0;
-            if (!ml.get_key(LLM_KV_VOCAB_SIZE, n_tokens, false)) {
-                LLAMA_LOG_WARN("%s: there is no vocab_size in metadata\n", __func__);
+            if (ml.get_key(LLM_KV_VOCAB_SIZE, n_tokens, false)) {
+                LLAMA_LOG_WARN("%s: adding %u dummy tokens\n", __func__, n_tokens);
+                id_to_token.resize(n_tokens);
             }
 
             return;

From b4d92a59a20eea400d8dd30844a339b76210daa0 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 14 Jan 2025 15:42:23 +0100
Subject: [PATCH 179/376] ci : add -no-cnv for tests (#11238)

---
 ci/run.sh | 66 +++++++++++++++++++++++++++----------------------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/ci/run.sh b/ci/run.sh
index abf08a4ff..77c32ce00 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -326,17 +326,17 @@ function gg_run_open_llama_7b_v2 {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -460,17 +460,17 @@ function gg_run_pythia_1_4b {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli --model ${model_f16}  -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -591,17 +591,17 @@ function gg_run_pythia_2_8b {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log

From f446c2cf6a56a750b67c967505e717a996d2f2fd Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Wed, 15 Jan 2025 08:50:17 +0530
Subject: [PATCH 180/376] SYCL: Add gated linear attention kernel (#11175)

* SYCL: Add Gated Linear attention kernel

* glahpp: add a space at the end of file

* gla: Put the barrier inside the main logic loop
---
 ggml/src/ggml-sycl/backend.hpp   |   1 +
 ggml/src/ggml-sycl/ggml-sycl.cpp |   4 ++
 ggml/src/ggml-sycl/gla.cpp       | 105 +++++++++++++++++++++++++++++++
 ggml/src/ggml-sycl/gla.hpp       |   8 +++
 4 files changed, 118 insertions(+)
 create mode 100644 ggml/src/ggml-sycl/gla.cpp
 create mode 100644 ggml/src/ggml-sycl/gla.hpp

diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp
index 85748a5b4..b1df4e5db 100644
--- a/ggml/src/ggml-sycl/backend.hpp
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -29,5 +29,6 @@
 #include "wkv6.hpp"
 #include "outprod.hpp"
 #include "element_wise.hpp"
+#include "gla.hpp"
 
 #endif // GGML_SYCL_BACKEND_HPP
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 037c8093e..5272ca454 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4040,6 +4040,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
         case GGML_OP_RWKV_WKV6:
             ggml_sycl_op_rwkv_wkv6(ctx, dst);
             break;
+        case GGML_OP_GATED_LINEAR_ATTN:
+            ggml_sycl_op_gated_linear_attn(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -4507,6 +4510,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_RWKV_WKV6:
+        case GGML_OP_GATED_LINEAR_ATTN:
             return true;
         default:
             return false;
diff --git a/ggml/src/ggml-sycl/gla.cpp b/ggml/src/ggml-sycl/gla.cpp
new file mode 100644
index 000000000..eedb47486
--- /dev/null
+++ b/ggml/src/ggml-sycl/gla.cpp
@@ -0,0 +1,105 @@
+#include <sycl/sycl.hpp>
+
+#include "common.hpp"
+
+template <u_int HEAD_SIZE>
+static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B, u_int T, u_int C, u_int H, float scale,
+                                         const float * k, const float * v, const float * r, const float * td,
+                                         const float * s, float * dst) {
+    const u_int head_size    = HEAD_SIZE;
+    const u_int state_size   = C * head_size;
+    const u_int n_seq_tokens = T / B;
+    sycl::range<1> block_dims((C / H));
+    sycl::range<1> grid_dims((B * H));
+    stream->submit([&](sycl::handler & cgh) {
+        /* local memory accessors*/
+        auto _k  = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+        auto _r  = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+        auto _td = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+
+        cgh.parallel_for(sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
+            u_int tid = item.get_local_id(0);
+            u_int bid = item.get_group(0);
+
+            u_int batch_i = bid / H;
+            u_int head_i  = bid % H;
+
+            float state[head_size];
+
+#pragma unroll
+            for (u_int i = 0; i < head_size; i++) {
+                state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
+            }
+
+            for (u_int t = batch_i * n_seq_tokens * C + head_i * head_size + tid;
+                 t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
+
+                item.barrier(sycl::access::fence_space::local_space);  //sync threads
+                _k[tid]  = k[t];
+                _r[tid]  = r[t];
+                _td[tid] = td[t];
+                item.barrier(sycl::access::fence_space::local_space);  //sync threads
+
+                const float _v = v[t];
+                float       y  = 0;
+
+                for (u_int j = 0; j < head_size; j += 4) {
+                    const sycl::float4 & k  = (sycl::float4 &) (_k[j]);
+                    const sycl::float4 & r  = (sycl::float4 &) (_r[j]);
+                    const sycl::float4 & td = (sycl::float4 &) (_td[j]);
+                    sycl::float4 &       s  = (sycl::float4 &) (state[j]);
+                    sycl::float4         kv;
+
+                    kv.x() = k.x() * _v;
+                    kv.y() = k.y() * _v;
+                    kv.z() = k.z() * _v;
+                    kv.w() = k.w() * _v;
+
+                    s.x() = s.x() * td.x() + kv.x();
+                    s.y() = s.y() * td.y() + kv.y();
+                    s.z() = s.z() * td.z() + kv.z();
+                    s.w() = s.w() * td.w() + kv.w();
+
+                    y += r.x() * s.x();
+                    y += r.y() * s.y();
+                    y += r.z() * s.z();
+                    y += r.w() * s.w();
+                }
+                dst[t] = y * scale;
+            }
+#pragma unroll
+            for (u_int i = 0; i < head_size; i++) {
+                dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
+            }
+        });
+    });
+}
+
+void ggml_sycl_op_gated_linear_attn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const float * k_d  = static_cast<const float *>(dst->src[0]->data);
+    const float * v_d  = static_cast<const float *>(dst->src[1]->data);
+    const float * r_d  = static_cast<const float *>(dst->src[2]->data);
+    const float * td_d = static_cast<const float *>(dst->src[3]->data);
+    const float * s_d  = static_cast<const float *>(dst->src[4]->data);
+
+    const int64_t B = dst->src[4]->ne[1];
+    const int64_t T = dst->src[0]->ne[2];
+    const int64_t C = dst->ne[0];
+    const int64_t H = dst->src[0]->ne[1];
+
+    dpct::queue_ptr stream = ctx.stream();
+    GGML_ASSERT(dst->src[4]->type == GGML_TYPE_F32);
+    GGML_ASSERT(C % H == 0);
+    GGML_ASSERT(C / H == 64 || C / H == 128);
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    float * dst_d = (float *) dst->data;
+
+    if (C / H == 64) {
+        gated_linear_attn_f32_kernel<64>(stream, B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+    } else {
+        gated_linear_attn_f32_kernel<128>(stream, B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+    }
+}
diff --git a/ggml/src/ggml-sycl/gla.hpp b/ggml/src/ggml-sycl/gla.hpp
new file mode 100644
index 000000000..607cf3a7f
--- /dev/null
+++ b/ggml/src/ggml-sycl/gla.hpp
@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_GLA_HPP
+#define GGML_SYCL_GLA_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_gated_linear_attn(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif  // GGML_SYCL_GLA_HPP

From 0ccd7f3eb2debe477ffe3c44d5353cc388c9418d Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 15 Jan 2025 05:44:38 +0100
Subject: [PATCH 181/376] examples : add embd_to_audio to tts-outetts.py [no
 ci] (#11235)

This commit contains a suggestion for adding the missing embd_to_audio
function from tts.cpp to tts-outetts.py. This introduces a depencency
numpy which I was not sure if that is acceptable or not (only PyTorch
was mentioned in referened PR).

Also the README has been updated with instructions to run the example
with llama-server and the python script.

Refs: https://github.com/ggerganov/llama.cpp/pull/10784#issuecomment-2548377734
---
 examples/tts/README.md      |  37 +++++++++++
 examples/tts/tts-outetts.py | 128 +++++++++++++++++++++++++++++++++++-
 2 files changed, 163 insertions(+), 2 deletions(-)

diff --git a/examples/tts/README.md b/examples/tts/README.md
index b0d20111a..4509763c6 100644
--- a/examples/tts/README.md
+++ b/examples/tts/README.md
@@ -78,3 +78,40 @@ play the audio:
 $ aplay output.wav
 ```
 
+### Running the example with llama-server
+Running this example with `llama-server` is also possible and requires two
+server instances to be started. One will serve the LLM model and the other
+will serve the voice decoder model.
+
+The LLM model server can be started with the following command:
+```console
+$ ./build/bin/llama-server -m ./models/outetts-0.2-0.5B-q8_0.gguf --port 8020
+```
+
+And the voice decoder model server can be started using:
+```console
+./build/bin/llama-server -m ./models/wavtokenizer-large-75-f16.gguf --port 8021 --embeddings --pooling none
+```
+
+Then we can run [tts-outetts.py](tts-outetts.py) to generate the audio.
+
+First create a virtual environment for python and install the required
+dependencies (this in only required to be done once):
+```console
+$ python3 -m venv venv
+$ source venv/bin/activate
+(venv) pip install requests numpy
+```
+
+And then run the python script using:
+```conole
+(venv) python ./examples/tts/tts-outetts.py http://localhost:8020 http://localhost:8021 "Hello world"
+spectrogram generated: n_codes: 90, n_embd: 1282
+converting to audio ...
+audio generated: 28800 samples
+audio written to file "output.wav"
+```
+And to play the audio we can again use aplay or any other media player:
+```console
+$ aplay output.wav
+```
diff --git a/examples/tts/tts-outetts.py b/examples/tts/tts-outetts.py
index 0f81192fc..3791f9fc3 100644
--- a/examples/tts/tts-outetts.py
+++ b/examples/tts/tts-outetts.py
@@ -3,6 +3,121 @@ import sys
 #import struct
 import requests
 import re
+import struct
+import numpy as np
+from concurrent.futures import ThreadPoolExecutor
+
+
+def fill_hann_window(size, periodic=True):
+    if periodic:
+        return np.hanning(size + 1)[:-1]
+    return np.hanning(size)
+
+
+def irfft(n_fft, complex_input):
+    return np.fft.irfft(complex_input, n=n_fft)
+
+
+def fold(buffer, n_out, n_win, n_hop, n_pad):
+    result = np.zeros(n_out)
+    n_frames = len(buffer) // n_win
+
+    for i in range(n_frames):
+        start = i * n_hop
+        end = start + n_win
+        result[start:end] += buffer[i * n_win:(i + 1) * n_win]
+
+    return result[n_pad:-n_pad] if n_pad > 0 else result
+
+
+def process_frame(args):
+    l, n_fft, ST, hann = args
+    frame = irfft(n_fft, ST[l])
+    frame = frame * hann
+    hann2 = hann * hann
+    return frame, hann2
+
+
+def embd_to_audio(embd, n_codes, n_embd, n_thread=4):
+    embd = np.asarray(embd, dtype=np.float32).reshape(n_codes, n_embd)
+
+    n_fft = 1280
+    n_hop = 320
+    n_win = 1280
+    n_pad = (n_win - n_hop) // 2
+    n_out = (n_codes - 1) * n_hop + n_win
+
+    hann = fill_hann_window(n_fft, True)
+
+    E = np.zeros((n_embd, n_codes), dtype=np.float32)
+    for l in range(n_codes):
+        for k in range(n_embd):
+            E[k, l] = embd[l, k]
+
+    half_embd = n_embd // 2
+    S = np.zeros((n_codes, half_embd + 1), dtype=np.complex64)
+
+    for k in range(half_embd):
+        for l in range(n_codes):
+            mag = E[k, l]
+            phi = E[k + half_embd, l]
+
+            mag = np.clip(np.exp(mag), 0, 1e2)
+            S[l, k] = mag * np.exp(1j * phi)
+
+    res = np.zeros(n_codes * n_fft)
+    hann2_buffer = np.zeros(n_codes * n_fft)
+
+    with ThreadPoolExecutor(max_workers=n_thread) as executor:
+        args = [(l, n_fft, S, hann) for l in range(n_codes)]
+        results = list(executor.map(process_frame, args))
+
+        for l, (frame, hann2) in enumerate(results):
+            res[l*n_fft:(l+1)*n_fft] = frame
+            hann2_buffer[l*n_fft:(l+1)*n_fft] = hann2
+
+    audio = fold(res, n_out, n_win, n_hop, n_pad)
+    env = fold(hann2_buffer, n_out, n_win, n_hop, n_pad)
+
+    mask = env > 1e-10
+    audio[mask] /= env[mask]
+
+    return audio
+
+
+def save_wav(filename, audio_data, sample_rate):
+    num_channels = 1
+    bits_per_sample = 16
+    bytes_per_sample = bits_per_sample // 8
+    data_size = len(audio_data) * bytes_per_sample
+    byte_rate = sample_rate * num_channels * bytes_per_sample
+    block_align = num_channels * bytes_per_sample
+    chunk_size = 36 + data_size  # 36 = size of header minus first 8 bytes
+
+    header = struct.pack(
+        '<4sI4s4sIHHIIHH4sI',
+        b'RIFF',
+        chunk_size,
+        b'WAVE',
+        b'fmt ',
+        16,                # fmt chunk size
+        1,                 # audio format (PCM)
+        num_channels,
+        sample_rate,
+        byte_rate,
+        block_align,
+        bits_per_sample,
+        b'data',
+        data_size
+    )
+
+    audio_data = np.clip(audio_data * 32767, -32768, 32767)
+    pcm_data = audio_data.astype(np.int16)
+
+    with open(filename, 'wb') as f:
+        f.write(header)
+        f.write(pcm_data.tobytes())
+
 
 def process_text(text: str):
     text = re.sub(r'\d+(\.\d+)?', lambda x: x.group(), text.lower()) # TODO this needs to be fixed
@@ -170,6 +285,15 @@ n_embd = len(embd[0])
 print('spectrogram generated: n_codes: %d, n_embd: %d' % (n_codes, n_embd))
 
 # post-process the spectrogram to convert to audio
-# TODO: see the tts.cpp:embd_to_audio() and implement it in Python
 print('converting to audio ...')
-print('TODO: see the tts.cpp:embd_to_audio() and implement it in Python')
+audio = embd_to_audio(embd, n_codes, n_embd)
+print('audio generated: %d samples' % len(audio))
+
+filename = "output.wav"
+sample_rate = 24000 # sampling rate
+
+# zero out first 0.25 seconds
+audio[:24000 // 4] = 0.0
+
+save_wav(filename, audio, sample_rate)
+print('audio written to file "%s"' % filename)

From 432df2d5f901a8ac93d1befad916144a0478bd9e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 15 Jan 2025 12:51:37 +0100
Subject: [PATCH 182/376] RoPE: fix back, CUDA support for back + noncont.
 (#11240)

* RoPE: fix back, CUDA support for back + noncont.

* fix comments reg. non-cont. RoPE support [no-ci]
---
 ggml/include/ggml.h             |  19 +-
 ggml/src/ggml-cpu/ggml-cpu.c    |   1 +
 ggml/src/ggml-cpu/ggml-cpu.cpp  |   2 -
 ggml/src/ggml-cuda/ggml-cuda.cu |  10 +-
 ggml/src/ggml-cuda/rope.cu      | 366 ++++++++++++++------------------
 ggml/src/ggml-cuda/rope.cuh     |   2 +
 ggml/src/ggml.c                 |  56 ++---
 src/llama.cpp                   |   8 +-
 tests/test-backend-ops.cpp      |  63 ++++--
 9 files changed, 269 insertions(+), 258 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 8f8cb9e1a..a9c051cd5 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1500,7 +1500,7 @@ extern "C" {
 
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
-    GGML_API struct ggml_tensor * ggml_rope_back(
+    GGML_API struct ggml_tensor * ggml_rope_ext_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a, // gradients of ggml_rope result
             struct ggml_tensor  * b, // positions
@@ -1515,6 +1515,23 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    GGML_API struct ggml_tensor * ggml_rope_multi_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[4],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+
     // clamp
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_clamp(
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 2966ff768..7c2e45f86 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -13668,6 +13668,7 @@ struct ggml_cplan ggml_graph_plan(
                     } break;
                 case GGML_OP_SOFT_MAX:
                 case GGML_OP_ROPE:
+                case GGML_OP_ROPE_BACK:
                     {
                         cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                     } break;
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index f11399cc6..5c47ceb73 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -403,8 +403,6 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
                 op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return src1->type == GGML_TYPE_F32 || src1->type == ggml_get_type_traits_cpu(src0->type)->vec_dot_type;
-        case GGML_OP_ROPE_BACK:
-            return op->src[2] == NULL && (op->op_params[2] & 4) == 0;
         case GGML_OP_IM2COL_BACK:
             return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
         case GGML_OP_OUT_PROD:
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 1dac397c4..9118edc72 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2141,6 +2141,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_ROPE:
             ggml_cuda_op_rope(ctx, dst);
             break;
+        case GGML_OP_ROPE_BACK:
+            ggml_cuda_op_rope_back(ctx, dst);
+            break;
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
@@ -3025,7 +3028,11 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SOFT_MAX:
             return true;
         case GGML_OP_ROPE:
-            return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_ROPE_BACK: {
+            const size_t ts = ggml_type_size(op->src[0]->type);
+            const int64_t ne0_012 = op->src[0]->ne[0] * op->src[0]->ne[1] * op->src[0]->ne[2];
+            return op->src[0]->nb[0] == ts && op->src[0]->nb[3] == ne0_012*ts;
+        }
         case GGML_OP_IM2COL:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM:
@@ -3081,6 +3088,7 @@ static int64_t get_op_batch_size(const ggml_tensor * op) {
             return op->ne[1];
         case GGML_OP_MUL_MAT_ID:
         case GGML_OP_ROPE:
+        case GGML_OP_ROPE_BACK:
             return op->ne[2];
         default:
             return ggml_nrows(op);
diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu
index 2c84778d2..e1912fee1 100644
--- a/ggml/src/ggml-cuda/rope.cu
+++ b/ggml/src/ggml-cuda/rope.cu
@@ -16,9 +16,10 @@ static __device__ float rope_yarn_ramp(const float low, const float high, const
 
 // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
 // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+template<bool forward>
 static __device__ void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta) {
+        const float theta_extrap, const float freq_scale, const rope_corr_dims corr_dims, const int64_t i0, const float ext_factor,
+        float mscale, float & cos_theta, float & sin_theta) {
     // Get n-d rotational scaling corrected for extrapolation
     float theta_interp = freq_scale * theta_extrap;
     float theta = theta_interp;
@@ -29,24 +30,28 @@ static __device__ void rope_yarn(
         // Get n-d magnitude scaling corrected for interpolation
         mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
     }
-    *cos_theta = cosf(theta) * mscale;
-    *sin_theta = sinf(theta) * mscale;
+    cos_theta = cosf(theta) * mscale;
+    sin_theta = sinf(theta) * mscale;
+    if (!forward) {
+        sin_theta *= -1.0f;
+    }
 }
 
-template<typename T, bool has_ff>
+template<bool forward, bool has_ff, typename T>
 static __global__ void rope_norm(
-    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
+        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
         return;
     }
 
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i0 >= n_dims) {
-        const int i = row*ne0 + i0;
+        const int i = row_dst*ne0 + i0;
 
         dst[i + 0] = x[i + 0];
         dst[i + 1] = x[i + 1];
@@ -54,39 +59,43 @@ static __global__ void rope_norm(
         return;
     }
 
-    const int i  = row*ne0 + i0;
-    const int i2 = row/p_delta_rows;
+    const int row_x     = row_dst % ne1;
+    const int channel_x = row_dst / ne1;
 
-    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const int idst = row_dst*ne0 + i0;
+    const int ix   = channel_x*s2 + row_x*s1 + i0;
+
+    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
     float cos_theta;
     float sin_theta;
 
-    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn<forward>(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, cos_theta, sin_theta);
 
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
+    const float x0 = x[ix + 0];
+    const float x1 = x[ix + 1];
 
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+    dst[idst + 0] = x0*cos_theta - x1*sin_theta;
+    dst[idst + 1] = x0*sin_theta + x1*cos_theta;
 }
 
-template<typename T, bool has_ff>
+template<bool forward, bool has_ff, typename T>
 static __global__ void rope_neox(
-    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
+        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
         return;
     }
 
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i0 >= n_dims) {
-        const int i = row*ne0 + i0;
+        const int i = row_dst*ne0 + i0;
 
         dst[i + 0] = x[i + 0];
         dst[i + 1] = x[i + 1];
@@ -94,39 +103,43 @@ static __global__ void rope_neox(
         return;
     }
 
-    const int i  = row*ne0 + i0/2;
-    const int i2 = row/p_delta_rows;
+    const int row_x     = row_dst % ne1;
+    const int channel_x = row_dst / ne1;
 
-    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const int idst = row_dst*ne0 + i0/2;
+    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+
+    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
     float cos_theta;
     float sin_theta;
 
-    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn<forward>(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, cos_theta, sin_theta);
 
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
+    const float x0 = x[ix + 0];
+    const float x1 = x[ix + n_dims/2];
 
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+    dst[idst + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
-template<typename T, bool has_ff>
+template<bool forward, bool has_ff, typename T>
 static __global__ void rope_multi(
-    const T * x, T * dst, int ne0, int ne2, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, mrope_sections sections) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
+        const int n_dims, const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors, const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
         return;
     }
 
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i0 >= n_dims) {
-        const int i = row*ne0 + i0;
+        const int i = row_dst*ne0 + i0;
 
         dst[i + 0] = x[i + 0];
         dst[i + 1] = x[i + 1];
@@ -134,25 +147,28 @@ static __global__ void rope_multi(
         return;
     }
 
-    const int i  = row*ne0 + i0/2;
-    const int i2 = row/p_delta_rows;
+    const int row_x     = row_dst % ne1;
+    const int channel_x = row_dst / ne1;
 
-    int sect_dims = sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3];
-    int sec_w = sections.v[1] + sections.v[0];
-    int sector = (i0 / 2) % sect_dims;
+    const int idst = row_dst*ne0 + i0/2;
+    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+
+    const int sect_dims = sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3];
+    const int sec_w = sections.v[1] + sections.v[0];
+    const int sector = (i0 / 2) % sect_dims;
 
     float theta_base = 0.0;
     if (sector < sections.v[0]) {
-        theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+        theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
     }
     else if (sector >= sections.v[0] && sector < sec_w) {
-        theta_base = pos[i2 + ne2 * 1]*powf(theta_scale, i0/2.0f);
+        theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
     }
     else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
-        theta_base = pos[i2 + ne2 * 2]*powf(theta_scale, i0/2.0f);
+        theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
     }
     else if (sector >= sec_w + sections.v[2]) {
-        theta_base = pos[i2 + ne2 * 3]*powf(theta_scale, i0/2.0f);
+        theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
     }
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
@@ -160,42 +176,46 @@ static __global__ void rope_multi(
     float cos_theta;
     float sin_theta;
 
-    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn<forward>(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, cos_theta, sin_theta);
 
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
+    const float x0 = x[ix + 0];
+    const float x1 = x[ix + n_dims/2];
 
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+    dst[idst + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
-template<typename T, bool has_ff>
+template<bool forward, bool has_ff, typename T>
 static __global__ void rope_vision(
-    const T * x, T * dst, int ne0, int ne2, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors, mrope_sections sections) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
+        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
+        const float theta_scale, const float * __restrict__ freq_factors, const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
         return;
     }
 
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int i  = row*ne0 + i0/2;
-    const int i2 = row/p_delta_rows; // i2-th tokens
+    const int row_x     = row_dst % ne1;
+    const int channel_x = row_dst / ne1;
 
-    int sect_dims = sections.v[0] + sections.v[1];
-    int sec_w = sections.v[1] + sections.v[0];
-    int sector = (i0 / 2) % sect_dims;
+    const int idst = row_dst*ne0 + i0/2;
+    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+
+    const int sect_dims = sections.v[0] + sections.v[1];
+    const int sec_w = sections.v[1] + sections.v[0];
+    const int sector = (i0 / 2) % sect_dims;
 
     float theta_base = 0.0;
     if (sector < sections.v[0]) {
         const int p = sector;
-        theta_base = pos[i2]*powf(theta_scale, p);
+        theta_base = pos[channel_x]*powf(theta_scale, p);
     }
     else if (sector >= sections.v[0] && sector < sec_w) {
         const int p = sector - sections.v[0];
-        theta_base = pos[i2 + ne2]*powf(theta_scale, p);
+        theta_base = pos[channel_x + ne2]*powf(theta_scale, p);
     }
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
@@ -203,19 +223,20 @@ static __global__ void rope_vision(
     float cos_theta;
     float sin_theta;
 
-    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn<forward>(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, cos_theta, sin_theta);
 
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims];
+    const float x0 = x[ix + 0];
+    const float x1 = x[ix + n_dims];
 
-    dst[i + 0]      = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims] = x0*sin_theta + x1*cos_theta;
+    dst[idst + 0]      = x0*cos_theta - x1*sin_theta;
+    dst[idst + n_dims] = x0*sin_theta + x1*cos_theta;
 }
 
-template<typename T>
+template<bool forward, typename T>
 static void rope_norm_cuda(
-    const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -224,22 +245,21 @@ static void rope_norm_cuda(
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     if (freq_factors == nullptr) {
-        rope_norm<T, false><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors
-                );
+        rope_norm<forward, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors);
     } else {
-        rope_norm<T, true><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors
-                );
+        rope_norm<forward, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors);
     }
 }
 
-template<typename T>
+template<bool forward, typename T>
 static void rope_neox_cuda(
-    const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -248,22 +268,21 @@ static void rope_neox_cuda(
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     if (freq_factors == nullptr) {
-        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors
-                );
+        rope_neox<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors);
     } else {
-        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors
-                );
+        rope_neox<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors);
     }
 }
 
-template<typename T>
+template<bool forward, typename T>
 static void rope_multi_cuda(
-    const T * x, T * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, const mrope_sections sections, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -272,22 +291,21 @@ static void rope_multi_cuda(
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     if (freq_factors == nullptr) {
-        rope_multi<T, false><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors, sections
-                );
+        rope_multi<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, sections);
     } else {
-        rope_multi<T, true><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors, sections
-                );
+        rope_multi<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, sections);
     }
 }
 
-template<typename T>
+template<bool forward, typename T>
 static void rope_vision_cuda(
-    const T * x, T * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream) {
+        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, const mrope_sections sections, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -298,80 +316,18 @@ static void rope_vision_cuda(
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     if (freq_factors == nullptr) {
-        rope_vision<T, false><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors, sections
-                );
+        rope_vision<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, sections);
     } else {
-        rope_vision<T, true><<<block_nums, block_dims, 0, stream>>>(
-                x, dst, ne0, ne2, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-                theta_scale, freq_factors, sections
-                );
+        rope_vision<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, sections);
     }
 }
 
-static void rope_norm_cuda_f16(
-    const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
-
-    rope_norm_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-}
-
-static void rope_norm_cuda_f32(
-    const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
-
-    rope_norm_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-}
-
-static void rope_neox_cuda_f16(
-    const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
-
-    rope_neox_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-}
-
-static void rope_neox_cuda_f32(
-    const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream
-) {
-
-    rope_neox_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-}
-
-static void rope_multi_cuda_f16(
-    const half * x, half * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
-) {
-
-    rope_multi_cuda<half>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
-}
-
-static void rope_multi_cuda_f32(
-    const float * x, float * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
-) {
-
-    rope_multi_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
-}
-
-static void rope_vision_cuda_f16(
-    const half * x, half * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
-) {
-
-    rope_vision_cuda<half>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
-}
-
-static void rope_vision_cuda_f32(
-    const float * x, float * dst, int ne0, int ne2, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, mrope_sections sections, cudaStream_t stream
-) {
-
-    rope_vision_cuda<float>(x, dst, ne0, ne2, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
-}
-
-void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+template <bool forward>
+void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
     const ggml_tensor * src2 = dst->src[2];
@@ -382,7 +338,6 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
-    GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
     GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
     GGML_ASSERT(src0->type == dst->type);
@@ -392,6 +347,9 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t ne02 = src0->ne[2]; // num heads
     const int64_t nr = ggml_nrows(src0);
 
+    const size_t s01 = src0->nb[1] / ggml_type_size(src0->type);
+    const size_t s02 = src0->nb[2] / ggml_type_size(src0->type);
+
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
     const int mode       = ((int32_t *) dst->op_params)[2];
@@ -440,59 +398,59 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     // compute
     if (is_neox) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, stream
-            );
+            rope_neox_cuda<forward>(
+                (const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, stream
-            );
+            rope_neox_cuda<forward>(
+                (const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_mrope && !is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_multi_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, sections, stream
-            );
+            rope_multi_cuda<forward>(
+                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_multi_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, sections, stream
-            );
+            rope_multi_cuda<forward>(
+                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_vision_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, sections, stream
-            );
+            rope_vision_cuda<forward>(
+                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_vision_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, ne02, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, sections, stream
-            );
+            rope_vision_cuda<forward>(
+                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else {
         if (src0->type == GGML_TYPE_F32) {
-            rope_norm_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, stream
-            );
+            rope_norm_cuda<forward>(
+                (const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_norm_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, stream
-            );
+            rope_norm_cuda<forward>(
+                (const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     }
 }
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_rope_impl<true>(ctx, dst);
+}
+
+void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_rope_impl<false>(ctx, dst);
+}
diff --git a/ggml/src/ggml-cuda/rope.cuh b/ggml/src/ggml-cuda/rope.cuh
index 0f787a0b2..9139f3b22 100644
--- a/ggml/src/ggml-cuda/rope.cuh
+++ b/ggml/src/ggml-cuda/rope.cuh
@@ -3,3 +3,5 @@
 #define CUDA_ROPE_BLOCK_SIZE 256
 
 void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index da5b817e1..ecfb84a80 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3695,7 +3695,7 @@ void ggml_rope_yarn_corr_dims(
 
 // ggml_rope_back
 
-struct ggml_tensor * ggml_rope_back(
+struct ggml_tensor * ggml_rope_ext_back(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
@@ -3709,29 +3709,32 @@ struct ggml_tensor * ggml_rope_back(
         float                 attn_factor,
         float                 beta_fast,
         float                 beta_slow) {
-    GGML_ASSERT(ggml_is_vector(b));
-    GGML_ASSERT(b->type == GGML_TYPE_I32);
-    GGML_ASSERT(a->ne[2] == b->ne[0]);
-
-    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
-
-    int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
-    memcpy(params +  5, &freq_base,    sizeof(float));
-    memcpy(params +  6, &freq_scale,   sizeof(float));
-    memcpy(params +  7, &ext_factor,   sizeof(float));
-    memcpy(params +  8, &attn_factor,  sizeof(float));
-    memcpy(params +  9, &beta_fast,    sizeof(float));
-    memcpy(params + 10, &beta_slow,    sizeof(float));
-    ggml_set_op_params(result, params, sizeof(params));
-
-    result->op     = GGML_OP_ROPE_BACK;
-    result->src[0] = a;
-    result->src[1] = b;
-    result->src[2] = c;
-
+    struct ggml_tensor * result = ggml_rope_ext(
+        ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    result->op = GGML_OP_ROPE_BACK;
     return result;
 }
 
+struct ggml_tensor * ggml_rope_multi_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
+        int                   n_dims,
+        int                   sections[4],
+        int                   mode,
+        int                   n_ctx_orig,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    struct ggml_tensor * result = ggml_rope_multi(
+        ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    result->op = GGML_OP_ROPE_BACK;
+    return result;
+}
 // ggml_clamp
 
 struct ggml_tensor * ggml_clamp(
@@ -5594,6 +5597,7 @@ static void ggml_compute_backward(
                 //const int n_ctx      = ((int32_t *) tensor->op_params)[3];
                 const int n_ctx_orig = ((const int32_t *) tensor->op_params)[4];
                 float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+                int sections[4] = {0, 0, 0, 0};
 
                 memcpy(&freq_base,   (const float *) tensor->op_params +  5, sizeof(float));
                 memcpy(&freq_scale,  (const float *) tensor->op_params +  6, sizeof(float));
@@ -5601,10 +5605,14 @@ static void ggml_compute_backward(
                 memcpy(&attn_factor, (const float *) tensor->op_params +  8, sizeof(float));
                 memcpy(&beta_fast,   (const float *) tensor->op_params +  9, sizeof(float));
                 memcpy(&beta_slow,   (const float *) tensor->op_params + 10, sizeof(float));
+                memcpy(&sections,                    tensor->op_params + 11, sizeof(sections));
 
-                ggml_add_or_set(ctx, cgraph, isrc0,
-                    ggml_rope_back(ctx, grad, src1, src2, n_dims, mode, n_ctx_orig, freq_base,
-                        freq_scale, ext_factor, attn_factor, beta_fast, beta_slow));
+                struct ggml_tensor * rope_back = grad->ne[2] == src1->ne[0] ?
+                    ggml_rope_ext_back(ctx, grad, src1, src2, n_dims,
+                        mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow) :
+                    ggml_rope_multi_back(ctx, grad, src1, src2, n_dims, sections,
+                        mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+                ggml_add_or_set(ctx, cgraph, isrc0, rope_back);
             }
             GGML_ASSERT((!src2 || !src2_needs_grads) && "gradients for freq factors not implemented");
         } break;
diff --git a/src/llama.cpp b/src/llama.cpp
index daf1b7c97..2e391b3b6 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -4642,7 +4642,7 @@ struct llm_build_context {
                     0);
                 cb(v_states, "v_states", il);
 
-                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend used to not support non-cont. RoPE, investigate removing this
                 q_pe = ggml_rope_ext(
                     ctx0, q_pe, inp_pos, rope_factors,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -4651,7 +4651,7 @@ struct llm_build_context {
                 cb(q_pe, "q_pe", il);
 
                 // shared RoPE key
-                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend used to not support non-cont. RoPE, investigate removing this
                 k_pe = ggml_rope_ext(
                     ctx0, k_pe, inp_pos, rope_factors,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -6496,7 +6496,7 @@ struct llm_build_context {
                     0);
                 cb(v_states, "v_states", il);
 
-                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend used to not support non-cont. RoPE, investigate removing this
                 q_pe = ggml_rope_ext(
                     ctx0, q_pe, inp_pos, nullptr,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -6505,7 +6505,7 @@ struct llm_build_context {
                 cb(q_pe, "q_pe", il);
 
                 // shared RoPE key
-                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend used to not support non-cont. RoPE, investigate removing this
                 k_pe = ggml_rope_ext(
                     ctx0, k_pe, inp_pos, nullptr,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 3834e0f84..4c8464d8b 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2192,7 +2192,7 @@ struct test_soft_max : public test_case {
 };
 
 
-// GGML_OP_ROPE
+// GGML_OP_ROPE + GGML_OP_ROPE_BACK
 struct test_rope : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne_a;
@@ -2204,29 +2204,36 @@ struct test_rope : public test_case {
     float af; // attn_factor
     bool ff;
     int v; // view (1 : non-contiguous a)
+    bool forward;
 
     std::string vars() override {
+        // forward can be inferred from the op, does not need to be printed
         return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v);
     }
 
     test_rope(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne_a = {10, 5, 3, 1},
-            int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0)
-        : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {}
+            int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f,
+            float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0, bool forward = true)
+        : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v), forward(forward) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a;
         if (v & 1) {
             auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
             a = ggml_new_tensor(ctx, type, 4, ne.data());
-            ggml_set_param(ctx, a);
+            if (forward) {
+                ggml_set_param(ctx, a);
+            }
             ggml_set_name(a, "a");
 
             a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
             ggml_set_name(a, "view_of_a");
         } else {
             a = ggml_new_tensor(ctx, type, 4, ne_a.data());
-            ggml_set_param(ctx, a);
+            if (forward) {
+                ggml_set_param(ctx, a);
+            }
             ggml_set_name(a, "a");
         }
 
@@ -2252,14 +2259,26 @@ struct test_rope : public test_case {
             if (is_vision) {
                 GGML_ASSERT(n_dims/4 > 0);
                 int rope_sections[4] = {n_dims/4, n_dims/4, 0, 0}; // Vision-RoPE only use first two dimension for image (x, y) coordinate
-                out = ggml_rope_multi(ctx, a, pos, freq, n_dims/2, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                if (forward) {
+                    out = ggml_rope_multi     (ctx, a, pos, freq, n_dims/2, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                } else {
+                    out = ggml_rope_multi_back(ctx, a, pos, freq, n_dims/2, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                }
             } else {
                 GGML_ASSERT(n_dims/3 > 0);
                 int rope_sections[4] = {n_dims/3, n_dims/3, n_dims/3, 0};
-                out = ggml_rope_multi(ctx, a, pos, freq, n_dims, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                if (forward) {
+                    out = ggml_rope_multi     (ctx, a, pos, freq, n_dims, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                } else {
+                    out = ggml_rope_multi_back(ctx, a, pos, freq, n_dims, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+                }
             }
         } else {
-            out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+            if (forward) {
+                out = ggml_rope_ext     (ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+            } else {
+                out = ggml_rope_ext_back(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
+            }
         }
         ggml_set_name(out, "out");
 
@@ -3844,7 +3863,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
-    {
+    for (bool fw : {true, false}) { // fw == forward
         bool all = true;
 
         for (float v : { 0, 1 }) {
@@ -3853,29 +3872,29 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                     for (float af : { 1.0f, 1.4245f }) {
                         for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
                             for (bool ff : {false, true}) { // freq_factors
-                                test_cases.emplace_back(new test_rope(type, {128,  32, 2, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 7B
+                                test_cases.emplace_back(new test_rope(type, {128,  32, 2, 1}, 128, 0, 512, fs, ef, af, ff, v, fw)); // llama 7B
 
                                 if (all) {
-                                    test_cases.emplace_back(new test_rope(type, {128,  40, 2, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 13B
-                                    test_cases.emplace_back(new test_rope(type, {128,  52, 2, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 30B
-                                    test_cases.emplace_back(new test_rope(type, {128,  64, 2, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 65B
+                                    test_cases.emplace_back(new test_rope(type, {128,  40, 2, 1}, 128, 0, 512, fs, ef, af, ff, v, fw)); // llama 13B
+                                    test_cases.emplace_back(new test_rope(type, {128,  52, 2, 1}, 128, 0, 512, fs, ef, af, ff, v, fw)); // llama 30B
+                                    test_cases.emplace_back(new test_rope(type, {128,  64, 2, 1}, 128, 0, 512, fs, ef, af, ff, v, fw)); // llama 65B
                                 }
 
                                 if (all) {
-                                    test_cases.emplace_back(new test_rope(type, { 64,   1, 2, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
-                                    test_cases.emplace_back(new test_rope(type, { 64,  71, 2, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
-                                    test_cases.emplace_back(new test_rope(type, { 64,   8, 2, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
-                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 2, 1},  20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm)
-                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 2, 1},  32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2)
+                                    test_cases.emplace_back(new test_rope(type, { 64,   1, 2, 1},  64, 2, 512, fs, ef, af, ff, v, fw)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,  71, 2, 1},  64, 2, 512, fs, ef, af, ff, v, fw)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,   8, 2, 1},  64, 2, 512, fs, ef, af, ff, v, fw)); // neox (falcon 40B)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 2, 1},  20, 2, 512, fs, ef, af, ff, v, fw)); // neox (stablelm)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 2, 1},  32, 2, 512, fs, ef, af, ff, v, fw)); // neox (phi-2)
                                 }
 
                                 if (all) {
-                                    test_cases.emplace_back(new test_rope(type, {128,  12, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl 2B)
-                                    test_cases.emplace_back(new test_rope(type, {128,  28, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl 7B)
-                                    test_cases.emplace_back(new test_rope(type, { 80,  16, 2, 1},  80, GGML_ROPE_TYPE_VISION, 512, fs, ef, af, ff, v)); // rope_multi,m-rope (qwen2vl ViT)
+                                    test_cases.emplace_back(new test_rope(type, {128,  12, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v, fw)); // rope_multi,m-rope (qwen2vl 2B)
+                                    test_cases.emplace_back(new test_rope(type, {128,  28, 2, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, fs, ef, af, ff, v, fw)); // rope_multi,m-rope (qwen2vl 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  16, 2, 1},  80, GGML_ROPE_TYPE_VISION, 512, fs, ef, af, ff, v, fw)); // rope_multi,m-rope (qwen2vl ViT)
                                 }
 
-                                test_cases.emplace_back(new test_rope(type, { 64, 128, 2, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
+                                test_cases.emplace_back(new test_rope(type, { 64, 128, 2, 1},  64, 2, 512, fs, ef, af, ff, v, fw)); // neox (falcon 40B)
                             }
                         }
 

From 1d8504338ea27c7331998c11afdbd47ce5a9daac Mon Sep 17 00:00:00 2001
From: Junil Kim <logyourself@gmail.com>
Date: Wed, 15 Jan 2025 22:17:42 +0900
Subject: [PATCH 183/376] fix: ggml: fix vulkan-shaders-gen build (#10448)

* fix: ggml: fix vulkan-shaders-gen build

The vulkan-shaders-gen target was not being built correctly
in case of cross-compilation.
Other outputs need to be built for the cross compile target,
but vulkan-shaders-gen needs to be built for the host.

* refactor: ggml: Improve vulkan-shaders-gen toolchain setup

- Add GGML_SHADERS_GEN_TOOLCHAIN CMake option.
- Auto-detect host toolchain if not set.

* refactor: ggml: Improve vulkan-shaders-gen toolchain setup

Use configure_file to generate host_toolchain.cmake from template

* fix: ggml: Fix compile error

Fix compile error not finding vulkan-shaders-gen

* fix: vulkan-shaders-gen build and path handling

Fix build issues with vulkan-shaders-gen:
- Add target dependency for correct build order
- Use CMAKE_HOST_SYSTEM_NAME for executable suffix
- Fix MSVC output directory in host toolchain
- Normalize path handling for cross-compilation

* fix: improve host compiler detection in vulkan shader build

Improve host compiler detection for vulkan shader generation:
- Add NO_CMAKE_FIND_ROOT_PATH to all compiler searches
- Consolidate compiler detection logic
- Fix Windows-specific MSVC detection
- Ensure correct compiler search in cross-compilation

* refactor: Simplify CMake function for detecting host compiler

Simplified the CMake function to improve the process of detecting the host compiler.

* fix: Remove unnecessary Vulkan library linkage in CMakeLists.txt

Since `vulkan-shader-gen.cpp` only requires the `glslc` executable
and not the Vulkan headers or libraries, CMakeLists.txt needs to
be corrected.
(See: ecc93d0558fc3ecb8a5af69d2ece02fae4710ade)

* refactor: Rename host_toolchain.cmake.in

- Rename host_toolchain.cmake.in to cmake/host-toolchain.cmake.in

* refactor: GGML_VULKAN_SHADERS_GEN_TOOLCHAIN

Rename the macro GGML_SHADERS_GEN_TOOLCHAIN to GGML_VULKAN_SHADERS_GEN_TOOLCHAIN
---
 ggml/CMakeLists.txt                           |  3 +
 ggml/src/ggml-vulkan/CMakeLists.txt           | 64 +++++++++++++++++--
 .../ggml-vulkan/cmake/host-toolchain.cmake.in | 15 +++++
 .../ggml-vulkan/vulkan-shaders/CMakeLists.txt |  6 +-
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  2 -
 5 files changed, 80 insertions(+), 10 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index fe8acc803..185079aa4 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -185,6 +185,9 @@ option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increas
 option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
 option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
 
+# toolchain for vulkan-shaders-gen
+set   (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
+
 # extra artifacts
 option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
 option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt
index c0ddaac82..d970f7e20 100644
--- a/ggml/src/ggml-vulkan/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/CMakeLists.txt
@@ -1,5 +1,20 @@
+cmake_minimum_required(VERSION 3.19)
+cmake_policy(SET CMP0114 NEW)
+
 find_package(Vulkan COMPONENTS glslc REQUIRED)
 
+function(detect_host_compiler)
+    if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows")
+        find_program(HOST_C_COMPILER NAMES cl gcc clang NO_CMAKE_FIND_ROOT_PATH)
+        find_program(HOST_CXX_COMPILER NAMES cl g++ clang++ NO_CMAKE_FIND_ROOT_PATH)
+    else()
+        find_program(HOST_C_COMPILER NAMES gcc clang NO_CMAKE_FIND_ROOT_PATH)
+        find_program(HOST_CXX_COMPILER NAMES g++ clang++ NO_CMAKE_FIND_ROOT_PATH)
+    endif()
+    set(HOST_C_COMPILER "${HOST_C_COMPILER}" PARENT_SCOPE)
+    set(HOST_CXX_COMPILER "${HOST_CXX_COMPILER}" PARENT_SCOPE)
+endfunction()
+
 if (Vulkan_FOUND)
     message(STATUS "Vulkan found")
 
@@ -73,19 +88,56 @@ if (Vulkan_FOUND)
         add_compile_definitions(GGML_VULKAN_RUN_TESTS)
     endif()
 
-    add_subdirectory(vulkan-shaders)
+    if (NOT CMAKE_CROSSCOMPILING)
+        add_subdirectory(vulkan-shaders)
+        if (MSVC)
+            foreach(CONFIG ${CMAKE_CONFIGURATION_TYPES})
+                string(TOUPPER ${CONFIG} CONFIG)
+                set_target_properties(vulkan-shaders-gen PROPERTIES
+                    RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
+            endforeach()
+        endif()
+    else()
+        if (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN)
+            set(HOST_CMAKE_TOOLCHAIN_FILE ${GGML_VULKAN_SHADERS_GEN_TOOLCHAIN})
+        else()
+            detect_host_compiler()
+            if (NOT HOST_C_COMPILER OR NOT HOST_CXX_COMPILER)
+                message(FATAL_ERROR "Host compiler not found")
+            else()
+                message(STATUS "Host compiler: ${HOST_C_COMPILER} ${HOST_CXX_COMPILER}")
+            endif()
+            configure_file(${CMAKE_CURRENT_SOURCE_DIR}/cmake/host-toolchain.cmake.in ${CMAKE_BINARY_DIR}/host-toolchain.cmake @ONLY)
+            set(HOST_CMAKE_TOOLCHAIN_FILE ${CMAKE_BINARY_DIR}/host-toolchain.cmake)
+        endif()
+        message(STATUS "vulkan-shaders-gen toolchain file: ${HOST_CMAKE_TOOLCHAIN_FILE}")
 
-    set (_ggml_vk_genshaders_cmd vulkan-shaders-gen)
+        include(ExternalProject)
+        # Native build through ExternalProject_Add
+        ExternalProject_Add(
+            vulkan-shaders-gen
+            SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders
+            CMAKE_ARGS -DCMAKE_TOOLCHAIN_FILE=${HOST_CMAKE_TOOLCHAIN_FILE}
+                    -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}
+            BUILD_COMMAND ${CMAKE_COMMAND} --build .
+            INSTALL_COMMAND ${CMAKE_COMMAND} --install .
+            INSTALL_DIR ${CMAKE_BINARY_DIR}
+        )
+        ExternalProject_Add_StepTargets(vulkan-shaders-gen build install)
+    endif()
+    set (_ggml_vk_host_suffix $<IF:$<STREQUAL:${CMAKE_HOST_SYSTEM_NAME},Windows>,.exe,>)
+    set (_ggml_vk_genshaders_cmd ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/vulkan-shaders-gen${_ggml_vk_host_suffix})
     set (_ggml_vk_header     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
     set (_ggml_vk_source     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp)
     set (_ggml_vk_input_dir  ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders)
     set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv)
 
     file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
+    set (_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen)
 
-    if (NOT CMAKE_CROSSCOMPILING)
-        set(_ggml_vk_genshaders_cmd "$<TARGET_FILE_DIR:vulkan-shaders-gen>/${_ggml_vk_genshaders_cmd}")
-    endif ()
+    if (CMAKE_CROSSCOMPILING)
+        set(_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen-build vulkan-shaders-gen-install)
+    endif()
 
     add_custom_command(
         OUTPUT ${_ggml_vk_header}
@@ -99,7 +151,7 @@ if (Vulkan_FOUND)
             --target-cpp ${_ggml_vk_source}
             --no-clean
 
-        DEPENDS ${_ggml_vk_shader_deps} ${_ggml_vk_genshaders_cmd}
+        DEPENDS ${_ggml_vk_shader_deps}
         COMMENT "Generate vulkan shaders"
     )
 
diff --git a/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in b/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in
new file mode 100644
index 000000000..b6af747a5
--- /dev/null
+++ b/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in
@@ -0,0 +1,15 @@
+set(CMAKE_BUILD_TYPE Release)
+set(CMAKE_C_FLAGS -O2)
+set(CMAKE_CXX_FLAGS -O2)
+set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
+set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER)
+set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER)
+set(CMAKE_C_COMPILER @HOST_C_COMPILER@)
+set(CMAKE_CXX_COMPILER @HOST_CXX_COMPILER@)
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY @CMAKE_RUNTIME_OUTPUT_DIRECTORY@)
+
+if("@CMAKE_C_COMPILER_ID@" STREQUAL "MSVC")
+    foreach(CONFIG IN ITEMS DEBUG RELEASE MINSIZEREL RELWITHDEBINFO)
+        set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
+    endforeach()
+endif()
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
index bd0c74cb1..074031087 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
@@ -1,9 +1,11 @@
 find_package (Threads REQUIRED)
-find_package(Vulkan COMPONENTS glslc REQUIRED)
+find_program(GLSLC_EXECUTABLE glslc)
+if(NOT GLSLC_EXECUTABLE)
+    message(FATAL_ERROR "glslc not found.")
+endif()
 
 set(TARGET vulkan-shaders-gen)
 add_executable(${TARGET} vulkan-shaders-gen.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads)
-target_link_libraries(vulkan-shaders-gen PRIVATE Vulkan::Vulkan)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 7b5044798..243839917 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -30,8 +30,6 @@
     #include <fcntl.h>
 #endif
 
-#include <vulkan/vulkan_core.h>
-
 #define ASYNCIO_CONCURRENCY 64
 
 std::mutex lock;

From f11cfdfd7fe29436fce512d934c2ff6b94bd89d2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 15 Jan 2025 18:28:35 +0200
Subject: [PATCH 184/376] ci : use -no-cnv in gguf-split tests (#11254)

* ci : use -no-cnv in gguf-split tests

ggml-ci

* ci : use -no-cnv in requantize tests

ggml-ci

* scripts : fix [no ci]
---
 examples/gguf-split/tests.sh            |  10 +--
 examples/quantize/tests.sh              |   4 +-
 scripts/hf.sh                           | 112 ------------------------
 tests/test-lora-conversion-inference.sh |   6 +-
 4 files changed, 10 insertions(+), 122 deletions(-)
 delete mode 100755 scripts/hf.sh

diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh
index d5a92d605..05a932227 100755
--- a/examples/gguf-split/tests.sh
+++ b/examples/gguf-split/tests.sh
@@ -41,7 +41,7 @@ echo PASS
 echo
 
 # 2b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
 echo PASS
 echo
 
@@ -51,7 +51,7 @@ echo PASS
 echo
 
 # 3b. Test the merged model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
 echo PASS
 echo
 
@@ -61,7 +61,7 @@ echo PASS
 echo
 
 # 4b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
 echo PASS
 echo
 
@@ -71,7 +71,7 @@ echo
 #echo
 
 # 5b. Test the merged model is loading properly
-#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
+#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
 #echo PASS
 #echo
 
@@ -81,7 +81,7 @@ echo PASS
 echo
 
 # 6b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
 echo PASS
 echo
 
diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh
index 24bc970e8..70f7610f9 100644
--- a/examples/quantize/tests.sh
+++ b/examples/quantize/tests.sh
@@ -47,7 +47,7 @@ echo PASS
 echo
 
 # 3a. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
 echo PASS
 echo
 
@@ -57,7 +57,7 @@ echo PASS
 echo
 
 # 4b. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
+$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
 echo PASS
 echo
 
diff --git a/scripts/hf.sh b/scripts/hf.sh
deleted file mode 100755
index b251925fa..000000000
--- a/scripts/hf.sh
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/bin/bash
-#
-# Shortcut for downloading HF models
-#
-# Usage:
-#   ./llama-cli -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./llama-cli -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./llama-cli -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#
-
-# all logs go to stderr
-function log {
-    echo "$@" 1>&2
-}
-
-function usage {
-    log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [--outdir <dir> [-h|--help]"
-    exit 1
-}
-
-# check for curl or wget
-function has_cmd {
-    if ! [ -x "$(command -v $1)" ]; then
-        return 1
-    fi
-}
-
-if has_cmd wget; then
-    cmd="wget -q -c -O %s/%s %s"
-elif has_cmd curl; then
-    cmd="curl -C - -f --output-dir %s -o %s -L %s"
-else
-    log "[E] curl or wget not found"
-    exit 1
-fi
-
-url=""
-repo=""
-file=""
-outdir="."
-
-# parse args
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --url)
-            url="$2"
-            shift 2
-            ;;
-        --repo)
-            repo="$2"
-            shift 2
-            ;;
-        --file)
-            file="$2"
-            shift 2
-            ;;
-        --outdir)
-            outdir="$2"
-            shift 2
-            ;;
-        -h|--help)
-            usage
-            ;;
-        *)
-            url="$1"
-            shift
-            ;;
-    esac
-done
-
-if [ -n "$repo" ] && [ -n "$file" ]; then
-    url="https://huggingface.co/$repo/resolve/main/$file"
-fi
-
-if [ -z "$url" ]; then
-    log "[E] missing --url"
-    usage
-fi
-
-# check if the URL is a HuggingFace model, and if so, try to download it
-is_url=false
-
-if [[ ${#url} -gt 22 ]]; then
-    if [[ ${url:0:22} == "https://huggingface.co" ]]; then
-        is_url=true
-    fi
-fi
-
-if [ "$is_url" = false ]; then
-    log "[E] invalid URL, must start with https://huggingface.co"
-    exit 0
-fi
-
-# replace "blob/main" with "resolve/main"
-url=${url/blob\/main/resolve\/main}
-
-basename=$(basename $url)
-
-log "[+] attempting to download $basename"
-
-if [ -n "$cmd" ]; then
-    cmd=$(printf "$cmd" "$outdir" "$basename" "$url")
-    log "[+] $cmd"
-    if $cmd; then
-        echo $outdir/$basename
-        exit 0
-    fi
-fi
-
-log "[-] failed to download"
-
-exit 1
diff --git a/tests/test-lora-conversion-inference.sh b/tests/test-lora-conversion-inference.sh
index fb308a9ff..1d1f4886c 100755
--- a/tests/test-lora-conversion-inference.sh
+++ b/tests/test-lora-conversion-inference.sh
@@ -80,18 +80,18 @@ run_conversion_and_inference_lora() {
     # Run inference
     echo -e "\n\n---------------------------\n\n"
     echo "Running llama-cli without lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_BASE=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
+    OUTPUT_BASE=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
         -p "$EXPECTED_BASE_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     echo -e "\n\n---------------------------\n\n"
     echo "Running llama-cli with hot lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_LORA_HOT=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
+    OUTPUT_LORA_HOT=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
         --lora $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora/Lora-F32-LoRA.gguf \
         -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     echo -e "\n\n---------------------------\n\n"
     echo "Running llama-cli with merged lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_LORA_MERGED=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \
+    OUTPUT_LORA_MERGED=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \
         -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     # Remove any initial white space

From adc5dd92e8aea98f5e7ac84f6e1bc15de35130b5 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Wed, 15 Jan 2025 19:50:13 +0000
Subject: [PATCH 185/376] vulkan: scale caching for k quants + misc fixes
 (#11081)

* q6_k scale caching

* 16 bit unpack

* q4_k test (slow)

* revert it

* q3_k

* q2_k

* little stuff

* try precalculating products of a and q2_k scales

* Revert "try precalculating products of a and q2_k scales"

This reverts commit 65110b81f23f66331a50c6e889a7c1ab9470a86b.

* unpack should be u16, add vim swap to gitignore (about time)

* better q4_k scales

* q5_k

* better q6_k with separate paths for all threads and partial threads in use, plus some more optimizations

* q2_k better dequant

* q3_k optimizations

* q3_k use hmask simd from cpu avx version

* make the caches happy

* q3_k separate out calculation

* q2_k separate out

* little stuff

* use calc_superblock everywhere

* q2_k optimize scale calculation

* more barriers
---
 .gitignore                                    |   1 +
 .../vulkan-shaders/mul_mat_vec_q2_k.comp      | 154 ++++++------
 .../vulkan-shaders/mul_mat_vec_q3_k.comp      | 141 ++++++-----
 .../vulkan-shaders/mul_mat_vec_q4_k.comp      | 169 ++++++-------
 .../vulkan-shaders/mul_mat_vec_q5_k.comp      | 229 +++++++++---------
 .../vulkan-shaders/mul_mat_vec_q6_k.comp      | 144 ++++++-----
 6 files changed, 454 insertions(+), 384 deletions(-)

diff --git a/.gitignore b/.gitignore
index 1df7cf4a1..694f36e04 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,6 +18,7 @@
 *.metallib
 *.o
 *.so
+*.swp
 *.tmp
 
 # IDE / OS
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
index 6a9b9b2d1..8cdc640e8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -5,6 +5,80 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+shared FLOAT_TYPE sccache1[BLOCK_SIZE/16][16];
+shared FLOAT_TYPE sccache2[BLOCK_SIZE/16][16];
+
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint v_im, const uint ix, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
+    const uint y_idx = i * QUANT_K + y_offset;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+
+        barrier();
+        if (!all_threads) { // when we don't have enough blocks to use all threads
+            if (i < num_blocks_per_row) {
+                const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]);
+                sccache1[ix][itid] = FLOAT_TYPE(scale & 0xF);
+                sccache2[ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
+            }
+            barrier();
+
+            if (i >= num_blocks_per_row)
+                continue;
+        } else {
+            const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]);
+            sccache1[ix][itid] = FLOAT_TYPE(scale & 0xF);
+            sccache2[ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
+            barrier();
+        }
+
+        const uint32_t qs_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
+        const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303));
+        const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303));
+        const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303));
+        const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303));
+
+        vec2 d = vec2(data_a[ib0 + i].d);
+        const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+            vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+            vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+            vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+            vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+            vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+            vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+            vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
+
+            FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+            FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sum1 = fma(FLOAT_TYPE(b0[l]),   sccache1[ix][    8*v_im] * qs_u32_0[l  ],
+                       fma(FLOAT_TYPE(b16[l]),  sccache1[ix][1 + 8*v_im] * qs_u32_0[l+2],
+                       fma(FLOAT_TYPE(b32[l]),  sccache1[ix][2 + 8*v_im] * qs_u32_2[l  ],
+                       fma(FLOAT_TYPE(b48[l]),  sccache1[ix][3 + 8*v_im] * qs_u32_2[l+2],
+                       fma(FLOAT_TYPE(b64[l]),  sccache1[ix][4 + 8*v_im] * qs_u32_4[l  ],
+                       fma(FLOAT_TYPE(b80[l]),  sccache1[ix][5 + 8*v_im] * qs_u32_4[l+2],
+                       fma(FLOAT_TYPE(b96[l]),  sccache1[ix][6 + 8*v_im] * qs_u32_6[l  ],
+                       fma(FLOAT_TYPE(b112[l]), sccache1[ix][7 + 8*v_im] * qs_u32_6[l+2], sum1))))))));
+                sum2 = fma(FLOAT_TYPE(b0[l]),   sccache2[ix][    8*v_im],
+                       fma(FLOAT_TYPE(b16[l]),  sccache2[ix][1 + 8*v_im],
+                       fma(FLOAT_TYPE(b32[l]),  sccache2[ix][2 + 8*v_im],
+                       fma(FLOAT_TYPE(b48[l]),  sccache2[ix][3 + 8*v_im],
+                       fma(FLOAT_TYPE(b64[l]),  sccache2[ix][4 + 8*v_im],
+                       fma(FLOAT_TYPE(b80[l]),  sccache2[ix][5 + 8*v_im],
+                       fma(FLOAT_TYPE(b96[l]),  sccache2[ix][6 + 8*v_im],
+                       fma(FLOAT_TYPE(b112[l]), sccache2[ix][7 + 8*v_im], sum2))))))));
+            }
+            temp[j][n] = fma(dall, sum1, fma(-dmin, sum2, temp[j][n]));
+        }
+    }
+}
+
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -14,88 +88,28 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
     const uint tid = gl_LocalInvocationID.x;
-    const uint itid = tid%16;  // 0...16
-    const uint ix  = tid/16;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
 
-    const uint step = 8;
-
-    const uint v_im = itid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = itid - step*v_im;                      // 0...15 or 0...7
+    const uint v_im = itid/8;                                // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = itid - 8*v_im;                         // 0...7
 
     const uint l0 = 2*v_in;                                  // 0...15
     const uint q_offset = 32*v_im + l0;
-    const uint s_offset = 8*v_im;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
-
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
             temp[j][i] = FLOAT_TYPE(0);
         }
     }
 
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y_idx = i * QUANT_K + y_offset;
-
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            vec2 d = vec2(data_a[ib0 + i].d);
-            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
-            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
-
-            uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0];
-            uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1];
-
-            uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F;
-            uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F;
-            uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F;
-            uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F;
-
-            uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32));
-            uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32));
-            uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32));
-            uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32));
-
-            uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0];
-            uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8];
-            uvec2 qs0 =  uvec2(unpack8(qs0_u16));
-            uvec2 qs16 = uvec2(unpack8(qs16_u16));
-
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
-                vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
-                vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
-                vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
-                vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
-                vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
-                vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
-                vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
-
-                FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
-                FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
-                [[unroll]] for (int l = 0; l < 2; ++l) {
-                    sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),
-                           fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),
-                           fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),
-                           fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),
-                           fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),
-                           fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),
-                           fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),
-                           fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));
-                    sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),
-                           fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),
-                           fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),
-                           fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),
-                           fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),
-                           fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),
-                           fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),
-                           fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));
-                }
-                temp[j][n] = fma(dall, sum1, fma(-dmin, sum2, temp[j][n]));
-            }
-        }
-    }
+    const uint nbr_par_th = num_blocks_per_row%it_size;
+    const uint nbr_all_th = num_blocks_per_row - nbr_par_th;
+    uint i0 = 0;
+    [[unroll]] for (; i0 < nbr_all_th; i0 += it_size)
+        calc_superblock(a_offset, b_offset, itid, v_im, ix, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, true);
+    calc_superblock(a_offset, b_offset, itid, v_im, ix, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, false);
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
index 96ef50fdd..3116fad16 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -5,6 +5,74 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+shared FLOAT_TYPE sccache[BLOCK_SIZE/16][2][8];
+
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, const uint itid8, const uint v_im, const uint v_im4, const uint v_in, const uint32_t hm_m[4], const uint q_offset, const uint y_offset, const uint s_shift, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
+    const uint y_idx = i * QUANT_K + y_offset;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+
+        if (!all_threads) { // when we don't have enough blocks to use all threads
+            barrier();
+            if (i < num_blocks_per_row)
+                sccache[ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
+            barrier();
+
+            if (i >= num_blocks_per_row)
+                continue;
+        }
+
+        const uint32_t hmk = ~(uint32_t(data_a_packed16[ib0 + i].hmask[v_in]) | (uint32_t(data_a_packed16[ib0 + i].hmask[v_in + 8]) << 16));
+        const vec4 hmk_0 = vec4(unpack8(((hmk & hm_m[0]) >> (    v_im4)) << 2));
+        const vec4 hmk_1 = vec4(unpack8(((hmk & hm_m[1]) >> (1 + v_im4)) << 2));
+        const vec4 hmk_2 = vec4(unpack8(((hmk & hm_m[2]) >> (2 + v_im4)) << 2));
+        const vec4 hmk_3 = vec4(unpack8(((hmk & hm_m[3]) >> (3 + v_im4)) << 2));
+
+        // 0, 1, 16, 17
+        uint32_t qs_u32 = uint32_t(data_a[ib0 + i].qs[q_offset]) | (uint32_t(data_a[ib0 + i].qs[q_offset + 1]) << 8);
+        qs_u32 |= (uint32_t(data_a[ib0 + i].qs[q_offset + 16]) | (uint32_t(data_a[ib0 + i].qs[q_offset + 17]) << 8)) << 16;
+        const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303));
+        const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303));
+        const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303));
+        const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303));
+
+        if (all_threads) {
+            barrier();
+            sccache[ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
+            barrier();
+        }
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+            vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+            vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+            vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+            vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+            vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+            vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+            vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
+
+            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sum = fma(FLOAT_TYPE(  b0[l]) * sccache[ix][v_im][0], qs_u32_0[l  ] - hmk_0[l  ],
+                      fma(FLOAT_TYPE( b16[l]) * sccache[ix][v_im][1], qs_u32_0[l+2] - hmk_0[l+2],
+                      fma(FLOAT_TYPE( b32[l]) * sccache[ix][v_im][2], qs_u32_2[l  ] - hmk_1[l  ],
+                      fma(FLOAT_TYPE( b48[l]) * sccache[ix][v_im][3], qs_u32_2[l+2] - hmk_1[l+2],
+                      fma(FLOAT_TYPE( b64[l]) * sccache[ix][v_im][4], qs_u32_4[l  ] - hmk_2[l  ],
+                      fma(FLOAT_TYPE( b80[l]) * sccache[ix][v_im][5], qs_u32_4[l+2] - hmk_2[l+2],
+                      fma(FLOAT_TYPE( b96[l]) * sccache[ix][v_im][6], qs_u32_6[l  ] - hmk_3[l  ],
+                      fma(FLOAT_TYPE(b112[l]) * sccache[ix][v_im][7], qs_u32_6[l+2] - hmk_3[l+2], sum))))))));
+            }
+            temp[j][n] = fma(d, sum, temp[j][n]);
+        }
+    }
+}
+
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -14,76 +82,37 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
     const uint tid = gl_LocalInvocationID.x;
-    const uint itid = tid%16;  // 0...16
-    const uint ix  = tid/16;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
+    const uint itid8 = itid%8;
 
-    const uint step = 8;
+    const uint v_im = itid/8;                               // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_im4 = v_im*4;
+    const uint v_in = itid - 8*v_im;                        // 0...7
 
-    const uint v_im = itid/step;                            // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = itid - step*v_im;                     // 0...15 or 0...7
-
-    const uint8_t m = uint8_t(1 << (4 * v_im));
+    const uint32_t m = 0x01010101 << (4 * v_im);
+    uint32_t hm_m[4];
+    [[unroll]] for (uint j = 0; j < 4; ++j)
+        hm_m[j] = m << j;
 
     const uint l0 = 2*v_in;                                 // 0...15
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
-
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
             temp[j][i] = FLOAT_TYPE(0);
         }
     }
 
-    const uint s_shift = 4 * v_im;
+    const uint s_shift = v_im4 + 2*(itid8/4);
 
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y_idx = i * QUANT_K + y_offset;
-
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
-            uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0];
-            uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1];
-            uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2];
-            uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3];
-            uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4];
-            uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5];
-            u8vec2 s0 = unpack8(s0_16);
-            u8vec2 s2 = unpack8(s2_16);
-            u8vec2 s4 = unpack8(s4_16);
-            u8vec2 s6 = unpack8(s6_16);
-            u8vec2 s8 = unpack8(s8_16);
-            u8vec2 s10 = unpack8(s10_16);
-
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-
-                vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
-                vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
-                vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
-                vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
-                vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
-                vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
-                vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
-                vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
-
-                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-                [[unroll]] for (int l = 0; l < 2; ++l) {
-                    sum = fma(FLOAT_TYPE(b0[l])   * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b32[l])  * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b64[l])  * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b96[l])  * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b16[l])  * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b48[l])  * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b80[l])  * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1]  >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
-                          fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
-                }
-                temp[j][n] = fma(d, sum, temp[j][n]);
-            }
-        }
-    }
+    const uint nbr_par_th = num_blocks_per_row%it_size;
+    const uint nbr_all_th = num_blocks_per_row - nbr_par_th;
+    uint i0 = 0;
+    [[unroll]] for (; i0 < nbr_all_th; i0 += it_size)
+        calc_superblock(a_offset, b_offset, ix, itid8, v_im, v_im4, v_in, hm_m, q_offset, y_offset, s_shift, i0 + ix, num_blocks_per_row, first_row, num_rows, true);
+    calc_superblock(a_offset, b_offset, ix, itid8, v_im, v_im4, v_in, hm_m, q_offset, y_offset, s_shift, i0 + ix, num_blocks_per_row, first_row, num_rows, false);
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
index f97eb8744..f9cde0648 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -6,6 +6,86 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y1_idx = i * QUANT_K + y_offset;
+    const uint y2_idx = y1_idx + 128;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        vec2 d = vec2(data_a[ib0 + i].d);
+        const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
+
+        const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
+        const uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
+        const uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
+
+        const uint32_t scale_0_4_l = (scale4_u32 << 16) | scale0_u32;
+        const uint32_t scale_0_4_h = (scale_0_4_l & 0xC0C0C0C0) >> 2;
+        const vec4 scale_0_4_l_f = vec4(unpack8(scale_0_4_l & 0x3F3F3F3F));
+        const vec4 scale8_f = vec4(unpack8((((scale8_u32 << 12) | scale8_u32) & 0x0F0F0F0F) | scale_0_4_h));
+
+        const FLOAT_TYPE sc0 = scale_0_4_l_f.x;
+        const FLOAT_TYPE sc1 = scale_0_4_l_f.y;
+        const FLOAT_TYPE sc2 = scale_0_4_l_f.z;
+        const FLOAT_TYPE sc3 = scale_0_4_l_f.w;
+        const FLOAT_TYPE sc4 = scale8_f.x;
+        const FLOAT_TYPE sc5 = scale8_f.y;
+        const FLOAT_TYPE sc6 = scale8_f.z;
+        const FLOAT_TYPE sc7 = scale8_f.w;
+
+        const uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4];
+        const uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16];
+
+        const uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F;
+        const uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F;
+        const uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F;
+        const uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F;
+
+        const vec4 qs0_lo4 = vec4(unpack8(qs0_u32_lo4));
+        const vec4 qs64_lo4 = vec4(unpack8(qs64_u32_lo4));
+        const vec4 qs0_hi4 = vec4(unpack8(qs0_u32_hi4));
+        const vec4 qs64_hi4 = vec4(unpack8(qs64_u32_hi4));
+
+        const FLOAT_TYPE q4_0  = qs0_lo4.x;
+        const FLOAT_TYPE q4_1  = qs0_lo4.y;
+        const FLOAT_TYPE q4_2  = qs0_lo4.z;
+        const FLOAT_TYPE q4_3  = qs0_lo4.w;
+        const FLOAT_TYPE q4_4  = qs0_hi4.x;
+        const FLOAT_TYPE q4_5  = qs0_hi4.y;
+        const FLOAT_TYPE q4_6  = qs0_hi4.z;
+        const FLOAT_TYPE q4_7  = qs0_hi4.w;
+        const FLOAT_TYPE q4_8  = qs64_lo4.x;
+        const FLOAT_TYPE q4_9  = qs64_lo4.y;
+        const FLOAT_TYPE q4_10 = qs64_lo4.z;
+        const FLOAT_TYPE q4_11 = qs64_lo4.w;
+        const FLOAT_TYPE q4_12 = qs64_hi4.x;
+        const FLOAT_TYPE q4_13 = qs64_hi4.y;
+        const FLOAT_TYPE q4_14 = qs64_hi4.z;
+        const FLOAT_TYPE q4_15 = qs64_hi4.w;
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec4 by10 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4    ]);
+            vec4 by132 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8]);
+            vec4 by20 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4    ]);
+            vec4 by232 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8]);
+
+            const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
+            const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
+            const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
+            const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
+            const FLOAT_TYPE smin =
+                fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
+                fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
+                fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
+                fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
+            temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
+        }
+    }
+}
+
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -15,13 +95,11 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
     const uint tid = gl_LocalInvocationID.x;
-    const uint itid = tid%16;  // 0...16
-    const uint ix  = tid/16;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
 
-    const uint step = 4;
-
-    const uint il = itid/step;                      // 0...3
-    const uint ir = itid - step*il;                 // 0...7 or 0...3
+    const uint il = itid/4;                         // 0...3
+    const uint ir = itid - 4*il;                    // 0...3
     const uint n =  4;
 
     const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
@@ -31,89 +109,14 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
-
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
             temp[j][i] = FLOAT_TYPE(0);
         }
     }
 
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y1_idx = i * QUANT_K + y_offset;
-        const uint y2_idx = y1_idx + 128;
-
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            vec2 d = vec2(data_a[ib0 + i].d);
-            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
-            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
-
-            uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
-            uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
-            uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
-            uvec4 scale0 = uvec4(unpack8(scale0_u32));
-            uvec4 scale4 = uvec4(unpack8(scale4_u32));
-            uvec4 scale8 = uvec4(unpack8(scale8_u32));
-
-            const uint32_t sc0 = (  scale0.x       & 0x3f);
-            const uint32_t sc1 = (  scale0.y       & 0x3f);
-            const uint32_t sc2 = (  scale4.x       & 0x3f);
-            const uint32_t sc3 = (  scale4.y       & 0x3f);
-            const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
-            const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
-            const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
-            const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
-
-            uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4];
-            uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16];
-
-            uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F;
-            uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F;
-            uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F;
-            uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F;
-
-            uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4));
-            uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4));
-            uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4));
-            uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4));
-
-            const uint32_t q4_0  = qs0_lo4.x;
-            const uint32_t q4_1  = qs0_lo4.y;
-            const uint32_t q4_2  = qs0_lo4.z;
-            const uint32_t q4_3  = qs0_lo4.w;
-            const uint32_t q4_4  = qs0_hi4.x;
-            const uint32_t q4_5  = qs0_hi4.y;
-            const uint32_t q4_6  = qs0_hi4.z;
-            const uint32_t q4_7  = qs0_hi4.w;
-            const uint32_t q4_8  = qs64_lo4.x;
-            const uint32_t q4_9  = qs64_lo4.y;
-            const uint32_t q4_10 = qs64_lo4.z;
-            const uint32_t q4_11 = qs64_lo4.w;
-            const uint32_t q4_12 = qs64_hi4.x;
-            const uint32_t q4_13 = qs64_hi4.y;
-            const uint32_t q4_14 = qs64_hi4.z;
-            const uint32_t q4_15 = qs64_hi4.w;
-
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec4 by10 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4    ]);
-                vec4 by132 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8]);
-                vec4 by20 =  vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4    ]);
-                vec4 by232 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8]);
-
-                const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x),      q4_0,  fma(FLOAT_TYPE(by10.y),  q4_1,  fma(FLOAT_TYPE(by10.z),  q4_2,  FLOAT_TYPE(by10.w) *  q4_3)));
-                const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x),     q4_4,  fma(FLOAT_TYPE(by132.y), q4_5,  fma(FLOAT_TYPE(by132.z), q4_6,  FLOAT_TYPE(by132.w) * q4_7)));
-                const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x),      q4_8,  fma(FLOAT_TYPE(by20.y),  q4_9,  fma(FLOAT_TYPE(by20.z),  q4_10, FLOAT_TYPE(by20.w) *  q4_11)));
-                const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x),     q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15)));
-                const FLOAT_TYPE smin =
-                    fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7,
-                    fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7,
-                    fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7,
-                    fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6,     FLOAT_TYPE(by232.w) * sc7)))))))))))))));
-                temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
-            }
-        }
-    }
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size)
+        calc_superblock(a_offset, b_offset, v_im, q_offset, y_offset, i, num_blocks_per_row, first_row, num_rows);
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
index 79d7db0e3..6c84ef3cd 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -6,6 +6,118 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint l0, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y1_idx = i * QUANT_K + y_offset;
+    const uint y2_idx = y1_idx + 128;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        vec2 d = vec2(data_a[ib0 + i].d);
+        const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
+
+        const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
+        const uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
+        const uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
+
+        const uint32_t scale_0_4_l = (scale4_u32 << 16) | scale0_u32;
+        const uint32_t scale_0_4_h = (scale_0_4_l & 0xC0C0C0C0) >> 2;
+        const vec4 scale_0_4_l_f = vec4(unpack8(scale_0_4_l & 0x3F3F3F3F));
+        const vec4 scale8_f = vec4(unpack8((((scale8_u32 << 12) | scale8_u32) & 0x0F0F0F0F) | scale_0_4_h));
+
+        const FLOAT_TYPE sc0 = scale_0_4_l_f.x;
+        const FLOAT_TYPE sc1 = scale_0_4_l_f.y;
+        const FLOAT_TYPE sc2 = scale_0_4_l_f.z;
+        const FLOAT_TYPE sc3 = scale_0_4_l_f.w;
+        const FLOAT_TYPE sc4 = scale8_f.x;
+        const FLOAT_TYPE sc5 = scale8_f.y;
+        const FLOAT_TYPE sc6 = scale8_f.z;
+        const FLOAT_TYPE sc7 = scale8_f.w;
+
+        const uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
+        const uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16);
+
+        uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F;
+        uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F;
+        uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F;
+        uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F;
+
+        const uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8]));
+
+        const uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4;
+        const uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3;
+        const uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010);
+        const uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1;
+
+        qs0_16_u32_lo4 += qs0_16_lo4_offset16;
+        qs0_16_u32_hi4 += qs0_16_hi4_offset16;
+        qs64_80_u32_lo4 += qs64_80_lo4_offset16;
+        qs64_80_u32_hi4 += qs64_80_hi4_offset16;
+
+        const vec4 qs0_16_lo4 = vec4(unpack8(qs0_16_u32_lo4));
+        const vec4 qs64_80_lo4 = vec4(unpack8(qs64_80_u32_lo4));
+        const vec4 qs0_16_hi4 = vec4(unpack8(qs0_16_u32_hi4));
+        const vec4 qs64_80_hi4 = vec4(unpack8(qs64_80_u32_hi4));
+
+        const FLOAT_TYPE q4_0  = qs0_16_lo4.x;
+        const FLOAT_TYPE q4_1  = qs0_16_lo4.y;
+        const FLOAT_TYPE q4_2  = qs0_16_lo4.z;
+        const FLOAT_TYPE q4_3  = qs0_16_lo4.w;
+        const FLOAT_TYPE q4_4  = qs0_16_hi4.x;
+        const FLOAT_TYPE q4_5  = qs0_16_hi4.y;
+        const FLOAT_TYPE q4_6  = qs0_16_hi4.z;
+        const FLOAT_TYPE q4_7  = qs0_16_hi4.w;
+        const FLOAT_TYPE q4_8  = qs64_80_lo4.x;
+        const FLOAT_TYPE q4_9  = qs64_80_lo4.y;
+        const FLOAT_TYPE q4_10 = qs64_80_lo4.z;
+        const FLOAT_TYPE q4_11 = qs64_80_lo4.w;
+        const FLOAT_TYPE q4_12 = qs64_80_hi4.x;
+        const FLOAT_TYPE q4_13 = qs64_80_hi4.y;
+        const FLOAT_TYPE q4_14 = qs64_80_hi4.z;
+        const FLOAT_TYPE q4_15 = qs64_80_hi4.w;
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec2 by10 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2     ]);
+            vec2 by116 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 +  8]);
+            vec2 by132 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16]);
+            vec2 by148 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24]);
+            vec2 by20 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2     ]);
+            vec2 by216 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 +  8]);
+            vec2 by232 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16]);
+            vec2 by248 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24]);
+
+            const FLOAT_TYPE sx =
+              fma(FLOAT_TYPE(by10.x), q4_0,
+              fma(FLOAT_TYPE(by10.y), q4_1,
+              fma(FLOAT_TYPE(by116.x), q4_2,
+                 FLOAT_TYPE(by116.y) * q4_3)));
+            const FLOAT_TYPE sy =
+              fma(FLOAT_TYPE(by132.x), q4_4,
+              fma(FLOAT_TYPE(by132.y), q4_5,
+              fma(FLOAT_TYPE(by148.x), q4_6,
+                 FLOAT_TYPE(by148.y) * q4_7)));
+            const FLOAT_TYPE sz =
+              fma(FLOAT_TYPE(by20.x), q4_8,
+              fma(FLOAT_TYPE(by20.y), q4_9,
+              fma(FLOAT_TYPE(by216.x), q4_10,
+                 FLOAT_TYPE(by216.y) * q4_11)));
+            const FLOAT_TYPE sw =
+              fma(FLOAT_TYPE(by232.x), q4_12,
+              fma(FLOAT_TYPE(by232.y), q4_13,
+              fma(FLOAT_TYPE(by248.x), q4_14,
+                 FLOAT_TYPE(by248.y) * q4_15)));
+            const FLOAT_TYPE smin =
+              fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
+              fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
+              fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
+                  (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
+            temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
+        }
+    }
+}
+
 void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
@@ -15,11 +127,11 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
     const uint tid = gl_LocalInvocationID.x;
-    const uint itid = tid%16;  // 0...16
-    const uint ix  = tid/16;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
 
     const uint il = itid/4;                          // 0...3
-    const uint ir = itid - 4*il;                     // 0...7 or 0...3
+    const uint ir = itid - 4*il;                     // 0...3
 
     const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
     const uint v_in = il % 2;
@@ -28,121 +140,14 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint q_offset = 32*v_im + l0;
     const uint y_offset = 64*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
-
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
             temp[j][i] = FLOAT_TYPE(0);
         }
     }
 
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y1_idx = i * QUANT_K + y_offset;
-        const uint y2_idx = y1_idx + 128;
-
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            vec2 d = vec2(data_a[ib0 + i].d);
-            const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
-            const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
-
-            uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];
-            uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
-            uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
-            uvec4 scale0 = uvec4(unpack8(scale0_u32));
-            uvec4 scale4 = uvec4(unpack8(scale4_u32));
-            uvec4 scale8 = uvec4(unpack8(scale8_u32));
-
-            const uint32_t sc0 = (  scale0.x       & 0x3f);
-            const uint32_t sc1 = (  scale0.y       & 0x3f);
-            const uint32_t sc2 = (  scale4.x       & 0x3f);
-            const uint32_t sc3 = (  scale4.y       & 0x3f);
-            const uint32_t sc4 = (( scale8.x       & 0x0f) | ((scale0.x & 0xc0) >> 2));
-            const uint32_t sc5 = (( scale8.y       & 0x0f) | ((scale0.y & 0xc0) >> 2));
-            const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
-            const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
-
-            uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
-            uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16);
-
-            uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F;
-            uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F;
-            uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F;
-            uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F;
-
-            uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8]));
-
-            uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4;
-            uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3;
-            uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0;
-            uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1;
-
-            qs0_16_u32_lo4 += qs0_16_lo4_offset16;
-            qs0_16_u32_hi4 += qs0_16_hi4_offset16;
-            qs64_80_u32_lo4 += qs64_80_lo4_offset16;
-            qs64_80_u32_hi4 += qs64_80_hi4_offset16;
-
-            uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4));
-            uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4));
-            uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4));
-            uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4));
-
-            const uint32_t q4_0  = qs0_16_lo4.x;
-            const uint32_t q4_1  = qs0_16_lo4.y;
-            const uint32_t q4_2  = qs0_16_lo4.z;
-            const uint32_t q4_3  = qs0_16_lo4.w;
-            const uint32_t q4_4  = qs0_16_hi4.x;
-            const uint32_t q4_5  = qs0_16_hi4.y;
-            const uint32_t q4_6  = qs0_16_hi4.z;
-            const uint32_t q4_7  = qs0_16_hi4.w;
-            const uint32_t q4_8  = qs64_80_lo4.x;
-            const uint32_t q4_9  = qs64_80_lo4.y;
-            const uint32_t q4_10 = qs64_80_lo4.z;
-            const uint32_t q4_11 = qs64_80_lo4.w;
-            const uint32_t q4_12 = qs64_80_hi4.x;
-            const uint32_t q4_13 = qs64_80_hi4.y;
-            const uint32_t q4_14 = qs64_80_hi4.z;
-            const uint32_t q4_15 = qs64_80_hi4.w;
-
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec2 by10 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2     ]);
-                vec2 by116 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 +  8]);
-                vec2 by132 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16]);
-                vec2 by148 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24]);
-                vec2 by20 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2     ]);
-                vec2 by216 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 +  8]);
-                vec2 by232 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16]);
-                vec2 by248 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24]);
-
-                const FLOAT_TYPE sx =
-                  fma(FLOAT_TYPE(by10.x), q4_0,
-                  fma(FLOAT_TYPE(by10.y), q4_1,
-                  fma(FLOAT_TYPE(by116.x), q4_2,
-                     FLOAT_TYPE(by116.y) * q4_3)));
-                const FLOAT_TYPE sy =
-                  fma(FLOAT_TYPE(by132.x), q4_4,
-                  fma(FLOAT_TYPE(by132.y), q4_5,
-                  fma(FLOAT_TYPE(by148.x), q4_6,
-                     FLOAT_TYPE(by148.y) * q4_7)));
-                const FLOAT_TYPE sz =
-                  fma(FLOAT_TYPE(by20.x), q4_8,
-                  fma(FLOAT_TYPE(by20.y), q4_9,
-                  fma(FLOAT_TYPE(by216.x), q4_10,
-                     FLOAT_TYPE(by216.y) * q4_11)));
-                const FLOAT_TYPE sw =
-                  fma(FLOAT_TYPE(by232.x), q4_12,
-                  fma(FLOAT_TYPE(by232.y), q4_13,
-                  fma(FLOAT_TYPE(by248.x), q4_14,
-                     FLOAT_TYPE(by248.y) * q4_15)));
-                const FLOAT_TYPE smin =
-                  fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
-                  fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
-                  fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
-                      (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
-                temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n]));
-            }
-        }
-    }
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size)
+        calc_superblock(a_offset, b_offset, v_im, l0, q_offset, y_offset, i, num_blocks_per_row, first_row, num_rows);
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
index 041fd27c1..f05f96b5e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -6,7 +6,77 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
-void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
+shared FLOAT_TYPE sccache[BLOCK_SIZE/16][16];
+
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint ix, const uint ql_offset, const uint qh_offset, const uint s_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
+    const uint y_idx = i * QUANT_K + y_offset;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+
+        if (!all_threads) { // when we don't have enough blocks to use all threads
+            barrier();
+            if (i < num_blocks_per_row)
+                sccache[ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
+            barrier();
+
+            if (i >= num_blocks_per_row)
+                continue;
+        }
+
+        const uint32_t ql0_u32 =  uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16);
+        const uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16);
+
+        const uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F;
+        const uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F;
+        const uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F;
+        const uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F;
+
+        const uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16);
+        const uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4;
+        const uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2;
+        const uint32_t qh4_u32 = (qh_u32 & 0x30303030);
+        const uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2;
+
+        const uint32_t q0_u32 = ql0_u32_lo4  | qh0_u32;
+        const uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32;
+        const uint32_t q2_u32 = ql0_u32_hi4  | qh4_u32;
+        const uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32;
+
+        const vec4 q0 = vec4(unpack8(q0_u32)) - 32;
+        const vec4 q1 = vec4(unpack8(q1_u32)) - 32;
+        const vec4 q2 = vec4(unpack8(q2_u32)) - 32;
+        const vec4 q3 = vec4(unpack8(q3_u32)) - 32;
+
+        if (all_threads) {
+            barrier();
+            sccache[ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
+            barrier();
+        }
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec4 by0  = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4     ]);
+            vec4 by32 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 +  8]);
+            vec4 by64 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16]);
+            vec4 by96 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24]);
+
+            FLOAT_TYPE sum[4] = {0, 0, 0, 0};
+            [[unroll]] for (uint l = 0; l < 4; ++l) {
+                sum[0] = fma(FLOAT_TYPE(by0[l]), q0[l], sum[0]);
+                sum[1] = fma(FLOAT_TYPE(by32[l]), q1[l], sum[1]);
+                sum[2] = fma(FLOAT_TYPE(by64[l]), q2[l], sum[2]);
+                sum[3] = fma(FLOAT_TYPE(by96[l]), q3[l], sum[3]);
+            }
+            temp[j][n] = fma(fma(sum[0], sccache[ix][s_offset], fma(sum[1], sccache[ix][s_offset + 2], fma(sum[2], sccache[ix][s_offset + 4], sum[3] * sccache[ix][s_offset + 6]))), d, temp[j][n]);
+        }
+    }
+}
+
+void compute_outputs(const uint first_row, const uint num_rows) {
     uint a_offset, b_offset, d_offset;
     get_offsets(a_offset, b_offset, d_offset);
 
@@ -15,13 +85,11 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     // 16 threads are used to process each block
     const uint it_size = gl_WorkGroupSize.x/16;
     const uint tid = gl_LocalInvocationID.x;
-    const uint itid = tid%16;  // 0...16
-    const uint ix  = tid/16;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
 
-    const uint step = 8;
-
-    const uint v_im = itid/step;                            // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = itid - step*v_im;                     // 0...15 or 0...7
+    const uint v_im = itid/8;                               // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = itid - 8*v_im;                        // 0...7
 
     const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
     const uint is = v_in / 4;
@@ -31,68 +99,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint s_offset  =  8*v_im + is;
     const uint y_offset = 128*v_im + l0;
 
-    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
-
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
             temp[j][i] = FLOAT_TYPE(0);
         }
     }
 
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) {
-        const uint y_idx = i * QUANT_K + y_offset;
-
-        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
-            const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
-            FLOAT_TYPE scales[4];
-            scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]);
-            scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]);
-            scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]);
-            scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]);
-
-            uint32_t ql0_u32 =  uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16);
-            uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16);
-
-            uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F;
-            uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F;
-            uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F;
-            uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F;
-
-            uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16);
-            uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4;
-            uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2;
-            uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0;
-            uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2;
-
-            uint32_t q0_u32 = ql0_u32_lo4  | qh0_u32;
-            uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32;
-            uint32_t q2_u32 = ql0_u32_hi4  | qh4_u32;
-            uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32;
-
-            uvec4 q0 = uvec4(unpack8(q0_u32));
-            uvec4 q1 = uvec4(unpack8(q1_u32));
-            uvec4 q2 = uvec4(unpack8(q2_u32));
-            uvec4 q3 = uvec4(unpack8(q3_u32));
-
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec4 by0  = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4     ]);
-                vec4 by32 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 +  8]);
-                vec4 by64 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16]);
-                vec4 by96 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24]);
-
-                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-                [[unroll]] for (int l = 0; l < 4; ++l) {
-                    sum = fma(FLOAT_TYPE(by0[l])  * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
-                          fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
-                          fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
-                          fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
-                }
-                temp[j][n] += sum * d;
-            }
-        }
-    }
+    const uint nbr_par_th = num_blocks_per_row%it_size;
+    const uint nbr_all_th = num_blocks_per_row - nbr_par_th;
+    uint i0 = 0;
+    [[unroll]] for (; i0 < nbr_all_th; i0 += it_size)
+        calc_superblock(a_offset, b_offset, itid, ix, ql_offset, qh_offset, s_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, true);
+    calc_superblock(a_offset, b_offset, itid, ix, ql_offset, qh_offset, s_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, false);
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);
 }

From c67cc9837d48ea7f612b5666b90d189e63dfd7d3 Mon Sep 17 00:00:00 2001
From: fj-y-saito <85871716+fj-y-saito@users.noreply.github.com>
Date: Thu, 16 Jan 2025 18:11:49 +0900
Subject: [PATCH 186/376] ggml: aarch64: implement SVE kernels for q4_K_q8_K
 vector dot (#11227)

* Add SVE support for q4_K_q8_K

* Update ggml/src/ggml-cpu/ggml-cpu-quants.c

change to use K_SCALE_SIZE

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-cpu/ggml-cpu-quants.c | 83 ++++++++++++++++++++++++++++-
 1 file changed, 82 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c
index 8e1472266..88303ff0e 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-quants.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c
@@ -5573,7 +5573,88 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     uint32_t utmp[4];
 
-#ifdef __ARM_NEON
+#ifdef __ARM_FEATURE_SVE
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8));
+
+        memcpy(utmp, x[i].scales, K_SCALE_SIZE);
+
+        uint32x2_t mins8 = { 0 };
+        mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0);
+        mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1);
+
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[0] &= kmask1;
+
+        const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8)));
+        const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)),
+                                         vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins)));
+        sumf -= dmin * vaddvq_s32(prod);
+
+        const uint8_t * scales = (const uint8_t *)utmp;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const int vector_length = ggml_cpu_get_sve_cnt()*8;
+        const svuint8_t m4b = svdup_n_u8(0xf);
+        const svint32_t mzero = svdup_n_s32(0);
+        svint32_t sumi1 = svdup_n_s32(0);
+        svint32_t sumi1_1 = svdup_n_s32(0);
+        svint32_t sumi1_2 = svdup_n_s32(0);
+        svint32_t sumi2 = svdup_n_s32(0);
+        svint32_t sumi2_1 = svdup_n_s32(0);
+        svint32_t sumi2_2 = svdup_n_s32(0);
+        switch (vector_length) {
+            case 128:
+                {
+                    for (int j = 0; j < QK_K/64; ++j) {
+                        svint8_t q4bytes = svreinterpret_s8_u8(svand_u8_x(svptrue_b8(), svld1_u8(svptrue_b8(), q4), m4b));
+                        svint8_t q8bytes = svld1_s8(svptrue_b8(), q8); q8 += 16;
+                        sumi1_1 = svmla_n_s32_x(svptrue_b32(), sumi1_1, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+0]);
+                        q4bytes = svreinterpret_s8_u8(svand_u8_x(svptrue_b8(), svld1_u8(svptrue_b8(), q4+16), m4b));
+                        q8bytes = svld1_s8(svptrue_b8(), q8); q8 += 16;
+                        sumi1_2 = svmla_n_s32_x(svptrue_b32(), sumi1_2, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+0]);
+
+                        q4bytes = svreinterpret_s8_u8(svlsr_n_u8_x(svptrue_b8(), svld1_u8(svptrue_b8(), q4), 4));
+                        q8bytes = svld1_s8(svptrue_b8(), q8); q8 += 16;
+                        sumi2_1 = svmla_n_s32_x(svptrue_b32(), sumi2_1, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+1]);
+                        q4bytes = svreinterpret_s8_u8(svlsr_n_u8_x(svptrue_b8(), svld1_u8(svptrue_b8(), q4+16), 4));
+                        q8bytes = svld1_s8(svptrue_b8(), q8); q8 += 16;
+                        sumi2_2 = svmla_n_s32_x(svptrue_b32(), sumi2_2, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+1]);
+                        q4 += 32;
+                    }
+                    sumi1 = svadd_s32_x(svptrue_b32(), sumi1_1, sumi1_2);
+                    sumi2 = svadd_s32_x(svptrue_b32(), sumi2_1, sumi2_2);
+                    sumf += d * (svaddv_s32(svptrue_b32(), svadd_s32_x(svptrue_b32(), sumi1, sumi2)));
+                } break;
+            case 256:
+            case 512:
+                {
+                    for (int j = 0; j < QK_K/64; ++j) {
+                        const svuint8_t q4bits  = svld1_u8(svptrue_pat_b8(SV_VL32), q4); q4 += 32;
+                        svint8_t q4bytes = svreinterpret_s8_u8(svand_u8_x(svptrue_pat_b8(SV_VL32), q4bits, m4b));
+                        svint8_t q8bytes = svld1_s8(svptrue_pat_b8(SV_VL32), q8); q8 += 32;
+                        sumi1 = svmla_n_s32_x(svptrue_pat_b32(SV_VL8), sumi1, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+0]);
+
+                        q4bytes = svreinterpret_s8_u8(svlsr_n_u8_x(svptrue_pat_b8(SV_VL32), q4bits, 4));
+                        q8bytes = svld1_s8(svptrue_pat_b8(SV_VL32), q8); q8 += 32;
+                        sumi2 = svmla_n_s32_x(svptrue_pat_b32(SV_VL8), sumi2, svdot_s32(mzero, q4bytes, q8bytes), scales[2*j+1]);
+                    }
+                    sumf += d * (svaddv_s32(svptrue_pat_b32(SV_VL8), svadd_s32_x(svptrue_pat_b32(SV_VL8), sumi1, sumi2)));
+                } break;
+            default:
+                assert(false && "Unsupported vector length");
+                break;
+        }
+    }
+    *s = sumf;
+#elif __ARM_NEON
     const uint8x16_t m4b = vdupq_n_u8(0xf);
     const int32x4_t mzero = vdupq_n_s32(0);
 

From 681149ced2582f48c24792403df1307fed5eb951 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 16 Jan 2025 13:54:08 +0100
Subject: [PATCH 187/376] llama : add `llama_model_load_from_splits` (#11255)

* llama : add `llama_model_load_from_splits`

* update
---
 include/llama.h            | 10 ++++++
 src/llama-model-loader.cpp | 74 ++++++++++++++++++++++++++++++++------
 src/llama-model-loader.h   |  7 +++-
 src/llama-quant.cpp        |  3 +-
 src/llama.cpp              | 46 ++++++++++++++++++------
 5 files changed, 116 insertions(+), 24 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index a184884c7..352c3417e 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -418,10 +418,20 @@ extern "C" {
               struct llama_model_params   params),
             "use llama_model_load_from_file instead");
 
+    // Load the model from a file
+    // If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
+    // If the split file name does not follow this pattern, use llama_model_load_from_splits
     LLAMA_API struct llama_model * llama_model_load_from_file(
                              const char * path_model,
               struct llama_model_params   params);
 
+    // Load the model from multiple splits (support custom naming scheme)
+    // The paths must be in the correct order
+    LLAMA_API struct llama_model * llama_model_load_from_splits(
+                             const char ** paths,
+                                 size_t    n_paths,
+              struct llama_model_params    params);
+
     DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
             "use llama_model_free instead");
 
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 53175f0e0..75073bf61 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -64,6 +64,33 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
     }
 }
 
+// return a list of splits for a given path
+// for example, given "<name>-00002-of-00004.gguf", returns list of all 4 splits
+static std::vector<std::string> llama_get_list_splits(const std::string & path, const int idx, const int n_split) {
+    std::vector<std::string> paths;
+    std::string split_prefix;
+    std::vector<char> buf(llama_path_max(), 0);
+
+    {
+        int ret = llama_split_prefix(buf.data(), buf.size(), path.c_str(), idx, n_split);
+        if (!ret) {
+            throw std::runtime_error(format("invalid split file name: %s", path.c_str()));
+        }
+        split_prefix = std::string(buf.data(), ret);
+    }
+
+    if (split_prefix.empty()) {
+        throw std::runtime_error(format("invalid split file: %s", path.c_str()));
+    }
+
+    for (int idx = 0; idx < n_split; ++idx) {
+        int ret = llama_split_path(buf.data(), buf.size(), split_prefix.c_str(), idx, n_split);
+        paths.push_back(std::string(buf.data(), ret));
+    }
+
+    return paths;
+}
+
 namespace GGUFMeta {
     template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int64_t)>
     struct GKV_Base_Type {
@@ -413,7 +440,12 @@ namespace GGUFMeta {
     template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
     template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
 
-llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
+llama_model_loader::llama_model_loader(
+        const std::string & fname,
+        std::vector<std::string> & splits,
+        bool use_mmap,
+        bool check_tensors,
+        const struct llama_model_kv_override * param_overrides_p) {
     int trace = 0;
     if (getenv("LLAMA_TRACE")) {
         trace = atoi(getenv("LLAMA_TRACE"));
@@ -425,6 +457,7 @@ llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap,
         }
     }
 
+    // Load the main GGUF
     struct ggml_context * ctx = NULL;
     struct gguf_init_params params = {
         /*.no_alloc = */ true,
@@ -460,35 +493,54 @@ llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap,
 
     // Load additional GGML contexts
     if (n_split > 1) {
+        // make sure the main file is loaded first
         uint16_t idx = 0;
-        get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
+        const std::string kv_split_no = llm_kv(LLM_KV_SPLIT_NO);
+        get_key(kv_split_no, idx);
         if (idx != 0) {
-            throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
+            throw std::runtime_error(format("illegal split file idx: %d (file: %s), model must be loaded with the first split", idx, fname.c_str()));
         }
 
-        std::vector<char> split_prefix(llama_path_max(), 0);
-        if (!llama_split_prefix(split_prefix.data(), split_prefix.size(), fname.c_str(), idx, n_split)) {
-            throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
+        // generate list of splits if needed
+        if (splits.empty()) {
+            splits = llama_get_list_splits(fname, idx, n_split);
+        }
+
+        // in case user give a custom list of splits, check if it matches the expected number
+        if (n_split != (uint16_t)splits.size()) {
+            throw std::runtime_error(format("invalid split count, given: %zu splits, but expected %d", splits.size(), n_split));
         }
 
         if (trace > 0) {
             LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
         }
 
-        std::vector<char> split_path(llama_path_max(), 0);
+        // load other splits
         for (idx = 1; idx < n_split; idx++) {
-            llama_split_path(split_path.data(), split_path.size(), split_prefix.data(), idx, n_split);
+            const char * fname_split = splits[idx].c_str();
 
             struct gguf_init_params split_params = {
                 /*.no_alloc = */ true,
                 /*.ctx      = */ &ctx,
             };
-            gguf_context_ptr ctx_gguf { gguf_init_from_file(split_path.data(), split_params) };
+            gguf_context_ptr ctx_gguf { gguf_init_from_file(fname_split, split_params) };
             if (!ctx_gguf) {
-                throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path.data()));
+                throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, fname_split));
             }
 
-            files.emplace_back(new llama_file(split_path.data(), "rb"));
+            // check idx
+            {
+                const int kid = gguf_find_key(ctx_gguf.get(), kv_split_no.c_str());
+                if (kid < 0) {
+                    throw std::runtime_error(format("missing key %s in GGUF split %s", kv_split_no.c_str(), fname_split));
+                }
+                int idx_gguf = gguf_get_val_u16(ctx_gguf.get(), kid);
+                if (idx_gguf != idx) {
+                    throw std::runtime_error(format("invalid split file idx: %d (file: %s), expected %d", idx_gguf, fname_split, idx));
+                }
+            }
+
+            files.emplace_back(new llama_file(fname_split, "rb"));
             contexts.emplace_back(ctx);
 
             // Save tensors data offset info of the shard.
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index b63d158d9..fe35404b2 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -90,7 +90,12 @@ struct llama_model_loader {
     size_t size_data = 0;
     std::vector<std::pair<size_t, size_t>> mmaps_used;
 
-    llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p);
+    llama_model_loader(
+        const std::string & fname,
+        std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
+        bool use_mmap,
+        bool check_tensors,
+        const struct llama_model_kv_override * param_overrides_p);
 
     template<typename T>
     typename std::enable_if<std::is_integral<T>::value, bool>::type
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index d4947a780..fb7982655 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -526,7 +526,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         kv_overrides = v->data();
     }
 
-    llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
+    std::vector<std::string> splits = {};
+    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides);
     ml.init_mappings(false); // no prefetching
 
     llama_model model(llama_model_default_params());
diff --git a/src/llama.cpp b/src/llama.cpp
index 2e391b3b6..fede23d19 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -31,7 +31,7 @@
 #endif
 
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
-static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
+static int llama_model_load(const std::string & fname, std::vector<std::string> & splits, llama_model & model, llama_model_params & params) {
     // loading time will be recalculated after the first eval, so
     // we take page faults deferred by mmap() into consideration
     model.t_load_us = 0;
@@ -40,7 +40,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
     model.t_start_us = tm.t_start_us;
 
     try {
-        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
+        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.kv_overrides);
 
         ml.print_info();
 
@@ -9374,14 +9374,9 @@ int64_t llama_time_us(void) {
     return ggml_time_us();
 }
 
-struct llama_model * llama_load_model_from_file(
-        const char * path_model,
-        struct llama_model_params params) {
-    return llama_model_load_from_file(path_model, params);
-}
-
-struct llama_model * llama_model_load_from_file(
-        const char * path_model,
+static struct llama_model * llama_model_load_from_file_impl(
+        const std::string & path_model,
+        std::vector<std::string> & splits,
         struct llama_model_params params) {
     ggml_time_init();
 
@@ -9485,7 +9480,7 @@ struct llama_model * llama_model_load_from_file(
         LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024);
     }
 
-    const int status = llama_model_load(path_model, *model, params);
+    const int status = llama_model_load(path_model, splits, *model, params);
     GGML_ASSERT(status <= 0);
     if (status < 0) {
         if (status == -1) {
@@ -9501,6 +9496,35 @@ struct llama_model * llama_model_load_from_file(
     return model;
 }
 
+// deprecated
+struct llama_model * llama_load_model_from_file(
+        const char * path_model,
+        struct llama_model_params params) {
+    return llama_model_load_from_file(path_model, params);
+}
+
+struct llama_model * llama_model_load_from_file(
+        const char * path_model,
+        struct llama_model_params params) {
+    std::vector<std::string> splits = {};
+    return llama_model_load_from_file_impl(path_model, splits, params);
+}
+
+struct llama_model * llama_model_load_from_splits(
+        const char ** paths,
+        size_t n_paths,
+        struct llama_model_params params) {
+    std::vector<std::string> splits;
+    if (n_paths == 0) {
+        LLAMA_LOG_ERROR("%s: list of splits is empty\n", __func__);
+        return nullptr;
+    }
+    for (size_t i = 0; i < n_paths; ++i) {
+        splits.push_back(paths[i]);
+    }
+    return llama_model_load_from_file_impl(splits.front(), splits, params);
+}
+
 struct llama_context * llama_init_from_model(
                  struct llama_model * model,
         struct llama_context_params   params) {

From 9c8dcefe171ba70ed14f2a64d1433da12d0dd1c1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 16 Jan 2025 16:43:38 +0100
Subject: [PATCH 188/376] CUDA: backwards pass for misc. ops, add tests
 (#11257)

* CUDA: backwards pass for misc. ops, add tests

* remove restrict from pointers
---
 ggml/include/ggml.h                      |  12 +-
 ggml/src/ggml-alloc.c                    |   5 +
 ggml/src/ggml-cpu/ggml-cpu.c             | 133 ++++++-----
 ggml/src/ggml-cpu/ggml-cpu.cpp           |  10 +
 ggml/src/ggml-cuda/cross-entropy-loss.cu | 175 ++++++++-------
 ggml/src/ggml-cuda/getrows.cu            | 157 ++++++++-----
 ggml/src/ggml-cuda/getrows.cuh           |   3 +
 ggml/src/ggml-cuda/ggml-cuda.cu          |  27 ++-
 ggml/src/ggml-cuda/norm.cu               | 155 ++++++++++---
 ggml/src/ggml-cuda/norm.cuh              |   2 +
 ggml/src/ggml-cuda/out-prod.cu           |  38 +++-
 ggml/src/ggml-cuda/rope.cu               |  48 ++--
 ggml/src/ggml-cuda/softmax.cu            | 134 ++++++++---
 ggml/src/ggml-cuda/softmax.cuh           |   2 +
 ggml/src/ggml-cuda/unary.cu              |  36 +++
 ggml/src/ggml-cuda/unary.cuh             |   3 +
 ggml/src/ggml.c                          |  49 ++--
 tests/test-backend-ops.cpp               | 273 ++++++++++++++++++++---
 18 files changed, 930 insertions(+), 332 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index a9c051cd5..1198dc1fd 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1384,16 +1384,20 @@ extern "C" {
             float                 scale,
             float                 max_bias);
 
-    GGML_API struct ggml_tensor * ggml_soft_max_back(
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            float                 scale,
+            float                 max_bias);
 
     // in-place, returns view(a)
-    GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_back_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            float                 scale,
+            float                 max_bias);
 
     // rotary position embedding
     // if (mode & 1) - skip n_past elements (NOT SUPPORTED)
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index 8dc8226ac..9a3bf9f29 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -37,6 +37,7 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
     return true;
 }
 
+// ops that return true for this function must not use restrict pointers for their backend implementations
 static bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
         case GGML_OP_SCALE:
@@ -52,8 +53,12 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_LOG:
         case GGML_OP_UNARY:
         case GGML_OP_ROPE:
+        case GGML_OP_ROPE_BACK:
+        case GGML_OP_SILU_BACK:
         case GGML_OP_RMS_NORM:
+        case GGML_OP_RMS_NORM_BACK:
         case GGML_OP_SOFT_MAX:
+        case GGML_OP_SOFT_MAX_BACK:
             return true;
 
         default:
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 7c2e45f86..dd9995562 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -6691,20 +6691,20 @@ static void ggml_compute_forward_silu_back_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[0];
-    const struct ggml_tensor * grad = dst->src[1];
+    const struct ggml_tensor * grad = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
 
     assert(ggml_is_contiguous_1(grad));
-    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(src1));
     assert(ggml_is_contiguous_1(dst));
-    assert(ggml_are_same_shape(src0, dst));
-    assert(ggml_are_same_shape(src0, grad));
+    assert(ggml_are_same_shape(src1, dst));
+    assert(ggml_are_same_shape(src1, grad));
 
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int nc = src0->ne[0];
-    const int nr = ggml_nrows(src0);
+    const int nc = src1->ne[0];
+    const int nr = ggml_nrows(src1);
 
     // rows per thread
     const int dr = (nr + nth - 1)/nth;
@@ -6716,7 +6716,7 @@ static void ggml_compute_forward_silu_back_f32(
     for (int i1 = ir0; i1 < ir1; i1++) {
         ggml_vec_silu_backward_f32(nc,
                 (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])),
+                (float *) ((char *) src1->data + i1*(src1->nb[1])),
                 (float *) ((char *) grad->data + i1*(grad->nb[1])));
 
 #ifndef NDEBUG
@@ -6895,7 +6895,7 @@ static void ggml_compute_forward_norm_f32(
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
 
-    GGML_ASSERT(eps > 0.0f);
+    GGML_ASSERT(eps >= 0.0f);
 
     // TODO: optimize
     for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -6966,7 +6966,7 @@ static void ggml_compute_forward_rms_norm_f32(
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
 
-    GGML_ASSERT(eps > 0.0f);
+    GGML_ASSERT(eps >= 0.0f);
 
     // TODO: optimize
     for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -7018,12 +7018,13 @@ static void ggml_compute_forward_rms_norm_back_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src0 = dst->src[0]; // gradients from forward pass output
+    const struct ggml_tensor * src1 = dst->src[1]; // src1 from forward pass
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));
 
     GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -7042,8 +7043,8 @@ static void ggml_compute_forward_rms_norm_back_f32(
                 const int64_t i12 = i02;
                 const int64_t i13 = i03;
 
-                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
-                const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+                const float * dz = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const float * x  = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);
 
                 ggml_float sum_xx  = 0.0;
                 ggml_float sum_xdz = 0.0;
@@ -7066,9 +7067,9 @@ static void ggml_compute_forward_rms_norm_back_f32(
                 {
                     // z = rms_norm(x)
                     //
-                    // rms_norm(src0) =
+                    // rms_norm(src1) =
                     //     scale(
-                    //         src0,
+                    //         src1,
                     //         div(
                     //             1,
                     //             sqrt(
@@ -7076,13 +7077,13 @@ static void ggml_compute_forward_rms_norm_back_f32(
                     //                     scale(
                     //                         sum(
                     //                             sqr(
-                    //                                 src0)),
+                    //                                 src1)),
                     //                         (1.0/N)),
                     //                     eps))));
 
                     // postorder:
                     // ## op    args         grad
-                    // 00 param src0         grad[#00]
+                    // 00 param src1         grad[#00]
                     // 01 const 1
                     // 02 sqr   (#00)        grad[#02]
                     // 03 sum   (#02)        grad[#03]
@@ -7159,6 +7160,7 @@ static void ggml_compute_forward_rms_norm_back_f32(
                 // dx := scale(dx, rrms)
                 float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
 
+                // dx[i00] = (x*(-sum_xdz/sum_eps) + dz) / sqrtf(mean_eps)
                 ggml_vec_cpy_f32  (ne00, dx, x);
                 // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps);
                 ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps);
@@ -7750,12 +7752,13 @@ static void ggml_compute_forward_out_prod_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    GGML_ASSERT(ne0  == ne00);
-    GGML_ASSERT(ne1  == ne10);
-    GGML_ASSERT(ne2  == ne02);
-    GGML_ASSERT(ne02 == ne12);
-    GGML_ASSERT(ne3  == ne13);
-    GGML_ASSERT(ne03 == ne13);
+    GGML_ASSERT(ne0 == ne00);
+    GGML_ASSERT(ne1 == ne10);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    GGML_ASSERT(ne2 % ne02 == 0);
+    GGML_ASSERT(ne3 % ne03 == 0);
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == sizeof(float));
@@ -7797,6 +7800,10 @@ static void ggml_compute_forward_out_prod_f32(
     const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32);
     const int64_t blck_1 = 16;
 
+    // dps == dst per src0, used for group query attention
+    const int64_t dps2 = ne2 / ne02;
+    const int64_t dps3 = ne3 / ne03;
+
     for (int64_t bir = ir0; bir < ir1; bir += blck_1) {
         const int64_t bir1 = MIN(bir + blck_1, ir1);
         for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) {
@@ -7807,8 +7814,8 @@ static void ggml_compute_forward_out_prod_f32(
                 const int64_t i2 = (ir - i3*ne2*ne1)/ne1;
                 const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1);
 
-                const int64_t i02 = i2;
-                const int64_t i03 = i3;
+                const int64_t i02 = i2 / dps2;
+                const int64_t i03 = i3 / dps3;
 
                 //const int64_t i10 = i1;
                 const int64_t i12 = i2;
@@ -8906,9 +8913,9 @@ static void ggml_compute_forward_soft_max(
 }
 
 
-// ggml_compute_forward_soft_max_back
+// ggml_compute_forward_soft_max_ext_back
 
-static void ggml_compute_forward_soft_max_back_f32(
+static void ggml_compute_forward_soft_max_ext_back_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
@@ -8921,6 +8928,14 @@ static void ggml_compute_forward_soft_max_back_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src1, dst));
 
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (const float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
+
+    GGML_ASSERT(max_bias == 0.0f);
+
     // TODO: handle transposed/permuted matrices
 
     const int ith = params->ith;
@@ -8969,10 +8984,11 @@ static void ggml_compute_forward_soft_max_back_f32(
 
         // linear runtime, no additional memory
         float dot_y_dy = 0;
-        ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1);
-        ggml_vec_cpy_f32 (nc, dx, dy);
-        ggml_vec_acc1_f32(nc, dx, -dot_y_dy);
-        ggml_vec_mul_f32 (nc, dx, dx, y);
+        ggml_vec_dot_f32  (nc, &dot_y_dy, 0, y, 0, dy, 0, 1);
+        ggml_vec_cpy_f32  (nc, dx, dy);
+        ggml_vec_acc1_f32 (nc, dx, -dot_y_dy);
+        ggml_vec_mul_f32  (nc, dx, dx, y);
+        ggml_vec_scale_f32(nc, dx, scale);
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
@@ -8983,7 +8999,7 @@ static void ggml_compute_forward_soft_max_back_f32(
     }
 }
 
-static void ggml_compute_forward_soft_max_back(
+static void ggml_compute_forward_soft_max_ext_back(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
@@ -8992,7 +9008,7 @@ static void ggml_compute_forward_soft_max_back(
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_back_f32(params, dst);
+                ggml_compute_forward_soft_max_ext_back_f32(params, dst);
             } break;
         default:
             {
@@ -9985,9 +10001,10 @@ static void ggml_compute_forward_im2col_back_f32(
         const struct ggml_compute_params * params,
               struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src0 = dst->src[0]; // gradients of forward pass output
+    const struct ggml_tensor * src1 = dst->src[1]; // convolution kernel
 
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -10009,11 +10026,11 @@ static void ggml_compute_forward_im2col_back_f32(
     const int64_t IH = is_2D ? ne1 : 1;
     const int64_t IW = ne0;
 
-    const int64_t KH = is_2D ? ne01 : 1;
-    const int64_t KW = ne00;
+    const int64_t KH = is_2D ? ne11 : 1;
+    const int64_t KW = ne10;
 
-    const int64_t OH = is_2D ? ne12 : 1;
-    const int64_t OW = ne11;
+    const int64_t OH = is_2D ? ne02 : 1;
+    const int64_t OW = ne01;
 
     int ofs0 = is_2D ? nb3 : nb2;
     int ofs1 = is_2D ? nb2 : nb1;
@@ -10059,9 +10076,9 @@ static void ggml_compute_forward_im2col_back_f32(
                                     continue;
                                 }
 
-                                const float * const src_data = (const float *) src1->data
+                                const float * const grad_in = (const float *) src0->data
                                     + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
-                                grad += src_data[iic*(KH*KW) + ikh*KW + ikw];
+                                grad += grad_in[iic*(KH*KW) + ikh*KW + ikw];
                             }
                         }
                         float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW]
@@ -12484,22 +12501,22 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
-    const struct ggml_tensor * opt0 = dst->src[2];
+    const struct ggml_tensor * grad  = dst->src[0]; // gradient of forward pass output
+    const struct ggml_tensor * src0f = dst->src[1]; // src0 of forward pass
+    const struct ggml_tensor * src1f = dst->src[2]; // src1 of forward pass
 
     GGML_ASSERT(ggml_is_contiguous(dst));
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    GGML_ASSERT(ggml_is_contiguous(src1));
-    GGML_ASSERT(ggml_is_contiguous(opt0));
-    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_contiguous(src0f));
+    GGML_ASSERT(ggml_is_contiguous(src1f));
+    GGML_ASSERT(ggml_is_contiguous(grad));
+    GGML_ASSERT(ggml_are_same_shape(src0f, src1f) && ggml_are_same_shape(src0f, dst));
 
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;
 
     // TODO: handle transposed/permuted matrices
-    const int64_t nc = src0->ne[0];
-    const int64_t nr = ggml_nrows(src0);
+    const int64_t nc = src0f->ne[0];
+    const int64_t nr = ggml_nrows(src0f);
 
     // rows per thread
     const int64_t dr = (nr + nth - 1)/nth;
@@ -12508,12 +12525,12 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
     const int64_t ir0 = dr*ith;
     const int64_t ir1 = MIN(ir0 + dr, nr);
 
-    const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr;
+    const float d_by_nr = ((const float *) grad->data)[0] / (float) nr;
 
     for (int64_t i1 = ir0; i1 < ir1; i1++) {
-        float * ds0 = (float *)((char *) dst->data  + i1*dst->nb[1]);
-        float * s0  = (float *)((char *) src0->data + i1*src0->nb[1]);
-        float * s1  = (float *)((char *) src1->data + i1*src1->nb[1]);
+        float       * ds0 = (float       *)((char       *) dst->data   + i1*dst->nb[1]);
+        const float * s0  = (const float *)((const char *) src0f->data + i1*src0f->nb[1]);
+        const float * s1  = (const float *)((const char *) src1f->data + i1*src1f->nb[1]);
 
 #ifndef NDEBUG
         for (int64_t i = 0; i < nc; ++i) {
@@ -12526,11 +12543,11 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
         // soft_max
         float max = -INFINITY;
         ggml_vec_max_f32(nc, &max, s0);
-        ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max);
+        const ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max);
         assert(sum > 0.0);
         ggml_vec_scale_f32(nc, ds0, 1.0/sum);
 
-        // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr
+        // grad(src0f) = (softmax(src0f) - src1f) * grad(cross_entropy_loss(src0f, src1f)) / nr
         ggml_vec_sub_f32(nc, ds0, ds0, s1);
         ggml_vec_scale_f32(nc, ds0, d_by_nr);
 
@@ -12827,7 +12844,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_SOFT_MAX_BACK:
             {
-                ggml_compute_forward_soft_max_back(params, tensor);
+                ggml_compute_forward_soft_max_ext_back(params, tensor);
             } break;
         case GGML_OP_ROPE:
             {
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 5c47ceb73..35a1c876c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -403,6 +403,16 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
                 op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return src1->type == GGML_TYPE_F32 || src1->type == ggml_get_type_traits_cpu(src0->type)->vec_dot_type;
+        case GGML_OP_SOFT_MAX_BACK: {
+            if (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type != GGML_TYPE_F32) {
+                return false;
+            }
+            float max_bias = 0.0f;
+
+            memcpy(&max_bias, (const float *) op->op_params + 1, sizeof(float));
+
+            return max_bias == 0.0f;
+        }
         case GGML_OP_IM2COL_BACK:
             return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
         case GGML_OP_OUT_PROD:
diff --git a/ggml/src/ggml-cuda/cross-entropy-loss.cu b/ggml/src/ggml-cuda/cross-entropy-loss.cu
index ed09406a8..27599a2b0 100644
--- a/ggml/src/ggml-cuda/cross-entropy-loss.cu
+++ b/ggml/src/ggml-cuda/cross-entropy-loss.cu
@@ -5,95 +5,89 @@
 #include <cmath>
 #include <cstdint>
 
-static __global__ void cross_entropy_loss_f32(const float * logits, const float * labels, float * dst, const int nclasses, const int k) {
-    const int warp_id = threadIdx.x / WARP_SIZE;
-    const int lane_id = threadIdx.x % WARP_SIZE;
-    const int i0 = blockDim.x*blockIdx.x + warp_id*WARP_SIZE;
+template <bool use_shared>
+static __global__ void cross_entropy_loss_f32(
+        const float * __restrict__ logits, const float * __restrict__ labels, float * __restrict__ dst, const int nclasses, const int k) {
+    extern __shared__ float tmp[];
 
-    const int ne_tmp = WARP_SIZE*nclasses;
-
-    extern __shared__ float tmp_all[];
-    float * tmp_logits = tmp_all + (2*warp_id + 0)*ne_tmp;
-    float * tmp_labels = tmp_all + (2*warp_id + 1)*ne_tmp;
-
-    // Each warp first loads ne_tmp logits/labels into shared memory:
-    for (int i = lane_id; i < ne_tmp; i += WARP_SIZE) {
-        const int ig = i0*nclasses + i; // ig == i global
-
-        tmp_logits[i] = ig < k*nclasses ? logits[ig] : 0.0f;
-        tmp_labels[i] = ig < k*nclasses ? labels[ig] : 0.0f;
-    }
-
-    // Each thread in the warp then calculates the cross entropy loss for a single row.
-    // TODO: pad in order to avoid shared memory bank conflicts.
+    logits += int64_t(blockIdx.x)*nclasses;
+    labels += int64_t(blockIdx.x)*nclasses;
 
     // Find maximum for softmax:
-    float max = -INFINITY;
-    for (int i = 0; i < nclasses; ++i) {
-        max = fmaxf(max, tmp_logits[lane_id*nclasses + i]);
+    float max_logit = -INFINITY;
+    for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
+        const float val = logits[i];
+        max_logit = fmaxf(max_logit, val);
+
+        if (use_shared) {
+            tmp[i] = val;
+        }
     }
+    max_logit = warp_reduce_max(max_logit);
 
     // Calculate log(softmax(logits)) which is just logits - max:
     float sum = 0.0f;
-    for (int i = 0; i < nclasses; ++i) {
-        float val = tmp_logits[lane_id*nclasses + i] - max;
-        sum += expf(val);
-        tmp_logits[lane_id*nclasses + i] = val;
+    for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
+        const float logit_i = use_shared ? tmp[i] : logits[i];
+        sum += expf(logit_i - max_logit);
     }
+    sum = warp_reduce_sum(sum);
     sum = logf(sum);
 
     // log(exp(logits - max) / sum) = (logits - max) - log(sum)
     float loss = 0.0f;
-    for (int i = 0; i < nclasses; ++i) {
-        loss += (tmp_logits[lane_id*nclasses + i] - sum) * tmp_labels[lane_id*nclasses + i];
+    for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
+        const float logit_i = use_shared ? tmp[i] : logits[i];
+        loss += (logit_i - max_logit - sum) * labels[i];
     }
     loss = -warp_reduce_sum(loss) / (float)k;
 
-    __syncthreads();
-
-    if (lane_id == 0) {
-        tmp_all[warp_id] = loss;
-    }
-
-    __syncthreads();
-
-    if (warp_id != 0) {
-        return;
-    }
-
-    loss = lane_id < CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE/WARP_SIZE ? tmp_all[lane_id] : 0.0f;
-    loss = warp_reduce_sum(loss);
-
-    if (lane_id != 0) {
+    if (threadIdx.x != 0) {
         return;
     }
 
     dst[blockIdx.x] = loss;
 }
 
-static __global__ void cross_entropy_loss_back_f32(const float * logits, const float * labels, const float * loss, float * dst, const int nclasses) {
+template <bool use_shared>
+static __global__ void cross_entropy_loss_back_f32(
+        const float * __restrict__ grad, const float * __restrict__ logits, const float * __restrict__ labels,
+        float * __restrict__ dst, const int nclasses) {
     extern __shared__ float tmp[];
 
+    logits += int64_t(blockIdx.x)*nclasses;
+    labels += int64_t(blockIdx.x)*nclasses;
+    dst    += int64_t(blockIdx.x)*nclasses;
+
     float maxval = -INFINITY;
     for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
-        const float val = logits[blockIdx.x*nclasses + i];
+        const float val = logits[i];
         maxval = fmaxf(maxval, val);
-        tmp[i] = val;
+
+        if (use_shared) {
+            tmp[i] = val;
+        }
     }
     maxval = warp_reduce_max(maxval);
 
     float sum = 0.0f;
     for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
-        const float val = expf(tmp[i] - maxval);
+        const float val = expf((use_shared ? tmp[i] : logits[i]) - maxval);
         sum += val;
-        tmp[i] = val;
+
+        if (use_shared) {
+            tmp[i] = val;
+        } else {
+            dst[i] = val;
+        }
     }
     sum = warp_reduce_sum(sum);
     const float sm_scale = 1.0f/sum;
 
-    const float d_by_nrows = *loss/gridDim.x;
+    const float d_by_nrows = *grad/gridDim.x;
     for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
-        dst[blockIdx.x*nclasses + i] = (tmp[i]*sm_scale - labels[blockIdx.x*nclasses + i])*d_by_nrows;
+        const float val = use_shared ? tmp[i] : dst[i];
+        dst[i] = (val*sm_scale - labels[i])*d_by_nrows;
     }
 }
 
@@ -119,48 +113,77 @@ void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor *
     ggml_cuda_pool & pool = ctx.pool();
     cudaStream_t stream = ctx.stream();
 
-    const dim3 blocks_dim(CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE, 1, 1);
-    const dim3 blocks_num((nrows + CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE - 1) / CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE, 1, 1);
-    const int shmem = 2*CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE*ne00*sizeof(float);
+    const dim3 blocks_dim(WARP_SIZE, 1, 1);
+    const dim3 blocks_num(nrows, 1, 1);
+    const size_t nbytes_shared = ne00*sizeof(float);
+
+    const int    id    = ggml_cuda_get_device();
+    const size_t smpbo = ggml_cuda_info().devices[id].smpbo;
 
     ggml_cuda_pool_alloc<float> dst_tmp(pool, blocks_num.x);
 
-    cross_entropy_loss_f32<<<blocks_num, blocks_dim, shmem, stream>>>(src0_d, src1_d, dst_tmp.ptr, ne00, nrows);
+    if (nbytes_shared <= smpbo) {
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+        static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
+        if (!shared_memory_limit_raised[id]) {
+            CUDA_CHECK(cudaFuncSetAttribute(cross_entropy_loss_back_f32<true>, cudaFuncAttributeMaxDynamicSharedMemorySize, smpbo));
+            shared_memory_limit_raised[id] = true;
+        }
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+        cross_entropy_loss_f32<true><<<blocks_num, blocks_dim, nbytes_shared, stream>>>(src0_d, src1_d, dst_tmp.ptr, ne00, nrows);
+    } else {
+        cross_entropy_loss_f32<false><<<blocks_num, blocks_dim, 0, stream>>>(src0_d, src1_d, dst_tmp.ptr, ne00, nrows);
+    }
+    CUDA_CHECK(cudaGetLastError());
 
     // Combine results from individual blocks:
     sum_f32_cuda(pool, dst_tmp.ptr, dst_d, blocks_num.x, stream);
 }
 
 void ggml_cuda_cross_entropy_loss_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * opt0 = dst->src[2];
+    const ggml_tensor * grad  = dst->src[0];
+    const ggml_tensor * src0f = dst->src[1];
+    const ggml_tensor * src1f = dst->src[2];
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(opt0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0f->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1f->type == GGML_TYPE_F32);
+    GGML_ASSERT( grad->type == GGML_TYPE_F32);
+    GGML_ASSERT(  dst->type == GGML_TYPE_F32);
 
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    GGML_ASSERT(ggml_is_contiguous(src1));
-    GGML_ASSERT(ggml_is_contiguous(opt0));
+    GGML_ASSERT(ggml_is_scalar(grad));
+    GGML_ASSERT(ggml_is_contiguous(src0f));
+    GGML_ASSERT(ggml_is_contiguous(src1f));
     GGML_ASSERT(ggml_is_contiguous(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, src1));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_are_same_shape(src0f, src1f));
+    GGML_ASSERT(ggml_are_same_shape(src0f, dst));
 
-    const int64_t ne00  = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
+    const int64_t ne00  = src0f->ne[0];
+    const int64_t nrows = ggml_nrows(src0f);
 
-    const float * src0_d = (const float *) src0->data;
-    const float * src1_d = (const float *) src1->data;
-    const float * opt0_d = (const float *) opt0->data;
-    float       * dst_d  = (float       *) dst->data;
+    const float * grad_d  = (const float *) grad->data;
+    const float * src0f_d = (const float *) src0f->data;
+    const float * src1f_d = (const float *) src1f->data;
+    float       * dst_d   = (float       *) dst->data;
 
     cudaStream_t stream = ctx.stream();
 
     const dim3 blocks_dim(WARP_SIZE, 1, 1);
     const dim3 blocks_num(nrows, 1, 1);
-    const int shmem = ne00*sizeof(float);
+    const size_t nbytes_shared = ne00*sizeof(float);
 
-    cross_entropy_loss_back_f32<<<blocks_num, blocks_dim, shmem, stream>>>(src0_d, src1_d, opt0_d, dst_d, ne00);
+    const int    id    = ggml_cuda_get_device();
+    const size_t smpbo = ggml_cuda_info().devices[id].smpbo;
+
+    if (nbytes_shared <= smpbo) {
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+        static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
+        if (!shared_memory_limit_raised[id]) {
+            CUDA_CHECK(cudaFuncSetAttribute(cross_entropy_loss_back_f32<true>, cudaFuncAttributeMaxDynamicSharedMemorySize, smpbo));
+            shared_memory_limit_raised[id] = true;
+        }
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+        cross_entropy_loss_back_f32<true><<<blocks_num, blocks_dim, nbytes_shared, stream>>>(grad_d, src0f_d, src1f_d, dst_d, ne00);
+    } else {
+        cross_entropy_loss_back_f32<false><<<blocks_num, blocks_dim, 0, stream>>>(grad_d, src0f_d, src1f_d, dst_d, ne00);
+    }
 }
diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index 4c3703238..4cef53a98 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -3,15 +3,15 @@
 
 template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void k_get_rows(
-            const void * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+        const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
+        const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
+        /*const int64_t ne10, const int64_t ne11,*/ const int64_t ne12, /*const int64_t ne13,*/
+        /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
+        /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
+        const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
     const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i10 =  blockDim.y*blockIdx.y + threadIdx.y;
     const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
     const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
 
@@ -22,10 +22,10 @@ static __global__ void k_get_rows(
     const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
 
     dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
+    const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03;
 
-    const int ib = i00/qk; // block index
-    const int iqs = (i00%qk)/qr; // quant index
+    const int ib   =  i00/qk;      // block index
+    const int iqs  = (i00%qk)/qr;  // quant index
     const int iybs = i00 - i00%qk; // dst block start index
     const int y_offset = qr == 1 ? 1 : qk/2;
 
@@ -39,15 +39,15 @@ static __global__ void k_get_rows(
 
 template<typename src0_t, typename dst_t>
 static __global__ void k_get_rows_float(
-            const src0_t * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+        const src0_t * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
+        const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
+        /*const int64_t ne10, const int64_t ne11,*/ const int64_t ne12, /*const int64_t ne13,*/
+        /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
+        /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
+        const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i00 =  blockIdx.x*blockDim.x + threadIdx.x;
+    const int i10 =  blockDim.y*blockIdx.y + threadIdx.y;
     const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
     const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
 
@@ -58,14 +58,38 @@ static __global__ void k_get_rows_float(
     const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
 
     dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
+    const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
 
     dst_row[i00] = src0_row[i00];
 }
 
+template<typename grad_t, typename dst_t>
+static __global__ void k_get_rows_back_float(
+        const grad_t * __restrict__ grad, const int32_t * __restrict__ rows, dst_t * __restrict__ dst, const int64_t ncols, const int64_t nrows_grad) {
+    const int col = blockIdx.x*blockDim.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int dst_row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    float sum = 0.0f;
+
+    for (int64_t i = 0; i < nrows_grad; ++i) {
+        if (rows[i] != dst_row) {
+            continue;
+        }
+        sum += grad[i*ncols + col];
+    }
+
+    dst[dst_row*ncols + col] = sum;
+}
+
 template<int qk, int qr, dequantize_kernel_t dq>
-static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                            const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+static void get_rows_cuda(
+        const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+        const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
@@ -87,22 +111,25 @@ static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, gg
     GGML_ASSERT(ne00 % 2 == 0);
 
     k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
+        src0_dd, src1_dd, dst_dd,
+        ne00, /*ne01, ne02, ne03,*/
+        /*ne10, ne11,*/ ne12, /*ne13,*/
+        /* s0,*/ s1, s2, s3,
+        /* nb00,*/ nb01, nb02, nb03,
+        s10, s11, s12/*, s13*/);
 
     GGML_UNUSED(dst);
 }
 
 template<typename src0_t>
-static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                                const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+static void get_rows_cuda_float(
+        const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+        const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
+    GGML_ASSERT(ne13 == 1);
+
     const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
     const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
     const dim3 block_nums(block_num_x, ne10, ne11*ne12);
@@ -119,12 +146,12 @@ static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * sr
     //const size_t s13 = nb13 / ggml_element_size(src1);
 
     k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
+        src0_dd, src1_dd, dst_dd,
+        ne00, /*ne01, ne02, ne03,*/
+        /*ne10, ne11,*/ ne12, /*ne13,*/
+        /* s0,*/ s1, s2, s3,
+        /* nb00,*/ nb01, nb02, nb03,
+        s10, s11, s12/*, s13*/);
 
     GGML_UNUSED(dst);
 }
@@ -132,42 +159,41 @@ static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * sr
 void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const float * src0_d = (const float *)src0->data;
-    const float * src1_d = (const float *)src1->data;
-    float * dst_d = (float *)dst->data;
+
+    const void    * src0_d = (const void    *) src0->data;
+    const int32_t * src1_d = (const int32_t *) src1->data;
+    float         * dst_d  = (float         *) dst->data;
+
     cudaStream_t stream = ctx.stream();
 
-
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
 
     GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
     GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
-    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
-
-    const int32_t * src1_i32 = (const int32_t *) src1_d;
+    GGML_ASSERT(dst->nb[0]  == ggml_type_size(dst->type));
 
     switch (src0->type) {
         case GGML_TYPE_F16:
-            get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda_float(src0, src1, dst, (const half *) src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_F32:
-            get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda_float(src0, src1, dst, (const float *) src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_Q4_0:
-            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_Q4_1:
-            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_Q5_0:
-            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_Q5_1:
-            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
             break;
         case GGML_TYPE_Q8_0:
-            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_d, dst_d, stream);
             break;
         default:
             // TODO: k-quants
@@ -175,3 +201,34 @@ void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             break;
     }
 }
+
+void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0]; // gradients of forward pass output
+    const ggml_tensor * src1 = dst->src[1]; // src1 in forward pass
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const float   * src0_d = (const float   *) src0->data;
+    const int32_t * src1_d = (const int32_t *) src1->data;
+    float         * dst_d  = (float         *) dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    GGML_ASSERT(ne02*ne03 == 1);
+    GGML_ASSERT(ne12*ne13 == 1);
+    GGML_ASSERT(ne2*ne3 == 1);
+
+    const dim3 block_dims(CUDA_GET_ROWS_BACK_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ne00 + CUDA_GET_ROWS_BACK_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BACK_BLOCK_SIZE;
+    const dim3 block_nums(block_num_x, ne1, 1);
+
+    k_get_rows_back_float<<<block_nums, block_dims, 0, stream>>>(src0_d, src1_d, dst_d, ne00, ne10);
+}
diff --git a/ggml/src/ggml-cuda/getrows.cuh b/ggml/src/ggml-cuda/getrows.cuh
index bbf130232..a1ca643f1 100644
--- a/ggml/src/ggml-cuda/getrows.cuh
+++ b/ggml/src/ggml-cuda/getrows.cuh
@@ -1,5 +1,8 @@
 #include "common.cuh"
 
 #define CUDA_GET_ROWS_BLOCK_SIZE 256
+#define CUDA_GET_ROWS_BACK_BLOCK_SIZE 256
 
 void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 9118edc72..7fd1fc853 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2003,6 +2003,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_GET_ROWS:
             ggml_cuda_op_get_rows(ctx, dst);
             break;
+        case GGML_OP_GET_ROWS_BACK:
+            ggml_cuda_op_get_rows_back(ctx, dst);
+            break;
         case GGML_OP_DUP:
             ggml_cuda_dup(ctx, dst);
             break;
@@ -2091,9 +2094,15 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_LEAKY_RELU:
             ggml_cuda_op_leaky_relu(ctx, dst);
             break;
+        case GGML_OP_SILU_BACK:
+            ggml_cuda_op_silu_back(ctx, dst);
+            break;
         case GGML_OP_RMS_NORM:
             ggml_cuda_op_rms_norm(ctx, dst);
             break;
+        case GGML_OP_RMS_NORM_BACK:
+            ggml_cuda_op_rms_norm_back(ctx, dst);
+            break;
         case GGML_OP_MUL_MAT:
             if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
                 GGML_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
@@ -2138,6 +2147,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SOFT_MAX:
             ggml_cuda_op_soft_max(ctx, dst);
             break;
+        case GGML_OP_SOFT_MAX_BACK:
+            ggml_cuda_op_soft_max_back(ctx, dst);
+            break;
         case GGML_OP_ROPE:
             ggml_cuda_op_rope(ctx, dst);
             break;
@@ -2912,7 +2924,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 }
             } break;
         case GGML_OP_OUT_PROD:
-            return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1;
+            return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
         case GGML_OP_GET_ROWS:
             {
                 switch (op->src[0]->type) {
@@ -2928,6 +2940,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                         return false;
                 }
             } break;
+        case GGML_OP_GET_ROWS_BACK:
+            {
+                return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1;
+            } break;
         case GGML_OP_CPY:
             {
                 ggml_type src0_type = op->src[0]->type;
@@ -3001,8 +3017,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 }
                 return false;
             } break;
+        case GGML_OP_SILU_BACK:
+            return ggml_is_contiguous(op->src[0]);
+            break;
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
+        case GGML_OP_RMS_NORM_BACK:
             return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
             break;
         case GGML_OP_NONE:
@@ -3027,6 +3047,11 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
             return true;
+        case GGML_OP_SOFT_MAX_BACK: {
+            float max_bias = 0.0f;
+            memcpy(&max_bias, (const float *) op->op_params + 1, sizeof(float));
+            return max_bias == 0.0f;
+        }
         case GGML_OP_ROPE:
         case GGML_OP_ROPE_BACK: {
             const size_t ts = ggml_type_size(op->src[0]->type);
diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
index 133e219f0..d991ec972 100644
--- a/ggml/src/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -5,20 +5,24 @@ static __global__ void norm_f32(const float * x, float * dst, const int ncols, c
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
 
-    float2 mean_var = make_float2(0.f, 0.f);
+    x   += int64_t(row)*ncols;
+    dst += int64_t(row)*ncols;
+
+    float2 mean_var = make_float2(0.0f, 0.0f);
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
+        const float xi = x[col];
         mean_var.x += xi;
         mean_var.y += xi * xi;
     }
 
     // sum up partial sums
     mean_var = warp_reduce_sum(mean_var);
-    if (block_size > WARP_SIZE) {
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
         __shared__ float2 s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
         if (lane_id == 0) {
             s_sum[warp_id] = mean_var;
         }
@@ -32,7 +36,7 @@ static __global__ void norm_f32(const float * x, float * dst, const int ncols, c
     const float inv_std = rsqrtf(var + eps);
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
+        dst[col] = (x[col] - mean) * inv_std;
     }
 }
 
@@ -40,14 +44,8 @@ template <int block_size>
 static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
     // blockIdx.x: num_groups idx
     // threadIdx.x: block_size idx
-    int start = blockIdx.x * group_size;
-    int end = start + group_size;
-
-    start += threadIdx.x;
-
-    if (end >= ne_elements) {
-        end = ne_elements;
-    }
+    const int start =     blockIdx.x*group_size + threadIdx.x;
+    const int end   = min(blockIdx.x*group_size + group_size,  ne_elements);
 
     float tmp = 0.0f; // partial sum for thread in warp
 
@@ -56,10 +54,11 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
     }
 
     tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
         __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
         if (lane_id == 0) {
             s_sum[warp_id] = tmp;
         }
@@ -68,11 +67,11 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
         tmp = warp_reduce_sum(tmp);
     }
 
-    float mean = tmp / group_size;
+    const float mean = tmp / group_size;
     tmp = 0.0f;
 
     for (int j = start; j < end; j += block_size) {
-        float xi = x[j] - mean;
+        const float xi = x[j] - mean;
         dst[j] = xi;
         tmp += xi * xi;
     }
@@ -80,8 +79,8 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
     tmp = warp_reduce_sum(tmp);
     if (block_size > WARP_SIZE) {
         __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
         if (lane_id == 0) {
             s_sum[warp_id] = tmp;
         }
@@ -90,8 +89,8 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
         tmp = warp_reduce_sum(tmp);
     }
 
-    float variance = tmp / group_size;
-    float scale = rsqrtf(variance + eps);
+    const float variance = tmp / group_size;
+    const float scale = rsqrtf(variance + eps);
     for (int j = start; j < end; j += block_size) {
         dst[j] *= scale;
     }
@@ -102,19 +101,23 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
 
+    x   += int64_t(row)*ncols;
+    dst += int64_t(row)*ncols;
+
     float tmp = 0.0f; // partial sum for thread in warp
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
+        const float xi = x[col];
         tmp += xi * xi;
     }
 
     // sum up partial sums
     tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
         __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
         if (lane_id == 0) {
             s_sum[warp_id] = tmp;
         }
@@ -127,12 +130,63 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
     const float scale = rsqrtf(mean + eps);
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = scale * x[row*ncols + col];
+        dst[col] = scale * x[col];
+    }
+}
+
+template <int block_size>
+static __global__ void rms_norm_back_f32(
+        const float * grad, const float * xf, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    grad += int64_t(row)*ncols;
+    xf   += int64_t(row)*ncols;
+    dst  += int64_t(row)*ncols;
+
+    float sum_xx = 0.0f; // sum for squares of x, equivalent to forward pass
+    float sum_xg = 0.0f; // sum for x * gradient, needed because RMS norm mixes inputs
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xfi = xf[col];
+        sum_xx += xfi * xfi;
+        sum_xg += xfi * grad[col];
+    }
+
+    // sum up partial sums
+    sum_xx = warp_reduce_sum(sum_xx);
+    sum_xg = warp_reduce_sum(sum_xg);
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
+        __shared__ float s_sum_xx[32];
+        __shared__ float s_sum_xg[32];
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum_xx[warp_id] = sum_xx;
+            s_sum_xg[warp_id] = sum_xg;
+        }
+        __syncthreads();
+
+        sum_xx = s_sum_xx[lane_id];
+        sum_xx = warp_reduce_sum(sum_xx);
+
+        sum_xg = s_sum_xg[lane_id];
+        sum_xg = warp_reduce_sum(sum_xg);
+    }
+
+    const float mean_eps = sum_xx / ncols + eps;
+    const float sum_eps  = sum_xx + ncols*eps;
+
+    const float scale_grad = rsqrtf(mean_eps);
+    const float scale_x    = -scale_grad * sum_xg/sum_eps;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[col] = scale_grad*grad[col] + scale_x*xf[col];
     }
 }
 
 static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
     if (ncols < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
         norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
@@ -142,7 +196,8 @@ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const i
     }
 }
 
-static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const float eps, const int group_size, const int ne_elements, cudaStream_t stream) {
+static void group_norm_f32_cuda(
+        const float * x, float * dst, const int num_groups, const float eps, const int group_size, const int ne_elements, cudaStream_t stream) {
     if (group_size < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
         group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
@@ -153,7 +208,6 @@ static void group_norm_f32_cuda(const float * x, float * dst, const int num_grou
 }
 
 static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
     if (ncols < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
         rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
@@ -163,6 +217,16 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con
     }
 }
 
+static void rms_norm_back_f32_cuda(const float * grad, const float * xf, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        rms_norm_back_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(grad, xf, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        rms_norm_back_f32<1024><<<nrows, block_dims, 0, stream>>>(grad, xf, dst, ncols, eps);
+    }
+}
+
 void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;
@@ -179,6 +243,7 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
 
     norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
 }
@@ -198,6 +263,7 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
 
     float eps;
     memcpy(&eps, dst->op_params + 1, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
 
     int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
     group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], eps, group_size, ggml_nelements(src0), stream);
@@ -219,6 +285,33 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
 
     rms_norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
 }
+
+void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * grad  = dst->src[0]; // gradients
+    const ggml_tensor * src0f = dst->src[1]; // src0 from forward pass
+
+    const float * grad_d  = (const float *) grad->data;
+    const float * src0f_d = (const float *) src0f->data;
+    float       * dst_d   = (float       *) dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(grad));
+
+    GGML_ASSERT( grad->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0f->type == GGML_TYPE_F32);
+    GGML_ASSERT(  dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0f->ne[0];
+    const int64_t nrows = ggml_nrows(src0f);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
+
+    rms_norm_back_f32_cuda(grad_d, src0f_d, dst_d, ne00, nrows, eps, stream);
+}
diff --git a/ggml/src/ggml-cuda/norm.cuh b/ggml/src/ggml-cuda/norm.cuh
index 431a8f74d..d63d34380 100644
--- a/ggml/src/ggml-cuda/norm.cuh
+++ b/ggml/src/ggml-cuda/norm.cuh
@@ -5,3 +5,5 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/out-prod.cu b/ggml/src/ggml-cuda/out-prod.cu
index 619cfdcb5..73e3e2c47 100644
--- a/ggml/src/ggml-cuda/out-prod.cu
+++ b/ggml/src/ggml-cuda/out-prod.cu
@@ -11,16 +11,15 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    GGML_ASSERT(ggml_is_contiguous(dst));
 
     GGML_ASSERT(ne01 == ne11);
     GGML_ASSERT(ne0 == ne00);
     GGML_ASSERT(ne1 == ne10);
 
-    GGML_ASSERT(ne2 == src0->ne[2]);
+    GGML_ASSERT(ne2 % src0->ne[2] == 0);
+    GGML_ASSERT(ne3 % src0->ne[3] == 0);
+
     GGML_ASSERT(ne2 == src1->ne[2]);
-    GGML_ASSERT(ne3 == src0->ne[3]);
     GGML_ASSERT(ne3 == src1->ne[3]);
 
     const float * src0_d = (const float *) src0->data;
@@ -33,8 +32,6 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const float alpha = 1.0f;
     const float beta = 0.0f;
 
-    GGML_ASSERT(ne2 == 1);
-    GGML_ASSERT(ne3 == 1);
     CUBLAS_CHECK(cublasSetStream(handle, stream));
 
     const bool src1_T = ggml_is_transposed(src1);
@@ -42,10 +39,27 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t           ldb            = (src1_T ?        nb10 :        nb11) /  sizeof(float);
     GGML_ASSERT(                             (src1_T ?        nb11 :        nb10) == sizeof(float));
 
-    CUBLAS_CHECK(
-        cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
-                ne0, ne1, ne01,
-                &alpha, src0_d, ne00,
-                        src1_d, ldb,
-                &beta,  dst_d,  ne0));
+    // data strides in dimensions 2/3
+    const size_t s02 = nb02 / sizeof(float);
+    const size_t s03 = nb03 / sizeof(float);
+    const size_t s12 = nb12 / sizeof(float);
+    const size_t s13 = nb13 / sizeof(float);
+    const size_t s2  = nb2  / sizeof(float);
+    const size_t s3  = nb3  / sizeof(float);
+
+    // dps == dst per src0, used for group query attention
+    const int64_t dps2 = ne2 / ne02;
+    const int64_t dps3 = ne3 / ne03;
+
+    // TODO batched matrix multiplication
+    for (int64_t i3 = 0; i3 < ne3; ++i3) {
+        for (int64_t i2 = 0; i2 < ne2; ++i2) {
+            CUBLAS_CHECK(
+                cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
+                        ne0, ne1, ne01,
+                        &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, ne00,
+                                src1_d +  i3      *s13 +  i2      *s12, ldb,
+                        &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ne0));
+        }
+    }
 }
diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu
index e1912fee1..18f691b2d 100644
--- a/ggml/src/ggml-cuda/rope.cu
+++ b/ggml/src/ggml-cuda/rope.cu
@@ -39,9 +39,9 @@ static __device__ void rope_yarn(
 
 template<bool forward, bool has_ff, typename T>
 static __global__ void rope_norm(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
-        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors) {
+        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
+        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -83,9 +83,9 @@ static __global__ void rope_norm(
 
 template<bool forward, bool has_ff, typename T>
 static __global__ void rope_neox(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
-        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors) {
+        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
+        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -127,9 +127,9 @@ static __global__ void rope_neox(
 
 template<bool forward, bool has_ff, typename T>
 static __global__ void rope_multi(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
-        const int n_dims, const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * __restrict__ freq_factors, const mrope_sections sections) {
+        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
+        const int n_dims, const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -187,9 +187,9 @@ static __global__ void rope_multi(
 
 template<bool forward, bool has_ff, typename T>
 static __global__ void rope_vision(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
-        const int32_t * __restrict__ pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
-        const float theta_scale, const float * __restrict__ freq_factors, const mrope_sections sections) {
+        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
+        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
+        const float theta_scale, const float * freq_factors, const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -234,9 +234,9 @@ static __global__ void rope_vision(
 
 template<bool forward, typename T>
 static void rope_norm_cuda(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, cudaStream_t stream) {
+        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -257,9 +257,9 @@ static void rope_norm_cuda(
 
 template<bool forward, typename T>
 static void rope_neox_cuda(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, cudaStream_t stream) {
+        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -280,9 +280,9 @@ static void rope_neox_cuda(
 
 template<bool forward, typename T>
 static void rope_multi_cuda(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, const mrope_sections sections, cudaStream_t stream) {
+        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -303,9 +303,9 @@ static void rope_multi_cuda(
 
 template<bool forward, typename T>
 static void rope_vision_cuda(
-        const T * __restrict__ x, T * __restrict__ dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * __restrict__ pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * __restrict__ freq_factors, const mrope_sections sections, cudaStream_t stream) {
+        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
+        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, cudaStream_t stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu
index c24abae1f..9aa4b8489 100644
--- a/ggml/src/ggml-cuda/softmax.cu
+++ b/ggml/src/ggml-cuda/softmax.cu
@@ -1,5 +1,7 @@
 #include "common.cuh"
+#include "ggml.h"
 #include "softmax.cuh"
+#include <cstdint>
 
 template <typename T>
 static __device__ __forceinline__ float t2f32(T val) {
@@ -11,14 +13,20 @@ __device__ float __forceinline__ t2f32<half>(half val) {
     return __half2float(val);
 }
 
-template <bool vals_smem, int ncols_template, int block_size_template, typename T>
-static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
+template <bool use_shared, int ncols_template, int block_size_template, typename T>
+static __global__ void soft_max_f32(
+        const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y,
+        const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
     const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
 
     const int tid  = threadIdx.x;
     const int rowx = blockIdx.x;
     const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
 
+    x    += int64_t(rowx)*ncols;
+    mask += int64_t(rowy)*ncols * (mask != nullptr);
+    dst  += int64_t(rowx)*ncols;
+
     const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
 
     const int warp_id = threadIdx.x / WARP_SIZE;
@@ -29,7 +37,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst
     extern __shared__ float data_soft_max_f32[];
     float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
     // shared memory buffer to cache values between iterations:
-    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;
+    float * vals = use_shared ? buf_iw + WARP_SIZE : dst;
 
     float max_val = -INFINITY;
 
@@ -41,10 +49,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst
             break;
         }
 
-        const int64_t ix = (int64_t)rowx*ncols + col;
-        const int64_t iy = (int64_t)rowy*ncols + col;
-
-        const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
+        const float val = x[col]*scale + (mask ? slope*t2f32(mask[col]) : 0.0f);
 
         vals[col] = val;
         max_val = max(max_val, val);
@@ -110,8 +115,29 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst
             return;
         }
 
-        const int64_t idst = (int64_t)rowx*ncols + col;
-        dst[idst] = vals[col] * inv_sum;
+        dst[col] = vals[col] * inv_sum;
+    }
+}
+
+static __global__ void soft_max_back_f32(
+        const float * grad, const float * dstf, float * dst, const int ncols, const float scale) {
+    const int tid  = threadIdx.x;
+    const int rowx = blockIdx.x;
+
+    grad += int64_t(rowx)*ncols;
+    dstf += int64_t(rowx)*ncols;
+    dst  += int64_t(rowx)*ncols;
+
+    float dgf_dot = 0.0f; // dot product of dst from forward pass and gradients
+
+    for (int col = tid; col < ncols; col += WARP_SIZE) {
+        dgf_dot += dstf[col]*grad[col];
+    }
+
+    dgf_dot = warp_reduce_sum(dgf_dot);
+
+    for (int col = tid; col < ncols; col += WARP_SIZE) {
+        dst[col] = scale * (grad[col] - dgf_dot) * dstf[col];
     }
 }
 
@@ -121,7 +147,7 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons
     while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
     const dim3 block_dims(nth,     1, 1);
     const dim3 block_nums(nrows_x, 1, 1);
-    const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
+    const size_t nbytes_shared = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
     static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
 
     const uint32_t n_head      = nrows_x/nrows_y;
@@ -131,50 +157,68 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
     // FIXME: this limit could be raised by ~2-4x on Ampere or newer
-    if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
+    if (nbytes_shared < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
         switch (ncols_x) {
             case 32:
-                soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,   32,   32><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 64:
-                soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,   64,   64><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 128:
-                soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,  128,  128><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 256:
-                soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,  256,  256><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 512:
-                soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,  512,  512><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 1024:
-                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 2048:
-                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             case 4096:
-                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
             default:
-                soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                soft_max_f32<true,    0,    0><<<block_nums, block_dims, nbytes_shared, stream>>>
+                    (x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
                 break;
         }
     } else {
-        const size_t shmem_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+        const size_t nbytes_shared_low = WARP_SIZE*sizeof(float);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
     }
 }
 
+static void soft_max_back_f32_cuda(
+        const float * grad, const float * dstf, float * dst,
+        const int ncols, const int nrows, const float scale, cudaStream_t stream) {
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_nums(nrows,     1, 1);
+
+    soft_max_back_f32<<<block_nums, block_dims, 0, stream>>>(grad, dstf, dst, ncols, scale);
+}
+
 void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
 
-    const float * src0_d = (const float *)src0->data;
-    const void  * src1_d = src1 ? (const void *)src1->data : nullptr;
+    const float * src0_d = (const float *) src0->data;
+    const void  * src1_d = src1 ? (const void *) src1->data : nullptr;
+    float       *  dst_d = (float *) dst->data;
 
-    float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
@@ -189,18 +233,42 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float scale    = 1.0f;
     float max_bias = 0.0f;
 
-    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+    memcpy(&scale,    (const float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
 
     const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
 
     if (use_f16) {
-        const half * src1_dd = (const half *)src1_d;
-
-        soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+        soft_max_f32_cuda(src0_d, (const half  *) src1_d, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
     } else {
-        const float * src1_dd = (const float *)src1_d;
-
-        soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+        soft_max_f32_cuda(src0_d, (const float *) src1_d, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
     }
 }
+
+void ggml_cuda_op_soft_max_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0]; // grad
+    const ggml_tensor * src1 = dst->src[1]; // forward pass output
+
+    const float * src0_d = (const float *) src0->data;
+    const float * src1_d = (const float *) src1->data;
+    float       * dst_d  = (float       *) dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (const float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
+
+    GGML_ASSERT(max_bias == 0.0f);
+
+    soft_max_back_f32_cuda(src0_d, src1_d, dst_d, ncols, nrows, scale, stream);
+}
diff --git a/ggml/src/ggml-cuda/softmax.cuh b/ggml/src/ggml-cuda/softmax.cuh
index 4ef4ff86c..93dfee835 100644
--- a/ggml/src/ggml-cuda/softmax.cuh
+++ b/ggml/src/ggml-cuda/softmax.cuh
@@ -3,3 +3,5 @@
 #define CUDA_SOFT_MAX_BLOCK_SIZE 1024
 
 void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_soft_max_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/unary.cu b/ggml/src/ggml-cuda/unary.cu
index 81fc92202..6b21f407d 100644
--- a/ggml/src/ggml-cuda/unary.cu
+++ b/ggml/src/ggml-cuda/unary.cu
@@ -51,6 +51,19 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) {
     dst[i] = x[i] / (1.0f + expf(-x[i]));
 }
 
+static __global__ void silu_back_f32(
+        const float * grad, const float * xf, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    const float xfi = xf[i];
+    const float s = 1.0f / (1.0f + expf(-xfi));
+    dst[i] = grad[i] * s * (1.0f + xfi * (1.0f - s));
+}
+
 static __global__ void tanh_f32(const float * x, float * dst, int k) {
     const int i  = blockDim.x*blockIdx.x + threadIdx.x;
     if (i >= k) {
@@ -173,6 +186,11 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
     silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
 }
 
+static void silu_back_f32_cuda(const float * grad, const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SILU_BACK_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
+    silu_back_f32<<<num_blocks, CUDA_SILU_BACK_BLOCK_SIZE, 0, stream>>>(grad, x, dst, k);
+}
+
 static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
     tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
@@ -284,6 +302,24 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     silu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
 }
 
+void ggml_cuda_op_silu_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0]; // input from forward pass
+    const ggml_tensor * src1 = dst->src[1]; // grads of forward pass output
+
+    const float * src0_d = (const float *) src0->data;
+    const float * src1_d = (const float *) src1->data;
+    float       * dst_d  = (float       *) dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_back_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(src0), stream);
+}
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;
diff --git a/ggml/src/ggml-cuda/unary.cuh b/ggml/src/ggml-cuda/unary.cuh
index c91936728..e7f62643a 100644
--- a/ggml/src/ggml-cuda/unary.cuh
+++ b/ggml/src/ggml-cuda/unary.cuh
@@ -4,6 +4,7 @@
 #define CUDA_STEP_BLOCK_SIZE 256
 #define CUDA_GELU_BLOCK_SIZE 256
 #define CUDA_SILU_BLOCK_SIZE 256
+#define CUDA_SILU_BACK_BLOCK_SIZE 256
 #define CUDA_TANH_BLOCK_SIZE 256
 #define CUDA_RELU_BLOCK_SIZE 256
 #define CUDA_SIGMOID_BLOCK_SIZE 256
@@ -23,6 +24,8 @@ void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
+void ggml_cuda_op_silu_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index ecfb84a80..b1d0d4913 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3450,12 +3450,14 @@ struct ggml_tensor * ggml_soft_max_ext(
     return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
 }
 
-// ggml_soft_max_back
+// ggml_soft_max_ext_back
 
-static struct ggml_tensor * ggml_soft_max_back_impl(
+static struct ggml_tensor * ggml_soft_max_ext_back_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
+        float                 scale,
+        float                 max_bias,
         bool                  inplace) {
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
@@ -3463,21 +3465,28 @@ static struct ggml_tensor * ggml_soft_max_back_impl(
     result->src[0] = a;
     result->src[1] = b;
 
+    memcpy((float *) result->op_params + 0, &scale,    sizeof(float));
+    memcpy((float *) result->op_params + 1, &max_bias, sizeof(float));
+
     return result;
 }
 
-struct ggml_tensor * ggml_soft_max_back(
+struct ggml_tensor * ggml_soft_max_ext_back(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
-    return ggml_soft_max_back_impl(ctx, a, b, false);
+        struct ggml_tensor  * b,
+        float                 scale,
+        float                 max_bias) {
+    return ggml_soft_max_ext_back_impl(ctx, a, b, scale, max_bias, false);
 }
 
-struct ggml_tensor * ggml_soft_max_back_inplace(
+struct ggml_tensor * ggml_soft_max_ext_back_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
-    return ggml_soft_max_back_impl(ctx, a, b, true);
+        struct ggml_tensor  * b,
+        float                 scale,
+        float                 max_bias) {
+    return ggml_soft_max_ext_back_impl(ctx, a, b, scale, max_bias, true);
 }
 
 // ggml_rope
@@ -5076,10 +5085,10 @@ struct ggml_tensor * ggml_cross_entropy_loss_back(
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
         struct ggml_tensor  * c) {
-    GGML_ASSERT(ggml_are_same_shape(a, b));
-    GGML_ASSERT(ggml_is_scalar(c));
+    GGML_ASSERT(ggml_is_scalar(a));
+    GGML_ASSERT(ggml_are_same_shape(b, c));
 
-    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, b);
 
     result->op     = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
     result->src[0] = a;
@@ -5258,7 +5267,7 @@ static void ggml_sub_or_set(
 }
 
 static void ggml_compute_backward(
-        struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i, bool * grads_needed) {
+        struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i, const bool * grads_needed) {
     struct ggml_tensor * tensor = cgraph->nodes[i];
     struct ggml_tensor * grad   = ggml_graph_get_grad(cgraph, tensor);
 
@@ -5402,7 +5411,7 @@ static void ggml_compute_backward(
             if (src0_needs_grads) {
                 float eps;
                 memcpy(&eps, tensor->op_params, sizeof(float));
-                ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, src0, grad, eps));
+                ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, grad, src0, eps));
             }
         } break;
         case GGML_OP_MUL_MAT: {
@@ -5585,7 +5594,13 @@ static void ggml_compute_backward(
         } break;
         case GGML_OP_SOFT_MAX: {
             if (src0_needs_grads) {
-                ggml_add_or_set(ctx, cgraph, isrc0, ggml_soft_max_back(ctx, grad, tensor));
+                float scale    = 1.0f;
+                float max_bias = 0.0f;
+
+                memcpy(&scale,    (const float *) tensor->op_params + 0, sizeof(float));
+                memcpy(&max_bias, (const float *) tensor->op_params + 1, sizeof(float));
+
+                ggml_add_or_set(ctx, cgraph, isrc0, ggml_soft_max_ext_back(ctx, grad, tensor, scale, max_bias));
             }
             GGML_ASSERT((!src1 || !src1_needs_grads) && "backward pass for softmax mask not implemented");
         } break;
@@ -5626,7 +5641,7 @@ static void ggml_compute_backward(
                 const int32_t d1    = ggml_get_op_params_i32(tensor, 5);
                 const bool    is_2D = ggml_get_op_params_i32(tensor, 6) == 1;
 
-                ggml_add_or_set(ctx, cgraph, isrc1, ggml_im2col_back(ctx, src0, grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D));
+                ggml_add_or_set(ctx, cgraph, isrc1, ggml_im2col_back(ctx, grad, src0, src1->ne, s0, s1, p0, p1, d0, d1, is_2D));
             }
         } break;
         case GGML_OP_POOL_2D: {
@@ -5669,7 +5684,7 @@ static void ggml_compute_backward(
                 } break;
                 case GGML_UNARY_OP_SILU: {
                     if (src0_needs_grads) {
-                        ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, src0, grad));
+                        ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, grad, src0));
                     }
                 } break;
                 case GGML_UNARY_OP_EXP: {
@@ -5686,7 +5701,7 @@ static void ggml_compute_backward(
         } break;
         case GGML_OP_CROSS_ENTROPY_LOSS: {
             if (src0_needs_grads) {
-                ggml_add_or_set(ctx, cgraph, isrc0, ggml_cross_entropy_loss_back(ctx, src0, src1, grad));
+                ggml_add_or_set(ctx, cgraph, isrc0, ggml_cross_entropy_loss_back(ctx, grad, src0, src1));
             }
             GGML_ASSERT(!src1_needs_grads && "backward pass for labels not implemented");
         } break;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 4c8464d8b..0ed3e98e1 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -780,7 +780,7 @@ struct test_case {
             }
         }
         if (!any_params) {
-            printf("not supported [%s] \n", op_name);
+            printf("not supported [%s] \n", op_desc(out).c_str());
             supported = false;
         }
         if (!supported) {
@@ -1130,6 +1130,59 @@ struct test_get_rows : public test_case {
     }
 };
 
+// GGML_OP_GET_ROWS_BACK
+struct test_get_rows_back : public test_case {
+    const ggml_type type;
+    const int n; // cols
+    const int m; // rows
+    const int r; // rows to get
+    const int b; // batch size
+    const bool v; // view (non-contiguous src1)
+
+    std::string vars() override {
+        return VARS_TO_STR6(type, n, m, r, b, v);
+    }
+
+    test_get_rows_back(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int b = 1, bool v = false)
+        : type(type), n(n), m(m), r(r), b(b), v(v) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * in_forward = ggml_new_tensor_3d(ctx, type, n, m, b);
+        ggml_set_name(in_forward, "in_forward");
+
+        ggml_tensor * rows = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, r, b);
+        ggml_set_name(rows, "rows");
+        if (v) {
+            rows = ggml_view_2d(ctx, rows, r/2, b, rows->nb[1], 0);
+            ggml_set_name(rows, "view_of_rows");
+        }
+
+        ggml_tensor * grad = ggml_new_tensor_3d(ctx, type, n, r, b);
+        ggml_set_name(grad, "grad");
+
+        ggml_tensor * out = ggml_get_rows_back(ctx, grad, rows, in_forward);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->type == GGML_TYPE_I32) {
+                if (ggml_is_view_op(t->op)) { continue; }
+                // rows
+                std::vector<int> data(r*b);
+                for (int i = 0; i < r*b; i++) {
+                    data[i] = rand() % m;
+                }
+                ggml_backend_tensor_set(t, data.data(), 0, r * b * sizeof(int));
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+};
+
 // GGML_OP_ARGMAX
 struct test_argmax : public test_case {
     const ggml_type type;
@@ -1531,6 +1584,39 @@ struct test_scale : public test_case {
     }
 };
 
+// GGML_OP_SILU_BACK
+struct test_silu_back : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    float eps;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne, eps);
+    }
+
+    test_silu_back(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {64, 5, 4, 3},
+            float eps = 1e-6f)
+        : type(type), ne(ne), eps(eps) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * grad = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(grad, "grad");
+
+        ggml_tensor * out = ggml_silu_back(ctx, a, grad);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    bool grad_precise() override {
+        return true;
+    }
+};
+
 // GGML_OP_NORM
 struct test_norm : public test_case {
     const ggml_type type;
@@ -1583,11 +1669,56 @@ struct test_rms_norm : public test_case {
         return out;
     }
 
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, -10.f, 10.f);
+        }
+    }
+
+    float grad_eps() override {
+        return 1.0f;
+    }
+
     bool grad_precise() override {
         return true;
     }
 };
 
+// GGML_OP_RMS_NORM_BACK
+struct test_rms_norm_back : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    float eps;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne, eps);
+    }
+
+    test_rms_norm_back(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {64, 5, 4, 3},
+            float eps = 1e-6f)
+        : type(type), ne(ne), eps(eps) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(b, "b");
+
+        ggml_tensor * out = ggml_rms_norm_back(ctx, a, b, eps);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, -10.f, 10.f);
+        }
+    }
+};
+
 // GGML_OP_SSM_CONV
 struct test_ssm_conv : public test_case {
     const ggml_type type;
@@ -1855,10 +1986,11 @@ struct test_out_prod : public test_case {
     const int64_t n;
     const int64_t k;
     const std::array<int64_t, 2> bs; // dims 3 and 4
+    const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
     const bool trans_b;
 
     std::string vars() override {
-        return VARS_TO_STR7(type_a, type_b, m, n, k, bs, trans_b);
+        return VARS_TO_STR8(type_a, type_b, m, n, k, bs, nr, trans_b);
     }
 
     double max_nmse_err() override {
@@ -1868,8 +2000,9 @@ struct test_out_prod : public test_case {
     test_out_prod(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
             int64_t m = 32, int64_t n = 32, int64_t k = 32,
             std::array<int64_t, 2> bs = {10, 10},
+            std::array<int64_t, 2> nr = {2, 2},
             bool trans_b = false)
-        : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), trans_b(trans_b) {}
+        : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), trans_b(trans_b) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, m, k, bs[0], bs[1]);
@@ -1877,10 +2010,10 @@ struct test_out_prod : public test_case {
 
         ggml_tensor * b;
         if (trans_b) {
-            b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0], bs[1]);
+            b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
             b = ggml_transpose(ctx, b);
         } else {
-            b = ggml_new_tensor_4d(ctx, type_b, n, k, bs[0], bs[1]);
+            b = ggml_new_tensor_4d(ctx, type_b, n, k, bs[0]*nr[0], bs[1]*nr[1]);
         }
         ggml_set_name(b, "b");
 
@@ -2191,6 +2324,36 @@ struct test_soft_max : public test_case {
     }
 };
 
+// GGML_OP_SOFT_MAX_BACK
+struct test_soft_max_back : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    const float scale;
+    const float max_bias;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne, scale, max_bias);
+    }
+
+    test_soft_max_back(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {10, 5, 4, 3},
+            float scale = 1.0f,
+            float max_bias = 0.0f)
+        : type(type), ne(ne), scale(scale), max_bias(max_bias) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_soft_max_ext_back(ctx, a, b, scale, max_bias);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
 
 // GGML_OP_ROPE + GGML_OP_ROPE_BACK
 struct test_rope : public test_case {
@@ -2980,6 +3143,40 @@ struct test_cross_entropy_loss : public test_case {
     }
 };
 
+// GGML_OP_CROSS_ENTROPY_LOSS_BACK
+struct test_cross_entropy_loss_back : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_cross_entropy_loss_back(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {10, 5, 4, 3})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * grad = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+        ggml_set_name(grad, "grad");
+
+        ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(logits, "logits");
+
+        ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(labels, "labels");
+
+        // Ensure labels add up to 1:
+        labels = ggml_soft_max(ctx, labels);
+        ggml_set_name(labels, "labels_normalized");
+
+        ggml_tensor * out = ggml_cross_entropy_loss_back(ctx, grad, logits, labels);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // GGML_OP_OPT_STEP_ADAMW
 struct test_opt_step_adamw : public test_case {
     const ggml_type type;
@@ -3479,6 +3676,16 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_F32, 1, 8, 2, 1, false));
+    for (ggml_type type : all_types) {
+        for (bool v : {false, true}) {
+            test_cases.emplace_back(new test_get_rows_back(type, 256, 5, 4, 1, v));
+        }
+    }
+    for (bool v : {false, true}) {
+        test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_I32, 256, 5, 4, 1, v));
+    }
+
     for (ggml_type type_input : {GGML_TYPE_F32}) {
         for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
             for (int k0 : {1, 3}) {
@@ -3657,10 +3864,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     test_cases.emplace_back(new test_add1());
     test_cases.emplace_back(new test_scale());
+    test_cases.emplace_back(new test_silu_back());
 
-    for (float eps : {1e-6f, 1e-5f, 1e-3f, 1e-1f}) {
-        test_cases.emplace_back(new test_norm(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
-        test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
+    for (float eps : {0.0f, 1e-7f, 1e-4f, 1e-1f}) {
+        test_cases.emplace_back(new test_norm         (GGML_TYPE_F32, {64, 5, 4, 3}, eps));
+        test_cases.emplace_back(new test_rms_norm     (GGML_TYPE_F32, {64, 5, 4, 3}, eps));
+        test_cases.emplace_back(new test_rms_norm_back(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
     }
 
     test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 1536, 1, 1}, {4, 1536, 1, 1}));
@@ -3800,22 +4009,19 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, { 1,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
-
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, { 1,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, { 1,  1}, true));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10,  1}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
-            test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
+            for (int n : {1, 16}) {
+                for (int k : {1, 16}) {
+                    for (int bs2 : {1, 3}) {
+                        for (int bs3 : {1, 3}) {
+                            for (int nr2 : {1, 2}) {
+                                for (int nr3 : {1, 2}) {
+                                    test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, n, k, {bs2, bs3}, {nr2, nr3}));
+                                }
+                            }
+                        }
+                    }
+                }
+            }
         }
     }
 
@@ -3858,11 +4064,22 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             }
         }
     }
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true, 0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
+    for (float max_bias : {0.0f, 8.0f}) {
+        for (float scale : {1.0f, 0.1f}) {
+            for (int64_t ne0 : {16, 1024}) {
+                for (int64_t ne1 : {16, 1024}) {
+                    test_cases.emplace_back(new test_soft_max_back(GGML_TYPE_F32, {ne0,   ne1,   1, 1}, scale, max_bias));
+                    test_cases.emplace_back(new test_soft_max_back(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, scale, max_bias));
+                }
+            }
+        }
+    }
+
     for (bool fw : {true, false}) { // fw == forward
         bool all = true;
 
@@ -3953,7 +4170,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
-    test_cases.emplace_back(new test_cross_entropy_loss());
+    test_cases.emplace_back(new test_cross_entropy_loss     (GGML_TYPE_F32, {   10, 5, 4, 3}));
+    test_cases.emplace_back(new test_cross_entropy_loss     (GGML_TYPE_F32, {30000, 1, 1, 1}));
+    test_cases.emplace_back(new test_cross_entropy_loss_back(GGML_TYPE_F32, {   10, 5, 4, 3}));
+    test_cases.emplace_back(new test_cross_entropy_loss_back(GGML_TYPE_F32, {30000, 1, 1, 1}));
+
     test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}));
 
     // these tests are disabled to save execution time, but they can be handy for debugging

From 4dbc8b9cb71876e005724f4e8f73a3544646bcf5 Mon Sep 17 00:00:00 2001
From: RunningLeon <mnsheng@yeah.net>
Date: Fri, 17 Jan 2025 02:10:38 +0800
Subject: [PATCH 189/376] llama : add internlm3 support (#11233)

* support internlm3

* fix lint
---
 convert_hf_to_gguf.py | 60 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 4dc9837ab..95f112043 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2882,6 +2882,66 @@ class InternLM2Model(Model):
             return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("InternLM3ForCausalLM")
+class InternLM3Model(Model):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    def set_vocab(self):
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+                if "added_tokens_decoder" in tokenizer_config_json:
+                    for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items():
+                        if token_data.get("special"):
+                            token_id = int(token_id)
+                            token = token_data["content"]
+                            special_vocab._set_special_token(token, token_id)
+                            # update eos token
+                            if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids:
+                                special_vocab.special_token_ids["eos"] = token_id
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear" or self.hparams["rope_scaling"].get("rope_type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @Model.register("BertModel", "BertForMaskedLM", "CamembertModel")
 class BertModel(Model):
     model_arch = gguf.MODEL_ARCH.BERT

From 206bc53422521a67de5e2caba661e21d590d2bae Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 16 Jan 2025 15:16:39 -0600
Subject: [PATCH 190/376] vulkan: optimize coopmat2 q2_k dequant function
 (#11130)

---
 .../vulkan-shaders/dequant_funcs_cm2.comp      | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index 94b78598e..e768b8930 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -101,19 +101,25 @@ layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_
    block_q2_K block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ2_K_packed16 {
+   block_q2_K_packed16 block;
+};
+
 float16_t dequantFuncQ2_K(const in decodeBufQ2_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
+    decodeBufQ2_K_packed16 bl16 = decodeBufQ2_K_packed16(bl);
     const f16vec2 d = bl.block.d;
     const uint idx = coordInBlock[1];
-    const uint iqs = idx;
 
-    const uint qsi = (iqs / 128) * 32 + (iqs % 32);     // 0..31
-    const uint scalesi = iqs / 16;                      // 0..15
-    const uint qsshift = ((iqs % 128) / 32) * 2;        // 0,2,4,6
+    const uint scalesi = (idx & 0xF0) >> 4;             // 0..15
+    const uint qsshift = (idx & 0x60) >> 4;             // 0,2,4,6
+
+    uint qs = uint32_t(bl16.block.qs[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]);
+    qs = (qs >> qsshift) & 0x0303;
+    qs = unpack8(qs)[idx & 1];
 
-    uint32_t qs = bl.block.qs[qsi];
     const uint scales = bl.block.scales[scalesi];
-    float16_t ret = d.x * float16_t(scales & 0xF) * float16_t((qs >> qsshift) & 3) - d.y * float16_t(scales >> 4);
+    float16_t ret = d.x * float16_t(scales & 0xF) * float16_t(qs) - d.y * float16_t(scales >> 4);
     return ret;
 }
 

From 466300fe1416de2802b710215817db28d4496f41 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 16 Jan 2025 15:23:49 -0600
Subject: [PATCH 191/376] vulkan: optimize coopmat2 q4_k/q5_k dequant
 functions. (#11206)

Do masking on whole dwords, fetch all scales at once.
---
 .../vulkan-shaders/dequant_funcs_cm2.comp     | 80 +++++++++++--------
 .../src/ggml-vulkan/vulkan-shaders/types.comp | 10 +++
 2 files changed, 57 insertions(+), 33 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index e768b8930..175e31fa7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -163,39 +163,47 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4
    block_q4_K_packed16 block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed128 {
+   block_q4_K_packed128 block;
+};
+
 float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
     decodeBufQ4_K_packed16 bl16 = decodeBufQ4_K_packed16(bl);
+    decodeBufQ4_K_packed128 bl128 = decodeBufQ4_K_packed128(bl);
     const uint idx = coordInBlock[1];
 
     const uint b = (idx & 0x20) >> 5;            // 0,1
     const uint is = (idx & 0xE0) >> 5;         // 0..7
 
-    const f16vec2 loadd = bl.block.d;
+    uvec4 v = bl128.block.q4k[0];
+
+    const f16vec2 loadd = unpackFloat2x16(v.x);
 
     uint32_t sc;
     uint32_t mbyte;
 
-    uint32_t scidx0 = (is < 4) ? is : (is + 4);
-    uint32_t scidx1 = (is < 4) ? is : (is - 4);
-    uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-    uint32_t scidxshift1 = (is < 4) ? 0 : 2;
-    uint32_t mbidx0 = is + 4;
-    uint32_t mbidx1 = (is < 4) ? is + 4 : is;
-    uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-    uint32_t mbidxshift0 = (is < 4) ? 0 : 4;
-    uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-    uint32_t mbidxshift1 = (is < 4) ? 0 : 2;
+    uint32_t scale0 = v.y;
+    uint32_t scale4 = v.z;
+    uint32_t scale8 = v.w;
 
-    sc    = uint8_t((bl.block.scales[scidx0] & 0xF)                         | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1));
-    mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+    uint32_t sc_lo = scale0;
+    uint32_t mb_lo = scale4;
+    uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2);
+    uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2);
+
+    sc = is < 4 ? sc_lo : sc_hi;
+    mbyte = is < 4 ? mb_lo : mb_hi;
+    sc = sc >> (8 * (is & 3));
+    mbyte = mbyte >> (8 * (is & 3));
+    sc &= 0x3F;
+    mbyte &= 0x3F;
 
     const float16_t d = loadd.x * float16_t(sc);
     const float16_t m = loadd.y * float16_t(mbyte);
 
     uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]);
-    qs = (qs >> (b * 4)) & 0x0F0F;
-    qs = unpack8(qs)[idx & 1];
+    qs = (qs >> (b * 4 + 8 * (idx & 1))) & 0xF;
 
     float16_t ret = d * float16_t(qs) - m;
 
@@ -210,47 +218,53 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5
    block_q5_K_packed16 block;
 };
 
+layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed128 {
+   block_q5_K_packed128 block;
+};
+
 float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
     decodeBufQ5_K_packed16 bl16 = decodeBufQ5_K_packed16(bl);
+    decodeBufQ5_K_packed128 bl128 = decodeBufQ5_K_packed128(bl);
     const uint idx = coordInBlock[1];
 
     const uint b = (idx & 0x20) >> 5;          // 0,1
     const uint is = (idx & 0xE0) >> 5;         // 0..7
 
-    const uint32_t hm = 0x0101 << is;
+    uvec4 v = bl128.block.q5k[0];
 
-    const f16vec2 loadd = bl.block.d;
+    const f16vec2 loadd = unpackFloat2x16(v.x);
 
     uint32_t sc;
     uint32_t mbyte;
 
-    uint32_t scidx0 = (is < 4) ? is : (is + 4);
-    uint32_t scidx1 = (is < 4) ? is : (is - 4);
-    uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-    uint32_t scidxshift1 = (is < 4) ? 0 : 2;
-    uint32_t mbidx0 = is + 4;
-    uint32_t mbidx1 = (is < 4) ? is + 4 : is;
-    uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-    uint32_t mbidxshift0 = (is < 4) ? 0 : 4;
-    uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-    uint32_t mbidxshift1 = (is < 4) ? 0 : 2;
+    uint32_t scale0 = v.y;
+    uint32_t scale4 = v.z;
+    uint32_t scale8 = v.w;
 
-    sc    = uint8_t((bl.block.scales[scidx0] & 0xF)                         | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1));
-    mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+    uint32_t sc_lo = scale0;
+    uint32_t mb_lo = scale4;
+    uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2);
+    uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2);
+
+    sc = is < 4 ? sc_lo : sc_hi;
+    mbyte = is < 4 ? mb_lo : mb_hi;
+    sc = sc >> (8 * (is & 3));
+    mbyte = mbyte >> (8 * (is & 3));
+    sc &= 0x3F;
+    mbyte &= 0x3F;
 
     const float16_t d = loadd.x * float16_t(sc);
     const float16_t m = loadd.y * float16_t(mbyte);
 
     uint qh = uint32_t(bl16.block.qh[(idx & 0x1E) >> 1]);
-    qh = qh & hm;
-    qh = unpack8(qh)[idx & 1];
+    qh = ((qh >> is) & 0x101) << 4;
 
     uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]);
     qs = (qs >> (b * 4)) & 0x0F0F;
-    qs = unpack8(qs)[idx & 1];
+    qs = unpack8(qs | qh)[idx & 1];
 
-    float16_t ret = d * (float16_t(qs) + (qh != 0 ? float16_t(16) : float16_t(0))) - m;
+    float16_t ret = d * (float16_t(qs)) - m;
 
     return ret;
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
index f12e61bbe..1e35b6652 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
@@ -227,6 +227,11 @@ struct block_q4_K_packed32
     uint32_t qs[QUANT_K_Q4_K/2/4];
 };
 
+struct block_q4_K_packed128
+{
+    uvec4 q4k[9];
+};
+
 #if defined(DATA_A_Q4_K)
 #define QUANT_K QUANT_K_Q4_K
 #define A_TYPE block_q4_K
@@ -252,6 +257,11 @@ struct block_q5_K_packed16
     uint16_t qs[QUANT_K_Q5_K/2/2];
 };
 
+struct block_q5_K_packed128
+{
+    uvec4 q5k[11];
+};
+
 #if defined(DATA_A_Q5_K)
 #define QUANT_K QUANT_K_Q5_K
 #define A_TYPE block_q5_K

From bd38ddea0181bc717de7cae66fd4323975c85656 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 16 Jan 2025 15:47:10 -0600
Subject: [PATCH 192/376] vulkan: support copy from f32 to
 q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl (#11166)

* vulkan: support copy from f32 to q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl

Shaders are based on cpy.cu.

* vulkan: support copy from q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl to f32

* ggml: copy q->f32 assumes some contiguity in the destination
---
 ggml/src/ggml-cpu/ggml-cpu.c                  |  55 ++++
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  77 +++++-
 .../vulkan-shaders/copy_from_quant.comp       |  51 ++++
 .../vulkan-shaders/copy_to_quant.comp         | 237 ++++++++++++++++++
 .../vulkan-shaders/generic_unary_head.comp    |  20 ++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   5 +
 tests/test-backend-ops.cpp                    |   6 +
 7 files changed, 446 insertions(+), 5 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index dd9995562..0ed92b3ff 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -3967,6 +3967,57 @@ static void ggml_compute_forward_dup_bytes(
     }
 }
 
+static void ggml_compute_forward_dup_q(
+        const struct ggml_compute_params * params,
+              struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const enum ggml_type type = src0->type;
+    ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float;
+
+    size_t qk = ggml_blck_size(type);
+    const int64_t nr = ggml_nelements(src1) / qk;
+
+    // destination must be contiguous in the first dimension
+    GGML_ASSERT(nb10 == ggml_type_size(dst->type));
+    // must either have first dimension large enough to hold a row, or fully contiguous
+    GGML_ASSERT((ne10 % qk) == 0 || ggml_is_contiguous(dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+
+        uint32_t i = ir * qk;
+
+        const int64_t i03 = i/(ne00 * ne01 * ne02);
+        const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+        const int64_t i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+        const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+        const int64_t x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+        const int64_t i13 = i/(ne10 * ne11 * ne12);
+        const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+        const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+        const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+        const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
+
+        dequantize_row_q(
+                (const void *) ((char *) src0->data + x_offset),
+                     (float *) ((char *)  dst->data + dst_offset), qk);
+    }
+}
+
 static void ggml_compute_forward_dup(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
@@ -3993,6 +4044,10 @@ static void ggml_compute_forward_dup(
             } break;
         default:
             {
+                if (ggml_is_quantized(src0->type) && dst->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_dup_q(params, dst);
+                    break;
+                }
                 GGML_ABORT("fatal error");
             }
     }
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 649146d7b..8e3e91495 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -228,6 +228,8 @@ struct vk_device_struct {
     vk_pipeline pipeline_repeat_f32;
     vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
     vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16;
+    vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT];
     vk_pipeline pipeline_norm_f32;
     vk_pipeline pipeline_group_norm_f32;
     vk_pipeline pipeline_rms_norm_f32;
@@ -1965,6 +1967,20 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_1], "cpy_f32_q5_1", cpy_f32_q5_1_len, cpy_f32_q5_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_1), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q8_0], "cpy_f32_q8_0", cpy_f32_q8_0_len, cpy_f32_q8_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_IQ4_NL], "cpy_f32_iq4_nl", cpy_f32_iq4_nl_len, cpy_f32_iq4_nl_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_0], "cpy_q5_0_f32", cpy_q5_0_f32_len, cpy_q5_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_1], "cpy_q5_1_f32", cpy_q5_1_f32_len, cpy_q5_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_1), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q8_0], "cpy_q8_0_f32", cpy_q8_0_f32_len, cpy_q8_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_IQ4_NL], "cpy_iq4_nl_f32", cpy_iq4_nl_f32_len, cpy_iq4_nl_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1);
+
     ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_add_f32_norepeat, "add_f32_norepeat", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1);
@@ -3689,6 +3705,33 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_cpy_f16_f16;
         }
     }
+    if (src->type == GGML_TYPE_F32) {
+        switch (to) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_IQ4_NL:
+            return ctx->device->pipeline_cpy_f32_quant[to];
+        default:
+            break;
+        }
+    }
+
+    if (to == GGML_TYPE_F32) {
+        switch (src->type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_IQ4_NL:
+            return ctx->device->pipeline_cpy_quant_f32[src->type];
+        default:
+            break;
+        }
+    }
 
     std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl;
     GGML_ABORT("fatal error");
@@ -5160,7 +5203,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     }
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
     std::cerr << "), " << ggml_op_name(op) << ", " << (dryrun ? "dryrun" : "") << ")");
-    GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
+    GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
     GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0));  // NOLINT
     GGML_ASSERT(dst->buffer != nullptr);
     const uint64_t ne00 = src0->ne[0];
@@ -7905,12 +7948,36 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
             {
                 ggml_type src0_type = op->src[0]->type;
                 ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-                    return true;
+
+                if (src0_type == GGML_TYPE_F32) {
+                    switch (src1_type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_IQ4_NL:
+                        return true;
+                    default:
+                        break;
+                    }
                 }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
-                    return true;
+                if (src1_type == GGML_TYPE_F32) {
+                    switch (src0_type) {
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_IQ4_NL:
+                        return true;
+                    default:
+                        break;
+                    }
                 }
+
                 if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
                     return true;
                 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
new file mode 100644
index 000000000..c09bf496b
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
@@ -0,0 +1,51 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+#include "dequant_funcs.comp"
+
+#if defined(DATA_A_IQ4_NL)
+// 16 invocations needed for init_iq4nl_shmem
+layout(local_size_x = 16, local_size_y = 1, local_size_z = 1) in;
+#else
+layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
+#endif
+
+void main() {
+#if defined(DATA_A_IQ4_NL)
+    init_iq4nl_shmem();
+    if (gl_LocalInvocationIndex.x != 0) {
+        return;
+    }
+#endif
+
+    const uint idx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x * QUANT_K;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    uint dst_idx = get_doffset() + dst_idx(idx);
+    uint src_idx = src0_idx_quant(idx, QUANT_K);
+
+    const uint a_offset = 0;
+    const uint ib = src_idx;
+    const vec2 dm = get_dm(ib, a_offset);
+
+    [[unroll]] for (int j = 0; j < QUANT_K; j += 4) {
+        vec4 v = dequantize4(ib, j / QUANT_R, a_offset);
+        v = v * dm.x + vec4(dm.y);
+
+#if QUANT_R == 2
+        data_d[dst_idx + j/2 +             0] = v[0];
+        data_d[dst_idx + j/2 + QUANT_K/2 + 0] = v[1];
+        data_d[dst_idx + j/2 +             1] = v[2];
+        data_d[dst_idx + j/2 + QUANT_K/2 + 1] = v[3];
+#else
+        data_d[dst_idx + j + 0] = v[0];
+        data_d[dst_idx + j + 1] = v[1];
+        data_d[dst_idx + j + 2] = v[2];
+        data_d[dst_idx + j + 3] = v[3];
+#endif
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
new file mode 100644
index 000000000..ccf5b980a
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
@@ -0,0 +1,237 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+#if defined(DATA_A_IQ4_NL)
+// 16 invocations needed for init_iq4nl_shmem
+layout(local_size_x = 16, local_size_y = 1, local_size_z = 1) in;
+#else
+layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
+#endif
+
+layout (binding = 0) readonly buffer S {float data_s[];};
+layout (binding = 1) writeonly buffer Q {A_TYPE data_q[];};
+
+#if defined(DATA_A_Q4_0)
+void quantize(uint dst_idx, uint src_idx)
+{
+    float amax = 0.0;
+    float vmax = 0.0;
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q4_0; ++j) {
+        const float v = data_s[src_idx + j];
+        if (amax < abs(v)) {
+            amax = abs(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = (d != 0.0) ? 1.0/d : 0.0;
+
+    data_q[dst_idx].d = float16_t(d);
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q4_0/2; ++j) {
+        const float x0 = data_s[src_idx + 0              + j]*id;
+        const float x1 = data_s[src_idx + QUANT_K_Q4_0/2 + j]*id;
+
+        const uint xi0 = min(15, int(x0 + 8.5));
+        const uint xi1 = min(15, int(x1 + 8.5));
+
+        data_q[dst_idx].qs[j]  = uint8_t(xi0 | (xi1 << 4));
+    }
+}
+#endif
+
+#if defined(DATA_A_Q4_1)
+void quantize(uint dst_idx, uint src_idx)
+{
+    float vmin = 1.0/0.0;
+    float vmax = -vmin;
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q4_1; ++j) {
+        const float v = data_s[src_idx + j];
+
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = (d != 0.0) ? 1.0/d : 0.0;
+
+    data_q[dst_idx].d = float16_t(d);
+    data_q[dst_idx].m = float16_t(vmin);
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q4_1/2; ++j) {
+        const float x0 = (data_s[src_idx + 0              + j] - vmin)*id;
+        const float x1 = (data_s[src_idx + QUANT_K_Q4_1/2 + j] - vmin)*id;
+
+        const uint xi0 = min(15, int(x0 + 0.5));
+        const uint xi1 = min(15, int(x1 + 0.5));
+
+        data_q[dst_idx].qs[j]  = uint8_t(xi0 | (xi1 << 4));
+    }
+}
+#endif
+
+#if defined(DATA_A_Q5_0)
+void quantize(uint dst_idx, uint src_idx)
+{
+    float amax = 0.0;
+    float vmax = 0.0;
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q5_0; ++j) {
+        const float v = data_s[src_idx + j];
+        if (amax < abs(v)) {
+            amax = abs(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -16;
+    const float id = (d != 0.0) ? 1.0/d : 0.0;
+
+    data_q[dst_idx].d = float16_t(d);
+
+    uint32_t qh = 0;
+    [[unroll]] for (int j = 0; j < QUANT_K_Q5_0/2; ++j) {
+        const float x0 = data_s[src_idx + 0              + j]*id;
+        const float x1 = data_s[src_idx + QUANT_K_Q5_0/2 + j]*id;
+
+        const uint xi0 = min(31, int(x0 + 16.5));
+        const uint xi1 = min(31, int(x1 + 16.5));
+
+        data_q[dst_idx].qs[j]  = uint8_t((xi0 & 0xf) | ((xi1 & 0xf) << 4));
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QUANT_K_Q5_0/2);
+    }
+    data_q[dst_idx].qh[0] = uint16_t(qh & 0xFFFF);
+    data_q[dst_idx].qh[1] = uint16_t(qh >> 16);
+}
+#endif
+
+#if defined(DATA_A_Q5_1)
+void quantize(uint dst_idx, uint src_idx)
+{
+    float min = data_s[src_idx + 0];
+    float max = min;
+
+    [[unroll]] for (int j = 1; j < QUANT_K_Q5_1; ++j) {
+        const float v = data_s[src_idx + j];
+        min = v < min ? v : min;
+        max = v > max ? v : max;
+    }
+
+    const float d  = (max - min) / 31;
+    const float id = (d != 0) ? 1.0/d : 0.0;
+
+    data_q[dst_idx].d = float16_t(d);
+    data_q[dst_idx].m = float16_t(min);
+
+    uint32_t qh = 0;
+    [[unroll]] for (int j = 0; j < QUANT_K_Q5_1/2; ++j) {
+        const float x0 = (data_s[src_idx + 0              + j] - min)*id;
+        const float x1 = (data_s[src_idx + QUANT_K_Q5_1/2 + j] - min)*id;
+
+        const uint xi0 = uint(x0 + 0.5);
+        const uint xi1 = uint(x1 + 0.5);
+
+        data_q[dst_idx].qs[j]  = uint8_t((xi0 & 0xf) | ((xi1 & 0xf) << 4));
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QUANT_K_Q5_1/2);
+    }
+    data_q[dst_idx].qh = qh;
+}
+#endif
+
+#if defined(DATA_A_Q8_0)
+void quantize(uint dst_idx, uint src_idx)
+{
+    float amax = 0.0; // absolute max
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q8_0; j++) {
+        const float v = data_s[src_idx + j];
+        amax = max(amax, abs(v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = (d != 0.0) ? 1.0/d : 0.0;
+
+    data_q[dst_idx].d = float16_t(d);
+
+    [[unroll]] for (int j = 0; j < QUANT_K_Q8_0; ++j) {
+        const float x0 = data_s[src_idx + j]*id;
+
+        data_q[dst_idx].qs[j] = int8_t(round(x0));
+    }
+}
+#endif
+
+#if defined(DATA_A_IQ4_NL)
+uint best_index(float x) {
+    if (x <= kvalues_iq4nl[0]) return 0;
+    if (x >= kvalues_iq4nl[15]) return 15;
+    int ml = 0, mu = 15;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < kvalues_iq4nl[mav]) mu = mav; else ml = mav;
+    }
+    return x - kvalues_iq4nl[mu-1] < kvalues_iq4nl[mu] - x ? mu-1 : mu;
+}
+
+void quantize(uint dst_idx, uint src_idx)
+{
+    float amax = 0.0;
+    float vmax = 0.0;
+
+    [[unroll]] for (int j = 0; j < QUANT_K_IQ4_NL; ++j) {
+        const float v = data_s[src_idx + j];
+        if (amax < abs(v)) {
+            amax = abs(v);
+            vmax = v;
+        }
+    }
+
+    float d = vmax / kvalues_iq4nl[0];
+    const float id = (d != 0.0) ? 1.0/d : 0.0;
+
+    float sumqx = 0, sumq2 = 0;
+    [[unroll]] for (int j = 0; j < QUANT_K_IQ4_NL/2; ++j) {
+        const float x0 = data_s[src_idx + 0                + j]*id;
+        const float x1 = data_s[src_idx + QUANT_K_IQ4_NL/2 + j]*id;
+        const uint xi0 = best_index(x0);
+        const uint xi1 = best_index(x1);
+        data_q[dst_idx].qs[j] = uint8_t(xi0 | (xi1 << 4));
+        const float v0 = kvalues_iq4nl[xi0];
+        const float v1 = kvalues_iq4nl[xi1];
+        const float w0 = data_s[src_idx + 0                + j]*data_s[src_idx + 0                + j];
+        const float w1 = data_s[src_idx + QUANT_K_IQ4_NL/2 + j]*data_s[src_idx + QUANT_K_IQ4_NL/2 + j];
+        sumqx += w0*v0*data_s[src_idx + j] + w1*v1*data_s[src_idx + QUANT_K_IQ4_NL/2 + j];
+        sumq2 += w0*v0*v0 + w1*v1*v1;
+    }
+
+    data_q[dst_idx].d = float16_t(sumq2 > 0 ? sumqx/sumq2 : d);
+
+}
+#endif
+
+void main() {
+#if defined(DATA_A_IQ4_NL)
+    init_iq4nl_shmem();
+    if (gl_LocalInvocationIndex.x != 0) {
+        return;
+    }
+#endif
+
+    const uint idx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x * QUANT_K;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    uint dst_idx = dst_idx_quant(idx, QUANT_K);
+    uint src_idx = get_aoffset() + src0_idx(idx);
+
+    quantize(dst_idx, src_idx);
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
index 68d1bc9f1..8dc9d360d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp
@@ -54,3 +54,23 @@ uint dst_idx(uint idx) {
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
 }
+
+uint src0_idx_quant(uint idx, uint qk) {
+    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + (i00/qk)*p.nb00;
+}
+
+uint dst_idx_quant(uint idx, uint qk) {
+    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + (i10/qk)*p.nb10;
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 243839917..b7890ef1e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -417,6 +417,11 @@ void process_shaders() {
     string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
     string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
 
+    for (std::string t : {"q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) {
+        string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+        string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }
+
     string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
     string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 0ed3e98e1..5cde8289f 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3808,6 +3808,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
         }
     }
+    for (ggml_type type_dst : {GGML_TYPE_F32}) {
+        for (ggml_type type_src : all_types) {
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
+        }
+    }
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : {GGML_TYPE_F16, GGML_TYPE_F32}) {
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {1, 0, 2, 3})); // cpy not-contiguous

From 7a689c415e2aecea1a5ae438542afeaf69815d52 Mon Sep 17 00:00:00 2001
From: musoles <135031143+musoles@users.noreply.github.com>
Date: Fri, 17 Jan 2025 00:10:49 +0000
Subject: [PATCH 193/376] README : added kalavai to infrastructure list
 (#11216)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 413a16422..784669ce1 100644
--- a/README.md
+++ b/README.md
@@ -204,6 +204,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
 - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
 - [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
+- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
 
 </details>
 

From 960ec65273a9554ff2510ba285a970289256ebfb Mon Sep 17 00:00:00 2001
From: David Renshaw <dwrenshaw@gmail.com>
Date: Fri, 17 Jan 2025 02:12:01 -0500
Subject: [PATCH 194/376] llama : fix deprecation message: vocabable -> vocab
 (#11269)

---
 include/llama.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/llama.h b/include/llama.h
index 352c3417e..be6802eef 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -961,7 +961,7 @@ extern "C" {
     LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab);
     LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab);
 
-    DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocabable_get_text instead");
+    DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead");
     DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead");
     DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead");
     DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead");

From a133566d34a1dd3693c504786963bf1b7b7d8c0e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 17 Jan 2025 09:28:00 +0200
Subject: [PATCH 195/376] vocab : fix double-eos check (#11273)

ggml-ci
---
 src/llama-vocab.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 4969d2628..9a680aed4 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -439,7 +439,7 @@ struct llm_tokenizer_bpe_session {
                 "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
                 "Are you sure this is what you want?\n", __FUNCTION__);
         }
-        if (vocab.get_add_bos() && output.size() >= 2 && *(output.end()-2) == vocab.token_eos()) {
+        if (vocab.get_add_eos() && output.size() >= 2 && *(output.end()-2) == vocab.token_eos()) {
             LLAMA_LOG_WARN(
                 "%s: Added a EOS token to the prompt as specified by the model but the prompt "
                 "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "

From 667d72846c06b2cf4f7c8a4265e210991a49706b Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Fri, 17 Jan 2025 10:57:09 +0200
Subject: [PATCH 196/376] rpc : early register backend devices (#11262)

Early register RPC devices and do not propagate RPC specifics in the
llama model structures.

ref: #10609
---
 common/arg.cpp                       | 27 +++++++++++++++++-
 common/common.cpp                    |  1 -
 common/common.h                      |  1 -
 examples/llama-bench/llama-bench.cpp | 37 ++++++++++++++++++++++---
 ggml/include/ggml-backend.h          |  2 ++
 ggml/src/ggml-backend-impl.h         |  1 -
 include/llama.h                      |  3 --
 src/llama-model.cpp                  |  1 -
 src/llama-model.h                    |  2 --
 src/llama.cpp                        | 41 ----------------------------
 10 files changed, 61 insertions(+), 55 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index dd10b6352..9069950eb 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -376,6 +376,30 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     return devices;
 }
 
+static void add_rpc_devices(std::string servers) {
+    auto rpc_servers = string_split<std::string>(servers, ',');
+    if (rpc_servers.empty()) {
+        throw std::invalid_argument("no RPC servers specified");
+    }
+    ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
+    if (!rpc_reg) {
+        throw std::invalid_argument("failed to find RPC backend");
+    }
+    typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
+    ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
+    if (!ggml_backend_rpc_add_device_fn) {
+        throw std::invalid_argument("failed to find RPC device add function");
+    }
+    for (const auto & server : rpc_servers) {
+        ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
+        if (dev) {
+            ggml_backend_device_register(dev);
+        } else {
+            throw std::invalid_argument("failed to register RPC device");
+        }
+    }
+}
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
@@ -1385,7 +1409,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             {"--rpc"}, "SERVERS",
             "comma separated list of RPC servers",
             [](common_params & params, const std::string & value) {
-                params.rpc_servers = value;
+                add_rpc_devices(value);
+                GGML_UNUSED(params);
             }
         ).set_env("LLAMA_ARG_RPC"));
     }
diff --git a/common/common.cpp b/common/common.cpp
index a6f9252b2..451826d5d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1043,7 +1043,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     if (params.n_gpu_layers != -1) {
         mparams.n_gpu_layers = params.n_gpu_layers;
     }
-    mparams.rpc_servers     = params.rpc_servers.c_str();
     mparams.main_gpu        = params.main_gpu;
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
diff --git a/common/common.h b/common/common.h
index 4fab1319a..691141d6b 100644
--- a/common/common.h
+++ b/common/common.h
@@ -246,7 +246,6 @@ struct common_params {
     std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
-    std::string rpc_servers          = ""; // comma separated list of RPC servers                           // NOLINT
 
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index a3b4c5ac8..4ac19ca86 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -683,7 +683,7 @@ struct cmd_params_instance {
     bool               cpu_strict;
     int                poll;
     int                n_gpu_layers;
-    std::string        rpc_servers;
+    std::string        rpc_servers_str;
     llama_split_mode   split_mode;
     int                main_gpu;
     bool               no_kv_offload;
@@ -696,8 +696,37 @@ struct cmd_params_instance {
         llama_model_params mparams = llama_model_default_params();
 
         mparams.n_gpu_layers = n_gpu_layers;
-        if (!rpc_servers.empty()) {
-            mparams.rpc_servers = rpc_servers.c_str();
+        if (!rpc_servers_str.empty()) {
+            auto rpc_servers = string_split<std::string>(rpc_servers_str, ',');
+
+            // add RPC devices
+            if (!rpc_servers.empty()) {
+                ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
+                if (!rpc_reg) {
+                    fprintf(stderr, "%s: failed to find RPC backend\n", __func__);
+                    exit(1);
+                }
+
+                typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
+                ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
+                if (!ggml_backend_rpc_add_device_fn) {
+                    fprintf(stderr, "%s: failed to find RPC device add function\n", __func__);
+                    exit(1);
+                }
+                static std::vector<ggml_backend_dev_t> devices;
+                devices.clear();
+                for (const std::string & server : rpc_servers) {
+                    ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
+                    if (dev) {
+                        devices.push_back(dev);
+                    } else {
+                        fprintf(stderr, "%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
+                        exit(1);
+                    }
+                }
+                devices.push_back(nullptr);
+                mparams.devices = devices.data();
+            }
         }
         mparams.split_mode   = split_mode;
         mparams.main_gpu     = main_gpu;
@@ -708,7 +737,7 @@ struct cmd_params_instance {
     }
 
     bool equal_mparams(const cmd_params_instance & other) const {
-        return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers &&
+        return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str &&
                split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap &&
                tensor_split == other.tensor_split;
     }
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 7221a0830..fc9571c82 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -203,6 +203,8 @@ extern "C" {
     // Backend registry
     //
 
+    GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
+
     // Backend (reg) enumeration
     GGML_API size_t             ggml_backend_reg_count(void);
     GGML_API ggml_backend_reg_t ggml_backend_reg_get(size_t index);
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
index 36d72e95f..d1c2d76d8 100644
--- a/ggml/src/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -208,7 +208,6 @@ extern "C" {
 
     // Internal backend registry API
     GGML_API void ggml_backend_register(ggml_backend_reg_t reg);
-    GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
 
     // Add backend dynamic loading support to the backend
 
diff --git a/include/llama.h b/include/llama.h
index be6802eef..298b8d1bc 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -288,9 +288,6 @@ extern "C" {
         // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
         const float * tensor_split;
 
-        // comma separated list of RPC servers to use for offloading
-        const char * rpc_servers;
-
         // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
         // If the provided progress_callback returns true, model loading continues.
         // If it returns false, model loading is immediately aborted.
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index f90f5e746..c2d23a8d3 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -3717,7 +3717,6 @@ struct llama_model_params llama_model_default_params() {
         /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
-        /*.rpc_servers                 =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
         /*.progress_callback_user_data =*/ nullptr,
         /*.kv_overrides                =*/ nullptr,
diff --git a/src/llama-model.h b/src/llama-model.h
index 4cc8abb75..a7c304447 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -323,8 +323,6 @@ struct llama_model {
     // gguf metadata
     std::unordered_map<std::string, std::string> gguf_kv;
 
-    std::vector<std::string> rpc_servers;
-
     // list of devices used in this model
     std::vector<ggml_backend_dev_t> devices;
 
diff --git a/src/llama.cpp b/src/llama.cpp
index fede23d19..e8cfe5012 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -9399,47 +9399,6 @@ static struct llama_model * llama_model_load_from_file_impl(
         };
     }
 
-    if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
-        // split the servers set them into model->rpc_servers
-        std::string servers(params.rpc_servers);
-        size_t pos = 0;
-        while ((pos = servers.find(',')) != std::string::npos) {
-            std::string server = servers.substr(0, pos);
-            model->rpc_servers.push_back(server);
-            servers.erase(0, pos + 1);
-        }
-        model->rpc_servers.push_back(servers);
-    }
-
-    // add RPC devices
-    if (!model->rpc_servers.empty()) {
-        ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
-        if (!rpc_reg) {
-            LLAMA_LOG_ERROR("%s: failed to find RPC backend\n", __func__);
-            llama_model_free(model);
-            return nullptr;
-        }
-
-        typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
-        ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
-        if (!ggml_backend_rpc_add_device_fn) {
-            LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__);
-            llama_model_free(model);
-            return nullptr;
-        }
-
-        for (const std::string & server : model->rpc_servers) {
-            ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
-            if (dev) {
-                model->devices.push_back(dev);
-            } else {
-                LLAMA_LOG_ERROR("%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
-                llama_model_free(model);
-                return nullptr;
-            }
-        }
-    }
-
     // create list of devices to use with this model
     if (params.devices) {
         for (ggml_backend_dev_t * dev = params.devices; *dev; ++dev) {

From 3edfa7d3753c29e44b964c0ff424d2ea8d5fdee6 Mon Sep 17 00:00:00 2001
From: codezjx <code.zjx@gmail.com>
Date: Fri, 17 Jan 2025 20:57:56 +0800
Subject: [PATCH 197/376] llama.android: add field formatChat to control
 whether to parse special tokens when send message (#11270)

---
 examples/llama.android/llama/src/main/cpp/llama-android.cpp | 6 ++++--
 .../llama/src/main/java/android/llama/cpp/LLamaAndroid.kt   | 5 +++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
index 99b14961d..2a73983a9 100644
--- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -347,6 +347,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
         jlong context_pointer,
         jlong batch_pointer,
         jstring jtext,
+        jboolean format_chat,
         jint n_len
     ) {
 
@@ -356,7 +357,8 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
     const auto context = reinterpret_cast<llama_context *>(context_pointer);
     const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
 
-    const auto tokens_list = common_tokenize(context, text, 1);
+    bool parse_special = (format_chat == JNI_TRUE);
+    const auto tokens_list = common_tokenize(context, text, true, parse_special);
 
     auto n_ctx = llama_n_ctx(context);
     auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
@@ -368,7 +370,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
     }
 
     for (auto id : tokens_list) {
-        LOGi("%s", common_token_to_piece(context, id).c_str());
+        LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id);
     }
 
     common_batch_clear(*batch);
diff --git a/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
index cf520e459..b964d93e3 100644
--- a/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
+++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
@@ -65,6 +65,7 @@ class LLamaAndroid {
         context: Long,
         batch: Long,
         text: String,
+        formatChat: Boolean,
         nLen: Int
     ): Int
 
@@ -115,10 +116,10 @@ class LLamaAndroid {
         }
     }
 
-    fun send(message: String): Flow<String> = flow {
+    fun send(message: String, formatChat: Boolean = false): Flow<String> = flow {
         when (val state = threadLocalState.get()) {
             is State.Loaded -> {
-                val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
+                val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen))
                 while (ncur.value <= nlen) {
                     val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur)
                     if (str == null) {

From 44e18ef93995f3040660750b527e5becf85899d0 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 18 Jan 2025 02:26:50 -0600
Subject: [PATCH 198/376] vulkan: fix coopmat2 flash attention for
 non-contiguous inputs (#11281)

Add code similar to mul_mm_cm2 to force alignment of strides, to avoid
a performance regression.

Add noncontiguous FA tests in test-backend-ops.

Fixes #11268.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 43 ++++++++++++++++---
 .../vulkan-shaders/flash_attn_cm2.comp        | 20 +++++++++
 tests/test-backend-ops.cpp                    | 31 ++++++++++---
 3 files changed, 82 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 8e3e91495..437e9cdcc 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -386,10 +386,13 @@ struct vk_flash_attn_push_constants {
     uint32_t nev3;
     uint32_t nem1;
 
+    uint32_t nb01;
     uint32_t nb02;
     uint32_t nb03;
+    uint32_t nb11;
     uint32_t nb12;
     uint32_t nb13;
+    uint32_t nb21;
     uint32_t nb22;
     uint32_t nb23;
     uint32_t nb31;
@@ -4809,7 +4812,14 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     }
     assert(pipelines);
 
-    bool aligned = (KV % pipelines[1]->align) == 0;
+    const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type));
+    const uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type));
+    const uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type));
+
+    bool aligned = (KV % pipelines[1]->align) == 0 &&
+                   // the "aligned" shader variant will forcibly align strides, for performance
+                   (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0;
+
     vk_pipeline pipeline = pipelines[aligned];
     assert(pipeline);
 
@@ -4845,15 +4855,15 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     if (ctx->device->uma) {
         ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset);
-        ggml_vk_host_get(ctx->device, k->data, d_K, q_buf_offset);
-        ggml_vk_host_get(ctx->device, v->data, d_V, q_buf_offset);
-        ggml_vk_host_get(ctx->device, dst->data, d_D, q_buf_offset);
+        ggml_vk_host_get(ctx->device, k->data, d_K, k_buf_offset);
+        ggml_vk_host_get(ctx->device, v->data, d_V, v_buf_offset);
+        ggml_vk_host_get(ctx->device, dst->data, d_D, d_buf_offset);
         Q_uma = d_Q != nullptr;
         K_uma = d_K != nullptr;
         V_uma = d_V != nullptr;
         D_uma = d_D != nullptr;
         if (mask) {
-            ggml_vk_host_get(ctx->device, mask->data, d_M, q_buf_offset);
+            ggml_vk_host_get(ctx->device, mask->data, d_M, m_buf_offset);
             M_uma = d_M != nullptr;
         }
     }
@@ -4891,7 +4901,18 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         }
     }
 
-    const vk_flash_attn_push_constants pc = { N, KV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, (uint32_t)neq2, (uint32_t)neq3, (uint32_t)nek2, (uint32_t)nek3, (uint32_t)nev2, (uint32_t)nev3, nem1, (uint32_t)nbq2, (uint32_t)nbq3, (uint32_t)nbk2, (uint32_t)nbk3, (uint32_t)nbv2, (uint32_t)nbv3, nbm1, scale, max_bias, logit_softcap, mask != nullptr, n_head_log2, m0, m1 };
+    const vk_flash_attn_push_constants pc = { N, KV,
+                                              (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
+                                              (uint32_t)neq2, (uint32_t)neq3,
+                                              (uint32_t)nek2, (uint32_t)nek3,
+                                              (uint32_t)nev2, (uint32_t)nev3,
+                                              nem1,
+                                              q_stride, (uint32_t)nbq2, (uint32_t)nbq3,
+                                              k_stride, (uint32_t)nbk2, (uint32_t)nbk3,
+                                              v_stride, (uint32_t)nbv2, (uint32_t)nbv3,
+                                              nbm1,
+                                              scale, max_bias, logit_softcap,
+                                              mask != nullptr, n_head_log2, m0, m1 };
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
                                 {
                                     vk_subbuffer{d_Q, q_buf_offset, VK_WHOLE_SIZE},
@@ -8668,6 +8689,7 @@ static void ggml_vk_check_results_1(ggml_tensor * tensor) {
     ggml_tensor * src0 = tensor->src[0];
     ggml_tensor * src1 = tensor->src[1];
     ggml_tensor * src2 = tensor->src[2];
+    ggml_tensor * src3 = tensor->src[3];
 
     void * tensor_data = tensor->data;
 
@@ -8730,6 +8752,9 @@ static void ggml_vk_check_results_1(ggml_tensor * tensor) {
                         if (src2 != nullptr) {
                             std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
                         }
+                        if (src3 != nullptr) {
+                            std::cerr << "src3=" << src3 << " src3->name=" << src3->name << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
+                        }
                         std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
                         std::cerr << std::endl << "Result:" << std::endl;
                         ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
@@ -8774,6 +8799,9 @@ static void ggml_vk_check_results_1(ggml_tensor * tensor) {
         if (src2 != nullptr) {
             std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
         }
+        if (src3 != nullptr) {
+            std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
+        }
         std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
         std::cerr << std::endl << "Result:" << std::endl;
         ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
@@ -8796,6 +8824,9 @@ static void ggml_vk_check_results_1(ggml_tensor * tensor) {
         if (src2 != nullptr) {
             std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
         }
+        if (src3 != nullptr) {
+            std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
+        }
         std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
         std::cerr << std::endl << "Result:" << std::endl;
         ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index c5be8131b..ca3a59b8f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -42,10 +42,13 @@ layout (push_constant) uniform parameter {
     uint32_t nev3;
     uint32_t nem1;
 
+    uint32_t nb01;
     uint32_t nb02;
     uint32_t nb03;
+    uint32_t nb11;
     uint32_t nb12;
     uint32_t nb13;
+    uint32_t nb21;
     uint32_t nb22;
     uint32_t nb23;
     uint32_t nb31;
@@ -146,6 +149,23 @@ void main() {
     tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, D);
     tensorLayoutV = setTensorLayoutDimensionNV(tensorLayoutV, KV, D);
 
+    // nb?1 are already divided by the type size and are in units of elements
+    uint32_t q_stride = p.nb01;
+    uint32_t k_stride = p.nb11;
+    uint32_t v_stride = p.nb21;
+    // hint to the compiler that strides are aligned for the aligned variant of the shader
+    if (Clamp != gl_CooperativeMatrixClampModeConstantNV)
+    {
+        q_stride &= ~7;
+#if !defined(BLOCK_SIZE)
+        k_stride &= ~7;
+        v_stride &= ~7;
+#endif
+    }
+    tensorLayoutQ = setTensorLayoutStrideNV(tensorLayoutQ, q_stride, 1);
+    tensorLayoutK = setTensorLayoutStrideNV(tensorLayoutK, k_stride, 1);
+    tensorLayoutV = setTensorLayoutStrideNV(tensorLayoutV, v_stride, 1);
+
     coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Q;
     coopmat<float16_t, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Qf16;
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 5cde8289f..74d1bee39 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3046,9 +3046,10 @@ struct test_flash_attn_ext : public test_case {
     const float logit_softcap; // Gemma 2
 
     const ggml_type type_KV;
+    std::array<int32_t, 4> permute;
 
     std::string vars() override {
-        return VARS_TO_STR8(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV);
+        return VARS_TO_STR9(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV, permute);
     }
 
     double max_nmse_err() override {
@@ -3063,19 +3064,33 @@ struct test_flash_attn_ext : public test_case {
     }
 
     test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8,
-                        bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
-        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV) {}
+                        bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16,
+                        std::array<int32_t, 4> permute = {0, 1, 2, 3})
+        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV), permute(permute) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
 
-        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1);
+        auto const &create_permuted = [&](ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) -> ggml_tensor * {
+            int64_t ne[4] = {ne0, ne1, ne2, ne3};
+            int64_t ne_perm[4];
+            for (int i = 0; i < 4; ++i) {
+                ne_perm[permute[i]] = ne[i];
+            }
+            ggml_tensor * t = ggml_new_tensor_4d(ctx, type, ne_perm[0], ne_perm[1], ne_perm[2], ne_perm[3]);
+            if (permute != std::array<int32_t, 4>{0, 1, 2, 3}) {
+                t = ggml_permute(ctx, t, permute[0], permute[1], permute[2], permute[3]);
+            }
+            return t;
+        };
+
+        ggml_tensor * q = create_permuted(GGML_TYPE_F32, hs_padded, nb, nh, 1);
         ggml_set_name(q, "q");
 
-        ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
+        ggml_tensor * k = create_permuted(type_KV,       hs_padded, kv, nh, 1);
         ggml_set_name(k, "k");
 
-        ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
+        ggml_tensor * v = create_permuted(type_KV,       hs_padded, kv, nh, 1);
         ggml_set_name(v, "v");
 
         ggml_tensor * m = nullptr;
@@ -4167,6 +4182,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                             for (int nb : { 1, 3, 32, 35, }) {
                                 for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
                                     test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV));
+                                    // run fewer test cases permuted
+                                    if (mask == true && max_bias == 0.0f && logit_softcap == 0 && kv == 512) {
+                                        test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV, {0, 2, 1, 3}));
+                                    }
                                 }
                             }
                         }

From 6390a998bfc63241fde8509022b0768a71bf20bb Mon Sep 17 00:00:00 2001
From: LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 18 Jan 2025 18:20:57 +0800
Subject: [PATCH 199/376] tts : add guide tokens support (#11186)

* Added the ability to use guide tokens for OuteTTS, greatly improving TTS recitation accuracy over long input sequences.

* applied linting suggestions, updated to latest llama_vocab changes, added a safety check, added newline to guide token start
---
 common/arg.cpp       |  7 +++++++
 common/common.h      |  2 ++
 examples/tts/tts.cpp | 45 +++++++++++++++++++++++++++++++++++++++++++-
 3 files changed, 53 insertions(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 9069950eb..dede335fb 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2254,6 +2254,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.vocoder.model = value;
         }
     ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
+     add_opt(common_arg(
+        {"--tts-use-guide-tokens"},
+        "Use guide tokens to improve TTS word recall",
+        [](common_params & params) {
+            params.vocoder.use_guide_tokens = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
 
     // model-specific
     add_opt(common_arg(
diff --git a/common/common.h b/common/common.h
index 691141d6b..3bcc637cc 100644
--- a/common/common.h
+++ b/common/common.h
@@ -184,6 +184,8 @@ struct common_params_vocoder {
 
     std::string model     = ""; // model path                                                // NOLINT
     std::string model_url = ""; // model url to download                                     // NOLINT
+
+    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
 };
 
 struct common_params {
diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp
index 5a9161181..f78f76303 100644
--- a/examples/tts/tts.cpp
+++ b/examples/tts/tts.cpp
@@ -425,6 +425,33 @@ static void prompt_init(llama_tokens & prompt, const llama_vocab * vocab) {
     prompt_add(prompt, vocab, "<|im_start|>\n", true, true);
 }
 
+static std::vector<llama_token> prepare_guide_tokens(const llama_vocab * vocab, const std::string & str) {
+    const std::string& delimiter = "<|text_sep|>";
+
+    std::vector<llama_token> result;
+    size_t start = 0;
+    size_t end = str.find(delimiter);
+
+    //first token is always a newline, as it was not previously added
+    result.push_back(common_tokenize(vocab, "\n", false, true)[0]);
+
+    while (end != std::string::npos) {
+        std::string current_word = str.substr(start, end - start);
+        auto tmp = common_tokenize(vocab, current_word, false, true);
+        result.push_back(tmp[0]);
+        start = end + delimiter.length();
+        end = str.find(delimiter, start);
+    }
+
+    // Add the last part
+    std::string current_word = str.substr(start);
+    auto tmp = common_tokenize(vocab, current_word, false, true);
+    if (tmp.size() > 0) {
+        result.push_back(tmp[0]);
+    }
+    return result;
+}
+
 int main(int argc, char ** argv) {
     common_params params;
 
@@ -494,6 +521,7 @@ int main(int argc, char ** argv) {
     const auto t_main_start = ggml_time_us();
 
     std::vector<llama_token> codes;
+    std::vector<llama_token> guide_tokens;
 
     // process prompt and generate voice codes
     {
@@ -508,6 +536,9 @@ int main(int argc, char ** argv) {
         // convert the input text into the necessary format expected by OuteTTS
         {
             std::string prompt_clean = process_text(params.prompt);
+            if (params.vocoder.use_guide_tokens) {
+                guide_tokens = prepare_guide_tokens(vocab, prompt_clean);
+            }
 
             LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
 
@@ -717,6 +748,8 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
         int n_past   = batch.n_tokens;
         int n_decode = 0;
 
+        bool next_token_uses_guide_token = true;
+
         while (n_decode <= n_predict) {
             // prepare the next batch
             common_batch_clear(batch);
@@ -728,7 +761,17 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
                     continue;
                 }
 
-                const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
+                llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
+
+                //guide tokens help prevent hallucinations by forcing the TTS to use the correct word
+                if (!guide_tokens.empty() && next_token_uses_guide_token && !llama_vocab_is_control(vocab, new_token_id) && !llama_vocab_is_eog(vocab, new_token_id)) {
+                    llama_token guide_token = guide_tokens[0];
+                    guide_tokens.erase(guide_tokens.begin());
+                    new_token_id = guide_token; //ensure correct word fragment is used
+                }
+
+                //this is the token id that always precedes a new word
+                next_token_uses_guide_token = (new_token_id == 198);
 
                 common_sampler_accept(smpl[i], new_token_id, true);
 

From f26c87417999209e7f4576b4f3ecf7a5b9c66a29 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 18 Jan 2025 13:18:32 +0200
Subject: [PATCH 200/376] scripts : restore hf.sh (#11288)

ggml-ci
---
 scripts/hf.sh | 112 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100755 scripts/hf.sh

diff --git a/scripts/hf.sh b/scripts/hf.sh
new file mode 100755
index 000000000..b251925fa
--- /dev/null
+++ b/scripts/hf.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+#
+# Shortcut for downloading HF models
+#
+# Usage:
+#   ./llama-cli -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#
+
+# all logs go to stderr
+function log {
+    echo "$@" 1>&2
+}
+
+function usage {
+    log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [--outdir <dir> [-h|--help]"
+    exit 1
+}
+
+# check for curl or wget
+function has_cmd {
+    if ! [ -x "$(command -v $1)" ]; then
+        return 1
+    fi
+}
+
+if has_cmd wget; then
+    cmd="wget -q -c -O %s/%s %s"
+elif has_cmd curl; then
+    cmd="curl -C - -f --output-dir %s -o %s -L %s"
+else
+    log "[E] curl or wget not found"
+    exit 1
+fi
+
+url=""
+repo=""
+file=""
+outdir="."
+
+# parse args
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --url)
+            url="$2"
+            shift 2
+            ;;
+        --repo)
+            repo="$2"
+            shift 2
+            ;;
+        --file)
+            file="$2"
+            shift 2
+            ;;
+        --outdir)
+            outdir="$2"
+            shift 2
+            ;;
+        -h|--help)
+            usage
+            ;;
+        *)
+            url="$1"
+            shift
+            ;;
+    esac
+done
+
+if [ -n "$repo" ] && [ -n "$file" ]; then
+    url="https://huggingface.co/$repo/resolve/main/$file"
+fi
+
+if [ -z "$url" ]; then
+    log "[E] missing --url"
+    usage
+fi
+
+# check if the URL is a HuggingFace model, and if so, try to download it
+is_url=false
+
+if [[ ${#url} -gt 22 ]]; then
+    if [[ ${url:0:22} == "https://huggingface.co" ]]; then
+        is_url=true
+    fi
+fi
+
+if [ "$is_url" = false ]; then
+    log "[E] invalid URL, must start with https://huggingface.co"
+    exit 0
+fi
+
+# replace "blob/main" with "resolve/main"
+url=${url/blob\/main/resolve\/main}
+
+basename=$(basename $url)
+
+log "[+] attempting to download $basename"
+
+if [ -n "$cmd" ]; then
+    cmd=$(printf "$cmd" "$outdir" "$basename" "$url")
+    log "[+] $cmd"
+    if $cmd; then
+        echo $outdir/$basename
+        exit 0
+    fi
+fi
+
+log "[-] failed to download"
+
+exit 1

From f30f099228f774209aa3010b78dfbe5d262e69aa Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Sat, 18 Jan 2025 14:12:05 +0100
Subject: [PATCH 201/376] server : implement cancellable request (#11285)

* server : implement cancellable request

* fix typo

* httplib 0.18.5

* fix i underflow
---
 examples/server/httplib.h                     | 1726 +++++++++++++----
 examples/server/server.cpp                    |   73 +-
 examples/server/tests/unit/test_completion.py |   21 +
 examples/server/tests/utils.py                |    7 +-
 4 files changed, 1396 insertions(+), 431 deletions(-)

diff --git a/examples/server/httplib.h b/examples/server/httplib.h
index f360bd93e..c2f12dd2a 100644
--- a/examples/server/httplib.h
+++ b/examples/server/httplib.h
@@ -8,7 +8,7 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.15.3"
+#define CPPHTTPLIB_VERSION "0.18.5"
 
 /*
  * Configuration
@@ -18,8 +18,12 @@
 #define CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND 5
 #endif
 
+#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND
+#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND 10000
+#endif
+
 #ifndef CPPHTTPLIB_KEEPALIVE_MAX_COUNT
-#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 5
+#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 100
 #endif
 
 #ifndef CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND
@@ -30,20 +34,36 @@
 #define CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND 0
 #endif
 
-#ifndef CPPHTTPLIB_READ_TIMEOUT_SECOND
-#define CPPHTTPLIB_READ_TIMEOUT_SECOND 5
+#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND
+#define CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND 5
 #endif
 
-#ifndef CPPHTTPLIB_READ_TIMEOUT_USECOND
-#define CPPHTTPLIB_READ_TIMEOUT_USECOND 0
+#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND
+#define CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND 0
 #endif
 
-#ifndef CPPHTTPLIB_WRITE_TIMEOUT_SECOND
-#define CPPHTTPLIB_WRITE_TIMEOUT_SECOND 5
+#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND
+#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND 5
 #endif
 
-#ifndef CPPHTTPLIB_WRITE_TIMEOUT_USECOND
-#define CPPHTTPLIB_WRITE_TIMEOUT_USECOND 0
+#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND
+#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND 0
+#endif
+
+#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND
+#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND 300
+#endif
+
+#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND
+#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND 0
+#endif
+
+#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND
+#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND 5
+#endif
+
+#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND
+#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND 0
 #endif
 
 #ifndef CPPHTTPLIB_IDLE_INTERVAL_SECOND
@@ -90,8 +110,12 @@
 #define CPPHTTPLIB_TCP_NODELAY false
 #endif
 
+#ifndef CPPHTTPLIB_IPV6_V6ONLY
+#define CPPHTTPLIB_IPV6_V6ONLY false
+#endif
+
 #ifndef CPPHTTPLIB_RECV_BUFSIZ
-#define CPPHTTPLIB_RECV_BUFSIZ size_t(4096u)
+#define CPPHTTPLIB_RECV_BUFSIZ size_t(16384u)
 #endif
 
 #ifndef CPPHTTPLIB_COMPRESSION_BUFSIZ
@@ -145,11 +169,11 @@ using ssize_t = long;
 #endif // _MSC_VER
 
 #ifndef S_ISREG
-#define S_ISREG(m) (((m)&S_IFREG) == S_IFREG)
+#define S_ISREG(m) (((m) & S_IFREG) == S_IFREG)
 #endif // S_ISREG
 
 #ifndef S_ISDIR
-#define S_ISDIR(m) (((m)&S_IFDIR) == S_IFDIR)
+#define S_ISDIR(m) (((m) & S_IFDIR) == S_IFDIR)
 #endif // S_ISDIR
 
 #ifndef NOMINMAX
@@ -269,7 +293,12 @@ using socket_t = int;
 #include <iostream>
 #include <sstream>
 
-#if OPENSSL_VERSION_NUMBER < 0x30000000L
+#if defined(OPENSSL_IS_BORINGSSL) || defined(LIBRESSL_VERSION_NUMBER)
+#if OPENSSL_VERSION_NUMBER < 0x1010107f
+#error Please use OpenSSL or a current version of BoringSSL
+#endif
+#define SSL_get1_peer_certificate SSL_get_peer_certificate
+#elif OPENSSL_VERSION_NUMBER < 0x30000000L
 #error Sorry, OpenSSL versions prior to 3.0.0 are not supported
 #endif
 
@@ -312,16 +341,63 @@ make_unique(std::size_t n) {
   return std::unique_ptr<T>(new RT[n]);
 }
 
-struct ci {
-  bool operator()(const std::string &s1, const std::string &s2) const {
-    return std::lexicographical_compare(s1.begin(), s1.end(), s2.begin(),
-                                        s2.end(),
-                                        [](unsigned char c1, unsigned char c2) {
-                                          return ::tolower(c1) < ::tolower(c2);
-                                        });
+namespace case_ignore {
+
+inline unsigned char to_lower(int c) {
+  const static unsigned char table[256] = {
+      0,   1,   2,   3,   4,   5,   6,   7,   8,   9,   10,  11,  12,  13,  14,
+      15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,
+      30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43,  44,
+      45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59,
+      60,  61,  62,  63,  64,  97,  98,  99,  100, 101, 102, 103, 104, 105, 106,
+      107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
+      122, 91,  92,  93,  94,  95,  96,  97,  98,  99,  100, 101, 102, 103, 104,
+      105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
+      120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
+      135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
+      150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
+      165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,
+      180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 224, 225, 226,
+      227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241,
+      242, 243, 244, 245, 246, 215, 248, 249, 250, 251, 252, 253, 254, 223, 224,
+      225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
+      240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,
+      255,
+  };
+  return table[(unsigned char)(char)c];
+}
+
+inline bool equal(const std::string &a, const std::string &b) {
+  return a.size() == b.size() &&
+         std::equal(a.begin(), a.end(), b.begin(), [](char ca, char cb) {
+           return to_lower(ca) == to_lower(cb);
+         });
+}
+
+struct equal_to {
+  bool operator()(const std::string &a, const std::string &b) const {
+    return equal(a, b);
   }
 };
 
+struct hash {
+  size_t operator()(const std::string &key) const {
+    return hash_core(key.data(), key.size(), 0);
+  }
+
+  size_t hash_core(const char *s, size_t l, size_t h) const {
+    return (l == 0) ? h
+                    : hash_core(s + 1, l - 1,
+                                // Unsets the 6 high bits of h, therefore no
+                                // overflow happens
+                                (((std::numeric_limits<size_t>::max)() >> 6) &
+                                 h * 33) ^
+                                    static_cast<unsigned char>(to_lower(*s)));
+  }
+};
+
+} // namespace case_ignore
+
 // This is based on
 // "http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2014/n4189".
 
@@ -427,7 +503,9 @@ enum StatusCode {
   NetworkAuthenticationRequired_511 = 511,
 };
 
-using Headers = std::multimap<std::string, std::string, detail::ci>;
+using Headers =
+    std::unordered_multimap<std::string, std::string, detail::case_ignore::hash,
+                            detail::case_ignore::equal_to>;
 
 using Params = std::multimap<std::string, std::string>;
 using Match = std::smatch;
@@ -534,6 +612,7 @@ using Ranges = std::vector<Range>;
 struct Request {
   std::string method;
   std::string path;
+  Params params;
   Headers headers;
   std::string body;
 
@@ -545,11 +624,11 @@ struct Request {
   // for server
   std::string version;
   std::string target;
-  Params params;
   MultipartFormDataMap files;
   Ranges ranges;
   Match matches;
   std::unordered_map<std::string, std::string> path_params;
+  std::function<bool()> is_connection_closed = []() { return true; };
 
   // for client
   ResponseHandler response_handler;
@@ -560,8 +639,10 @@ struct Request {
 #endif
 
   bool has_header(const std::string &key) const;
-  std::string get_header_value(const std::string &key, size_t id = 0) const;
-  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
+  std::string get_header_value(const std::string &key, const char *def = "",
+                               size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0,
+                                size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
@@ -592,8 +673,10 @@ struct Response {
   std::string location; // Redirect location
 
   bool has_header(const std::string &key) const;
-  std::string get_header_value(const std::string &key, size_t id = 0) const;
-  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
+  std::string get_header_value(const std::string &key, const char *def = "",
+                               size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0,
+                                size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
@@ -614,6 +697,10 @@ struct Response {
       const std::string &content_type, ContentProviderWithoutLength provider,
       ContentProviderResourceReleaser resource_releaser = nullptr);
 
+  void set_file_content(const std::string &path,
+                        const std::string &content_type);
+  void set_file_content(const std::string &path);
+
   Response() = default;
   Response(const Response &) = default;
   Response &operator=(const Response &) = default;
@@ -631,6 +718,8 @@ struct Response {
   ContentProviderResourceReleaser content_provider_resource_releaser_;
   bool is_chunked_content_provider_ = false;
   bool content_provider_success_ = false;
+  std::string file_content_path_;
+  std::string file_content_content_type_;
 };
 
 class Stream {
@@ -646,8 +735,6 @@ public:
   virtual void get_local_ip_and_port(std::string &ip, int &port) const = 0;
   virtual socket_t socket() const = 0;
 
-  template <typename... Args>
-  ssize_t write_format(const char *fmt, const Args &...args);
   ssize_t write(const char *ptr);
   ssize_t write(const std::string &s);
 };
@@ -719,13 +806,18 @@ private:
 
           if (pool_.shutdown_ && pool_.jobs_.empty()) { break; }
 
-          fn = std::move(pool_.jobs_.front());
+          fn = pool_.jobs_.front();
           pool_.jobs_.pop_front();
         }
 
         assert(true == static_cast<bool>(fn));
         fn();
       }
+
+#if defined(CPPHTTPLIB_OPENSSL_SUPPORT) && !defined(OPENSSL_IS_BORINGSSL) &&   \
+    !defined(LIBRESSL_VERSION_NUMBER)
+      OPENSSL_thread_stop();
+#endif
     }
 
     ThreadPool &pool_;
@@ -787,7 +879,6 @@ public:
   bool match(Request &request) const override;
 
 private:
-  static constexpr char marker = ':';
   // Treat segment separators as the end of path parameter capture
   // Does not need to handle query parameters as they are parsed before path
   // matching
@@ -871,8 +962,13 @@ public:
   Server &set_default_file_mimetype(const std::string &mime);
   Server &set_file_request_handler(Handler handler);
 
-  Server &set_error_handler(HandlerWithResponse handler);
-  Server &set_error_handler(Handler handler);
+  template <class ErrorHandlerFunc>
+  Server &set_error_handler(ErrorHandlerFunc &&handler) {
+    return set_error_handler_core(
+        std::forward<ErrorHandlerFunc>(handler),
+        std::is_convertible<ErrorHandlerFunc, HandlerWithResponse>{});
+  }
+
   Server &set_exception_handler(ExceptionHandler handler);
   Server &set_pre_routing_handler(HandlerWithResponse handler);
   Server &set_post_routing_handler(Handler handler);
@@ -882,6 +978,7 @@ public:
 
   Server &set_address_family(int family);
   Server &set_tcp_nodelay(bool on);
+  Server &set_ipv6_v6only(bool on);
   Server &set_socket_options(SocketOptions socket_options);
 
   Server &set_default_headers(Headers headers);
@@ -914,21 +1011,24 @@ public:
   bool is_running() const;
   void wait_until_ready() const;
   void stop();
+  void decommission();
 
   std::function<TaskQueue *(void)> new_task_queue;
 
 protected:
-  bool process_request(Stream &strm, bool close_connection,
+  bool process_request(Stream &strm, const std::string &remote_addr,
+                       int remote_port, const std::string &local_addr,
+                       int local_port, bool close_connection,
                        bool &connection_closed,
                        const std::function<void(Request &)> &setup_request);
 
   std::atomic<socket_t> svr_sock_{INVALID_SOCKET};
   size_t keep_alive_max_count_ = CPPHTTPLIB_KEEPALIVE_MAX_COUNT;
   time_t keep_alive_timeout_sec_ = CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND;
-  time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND;
-  time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND;
-  time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND;
-  time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND;
+  time_t read_timeout_sec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND;
+  time_t read_timeout_usec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND;
+  time_t write_timeout_sec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND;
+  time_t write_timeout_usec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND;
   time_t idle_interval_sec_ = CPPHTTPLIB_IDLE_INTERVAL_SECOND;
   time_t idle_interval_usec_ = CPPHTTPLIB_IDLE_INTERVAL_USECOND;
   size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH;
@@ -943,6 +1043,9 @@ private:
   static std::unique_ptr<detail::MatcherBase>
   make_matcher(const std::string &pattern);
 
+  Server &set_error_handler_core(HandlerWithResponse handler, std::true_type);
+  Server &set_error_handler_core(Handler handler, std::false_type);
+
   socket_t create_server_socket(const std::string &host, int port,
                                 int socket_flags,
                                 SocketOptions socket_options) const;
@@ -985,7 +1088,7 @@ private:
   virtual bool process_and_close_socket(socket_t sock);
 
   std::atomic<bool> is_running_{false};
-  std::atomic<bool> done_{false};
+  std::atomic<bool> is_decommisioned{false};
 
   struct MountPointEntry {
     std::string mount_point;
@@ -1018,6 +1121,7 @@ private:
 
   int address_family_ = AF_UNSPEC;
   bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;
+  bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY;
   SocketOptions socket_options_ = default_socket_options;
 
   Headers default_headers_;
@@ -1037,6 +1141,7 @@ enum class Error {
   SSLConnection,
   SSLLoadingCerts,
   SSLServerVerification,
+  SSLServerHostnameVerification,
   UnsupportedMultipartBoundaryChars,
   Compression,
   ConnectionTimeout,
@@ -1074,9 +1179,10 @@ public:
   // Request Headers
   bool has_request_header(const std::string &key) const;
   std::string get_request_header_value(const std::string &key,
+                                       const char *def = "",
                                        size_t id = 0) const;
   uint64_t get_request_header_value_u64(const std::string &key,
-                                        size_t id = 0) const;
+                                        uint64_t def = 0, size_t id = 0) const;
   size_t get_request_header_value_count(const std::string &key) const;
 
 private:
@@ -1140,10 +1246,18 @@ public:
               const std::string &content_type);
   Result Post(const std::string &path, const Headers &headers, const char *body,
               size_t content_length, const std::string &content_type);
+  Result Post(const std::string &path, const Headers &headers, const char *body,
+              size_t content_length, const std::string &content_type,
+              Progress progress);
   Result Post(const std::string &path, const std::string &body,
               const std::string &content_type);
+  Result Post(const std::string &path, const std::string &body,
+              const std::string &content_type, Progress progress);
   Result Post(const std::string &path, const Headers &headers,
               const std::string &body, const std::string &content_type);
+  Result Post(const std::string &path, const Headers &headers,
+              const std::string &body, const std::string &content_type,
+              Progress progress);
   Result Post(const std::string &path, size_t content_length,
               ContentProvider content_provider,
               const std::string &content_type);
@@ -1159,6 +1273,8 @@ public:
   Result Post(const std::string &path, const Params &params);
   Result Post(const std::string &path, const Headers &headers,
               const Params &params);
+  Result Post(const std::string &path, const Headers &headers,
+              const Params &params, Progress progress);
   Result Post(const std::string &path, const MultipartFormDataItems &items);
   Result Post(const std::string &path, const Headers &headers,
               const MultipartFormDataItems &items);
@@ -1173,10 +1289,18 @@ public:
              const std::string &content_type);
   Result Put(const std::string &path, const Headers &headers, const char *body,
              size_t content_length, const std::string &content_type);
+  Result Put(const std::string &path, const Headers &headers, const char *body,
+             size_t content_length, const std::string &content_type,
+             Progress progress);
   Result Put(const std::string &path, const std::string &body,
              const std::string &content_type);
+  Result Put(const std::string &path, const std::string &body,
+             const std::string &content_type, Progress progress);
   Result Put(const std::string &path, const Headers &headers,
              const std::string &body, const std::string &content_type);
+  Result Put(const std::string &path, const Headers &headers,
+             const std::string &body, const std::string &content_type,
+             Progress progress);
   Result Put(const std::string &path, size_t content_length,
              ContentProvider content_provider, const std::string &content_type);
   Result Put(const std::string &path,
@@ -1191,6 +1315,8 @@ public:
   Result Put(const std::string &path, const Params &params);
   Result Put(const std::string &path, const Headers &headers,
              const Params &params);
+  Result Put(const std::string &path, const Headers &headers,
+             const Params &params, Progress progress);
   Result Put(const std::string &path, const MultipartFormDataItems &items);
   Result Put(const std::string &path, const Headers &headers,
              const MultipartFormDataItems &items);
@@ -1203,13 +1329,23 @@ public:
   Result Patch(const std::string &path);
   Result Patch(const std::string &path, const char *body, size_t content_length,
                const std::string &content_type);
+  Result Patch(const std::string &path, const char *body, size_t content_length,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const Headers &headers,
                const char *body, size_t content_length,
                const std::string &content_type);
+  Result Patch(const std::string &path, const Headers &headers,
+               const char *body, size_t content_length,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const std::string &body,
                const std::string &content_type);
+  Result Patch(const std::string &path, const std::string &body,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const Headers &headers,
                const std::string &body, const std::string &content_type);
+  Result Patch(const std::string &path, const Headers &headers,
+               const std::string &body, const std::string &content_type,
+               Progress progress);
   Result Patch(const std::string &path, size_t content_length,
                ContentProvider content_provider,
                const std::string &content_type);
@@ -1227,13 +1363,24 @@ public:
   Result Delete(const std::string &path, const Headers &headers);
   Result Delete(const std::string &path, const char *body,
                 size_t content_length, const std::string &content_type);
+  Result Delete(const std::string &path, const char *body,
+                size_t content_length, const std::string &content_type,
+                Progress progress);
   Result Delete(const std::string &path, const Headers &headers,
                 const char *body, size_t content_length,
                 const std::string &content_type);
+  Result Delete(const std::string &path, const Headers &headers,
+                const char *body, size_t content_length,
+                const std::string &content_type, Progress progress);
   Result Delete(const std::string &path, const std::string &body,
                 const std::string &content_type);
+  Result Delete(const std::string &path, const std::string &body,
+                const std::string &content_type, Progress progress);
   Result Delete(const std::string &path, const Headers &headers,
                 const std::string &body, const std::string &content_type);
+  Result Delete(const std::string &path, const Headers &headers,
+                const std::string &body, const std::string &content_type,
+                Progress progress);
 
   Result Options(const std::string &path);
   Result Options(const std::string &path, const Headers &headers);
@@ -1258,6 +1405,7 @@ public:
 
   void set_address_family(int family);
   void set_tcp_nodelay(bool on);
+  void set_ipv6_v6only(bool on);
   void set_socket_options(SocketOptions socket_options);
 
   void set_connection_timeout(time_t sec, time_t usec = 0);
@@ -1309,6 +1457,8 @@ public:
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
   void enable_server_certificate_verification(bool enabled);
+  void enable_server_hostname_verification(bool enabled);
+  void set_server_certificate_verifier(std::function<bool(SSL *ssl)> verifier);
 #endif
 
   void set_logger(Logger logger);
@@ -1375,10 +1525,10 @@ protected:
 
   time_t connection_timeout_sec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND;
   time_t connection_timeout_usec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND;
-  time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND;
-  time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND;
-  time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND;
-  time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND;
+  time_t read_timeout_sec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND;
+  time_t read_timeout_usec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND;
+  time_t write_timeout_sec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND;
+  time_t write_timeout_usec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND;
 
   std::string basic_auth_username_;
   std::string basic_auth_password_;
@@ -1395,6 +1545,7 @@ protected:
 
   int address_family_ = AF_UNSPEC;
   bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;
+  bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY;
   SocketOptions socket_options_ = nullptr;
 
   bool compress_ = false;
@@ -1422,6 +1573,8 @@ protected:
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
   bool server_certificate_verification_ = true;
+  bool server_hostname_verification_ = true;
+  std::function<bool(SSL *ssl)> server_certificate_verifier_;
 #endif
 
   Logger logger_;
@@ -1430,6 +1583,9 @@ private:
   bool send_(Request &req, Response &res, Error &error);
   Result send_(Request &&req);
 
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+  bool is_ssl_peer_could_be_closed(SSL *ssl) const;
+#endif
   socket_t create_client_socket(Error &error) const;
   bool read_response_line(Stream &strm, const Request &req,
                           Response &res) const;
@@ -1448,7 +1604,7 @@ private:
       const Headers &headers, const char *body, size_t content_length,
       ContentProvider content_provider,
       ContentProviderWithoutLength content_provider_without_length,
-      const std::string &content_type);
+      const std::string &content_type, Progress progress);
   ContentProviderWithoutLength get_multipart_content_provider(
       const std::string &boundary, const MultipartFormDataItems &items,
       const MultipartFormDataProviderItems &provider_items) const;
@@ -1477,6 +1633,7 @@ public:
                   const std::string &client_key_path);
 
   Client(Client &&) = default;
+  Client &operator=(Client &&) = default;
 
   ~Client();
 
@@ -1523,10 +1680,18 @@ public:
               const std::string &content_type);
   Result Post(const std::string &path, const Headers &headers, const char *body,
               size_t content_length, const std::string &content_type);
+  Result Post(const std::string &path, const Headers &headers, const char *body,
+              size_t content_length, const std::string &content_type,
+              Progress progress);
   Result Post(const std::string &path, const std::string &body,
               const std::string &content_type);
+  Result Post(const std::string &path, const std::string &body,
+              const std::string &content_type, Progress progress);
   Result Post(const std::string &path, const Headers &headers,
               const std::string &body, const std::string &content_type);
+  Result Post(const std::string &path, const Headers &headers,
+              const std::string &body, const std::string &content_type,
+              Progress progress);
   Result Post(const std::string &path, size_t content_length,
               ContentProvider content_provider,
               const std::string &content_type);
@@ -1542,6 +1707,8 @@ public:
   Result Post(const std::string &path, const Params &params);
   Result Post(const std::string &path, const Headers &headers,
               const Params &params);
+  Result Post(const std::string &path, const Headers &headers,
+              const Params &params, Progress progress);
   Result Post(const std::string &path, const MultipartFormDataItems &items);
   Result Post(const std::string &path, const Headers &headers,
               const MultipartFormDataItems &items);
@@ -1556,10 +1723,18 @@ public:
              const std::string &content_type);
   Result Put(const std::string &path, const Headers &headers, const char *body,
              size_t content_length, const std::string &content_type);
+  Result Put(const std::string &path, const Headers &headers, const char *body,
+             size_t content_length, const std::string &content_type,
+             Progress progress);
   Result Put(const std::string &path, const std::string &body,
              const std::string &content_type);
+  Result Put(const std::string &path, const std::string &body,
+             const std::string &content_type, Progress progress);
   Result Put(const std::string &path, const Headers &headers,
              const std::string &body, const std::string &content_type);
+  Result Put(const std::string &path, const Headers &headers,
+             const std::string &body, const std::string &content_type,
+             Progress progress);
   Result Put(const std::string &path, size_t content_length,
              ContentProvider content_provider, const std::string &content_type);
   Result Put(const std::string &path,
@@ -1574,6 +1749,8 @@ public:
   Result Put(const std::string &path, const Params &params);
   Result Put(const std::string &path, const Headers &headers,
              const Params &params);
+  Result Put(const std::string &path, const Headers &headers,
+             const Params &params, Progress progress);
   Result Put(const std::string &path, const MultipartFormDataItems &items);
   Result Put(const std::string &path, const Headers &headers,
              const MultipartFormDataItems &items);
@@ -1586,13 +1763,23 @@ public:
   Result Patch(const std::string &path);
   Result Patch(const std::string &path, const char *body, size_t content_length,
                const std::string &content_type);
+  Result Patch(const std::string &path, const char *body, size_t content_length,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const Headers &headers,
                const char *body, size_t content_length,
                const std::string &content_type);
+  Result Patch(const std::string &path, const Headers &headers,
+               const char *body, size_t content_length,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const std::string &body,
                const std::string &content_type);
+  Result Patch(const std::string &path, const std::string &body,
+               const std::string &content_type, Progress progress);
   Result Patch(const std::string &path, const Headers &headers,
                const std::string &body, const std::string &content_type);
+  Result Patch(const std::string &path, const Headers &headers,
+               const std::string &body, const std::string &content_type,
+               Progress progress);
   Result Patch(const std::string &path, size_t content_length,
                ContentProvider content_provider,
                const std::string &content_type);
@@ -1610,13 +1797,24 @@ public:
   Result Delete(const std::string &path, const Headers &headers);
   Result Delete(const std::string &path, const char *body,
                 size_t content_length, const std::string &content_type);
+  Result Delete(const std::string &path, const char *body,
+                size_t content_length, const std::string &content_type,
+                Progress progress);
   Result Delete(const std::string &path, const Headers &headers,
                 const char *body, size_t content_length,
                 const std::string &content_type);
+  Result Delete(const std::string &path, const Headers &headers,
+                const char *body, size_t content_length,
+                const std::string &content_type, Progress progress);
   Result Delete(const std::string &path, const std::string &body,
                 const std::string &content_type);
+  Result Delete(const std::string &path, const std::string &body,
+                const std::string &content_type, Progress progress);
   Result Delete(const std::string &path, const Headers &headers,
                 const std::string &body, const std::string &content_type);
+  Result Delete(const std::string &path, const Headers &headers,
+                const std::string &body, const std::string &content_type,
+                Progress progress);
 
   Result Options(const std::string &path);
   Result Options(const std::string &path, const Headers &headers);
@@ -1685,6 +1883,8 @@ public:
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
   void enable_server_certificate_verification(bool enabled);
+  void enable_server_hostname_verification(bool enabled);
+  void set_server_certificate_verifier(std::function<bool(SSL *ssl)> verifier);
 #endif
 
   void set_logger(Logger logger);
@@ -1730,6 +1930,9 @@ public:
 
   SSL_CTX *ssl_context() const;
 
+  void update_certs(X509 *cert, EVP_PKEY *private_key,
+                    X509_STORE *client_ca_cert_store = nullptr);
+
 private:
   bool process_and_close_socket(socket_t sock) override;
 
@@ -1810,68 +2013,58 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) {
   callback(static_cast<time_t>(sec), static_cast<time_t>(usec));
 }
 
+inline bool is_numeric(const std::string &str) {
+  return !str.empty() && std::all_of(str.begin(), str.end(), ::isdigit);
+}
+
 inline uint64_t get_header_value_u64(const Headers &headers,
-                                     const std::string &key, size_t id,
-                                     uint64_t def) {
+                                     const std::string &key, uint64_t def,
+                                     size_t id, bool &is_invalid_value) {
+  is_invalid_value = false;
   auto rng = headers.equal_range(key);
   auto it = rng.first;
   std::advance(it, static_cast<ssize_t>(id));
   if (it != rng.second) {
-    return std::strtoull(it->second.data(), nullptr, 10);
+    if (is_numeric(it->second)) {
+      return std::strtoull(it->second.data(), nullptr, 10);
+    } else {
+      is_invalid_value = true;
+    }
   }
   return def;
 }
 
+inline uint64_t get_header_value_u64(const Headers &headers,
+                                     const std::string &key, uint64_t def,
+                                     size_t id) {
+  bool dummy = false;
+  return get_header_value_u64(headers, key, def, id, dummy);
+}
+
 } // namespace detail
 
 inline uint64_t Request::get_header_value_u64(const std::string &key,
-                                              size_t id) const {
-  return detail::get_header_value_u64(headers, key, id, 0);
+                                              uint64_t def, size_t id) const {
+  return detail::get_header_value_u64(headers, key, def, id);
 }
 
 inline uint64_t Response::get_header_value_u64(const std::string &key,
-                                               size_t id) const {
-  return detail::get_header_value_u64(headers, key, id, 0);
-}
-
-template <typename... Args>
-inline ssize_t Stream::write_format(const char *fmt, const Args &...args) {
-  const auto bufsiz = 2048;
-  std::array<char, bufsiz> buf{};
-
-  auto sn = snprintf(buf.data(), buf.size() - 1, fmt, args...);
-  if (sn <= 0) { return sn; }
-
-  auto n = static_cast<size_t>(sn);
-
-  if (n >= buf.size() - 1) {
-    std::vector<char> glowable_buf(buf.size());
-
-    while (n >= glowable_buf.size() - 1) {
-      glowable_buf.resize(glowable_buf.size() * 2);
-      n = static_cast<size_t>(
-          snprintf(&glowable_buf[0], glowable_buf.size() - 1, fmt, args...));
-    }
-    return write(&glowable_buf[0], n);
-  } else {
-    return write(buf.data(), n);
-  }
+                                               uint64_t def, size_t id) const {
+  return detail::get_header_value_u64(headers, key, def, id);
 }
 
 inline void default_socket_options(socket_t sock) {
-  int yes = 1;
+  int opt = 1;
 #ifdef _WIN32
   setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
-             reinterpret_cast<const char *>(&yes), sizeof(yes));
-  setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE,
-             reinterpret_cast<const char *>(&yes), sizeof(yes));
+             reinterpret_cast<const char *>(&opt), sizeof(opt));
 #else
 #ifdef SO_REUSEPORT
   setsockopt(sock, SOL_SOCKET, SO_REUSEPORT,
-             reinterpret_cast<const void *>(&yes), sizeof(yes));
+             reinterpret_cast<const void *>(&opt), sizeof(opt));
 #else
   setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
-             reinterpret_cast<const void *>(&yes), sizeof(yes));
+             reinterpret_cast<const void *>(&opt), sizeof(opt));
 #endif
 #endif
 }
@@ -1997,6 +2190,8 @@ inline std::string to_string(const Error error) {
   case Error::SSLConnection: return "SSL connection failed";
   case Error::SSLLoadingCerts: return "SSL certificate loading failed";
   case Error::SSLServerVerification: return "SSL server verification failed";
+  case Error::SSLServerHostnameVerification:
+    return "SSL server hostname verification failed";
   case Error::UnsupportedMultipartBoundaryChars:
     return "Unsupported HTTP multipart boundary characters";
   case Error::Compression: return "Compression failed";
@@ -2016,8 +2211,9 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) {
 }
 
 inline uint64_t Result::get_request_header_value_u64(const std::string &key,
+                                                     uint64_t def,
                                                      size_t id) const {
-  return detail::get_header_value_u64(request_headers_, key, id, 0);
+  return detail::get_header_value_u64(request_headers_, key, def, id);
 }
 
 template <class Rep, class Period>
@@ -2080,6 +2276,36 @@ make_basic_authentication_header(const std::string &username,
 
 namespace detail {
 
+#if defined(_WIN32)
+inline std::wstring u8string_to_wstring(const char *s) {
+  std::wstring ws;
+  auto len = static_cast<int>(strlen(s));
+  auto wlen = ::MultiByteToWideChar(CP_UTF8, 0, s, len, nullptr, 0);
+  if (wlen > 0) {
+    ws.resize(wlen);
+    wlen = ::MultiByteToWideChar(
+        CP_UTF8, 0, s, len,
+        const_cast<LPWSTR>(reinterpret_cast<LPCWSTR>(ws.data())), wlen);
+    if (wlen != static_cast<int>(ws.size())) { ws.clear(); }
+  }
+  return ws;
+}
+#endif
+
+struct FileStat {
+  FileStat(const std::string &path);
+  bool is_file() const;
+  bool is_dir() const;
+
+private:
+#if defined(_WIN32)
+  struct _stat st_;
+#else
+  struct stat st_;
+#endif
+  int ret_ = -1;
+};
+
 std::string encode_query_param(const std::string &value);
 
 std::string decode_url(const std::string &s, bool convert_plus_to_space);
@@ -2088,6 +2314,16 @@ void read_file(const std::string &path, std::string &out);
 
 std::string trim_copy(const std::string &s);
 
+void divide(
+    const char *data, std::size_t size, char d,
+    std::function<void(const char *, std::size_t, const char *, std::size_t)>
+        fn);
+
+void divide(
+    const std::string &str, char d,
+    std::function<void(const char *, std::size_t, const char *, std::size_t)>
+        fn);
+
 void split(const char *b, const char *e, char d,
            std::function<void(const char *, const char *)> fn);
 
@@ -2099,18 +2335,23 @@ bool process_client_socket(socket_t sock, time_t read_timeout_sec,
                            time_t write_timeout_usec,
                            std::function<bool(Stream &)> callback);
 
-socket_t create_client_socket(
-    const std::string &host, const std::string &ip, int port,
-    int address_family, bool tcp_nodelay, SocketOptions socket_options,
-    time_t connection_timeout_sec, time_t connection_timeout_usec,
-    time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec,
-    time_t write_timeout_usec, const std::string &intf, Error &error);
+socket_t create_client_socket(const std::string &host, const std::string &ip,
+                              int port, int address_family, bool tcp_nodelay,
+                              bool ipv6_v6only, SocketOptions socket_options,
+                              time_t connection_timeout_sec,
+                              time_t connection_timeout_usec,
+                              time_t read_timeout_sec, time_t read_timeout_usec,
+                              time_t write_timeout_sec,
+                              time_t write_timeout_usec,
+                              const std::string &intf, Error &error);
 
 const char *get_header_value(const Headers &headers, const std::string &key,
-                             size_t id = 0, const char *def = nullptr);
+                             const char *def, size_t id);
 
 std::string params_to_query_str(const Params &params);
 
+void parse_query_text(const char *data, std::size_t size, Params &params);
+
 void parse_query_text(const std::string &s, Params &params);
 
 bool parse_multipart_boundary(const std::string &content_type,
@@ -2270,15 +2511,70 @@ public:
 
 private:
 #if defined(_WIN32)
-  HANDLE hFile_;
-  HANDLE hMapping_;
+  HANDLE hFile_ = NULL;
+  HANDLE hMapping_ = NULL;
 #else
-  int fd_;
+  int fd_ = -1;
 #endif
-  size_t size_;
-  void *addr_;
+  size_t size_ = 0;
+  void *addr_ = nullptr;
+  bool is_open_empty_file = false;
 };
 
+// NOTE: https://www.rfc-editor.org/rfc/rfc9110#section-5
+namespace fields {
+
+inline bool is_token_char(char c) {
+  return std::isalnum(c) || c == '!' || c == '#' || c == '$' || c == '%' ||
+         c == '&' || c == '\'' || c == '*' || c == '+' || c == '-' ||
+         c == '.' || c == '^' || c == '_' || c == '`' || c == '|' || c == '~';
+}
+
+inline bool is_token(const std::string &s) {
+  if (s.empty()) { return false; }
+  for (auto c : s) {
+    if (!is_token_char(c)) { return false; }
+  }
+  return true;
+}
+
+inline bool is_field_name(const std::string &s) { return is_token(s); }
+
+inline bool is_vchar(char c) { return c >= 33 && c <= 126; }
+
+inline bool is_obs_text(char c) { return 128 <= static_cast<unsigned char>(c); }
+
+inline bool is_field_vchar(char c) { return is_vchar(c) || is_obs_text(c); }
+
+inline bool is_field_content(const std::string &s) {
+  if (s.empty()) { return false; }
+
+  if (s.size() == 1) {
+    return is_field_vchar(s[0]);
+  } else if (s.size() == 2) {
+    return is_field_vchar(s[0]) && is_field_vchar(s[1]);
+  } else {
+    size_t i = 0;
+
+    if (!is_field_vchar(s[i])) { return false; }
+    i++;
+
+    while (i < s.size() - 1) {
+      auto c = s[i++];
+      if (c == ' ' || c == '\t' || is_field_vchar(c)) {
+      } else {
+        return false;
+      }
+    }
+
+    return is_field_vchar(s[i]);
+  }
+}
+
+inline bool is_field_value(const std::string &s) { return is_field_content(s); }
+
+} // namespace fields
+
 } // namespace detail
 
 // ----------------------------------------------------------------------------
@@ -2392,20 +2688,6 @@ inline std::string base64_encode(const std::string &in) {
   return out;
 }
 
-inline bool is_file(const std::string &path) {
-#ifdef _WIN32
-  return _access_s(path.c_str(), 0) == 0;
-#else
-  struct stat st;
-  return stat(path.c_str(), &st) >= 0 && S_ISREG(st.st_mode);
-#endif
-}
-
-inline bool is_dir(const std::string &path) {
-  struct stat st;
-  return stat(path.c_str(), &st) >= 0 && S_ISDIR(st.st_mode);
-}
-
 inline bool is_valid_path(const std::string &path) {
   size_t level = 0;
   size_t i = 0;
@@ -2448,6 +2730,21 @@ inline bool is_valid_path(const std::string &path) {
   return true;
 }
 
+inline FileStat::FileStat(const std::string &path) {
+#if defined(_WIN32)
+  auto wpath = u8string_to_wstring(path.c_str());
+  ret_ = _wstat(wpath.c_str(), &st_);
+#else
+  ret_ = stat(path.c_str(), &st_);
+#endif
+}
+inline bool FileStat::is_file() const {
+  return ret_ >= 0 && S_ISREG(st_.st_mode);
+}
+inline bool FileStat::is_dir() const {
+  return ret_ >= 0 && S_ISDIR(st_.st_mode);
+}
+
 inline std::string encode_query_param(const std::string &value) {
   std::ostringstream escaped;
   escaped.fill('0');
@@ -2579,6 +2876,27 @@ inline std::string trim_double_quotes_copy(const std::string &s) {
   return s;
 }
 
+inline void
+divide(const char *data, std::size_t size, char d,
+       std::function<void(const char *, std::size_t, const char *, std::size_t)>
+           fn) {
+  const auto it = std::find(data, data + size, d);
+  const auto found = static_cast<std::size_t>(it != data + size);
+  const auto lhs_data = data;
+  const auto lhs_size = static_cast<std::size_t>(it - data);
+  const auto rhs_data = it + found;
+  const auto rhs_size = size - lhs_size - found;
+
+  fn(lhs_data, lhs_size, rhs_data, rhs_size);
+}
+
+inline void
+divide(const std::string &str, char d,
+       std::function<void(const char *, std::size_t, const char *, std::size_t)>
+           fn) {
+  divide(str.data(), str.size(), d, std::move(fn));
+}
+
 inline void split(const char *b, const char *e, char d,
                   std::function<void(const char *, const char *)> fn) {
   return split(b, e, d, (std::numeric_limits<size_t>::max)(), std::move(fn));
@@ -2636,6 +2954,10 @@ inline bool stream_line_reader::getline() {
   fixed_buffer_used_size_ = 0;
   glowable_buffer_.clear();
 
+#ifndef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
+  char prev_byte = 0;
+#endif
+
   for (size_t i = 0;; i++) {
     char byte;
     auto n = strm_.read(&byte, 1);
@@ -2652,7 +2974,12 @@ inline bool stream_line_reader::getline() {
 
     append(byte);
 
+#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
     if (byte == '\n') { break; }
+#else
+    if (prev_byte == '\r' && byte == '\n') { break; }
+    prev_byte = byte;
+#endif
   }
 
   return true;
@@ -2671,16 +2998,7 @@ inline void stream_line_reader::append(char c) {
   }
 }
 
-inline mmap::mmap(const char *path)
-#if defined(_WIN32)
-    : hFile_(NULL), hMapping_(NULL)
-#else
-    : fd_(-1)
-#endif
-      ,
-      size_(0), addr_(nullptr) {
-  open(path);
-}
+inline mmap::mmap(const char *path) { open(path); }
 
 inline mmap::~mmap() { close(); }
 
@@ -2688,29 +3006,60 @@ inline bool mmap::open(const char *path) {
   close();
 
 #if defined(_WIN32)
-  std::wstring wpath;
-  for (size_t i = 0; i < strlen(path); i++) {
-    wpath += path[i];
-  }
+  auto wpath = u8string_to_wstring(path);
+  if (wpath.empty()) { return false; }
 
+#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
   hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ,
                          OPEN_EXISTING, NULL);
+#else
+  hFile_ = ::CreateFileW(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, NULL,
+                         OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL);
+#endif
 
   if (hFile_ == INVALID_HANDLE_VALUE) { return false; }
 
   LARGE_INTEGER size{};
   if (!::GetFileSizeEx(hFile_, &size)) { return false; }
+  // If the following line doesn't compile due to QuadPart, update Windows SDK.
+  // See:
+  // https://github.com/yhirose/cpp-httplib/issues/1903#issuecomment-2316520721
+  if (static_cast<ULONGLONG>(size.QuadPart) >
+      (std::numeric_limits<decltype(size_)>::max)()) {
+    // `size_t` might be 32-bits, on 32-bits Windows.
+    return false;
+  }
   size_ = static_cast<size_t>(size.QuadPart);
 
+#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
   hMapping_ =
       ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL);
+#else
+  hMapping_ = ::CreateFileMappingW(hFile_, NULL, PAGE_READONLY, 0, 0, NULL);
+#endif
+
+  // Special treatment for an empty file...
+  if (hMapping_ == NULL && size_ == 0) {
+    close();
+    is_open_empty_file = true;
+    return true;
+  }
 
   if (hMapping_ == NULL) {
     close();
     return false;
   }
 
+#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
   addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0);
+#else
+  addr_ = ::MapViewOfFile(hMapping_, FILE_MAP_READ, 0, 0, 0);
+#endif
+
+  if (addr_ == nullptr) {
+    close();
+    return false;
+  }
 #else
   fd_ = ::open(path, O_RDONLY);
   if (fd_ == -1) { return false; }
@@ -2723,22 +3072,26 @@ inline bool mmap::open(const char *path) {
   size_ = static_cast<size_t>(sb.st_size);
 
   addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0);
-#endif
 
-  if (addr_ == nullptr) {
+  // Special treatment for an empty file...
+  if (addr_ == MAP_FAILED && size_ == 0) {
     close();
+    is_open_empty_file = true;
     return false;
   }
+#endif
 
   return true;
 }
 
-inline bool mmap::is_open() const { return addr_ != nullptr; }
+inline bool mmap::is_open() const {
+  return is_open_empty_file ? true : addr_ != nullptr;
+}
 
 inline size_t mmap::size() const { return size_; }
 
 inline const char *mmap::data() const {
-  return static_cast<const char *>(addr_);
+  return is_open_empty_file ? "" : static_cast<const char *>(addr_);
 }
 
 inline void mmap::close() {
@@ -2757,6 +3110,8 @@ inline void mmap::close() {
     ::CloseHandle(hFile_);
     hFile_ = INVALID_HANDLE_VALUE;
   }
+
+  is_open_empty_file = false;
 #else
   if (addr_ != nullptr) {
     munmap(addr_, size_);
@@ -2782,7 +3137,10 @@ template <typename T> inline ssize_t handle_EINTR(T fn) {
   ssize_t res = 0;
   while (true) {
     res = fn();
-    if (res < 0 && errno == EINTR) { continue; }
+    if (res < 0 && errno == EINTR) {
+      std::this_thread::sleep_for(std::chrono::microseconds{1});
+      continue;
+    }
     break;
   }
   return res;
@@ -2991,23 +3349,37 @@ private:
 };
 #endif
 
-inline bool keep_alive(socket_t sock, time_t keep_alive_timeout_sec) {
+inline bool keep_alive(const std::atomic<socket_t> &svr_sock, socket_t sock,
+                       time_t keep_alive_timeout_sec) {
   using namespace std::chrono;
-  auto start = steady_clock::now();
+
+  const auto interval_usec =
+      CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND;
+
+  // Avoid expensive `steady_clock::now()` call for the first time
+  if (select_read(sock, 0, interval_usec) > 0) { return true; }
+
+  const auto start = steady_clock::now() - microseconds{interval_usec};
+  const auto timeout = seconds{keep_alive_timeout_sec};
+
   while (true) {
-    auto val = select_read(sock, 0, 10000);
+    if (svr_sock == INVALID_SOCKET) {
+      break; // Server socket is closed
+    }
+
+    auto val = select_read(sock, 0, interval_usec);
     if (val < 0) {
-      return false;
+      break; // Ssocket error
     } else if (val == 0) {
-      auto current = steady_clock::now();
-      auto duration = duration_cast<milliseconds>(current - start);
-      auto timeout = keep_alive_timeout_sec * 1000;
-      if (duration.count() > timeout) { return false; }
-      std::this_thread::sleep_for(std::chrono::milliseconds(1));
+      if (steady_clock::now() - start > timeout) {
+        break; // Timeout
+      }
     } else {
-      return true;
+      return true; // Ready for read
     }
   }
+
+  return false;
 }
 
 template <typename T>
@@ -3018,8 +3390,7 @@ process_server_socket_core(const std::atomic<socket_t> &svr_sock, socket_t sock,
   assert(keep_alive_max_count > 0);
   auto ret = false;
   auto count = keep_alive_max_count;
-  while (svr_sock != INVALID_SOCKET && count > 0 &&
-         keep_alive(sock, keep_alive_timeout_sec)) {
+  while (count > 0 && keep_alive(svr_sock, sock, keep_alive_timeout_sec)) {
     auto close_connection = count == 1;
     auto connection_closed = false;
     ret = callback(close_connection, connection_closed);
@@ -3063,10 +3434,29 @@ inline int shutdown_socket(socket_t sock) {
 #endif
 }
 
+inline std::string escape_abstract_namespace_unix_domain(const std::string &s) {
+  if (s.size() > 1 && s[0] == '\0') {
+    auto ret = s;
+    ret[0] = '@';
+    return ret;
+  }
+  return s;
+}
+
+inline std::string
+unescape_abstract_namespace_unix_domain(const std::string &s) {
+  if (s.size() > 1 && s[0] == '@') {
+    auto ret = s;
+    ret[0] = '\0';
+    return ret;
+  }
+  return s;
+}
+
 template <typename BindOrConnect>
 socket_t create_socket(const std::string &host, const std::string &ip, int port,
                        int address_family, int socket_flags, bool tcp_nodelay,
-                       SocketOptions socket_options,
+                       bool ipv6_v6only, SocketOptions socket_options,
                        BindOrConnect bind_or_connect) {
   // Get address info
   const char *node = nullptr;
@@ -3075,7 +3465,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 
   memset(&hints, 0, sizeof(struct addrinfo));
   hints.ai_socktype = SOCK_STREAM;
-  hints.ai_protocol = 0;
+  hints.ai_protocol = IPPROTO_IP;
 
   if (!ip.empty()) {
     node = ip.c_str();
@@ -3093,20 +3483,32 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
     const auto addrlen = host.length();
     if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; }
 
+#ifdef SOCK_CLOEXEC
+    auto sock = socket(hints.ai_family, hints.ai_socktype | SOCK_CLOEXEC,
+                       hints.ai_protocol);
+#else
     auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol);
+#endif
+
     if (sock != INVALID_SOCKET) {
       sockaddr_un addr{};
       addr.sun_family = AF_UNIX;
-      std::copy(host.begin(), host.end(), addr.sun_path);
+
+      auto unescaped_host = unescape_abstract_namespace_unix_domain(host);
+      std::copy(unescaped_host.begin(), unescaped_host.end(), addr.sun_path);
 
       hints.ai_addr = reinterpret_cast<sockaddr *>(&addr);
       hints.ai_addrlen = static_cast<socklen_t>(
           sizeof(addr) - sizeof(addr.sun_path) + addrlen);
 
+#ifndef SOCK_CLOEXEC
       fcntl(sock, F_SETFD, FD_CLOEXEC);
+#endif
+
       if (socket_options) { socket_options(sock); }
 
-      if (!bind_or_connect(sock, hints)) {
+      bool dummy;
+      if (!bind_or_connect(sock, hints, dummy)) {
         close_socket(sock);
         sock = INVALID_SOCKET;
       }
@@ -3123,6 +3525,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #endif
     return INVALID_SOCKET;
   }
+  auto se = detail::scope_exit([&] { freeaddrinfo(result); });
 
   for (auto rp = result; rp; rp = rp->ai_next) {
     // Create a socket
@@ -3148,11 +3551,18 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
       sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol);
     }
 #else
+
+#ifdef SOCK_CLOEXEC
+    auto sock =
+        socket(rp->ai_family, rp->ai_socktype | SOCK_CLOEXEC, rp->ai_protocol);
+#else
     auto sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol);
+#endif
+
 #endif
     if (sock == INVALID_SOCKET) { continue; }
 
-#ifndef _WIN32
+#if !defined _WIN32 && !defined SOCK_CLOEXEC
     if (fcntl(sock, F_SETFD, FD_CLOEXEC) == -1) {
       close_socket(sock);
       continue;
@@ -3160,39 +3570,38 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #endif
 
     if (tcp_nodelay) {
-      auto yes = 1;
+      auto opt = 1;
 #ifdef _WIN32
       setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
-                 reinterpret_cast<const char *>(&yes), sizeof(yes));
+                 reinterpret_cast<const char *>(&opt), sizeof(opt));
 #else
       setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
-                 reinterpret_cast<const void *>(&yes), sizeof(yes));
+                 reinterpret_cast<const void *>(&opt), sizeof(opt));
+#endif
+    }
+
+    if (rp->ai_family == AF_INET6) {
+      auto opt = ipv6_v6only ? 1 : 0;
+#ifdef _WIN32
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const char *>(&opt), sizeof(opt));
+#else
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const void *>(&opt), sizeof(opt));
 #endif
     }
 
     if (socket_options) { socket_options(sock); }
 
-    if (rp->ai_family == AF_INET6) {
-      auto no = 0;
-#ifdef _WIN32
-      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
-                 reinterpret_cast<const char *>(&no), sizeof(no));
-#else
-      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
-                 reinterpret_cast<const void *>(&no), sizeof(no));
-#endif
-    }
-
     // bind or connect
-    if (bind_or_connect(sock, *rp)) {
-      freeaddrinfo(result);
-      return sock;
-    }
+    auto quit = false;
+    if (bind_or_connect(sock, *rp, quit)) { return sock; }
 
     close_socket(sock);
+
+    if (quit) { break; }
   }
 
-  freeaddrinfo(result);
   return INVALID_SOCKET;
 }
 
@@ -3225,6 +3634,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) {
   hints.ai_protocol = 0;
 
   if (getaddrinfo(host.c_str(), "0", &hints, &result)) { return false; }
+  auto se = detail::scope_exit([&] { freeaddrinfo(result); });
 
   auto ret = false;
   for (auto rp = result; rp; rp = rp->ai_next) {
@@ -3235,7 +3645,6 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) {
     }
   }
 
-  freeaddrinfo(result);
   return ret;
 }
 
@@ -3247,6 +3656,8 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) {
 inline std::string if2ip(int address_family, const std::string &ifn) {
   struct ifaddrs *ifap;
   getifaddrs(&ifap);
+  auto se = detail::scope_exit([&] { freeifaddrs(ifap); });
+
   std::string addr_candidate;
   for (auto ifa = ifap; ifa; ifa = ifa->ifa_next) {
     if (ifa->ifa_addr && ifn == ifa->ifa_name &&
@@ -3256,7 +3667,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) {
         auto sa = reinterpret_cast<struct sockaddr_in *>(ifa->ifa_addr);
         char buf[INET_ADDRSTRLEN];
         if (inet_ntop(AF_INET, &sa->sin_addr, buf, INET_ADDRSTRLEN)) {
-          freeifaddrs(ifap);
           return std::string(buf, INET_ADDRSTRLEN);
         }
       } else if (ifa->ifa_addr->sa_family == AF_INET6) {
@@ -3269,7 +3679,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) {
             if (s6_addr_head == 0xfc || s6_addr_head == 0xfd) {
               addr_candidate = std::string(buf, INET6_ADDRSTRLEN);
             } else {
-              freeifaddrs(ifap);
               return std::string(buf, INET6_ADDRSTRLEN);
             }
           }
@@ -3277,20 +3686,21 @@ inline std::string if2ip(int address_family, const std::string &ifn) {
       }
     }
   }
-  freeifaddrs(ifap);
   return addr_candidate;
 }
 #endif
 
 inline socket_t create_client_socket(
     const std::string &host, const std::string &ip, int port,
-    int address_family, bool tcp_nodelay, SocketOptions socket_options,
-    time_t connection_timeout_sec, time_t connection_timeout_usec,
-    time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec,
+    int address_family, bool tcp_nodelay, bool ipv6_v6only,
+    SocketOptions socket_options, time_t connection_timeout_sec,
+    time_t connection_timeout_usec, time_t read_timeout_sec,
+    time_t read_timeout_usec, time_t write_timeout_sec,
     time_t write_timeout_usec, const std::string &intf, Error &error) {
   auto sock = create_socket(
-      host, ip, port, address_family, 0, tcp_nodelay, std::move(socket_options),
-      [&](socket_t sock2, struct addrinfo &ai) -> bool {
+      host, ip, port, address_family, 0, tcp_nodelay, ipv6_v6only,
+      std::move(socket_options),
+      [&](socket_t sock2, struct addrinfo &ai, bool &quit) -> bool {
         if (!intf.empty()) {
 #ifdef USE_IF2IP
           auto ip_from_if = if2ip(address_family, intf);
@@ -3314,7 +3724,10 @@ inline socket_t create_client_socket(
           }
           error = wait_until_socket_is_ready(sock2, connection_timeout_sec,
                                              connection_timeout_usec);
-          if (error != Error::Success) { return false; }
+          if (error != Error::Success) {
+            if (error == Error::ConnectionTimeout) { quit = true; }
+            return false;
+          }
         }
 
         set_nonblocking(sock2, false);
@@ -3439,7 +3852,7 @@ inline unsigned int str2tag(const std::string &s) {
 
 namespace udl {
 
-inline constexpr unsigned int operator"" _t(const char *s, size_t l) {
+inline constexpr unsigned int operator""_t(const char *s, size_t l) {
   return str2tag_core(s, l, 0);
 }
 
@@ -3524,8 +3937,9 @@ inline bool can_compress_content_type(const std::string &content_type) {
   case "application/protobuf"_t:
   case "application/xhtml+xml"_t: return true;
 
-  default:
-    return !content_type.rfind("text/", 0) && tag != "text/event-stream"_t;
+  case "text/event-stream"_t: return false;
+
+  default: return !content_type.rfind("text/", 0);
   }
 }
 
@@ -3762,8 +4176,8 @@ inline bool has_header(const Headers &headers, const std::string &key) {
 }
 
 inline const char *get_header_value(const Headers &headers,
-                                    const std::string &key, size_t id,
-                                    const char *def) {
+                                    const std::string &key, const char *def,
+                                    size_t id) {
   auto rng = headers.equal_range(key);
   auto it = rng.first;
   std::advance(it, static_cast<ssize_t>(id));
@@ -3771,14 +4185,6 @@ inline const char *get_header_value(const Headers &headers,
   return def;
 }
 
-inline bool compare_case_ignore(const std::string &a, const std::string &b) {
-  if (a.size() != b.size()) { return false; }
-  for (size_t i = 0; i < b.size(); i++) {
-    if (::tolower(a[i]) != ::tolower(b[i])) { return false; }
-  }
-  return true;
-}
-
 template <typename T>
 inline bool parse_header(const char *beg, const char *end, T fn) {
   // Skip trailing spaces and tabs.
@@ -3801,15 +4207,27 @@ inline bool parse_header(const char *beg, const char *end, T fn) {
     p++;
   }
 
-  if (p < end) {
+  if (p <= end) {
     auto key_len = key_end - beg;
     if (!key_len) { return false; }
 
     auto key = std::string(beg, key_end);
-    auto val = compare_case_ignore(key, "Location")
+    auto val = case_ignore::equal(key, "Location")
                    ? std::string(p, end)
                    : decode_url(std::string(p, end), false);
-    fn(std::move(key), std::move(val));
+
+    // NOTE: From RFC 9110:
+    // Field values containing CR, LF, or NUL characters are
+    // invalid and dangerous, due to the varying ways that
+    // implementations might parse and interpret those
+    // characters; a recipient of CR, LF, or NUL within a field
+    // value MUST either reject the message or replace each of
+    // those characters with SP before further processing or
+    // forwarding of that message.
+    static const std::string CR_LF_NUL("\r\n\0", 3);
+    if (val.find_first_of(CR_LF_NUL) != std::string::npos) { return false; }
+
+    fn(key, val);
     return true;
   }
 
@@ -3829,27 +4247,27 @@ inline bool read_headers(Stream &strm, Headers &headers) {
     if (line_reader.end_with_crlf()) {
       // Blank line indicates end of headers.
       if (line_reader.size() == 2) { break; }
-#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
     } else {
+#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
       // Blank line indicates end of headers.
       if (line_reader.size() == 1) { break; }
       line_terminator_len = 1;
-    }
 #else
-    } else {
       continue; // Skip invalid line.
-    }
 #endif
+    }
 
     if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }
 
     // Exclude line terminator
     auto end = line_reader.ptr() + line_reader.size() - line_terminator_len;
 
-    parse_header(line_reader.ptr(), end,
-                 [&](std::string &&key, std::string &&val) {
-                   headers.emplace(std::move(key), std::move(val));
-                 });
+    if (!parse_header(line_reader.ptr(), end,
+                      [&](const std::string &key, std::string &val) {
+                        headers.emplace(key, val);
+                      })) {
+      return false;
+    }
   }
 
   return true;
@@ -3937,8 +4355,19 @@ inline bool read_content_chunked(Stream &strm, T &x,
 
   assert(chunk_len == 0);
 
-  // Trailer
-  if (!line_reader.getline()) { return false; }
+  // NOTE: In RFC 9112, '7.1 Chunked Transfer Coding' mentiones "The chunked
+  // transfer coding is complete when a chunk with a chunk-size of zero is
+  // received, possibly followed by a trailer section, and finally terminated by
+  // an empty line". https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1
+  //
+  // In '7.1.3. Decoding Chunked', however, the pseudo-code in the section
+  // does't care for the existence of the final CRLF. In other words, it seems
+  // to be ok whether the final CRLF exists or not in the chunked data.
+  // https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1.3
+  //
+  // According to the reference code in RFC 9112, cpp-htpplib now allows
+  // chuncked transfer coding data without the final CRLF.
+  if (!line_reader.getline()) { return true; }
 
   while (strcmp(line_reader.ptr(), "\r\n") != 0) {
     if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }
@@ -3948,8 +4377,8 @@ inline bool read_content_chunked(Stream &strm, T &x,
     auto end = line_reader.ptr() + line_reader.size() - line_terminator_len;
 
     parse_header(line_reader.ptr(), end,
-                 [&](std::string &&key, std::string &&val) {
-                   x.headers.emplace(std::move(key), std::move(val));
+                 [&](const std::string &key, const std::string &val) {
+                   x.headers.emplace(key, val);
                  });
 
     if (!line_reader.getline()) { return false; }
@@ -3959,8 +4388,8 @@ inline bool read_content_chunked(Stream &strm, T &x,
 }
 
 inline bool is_chunked_transfer_encoding(const Headers &headers) {
-  return compare_case_ignore(
-      get_header_value(headers, "Transfer-Encoding", 0, ""), "chunked");
+  return case_ignore::equal(
+      get_header_value(headers, "Transfer-Encoding", "", 0), "chunked");
 }
 
 template <typename T, typename U>
@@ -4026,8 +4455,14 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
         } else if (!has_header(x.headers, "Content-Length")) {
           ret = read_content_without_length(strm, out);
         } else {
-          auto len = get_header_value_u64(x.headers, "Content-Length", 0, 0);
-          if (len > payload_max_length) {
+          auto is_invalid_value = false;
+          auto len = get_header_value_u64(x.headers, "Content-Length",
+                                          std::numeric_limits<uint64_t>::max(),
+                                          0, is_invalid_value);
+
+          if (is_invalid_value) {
+            ret = false;
+          } else if (len > payload_max_length) {
             exceed_payload_max_length = true;
             skip_content_with_length(strm, len);
             ret = false;
@@ -4042,13 +4477,36 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
         }
         return ret;
       });
-} // namespace detail
+}
+
+inline ssize_t write_request_line(Stream &strm, const std::string &method,
+                                  const std::string &path) {
+  std::string s = method;
+  s += " ";
+  s += path;
+  s += " HTTP/1.1\r\n";
+  return strm.write(s.data(), s.size());
+}
+
+inline ssize_t write_response_line(Stream &strm, int status) {
+  std::string s = "HTTP/1.1 ";
+  s += std::to_string(status);
+  s += " ";
+  s += httplib::status_message(status);
+  s += "\r\n";
+  return strm.write(s.data(), s.size());
+}
 
 inline ssize_t write_headers(Stream &strm, const Headers &headers) {
   ssize_t write_len = 0;
   for (const auto &x : headers) {
-    auto len =
-        strm.write_format("%s: %s\r\n", x.first.c_str(), x.second.c_str());
+    std::string s;
+    s = x.first;
+    s += ": ";
+    s += x.second;
+    s += "\r\n";
+
+    auto len = strm.write(s.data(), s.size());
     if (len < 0) { return len; }
     write_len += len;
   }
@@ -4302,22 +4760,22 @@ inline std::string params_to_query_str(const Params &params) {
   return query;
 }
 
-inline void parse_query_text(const std::string &s, Params &params) {
+inline void parse_query_text(const char *data, std::size_t size,
+                             Params &params) {
   std::set<std::string> cache;
-  split(s.data(), s.data() + s.size(), '&', [&](const char *b, const char *e) {
+  split(data, data + size, '&', [&](const char *b, const char *e) {
     std::string kv(b, e);
     if (cache.find(kv) != cache.end()) { return; }
-    cache.insert(kv);
+    cache.insert(std::move(kv));
 
     std::string key;
     std::string val;
-    split(b, e, '=', [&](const char *b2, const char *e2) {
-      if (key.empty()) {
-        key.assign(b2, e2);
-      } else {
-        val.assign(b2, e2);
-      }
-    });
+    divide(b, static_cast<std::size_t>(e - b), '=',
+           [&](const char *lhs_data, std::size_t lhs_size, const char *rhs_data,
+               std::size_t rhs_size) {
+             key.assign(lhs_data, lhs_size);
+             val.assign(rhs_data, rhs_size);
+           });
 
     if (!key.empty()) {
       params.emplace(decode_url(key, true), decode_url(val, true));
@@ -4325,6 +4783,10 @@ inline void parse_query_text(const std::string &s, Params &params) {
   });
 }
 
+inline void parse_query_text(const std::string &s, Params &params) {
+  parse_query_text(s.data(), s.size(), params);
+}
+
 inline bool parse_multipart_boundary(const std::string &content_type,
                                      std::string &boundary) {
   auto boundary_keyword = "boundary=";
@@ -4365,35 +4827,44 @@ inline bool parse_range_header(const std::string &s, Ranges &ranges) {
 #else
 inline bool parse_range_header(const std::string &s, Ranges &ranges) try {
 #endif
-  static auto re_first_range = std::regex(R"(bytes=(\d*-\d*(?:,\s*\d*-\d*)*))");
-  std::smatch m;
-  if (std::regex_match(s, m, re_first_range)) {
-    auto pos = static_cast<size_t>(m.position(1));
-    auto len = static_cast<size_t>(m.length(1));
+  auto is_valid = [](const std::string &str) {
+    return std::all_of(str.cbegin(), str.cend(),
+                       [](unsigned char c) { return std::isdigit(c); });
+  };
+
+  if (s.size() > 7 && s.compare(0, 6, "bytes=") == 0) {
+    const auto pos = static_cast<size_t>(6);
+    const auto len = static_cast<size_t>(s.size() - 6);
     auto all_valid_ranges = true;
     split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) {
       if (!all_valid_ranges) { return; }
-      static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))");
-      std::cmatch cm;
-      if (std::regex_match(b, e, cm, re_another_range)) {
-        ssize_t first = -1;
-        if (!cm.str(1).empty()) {
-          first = static_cast<ssize_t>(std::stoll(cm.str(1)));
-        }
 
-        ssize_t last = -1;
-        if (!cm.str(2).empty()) {
-          last = static_cast<ssize_t>(std::stoll(cm.str(2)));
-        }
-
-        if (first != -1 && last != -1 && first > last) {
-          all_valid_ranges = false;
-          return;
-        }
-        ranges.emplace_back(std::make_pair(first, last));
+      const auto it = std::find(b, e, '-');
+      if (it == e) {
+        all_valid_ranges = false;
+        return;
       }
+
+      const auto lhs = std::string(b, it);
+      const auto rhs = std::string(it + 1, e);
+      if (!is_valid(lhs) || !is_valid(rhs)) {
+        all_valid_ranges = false;
+        return;
+      }
+
+      const auto first =
+          static_cast<ssize_t>(lhs.empty() ? -1 : std::stoll(lhs));
+      const auto last =
+          static_cast<ssize_t>(rhs.empty() ? -1 : std::stoll(rhs));
+      if ((first == -1 && last == -1) ||
+          (first != -1 && last != -1 && first > last)) {
+        all_valid_ranges = false;
+        return;
+      }
+
+      ranges.emplace_back(first, last);
     });
-    return all_valid_ranges;
+    return all_valid_ranges && !ranges.empty();
   }
   return false;
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
@@ -4452,7 +4923,7 @@ public:
           const auto header = buf_head(pos);
 
           if (!parse_header(header.data(), header.data() + header.size(),
-                            [&](std::string &&, std::string &&) {})) {
+                            [&](const std::string &, const std::string &) {})) {
             is_valid_ = false;
             return false;
           }
@@ -4562,7 +5033,9 @@ private:
                               const std::string &b) const {
     if (a.size() < b.size()) { return false; }
     for (size_t i = 0; i < b.size(); i++) {
-      if (::tolower(a[i]) != ::tolower(b[i])) { return false; }
+      if (case_ignore::to_lower(a[i]) != case_ignore::to_lower(b[i])) {
+        return false;
+      }
     }
     return true;
   }
@@ -4645,16 +5118,6 @@ private:
   size_t buf_epos_ = 0;
 };
 
-inline std::string to_lower(const char *beg, const char *end) {
-  std::string out;
-  auto it = beg;
-  while (it != end) {
-    out += static_cast<char>(::tolower(*it));
-    it++;
-  }
-  return out;
-}
-
 inline std::string random_string(size_t length) {
   static const char data[] =
       "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
@@ -4768,7 +5231,18 @@ inline bool range_error(Request &req, Response &res) {
         last_pos = contant_len - 1;
       }
 
-      if (last_pos == -1) { last_pos = contant_len - 1; }
+      // NOTE: RFC-9110 '14.1.2. Byte Ranges':
+      // A client can limit the number of bytes requested without knowing the
+      // size of the selected representation. If the last-pos value is absent,
+      // or if the value is greater than or equal to the current length of the
+      // representation data, the byte range is interpreted as the remainder of
+      // the representation (i.e., the server replaces the value of last-pos
+      // with a value that is one less than the current length of the selected
+      // representation).
+      // https://www.rfc-editor.org/rfc/rfc9110.html#section-14.1.2-6
+      if (last_pos == -1 || last_pos >= contant_len) {
+        last_pos = contant_len - 1;
+      }
 
       // Range must be within content length
       if (!(0 <= first_pos && first_pos <= last_pos &&
@@ -4795,12 +5269,11 @@ inline bool range_error(Request &req, Response &res) {
 
 inline std::pair<size_t, size_t>
 get_range_offset_and_length(Range r, size_t content_length) {
-  (void)(content_length); // patch to get rid of "unused parameter" on release build
   assert(r.first != -1 && r.second != -1);
   assert(0 <= r.first && r.first < static_cast<ssize_t>(content_length));
   assert(r.first <= r.second &&
          r.second < static_cast<ssize_t>(content_length));
-
+  (void)(content_length);
   return std::make_pair(r.first, static_cast<size_t>(r.second - r.first) + 1);
 }
 
@@ -5230,6 +5703,7 @@ inline void hosted_at(const std::string &hostname,
 #endif
     return;
   }
+  auto se = detail::scope_exit([&] { freeaddrinfo(result); });
 
   for (auto rp = result; rp; rp = rp->ai_next) {
     const auto &addr =
@@ -5241,8 +5715,6 @@ inline void hosted_at(const std::string &hostname,
       addrs.push_back(ip);
     }
   }
-
-  freeaddrinfo(result);
 }
 
 inline std::string append_query_params(const std::string &path,
@@ -5291,8 +5763,8 @@ inline bool Request::has_header(const std::string &key) const {
 }
 
 inline std::string Request::get_header_value(const std::string &key,
-                                             size_t id) const {
-  return detail::get_header_value(headers, key, id, "");
+                                             const char *def, size_t id) const {
+  return detail::get_header_value(headers, key, def, id);
 }
 
 inline size_t Request::get_header_value_count(const std::string &key) const {
@@ -5302,7 +5774,8 @@ inline size_t Request::get_header_value_count(const std::string &key) const {
 
 inline void Request::set_header(const std::string &key,
                                 const std::string &val) {
-  if (!detail::has_crlf(key) && !detail::has_crlf(val)) {
+  if (detail::fields::is_field_name(key) &&
+      detail::fields::is_field_value(val)) {
     headers.emplace(key, val);
   }
 }
@@ -5356,8 +5829,9 @@ inline bool Response::has_header(const std::string &key) const {
 }
 
 inline std::string Response::get_header_value(const std::string &key,
+                                              const char *def,
                                               size_t id) const {
-  return detail::get_header_value(headers, key, id, "");
+  return detail::get_header_value(headers, key, def, id);
 }
 
 inline size_t Response::get_header_value_count(const std::string &key) const {
@@ -5367,13 +5841,14 @@ inline size_t Response::get_header_value_count(const std::string &key) const {
 
 inline void Response::set_header(const std::string &key,
                                  const std::string &val) {
-  if (!detail::has_crlf(key) && !detail::has_crlf(val)) {
+  if (detail::fields::is_field_name(key) &&
+      detail::fields::is_field_value(val)) {
     headers.emplace(key, val);
   }
 }
 
 inline void Response::set_redirect(const std::string &url, int stat) {
-  if (!detail::has_crlf(url)) {
+  if (detail::fields::is_field_value(url)) {
     set_header("Location", url);
     if (300 <= stat && stat < 400) {
       this->status = stat;
@@ -5436,14 +5911,25 @@ inline void Response::set_chunked_content_provider(
   is_chunked_content_provider_ = true;
 }
 
+inline void Response::set_file_content(const std::string &path,
+                                       const std::string &content_type) {
+  file_content_path_ = path;
+  file_content_content_type_ = content_type;
+}
+
+inline void Response::set_file_content(const std::string &path) {
+  file_content_path_ = path;
+}
+
 // Result implementation
 inline bool Result::has_request_header(const std::string &key) const {
   return request_headers_.find(key) != request_headers_.end();
 }
 
 inline std::string Result::get_request_header_value(const std::string &key,
+                                                    const char *def,
                                                     size_t id) const {
-  return detail::get_header_value(request_headers_, key, id, "");
+  return detail::get_header_value(request_headers_, key, def, id);
 }
 
 inline size_t
@@ -5584,6 +6070,8 @@ inline socket_t BufferStream::socket() const { return 0; }
 inline const std::string &BufferStream::get_buffer() const { return buffer; }
 
 inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) {
+  static constexpr char marker[] = "/:";
+
   // One past the last ending position of a path param substring
   std::size_t last_param_end = 0;
 
@@ -5596,13 +6084,14 @@ inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) {
 #endif
 
   while (true) {
-    const auto marker_pos = pattern.find(marker, last_param_end);
+    const auto marker_pos = pattern.find(
+        marker, last_param_end == 0 ? last_param_end : last_param_end - 1);
     if (marker_pos == std::string::npos) { break; }
 
     static_fragments_.push_back(
-        pattern.substr(last_param_end, marker_pos - last_param_end));
+        pattern.substr(last_param_end, marker_pos - last_param_end + 1));
 
-    const auto param_name_start = marker_pos + 1;
+    const auto param_name_start = marker_pos + 2;
 
     auto sep_pos = pattern.find(separator, param_name_start);
     if (sep_pos == std::string::npos) { sep_pos = pattern.length(); }
@@ -5664,7 +6153,7 @@ inline bool PathParamsMatcher::match(Request &request) const {
     request.path_params.emplace(
         param_name, request.path.substr(starting_pos, sep_pos - starting_pos));
 
-    // Mark everythin up to '/' as matched
+    // Mark everything up to '/' as matched
     starting_pos = sep_pos + 1;
   }
   // Returns false if the path is longer than the pattern
@@ -5763,7 +6252,8 @@ inline bool Server::set_base_dir(const std::string &dir,
 
 inline bool Server::set_mount_point(const std::string &mount_point,
                                     const std::string &dir, Headers headers) {
-  if (detail::is_dir(dir)) {
+  detail::FileStat stat(dir);
+  if (stat.is_dir()) {
     std::string mnt = !mount_point.empty() ? mount_point : "/";
     if (!mnt.empty() && mnt[0] == '/') {
       base_dirs_.push_back({mnt, dir, std::move(headers)});
@@ -5800,12 +6290,14 @@ inline Server &Server::set_file_request_handler(Handler handler) {
   return *this;
 }
 
-inline Server &Server::set_error_handler(HandlerWithResponse handler) {
+inline Server &Server::set_error_handler_core(HandlerWithResponse handler,
+                                              std::true_type) {
   error_handler_ = std::move(handler);
   return *this;
 }
 
-inline Server &Server::set_error_handler(Handler handler) {
+inline Server &Server::set_error_handler_core(Handler handler,
+                                              std::false_type) {
   error_handler_ = [handler](const Request &req, Response &res) {
     handler(req, res);
     return HandlerResponse::Handled;
@@ -5849,6 +6341,11 @@ inline Server &Server::set_tcp_nodelay(bool on) {
   return *this;
 }
 
+inline Server &Server::set_ipv6_v6only(bool on) {
+  ipv6_v6only_ = on;
+  return *this;
+}
+
 inline Server &Server::set_socket_options(SocketOptions socket_options) {
   socket_options_ = std::move(socket_options);
   return *this;
@@ -5900,27 +6397,27 @@ inline Server &Server::set_payload_max_length(size_t length) {
 
 inline bool Server::bind_to_port(const std::string &host, int port,
                                  int socket_flags) {
-  return bind_internal(host, port, socket_flags) >= 0;
+  auto ret = bind_internal(host, port, socket_flags);
+  if (ret == -1) { is_decommisioned = true; }
+  return ret >= 0;
 }
 inline int Server::bind_to_any_port(const std::string &host, int socket_flags) {
-  return bind_internal(host, 0, socket_flags);
+  auto ret = bind_internal(host, 0, socket_flags);
+  if (ret == -1) { is_decommisioned = true; }
+  return ret;
 }
 
-inline bool Server::listen_after_bind() {
-  auto se = detail::scope_exit([&]() { done_ = true; });
-  return listen_internal();
-}
+inline bool Server::listen_after_bind() { return listen_internal(); }
 
 inline bool Server::listen(const std::string &host, int port,
                            int socket_flags) {
-  auto se = detail::scope_exit([&]() { done_ = true; });
   return bind_to_port(host, port, socket_flags) && listen_internal();
 }
 
 inline bool Server::is_running() const { return is_running_; }
 
 inline void Server::wait_until_ready() const {
-  while (!is_running() && !done_) {
+  while (!is_running_ && !is_decommisioned) {
     std::this_thread::sleep_for(std::chrono::milliseconds{1});
   }
 }
@@ -5932,8 +6429,11 @@ inline void Server::stop() {
     detail::shutdown_socket(sock);
     detail::close_socket(sock);
   }
+  is_decommisioned = false;
 }
 
+inline void Server::decommission() { is_decommisioned = true; }
+
 inline bool Server::parse_request_line(const char *s, Request &req) const {
   auto len = strlen(s);
   if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; }
@@ -5972,26 +6472,13 @@ inline bool Server::parse_request_line(const char *s, Request &req) const {
       }
     }
 
-    size_t count = 0;
-
-    detail::split(req.target.data(), req.target.data() + req.target.size(), '?',
-                  2, [&](const char *b, const char *e) {
-                    switch (count) {
-                    case 0:
-                      req.path = detail::decode_url(std::string(b, e), false);
-                      break;
-                    case 1: {
-                      if (e - b > 0) {
-                        detail::parse_query_text(std::string(b, e), req.params);
-                      }
-                      break;
-                    }
-                    default: break;
-                    }
-                    count++;
-                  });
-
-    if (count > 2) { return false; }
+    detail::divide(req.target, '?',
+                   [&](const char *lhs_data, std::size_t lhs_size,
+                       const char *rhs_data, std::size_t rhs_size) {
+                     req.path = detail::decode_url(
+                         std::string(lhs_data, lhs_size), false);
+                     detail::parse_query_text(rhs_data, rhs_size, req.params);
+                   });
   }
 
   return true;
@@ -6030,23 +6517,24 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
   if (close_connection || req.get_header_value("Connection") == "close") {
     res.set_header("Connection", "close");
   } else {
-    std::stringstream ss;
-    ss << "timeout=" << keep_alive_timeout_sec_
-       << ", max=" << keep_alive_max_count_;
-    res.set_header("Keep-Alive", ss.str());
+    std::string s = "timeout=";
+    s += std::to_string(keep_alive_timeout_sec_);
+    s += ", max=";
+    s += std::to_string(keep_alive_max_count_);
+    res.set_header("Keep-Alive", s);
   }
 
-  if (!res.has_header("Content-Type") &&
-      (!res.body.empty() || res.content_length_ > 0 || res.content_provider_)) {
+  if ((!res.body.empty() || res.content_length_ > 0 || res.content_provider_) &&
+      !res.has_header("Content-Type")) {
     res.set_header("Content-Type", "text/plain");
   }
 
-  if (!res.has_header("Content-Length") && res.body.empty() &&
-      !res.content_length_ && !res.content_provider_) {
+  if (res.body.empty() && !res.content_length_ && !res.content_provider_ &&
+      !res.has_header("Content-Length")) {
     res.set_header("Content-Length", "0");
   }
 
-  if (!res.has_header("Accept-Ranges") && req.method == "HEAD") {
+  if (req.method == "HEAD" && !res.has_header("Accept-Ranges")) {
     res.set_header("Accept-Ranges", "bytes");
   }
 
@@ -6055,12 +6543,7 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
   // Response line and headers
   {
     detail::BufferStream bstrm;
-
-    if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status,
-                            status_message(res.status))) {
-      return false;
-    }
-
+    if (!detail::write_response_line(bstrm, res.status)) { return false; }
     if (!header_writer_(bstrm, res.headers)) { return false; }
 
     // Flush buffer
@@ -6254,7 +6737,14 @@ inline bool Server::handle_file_request(const Request &req, Response &res,
         auto path = entry.base_dir + sub_path;
         if (path.back() == '/') { path += "index.html"; }
 
-        if (detail::is_file(path)) {
+        detail::FileStat stat(path);
+
+        if (stat.is_dir()) {
+          res.set_redirect(sub_path + "/", StatusCode::MovedPermanently_301);
+          return true;
+        }
+
+        if (stat.is_file()) {
           for (const auto &kv : entry.headers) {
             res.set_header(kv.first, kv.second);
           }
@@ -6289,8 +6779,8 @@ Server::create_server_socket(const std::string &host, int port,
                              SocketOptions socket_options) const {
   return detail::create_socket(
       host, std::string(), port, address_family_, socket_flags, tcp_nodelay_,
-      std::move(socket_options),
-      [](socket_t sock, struct addrinfo &ai) -> bool {
+      ipv6_v6only_, std::move(socket_options),
+      [](socket_t sock, struct addrinfo &ai, bool & /*quit*/) -> bool {
         if (::bind(sock, ai.ai_addr, static_cast<socklen_t>(ai.ai_addrlen))) {
           return false;
         }
@@ -6301,6 +6791,8 @@ Server::create_server_socket(const std::string &host, int port,
 
 inline int Server::bind_internal(const std::string &host, int port,
                                  int socket_flags) {
+  if (is_decommisioned) { return -1; }
+
   if (!is_valid()) { return -1; }
 
   svr_sock_ = create_server_socket(host, port, socket_flags, socket_options_);
@@ -6326,6 +6818,8 @@ inline int Server::bind_internal(const std::string &host, int port,
 }
 
 inline bool Server::listen_internal() {
+  if (is_decommisioned) { return false; }
+
   auto ret = true;
   is_running_ = true;
   auto se = detail::scope_exit([&]() { is_running_ = false; });
@@ -6346,13 +6840,22 @@ inline bool Server::listen_internal() {
 #ifndef _WIN32
       }
 #endif
+
+#if defined _WIN32
+      // sockets conneced via WASAccept inherit flags NO_HANDLE_INHERIT,
+      // OVERLAPPED
+      socket_t sock = WSAAccept(svr_sock_, nullptr, nullptr, nullptr, 0);
+#elif defined SOCK_CLOEXEC
+      socket_t sock = accept4(svr_sock_, nullptr, nullptr, SOCK_CLOEXEC);
+#else
       socket_t sock = accept(svr_sock_, nullptr, nullptr);
+#endif
 
       if (sock == INVALID_SOCKET) {
         if (errno == EMFILE) {
           // The per-process limit of open file descriptors has been reached.
           // Try to accept new connections after a short sleep.
-          std::this_thread::sleep_for(std::chrono::milliseconds(1));
+          std::this_thread::sleep_for(std::chrono::microseconds{1});
           continue;
         } else if (errno == EINTR || errno == EAGAIN) {
           continue;
@@ -6406,6 +6909,7 @@ inline bool Server::listen_internal() {
     task_queue->shutdown();
   }
 
+  is_decommisioned = !ret;
   return ret;
 }
 
@@ -6503,7 +7007,7 @@ inline bool Server::dispatch_request(Request &req, Response &res,
 inline void Server::apply_ranges(const Request &req, Response &res,
                                  std::string &content_type,
                                  std::string &boundary) const {
-  if (req.ranges.size() > 1) {
+  if (req.ranges.size() > 1 && res.status == StatusCode::PartialContent_206) {
     auto it = res.headers.find("Content-Type");
     if (it != res.headers.end()) {
       content_type = it->second;
@@ -6521,7 +7025,7 @@ inline void Server::apply_ranges(const Request &req, Response &res,
   if (res.body.empty()) {
     if (res.content_length_ > 0) {
       size_t length = 0;
-      if (req.ranges.empty()) {
+      if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) {
         length = res.content_length_;
       } else if (req.ranges.size() == 1) {
         auto offset_and_length = detail::get_range_offset_and_length(
@@ -6550,7 +7054,7 @@ inline void Server::apply_ranges(const Request &req, Response &res,
       }
     }
   } else {
-    if (req.ranges.empty()) {
+    if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) {
       ;
     } else if (req.ranges.size() == 1) {
       auto offset_and_length =
@@ -6621,7 +7125,9 @@ inline bool Server::dispatch_request_for_content_reader(
 }
 
 inline bool
-Server::process_request(Stream &strm, bool close_connection,
+Server::process_request(Stream &strm, const std::string &remote_addr,
+                        int remote_port, const std::string &local_addr,
+                        int local_port, bool close_connection,
                         bool &connection_closed,
                         const std::function<void(Request &)> &setup_request) {
   std::array<char, 2048> buf{};
@@ -6675,11 +7181,13 @@ Server::process_request(Stream &strm, bool close_connection,
     connection_closed = true;
   }
 
-  strm.get_remote_ip_and_port(req.remote_addr, req.remote_port);
+  req.remote_addr = remote_addr;
+  req.remote_port = remote_port;
   req.set_header("REMOTE_ADDR", req.remote_addr);
   req.set_header("REMOTE_PORT", std::to_string(req.remote_port));
 
-  strm.get_local_ip_and_port(req.local_addr, req.local_port);
+  req.local_addr = local_addr;
+  req.local_port = local_port;
   req.set_header("LOCAL_ADDR", req.local_addr);
   req.set_header("LOCAL_PORT", std::to_string(req.local_port));
 
@@ -6701,13 +7209,20 @@ Server::process_request(Stream &strm, bool close_connection,
     switch (status) {
     case StatusCode::Continue_100:
     case StatusCode::ExpectationFailed_417:
-      strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status,
-                        status_message(status));
+      detail::write_response_line(strm, status);
+      strm.write("\r\n");
       break;
-    default: return write_response(strm, close_connection, req, res);
+    default:
+      connection_closed = true;
+      return write_response(strm, true, req, res);
     }
   }
 
+  // Setup `is_connection_closed` method
+  req.is_connection_closed = [&]() {
+    return !detail::is_socket_alive(strm.socket());
+  };
+
   // Routing
   auto routed = false;
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
@@ -6750,6 +7265,32 @@ Server::process_request(Stream &strm, bool close_connection,
                                       : StatusCode::PartialContent_206;
     }
 
+    // Serve file content by using a content provider
+    if (!res.file_content_path_.empty()) {
+      const auto &path = res.file_content_path_;
+      auto mm = std::make_shared<detail::mmap>(path.c_str());
+      if (!mm->is_open()) {
+        res.body.clear();
+        res.content_length_ = 0;
+        res.content_provider_ = nullptr;
+        res.status = StatusCode::NotFound_404;
+        return write_response(strm, close_connection, req, res);
+      }
+
+      auto content_type = res.file_content_content_type_;
+      if (content_type.empty()) {
+        content_type = detail::find_content_type(
+            path, file_extension_and_mimetype_map_, default_file_mimetype_);
+      }
+
+      res.set_content_provider(
+          mm->size(), content_type,
+          [mm](size_t offset, size_t length, DataSink &sink) -> bool {
+            sink.write(mm->data() + offset, length);
+            return true;
+          });
+    }
+
     if (detail::range_error(req, res)) {
       res.body.clear();
       res.content_length_ = 0;
@@ -6769,12 +7310,21 @@ Server::process_request(Stream &strm, bool close_connection,
 inline bool Server::is_valid() const { return true; }
 
 inline bool Server::process_and_close_socket(socket_t sock) {
+  std::string remote_addr;
+  int remote_port = 0;
+  detail::get_remote_ip_and_port(sock, remote_addr, remote_port);
+
+  std::string local_addr;
+  int local_port = 0;
+  detail::get_local_ip_and_port(sock, local_addr, local_port);
+
   auto ret = detail::process_server_socket(
       svr_sock_, sock, keep_alive_max_count_, keep_alive_timeout_sec_,
       read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,
       write_timeout_usec_,
-      [this](Stream &strm, bool close_connection, bool &connection_closed) {
-        return process_request(strm, close_connection, connection_closed,
+      [&](Stream &strm, bool close_connection, bool &connection_closed) {
+        return process_request(strm, remote_addr, remote_port, local_addr,
+                               local_port, close_connection, connection_closed,
                                nullptr);
       });
 
@@ -6793,8 +7343,8 @@ inline ClientImpl::ClientImpl(const std::string &host, int port)
 inline ClientImpl::ClientImpl(const std::string &host, int port,
                               const std::string &client_cert_path,
                               const std::string &client_key_path)
-    : host_(host), port_(port),
-      host_and_port_(adjust_host_string(host) + ":" + std::to_string(port)),
+    : host_(detail::escape_abstract_namespace_unix_domain(host)), port_(port),
+      host_and_port_(adjust_host_string(host_) + ":" + std::to_string(port)),
       client_cert_path_(client_cert_path), client_key_path_(client_key_path) {}
 
 inline ClientImpl::~ClientImpl() {
@@ -6825,6 +7375,7 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) {
   url_encode_ = rhs.url_encode_;
   address_family_ = rhs.address_family_;
   tcp_nodelay_ = rhs.tcp_nodelay_;
+  ipv6_v6only_ = rhs.ipv6_v6only_;
   socket_options_ = rhs.socket_options_;
   compress_ = rhs.compress_;
   decompress_ = rhs.decompress_;
@@ -6845,6 +7396,8 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) {
 #endif
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
   server_certificate_verification_ = rhs.server_certificate_verification_;
+  server_hostname_verification_ = rhs.server_hostname_verification_;
+  server_certificate_verifier_ = rhs.server_certificate_verifier_;
 #endif
   logger_ = rhs.logger_;
 }
@@ -6853,9 +7406,9 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const {
   if (!proxy_host_.empty() && proxy_port_ != -1) {
     return detail::create_client_socket(
         proxy_host_, std::string(), proxy_port_, address_family_, tcp_nodelay_,
-        socket_options_, connection_timeout_sec_, connection_timeout_usec_,
-        read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,
-        write_timeout_usec_, interface_, error);
+        ipv6_v6only_, socket_options_, connection_timeout_sec_,
+        connection_timeout_usec_, read_timeout_sec_, read_timeout_usec_,
+        write_timeout_sec_, write_timeout_usec_, interface_, error);
   }
 
   // Check is custom IP specified for host_
@@ -6864,10 +7417,10 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const {
   if (it != addr_map_.end()) { ip = it->second; }
 
   return detail::create_client_socket(
-      host_, ip, port_, address_family_, tcp_nodelay_, socket_options_,
-      connection_timeout_sec_, connection_timeout_usec_, read_timeout_sec_,
-      read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, interface_,
-      error);
+      host_, ip, port_, address_family_, tcp_nodelay_, ipv6_v6only_,
+      socket_options_, connection_timeout_sec_, connection_timeout_usec_,
+      read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,
+      write_timeout_usec_, interface_, error);
 }
 
 inline bool ClientImpl::create_and_connect_socket(Socket &socket,
@@ -6956,6 +7509,18 @@ inline bool ClientImpl::send(Request &req, Response &res, Error &error) {
   return ret;
 }
 
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+inline bool ClientImpl::is_ssl_peer_could_be_closed(SSL *ssl) const {
+  detail::set_nonblocking(socket_.sock, true);
+  auto se = detail::scope_exit(
+      [&]() { detail::set_nonblocking(socket_.sock, false); });
+
+  char buf[1];
+  return !SSL_peek(ssl, buf, 1) &&
+         SSL_get_error(ssl, 0) == SSL_ERROR_ZERO_RETURN;
+}
+#endif
+
 inline bool ClientImpl::send_(Request &req, Response &res, Error &error) {
   {
     std::lock_guard<std::mutex> guard(socket_mutex_);
@@ -6967,6 +7532,13 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) {
     auto is_alive = false;
     if (socket_.is_open()) {
       is_alive = detail::is_socket_alive(socket_.sock);
+
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+      if (is_alive && is_ssl()) {
+        if (is_ssl_peer_could_be_closed(socket_.ssl)) { is_alive = false; }
+      }
+#endif
+
       if (!is_alive) {
         // Attempt to avoid sigpipe by shutting down nongracefully if it seems
         // like the other side has already closed the connection Also, there
@@ -7144,7 +7716,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
   if (location.empty()) { return false; }
 
   const static std::regex re(
-      R"((?:(https?):)?(?://(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)");
+      R"((?:(https?):)?(?://(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)");
 
   std::smatch m;
   if (!std::regex_match(location, m, re)) { return false; }
@@ -7243,12 +7815,26 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
 
   if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); }
 
-#ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT
-  if (!req.has_header("User-Agent")) {
-    auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION;
-    req.set_header("User-Agent", agent);
-  }
+  if (!req.content_receiver) {
+    if (!req.has_header("Accept-Encoding")) {
+      std::string accept_encoding;
+#ifdef CPPHTTPLIB_BROTLI_SUPPORT
+      accept_encoding = "br";
 #endif
+#ifdef CPPHTTPLIB_ZLIB_SUPPORT
+      if (!accept_encoding.empty()) { accept_encoding += ", "; }
+      accept_encoding += "gzip, deflate";
+#endif
+      req.set_header("Accept-Encoding", accept_encoding);
+    }
+
+#ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT
+    if (!req.has_header("User-Agent")) {
+      auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION;
+      req.set_header("User-Agent", agent);
+    }
+#endif
+  };
 
   if (req.body.empty()) {
     if (req.content_provider_) {
@@ -7308,8 +7894,14 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
   {
     detail::BufferStream bstrm;
 
-    const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path;
-    bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str());
+    const auto &path_with_query =
+        req.params.empty() ? req.path
+                           : append_query_params(req.path, req.params);
+
+    const auto &path =
+        url_encode_ ? detail::encode_url(path_with_query) : path_with_query;
+
+    detail::write_request_line(bstrm, req.method, path);
 
     header_writer_(bstrm, req.headers);
 
@@ -7417,11 +8009,12 @@ inline Result ClientImpl::send_with_content_provider(
     const std::string &method, const std::string &path, const Headers &headers,
     const char *body, size_t content_length, ContentProvider content_provider,
     ContentProviderWithoutLength content_provider_without_length,
-    const std::string &content_type) {
+    const std::string &content_type, Progress progress) {
   Request req;
   req.method = method;
   req.headers = headers;
   req.path = path;
+  req.progress = progress;
 
   auto error = Error::Success;
 
@@ -7448,9 +8041,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
   if (is_ssl()) {
     auto is_proxy_enabled = !proxy_host_.empty() && proxy_port_ != -1;
     if (!is_proxy_enabled) {
-      char buf[1];
-      if (SSL_peek(socket_.ssl, buf, 1) == 0 &&
-          SSL_get_error(socket_.ssl, 0) == SSL_ERROR_ZERO_RETURN) {
+      if (is_ssl_peer_could_be_closed(socket_.ssl)) {
         error = Error::SSLPeerCouldBeClosed_;
         return false;
       }
@@ -7468,7 +8059,9 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
   // Body
   if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" &&
       req.method != "CONNECT") {
-    auto redirect = 300 < res.status && res.status < 400 && follow_location_;
+    auto redirect = 300 < res.status && res.status < 400 &&
+                    res.status != StatusCode::NotModified_304 &&
+                    follow_location_;
 
     if (req.response_handler && !redirect) {
       if (!req.response_handler(res)) {
@@ -7489,9 +8082,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
             : static_cast<ContentReceiverWithProgress>(
                   [&](const char *buf, size_t n, uint64_t /*off*/,
                       uint64_t /*len*/) {
-                    if (res.body.size() + n > res.body.max_size()) {
-                      return false;
-                    }
+                    assert(res.body.size() + n <= res.body.max_size());
                     res.body.append(buf, n);
                     return true;
                   });
@@ -7503,12 +8094,25 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
       return ret;
     };
 
-    int dummy_status;
-    if (!detail::read_content(strm, res, (std::numeric_limits<size_t>::max)(),
-                              dummy_status, std::move(progress), std::move(out),
-                              decompress_)) {
-      if (error != Error::Canceled) { error = Error::Read; }
-      return false;
+    if (res.has_header("Content-Length")) {
+      if (!req.content_receiver) {
+        auto len = res.get_header_value_u64("Content-Length");
+        if (len > res.body.max_size()) {
+          error = Error::Read;
+          return false;
+        }
+        res.body.reserve(static_cast<size_t>(len));
+      }
+    }
+
+    if (res.status != StatusCode::NotModified_304) {
+      int dummy_status;
+      if (!detail::read_content(strm, res, (std::numeric_limits<size_t>::max)(),
+                                dummy_status, std::move(progress),
+                                std::move(out), decompress_)) {
+        if (error != Error::Canceled) { error = Error::Read; }
+        return false;
+      }
     }
   }
 
@@ -7717,14 +8321,22 @@ inline Result ClientImpl::Post(const std::string &path,
 inline Result ClientImpl::Post(const std::string &path, const char *body,
                                size_t content_length,
                                const std::string &content_type) {
-  return Post(path, Headers(), body, content_length, content_type);
+  return Post(path, Headers(), body, content_length, content_type, nullptr);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
                                const char *body, size_t content_length,
                                const std::string &content_type) {
   return send_with_content_provider("POST", path, headers, body, content_length,
-                                    nullptr, nullptr, content_type);
+                                    nullptr, nullptr, content_type, nullptr);
+}
+
+inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
+                               const char *body, size_t content_length,
+                               const std::string &content_type,
+                               Progress progress) {
+  return send_with_content_provider("POST", path, headers, body, content_length,
+                                    nullptr, nullptr, content_type, progress);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const std::string &body,
@@ -7732,12 +8344,27 @@ inline Result ClientImpl::Post(const std::string &path, const std::string &body,
   return Post(path, Headers(), body, content_type);
 }
 
+inline Result ClientImpl::Post(const std::string &path, const std::string &body,
+                               const std::string &content_type,
+                               Progress progress) {
+  return Post(path, Headers(), body, content_type, progress);
+}
+
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
                                const std::string &body,
                                const std::string &content_type) {
   return send_with_content_provider("POST", path, headers, body.data(),
-                                    body.size(), nullptr, nullptr,
-                                    content_type);
+                                    body.size(), nullptr, nullptr, content_type,
+                                    nullptr);
+}
+
+inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
+                               const std::string &body,
+                               const std::string &content_type,
+                               Progress progress) {
+  return send_with_content_provider("POST", path, headers, body.data(),
+                                    body.size(), nullptr, nullptr, content_type,
+                                    progress);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Params &params) {
@@ -7763,14 +8390,15 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
                                const std::string &content_type) {
   return send_with_content_provider("POST", path, headers, nullptr,
                                     content_length, std::move(content_provider),
-                                    nullptr, content_type);
+                                    nullptr, content_type, nullptr);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
                                ContentProviderWithoutLength content_provider,
                                const std::string &content_type) {
   return send_with_content_provider("POST", path, headers, nullptr, 0, nullptr,
-                                    std::move(content_provider), content_type);
+                                    std::move(content_provider), content_type,
+                                    nullptr);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
@@ -7779,6 +8407,13 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
   return Post(path, headers, query, "application/x-www-form-urlencoded");
 }
 
+inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
+                               const Params &params, Progress progress) {
+  auto query = detail::params_to_query_str(params);
+  return Post(path, headers, query, "application/x-www-form-urlencoded",
+              progress);
+}
+
 inline Result ClientImpl::Post(const std::string &path,
                                const MultipartFormDataItems &items) {
   return Post(path, Headers(), items);
@@ -7816,7 +8451,7 @@ ClientImpl::Post(const std::string &path, const Headers &headers,
   return send_with_content_provider(
       "POST", path, headers, nullptr, 0, nullptr,
       get_multipart_content_provider(boundary, items, provider_items),
-      content_type);
+      content_type, nullptr);
 }
 
 inline Result ClientImpl::Put(const std::string &path) {
@@ -7833,7 +8468,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
                               const char *body, size_t content_length,
                               const std::string &content_type) {
   return send_with_content_provider("PUT", path, headers, body, content_length,
-                                    nullptr, nullptr, content_type);
+                                    nullptr, nullptr, content_type, nullptr);
+}
+
+inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
+                              const char *body, size_t content_length,
+                              const std::string &content_type,
+                              Progress progress) {
+  return send_with_content_provider("PUT", path, headers, body, content_length,
+                                    nullptr, nullptr, content_type, progress);
 }
 
 inline Result ClientImpl::Put(const std::string &path, const std::string &body,
@@ -7841,12 +8484,27 @@ inline Result ClientImpl::Put(const std::string &path, const std::string &body,
   return Put(path, Headers(), body, content_type);
 }
 
+inline Result ClientImpl::Put(const std::string &path, const std::string &body,
+                              const std::string &content_type,
+                              Progress progress) {
+  return Put(path, Headers(), body, content_type, progress);
+}
+
 inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
                               const std::string &body,
                               const std::string &content_type) {
   return send_with_content_provider("PUT", path, headers, body.data(),
-                                    body.size(), nullptr, nullptr,
-                                    content_type);
+                                    body.size(), nullptr, nullptr, content_type,
+                                    nullptr);
+}
+
+inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
+                              const std::string &body,
+                              const std::string &content_type,
+                              Progress progress) {
+  return send_with_content_provider("PUT", path, headers, body.data(),
+                                    body.size(), nullptr, nullptr, content_type,
+                                    progress);
 }
 
 inline Result ClientImpl::Put(const std::string &path, size_t content_length,
@@ -7868,14 +8526,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
                               const std::string &content_type) {
   return send_with_content_provider("PUT", path, headers, nullptr,
                                     content_length, std::move(content_provider),
-                                    nullptr, content_type);
+                                    nullptr, content_type, nullptr);
 }
 
 inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
                               ContentProviderWithoutLength content_provider,
                               const std::string &content_type) {
   return send_with_content_provider("PUT", path, headers, nullptr, 0, nullptr,
-                                    std::move(content_provider), content_type);
+                                    std::move(content_provider), content_type,
+                                    nullptr);
 }
 
 inline Result ClientImpl::Put(const std::string &path, const Params &params) {
@@ -7888,6 +8547,13 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
   return Put(path, headers, query, "application/x-www-form-urlencoded");
 }
 
+inline Result ClientImpl::Put(const std::string &path, const Headers &headers,
+                              const Params &params, Progress progress) {
+  auto query = detail::params_to_query_str(params);
+  return Put(path, headers, query, "application/x-www-form-urlencoded",
+             progress);
+}
+
 inline Result ClientImpl::Put(const std::string &path,
                               const MultipartFormDataItems &items) {
   return Put(path, Headers(), items);
@@ -7925,7 +8591,7 @@ ClientImpl::Put(const std::string &path, const Headers &headers,
   return send_with_content_provider(
       "PUT", path, headers, nullptr, 0, nullptr,
       get_multipart_content_provider(boundary, items, provider_items),
-      content_type);
+      content_type, nullptr);
 }
 inline Result ClientImpl::Patch(const std::string &path) {
   return Patch(path, std::string(), std::string());
@@ -7937,12 +8603,26 @@ inline Result ClientImpl::Patch(const std::string &path, const char *body,
   return Patch(path, Headers(), body, content_length, content_type);
 }
 
+inline Result ClientImpl::Patch(const std::string &path, const char *body,
+                                size_t content_length,
+                                const std::string &content_type,
+                                Progress progress) {
+  return Patch(path, Headers(), body, content_length, content_type, progress);
+}
+
 inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
                                 const char *body, size_t content_length,
                                 const std::string &content_type) {
+  return Patch(path, headers, body, content_length, content_type, nullptr);
+}
+
+inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
+                                const char *body, size_t content_length,
+                                const std::string &content_type,
+                                Progress progress) {
   return send_with_content_provider("PATCH", path, headers, body,
                                     content_length, nullptr, nullptr,
-                                    content_type);
+                                    content_type, progress);
 }
 
 inline Result ClientImpl::Patch(const std::string &path,
@@ -7951,12 +8631,26 @@ inline Result ClientImpl::Patch(const std::string &path,
   return Patch(path, Headers(), body, content_type);
 }
 
+inline Result ClientImpl::Patch(const std::string &path,
+                                const std::string &body,
+                                const std::string &content_type,
+                                Progress progress) {
+  return Patch(path, Headers(), body, content_type, progress);
+}
+
 inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
                                 const std::string &body,
                                 const std::string &content_type) {
+  return Patch(path, headers, body, content_type, nullptr);
+}
+
+inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
+                                const std::string &body,
+                                const std::string &content_type,
+                                Progress progress) {
   return send_with_content_provider("PATCH", path, headers, body.data(),
-                                    body.size(), nullptr, nullptr,
-                                    content_type);
+                                    body.size(), nullptr, nullptr, content_type,
+                                    progress);
 }
 
 inline Result ClientImpl::Patch(const std::string &path, size_t content_length,
@@ -7978,14 +8672,15 @@ inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
                                 const std::string &content_type) {
   return send_with_content_provider("PATCH", path, headers, nullptr,
                                     content_length, std::move(content_provider),
-                                    nullptr, content_type);
+                                    nullptr, content_type, nullptr);
 }
 
 inline Result ClientImpl::Patch(const std::string &path, const Headers &headers,
                                 ContentProviderWithoutLength content_provider,
                                 const std::string &content_type) {
   return send_with_content_provider("PATCH", path, headers, nullptr, 0, nullptr,
-                                    std::move(content_provider), content_type);
+                                    std::move(content_provider), content_type,
+                                    nullptr);
 }
 
 inline Result ClientImpl::Delete(const std::string &path) {
@@ -8003,14 +8698,30 @@ inline Result ClientImpl::Delete(const std::string &path, const char *body,
   return Delete(path, Headers(), body, content_length, content_type);
 }
 
+inline Result ClientImpl::Delete(const std::string &path, const char *body,
+                                 size_t content_length,
+                                 const std::string &content_type,
+                                 Progress progress) {
+  return Delete(path, Headers(), body, content_length, content_type, progress);
+}
+
 inline Result ClientImpl::Delete(const std::string &path,
                                  const Headers &headers, const char *body,
                                  size_t content_length,
                                  const std::string &content_type) {
+  return Delete(path, headers, body, content_length, content_type, nullptr);
+}
+
+inline Result ClientImpl::Delete(const std::string &path,
+                                 const Headers &headers, const char *body,
+                                 size_t content_length,
+                                 const std::string &content_type,
+                                 Progress progress) {
   Request req;
   req.method = "DELETE";
   req.headers = headers;
   req.path = path;
+  req.progress = progress;
 
   if (!content_type.empty()) { req.set_header("Content-Type", content_type); }
   req.body.assign(body, content_length);
@@ -8024,6 +8735,14 @@ inline Result ClientImpl::Delete(const std::string &path,
   return Delete(path, Headers(), body.data(), body.size(), content_type);
 }
 
+inline Result ClientImpl::Delete(const std::string &path,
+                                 const std::string &body,
+                                 const std::string &content_type,
+                                 Progress progress) {
+  return Delete(path, Headers(), body.data(), body.size(), content_type,
+                progress);
+}
+
 inline Result ClientImpl::Delete(const std::string &path,
                                  const Headers &headers,
                                  const std::string &body,
@@ -8031,6 +8750,15 @@ inline Result ClientImpl::Delete(const std::string &path,
   return Delete(path, headers, body.data(), body.size(), content_type);
 }
 
+inline Result ClientImpl::Delete(const std::string &path,
+                                 const Headers &headers,
+                                 const std::string &body,
+                                 const std::string &content_type,
+                                 Progress progress) {
+  return Delete(path, headers, body.data(), body.size(), content_type,
+                progress);
+}
+
 inline Result ClientImpl::Options(const std::string &path) {
   return Options(path, Headers());
 }
@@ -8138,6 +8866,8 @@ inline void ClientImpl::set_address_family(int family) {
 
 inline void ClientImpl::set_tcp_nodelay(bool on) { tcp_nodelay_ = on; }
 
+inline void ClientImpl::set_ipv6_v6only(bool on) { ipv6_v6only_ = on; }
+
 inline void ClientImpl::set_socket_options(SocketOptions socket_options) {
   socket_options_ = std::move(socket_options);
 }
@@ -8187,13 +8917,11 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) {
 inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert,
                                                     std::size_t size) const {
   auto mem = BIO_new_mem_buf(ca_cert, static_cast<int>(size));
+  auto se = detail::scope_exit([&] { BIO_free_all(mem); });
   if (!mem) { return nullptr; }
 
   auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr);
-  if (!inf) {
-    BIO_free_all(mem);
-    return nullptr;
-  }
+  if (!inf) { return nullptr; }
 
   auto cts = X509_STORE_new();
   if (cts) {
@@ -8207,13 +8935,21 @@ inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert,
   }
 
   sk_X509_INFO_pop_free(inf, X509_INFO_free);
-  BIO_free_all(mem);
   return cts;
 }
 
 inline void ClientImpl::enable_server_certificate_verification(bool enabled) {
   server_certificate_verification_ = enabled;
 }
+
+inline void ClientImpl::enable_server_hostname_verification(bool enabled) {
+  server_hostname_verification_ = enabled;
+}
+
+inline void ClientImpl::set_server_certificate_verifier(
+    std::function<bool(SSL *ssl)> verifier) {
+  server_certificate_verifier_ = verifier;
+}
 #endif
 
 inline void ClientImpl::set_logger(Logger logger) {
@@ -8257,13 +8993,30 @@ inline SSL *ssl_new(socket_t sock, SSL_CTX *ctx, std::mutex &ctx_mutex,
   return ssl;
 }
 
-inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl,
+inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl, socket_t sock,
                        bool shutdown_gracefully) {
   // sometimes we may want to skip this to try to avoid SIGPIPE if we know
   // the remote has closed the network connection
   // Note that it is not always possible to avoid SIGPIPE, this is merely a
   // best-efforts.
-  if (shutdown_gracefully) { SSL_shutdown(ssl); }
+  if (shutdown_gracefully) {
+#ifdef _WIN32
+    (void)(sock);
+    SSL_shutdown(ssl);
+#else
+    timeval tv;
+    tv.tv_sec = 1;
+    tv.tv_usec = 0;
+    setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO,
+               reinterpret_cast<const void *>(&tv), sizeof(tv));
+
+    auto ret = SSL_shutdown(ssl);
+    while (ret == 0) {
+      std::this_thread::sleep_for(std::chrono::milliseconds{100});
+      ret = SSL_shutdown(ssl);
+    }
+#endif
+  }
 
   std::lock_guard<std::mutex> guard(ctx_mutex);
   SSL_free(ssl);
@@ -8366,7 +9119,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) {
         if (SSL_pending(ssl_) > 0) {
           return SSL_read(ssl_, ptr, static_cast<int>(size));
         } else if (is_readable()) {
-          std::this_thread::sleep_for(std::chrono::milliseconds(1));
+          std::this_thread::sleep_for(std::chrono::microseconds{10});
           ret = SSL_read(ssl_, ptr, static_cast<int>(size));
           if (ret >= 0) { return ret; }
           err = SSL_get_error(ssl_, ret);
@@ -8397,7 +9150,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) {
       while (--n >= 0 && err == SSL_ERROR_WANT_WRITE) {
 #endif
         if (is_writable()) {
-          std::this_thread::sleep_for(std::chrono::milliseconds(1));
+          std::this_thread::sleep_for(std::chrono::microseconds{10});
           ret = SSL_write(ssl_, ptr, static_cast<int>(handle_size));
           if (ret >= 0) { return ret; }
           err = SSL_get_error(ssl_, ret);
@@ -8439,7 +9192,7 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,
                         SSL_OP_NO_COMPRESSION |
                             SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION);
 
-    SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);
+    SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION);
 
     if (private_key_password != nullptr && (private_key_password[0] != '\0')) {
       SSL_CTX_set_default_passwd_cb_userdata(
@@ -8449,7 +9202,8 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,
 
     if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 ||
         SSL_CTX_use_PrivateKey_file(ctx_, private_key_path, SSL_FILETYPE_PEM) !=
-            1) {
+            1 ||
+        SSL_CTX_check_private_key(ctx_) != 1) {
       SSL_CTX_free(ctx_);
       ctx_ = nullptr;
     } else if (client_ca_cert_file_path || client_ca_cert_dir_path) {
@@ -8471,7 +9225,7 @@ inline SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key,
                         SSL_OP_NO_COMPRESSION |
                             SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION);
 
-    SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);
+    SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION);
 
     if (SSL_CTX_use_certificate(ctx_, cert) != 1 ||
         SSL_CTX_use_PrivateKey(ctx_, private_key) != 1) {
@@ -8505,6 +9259,19 @@ inline bool SSLServer::is_valid() const { return ctx_; }
 
 inline SSL_CTX *SSLServer::ssl_context() const { return ctx_; }
 
+inline void SSLServer::update_certs(X509 *cert, EVP_PKEY *private_key,
+                                    X509_STORE *client_ca_cert_store) {
+
+  std::lock_guard<std::mutex> guard(ctx_mutex_);
+
+  SSL_CTX_use_certificate(ctx_, cert);
+  SSL_CTX_use_PrivateKey(ctx_, private_key);
+
+  if (client_ca_cert_store != nullptr) {
+    SSL_CTX_set_cert_store(ctx_, client_ca_cert_store);
+  }
+}
+
 inline bool SSLServer::process_and_close_socket(socket_t sock) {
   auto ssl = detail::ssl_new(
       sock, ctx_, ctx_mutex_,
@@ -8516,20 +9283,29 @@ inline bool SSLServer::process_and_close_socket(socket_t sock) {
 
   auto ret = false;
   if (ssl) {
+    std::string remote_addr;
+    int remote_port = 0;
+    detail::get_remote_ip_and_port(sock, remote_addr, remote_port);
+
+    std::string local_addr;
+    int local_port = 0;
+    detail::get_local_ip_and_port(sock, local_addr, local_port);
+
     ret = detail::process_server_socket_ssl(
         svr_sock_, ssl, sock, keep_alive_max_count_, keep_alive_timeout_sec_,
         read_timeout_sec_, read_timeout_usec_, write_timeout_sec_,
         write_timeout_usec_,
-        [this, ssl](Stream &strm, bool close_connection,
-                    bool &connection_closed) {
-          return process_request(strm, close_connection, connection_closed,
+        [&](Stream &strm, bool close_connection, bool &connection_closed) {
+          return process_request(strm, remote_addr, remote_port, local_addr,
+                                 local_port, close_connection,
+                                 connection_closed,
                                  [&](Request &req) { req.ssl = ssl; });
         });
 
     // Shutdown gracefully if the result seemed successful, non-gracefully if
     // the connection appeared to be closed.
     const bool shutdown_gracefully = ret;
-    detail::ssl_delete(ctx_mutex_, ssl, shutdown_gracefully);
+    detail::ssl_delete(ctx_mutex_, ssl, sock, shutdown_gracefully);
   }
 
   detail::shutdown_socket(sock);
@@ -8551,6 +9327,8 @@ inline SSLClient::SSLClient(const std::string &host, int port,
     : ClientImpl(host, port, client_cert_path, client_key_path) {
   ctx_ = SSL_CTX_new(TLS_client_method());
 
+  SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION);
+
   detail::split(&host_[0], &host_[host_.size()], '.',
                 [&](const char *b, const char *e) {
                   host_components_.emplace_back(b, e);
@@ -8758,36 +9536,47 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) {
         }
 
         if (server_certificate_verification_) {
-          verify_result_ = SSL_get_verify_result(ssl2);
+          if (server_certificate_verifier_) {
+            if (!server_certificate_verifier_(ssl2)) {
+              error = Error::SSLServerVerification;
+              return false;
+            }
+          } else {
+            verify_result_ = SSL_get_verify_result(ssl2);
 
-          if (verify_result_ != X509_V_OK) {
-            error = Error::SSLServerVerification;
-            return false;
+            if (verify_result_ != X509_V_OK) {
+              error = Error::SSLServerVerification;
+              return false;
+            }
+
+            auto server_cert = SSL_get1_peer_certificate(ssl2);
+            auto se = detail::scope_exit([&] { X509_free(server_cert); });
+
+            if (server_cert == nullptr) {
+              error = Error::SSLServerVerification;
+              return false;
+            }
+
+            if (server_hostname_verification_) {
+              if (!verify_host(server_cert)) {
+                error = Error::SSLServerHostnameVerification;
+                return false;
+              }
+            }
           }
-
-          auto server_cert = SSL_get1_peer_certificate(ssl2);
-
-          if (server_cert == nullptr) {
-            error = Error::SSLServerVerification;
-            return false;
-          }
-
-          if (!verify_host(server_cert)) {
-            X509_free(server_cert);
-            error = Error::SSLServerVerification;
-            return false;
-          }
-          X509_free(server_cert);
         }
 
         return true;
       },
       [&](SSL *ssl2) {
+#if defined(OPENSSL_IS_BORINGSSL)
+        SSL_set_tlsext_host_name(ssl2, host_.c_str());
+#else
         // NOTE: Direct call instead of using the OpenSSL macro to suppress
         // -Wold-style-cast warning
-        // SSL_set_tlsext_host_name(ssl2, host_.c_str());
         SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name,
                  static_cast<void *>(const_cast<char *>(host_.c_str())));
+#endif
         return true;
       });
 
@@ -8812,7 +9601,8 @@ inline void SSLClient::shutdown_ssl_impl(Socket &socket,
     return;
   }
   if (socket.ssl) {
-    detail::ssl_delete(ctx_mutex_, socket.ssl, shutdown_gracefully);
+    detail::ssl_delete(ctx_mutex_, socket.ssl, socket.sock,
+                       shutdown_gracefully);
     socket.ssl = nullptr;
   }
   assert(socket.ssl == nullptr);
@@ -8861,8 +9651,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
 
   auto type = GEN_DNS;
 
-  struct in6_addr addr6 {};
-  struct in_addr addr {};
+  struct in6_addr addr6{};
+  struct in_addr addr{};
   size_t addr_len = 0;
 
 #ifndef __MINGW32__
@@ -8965,7 +9755,7 @@ inline Client::Client(const std::string &scheme_host_port,
                       const std::string &client_cert_path,
                       const std::string &client_key_path) {
   const static std::regex re(
-      R"((?:([a-z]+):\/\/)?(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)");
+      R"((?:([a-z]+):\/\/)?(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)");
 
   std::smatch m;
   if (std::regex_match(scheme_host_port, m, re)) {
@@ -9002,10 +9792,12 @@ inline Client::Client(const std::string &scheme_host_port,
                                              client_key_path);
     }
   } else {
+    // NOTE: Update TEST(UniversalClientImplTest, Ipv6LiteralAddress)
+    // if port param below changes.
     cli_ = detail::make_unique<ClientImpl>(scheme_host_port, 80,
                                            client_cert_path, client_key_path);
   }
-}
+} // namespace detail
 
 inline Client::Client(const std::string &host, int port)
     : cli_(detail::make_unique<ClientImpl>(host, port)) {}
@@ -9111,15 +9903,30 @@ inline Result Client::Post(const std::string &path, const Headers &headers,
                            const std::string &content_type) {
   return cli_->Post(path, headers, body, content_length, content_type);
 }
+inline Result Client::Post(const std::string &path, const Headers &headers,
+                           const char *body, size_t content_length,
+                           const std::string &content_type, Progress progress) {
+  return cli_->Post(path, headers, body, content_length, content_type,
+                    progress);
+}
 inline Result Client::Post(const std::string &path, const std::string &body,
                            const std::string &content_type) {
   return cli_->Post(path, body, content_type);
 }
+inline Result Client::Post(const std::string &path, const std::string &body,
+                           const std::string &content_type, Progress progress) {
+  return cli_->Post(path, body, content_type, progress);
+}
 inline Result Client::Post(const std::string &path, const Headers &headers,
                            const std::string &body,
                            const std::string &content_type) {
   return cli_->Post(path, headers, body, content_type);
 }
+inline Result Client::Post(const std::string &path, const Headers &headers,
+                           const std::string &body,
+                           const std::string &content_type, Progress progress) {
+  return cli_->Post(path, headers, body, content_type, progress);
+}
 inline Result Client::Post(const std::string &path, size_t content_length,
                            ContentProvider content_provider,
                            const std::string &content_type) {
@@ -9150,6 +9957,10 @@ inline Result Client::Post(const std::string &path, const Headers &headers,
                            const Params &params) {
   return cli_->Post(path, headers, params);
 }
+inline Result Client::Post(const std::string &path, const Headers &headers,
+                           const Params &params, Progress progress) {
+  return cli_->Post(path, headers, params, progress);
+}
 inline Result Client::Post(const std::string &path,
                            const MultipartFormDataItems &items) {
   return cli_->Post(path, items);
@@ -9180,15 +9991,29 @@ inline Result Client::Put(const std::string &path, const Headers &headers,
                           const std::string &content_type) {
   return cli_->Put(path, headers, body, content_length, content_type);
 }
+inline Result Client::Put(const std::string &path, const Headers &headers,
+                          const char *body, size_t content_length,
+                          const std::string &content_type, Progress progress) {
+  return cli_->Put(path, headers, body, content_length, content_type, progress);
+}
 inline Result Client::Put(const std::string &path, const std::string &body,
                           const std::string &content_type) {
   return cli_->Put(path, body, content_type);
 }
+inline Result Client::Put(const std::string &path, const std::string &body,
+                          const std::string &content_type, Progress progress) {
+  return cli_->Put(path, body, content_type, progress);
+}
 inline Result Client::Put(const std::string &path, const Headers &headers,
                           const std::string &body,
                           const std::string &content_type) {
   return cli_->Put(path, headers, body, content_type);
 }
+inline Result Client::Put(const std::string &path, const Headers &headers,
+                          const std::string &body,
+                          const std::string &content_type, Progress progress) {
+  return cli_->Put(path, headers, body, content_type, progress);
+}
 inline Result Client::Put(const std::string &path, size_t content_length,
                           ContentProvider content_provider,
                           const std::string &content_type) {
@@ -9219,6 +10044,10 @@ inline Result Client::Put(const std::string &path, const Headers &headers,
                           const Params &params) {
   return cli_->Put(path, headers, params);
 }
+inline Result Client::Put(const std::string &path, const Headers &headers,
+                          const Params &params, Progress progress) {
+  return cli_->Put(path, headers, params, progress);
+}
 inline Result Client::Put(const std::string &path,
                           const MultipartFormDataItems &items) {
   return cli_->Put(path, items);
@@ -9246,20 +10075,44 @@ inline Result Client::Patch(const std::string &path, const char *body,
                             const std::string &content_type) {
   return cli_->Patch(path, body, content_length, content_type);
 }
+inline Result Client::Patch(const std::string &path, const char *body,
+                            size_t content_length,
+                            const std::string &content_type,
+                            Progress progress) {
+  return cli_->Patch(path, body, content_length, content_type, progress);
+}
 inline Result Client::Patch(const std::string &path, const Headers &headers,
                             const char *body, size_t content_length,
                             const std::string &content_type) {
   return cli_->Patch(path, headers, body, content_length, content_type);
 }
+inline Result Client::Patch(const std::string &path, const Headers &headers,
+                            const char *body, size_t content_length,
+                            const std::string &content_type,
+                            Progress progress) {
+  return cli_->Patch(path, headers, body, content_length, content_type,
+                     progress);
+}
 inline Result Client::Patch(const std::string &path, const std::string &body,
                             const std::string &content_type) {
   return cli_->Patch(path, body, content_type);
 }
+inline Result Client::Patch(const std::string &path, const std::string &body,
+                            const std::string &content_type,
+                            Progress progress) {
+  return cli_->Patch(path, body, content_type, progress);
+}
 inline Result Client::Patch(const std::string &path, const Headers &headers,
                             const std::string &body,
                             const std::string &content_type) {
   return cli_->Patch(path, headers, body, content_type);
 }
+inline Result Client::Patch(const std::string &path, const Headers &headers,
+                            const std::string &body,
+                            const std::string &content_type,
+                            Progress progress) {
+  return cli_->Patch(path, headers, body, content_type, progress);
+}
 inline Result Client::Patch(const std::string &path, size_t content_length,
                             ContentProvider content_provider,
                             const std::string &content_type) {
@@ -9294,20 +10147,44 @@ inline Result Client::Delete(const std::string &path, const char *body,
                              const std::string &content_type) {
   return cli_->Delete(path, body, content_length, content_type);
 }
+inline Result Client::Delete(const std::string &path, const char *body,
+                             size_t content_length,
+                             const std::string &content_type,
+                             Progress progress) {
+  return cli_->Delete(path, body, content_length, content_type, progress);
+}
 inline Result Client::Delete(const std::string &path, const Headers &headers,
                              const char *body, size_t content_length,
                              const std::string &content_type) {
   return cli_->Delete(path, headers, body, content_length, content_type);
 }
+inline Result Client::Delete(const std::string &path, const Headers &headers,
+                             const char *body, size_t content_length,
+                             const std::string &content_type,
+                             Progress progress) {
+  return cli_->Delete(path, headers, body, content_length, content_type,
+                      progress);
+}
 inline Result Client::Delete(const std::string &path, const std::string &body,
                              const std::string &content_type) {
   return cli_->Delete(path, body, content_type);
 }
+inline Result Client::Delete(const std::string &path, const std::string &body,
+                             const std::string &content_type,
+                             Progress progress) {
+  return cli_->Delete(path, body, content_type, progress);
+}
 inline Result Client::Delete(const std::string &path, const Headers &headers,
                              const std::string &body,
                              const std::string &content_type) {
   return cli_->Delete(path, headers, body, content_type);
 }
+inline Result Client::Delete(const std::string &path, const Headers &headers,
+                             const std::string &body,
+                             const std::string &content_type,
+                             Progress progress) {
+  return cli_->Delete(path, headers, body, content_type, progress);
+}
 inline Result Client::Options(const std::string &path) {
   return cli_->Options(path);
 }
@@ -9417,6 +10294,15 @@ inline void Client::set_proxy_digest_auth(const std::string &username,
 inline void Client::enable_server_certificate_verification(bool enabled) {
   cli_->enable_server_certificate_verification(enabled);
 }
+
+inline void Client::enable_server_hostname_verification(bool enabled) {
+  cli_->enable_server_hostname_verification(enabled);
+}
+
+inline void Client::set_server_certificate_verifier(
+    std::function<bool(SSL *ssl)> verifier) {
+  cli_->set_server_certificate_verifier(verifier);
+}
 #endif
 
 inline void Client::set_logger(Logger logger) {
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 64c0c4ef6..d1e8ee829 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -19,6 +19,7 @@
 #include "loading.html.hpp"
 
 #include <atomic>
+#include <chrono>
 #include <condition_variable>
 #include <cstddef>
 #include <cinttypes>
@@ -32,6 +33,8 @@
 
 using json = nlohmann::ordered_json;
 
+constexpr int HTTP_POLLING_SECONDS = 1;
+
 enum stop_type {
     STOP_TYPE_NONE,
     STOP_TYPE_EOS,
@@ -1602,6 +1605,30 @@ struct server_response {
         // should never reach here
     }
 
+    // same as recv(), but have timeout in seconds
+    // if timeout is reached, nullptr is returned
+    server_task_result_ptr recv_with_timeout(const std::unordered_set<int> & id_tasks, int timeout) {
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex_results);
+            bool cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout), [&]{
+                return !queue_results.empty();
+            });
+            if (!cr_res) {
+                return nullptr;
+            }
+
+            for (int i = 0; i < (int) queue_results.size(); i++) {
+                if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) {
+                    server_task_result_ptr res = std::move(queue_results[i]);
+                    queue_results.erase(queue_results.begin() + i);
+                    return res;
+                }
+            }
+        }
+
+        // should never reach here
+    }
+
     // single-task version of recv()
     server_task_result_ptr recv(int id_task) {
         std::unordered_set<int> id_tasks = {id_task};
@@ -2322,10 +2349,21 @@ struct server_context {
     void receive_multi_results(
             const std::unordered_set<int> & id_tasks,
             const std::function<void(std::vector<server_task_result_ptr>&)> & result_handler,
-            const std::function<void(json)> & error_handler) {
+            const std::function<void(json)> & error_handler,
+            const std::function<bool()> & is_connection_closed) {
         std::vector<server_task_result_ptr> results(id_tasks.size());
-        for (size_t i = 0; i < id_tasks.size(); i++) {
-            server_task_result_ptr result = queue_results.recv(id_tasks);
+        for (int i = 0; i < (int)id_tasks.size(); i++) {
+            server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS);
+
+            if (is_connection_closed()) {
+                cancel_tasks(id_tasks);
+                return;
+            }
+
+            if (result == nullptr) {
+                i--; // retry
+                continue;
+            }
 
             if (result->is_error()) {
                 error_handler(result->to_json());
@@ -2349,10 +2387,20 @@ struct server_context {
     void receive_cmpl_results_stream(
             const std::unordered_set<int> & id_tasks,
             const std::function<bool(server_task_result_ptr&)> & result_handler,
-            const std::function<void(json)> & error_handler) {
+            const std::function<void(json)> & error_handler,
+            const std::function<bool()> & is_connection_closed) {
         size_t n_finished = 0;
         while (true) {
-            server_task_result_ptr result = queue_results.recv(id_tasks);
+            server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS);
+
+            if (is_connection_closed()) {
+                cancel_tasks(id_tasks);
+                return;
+            }
+
+            if (result == nullptr) {
+                continue; // retry
+            }
 
             if (result->is_error()) {
                 error_handler(result->to_json());
@@ -3633,6 +3681,7 @@ int main(int argc, char ** argv) {
     const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok](
             server_task_type type,
             json & data,
+            std::function<bool()> is_connection_closed,
             httplib::Response & res,
             oaicompat_type oaicompat) {
         GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
@@ -3694,7 +3743,7 @@ int main(int argc, char ** argv) {
                 }
             }, [&](const json & error_data) {
                 res_error(res, error_data);
-            });
+            }, is_connection_closed);
 
             ctx_server.queue_results.remove_waiting_task_ids(task_ids);
         } else {
@@ -3704,6 +3753,7 @@ int main(int argc, char ** argv) {
                     if (res_json.is_array()) {
                         for (const auto & res : res_json) {
                             if (!server_sent_event(sink, "data", res)) {
+                                // sending failed (HTTP connection closed), cancel the generation
                                 return false;
                             }
                         }
@@ -3713,6 +3763,9 @@ int main(int argc, char ** argv) {
                     }
                 }, [&](const json & error_data) {
                     server_sent_event(sink, "error", error_data);
+                }, [&sink]() {
+                    // note: do not use req.is_connection_closed here because req is already destroyed
+                    return !sink.is_writable();
                 });
                 if (oaicompat != OAICOMPAT_TYPE_NONE) {
                     static const std::string ev_done = "data: [DONE]\n\n";
@@ -3735,6 +3788,7 @@ int main(int argc, char ** argv) {
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
+            req.is_connection_closed,
             res,
             OAICOMPAT_TYPE_NONE);
     };
@@ -3744,6 +3798,7 @@ int main(int argc, char ** argv) {
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
+            req.is_connection_closed,
             res,
             OAICOMPAT_TYPE_COMPLETION);
     };
@@ -3820,6 +3875,7 @@ int main(int argc, char ** argv) {
         return handle_completions_impl(
             SERVER_TASK_TYPE_INFILL,
             data,
+            req.is_connection_closed,
             res,
             OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
     };
@@ -3834,6 +3890,7 @@ int main(int argc, char ** argv) {
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
+            req.is_connection_closed,
             res,
             OAICOMPAT_TYPE_CHAT);
     };
@@ -3980,7 +4037,7 @@ int main(int argc, char ** argv) {
             }, [&](const json & error_data) {
                 res_error(res, error_data);
                 error = true;
-            });
+            }, req.is_connection_closed);
 
             ctx_server.queue_results.remove_waiting_task_ids(task_ids);
         }
@@ -4070,7 +4127,7 @@ int main(int argc, char ** argv) {
             }, [&](const json & error_data) {
                 res_error(res, error_data);
                 error = true;
-            });
+            }, req.is_connection_closed);
         }
 
         if (error) {
diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index e5e3b6077..c1fc12462 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -1,4 +1,5 @@
 import pytest
+import requests
 import time
 from openai import OpenAI
 from utils import *
@@ -405,3 +406,23 @@ def test_n_probs_post_sampling():
             assert "bytes" in prob and type(prob["bytes"]) == list
         # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs
         assert any(prob["prob"] == 1.0 for prob in tok["top_probs"])
+
+
+def test_cancel_request():
+    global server
+    server.n_ctx = 4096
+    server.n_predict = -1
+    server.n_slots = 1
+    server.server_slots = True
+    server.start()
+    # send a request that will take a long time, but cancel it before it finishes
+    try:
+        server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+        }, timeout=0.1)
+    except requests.exceptions.ReadTimeout:
+        pass # expected
+    # make sure the slot is free
+    time.sleep(1) # wait for HTTP_POLLING_SECONDS
+    res = server.make_request("GET", "/slots")
+    assert res.body[0]["is_processing"] == False
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index a1a94d0f1..73be4c92f 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -219,17 +219,18 @@ class ServerProcess:
         path: str,
         data: dict | Any | None = None,
         headers: dict | None = None,
+        timeout: float | None = None,
     ) -> ServerResponse:
         url = f"http://{self.server_host}:{self.server_port}{path}"
         parse_body = False
         if method == "GET":
-            response = requests.get(url, headers=headers)
+            response = requests.get(url, headers=headers, timeout=timeout)
             parse_body = True
         elif method == "POST":
-            response = requests.post(url, headers=headers, json=data)
+            response = requests.post(url, headers=headers, json=data, timeout=timeout)
             parse_body = True
         elif method == "OPTIONS":
-            response = requests.options(url, headers=headers)
+            response = requests.options(url, headers=headers, timeout=timeout)
         else:
             raise ValueError(f"Unimplemented method: {method}")
         result = ServerResponse()

From 4dd34ff83165a483ebff7bd43621b28490fa1fd6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 18 Jan 2025 16:18:15 +0200
Subject: [PATCH 202/376] cmake : add sanitizer flags for llama.cpp (#11279)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cmake : add sanitizer flags for llama.cpp

ggml-ci

* tests : fix compile warnings

ggml-ci

* cmake : move sanitizer flags to llama_add_compile_flags

ggml-ci

* cmake : move llama.cpp compile flags to top level lists

ggml-ci

* cmake : apply only sanitizer flags at top level

ggml-ci

* tests : fix gguf context use in same_tensor_data

* gguf-test: tensor data comparison

* dummy : trigger ggml-ci

* unicode : silence gcc warnings

ggml-ci

* ci : use sanitizer builds only in Debug mode

ggml-ci

* cmake : add status messages [no ci]

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 .github/workflows/build.yml |  2 +-
 CMakeLists.txt              | 73 +++++++++++++++++++++++++------------
 ggml/src/gguf.cpp           |  4 ++
 src/unicode.cpp             |  5 +--
 tests/CMakeLists.txt        |  2 +
 tests/test-gguf.cpp         | 28 ++++++++------
 tests/test-sampling.cpp     |  1 -
 7 files changed, 74 insertions(+), 41 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index c85999b89..9e0c4a675 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -234,7 +234,7 @@ jobs:
     strategy:
       matrix:
         sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug, Release]
+        build_type: [Debug]
 
     steps:
       - name: Clone
diff --git a/CMakeLists.txt b/CMakeLists.txt
index a717a508f..42caed486 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -83,11 +83,8 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
 
 # override ggml options
-set(GGML_SANITIZE_THREAD    ${LLAMA_SANITIZE_THREAD})
-set(GGML_SANITIZE_ADDRESS   ${LLAMA_SANITIZE_ADDRESS})
-set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
-set(GGML_ALL_WARNINGS       ${LLAMA_ALL_WARNINGS})
-set(GGML_FATAL_WARNINGS     ${LLAMA_FATAL_WARNINGS})
+set(GGML_ALL_WARNINGS   ${LLAMA_ALL_WARNINGS})
+set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
 
 # change the default for these ggml options
 if (NOT DEFINED GGML_LLAMAFILE)
@@ -117,16 +114,62 @@ llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
 llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
 llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
+if (NOT MSVC)
+    if (LLAMA_SANITIZE_THREAD)
+        message(STATUS "Using -fsanitize=thread")
+
+        add_compile_options(-fsanitize=thread)
+        link_libraries     (-fsanitize=thread)
+    endif()
+
+    if (LLAMA_SANITIZE_ADDRESS)
+        message(STATUS "Using -fsanitize=address")
+
+        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
+        link_libraries     (-fsanitize=address)
+    endif()
+
+    if (LLAMA_SANITIZE_UNDEFINED)
+        message(STATUS "Using -fsanitize=undefined")
+
+        add_compile_options(-fsanitize=undefined)
+        link_libraries     (-fsanitize=undefined)
+    endif()
+endif()
+
 #
-# build the library
+# 3rd-party
 #
 
 if (NOT TARGET ggml)
     add_subdirectory(ggml)
     # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()
+
+#
+# build the library
+#
+
 add_subdirectory(src)
 
+#
+# utils, programs, examples and tests
+#
+
+if (LLAMA_BUILD_COMMON)
+    add_subdirectory(common)
+endif()
+
+if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
+    include(CTest)
+    add_subdirectory(tests)
+endif()
+
+if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
+    add_subdirectory(examples)
+    add_subdirectory(pocs)
+endif()
+
 #
 # install
 #
@@ -200,21 +243,3 @@ configure_file(cmake/llama.pc.in
 
 install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
         DESTINATION lib/pkgconfig)
-
-#
-# utils, programs, examples and tests
-#
-
-if (LLAMA_BUILD_COMMON)
-    add_subdirectory(common)
-endif()
-
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
-    include(CTest)
-    add_subdirectory(tests)
-endif()
-
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
-    add_subdirectory(examples)
-    add_subdirectory(pocs)
-endif()
diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp
index 655ed600a..ab13669c5 100644
--- a/ggml/src/gguf.cpp
+++ b/ggml/src/gguf.cpp
@@ -648,6 +648,10 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
             ok = ok && data != nullptr;
 
+            if (ok) {
+                ggml_set_name(data, "GGUF tensor data binary blob");
+            }
+
             // read the binary blob with the tensor data
             ok = ok && gr.read(data->data, ctx->size);
 
diff --git a/src/unicode.cpp b/src/unicode.cpp
index 7aca6544b..89180da41 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -7,18 +7,17 @@
 
 #include <algorithm>
 #include <cassert>
+#include <codecvt>
 #include <cstddef>
 #include <cstdint>
+#include <locale>
 #include <map>
 #include <regex>
 #include <stdexcept>
 #include <string>
 #include <unordered_map>
-#include <unordered_set>
 #include <utility>
 #include <vector>
-#include <locale>
-#include <codecvt>
 
 size_t unicode_len_utf8(char src) {
     const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 2b5e5fd4a..3fa43c295 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,3 +1,5 @@
+llama_add_compile_flags()
+
 function(llama_test target)
     include(CMakeParseArguments)
     set(options)
diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp
index 611957ac0..6ed696328 100644
--- a/tests/test-gguf.cpp
+++ b/tests/test-gguf.cpp
@@ -48,7 +48,7 @@ enum handcrafted_file_type {
     HANDCRAFTED_DATA_CUSTOM_ALIGN          = 810 + offset_has_data,
 };
 
-std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
+static std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) {
     switch (hft) {
         case HANDCRAFTED_HEADER_BAD_MAGIC:           return "HEADER_BAD_MAGIC";
         case HANDCRAFTED_HEADER_BAD_VERSION_1:       return "HEADER_BAD_VERSION_1";
@@ -99,7 +99,7 @@ static bool expect_context_not_null(const enum handcrafted_file_type hft) {
 
 typedef std::pair<enum ggml_type, std::array<int64_t, GGML_MAX_DIMS>> tensor_config_t;
 
-std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
+static std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
     std::vector<tensor_config_t> tensor_configs;
     tensor_configs.reserve(100);
 
@@ -122,7 +122,7 @@ std::vector<tensor_config_t> get_tensor_configs(std::mt19937 & rng) {
     return tensor_configs;
 }
 
-std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
+static std::vector<std::pair<enum gguf_type, enum gguf_type>> get_kv_types(std::mt19937 rng) {
     std::vector<std::pair<enum gguf_type, enum gguf_type>> kv_types;
     kv_types.reserve(100);
 
@@ -626,8 +626,6 @@ static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const u
 
     bool ok = true;
 
-    const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT;
-
     for (int i = 0; i < int(tensor_configs.size()); ++i) {
         const ggml_type                          type  = tensor_configs[i].first;
         const std::array<int64_t, GGML_MAX_DIMS> shape = tensor_configs[i].second;
@@ -866,13 +864,13 @@ static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t
                     case GGUF_TYPE_COUNT:
                     default: {
                         GGML_ABORT("fatal error");
-                    } break;
+                    }
                 }
             } break;
             case GGUF_TYPE_COUNT:
             default: {
                 GGML_ABORT("fatal error");
-            } break;
+            }
         }
     }
 
@@ -938,7 +936,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
         }
 
         if (type == GGUF_TYPE_ARRAY) {
-            const int arr_n = gguf_get_arr_n(ctx, id);
+            const size_t arr_n = gguf_get_arr_n(ctx, id);
             if (arr_n != gguf_get_arr_n(other, idx_other)) {
                 ok = false;
                 continue;
@@ -953,7 +951,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
             if (type_arr == GGUF_TYPE_BOOL) {
                 const int8_t * data       = reinterpret_cast<const int8_t *>(gguf_get_arr_data(ctx,   id));
                 const int8_t * data_other = reinterpret_cast<const int8_t *>(gguf_get_arr_data(other, idx_other));
-                for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
                     if (bool(data[arr_i]) != bool(data_other[arr_i])) {
                         ok = false;
                     }
@@ -962,7 +960,7 @@ static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other
             }
 
             if (type_arr == GGUF_TYPE_STRING) {
-                for (int arr_i = 0; arr_i < arr_n; ++arr_i) {
+                for (size_t arr_i = 0; arr_i < arr_n; ++arr_i) {
                     const std::string str       = gguf_get_arr_str(ctx,   id,       arr_i);
                     const std::string str_other = gguf_get_arr_str(other, idx_other, arr_i);
                     if (str != str_other) {
@@ -1033,6 +1031,12 @@ static bool same_tensor_data(const struct ggml_context * orig, const struct ggml
 
     struct ggml_tensor * t_orig = ggml_get_first_tensor(orig);
     struct ggml_tensor * t_read = ggml_get_first_tensor(read);
+
+    if (std::string(t_read->name) != "GGUF tensor data binary blob") {
+        return false;
+    }
+    t_read = ggml_get_next_tensor(read, t_read);
+
     while (t_orig) {
         if (!t_read) {
             ok = false;
@@ -1051,13 +1055,13 @@ static bool same_tensor_data(const struct ggml_context * orig, const struct ggml
         }
 
         t_orig = ggml_get_next_tensor(orig, t_orig);
-        t_read = ggml_get_next_tensor(orig, t_read);
+        t_read = ggml_get_next_tensor(read, t_read);
     }
     if (t_read) {
         ok = false;
     }
 
-    return true;
+    return ok;
 }
 
 static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) {
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index c0dcb4848..61bd67850 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -144,7 +144,6 @@ static void test_penalties(
 
     sampler_tester tester(probs, probs_expected);
 
-    const size_t n_vocab = probs.size();
     auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
 
     for (size_t i = 0; i < last_tokens.size(); i++) {

From a1649cc13f89946322358f92ea268ae1b7b5096c Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Sat, 18 Jan 2025 14:42:31 +0000
Subject: [PATCH 203/376] Adding linenoise.cpp to llama-run (#11252)

This is a fork of linenoise that is C++17 compatible. I intend on
adding it to llama-run so we can do things like traverse prompt
history via the up and down arrows:

https://github.com/ericcurtin/linenoise.cpp

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 .github/workflows/build.yml              |    4 +
 examples/run/CMakeLists.txt              |    2 +-
 examples/run/linenoise.cpp/LICENSE       |   26 +
 examples/run/linenoise.cpp/linenoise.cpp | 1351 ++++++++++++++++++++++
 examples/run/linenoise.cpp/linenoise.h   |  114 ++
 examples/run/run.cpp                     |   38 +-
 6 files changed, 1524 insertions(+), 11 deletions(-)
 create mode 100644 examples/run/linenoise.cpp/LICENSE
 create mode 100644 examples/run/linenoise.cpp/linenoise.cpp
 create mode 100644 examples/run/linenoise.cpp/linenoise.h

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 9e0c4a675..fe3b2cdfa 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -87,6 +87,7 @@ jobs:
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
           cp LICENSE ./build/bin/
+          cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
           zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
 
       - name: Upload artifacts
@@ -149,6 +150,7 @@ jobs:
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
           cp LICENSE ./build/bin/
+          cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
           zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
 
       - name: Upload artifacts
@@ -217,6 +219,7 @@ jobs:
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
           cp LICENSE ./build/bin/
+          cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
           zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/*
 
       - name: Upload artifacts
@@ -796,6 +799,7 @@ jobs:
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
           Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
+          Copy-Item .\examples\run\linenoise.cpp\LICENSE .\build\bin\Release\linenoise.cpp.txt
           7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\*
 
       - name: Upload artifacts
diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt
index 0686d6305..cd6b0520e 100644
--- a/examples/run/CMakeLists.txt
+++ b/examples/run/CMakeLists.txt
@@ -1,5 +1,5 @@
 set(TARGET llama-run)
-add_executable(${TARGET} run.cpp)
+add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/run/linenoise.cpp/LICENSE b/examples/run/linenoise.cpp/LICENSE
new file mode 100644
index 000000000..b006b3b24
--- /dev/null
+++ b/examples/run/linenoise.cpp/LICENSE
@@ -0,0 +1,26 @@
+Copyright (c) 2010-2014, Salvatore Sanfilippo <antirez at gmail dot com>
+Copyright (c) 2010-2013, Pieter Noordhuis <pcnoordhuis at gmail dot com>
+Copyright (c) 2025, Eric Curtin <ericcurtin17 at gmail dot com>
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/examples/run/linenoise.cpp/linenoise.cpp b/examples/run/linenoise.cpp/linenoise.cpp
new file mode 100644
index 000000000..050c23012
--- /dev/null
+++ b/examples/run/linenoise.cpp/linenoise.cpp
@@ -0,0 +1,1351 @@
+#ifndef _WIN32
+/*
+ * You can find the latest source code at:
+ *
+ *   http://github.com/ericcurtin/linenoise.cpp
+ *
+ * Does a number of crazy assumptions that happen to be true in 99.9999% of
+ * the 2010 UNIX computers around.
+ *
+ * ------------------------------------------------------------------------
+ *
+ * Copyright (c) 2010-2023, Salvatore Sanfilippo <antirez at gmail dot com>
+ * Copyright (c) 2010-2013, Pieter Noordhuis <pcnoordhuis at gmail dot com>
+ * Copyright (c) 2025, Eric Curtin <ericcurtin17 at gmail dot com>
+ *
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *  *  Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *
+ *  *  Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * ------------------------------------------------------------------------
+ *
+ * References:
+ * - http://invisible-island.net/xterm/ctlseqs/ctlseqs.html
+ * - http://www.3waylabs.com/nw/WWW/products/wizcon/vt220.html
+ *
+ * Todo list:
+ * - Filter bogus Ctrl+<char> combinations.
+ * - Win32 support
+ *
+ * Bloat:
+ * - History search like Ctrl+r in readline?
+ *
+ * List of escape sequences used by this program, we do everything just
+ * with three sequences. In order to be so cheap we may have some
+ * flickering effect with some slow terminal, but the lesser sequences
+ * the more compatible.
+ *
+ * EL (Erase Line)
+ *    Sequence: ESC [ n K
+ *    Effect: if n is 0 or missing, clear from cursor to end of line
+ *    Effect: if n is 1, clear from beginning of line to cursor
+ *    Effect: if n is 2, clear entire line
+ *
+ * CUF (CUrsor Forward)
+ *    Sequence: ESC [ n C
+ *    Effect: moves cursor forward n chars
+ *
+ * CUB (CUrsor Backward)
+ *    Sequence: ESC [ n D
+ *    Effect: moves cursor backward n chars
+ *
+ * The following is used to get the terminal width if getting
+ * the width with the TIOCGWINSZ ioctl fails
+ *
+ * DSR (Device Status Report)
+ *    Sequence: ESC [ 6 n
+ *    Effect: reports the current cusor position as ESC [ n ; m R
+ *            where n is the row and m is the column
+ *
+ * When multi line mode is enabled, we also use an additional escape
+ * sequence. However multi line editing is disabled by default.
+ *
+ * CUU (Cursor Up)
+ *    Sequence: ESC [ n A
+ *    Effect: moves cursor up of n chars.
+ *
+ * CUD (Cursor Down)
+ *    Sequence: ESC [ n B
+ *    Effect: moves cursor down of n chars.
+ *
+ * When linenoiseClearScreen() is called, two additional escape sequences
+ * are used in order to clear the screen and position the cursor at home
+ * position.
+ *
+ * CUP (Cursor position)
+ *    Sequence: ESC [ H
+ *    Effect: moves the cursor to upper left corner
+ *
+ * ED (Erase display)
+ *    Sequence: ESC [ 2 J
+ *    Effect: clear the whole screen
+ *
+ */
+
+#include <termios.h>
+#include <unistd.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <errno.h>
+#include <string.h>
+#include <stdlib.h>
+#include <ctype.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <vector>
+#include "linenoise.h"
+
+#define LINENOISE_DEFAULT_HISTORY_MAX_LEN 100
+#define LINENOISE_MAX_LINE 4096
+static std::vector<const char*> unsupported_term = {"dumb","cons25","emacs",nullptr};
+static linenoiseCompletionCallback *completionCallback = NULL;
+static linenoiseHintsCallback *hintsCallback = NULL;
+static linenoiseFreeHintsCallback *freeHintsCallback = NULL;
+static char *linenoiseNoTTY(void);
+static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags);
+static void refreshLineWithFlags(struct linenoiseState *l, int flags);
+
+static struct termios orig_termios; /* In order to restore at exit.*/
+static int maskmode = 0; /* Show "***" instead of input. For passwords. */
+static int rawmode = 0; /* For atexit() function to check if restore is needed*/
+static int mlmode = 0;  /* Multi line mode. Default is single line. */
+static int atexit_registered = 0; /* Register atexit just 1 time. */
+static int history_max_len = LINENOISE_DEFAULT_HISTORY_MAX_LEN;
+static int history_len = 0;
+static char **history = NULL;
+
+enum KEY_ACTION{
+        KEY_NULL = 0,            /* NULL */
+        CTRL_A = 1,         /* Ctrl+a */
+        CTRL_B = 2,         /* Ctrl-b */
+        CTRL_C = 3,         /* Ctrl-c */
+        CTRL_D = 4,         /* Ctrl-d */
+        CTRL_E = 5,         /* Ctrl-e */
+        CTRL_F = 6,         /* Ctrl-f */
+        CTRL_H = 8,         /* Ctrl-h */
+        TAB = 9,            /* Tab */
+        CTRL_K = 11,        /* Ctrl+k */
+        CTRL_L = 12,        /* Ctrl+l */
+        ENTER = 13,         /* Enter */
+        CTRL_N = 14,        /* Ctrl-n */
+        CTRL_P = 16,        /* Ctrl-p */
+        CTRL_T = 20,        /* Ctrl-t */
+        CTRL_U = 21,        /* Ctrl+u */
+        CTRL_W = 23,        /* Ctrl+w */
+        ESC = 27,           /* Escape */
+        BACKSPACE =  127    /* Backspace */
+};
+
+static void linenoiseAtExit(void);
+int linenoiseHistoryAdd(const char *line);
+#define REFRESH_CLEAN (1<<0)    // Clean the old prompt from the screen
+#define REFRESH_WRITE (1<<1)    // Rewrite the prompt on the screen.
+#define REFRESH_ALL (REFRESH_CLEAN|REFRESH_WRITE) // Do both.
+static void refreshLine(struct linenoiseState *l);
+
+__attribute__((format(printf, 1, 2)))
+/* Debugging function. */
+#if 0
+static void lndebug(const char *fmt, ...) {
+    static FILE *lndebug_fp = NULL;
+    if (lndebug_fp == NULL) {
+        lndebug_fp = fopen("/tmp/lndebug.txt", "a");
+    }
+
+    if (lndebug_fp != NULL) {
+        va_list args;
+        va_start(args, fmt);
+        vfprintf(lndebug_fp, fmt, args);
+        va_end(args);
+        fflush(lndebug_fp);
+    }
+}
+#else
+static void lndebug(const char *, ...) {
+}
+#endif
+
+/* ======================= Low level terminal handling ====================== */
+
+/* Enable "mask mode". When it is enabled, instead of the input that
+ * the user is typing, the terminal will just display a corresponding
+ * number of asterisks, like "****". This is useful for passwords and other
+ * secrets that should not be displayed. */
+void linenoiseMaskModeEnable(void) {
+    maskmode = 1;
+}
+
+/* Disable mask mode. */
+void linenoiseMaskModeDisable(void) {
+    maskmode = 0;
+}
+
+/* Set if to use or not the multi line mode. */
+void linenoiseSetMultiLine(int ml) {
+    mlmode = ml;
+}
+
+/* Return true if the terminal name is in the list of terminals we know are
+ * not able to understand basic escape sequences. */
+static int isUnsupportedTerm(void) {
+    char *term = getenv("TERM");
+    if (term == NULL) return 0;
+    for (int j = 0; unsupported_term[j]; ++j)
+        if (!strcasecmp(term, unsupported_term[j])) return 1;
+    return 0;
+}
+
+/* Raw mode: 1960 magic shit. */
+static int enableRawMode(int fd) {
+    struct termios raw;
+
+    if (!isatty(STDIN_FILENO)) goto fatal;
+    if (!atexit_registered) {
+        atexit(linenoiseAtExit);
+        atexit_registered = 1;
+    }
+    if (tcgetattr(fd,&orig_termios) == -1) goto fatal;
+
+    raw = orig_termios;  /* modify the original mode */
+    /* input modes: no break, no CR to NL, no parity check, no strip char,
+     * no start/stop output control. */
+    raw.c_iflag &= ~(BRKINT | ICRNL | INPCK | ISTRIP | IXON);
+    /* output modes - disable post processing */
+    raw.c_oflag &= ~(OPOST);
+    /* control modes - set 8 bit chars */
+    raw.c_cflag |= (CS8);
+    /* local modes - choing off, canonical off, no extended functions,
+     * no signal chars (^Z,^C) */
+    raw.c_lflag &= ~(ECHO | ICANON | IEXTEN | ISIG);
+    /* control chars - set return condition: min number of bytes and timer.
+     * We want read to return every single byte, without timeout. */
+    raw.c_cc[VMIN] = 1; raw.c_cc[VTIME] = 0; /* 1 byte, no timer */
+
+    /* put terminal in raw mode after flushing */
+    if (tcsetattr(fd,TCSAFLUSH,&raw) < 0) goto fatal;
+    rawmode = 1;
+    return 0;
+
+fatal:
+    errno = ENOTTY;
+    return -1;
+}
+
+static void disableRawMode(int fd) {
+    /* Don't even check the return value as it's too late. */
+    if (rawmode && tcsetattr(fd,TCSAFLUSH,&orig_termios) != -1)
+        rawmode = 0;
+}
+
+/* Use the ESC [6n escape sequence to query the horizontal cursor position
+ * and return it. On error -1 is returned, on success the position of the
+ * cursor. */
+static int getCursorPosition(int ifd, int ofd) {
+    char buf[32];
+    int cols, rows;
+    unsigned int i = 0;
+
+    /* Report cursor location */
+    if (write(ofd, "\x1b[6n", 4) != 4) return -1;
+
+    /* Read the response: ESC [ rows ; cols R */
+    while (i < sizeof(buf)-1) {
+        if (read(ifd,buf+i,1) != 1) break;
+        if (buf[i] == 'R') break;
+        i++;
+    }
+    buf[i] = '\0';
+
+    /* Parse it. */
+    if (buf[0] != ESC || buf[1] != '[') return -1;
+    if (sscanf(buf+2,"%d;%d",&rows,&cols) != 2) return -1;
+    return cols;
+}
+
+/* Try to get the number of columns in the current terminal, or assume 80
+ * if it fails. */
+static int getColumns(int ifd, int ofd) {
+    struct winsize ws;
+
+    if (ioctl(1, TIOCGWINSZ, &ws) == -1 || ws.ws_col == 0) {
+        /* ioctl() failed. Try to query the terminal itself. */
+        int start, cols;
+
+        /* Get the initial position so we can restore it later. */
+        start = getCursorPosition(ifd,ofd);
+        if (start == -1) goto failed;
+
+        /* Go to right margin and get position. */
+        if (write(ofd,"\x1b[999C",6) != 6) goto failed;
+        cols = getCursorPosition(ifd,ofd);
+        if (cols == -1) goto failed;
+
+        /* Restore position. */
+        if (cols > start) {
+            char seq[32];
+            snprintf(seq,32,"\x1b[%dD",cols-start);
+            if (write(ofd,seq,strlen(seq)) == -1) {
+                /* Can't recover... */
+            }
+        }
+        return cols;
+    } else {
+        return ws.ws_col;
+    }
+
+failed:
+    return 80;
+}
+
+/* Clear the screen. Used to handle ctrl+l */
+void linenoiseClearScreen(void) {
+    if (write(STDOUT_FILENO,"\x1b[H\x1b[2J",7) <= 0) {
+        /* nothing to do, just to avoid warning. */
+    }
+}
+
+/* Beep, used for completion when there is nothing to complete or when all
+ * the choices were already shown. */
+static void linenoiseBeep(void) {
+    fprintf(stderr, "\x7");
+    fflush(stderr);
+}
+
+/* ============================== Completion ================================ */
+
+/* Free a list of completion option populated by linenoiseAddCompletion(). */
+static void freeCompletions(linenoiseCompletions *lc) {
+    size_t i;
+    for (i = 0; i < lc->len; i++)
+        free(lc->cvec[i]);
+    if (lc->cvec != NULL)
+        free(lc->cvec);
+}
+
+/* Called by completeLine() and linenoiseShow() to render the current
+ * edited line with the proposed completion. If the current completion table
+ * is already available, it is passed as second argument, otherwise the
+ * function will use the callback to obtain it.
+ *
+ * Flags are the same as refreshLine*(), that is REFRESH_* macros. */
+static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags) {
+    /* Obtain the table of completions if the caller didn't provide one. */
+    linenoiseCompletions ctable = { 0, NULL };
+    if (lc == NULL) {
+        completionCallback(ls->buf,&ctable);
+        lc = &ctable;
+    }
+
+    /* Show the edited line with completion if possible, or just refresh. */
+    if (ls->completion_idx < lc->len) {
+        struct linenoiseState saved = *ls;
+        ls->len = ls->pos = strlen(lc->cvec[ls->completion_idx]);
+        ls->buf = lc->cvec[ls->completion_idx];
+        refreshLineWithFlags(ls,flags);
+        ls->len = saved.len;
+        ls->pos = saved.pos;
+        ls->buf = saved.buf;
+    } else {
+        refreshLineWithFlags(ls,flags);
+    }
+
+    /* Free the completions table if needed. */
+    if (lc != &ctable) freeCompletions(&ctable);
+}
+
+/* This is an helper function for linenoiseEdit*() and is called when the
+ * user types the <tab> key in order to complete the string currently in the
+ * input.
+ *
+ * The state of the editing is encapsulated into the pointed linenoiseState
+ * structure as described in the structure definition.
+ *
+ * If the function returns non-zero, the caller should handle the
+ * returned value as a byte read from the standard input, and process
+ * it as usually: this basically means that the function may return a byte
+ * read from the termianl but not processed. Otherwise, if zero is returned,
+ * the input was consumed by the completeLine() function to navigate the
+ * possible completions, and the caller should read for the next characters
+ * from stdin. */
+static int completeLine(struct linenoiseState *ls, int keypressed) {
+    linenoiseCompletions lc = { 0, NULL };
+    int nwritten;
+    char c = keypressed;
+
+    completionCallback(ls->buf,&lc);
+    if (lc.len == 0) {
+        linenoiseBeep();
+        ls->in_completion = 0;
+    } else {
+        switch(c) {
+            case 9: /* tab */
+                if (ls->in_completion == 0) {
+                    ls->in_completion = 1;
+                    ls->completion_idx = 0;
+                } else {
+                    ls->completion_idx = (ls->completion_idx+1) % (lc.len+1);
+                    if (ls->completion_idx == lc.len) linenoiseBeep();
+                }
+                c = 0;
+                break;
+            case 27: /* escape */
+                /* Re-show original buffer */
+                if (ls->completion_idx < lc.len) refreshLine(ls);
+                ls->in_completion = 0;
+                c = 0;
+                break;
+            default:
+                /* Update buffer and return */
+                if (ls->completion_idx < lc.len) {
+                    nwritten = snprintf(ls->buf,ls->buflen,"%s",
+                        lc.cvec[ls->completion_idx]);
+                    ls->len = ls->pos = nwritten;
+                }
+                ls->in_completion = 0;
+                break;
+        }
+
+        /* Show completion or original buffer */
+        if (ls->in_completion && ls->completion_idx < lc.len) {
+            refreshLineWithCompletion(ls,&lc,REFRESH_ALL);
+        } else {
+            refreshLine(ls);
+        }
+    }
+
+    freeCompletions(&lc);
+    return c; /* Return last read character */
+}
+
+/* Register a callback function to be called for tab-completion. */
+void linenoiseSetCompletionCallback(linenoiseCompletionCallback *fn) {
+    completionCallback = fn;
+}
+
+/* Register a hits function to be called to show hits to the user at the
+ * right of the prompt. */
+void linenoiseSetHintsCallback(linenoiseHintsCallback *fn) {
+    hintsCallback = fn;
+}
+
+/* Register a function to free the hints returned by the hints callback
+ * registered with linenoiseSetHintsCallback(). */
+void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *fn) {
+    freeHintsCallback = fn;
+}
+
+/* This function is used by the callback function registered by the user
+ * in order to add completion options given the input string when the
+ * user typed <tab>. See the example.c source code for a very easy to
+ * understand example. */
+void linenoiseAddCompletion(linenoiseCompletions *lc, const char *str) {
+    size_t len = strlen(str);
+    char *copy, **cvec;
+
+    copy = (char*) malloc(len + 1);
+    if (copy == NULL) return;
+    memcpy(copy,str,len+1);
+    cvec = (char**) realloc(lc->cvec,sizeof(char*)*(lc->len+1));
+    if (cvec == NULL) {
+        free(copy);
+        return;
+    }
+    lc->cvec = cvec;
+    lc->cvec[lc->len++] = copy;
+}
+
+/* =========================== Line editing ================================= */
+
+/* We define a very simple "append buffer" structure, that is an heap
+ * allocated string where we can append to. This is useful in order to
+ * write all the escape sequences in a buffer and flush them to the standard
+ * output in a single call, to avoid flickering effects. */
+struct abuf {
+    char *b;
+    int len;
+};
+
+static void abInit(struct abuf *ab) {
+    ab->b = NULL;
+    ab->len = 0;
+}
+
+static void abAppend(struct abuf *ab, const char *s, int len) {
+    char *new_ptr = (char*) realloc(ab->b,ab->len+len);
+
+    if (new_ptr == NULL) return;
+    memcpy(new_ptr+ab->len,s,len);
+    ab->b = new_ptr;
+    ab->len += len;
+}
+
+static void abFree(struct abuf *ab) {
+    free(ab->b);
+}
+
+/* Helper of refreshSingleLine() and refreshMultiLine() to show hints
+ * to the right of the prompt. */
+static void refreshShowHints(struct abuf * ab, struct linenoiseState * l, int plen) {
+    char seq[64];
+    if (hintsCallback && plen+l->len < l->cols) {
+        int color = -1, bold = 0;
+        const char *hint = hintsCallback(l->buf,&color,&bold);
+        if (hint) {
+            int hintlen = strlen(hint);
+            int hintmaxlen = l->cols-(plen+l->len);
+            if (hintlen > hintmaxlen) hintlen = hintmaxlen;
+            if (bold == 1 && color == -1) color = 37;
+            if (color != -1 || bold != 0)
+                snprintf(seq,64,"\033[%d;%d;49m",bold,color);
+            else
+                seq[0] = '\0';
+            abAppend(ab,seq,strlen(seq));
+            abAppend(ab,hint,hintlen);
+            if (color != -1 || bold != 0)
+                abAppend(ab,"\033[0m",4);
+            /* Call the function to free the hint returned. */
+            if (freeHintsCallback) freeHintsCallback(hint);
+        }
+    }
+}
+
+/* Single line low level line refresh.
+ *
+ * Rewrite the currently edited line accordingly to the buffer content,
+ * cursor position, and number of columns of the terminal.
+ *
+ * Flags is REFRESH_* macros. The function can just remove the old
+ * prompt, just write it, or both. */
+static void refreshSingleLine(struct linenoiseState *l, int flags) {
+    char seq[64];
+    size_t plen = strlen(l->prompt);
+    int fd = l->ofd;
+    char *buf = l->buf;
+    size_t len = l->len;
+    size_t pos = l->pos;
+    struct abuf ab;
+
+    while((plen+pos) >= l->cols) {
+        buf++;
+        len--;
+        pos--;
+    }
+    while (plen+len > l->cols) {
+        len--;
+    }
+
+    abInit(&ab);
+    /* Cursor to left edge */
+    snprintf(seq,sizeof(seq),"\r");
+    abAppend(&ab,seq,strlen(seq));
+
+    if (flags & REFRESH_WRITE) {
+        /* Write the prompt and the current buffer content */
+        abAppend(&ab,l->prompt,strlen(l->prompt));
+        if (maskmode == 1) {
+            while (len--) abAppend(&ab,"*",1);
+        } else {
+            abAppend(&ab,buf,len);
+        }
+        /* Show hits if any. */
+        refreshShowHints(&ab,l,plen);
+    }
+
+    /* Erase to right */
+    snprintf(seq,sizeof(seq),"\x1b[0K");
+    abAppend(&ab,seq,strlen(seq));
+
+    if (flags & REFRESH_WRITE) {
+        /* Move cursor to original position. */
+        snprintf(seq,sizeof(seq),"\r\x1b[%dC", (int)(pos+plen));
+        abAppend(&ab,seq,strlen(seq));
+    }
+
+    if (write(fd,ab.b,ab.len) == -1) {} /* Can't recover from write error. */
+    abFree(&ab);
+}
+
+/* Multi line low level line refresh.
+ *
+ * Rewrite the currently edited line accordingly to the buffer content,
+ * cursor position, and number of columns of the terminal.
+ *
+ * Flags is REFRESH_* macros. The function can just remove the old
+ * prompt, just write it, or both. */
+static void refreshMultiLine(struct linenoiseState *l, int flags) {
+    char seq[64];
+    int plen = strlen(l->prompt);
+    int rows = (plen+l->len+l->cols-1)/l->cols; /* rows used by current buf. */
+    int rpos = (plen+l->oldpos+l->cols)/l->cols; /* cursor relative row. */
+    int rpos2; /* rpos after refresh. */
+    int col; /* colum position, zero-based. */
+    int old_rows = l->oldrows;
+    int fd = l->ofd, j;
+    struct abuf ab;
+
+    l->oldrows = rows;
+
+    /* First step: clear all the lines used before. To do so start by
+     * going to the last row. */
+    abInit(&ab);
+
+    if (flags & REFRESH_CLEAN) {
+        if (old_rows-rpos > 0) {
+            lndebug("go down %d", old_rows-rpos);
+            snprintf(seq,64,"\x1b[%dB", old_rows-rpos);
+            abAppend(&ab,seq,strlen(seq));
+        }
+
+        /* Now for every row clear it, go up. */
+        for (j = 0; j < old_rows-1; j++) {
+            lndebug("clear+up");
+            snprintf(seq,64,"\r\x1b[0K\x1b[1A");
+            abAppend(&ab,seq,strlen(seq));
+        }
+    }
+
+    if (flags & REFRESH_ALL) {
+        /* Clean the top line. */
+        lndebug("clear");
+        snprintf(seq,64,"\r\x1b[0K");
+        abAppend(&ab,seq,strlen(seq));
+    }
+
+    if (flags & REFRESH_WRITE) {
+        /* Write the prompt and the current buffer content */
+        abAppend(&ab,l->prompt,strlen(l->prompt));
+        if (maskmode == 1) {
+            unsigned int i;
+            for (i = 0; i < l->len; i++) abAppend(&ab,"*",1);
+        } else {
+            abAppend(&ab,l->buf,l->len);
+        }
+
+        /* Show hits if any. */
+        refreshShowHints(&ab,l,plen);
+
+        /* If we are at the very end of the screen with our prompt, we need to
+         * emit a newline and move the prompt to the first column. */
+        if (l->pos &&
+            l->pos == l->len &&
+            (l->pos+plen) % l->cols == 0)
+        {
+            lndebug("<newline>");
+            abAppend(&ab,"\n",1);
+            snprintf(seq,64,"\r");
+            abAppend(&ab,seq,strlen(seq));
+            rows++;
+            if (rows > (int)l->oldrows) l->oldrows = rows;
+        }
+
+        /* Move cursor to right position. */
+        rpos2 = (plen+l->pos+l->cols)/l->cols; /* Current cursor relative row */
+        lndebug("rpos2 %d", rpos2);
+
+        /* Go up till we reach the expected positon. */
+        if (rows-rpos2 > 0) {
+            lndebug("go-up %d", rows-rpos2);
+            snprintf(seq,64,"\x1b[%dA", rows-rpos2);
+            abAppend(&ab,seq,strlen(seq));
+        }
+
+        /* Set column. */
+        col = (plen+(int)l->pos) % (int)l->cols;
+        lndebug("set col %d", 1+col);
+        if (col)
+            snprintf(seq,64,"\r\x1b[%dC", col);
+        else
+            snprintf(seq,64,"\r");
+        abAppend(&ab,seq,strlen(seq));
+    }
+
+    lndebug("\n");
+    l->oldpos = l->pos;
+
+    if (write(fd,ab.b,ab.len) == -1) {} /* Can't recover from write error. */
+    abFree(&ab);
+}
+
+/* Calls the two low level functions refreshSingleLine() or
+ * refreshMultiLine() according to the selected mode. */
+static void refreshLineWithFlags(struct linenoiseState *l, int flags) {
+    if (mlmode)
+        refreshMultiLine(l,flags);
+    else
+        refreshSingleLine(l,flags);
+}
+
+/* Utility function to avoid specifying REFRESH_ALL all the times. */
+static void refreshLine(struct linenoiseState *l) {
+    refreshLineWithFlags(l,REFRESH_ALL);
+}
+
+/* Hide the current line, when using the multiplexing API. */
+void linenoiseHide(struct linenoiseState *l) {
+    if (mlmode)
+        refreshMultiLine(l,REFRESH_CLEAN);
+    else
+        refreshSingleLine(l,REFRESH_CLEAN);
+}
+
+/* Show the current line, when using the multiplexing API. */
+void linenoiseShow(struct linenoiseState *l) {
+    if (l->in_completion) {
+        refreshLineWithCompletion(l,NULL,REFRESH_WRITE);
+    } else {
+        refreshLineWithFlags(l,REFRESH_WRITE);
+    }
+}
+
+/* Insert the character 'c' at cursor current position.
+ *
+ * On error writing to the terminal -1 is returned, otherwise 0. */
+static int linenoiseEditInsert(struct linenoiseState * l, char c) {
+    if (l->len < l->buflen) {
+        if (l->len == l->pos) {
+            l->buf[l->pos] = c;
+            l->pos++;
+            l->len++;
+            l->buf[l->len] = '\0';
+            if ((!mlmode && l->plen+l->len < l->cols && !hintsCallback)) {
+                /* Avoid a full update of the line in the
+                 * trivial case. */
+                char d = (maskmode==1) ? '*' : c;
+                if (write(l->ofd,&d,1) == -1) return -1;
+            } else {
+                refreshLine(l);
+            }
+        } else {
+            memmove(l->buf+l->pos+1,l->buf+l->pos,l->len-l->pos);
+            l->buf[l->pos] = c;
+            l->len++;
+            l->pos++;
+            l->buf[l->len] = '\0';
+            refreshLine(l);
+        }
+    }
+    return 0;
+}
+
+/* Move cursor on the left. */
+static void linenoiseEditMoveLeft(struct linenoiseState * l) {
+    if (l->pos > 0) {
+        l->pos--;
+        refreshLine(l);
+    }
+}
+
+/* Move cursor on the right. */
+static void linenoiseEditMoveRight(struct linenoiseState * l) {
+    if (l->pos != l->len) {
+        l->pos++;
+        refreshLine(l);
+    }
+}
+
+/* Move cursor to the start of the line. */
+static void linenoiseEditMoveHome(struct linenoiseState * l) {
+    if (l->pos != 0) {
+        l->pos = 0;
+        refreshLine(l);
+    }
+}
+
+/* Move cursor to the end of the line. */
+static void linenoiseEditMoveEnd(struct linenoiseState * l) {
+    if (l->pos != l->len) {
+        l->pos = l->len;
+        refreshLine(l);
+    }
+}
+
+/* Substitute the currently edited line with the next or previous history
+ * entry as specified by 'dir'. */
+#define LINENOISE_HISTORY_NEXT 0
+#define LINENOISE_HISTORY_PREV 1
+
+static void linenoiseEditHistoryNext(struct linenoiseState * l, int dir) {
+    if (history_len > 1) {
+        /* Update the current history entry before to
+         * overwrite it with the next one. */
+        free(history[history_len - 1 - l->history_index]);
+        history[history_len - 1 - l->history_index] = strdup(l->buf);
+        /* Show the new entry */
+        l->history_index += (dir == LINENOISE_HISTORY_PREV) ? 1 : -1;
+        if (l->history_index < 0) {
+            l->history_index = 0;
+            return;
+        } else if (l->history_index >= history_len) {
+            l->history_index = history_len-1;
+            return;
+        }
+        strncpy(l->buf,history[history_len - 1 - l->history_index],l->buflen);
+        l->buf[l->buflen-1] = '\0';
+        l->len = l->pos = strlen(l->buf);
+        refreshLine(l);
+    }
+}
+
+/* Delete the character at the right of the cursor without altering the cursor
+ * position. Basically this is what happens with the "Delete" keyboard key. */
+static void linenoiseEditDelete(struct linenoiseState * l) {
+    if (l->len > 0 && l->pos < l->len) {
+        memmove(l->buf+l->pos,l->buf+l->pos+1,l->len-l->pos-1);
+        l->len--;
+        l->buf[l->len] = '\0';
+        refreshLine(l);
+    }
+}
+
+/* Backspace implementation. */
+static void linenoiseEditBackspace(struct linenoiseState * l) {
+    if (l->pos > 0 && l->len > 0) {
+        memmove(l->buf+l->pos-1,l->buf+l->pos,l->len-l->pos);
+        l->pos--;
+        l->len--;
+        l->buf[l->len] = '\0';
+        refreshLine(l);
+    }
+}
+
+/* Delete the previosu word, maintaining the cursor at the start of the
+ * current word. */
+static void linenoiseEditDeletePrevWord(struct linenoiseState * l) {
+    size_t old_pos = l->pos;
+    size_t diff;
+
+    while (l->pos > 0 && l->buf[l->pos-1] == ' ')
+        l->pos--;
+    while (l->pos > 0 && l->buf[l->pos-1] != ' ')
+        l->pos--;
+    diff = old_pos - l->pos;
+    memmove(l->buf+l->pos,l->buf+old_pos,l->len-old_pos+1);
+    l->len -= diff;
+    refreshLine(l);
+}
+
+/* This function is part of the multiplexed API of Linenoise, that is used
+ * in order to implement the blocking variant of the API but can also be
+ * called by the user directly in an event driven program. It will:
+ *
+ * 1. Initialize the linenoise state passed by the user.
+ * 2. Put the terminal in RAW mode.
+ * 3. Show the prompt.
+ * 4. Return control to the user, that will have to call linenoiseEditFeed()
+ *    each time there is some data arriving in the standard input.
+ *
+ * The user can also call linenoiseEditHide() and linenoiseEditShow() if it
+ * is required to show some input arriving asyncronously, without mixing
+ * it with the currently edited line.
+ *
+ * When linenoiseEditFeed() returns non-NULL, the user finished with the
+ * line editing session (pressed enter CTRL-D/C): in this case the caller
+ * needs to call linenoiseEditStop() to put back the terminal in normal
+ * mode. This will not destroy the buffer, as long as the linenoiseState
+ * is still valid in the context of the caller.
+ *
+ * The function returns 0 on success, or -1 if writing to standard output
+ * fails. If stdin_fd or stdout_fd are set to -1, the default is to use
+ * STDIN_FILENO and STDOUT_FILENO.
+ */
+int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt) {
+    /* Populate the linenoise state that we pass to functions implementing
+     * specific editing functionalities. */
+    l->in_completion = 0;
+    l->ifd = stdin_fd != -1 ? stdin_fd : STDIN_FILENO;
+    l->ofd = stdout_fd != -1 ? stdout_fd : STDOUT_FILENO;
+    l->buf = buf;
+    l->buflen = buflen;
+    l->prompt = prompt;
+    l->plen = strlen(prompt);
+    l->oldpos = l->pos = 0;
+    l->len = 0;
+
+    /* Enter raw mode. */
+    if (enableRawMode(l->ifd) == -1) return -1;
+
+    l->cols = getColumns(stdin_fd, stdout_fd);
+    l->oldrows = 0;
+    l->history_index = 0;
+
+    /* Buffer starts empty. */
+    l->buf[0] = '\0';
+    l->buflen--; /* Make sure there is always space for the nulterm */
+
+    /* If stdin is not a tty, stop here with the initialization. We
+     * will actually just read a line from standard input in blocking
+     * mode later, in linenoiseEditFeed(). */
+    if (!isatty(l->ifd)) return 0;
+
+    /* The latest history entry is always our current buffer, that
+     * initially is just an empty string. */
+    linenoiseHistoryAdd("");
+
+    if (write(l->ofd,prompt,l->plen) == -1) return -1;
+    return 0;
+}
+
+const char* linenoiseEditMore = "If you see this, you are misusing the API: when linenoiseEditFeed() is called, if it returns linenoiseEditMore the user is yet editing the line. See the README file for more information.";
+
+/* This function is part of the multiplexed API of linenoise, see the top
+ * comment on linenoiseEditStart() for more information. Call this function
+ * each time there is some data to read from the standard input file
+ * descriptor. In the case of blocking operations, this function can just be
+ * called in a loop, and block.
+ *
+ * The function returns linenoiseEditMore to signal that line editing is still
+ * in progress, that is, the user didn't yet pressed enter / CTRL-D. Otherwise
+ * the function returns the pointer to the heap-allocated buffer with the
+ * edited line, that the user should free with linenoiseFree().
+ *
+ * On special conditions, NULL is returned and errno is populated:
+ *
+ * EAGAIN if the user pressed Ctrl-C
+ * ENOENT if the user pressed Ctrl-D
+ *
+ * Some other errno: I/O error.
+ */
+const char *linenoiseEditFeed(struct linenoiseState *l) {
+    /* Not a TTY, pass control to line reading without character
+     * count limits. */
+    if (!isatty(l->ifd)) return linenoiseNoTTY();
+
+    char c;
+    int nread;
+    char seq[3];
+
+    nread = read(l->ifd,&c,1);
+    if (nread <= 0) return NULL;
+
+    /* Only autocomplete when the callback is set. It returns < 0 when
+     * there was an error reading from fd. Otherwise it will return the
+     * character that should be handled next. */
+    if ((l->in_completion || c == 9) && completionCallback != NULL) {
+        c = completeLine(l,c);
+        /* Read next character when 0 */
+        if (c == 0) return linenoiseEditMore;
+    }
+
+    switch(c) {
+    case ENTER:    /* enter */
+        history_len--;
+        free(history[history_len]);
+        if (mlmode) linenoiseEditMoveEnd(l);
+        if (hintsCallback) {
+            /* Force a refresh without hints to leave the previous
+             * line as the user typed it after a newline. */
+            linenoiseHintsCallback *hc = hintsCallback;
+            hintsCallback = NULL;
+            refreshLine(l);
+            hintsCallback = hc;
+        }
+        return strdup(l->buf);
+    case CTRL_C:     /* ctrl-c */
+        errno = EAGAIN;
+        return NULL;
+    case BACKSPACE:   /* backspace */
+    case 8:     /* ctrl-h */
+        linenoiseEditBackspace(l);
+        break;
+    case CTRL_D:     /* ctrl-d, remove char at right of cursor, or if the
+                        line is empty, act as end-of-file. */
+        if (l->len > 0) {
+            linenoiseEditDelete(l);
+        } else {
+            history_len--;
+            free(history[history_len]);
+            errno = ENOENT;
+            return NULL;
+        }
+        break;
+    case CTRL_T:    /* ctrl-t, swaps current character with previous. */
+        if (l->pos > 0 && l->pos < l->len) {
+            int aux = l->buf[l->pos-1];
+            l->buf[l->pos-1] = l->buf[l->pos];
+            l->buf[l->pos] = aux;
+            if (l->pos != l->len-1) l->pos++;
+            refreshLine(l);
+        }
+        break;
+    case CTRL_B:     /* ctrl-b */
+        linenoiseEditMoveLeft(l);
+        break;
+    case CTRL_F:     /* ctrl-f */
+        linenoiseEditMoveRight(l);
+        break;
+    case CTRL_P:    /* ctrl-p */
+        linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV);
+        break;
+    case CTRL_N:    /* ctrl-n */
+        linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT);
+        break;
+    case ESC:    /* escape sequence */
+        /* Read the next two bytes representing the escape sequence.
+         * Use two calls to handle slow terminals returning the two
+         * chars at different times. */
+        if (read(l->ifd,seq,1) == -1) break;
+        if (read(l->ifd,seq+1,1) == -1) break;
+
+        /* ESC [ sequences. */
+        if (seq[0] == '[') {
+            if (seq[1] >= '0' && seq[1] <= '9') {
+                /* Extended escape, read additional byte. */
+                if (read(l->ifd,seq+2,1) == -1) break;
+                if (seq[2] == '~') {
+                    switch(seq[1]) {
+                    case '3': /* Delete key. */
+                        linenoiseEditDelete(l);
+                        break;
+                    }
+                }
+            } else {
+                switch(seq[1]) {
+                case 'A': /* Up */
+                    linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV);
+                    break;
+                case 'B': /* Down */
+                    linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT);
+                    break;
+                case 'C': /* Right */
+                    linenoiseEditMoveRight(l);
+                    break;
+                case 'D': /* Left */
+                    linenoiseEditMoveLeft(l);
+                    break;
+                case 'H': /* Home */
+                    linenoiseEditMoveHome(l);
+                    break;
+                case 'F': /* End*/
+                    linenoiseEditMoveEnd(l);
+                    break;
+                }
+            }
+        }
+
+        /* ESC O sequences. */
+        else if (seq[0] == 'O') {
+            switch(seq[1]) {
+            case 'H': /* Home */
+                linenoiseEditMoveHome(l);
+                break;
+            case 'F': /* End*/
+                linenoiseEditMoveEnd(l);
+                break;
+            }
+        }
+        break;
+    default:
+        if (linenoiseEditInsert(l,c)) return NULL;
+        break;
+    case CTRL_U: /* Ctrl+u, delete the whole line. */
+        l->buf[0] = '\0';
+        l->pos = l->len = 0;
+        refreshLine(l);
+        break;
+    case CTRL_K: /* Ctrl+k, delete from current to end of line. */
+        l->buf[l->pos] = '\0';
+        l->len = l->pos;
+        refreshLine(l);
+        break;
+    case CTRL_A: /* Ctrl+a, go to the start of the line */
+        linenoiseEditMoveHome(l);
+        break;
+    case CTRL_E: /* ctrl+e, go to the end of the line */
+        linenoiseEditMoveEnd(l);
+        break;
+    case CTRL_L: /* ctrl+l, clear screen */
+        linenoiseClearScreen();
+        refreshLine(l);
+        break;
+    case CTRL_W: /* ctrl+w, delete previous word */
+        linenoiseEditDeletePrevWord(l);
+        break;
+    }
+    return linenoiseEditMore;
+}
+
+/* This is part of the multiplexed linenoise API. See linenoiseEditStart()
+ * for more information. This function is called when linenoiseEditFeed()
+ * returns something different than NULL. At this point the user input
+ * is in the buffer, and we can restore the terminal in normal mode. */
+void linenoiseEditStop(struct linenoiseState *l) {
+    if (!isatty(l->ifd)) return;
+    disableRawMode(l->ifd);
+    printf("\n");
+}
+
+/* This just implements a blocking loop for the multiplexed API.
+ * In many applications that are not event-drivern, we can just call
+ * the blocking linenoise API, wait for the user to complete the editing
+ * and return the buffer. */
+static const char *linenoiseBlockingEdit(int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt)
+{
+    struct linenoiseState l;
+
+    /* Editing without a buffer is invalid. */
+    if (buflen == 0) {
+        errno = EINVAL;
+        return NULL;
+    }
+
+    linenoiseEditStart(&l,stdin_fd,stdout_fd,buf,buflen,prompt);
+    const char *res;
+    while((res = linenoiseEditFeed(&l)) == linenoiseEditMore);
+    linenoiseEditStop(&l);
+    return res;
+}
+
+/* This special mode is used by linenoise in order to print scan codes
+ * on screen for debugging / development purposes. It is implemented
+ * by the linenoise_example program using the --keycodes option. */
+void linenoisePrintKeyCodes(void) {
+    char quit[4];
+
+    printf("Linenoise key codes debugging mode.\n"
+            "Press keys to see scan codes. Type 'quit' at any time to exit.\n");
+    if (enableRawMode(STDIN_FILENO) == -1) return;
+    memset(quit,' ',4);
+    while(1) {
+        char c;
+        int nread;
+
+        nread = read(STDIN_FILENO,&c,1);
+        if (nread <= 0) continue;
+        memmove(quit,quit+1,sizeof(quit)-1); /* shift string to left. */
+        quit[sizeof(quit)-1] = c; /* Insert current char on the right. */
+        if (memcmp(quit,"quit",sizeof(quit)) == 0) break;
+
+        printf("'%c' %02x (%d) (type quit to exit)\n",
+            isprint(c) ? c : '?', (int)c, (int)c);
+        printf("\r"); /* Go left edge manually, we are in raw mode. */
+        fflush(stdout);
+    }
+    disableRawMode(STDIN_FILENO);
+}
+
+/* This function is called when linenoise() is called with the standard
+ * input file descriptor not attached to a TTY. So for example when the
+ * program using linenoise is called in pipe or with a file redirected
+ * to its standard input. In this case, we want to be able to return the
+ * line regardless of its length (by default we are limited to 4k). */
+static char *linenoiseNoTTY(void) {
+    char *line = NULL;
+    size_t len = 0, maxlen = 0;
+
+    while(1) {
+        if (len == maxlen) {
+            if (maxlen == 0) maxlen = 16;
+            maxlen *= 2;
+            char *oldval = line;
+            line = (char*) realloc(line,maxlen);
+            if (line == NULL) {
+                if (oldval) free(oldval);
+                return NULL;
+            }
+        }
+        int c = fgetc(stdin);
+        if (c == EOF || c == '\n') {
+            if (c == EOF && len == 0) {
+                free(line);
+                return NULL;
+            } else {
+                line[len] = '\0';
+                return line;
+            }
+        } else {
+            line[len] = c;
+            len++;
+        }
+    }
+}
+
+/* The high level function that is the main API of the linenoise library.
+ * This function checks if the terminal has basic capabilities, just checking
+ * for a blacklist of stupid terminals, and later either calls the line
+ * editing function or uses dummy fgets() so that you will be able to type
+ * something even in the most desperate of the conditions. */
+const char *linenoise(const char *prompt) {
+    char buf[LINENOISE_MAX_LINE];
+
+    if (!isatty(STDIN_FILENO)) {
+        /* Not a tty: read from file / pipe. In this mode we don't want any
+         * limit to the line size, so we call a function to handle that. */
+        return linenoiseNoTTY();
+    } else if (isUnsupportedTerm()) {
+        size_t len;
+
+        printf("%s",prompt);
+        fflush(stdout);
+        if (fgets(buf,LINENOISE_MAX_LINE,stdin) == NULL) return NULL;
+        len = strlen(buf);
+        while(len && (buf[len-1] == '\n' || buf[len-1] == '\r')) {
+            len--;
+            buf[len] = '\0';
+        }
+        return strdup(buf);
+    } else {
+        const char *retval = linenoiseBlockingEdit(STDIN_FILENO,STDOUT_FILENO,buf,LINENOISE_MAX_LINE,prompt);
+        return retval;
+    }
+}
+
+/* This is just a wrapper the user may want to call in order to make sure
+ * the linenoise returned buffer is freed with the same allocator it was
+ * created with. Useful when the main program is using an alternative
+ * allocator. */
+void linenoiseFree(void *ptr) {
+    if (ptr == linenoiseEditMore) return; // Protect from API misuse.
+    free(ptr);
+}
+
+/* ================================ History ================================= */
+
+/* Free the history, but does not reset it. Only used when we have to
+ * exit() to avoid memory leaks are reported by valgrind & co. */
+static void freeHistory(void) {
+    if (history) {
+        int j;
+
+        for (j = 0; j < history_len; j++)
+            free(history[j]);
+        free(history);
+    }
+}
+
+/* At exit we'll try to fix the terminal to the initial conditions. */
+static void linenoiseAtExit(void) {
+    disableRawMode(STDIN_FILENO);
+    freeHistory();
+}
+
+/* This is the API call to add a new entry in the linenoise history.
+ * It uses a fixed array of char pointers that are shifted (memmoved)
+ * when the history max length is reached in order to remove the older
+ * entry and make room for the new one, so it is not exactly suitable for huge
+ * histories, but will work well for a few hundred of entries.
+ *
+ * Using a circular buffer is smarter, but a bit more complex to handle. */
+int linenoiseHistoryAdd(const char *line) {
+    char *linecopy;
+
+    if (history_max_len == 0) return 0;
+
+    /* Initialization on first call. */
+    if (history == NULL) {
+        history = (char**) malloc(sizeof(char*)*history_max_len);
+        if (history == NULL) return 0;
+        memset(history,0,(sizeof(char*)*history_max_len));
+    }
+
+    /* Don't add duplicated lines. */
+    if (history_len && !strcmp(history[history_len-1], line)) return 0;
+
+    /* Add an heap allocated copy of the line in the history.
+     * If we reached the max length, remove the older line. */
+    linecopy = strdup(line);
+    if (!linecopy) return 0;
+    if (history_len == history_max_len) {
+        free(history[0]);
+        memmove(history,history+1,sizeof(char*)*(history_max_len-1));
+        history_len--;
+    }
+    history[history_len] = linecopy;
+    history_len++;
+    return 1;
+}
+
+/* Set the maximum length for the history. This function can be called even
+ * if there is already some history, the function will make sure to retain
+ * just the latest 'len' elements if the new history length value is smaller
+ * than the amount of items already inside the history. */
+int linenoiseHistorySetMaxLen(int len) {
+    char **new_ptr;
+
+    if (len < 1) return 0;
+    if (history) {
+        int tocopy = history_len;
+
+        new_ptr = (char**) malloc(sizeof(char*)*len);
+        if (new_ptr == NULL) return 0;
+
+        /* If we can't copy everything, free the elements we'll not use. */
+        if (len < tocopy) {
+            int j;
+
+            for (j = 0; j < tocopy-len; j++) free(history[j]);
+            tocopy = len;
+        }
+        memset(new_ptr,0,sizeof(char*)*len);
+        memcpy(new_ptr,history+(history_len-tocopy), sizeof(char*)*tocopy);
+        free(history);
+        history = new_ptr;
+    }
+    history_max_len = len;
+    if (history_len > history_max_len)
+        history_len = history_max_len;
+    return 1;
+}
+
+/* Save the history in the specified file. On success 0 is returned
+ * otherwise -1 is returned. */
+int linenoiseHistorySave(const char *filename) {
+    mode_t old_umask = umask(S_IXUSR|S_IRWXG|S_IRWXO);
+    FILE *fp;
+    int j;
+
+    fp = fopen(filename,"w");
+    umask(old_umask);
+    if (fp == NULL) return -1;
+    chmod(filename,S_IRUSR|S_IWUSR);
+    for (j = 0; j < history_len; j++)
+        fprintf(fp,"%s\n",history[j]);
+    fclose(fp);
+    return 0;
+}
+
+/* Load the history from the specified file. If the file does not exist
+ * zero is returned and no operation is performed.
+ *
+ * If the file exists and the operation succeeded 0 is returned, otherwise
+ * on error -1 is returned. */
+int linenoiseHistoryLoad(const char *filename) {
+    FILE *fp = fopen(filename,"r");
+    char buf[LINENOISE_MAX_LINE];
+
+    if (fp == NULL) return -1;
+
+    while (fgets(buf,LINENOISE_MAX_LINE,fp) != NULL) {
+        char *p;
+
+        p = strchr(buf,'\r');
+        if (!p) p = strchr(buf,'\n');
+        if (p) *p = '\0';
+        linenoiseHistoryAdd(buf);
+    }
+    fclose(fp);
+    return 0;
+}
+#endif
diff --git a/examples/run/linenoise.cpp/linenoise.h b/examples/run/linenoise.cpp/linenoise.h
new file mode 100644
index 000000000..3e25f4de3
--- /dev/null
+++ b/examples/run/linenoise.cpp/linenoise.h
@@ -0,0 +1,114 @@
+/* linenoise.h -- VERSION 1.0
+ *
+ * Guerrilla line editing library against the idea that a line editing lib
+ * needs to be 20,000 lines of C++ code.
+ *
+ * See linenoise.cpp for more information.
+ *
+ * ------------------------------------------------------------------------
+ *
+ * Copyright (c) 2010-2023, Salvatore Sanfilippo <antirez at gmail dot com>
+ * Copyright (c) 2010-2013, Pieter Noordhuis <pcnoordhuis at gmail dot com>
+ * Copyright (c) 2025, Eric Curtin <ericcurtin17 at gmail dot com>
+ *
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *  *  Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *
+ *  *  Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#ifndef __LINENOISE_H
+#define __LINENOISE_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <stddef.h> /* For size_t. */
+
+extern const char *linenoiseEditMore;
+
+/* The linenoiseState structure represents the state during line editing.
+ * We pass this state to functions implementing specific editing
+ * functionalities. */
+struct linenoiseState {
+    int in_completion;  /* The user pressed TAB and we are now in completion
+                         * mode, so input is handled by completeLine(). */
+    size_t completion_idx; /* Index of next completion to propose. */
+    int ifd;            /* Terminal stdin file descriptor. */
+    int ofd;            /* Terminal stdout file descriptor. */
+    char *buf;          /* Edited line buffer. */
+    size_t buflen;      /* Edited line buffer size. */
+    const char *prompt; /* Prompt to display. */
+    size_t plen;        /* Prompt length. */
+    size_t pos;         /* Current cursor position. */
+    size_t oldpos;      /* Previous refresh cursor position. */
+    size_t len;         /* Current edited line length. */
+    size_t cols;        /* Number of columns in terminal. */
+    size_t oldrows;     /* Rows used by last refrehsed line (multiline mode) */
+    int history_index;  /* The history index we are currently editing. */
+};
+
+typedef struct linenoiseCompletions {
+  size_t len;
+  char **cvec;
+} linenoiseCompletions;
+
+/* Non blocking API. */
+int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt);
+const char *linenoiseEditFeed(struct linenoiseState *l);
+void linenoiseEditStop(struct linenoiseState *l);
+void linenoiseHide(struct linenoiseState *l);
+void linenoiseShow(struct linenoiseState *l);
+
+/* Blocking API. */
+const char *linenoise(const char *prompt);
+void linenoiseFree(void *ptr);
+
+/* Completion API. */
+typedef void(linenoiseCompletionCallback)(const char *, linenoiseCompletions *);
+typedef const char*(linenoiseHintsCallback)(const char *, int *color, int *bold);
+typedef void(linenoiseFreeHintsCallback)(const char *);
+void linenoiseSetCompletionCallback(linenoiseCompletionCallback *);
+void linenoiseSetHintsCallback(linenoiseHintsCallback *);
+void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *);
+void linenoiseAddCompletion(linenoiseCompletions *, const char *);
+
+/* History API. */
+int linenoiseHistoryAdd(const char *line);
+int linenoiseHistorySetMaxLen(int len);
+int linenoiseHistorySave(const char *filename);
+int linenoiseHistoryLoad(const char *filename);
+
+/* Other utilities. */
+void linenoiseClearScreen(void);
+void linenoiseSetMultiLine(int ml);
+void linenoisePrintKeyCodes(void);
+void linenoiseMaskModeEnable(void);
+void linenoiseMaskModeDisable(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* __LINENOISE_H */
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 0ad8bb15b..dd9ea79e8 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -19,12 +19,14 @@
 #include <cstring>
 #include <filesystem>
 #include <iostream>
+#include <list>
 #include <sstream>
 #include <string>
 #include <vector>
 
 #include "common.h"
 #include "json.hpp"
+#include "linenoise.cpp/linenoise.h"
 #include "llama-cpp.h"
 
 #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
@@ -536,7 +538,7 @@ class LlamaData {
     llama_sampler_ptr               sampler;
     llama_context_ptr               context;
     std::vector<llama_chat_message> messages;
-    std::vector<std::string>        msg_strs;
+    std::list<std::string>          msg_strs;
     std::vector<char>               fmtted;
 
     int init(Opt & opt) {
@@ -807,24 +809,44 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
         batch = llama_batch_get_one(&new_token_id, 1);
     }
 
+    printf("\033[0m");
     return 0;
 }
 
-static int read_user_input(std::string & user) {
-    std::getline(std::cin, user);
+static int read_user_input(std::string & user_input) {
+    static const char * prompt_prefix = "> ";
+#ifdef WIN32
+    printf(
+        "\r%*s"
+        "\r\033[0m%s",
+        get_terminal_width(), " ", prompt_prefix);
+
+    std::getline(std::cin, user_input);
     if (std::cin.eof()) {
         printf("\n");
         return 1;
     }
-
-    if (user == "/bye") {
+#else
+    std::unique_ptr<char, decltype(&std::free)> line(const_cast<char *>(linenoise(prompt_prefix)), free);
+    if (!line) {
         return 1;
     }
 
-    if (user.empty()) {
+    user_input = line.get();
+#endif
+
+    if (user_input == "/bye") {
+        return 1;
+    }
+
+    if (user_input.empty()) {
         return 2;
     }
 
+#ifndef WIN32
+    linenoiseHistoryAdd(line.get());
+#endif
+
     return 0;  // Should have data in happy path
 }
 
@@ -865,10 +887,6 @@ static int handle_user_input(std::string & user_input, const std::string & user)
         return 0;  // No need for interactive input
     }
 
-    printf(
-        "\r%*s"
-        "\r\033[32m> \033[0m",
-        get_terminal_width(), " ");
     return read_user_input(user_input);  // Returns true if input ends the loop
 }
 

From 99487b57d47e14dc342b7b89d238ca11c0345241 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Nicol=C3=B2=20Scipione?= <nicolo.scipione@codeplay.com>
Date: Sun, 19 Jan 2025 14:33:34 +0100
Subject: [PATCH 204/376] SYCL: Introducing memory host pool (#11251)

* Implement host pool for matrix_info

Creating a new memory pool on the host to store memory location for
matrix_info needed to launch gemm_batch from oneMKL/oneMath.
Removing complex support in gemm_batch since it is not used in llama.cpp

* Remove unnecessary headers and cast

* Reorder member variable to avoid warning on initialization

* Formatting

* Remove unused variable

* Address PR review feedback - remove warning

---------

Signed-off-by: nscipione <nicolo.scipione@codeplay.com>
---
 ggml/src/ggml-sycl/common.hpp      |  13 +++
 ggml/src/ggml-sycl/dpct/helper.hpp | 135 +++++++++--------------------
 ggml/src/ggml-sycl/ggml-sycl.cpp   |  92 ++++++++++++++++++--
 3 files changed, 137 insertions(+), 103 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index e9500f3a1..abad847ca 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -333,8 +333,12 @@ struct ggml_backend_sycl_context {
     // pool
     std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];
 
+    std::unique_ptr<ggml_sycl_pool> host_pools[GGML_SYCL_MAX_DEVICES];
+
     static std::unique_ptr<ggml_sycl_pool> new_pool_for_device(queue_ptr qptr, int device);
 
+    static std::unique_ptr<ggml_sycl_pool> new_pool_for_host(queue_ptr qptr, int device);
+
     ggml_sycl_pool & pool(int device) {
         if (pools[device] == nullptr) {
             pools[device] = new_pool_for_device(stream(device,0), device);
@@ -345,6 +349,15 @@ struct ggml_backend_sycl_context {
     ggml_sycl_pool & pool() {
         return pool(device);
     }
+
+    ggml_sycl_pool & host_pool(int device) {
+        if (host_pools[device] == nullptr) {
+            host_pools[device] = new_pool_for_host(stream(device, 0), device);
+        }
+        return *host_pools[device];
+    }
+
+    ggml_sycl_pool & host_pool() { return host_pool(device); }
 };
 
 // common device functions
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index e167948e7..c96395be6 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -82,6 +82,14 @@ inline std::string get_device_backend_and_type(const sycl::device &device) {
     return device_type.str();
 }
 
+template <typename Ts> struct matrix_info_t {
+    oneapi::mkl::transpose transpose_info[2];
+    Ts                     value_info[2];
+    std::int64_t           size_info[3];
+    std::int64_t           ld_info[3];
+    std::int64_t           groupsize_info;
+};
+
 namespace dpct
 {
     typedef sycl::queue *queue_ptr;
@@ -1727,26 +1735,13 @@ namespace dpct
         };
 
         template <class Ta, class Tb, class Tc, class Ts>
-        inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                                    oneapi::mkl::transpose b_trans, int m, int n, int k,
-                                    const void *alpha, const void **a, int lda,
-                                    const void **b, int ldb, const void *beta, void **c,
-                                    int ldc, int batch_size)
-        {
-            struct matrix_info_t
-            {
-                oneapi::mkl::transpose transpose_info[2];
-                Ts value_info[2];
-                std::int64_t size_info[3];
-                std::int64_t ld_info[3];
-                std::int64_t groupsize_info;
-            };
-
+        inline void gemm_batch_impl(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans,
+                                    int m, int n, int k, const void * alpha, const void ** a, int lda, const void ** b,
+                                    int ldb, const void * beta, void ** c, int ldc, int batch_size,
+                                    matrix_info_t<float> * matrix_info) {
             Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
             Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
 
-            matrix_info_t *matrix_info =
-                (matrix_info_t *)std::malloc(sizeof(matrix_info_t));
             matrix_info->transpose_info[0] = a_trans;
             matrix_info->transpose_info[1] = b_trans;
             matrix_info->value_info[0] = alpha_value;
@@ -1763,23 +1758,18 @@ namespace dpct
             sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
                 oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ q }, matrix_info->transpose_info,
                 matrix_info->transpose_info + 1, matrix_info->size_info, matrix_info->size_info + 1,
-                matrix_info->size_info + 2, matrix_info->value_info, reinterpret_cast<const Ta **>(a),
-                matrix_info->ld_info, reinterpret_cast<const Tb **>(b), matrix_info->ld_info + 1,
-                matrix_info->value_info + 1, reinterpret_cast<Tc **>(c), matrix_info->ld_info + 2, 1,
-                &(matrix_info->groupsize_info));
+                matrix_info->size_info + 2, reinterpret_cast<Ts *>(matrix_info->value_info),
+                reinterpret_cast<const Ta **>(a), matrix_info->ld_info, reinterpret_cast<const Tb **>(b),
+                matrix_info->ld_info + 1, reinterpret_cast<Ts *>(matrix_info->value_info + 1),
+                reinterpret_cast<Tc **>(c), matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
 #else
             sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
                 q, matrix_info->transpose_info, matrix_info->transpose_info + 1, matrix_info->size_info,
-                matrix_info->size_info + 1, matrix_info->size_info + 2, matrix_info->value_info,
+                matrix_info->size_info + 1, matrix_info->size_info + 2, reinterpret_cast<Ts *>(matrix_info->value_info),
                 reinterpret_cast<const Ta **>(a), matrix_info->ld_info, reinterpret_cast<const Tb **>(b),
-                matrix_info->ld_info + 1, matrix_info->value_info + 1, reinterpret_cast<Tc **>(c),
-                matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
+                matrix_info->ld_info + 1, reinterpret_cast<Ts *>(matrix_info->value_info + 1),
+                reinterpret_cast<Tc **>(c), matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
 #endif
-
-            q.submit([&](sycl::handler &cgh)
-                     {
-    cgh.depends_on(e);
-    cgh.host_task([=] { std::free(matrix_info); }); });
         }
 
         template <class Ta, class Tb, class Tc, class Ts>
@@ -2422,25 +2412,11 @@ namespace dpct
     /// \param [in] ldc Leading dimension of C.
     /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
     /// \param [in] scaling_type Data type of the scaling factors.
-    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                           oneapi::mkl::transpose b_trans, int m, int n, int k,
-                           const void *alpha, const void *a[],
-                           library_data_t a_type, int lda, const void *b[],
-                           library_data_t b_type, int ldb, const void *beta,
-                           void *c[], library_data_t c_type, int ldc,
-                           int batch_size, library_data_t scaling_type)
-    {
-        if (scaling_type == library_data_t::real_float &&
-            c_type == library_data_t::complex_float)
-        {
-            scaling_type = library_data_t::complex_float;
-        }
-        else if (scaling_type == library_data_t::real_double &&
-                 c_type == library_data_t::complex_double)
-        {
-            scaling_type = library_data_t::complex_double;
-        }
-
+    inline void gemm_batch(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m,
+                           int n, int k, const void * alpha, const void * a[], library_data_t a_type, int lda,
+                           const void * b[], library_data_t b_type, int ldb, const void * beta, void * c[],
+                           library_data_t c_type, int ldc, int batch_size, library_data_t scaling_type,
+                           matrix_info_t<float> * matrix_info) {
         std::uint64_t key =
             detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
         switch (key)
@@ -2449,48 +2425,24 @@ namespace dpct
             library_data_t::real_float, library_data_t::real_float,
             library_data_t::real_float, library_data_t::real_float):
         {
-            detail::gemm_batch_impl<float, float, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
+            detail::gemm_batch_impl<float, float, float, float>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb,
+                                                                beta, c, ldc, batch_size, matrix_info);
             break;
         }
         case detail::get_type_combination_id(
             library_data_t::real_double, library_data_t::real_double,
             library_data_t::real_double, library_data_t::real_double):
         {
-            detail::gemm_batch_impl<double, double, double, double>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_float, library_data_t::complex_float,
-            library_data_t::complex_float, library_data_t::complex_float):
-        {
-            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
-                                    std::complex<float>, std::complex<float>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_double, library_data_t::complex_double,
-            library_data_t::complex_double, library_data_t::complex_double):
-        {
-            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
-                                    std::complex<double>, std::complex<double>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
+            detail::gemm_batch_impl<double, double, double, double>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb,
+                                                                    beta, c, ldc, batch_size, matrix_info);
             break;
         }
         case detail::get_type_combination_id(
             library_data_t::real_half, library_data_t::real_half,
             library_data_t::real_half, library_data_t::real_half):
         {
-            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
-                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
-                                                a, lda, b, ldb, beta, c, ldc,
-                                                batch_size);
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
             break;
         }
 #ifdef __INTEL_MKL__
@@ -2498,19 +2450,16 @@ namespace dpct
             library_data_t::real_bfloat16, library_data_t::real_bfloat16,
             library_data_t::real_bfloat16, library_data_t::real_float):
         {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
-                                    oneapi::mkl::bfloat16, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
             break;
         }
         case detail::get_type_combination_id(
             library_data_t::real_bfloat16, library_data_t::real_bfloat16,
             library_data_t::real_float, library_data_t::real_float):
         {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
-                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
-                                           b, ldb, beta, c, ldc, batch_size);
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
             break;
         }
 #endif
@@ -2522,10 +2471,9 @@ namespace dpct
                 dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
             float beta_float =
                 dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
-            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
-                                    float>(q, a_trans, b_trans, m, n, k, &alpha_float,
-                                           a, lda, b, ldb, &beta_float, c, ldc,
-                                           batch_size);
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t, float>(
+                q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc, batch_size,
+                matrix_info);
             break;
         }
         case detail::get_type_combination_id(
@@ -2533,8 +2481,7 @@ namespace dpct
             library_data_t::real_float, library_data_t::real_float):
         {
             detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
             break;
         }
         case detail::get_type_combination_id(
@@ -2542,8 +2489,7 @@ namespace dpct
             library_data_t::real_float, library_data_t::real_float):
         {
             detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info);
             break;
         }
         case detail::get_type_combination_id(
@@ -2557,8 +2503,7 @@ namespace dpct
             sycl::half alpha_half(alpha_value);
             sycl::half beta_half(beta_value);
             detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
-                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc,
-                batch_size);
+                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc, batch_size, matrix_info);
             break;
         }
         default:
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 5272ca454..ed4d8bb8b 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -1173,6 +1173,85 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
     }
 };
 
+struct ggml_sycl_pool_host : public ggml_sycl_pool {
+    queue_ptr qptr;
+    int       device;
+
+    inline static int counter{ 0 };
+
+    struct ggml_sycl_buffer {
+        void * ptr  = nullptr;
+        size_t size = 0;
+    };
+
+    // Set arbitrarly to 64
+    static constexpr int          MAX_POOL_SIZE{ 64 };
+    std::vector<ggml_sycl_buffer> buffer_pool = std::vector<ggml_sycl_buffer>(MAX_POOL_SIZE);
+    size_t                        pool_size   = 0;
+
+    explicit ggml_sycl_pool_host(queue_ptr qptr_, int device_) : qptr(qptr_), device(device_) {}
+
+    ~ggml_sycl_pool_host() {
+        for (int i = 0; i < MAX_POOL_SIZE; ++i) {
+            ggml_sycl_buffer & b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr)));
+                b.ptr = nullptr;
+                pool_size -= b.size;
+                b.size = 0;
+            }
+        }
+        counter = 0;
+    }
+
+    void * alloc(size_t size, size_t * actual_size) override {
+        if (counter == MAX_POOL_SIZE) {
+            ggml_sycl_buffer b               = buffer_pool[0];
+            void *           ptr             = b.ptr;
+            *actual_size                     = b.size;
+            counter                          = 1;
+            return ptr;
+        }
+        ggml_sycl_buffer & b = buffer_pool[counter];
+
+        if (b.ptr == nullptr) {
+            void * ptr;
+
+            SYCL_CHECK(CHECK_TRY_ERROR(ptr = (void *) sycl::malloc_host(size, *qptr)));
+            if (!ptr) {
+                GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on host\n", __func__, size);
+                return nullptr;
+            }
+            pool_size += size;
+            *actual_size = size;
+            counter      = counter + 1;
+            return ptr;
+        } else {
+            ++counter;
+            b.size = size;
+            return b.ptr;
+        }
+    }
+
+    void free(void * ptr, size_t size) override {
+        // if the pool is not completed add the pointer to it in place of the first nullptr found.
+        // Otherwise do nothing, pointers will be freed once the pool is deallocated.
+        for (int i = 0; i < MAX_POOL_SIZE; ++i) {
+            ggml_sycl_buffer & b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr  = ptr;
+                b.size = size;
+                return;
+            }
+        }
+    }
+};
+
+std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_host(queue_ptr qptr, int device) {
+    // return pool for the host to speed up memory management
+    return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_host(qptr, device));
+}
+
 std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) {
     // TBD: NO VMM support
     // if (ggml_sycl_info().devices[device].vmm) {
@@ -3363,6 +3442,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
 
         ggml_sycl_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
         ggml_sycl_pool_alloc<      void *> ptrs_dst(ctx.pool(), 1*ne23);
+        ggml_sycl_pool_alloc<matrix_info_t<float>> matrix_info(ctx.host_pool(), 1);
 
         sycl::range<3> block_dims(1, ne12, ne13);
         /*
@@ -3391,14 +3471,10 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
             });
         }
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
-            *main_stream, oneapi::mkl::transpose::trans,
-            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
-            (const void **)(ptrs_src.get() + 0 * ne23),
-            dpct::library_data_t::real_half, nb01 / nb00,
-            (const void **)(ptrs_src.get() + 1 * ne23),
-            dpct::library_data_t::real_half, nb11 / nb10, beta,
-            (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
-            cu_compute_type)));
+            *main_stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
+            (const void **) (ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00,
+            (const void **) (ptrs_src.get() + 1 * ne23), dpct::library_data_t::real_half, nb11 / nb10, beta,
+            (void **) (ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type, matrix_info.get())));
     }
 }
 catch (sycl::exception const &exc) {

From b9daaffe02d6a77d85f0420bce5dfe0e00daeff6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 19 Jan 2025 18:12:09 +0200
Subject: [PATCH 205/376] simple-chat : fix BOS being added to each message
 (#11278)

---
 examples/simple-chat/simple-chat.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index e8eda9c22..26422601d 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -95,11 +95,11 @@ int main(int argc, char ** argv) {
     llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
 
     // helper function to evaluate a prompt and generate a response
-    auto generate = [&](const std::string & prompt) {
+    auto generate = [&](const std::string & prompt, bool is_first) {
         std::string response;
 
         // tokenize the prompt
-        const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+        const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
         std::vector<llama_token> prompt_tokens(n_prompt_tokens);
         if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) {
             GGML_ABORT("failed to tokenize the prompt\n");
@@ -180,7 +180,7 @@ int main(int argc, char ** argv) {
 
         // generate a response
         printf("\033[33m");
-        std::string response = generate(prompt);
+        std::string response = generate(prompt, prev_len == 0);
         printf("\n\033[0m");
 
         // add the response to the messages

From 92bc493917d43b83e592349e138b54c90b1c3ea7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 19 Jan 2025 20:22:30 +0200
Subject: [PATCH 206/376] tests : increase timeout when sanitizers are enabled
 (#11300)

* tests : increase timeout when sanitizers are enabled

* tests : add DEFAULT_HTTP_TIMEOUT
---
 .github/workflows/server.yml   | 25 ++++++++++++++++++++++---
 examples/server/tests/utils.py |  5 ++++-
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 671fe595c..ed1c357a5 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -112,9 +112,9 @@ jobs:
               -DGGML_OPENMP=OFF ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
-      - name: Build
-        id: cmake_build
-        if: ${{ matrix.sanitizer != 'THREAD' }}
+      - name: Build (sanitizers)
+        id: cmake_build_sanitizers
+        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
@@ -124,12 +124,31 @@ jobs:
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
+      - name: Build (sanitizers)
+        id: cmake_build
+        if: ${{ matrix.sanitizer == '' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
       - name: Tests
         id: server_integration_tests
+        if: ${{ matrix.sanitizer == '' }}
         run: |
           cd examples/server/tests
           ./tests.sh
 
+      - name: Tests (sanitizers)
+        id: server_integration_tests_sanitizers
+        if: ${{ matrix.sanitizer != '' }}
+        run: |
+          cd examples/server/tests
+          LLAMA_SANITIZE=1 ./tests.sh
+
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index 73be4c92f..9d1a7a5b0 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -26,6 +26,9 @@ from re import RegexFlag
 import wget
 
 
+DEFAULT_HTTP_TIMEOUT = 10 if "LLAMA_SANITIZE" not in os.environ else 30
+
+
 class ServerResponse:
     headers: dict
     status_code: int
@@ -88,7 +91,7 @@ class ServerProcess:
         if "PORT" in os.environ:
             self.server_port = int(os.environ["PORT"])
 
-    def start(self, timeout_seconds: int = 10) -> None:
+    def start(self, timeout_seconds: int | None = DEFAULT_HTTP_TIMEOUT) -> None:
         if "LLAMA_SERVER_BIN_PATH" in os.environ:
             server_path = os.environ["LLAMA_SERVER_BIN_PATH"]
         elif os.name == "nt":

From ae3c1db2f9c4beec0737c6a82d1f3791fd6fcdb2 Mon Sep 17 00:00:00 2001
From: Kyle Bruene <KyleBruene@users.noreply.github.com>
Date: Mon, 20 Jan 2025 01:21:01 -0600
Subject: [PATCH 207/376] llama : re-add LLM_ARCH_PHIMOE (#11305)

Phi 3.5 MoE was partially removed during a refactor. The code was originally in llama.cpp and should be in llama-model.cpp after the refactor.
---
 src/llama-model.cpp | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c2d23a8d3..6dfcd5f59 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2203,6 +2203,50 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                     }
                 } break;
+            case LLM_ARCH_PHIMOE:
+                {
+                    const int64_t n_embd_head = n_embd / n_head;
+ 
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+ 
+                    // output
+                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
+                    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
+ 
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+ 
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
+ 
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        if (layer.wqkv == nullptr) {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias",   i), {n_embd}, 0);
+ 
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
+ 
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
+                        }
+                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
+ 
+                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
+ 
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+ 
+                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                     }
+                } break;
             case LLM_ARCH_PLAMO:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

From ef6dada60ca710f4edfbb6fd9e1258685d8ea49d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 20 Jan 2025 09:29:32 +0200
Subject: [PATCH 208/376] cont : fix whitespaces (#11305)

---
 src/llama-model.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 6dfcd5f59..590386e64 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2206,43 +2206,43 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_PHIMOE:
                 {
                     const int64_t n_embd_head = n_embd / n_head;
- 
+
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
- 
+
                     // output
                     output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
                     output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                     output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
                     output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
- 
+
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
- 
+
                         layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
                         layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
- 
+
                         layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (layer.wqkv == nullptr) {
                             layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
                             layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias",   i), {n_embd}, 0);
- 
+
                             layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
                             layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);
- 
+
                             layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
                             layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);
                         }
                         layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
                         layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
- 
+
                         layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
                         layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
- 
+
                         layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
                         layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
                         layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
- 
+
                         layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                         layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                      }

From ec7f3ac9ab33e46b136eb5ab6a76c4d81f57c7f1 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 20 Jan 2025 14:35:07 +0100
Subject: [PATCH 209/376] llama : add support for Deepseek-R1-Qwen distill
 model (#11310)

* llama : add support for Deepseek-R1-Qwen distill model

* coding style
---
 convert_hf_to_gguf.py                       |   3 +
 convert_hf_to_gguf_update.py                |  87 +++++++--------
 models/ggml-vocab-deepseek-r1-qwen.gguf.inp | 112 ++++++++++++++++++++
 models/ggml-vocab-deepseek-r1-qwen.gguf.out |  46 ++++++++
 src/llama-chat.cpp                          |   2 +-
 src/llama-vocab.cpp                         |   3 +-
 6 files changed, 208 insertions(+), 45 deletions(-)
 create mode 100644 models/ggml-vocab-deepseek-r1-qwen.gguf.inp
 create mode 100644 models/ggml-vocab-deepseek-r1-qwen.gguf.out

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 95f112043..63b54a9cf 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -696,6 +696,9 @@ class Model:
         if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
             # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
             res = "deepseek-v3"
+        if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
+            # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+            res = "deepseek-r1-qwen"
 
         if res is None:
             logger.warning("\n")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 56edc64a7..cea34413f 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -65,49 +65,50 @@ else:
 
 # TODO: add models here, base models preferred
 models = [
-    {"name": "llama-spm",      "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
-    {"name": "llama-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
-    {"name": "phi-3",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
-    {"name": "deepseek-llm",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
-    {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
-    {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
-    {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
-    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
-    {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
-    {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
-    {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
-    {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
-    {"name": "stablelm2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
-    {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
-    {"name": "command-r",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
-    {"name": "qwen2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
-    {"name": "olmo",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
-    {"name": "dbrx",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
-    {"name": "jina-v1-en",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
-    {"name": "jina-v2-en",     "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
-    {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
-    {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
-    {"name": "smaug-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
-    {"name": "poro-chat",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
-    {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
-    {"name": "viking",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
-    {"name": "gemma",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
-    {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
-    {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
-    {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
-    {"name": "codeshell",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
-    {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
-    {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
-    {'name': "bloom",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
-    {'name': "gpt3-finnish",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
-    {"name": "exaone",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
-    {"name": "phi-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
-    {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
-    {"name": "minerva-7b",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
-    {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
-    {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
-    {"name": "megrez",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
-    {"name": "deepseek-v3",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
+    {"name": "llama-spm",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
+    {"name": "llama-bpe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
+    {"name": "phi-3",            "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
+    {"name": "deepseek-llm",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
+    {"name": "deepseek-coder",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
+    {"name": "falcon",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
+    {"name": "bert-bge",         "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+    {"name": "falcon3",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
+    {"name": "bert-bge-large",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
+    {"name": "mpt",              "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
+    {"name": "starcoder",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
+    {"name": "gpt-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
+    {"name": "stablelm2",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
+    {"name": "refact",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
+    {"name": "command-r",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
+    {"name": "qwen2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
+    {"name": "olmo",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
+    {"name": "dbrx",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
+    {"name": "jina-v1-en",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
+    {"name": "jina-v2-en",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
+    {"name": "jina-v2-es",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
+    {"name": "jina-v2-de",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
+    {"name": "smaug-bpe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
+    {"name": "poro-chat",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
+    {"name": "jina-v2-code",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
+    {"name": "viking",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
+    {"name": "gemma",            "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
+    {"name": "gemma-2",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
+    {"name": "jais",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
+    {"name": "t5",               "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
+    {"name": "codeshell",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
+    {"name": "tekken",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
+    {"name": "smollm",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
+    {'name': "bloom",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
+    {'name': "gpt3-finnish",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
+    {"name": "exaone",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
+    {"name": "phi-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
+    {"name": "chameleon",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
+    {"name": "minerva-7b",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
+    {"name": "roberta-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
+    {"name": "gigachat",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
+    {"name": "megrez",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
+    {"name": "deepseek-v3",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
+    {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
 ]
 
 
diff --git a/models/ggml-vocab-deepseek-r1-qwen.gguf.inp b/models/ggml-vocab-deepseek-r1-qwen.gguf.inp
new file mode 100644
index 000000000..9baf7d77a
--- /dev/null
+++ b/models/ggml-vocab-deepseek-r1-qwen.gguf.inp
@@ -0,0 +1,112 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
diff --git a/models/ggml-vocab-deepseek-r1-qwen.gguf.out b/models/ggml-vocab-deepseek-r1-qwen.gguf.out
new file mode 100644
index 000000000..18b4b45cd
--- /dev/null
+++ b/models/ggml-vocab-deepseek-r1-qwen.gguf.out
@@ -0,0 +1,46 @@
+ 1122 220 19 220 26062 3951
+ 37 50753 261
+
+ 220
+ 256
+ 262
+ 197
+ 198
+ 271
+ 1406
+ 1572
+ 9707 1879
+ 21927 1879
+ 9707 4337
+ 21927 4337
+ 21927 4337 0
+ 9707 11 1879 0
+ 21927 11 1879 0
+ 419 374 11162 99 247 13 10821
+ 86 15 19 23 220 22 83 1963 41808 11472 2940 16739
+ 78762 14144 1456 13073 63471 33594 3038 133178 79012
+ 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 147805 148301 147270 44258 223 146848
+ 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 320 3243 42365 429 702 1181 1828 3950 8
+ 9707
+ 21927
+ 220 21927
+ 256 21927
+ 262 21927
+ 262 21927 198 262 21927
+ 320
+ 198 284
+ 6 11385
+ 9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216
+ 17085 2928
+ 18
+ 18 18
+ 18 18 18
+ 18 18 18 18
+ 18 18 18 18 18
+ 18 18 18 18 18 18
+ 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18
+ 18 18 18 18 18 18 18 18 18
+ 34 90063 128324
+ 2560 2347
+ 198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43
diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 1347ec156..5c19bab24 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -152,7 +152,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_MINICPM;
     } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
         return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
-    } else if (tmpl_contains(LU8("'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'"))) {
+    } else if (tmpl_contains(LU8("<｜Assistant｜>")) && tmpl_contains(LU8("<｜User｜>")) && tmpl_contains(LU8("<｜end▁of▁sentence｜>"))) {
         return LLM_CHAT_TEMPLATE_DEEPSEEK_3;
     } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
         // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 9a680aed4..0782d3a41 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1523,7 +1523,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
                 clean_spaces = false;
             } else if (
-                tokenizer_pre == "qwen2") {
+                    tokenizer_pre == "qwen2" ||
+                    tokenizer_pre == "deepseek-r1-qwen") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                 clean_spaces = false;
             } else if (

From a4251edd6fc16d168f517a7e4b0936212b296603 Mon Sep 17 00:00:00 2001
From: Michael Podvitskiy <podvitskiymichael@gmail.com>
Date: Mon, 20 Jan 2025 15:02:15 +0100
Subject: [PATCH 210/376] cmake: fix shell command quoting in build-info script
 (#11309)

---
 cmake/build-info.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/build-info.cmake b/cmake/build-info.cmake
index ea3dc55c8..c1a456e17 100644
--- a/cmake/build-info.cmake
+++ b/cmake/build-info.cmake
@@ -44,7 +44,7 @@ if(MSVC)
     set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
 else()
     execute_process(
-        COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER}
+        COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER}
         OUTPUT_VARIABLE OUT
         OUTPUT_STRIP_TRAILING_WHITESPACE
     )

From 90d987b105db6016cb395f673b4d21e02129721e Mon Sep 17 00:00:00 2001
From: Christopher Nielsen <62156882+mascguy@users.noreply.github.com>
Date: Mon, 20 Jan 2025 09:02:43 -0500
Subject: [PATCH 211/376] mmap: add include for cerrno (#11296)

ggml-ci

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 src/llama-mmap.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/llama-mmap.cpp b/src/llama-mmap.cpp
index 57c6e4f51..b716630a8 100644
--- a/src/llama-mmap.cpp
+++ b/src/llama-mmap.cpp
@@ -7,6 +7,7 @@
 #include <cstring>
 #include <climits>
 #include <stdexcept>
+#include <cerrno>
 
 #ifdef __has_include
     #if __has_include(<unistd.h>)

From 9f7add1cde670ff744b791f5ed6649e86a0c586c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 20 Jan 2025 16:36:08 +0200
Subject: [PATCH 212/376] examples : fix add_special conditions (#11311)

---
 examples/run/run.cpp                 | 10 ++++++----
 examples/simple-chat/simple-chat.cpp |  8 +++++---
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index dd9ea79e8..d04108e71 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -729,10 +729,12 @@ static int apply_chat_template(LlamaData & llama_data, const bool append) {
 
 // Function to tokenize the prompt
 static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
-                           std::vector<llama_token> & prompt_tokens) {
-    const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+                           std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
+    const bool is_first = llama_get_kv_cache_used_cells(llama_data.context.get()) == 0;
+
+    const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
     prompt_tokens.resize(n_prompt_tokens);
-    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
+    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first,
                        true) < 0) {
         printe("failed to tokenize the prompt\n");
         return -1;
@@ -778,7 +780,7 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
     const llama_vocab * vocab = llama_model_get_vocab(llama_data.model.get());
 
     std::vector<llama_token> tokens;
-    if (tokenize_prompt(vocab, prompt, tokens) < 0) {
+    if (tokenize_prompt(vocab, prompt, tokens, llama_data) < 0) {
         return 1;
     }
 
diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index 26422601d..212b3fd79 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -95,13 +95,15 @@ int main(int argc, char ** argv) {
     llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
 
     // helper function to evaluate a prompt and generate a response
-    auto generate = [&](const std::string & prompt, bool is_first) {
+    auto generate = [&](const std::string & prompt) {
         std::string response;
 
+        const bool is_first = llama_get_kv_cache_used_cells(ctx) == 0;
+
         // tokenize the prompt
         const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
         std::vector<llama_token> prompt_tokens(n_prompt_tokens);
-        if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) {
+        if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first, true) < 0) {
             GGML_ABORT("failed to tokenize the prompt\n");
         }
 
@@ -180,7 +182,7 @@ int main(int argc, char ** argv) {
 
         // generate a response
         printf("\033[33m");
-        std::string response = generate(prompt, prev_len == 0);
+        std::string response = generate(prompt);
         printf("\n\033[0m");
 
         // add the response to the messages

From aea8ddd5165d525a449e2fc3839db77a71f4a318 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 20 Jan 2025 10:38:32 -0600
Subject: [PATCH 213/376] vulkan: fix coopmat2 validation failures (#11284)

mul mat and flash attention shaders were loading f32 types directly into
A/B matrices, which happens to work but is technically invalid usage.
For FA, we can load it as an Accumulator matrix and convert and this
is not in the inner loop and is cheap enough. For mul mat, it's more
efficient to do this conversion in a separate pass and have the input(s)
be f16.

coopmat2 requires SPIR-V 1.6 (related using to LocalSizeId). LocalSizeId
requires maintenance4 be enabled, and SPIR-V 1.6 requires Vulkan 1.3.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 65 +++++++++++--------
 .../vulkan-shaders/flash_attn_cm2.comp        |  2 +-
 .../vulkan-shaders/mul_mm_cm2.comp            | 35 +++-------
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  7 +-
 4 files changed, 53 insertions(+), 56 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 437e9cdcc..78c2f5c45 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -29,8 +29,6 @@
 
 #include "ggml-vulkan-shaders.hpp"
 
-#define VK_API_VERSION VK_API_VERSION_1_2
-
 #define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
 
 #define VK_VENDOR_ID_AMD 0x1002
@@ -1614,11 +1612,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT)   \
         CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT)   \
 
-        CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3)
-        CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3)
-
         CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3)
-        CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
@@ -1631,21 +1625,18 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
 
-        CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
         CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
-
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
     } else
@@ -2287,6 +2278,14 @@ static vk_device ggml_vk_get_device(size_t idx) {
         }
 #endif
 
+        VkPhysicalDeviceMaintenance4Features maint4_features {};
+        maint4_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MAINTENANCE_4_FEATURES;
+        if (maintenance4_support) {
+            last_struct->pNext = (VkBaseOutStructure *)&maint4_features;
+            last_struct = (VkBaseOutStructure *)&maint4_features;
+            device_extensions.push_back("VK_KHR_maintenance4");
+        }
+
         vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
 
         device->fp16 = device->fp16 && vk12_features.shaderFloat16;
@@ -2662,7 +2661,14 @@ void ggml_vk_instance_init() {
 
     vk_instance_initialized = true;
 
-    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
+    uint32_t api_version = vk::enumerateInstanceVersion();
+
+    if (api_version < VK_API_VERSION_1_2) {
+        std::cerr << "ggml_vulkan: Error: Vulkan 1.2 required." << std::endl;
+        GGML_ABORT("fatal error");
+    }
+
+    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version };
 
     const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
     const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
@@ -2972,7 +2978,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         }
     }
 
-    GGML_ASSERT(src1_type == GGML_TYPE_F32);
+    GGML_ASSERT(src1_type == GGML_TYPE_F32 || (ctx->device->coopmat2 && src1_type == GGML_TYPE_F16));
 
     switch (src0_type) {
         case GGML_TYPE_Q4_0:
@@ -3812,8 +3818,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    // Reformat and convert to fp16 if src1 is non-contiguous, or for coopmat2 for better perf
+    // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf
+    const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) ||
+                              !ggml_vk_dim01_contiguous(src0);
     const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) ||
                               !ggml_vk_dim01_contiguous(src1);
 
@@ -4393,8 +4400,11 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
         ids_uma = d_ids != nullptr;
     }
 
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+    // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf
+    const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) ||
+                              !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) ||
+                              !ggml_vk_dim01_contiguous(src1);
 
     const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
 
@@ -4404,7 +4414,8 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
 
     if (qx_needs_dequant) {
-        GGML_ABORT("fatal error");
+        // Fall back to dequant + f16 mulmat
+        mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16, (ggml_prec)dst->op_params[0]);
     }
 
     // Not implemented
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index ca3a59b8f..3735d0dbb 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -166,7 +166,7 @@ void main() {
     tensorLayoutK = setTensorLayoutStrideNV(tensorLayoutK, k_stride, 1);
     tensorLayoutV = setTensorLayoutStrideNV(tensorLayoutV, v_stride, 1);
 
-    coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Q;
+    coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> Q;
     coopmat<float16_t, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Qf16;
 
     uint32_t q_offset = iq2*p.nb02+iq3*p.nb03;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index cbfa5dce1..57f9e7245 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -57,17 +57,13 @@ layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 
 #if QUANT_K > 1
 #define DECODEFUNCA , dequantFuncA
-#define MAT_A_TYPE float16_t
 
 #include "dequant_funcs_cm2.comp"
 
 #else
 #define DECODEFUNCA
-#define MAT_A_TYPE A_TYPE
 #endif
 
-#define MAT_B_TYPE B_TYPE
-
 #ifdef MUL_MAT_ID
 layout (binding = 3) readonly buffer IDS {int data_ids[];};
 
@@ -236,16 +232,13 @@ void main() {
 
         for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {
 
-            coopmat<MAT_A_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
-            coopmat<MAT_B_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
+            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
 
             coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
-            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
-
             coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose);
-            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
 
-            sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
+            sum = coopMatMulAdd(mat_a, mat_b, sum);
         }
     } else
 #endif // !defined(MUL_MAT_ID)
@@ -261,10 +254,8 @@ void main() {
         [[dont_unroll]]
         for (uint block_k = start_k; block_k < end_k; block_k += BK) {
 
-            coopmat<MAT_A_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
-            coopmat<MAT_B_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
-            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a_ft;
-            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b_ft;
+            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+            coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
 
             // Clamping is expensive, so detect different code paths for each combination
             // of A and B needing clamping.
@@ -281,16 +272,12 @@ void main() {
 #else
                 coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose);
 #endif
-                mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
-                mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
-                sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
+                sum = coopMatMulAdd(mat_a, mat_b, sum);
             } else if (unclampedA && !unclampedB) {
                 coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, (block_k & ~7), BK) DECODEFUNCA);
                 coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
 
-                mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
-                mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
-                sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
+                sum = coopMatMulAdd(mat_a, mat_b, sum);
             } else if (!unclampedA && unclampedB) {
                 coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
 #ifdef MUL_MAT_ID
@@ -298,16 +285,12 @@ void main() {
 #else
                 coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose);
 #endif
-                mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
-                mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
-                sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
+                sum = coopMatMulAdd(mat_a, mat_b, sum);
             } else if (!unclampedA && !unclampedB) {
                 coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
                 coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
 
-                mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
-                mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
-                sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
+                sum = coopMatMulAdd(mat_a, mat_b, sum);
             }
         }
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index b7890ef1e..8bcb64101 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -316,8 +316,11 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool
         // For aligned matmul loads
         std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16") ? load_vec : "2";
 
-        string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat, coopmat2, f16acc);
-        string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+        // don't generate f32 variants for coopmat2
+        if (!coopmat2) {
+            string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat, coopmat2, f16acc);
+            string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+        }
 
         if (tname != "f16" && tname != "f32") {
             string_to_spv(shader_name + "_" + tname + "_f16", source_name,          merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned},                           {"B_TYPE", "float16_t"},        {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat, coopmat2, f16acc);

From 80d0d6b4b7abca342fc990399e78af8d213eabaf Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 20 Jan 2025 22:29:43 +0200
Subject: [PATCH 214/376] common : add -hfd option for the draft model (#11318)

* common : add -hfd option for the draft model

* cont : fix env var

* cont : more fixes
---
 common/arg.cpp             | 17 +++++++++++++----
 common/common.h            |  8 +++++++-
 examples/server/server.cpp |  5 ++++-
 3 files changed, 24 insertions(+), 6 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index dede335fb..126970950 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -133,7 +133,8 @@ static void common_params_handle_model_default(
         const std::string & model_url,
         std::string & hf_repo,
         std::string & hf_file,
-        const std::string & hf_token) {
+        const std::string & hf_token,
+        const std::string & model_default) {
     if (!hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
         if (hf_file.empty()) {
@@ -163,7 +164,7 @@ static void common_params_handle_model_default(
             model = fs_get_cache_file(string_split<std::string>(f, '/').back());
         }
     } else if (model.empty()) {
-        model = DEFAULT_MODEL_PATH;
+        model = model_default;
     }
 }
 
@@ -299,8 +300,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     }
 
     // TODO: refactor model params in a common struct
-    common_params_handle_model_default(params.model,         params.model_url,         params.hf_repo,         params.hf_file,         params.hf_token);
-    common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file, params.hf_token);
+    common_params_handle_model_default(params.model,             params.model_url,             params.hf_repo,             params.hf_file,             params.hf_token, DEFAULT_MODEL_PATH);
+    common_params_handle_model_default(params.speculative.model, params.speculative.model_url, params.speculative.hf_repo, params.speculative.hf_file, params.hf_token, "");
+    common_params_handle_model_default(params.vocoder.model,     params.vocoder.model_url,     params.vocoder.hf_repo,     params.vocoder.hf_file,     params.hf_token, "");
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -1629,6 +1631,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.hf_repo = value;
         }
     ).set_env("LLAMA_ARG_HF_REPO"));
+    add_opt(common_arg(
+        {"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
+        "Same as --hf-repo, but for the draft model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.speculative.hf_repo = value;
+        }
+    ).set_env("LLAMA_ARG_HFD_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
         "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
diff --git a/common/common.h b/common/common.h
index 3bcc637cc..b2709c044 100644
--- a/common/common.h
+++ b/common/common.h
@@ -175,7 +175,11 @@ struct common_params_speculative {
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;
 
-    std::string model = ""; // draft model for speculative decoding                          // NOLINT
+    std::string hf_repo = ""; // HF repo                                                     // NOLINT
+    std::string hf_file = ""; // HF file                                                     // NOLINT
+
+    std::string model = "";     // draft model for speculative decoding                      // NOLINT
+    std::string model_url = ""; // model url to download                                     // NOLINT
 };
 
 struct common_params_vocoder {
@@ -508,12 +512,14 @@ struct llama_model * common_load_model_from_url(
     const std::string & local_path,
     const std::string & hf_token,
     const struct llama_model_params & params);
+
 struct llama_model * common_load_model_from_hf(
     const std::string & repo,
     const std::string & remote_path,
     const std::string & local_path,
     const std::string & hf_token,
     const struct llama_model_params & params);
+
 std::pair<std::string, std::string> common_get_hf_file(
     const std::string & hf_repo_with_tag,
     const std::string & hf_token);
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index d1e8ee829..f35206d7b 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1728,13 +1728,16 @@ struct server_context {
         add_bos_token = llama_vocab_get_add_bos(vocab);
         has_eos_token = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
 
-        if (!params_base.speculative.model.empty()) {
+        if (!params_base.speculative.model.empty() || !params_base.speculative.hf_repo.empty()) {
             SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str());
 
             auto params_dft = params_base;
 
             params_dft.devices      = params_base.speculative.devices;
+            params_dft.hf_file      = params_base.speculative.hf_file;
+            params_dft.hf_repo      = params_base.speculative.hf_repo;
             params_dft.model        = params_base.speculative.model;
+            params_dft.model_url    = params_base.speculative.model_url;
             params_dft.n_ctx        = params_base.speculative.n_ctx == 0 ? params_base.n_ctx / params_base.n_parallel : params_base.speculative.n_ctx;
             params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers;
             params_dft.n_parallel   = 1;

From 2139667ec419cdd953987f4df2ace9da87bbda28 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 21 Jan 2025 08:48:13 +0200
Subject: [PATCH 215/376] metal : fix out-of-bounds write (#11314)

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal.metal | 31 +++++++++++++++-------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 8ba43904d..44f04c909 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -4416,7 +4416,6 @@ void kernel_mul_mv_q2_K_f32_impl(
         device const half     * dh = &x[ib].d;
 
         for (int row = 0; row < N_DST; row++) {
-
             float4 acc1 = {0.f, 0.f, 0.f, 0.f};
             float4 acc2 = {0.f, 0.f, 0.f, 0.f};
             for (int i = 0; i < 8; i += 2) {
@@ -4447,7 +4446,7 @@ void kernel_mul_mv_q2_K_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -4613,7 +4612,7 @@ void kernel_mul_mv_q3_K_f32_impl(
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
     if (tiisg == 0) {
-        for (int row = 0; row < 2; ++row) {
+        for (int row = 0; row < 2 && first_row + row < args.ne0; ++row) {
             dst_f32[first_row + row] = sumf1[row];
         }
     }
@@ -4729,7 +4728,7 @@ void kernel_mul_mv_q4_K_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (int64_t)im*args.ne0*args.ne1 + (int64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -4861,7 +4860,7 @@ void kernel_mul_mv_q5_K_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < 2; ++row) {
+    for (int row = 0; row < 2 && first_row + row < args.ne0; ++row) {
         const float tot = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = tot;
@@ -4906,6 +4905,10 @@ void kernel_mul_mv_q6_K_f32_impl(
 
     const int row = 2*r0 + sgitg;
 
+    if (row >= args.ne0) {
+        return;
+    }
+
     const uint i12 = im%args.ne12;
     const uint i13 = im/args.ne12;
 
@@ -5061,7 +5064,7 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum * 0.25f;
@@ -5179,7 +5182,7 @@ void kernel_mul_mv_iq2_xs_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum * 0.25f;
@@ -5289,7 +5292,7 @@ void kernel_mul_mv_iq3_xxs_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum * 0.5f;
@@ -5401,7 +5404,7 @@ void kernel_mul_mv_iq3_s_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -5514,7 +5517,7 @@ void kernel_mul_mv_iq2_s_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum * 0.25f;
@@ -5614,7 +5617,7 @@ void kernel_mul_mv_iq1_s_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -5709,7 +5712,7 @@ void kernel_mul_mv_iq1_m_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < N_DST; ++row) {
+    for (int row = 0; row < N_DST && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -5799,7 +5802,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < 2 && first_row + row < args.ne01; ++row) {
+    for (int row = 0; row < 2 && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;
@@ -5888,7 +5891,7 @@ void kernel_mul_mv_iq4_xs_f32_impl(
 
     device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
 
-    for (int row = 0; row < 2; ++row) {
+    for (int row = 0; row < 2 && first_row + row < args.ne0; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst_f32[first_row + row] = all_sum;

From 2e2f8f093cd4fb6bbb87ba84f6b9684fa082f3fa Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Tue, 21 Jan 2025 09:32:35 +0000
Subject: [PATCH 216/376] linenoise.cpp refactoring (#11301)

More RAII mainly

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/linenoise.cpp/linenoise.cpp | 273 +++++++++++------------
 examples/run/linenoise.cpp/linenoise.h   |  22 +-
 2 files changed, 154 insertions(+), 141 deletions(-)

diff --git a/examples/run/linenoise.cpp/linenoise.cpp b/examples/run/linenoise.cpp/linenoise.cpp
index 050c23012..a68f12a1a 100644
--- a/examples/run/linenoise.cpp/linenoise.cpp
+++ b/examples/run/linenoise.cpp/linenoise.cpp
@@ -103,24 +103,26 @@
  *
  */
 
-#include <termios.h>
-#include <unistd.h>
-#include <stdlib.h>
-#include <stdio.h>
-#include <errno.h>
-#include <string.h>
-#include <stdlib.h>
-#include <ctype.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-#include <sys/ioctl.h>
-#include <unistd.h>
-#include <vector>
-#include "linenoise.h"
+#    include "linenoise.h"
 
-#define LINENOISE_DEFAULT_HISTORY_MAX_LEN 100
-#define LINENOISE_MAX_LINE 4096
-static std::vector<const char*> unsupported_term = {"dumb","cons25","emacs",nullptr};
+#    include <ctype.h>
+#    include <errno.h>
+#    include <stdio.h>
+#    include <string.h>
+#    include <sys/file.h>
+#    include <sys/ioctl.h>
+#    include <sys/stat.h>
+#    include <sys/types.h>
+#    include <termios.h>
+#    include <unistd.h>
+
+#    include <memory>
+#    include <string>
+#    include <vector>
+
+#    define LINENOISE_DEFAULT_HISTORY_MAX_LEN 100
+#    define LINENOISE_MAX_LINE                4096
+static std::vector<const char *>    unsupported_term   = { "dumb", "cons25", "emacs" };
 static linenoiseCompletionCallback *completionCallback = NULL;
 static linenoiseHintsCallback *hintsCallback = NULL;
 static linenoiseFreeHintsCallback *freeHintsCallback = NULL;
@@ -166,21 +168,58 @@ int linenoiseHistoryAdd(const char *line);
 #define REFRESH_ALL (REFRESH_CLEAN|REFRESH_WRITE) // Do both.
 static void refreshLine(struct linenoiseState *l);
 
+class File {
+  public:
+    FILE * file = nullptr;
+
+    FILE * open(const std::string & filename, const char * mode) {
+        file = fopen(filename.c_str(), mode);
+
+        return file;
+    }
+
+    int lock() {
+        if (file) {
+            fd = fileno(file);
+            if (flock(fd, LOCK_EX | LOCK_NB) != 0) {
+                fd = -1;
+
+                return 1;
+            }
+        }
+
+        return 0;
+    }
+
+    ~File() {
+        if (fd >= 0) {
+            flock(fd, LOCK_UN);
+        }
+
+        if (file) {
+            fclose(file);
+        }
+    }
+
+  private:
+    int fd = -1;
+};
+
 __attribute__((format(printf, 1, 2)))
 /* Debugging function. */
 #if 0
 static void lndebug(const char *fmt, ...) {
-    static FILE *lndebug_fp = NULL;
-    if (lndebug_fp == NULL) {
-        lndebug_fp = fopen("/tmp/lndebug.txt", "a");
+    static File file;
+    if (file.file == nullptr) {
+        file.open("/tmp/lndebug.txt", "a");
     }
 
-    if (lndebug_fp != NULL) {
+    if (file.file != nullptr) {
         va_list args;
         va_start(args, fmt);
-        vfprintf(lndebug_fp, fmt, args);
+        vfprintf(file.file, fmt, args);
         va_end(args);
-        fflush(lndebug_fp);
+        fflush(file.file);
     }
 }
 #else
@@ -213,8 +252,11 @@ void linenoiseSetMultiLine(int ml) {
 static int isUnsupportedTerm(void) {
     char *term = getenv("TERM");
     if (term == NULL) return 0;
-    for (int j = 0; unsupported_term[j]; ++j)
-        if (!strcasecmp(term, unsupported_term[j])) return 1;
+    for (size_t j = 0; j < unsupported_term.size(); ++j) {
+        if (!strcasecmp(term, unsupported_term[j])) {
+            return 1;
+        }
+    }
     return 0;
 }
 
@@ -334,17 +376,6 @@ static void linenoiseBeep(void) {
     fflush(stderr);
 }
 
-/* ============================== Completion ================================ */
-
-/* Free a list of completion option populated by linenoiseAddCompletion(). */
-static void freeCompletions(linenoiseCompletions *lc) {
-    size_t i;
-    for (i = 0; i < lc->len; i++)
-        free(lc->cvec[i]);
-    if (lc->cvec != NULL)
-        free(lc->cvec);
-}
-
 /* Called by completeLine() and linenoiseShow() to render the current
  * edited line with the proposed completion. If the current completion table
  * is already available, it is passed as second argument, otherwise the
@@ -353,9 +384,9 @@ static void freeCompletions(linenoiseCompletions *lc) {
  * Flags are the same as refreshLine*(), that is REFRESH_* macros. */
 static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags) {
     /* Obtain the table of completions if the caller didn't provide one. */
-    linenoiseCompletions ctable = { 0, NULL };
+    linenoiseCompletions ctable;
     if (lc == NULL) {
-        completionCallback(ls->buf,&ctable);
+        completionCallback(ls->buf, &ctable);
         lc = &ctable;
     }
 
@@ -364,16 +395,17 @@ static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseComple
         struct linenoiseState saved = *ls;
         ls->len = ls->pos = strlen(lc->cvec[ls->completion_idx]);
         ls->buf = lc->cvec[ls->completion_idx];
-        refreshLineWithFlags(ls,flags);
+        refreshLineWithFlags(ls, flags);
         ls->len = saved.len;
         ls->pos = saved.pos;
         ls->buf = saved.buf;
     } else {
-        refreshLineWithFlags(ls,flags);
+        refreshLineWithFlags(ls, flags);
     }
 
-    /* Free the completions table if needed. */
-    if (lc != &ctable) freeCompletions(&ctable);
+    if (lc == &ctable) {
+        ctable.to_free = false;
+    }
 }
 
 /* This is an helper function for linenoiseEdit*() and is called when the
@@ -391,11 +423,11 @@ static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseComple
  * possible completions, and the caller should read for the next characters
  * from stdin. */
 static int completeLine(struct linenoiseState *ls, int keypressed) {
-    linenoiseCompletions lc = { 0, NULL };
+    linenoiseCompletions lc;
     int nwritten;
     char c = keypressed;
 
-    completionCallback(ls->buf,&lc);
+    completionCallback(ls->buf, &lc);
     if (lc.len == 0) {
         linenoiseBeep();
         ls->in_completion = 0;
@@ -406,7 +438,7 @@ static int completeLine(struct linenoiseState *ls, int keypressed) {
                     ls->in_completion = 1;
                     ls->completion_idx = 0;
                 } else {
-                    ls->completion_idx = (ls->completion_idx+1) % (lc.len+1);
+                    ls->completion_idx = (ls->completion_idx + 1) % (lc.len + 1);
                     if (ls->completion_idx == lc.len) linenoiseBeep();
                 }
                 c = 0;
@@ -420,8 +452,7 @@ static int completeLine(struct linenoiseState *ls, int keypressed) {
             default:
                 /* Update buffer and return */
                 if (ls->completion_idx < lc.len) {
-                    nwritten = snprintf(ls->buf,ls->buflen,"%s",
-                        lc.cvec[ls->completion_idx]);
+                    nwritten = snprintf(ls->buf, ls->buflen, "%s", lc.cvec[ls->completion_idx]);
                     ls->len = ls->pos = nwritten;
                 }
                 ls->in_completion = 0;
@@ -430,13 +461,12 @@ static int completeLine(struct linenoiseState *ls, int keypressed) {
 
         /* Show completion or original buffer */
         if (ls->in_completion && ls->completion_idx < lc.len) {
-            refreshLineWithCompletion(ls,&lc,REFRESH_ALL);
+            refreshLineWithCompletion(ls, &lc, REFRESH_ALL);
         } else {
             refreshLine(ls);
         }
     }
 
-    freeCompletions(&lc);
     return c; /* Return last read character */
 }
 
@@ -462,53 +492,25 @@ void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *fn) {
  * user typed <tab>. See the example.c source code for a very easy to
  * understand example. */
 void linenoiseAddCompletion(linenoiseCompletions *lc, const char *str) {
-    size_t len = strlen(str);
-    char *copy, **cvec;
-
-    copy = (char*) malloc(len + 1);
-    if (copy == NULL) return;
-    memcpy(copy,str,len+1);
-    cvec = (char**) realloc(lc->cvec,sizeof(char*)*(lc->len+1));
-    if (cvec == NULL) {
-        free(copy);
+    const size_t len  = strlen(str);
+    auto         copy = std::make_unique<char[]>(len + 1);
+    if (!copy) {
         return;
     }
+
+    memcpy(copy.get(), str, len + 1);
+    char ** cvec = static_cast<char **>(std::realloc(lc->cvec, sizeof(char *) * (lc->len + 1)));
+    if (cvec == nullptr) {
+        return;
+    }
+
     lc->cvec = cvec;
-    lc->cvec[lc->len++] = copy;
-}
-
-/* =========================== Line editing ================================= */
-
-/* We define a very simple "append buffer" structure, that is an heap
- * allocated string where we can append to. This is useful in order to
- * write all the escape sequences in a buffer and flush them to the standard
- * output in a single call, to avoid flickering effects. */
-struct abuf {
-    char *b;
-    int len;
-};
-
-static void abInit(struct abuf *ab) {
-    ab->b = NULL;
-    ab->len = 0;
-}
-
-static void abAppend(struct abuf *ab, const char *s, int len) {
-    char *new_ptr = (char*) realloc(ab->b,ab->len+len);
-
-    if (new_ptr == NULL) return;
-    memcpy(new_ptr+ab->len,s,len);
-    ab->b = new_ptr;
-    ab->len += len;
-}
-
-static void abFree(struct abuf *ab) {
-    free(ab->b);
+    lc->cvec[lc->len++] = copy.release();
 }
 
 /* Helper of refreshSingleLine() and refreshMultiLine() to show hints
  * to the right of the prompt. */
-static void refreshShowHints(struct abuf * ab, struct linenoiseState * l, int plen) {
+static void refreshShowHints(std::string & ab, struct linenoiseState * l, int plen) {
     char seq[64];
     if (hintsCallback && plen+l->len < l->cols) {
         int color = -1, bold = 0;
@@ -522,10 +524,11 @@ static void refreshShowHints(struct abuf * ab, struct linenoiseState * l, int pl
                 snprintf(seq,64,"\033[%d;%d;49m",bold,color);
             else
                 seq[0] = '\0';
-            abAppend(ab,seq,strlen(seq));
-            abAppend(ab,hint,hintlen);
+            ab.append(seq);
+            ab.append(hint, hintlen);
             if (color != -1 || bold != 0)
-                abAppend(ab,"\033[0m",4);
+                ab.append("\033[0m");
+
             /* Call the function to free the hint returned. */
             if (freeHintsCallback) freeHintsCallback(hint);
         }
@@ -546,8 +549,7 @@ static void refreshSingleLine(struct linenoiseState *l, int flags) {
     char *buf = l->buf;
     size_t len = l->len;
     size_t pos = l->pos;
-    struct abuf ab;
-
+    std::string ab;
     while((plen+pos) >= l->cols) {
         buf++;
         len--;
@@ -557,35 +559,34 @@ static void refreshSingleLine(struct linenoiseState *l, int flags) {
         len--;
     }
 
-    abInit(&ab);
     /* Cursor to left edge */
     snprintf(seq,sizeof(seq),"\r");
-    abAppend(&ab,seq,strlen(seq));
+    ab.append(seq);
 
     if (flags & REFRESH_WRITE) {
         /* Write the prompt and the current buffer content */
-        abAppend(&ab,l->prompt,strlen(l->prompt));
+        ab.append(l->prompt);
         if (maskmode == 1) {
-            while (len--) abAppend(&ab,"*",1);
+            while (len--) {
+                ab.append("*");
+            }
         } else {
-            abAppend(&ab,buf,len);
+            ab.append(buf, len);
         }
         /* Show hits if any. */
-        refreshShowHints(&ab,l,plen);
+        refreshShowHints(ab, l, plen);
     }
 
     /* Erase to right */
     snprintf(seq,sizeof(seq),"\x1b[0K");
-    abAppend(&ab,seq,strlen(seq));
-
+    ab.append(seq);
     if (flags & REFRESH_WRITE) {
         /* Move cursor to original position. */
         snprintf(seq,sizeof(seq),"\r\x1b[%dC", (int)(pos+plen));
-        abAppend(&ab,seq,strlen(seq));
+        ab.append(seq);
     }
 
-    if (write(fd,ab.b,ab.len) == -1) {} /* Can't recover from write error. */
-    abFree(&ab);
+    (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */
 }
 
 /* Multi line low level line refresh.
@@ -604,26 +605,23 @@ static void refreshMultiLine(struct linenoiseState *l, int flags) {
     int col; /* colum position, zero-based. */
     int old_rows = l->oldrows;
     int fd = l->ofd, j;
-    struct abuf ab;
-
+    std::string ab;
     l->oldrows = rows;
 
     /* First step: clear all the lines used before. To do so start by
      * going to the last row. */
-    abInit(&ab);
-
     if (flags & REFRESH_CLEAN) {
         if (old_rows-rpos > 0) {
             lndebug("go down %d", old_rows-rpos);
             snprintf(seq,64,"\x1b[%dB", old_rows-rpos);
-            abAppend(&ab,seq,strlen(seq));
+            ab.append(seq);
         }
 
         /* Now for every row clear it, go up. */
         for (j = 0; j < old_rows-1; j++) {
             lndebug("clear+up");
             snprintf(seq,64,"\r\x1b[0K\x1b[1A");
-            abAppend(&ab,seq,strlen(seq));
+            ab.append(seq);
         }
     }
 
@@ -631,21 +629,22 @@ static void refreshMultiLine(struct linenoiseState *l, int flags) {
         /* Clean the top line. */
         lndebug("clear");
         snprintf(seq,64,"\r\x1b[0K");
-        abAppend(&ab,seq,strlen(seq));
+        ab.append(seq);
     }
 
     if (flags & REFRESH_WRITE) {
         /* Write the prompt and the current buffer content */
-        abAppend(&ab,l->prompt,strlen(l->prompt));
+        ab.append(l->prompt);
         if (maskmode == 1) {
-            unsigned int i;
-            for (i = 0; i < l->len; i++) abAppend(&ab,"*",1);
+            for (unsigned int i = 0; i < l->len; ++i) {
+                ab.append("*");
+            }
         } else {
-            abAppend(&ab,l->buf,l->len);
+            ab.append(l->buf, l->len);
         }
 
         /* Show hits if any. */
-        refreshShowHints(&ab,l,plen);
+        refreshShowHints(ab, l, plen);
 
         /* If we are at the very end of the screen with our prompt, we need to
          * emit a newline and move the prompt to the first column. */
@@ -654,9 +653,9 @@ static void refreshMultiLine(struct linenoiseState *l, int flags) {
             (l->pos+plen) % l->cols == 0)
         {
             lndebug("<newline>");
-            abAppend(&ab,"\n",1);
+            ab.append("\n");
             snprintf(seq,64,"\r");
-            abAppend(&ab,seq,strlen(seq));
+            ab.append(seq);
             rows++;
             if (rows > (int)l->oldrows) l->oldrows = rows;
         }
@@ -669,7 +668,7 @@ static void refreshMultiLine(struct linenoiseState *l, int flags) {
         if (rows-rpos2 > 0) {
             lndebug("go-up %d", rows-rpos2);
             snprintf(seq,64,"\x1b[%dA", rows-rpos2);
-            abAppend(&ab,seq,strlen(seq));
+            ab.append(seq);
         }
 
         /* Set column. */
@@ -679,14 +678,12 @@ static void refreshMultiLine(struct linenoiseState *l, int flags) {
             snprintf(seq,64,"\r\x1b[%dC", col);
         else
             snprintf(seq,64,"\r");
-        abAppend(&ab,seq,strlen(seq));
+        ab.append(seq);
     }
 
     lndebug("\n");
     l->oldpos = l->pos;
-
-    if (write(fd,ab.b,ab.len) == -1) {} /* Can't recover from write error. */
-    abFree(&ab);
+    (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */
 }
 
 /* Calls the two low level functions refreshSingleLine() or
@@ -1313,16 +1310,17 @@ int linenoiseHistorySetMaxLen(int len) {
  * otherwise -1 is returned. */
 int linenoiseHistorySave(const char *filename) {
     mode_t old_umask = umask(S_IXUSR|S_IRWXG|S_IRWXO);
-    FILE *fp;
-    int j;
-
-    fp = fopen(filename,"w");
+    File   file;
+    file.open(filename, "w");
     umask(old_umask);
-    if (fp == NULL) return -1;
+    if (file.file == NULL) {
+        return -1;
+    }
     chmod(filename,S_IRUSR|S_IWUSR);
-    for (j = 0; j < history_len; j++)
-        fprintf(fp,"%s\n",history[j]);
-    fclose(fp);
+    for (int j = 0; j < history_len; ++j) {
+        fprintf(file.file, "%s\n", history[j]);
+    }
+
     return 0;
 }
 
@@ -1332,12 +1330,14 @@ int linenoiseHistorySave(const char *filename) {
  * If the file exists and the operation succeeded 0 is returned, otherwise
  * on error -1 is returned. */
 int linenoiseHistoryLoad(const char *filename) {
-    FILE *fp = fopen(filename,"r");
+    File file;
+    file.open(filename, "r");
     char buf[LINENOISE_MAX_LINE];
+    if (file.file == NULL) {
+        return -1;
+    }
 
-    if (fp == NULL) return -1;
-
-    while (fgets(buf,LINENOISE_MAX_LINE,fp) != NULL) {
+    while (fgets(buf, LINENOISE_MAX_LINE, file.file) != NULL) {
         char *p;
 
         p = strchr(buf,'\r');
@@ -1345,7 +1345,6 @@ int linenoiseHistoryLoad(const char *filename) {
         if (p) *p = '\0';
         linenoiseHistoryAdd(buf);
     }
-    fclose(fp);
     return 0;
 }
 #endif
diff --git a/examples/run/linenoise.cpp/linenoise.h b/examples/run/linenoise.cpp/linenoise.h
index 3e25f4de3..a14ec6c74 100644
--- a/examples/run/linenoise.cpp/linenoise.h
+++ b/examples/run/linenoise.cpp/linenoise.h
@@ -45,6 +45,7 @@ extern "C" {
 #endif
 
 #include <stddef.h> /* For size_t. */
+#include <stdlib.h>
 
 extern const char *linenoiseEditMore;
 
@@ -69,10 +70,23 @@ struct linenoiseState {
     int history_index;  /* The history index we are currently editing. */
 };
 
-typedef struct linenoiseCompletions {
-  size_t len;
-  char **cvec;
-} linenoiseCompletions;
+struct linenoiseCompletions {
+    size_t  len     = 0;
+    char ** cvec    = nullptr;
+    bool    to_free = true;
+
+    ~linenoiseCompletions() {
+        if (!to_free) {
+            return;
+        }
+
+        for (size_t i = 0; i < len; ++i) {
+            free(cvec[i]);
+        }
+
+        free(cvec);
+    }
+};
 
 /* Non blocking API. */
 int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt);

From 6da5bec81c34f3b8a8f1b367cf23ad016e83d332 Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Tue, 21 Jan 2025 15:06:41 +0200
Subject: [PATCH 217/376] rpc : better caching of the base buffer pointer
 (#11331)

There is no need to use map, just store the base pointer in the buffer
context.
---
 ggml/src/ggml-rpc/ggml-rpc.cpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 63da2b86b..3d0c46578 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -181,7 +181,7 @@ struct ggml_backend_rpc_context {
 
 struct ggml_backend_rpc_buffer_context {
     std::shared_ptr<socket_t> sock;
-    std::unordered_map<ggml_backend_buffer_t, void *> base_cache;
+    void * base_ptr;
     uint64_t remote_ptr;
 };
 
@@ -423,16 +423,15 @@ static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
 
 static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) {
     ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
-    if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) {
-        return ctx->base_cache[buffer];
+    if (ctx->base_ptr != nullptr) {
+        return ctx->base_ptr;
     }
     rpc_msg_buffer_get_base_req request = {ctx->remote_ptr};
     rpc_msg_buffer_get_base_rsp response;
     bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, &request, sizeof(request), &response, sizeof(response));
     GGML_ASSERT(status);
-    void * base_ptr = reinterpret_cast<void *>(response.base_ptr);
-    ctx->base_cache[buffer] = base_ptr;
-    return base_ptr;
+    ctx->base_ptr = reinterpret_cast<void *>(response.base_ptr);
+    return ctx->base_ptr;
 }
 
 static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
@@ -557,7 +556,7 @@ static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_back
     if (response.remote_ptr != 0) {
         ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft,
             ggml_backend_rpc_buffer_interface,
-            new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr},
+            new ggml_backend_rpc_buffer_context{sock, nullptr, response.remote_ptr},
             response.remote_size);
         return buffer;
     } else {

From e28245f35f2faaf249dd352998b3693a8cc28c51 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 21 Jan 2025 14:07:12 +0100
Subject: [PATCH 218/376] export-lora : fix tok_embd tensor (#11330)

---
 examples/export-lora/export-lora.cpp | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 99063b5d5..91238e4be 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -345,8 +345,18 @@ struct lora_merge_ctx {
             gf = ggml_new_graph(ctx0);
             struct ggml_tensor * cur = inp_base;
             for (size_t i = 0; i < adapters.size(); ++i) {
-                struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)));
-                struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
+                struct ggml_tensor * delta;
+                bool is_tok_embd = string_starts_with(name_base, "token_embd");
+                if (is_tok_embd) {
+                    printf("%s :     detected token embeddings tensor\n", __func__);
+                    delta = ggml_mul_mat(ctx0,
+                        ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32),
+                        ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32));
+                } else {
+                    delta = ggml_mul_mat(ctx0,
+                        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))),
+                        ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
+                }
                 // scale
                 const float alpha = adapters[i]->alpha;
                 const float rank  = (float) inp_b[i]->ne[0];

From 6171c9d25820ccf676b243c172868819d882848f Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Tue, 21 Jan 2025 13:18:51 +0000
Subject: [PATCH 219/376] Add Jinja template support (#11016)

* Copy minja from https://github.com/google/minja/commit/58f0ca6dd74bcbfbd4e71229736640322b31c7f9

* Add --jinja and --chat-template-file flags

* Add missing <optional> include

* Avoid print in get_hf_chat_template.py

* No designated initializers yet

* Try and work around msvc++ non-macro max resolution quirk

* Update test_chat_completion.py

* Wire LLM_KV_TOKENIZER_CHAT_TEMPLATE_N in llama_model_chat_template

* Refactor test-chat-template

* Test templates w/ minja

* Fix deprecation

* Add --jinja to llama-run

* Update common_chat_format_example to use minja template wrapper

* Test chat_template in e2e test

* Update utils.py

* Update test_chat_completion.py

* Update run.cpp

* Update arg.cpp

* Refactor common_chat_* functions to accept minja template + use_jinja option

* Attempt to fix linkage of LLAMA_CHATML_TEMPLATE

* Revert LLAMA_CHATML_TEMPLATE refactor

* Normalize newlines in test-chat-templates for windows tests

* Forward decl minja::chat_template to avoid eager json dep

* Flush stdout in chat template before potential crash

* Fix copy elision warning

* Rm unused optional include

* Add missing optional include to server.cpp

* Disable jinja test that has a cryptic windows failure

* minja: fix vigogne (https://github.com/google/minja/pull/22)

* Apply suggestions from code review

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Finish suggested renamings

* Move chat_templates inside server_context + remove mutex

* Update --chat-template-file w/ recent change to --chat-template

* Refactor chat template validation

* Guard against missing eos/bos tokens (null token otherwise throws in llama_vocab::impl::token_get_attr)

* Warn against missing eos / bos tokens when jinja template references them

* rename: common_chat_template[s]

* reinstate assert on chat_templates.template_default

* Update minja to https://github.com/google/minja/commit/b8437df626ac6cd0ce3b333b3c74ed1129c19f25

* Update minja to https://github.com/google/minja/pull/25

* Update minja from https://github.com/google/minja/pull/27

* rm unused optional header

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 Makefile                                      |    2 +
 common/CMakeLists.txt                         |    2 +
 common/arg.cpp                                |   42 +-
 common/chat-template.hpp                      |  249 ++
 common/common.cpp                             |  126 +-
 common/common.h                               |   38 +-
 common/minja.hpp                              | 2788 +++++++++++++++++
 examples/main/main.cpp                        |   28 +-
 examples/run/run.cpp                          |   42 +-
 examples/server/README.md                     |    2 +-
 examples/server/server.cpp                    |   51 +-
 .../server/tests/unit/test_chat_completion.py |   16 +-
 examples/server/tests/utils.py                |    7 +-
 examples/server/utils.hpp                     |   36 +-
 examples/simple-chat/simple-chat.cpp          |    2 +-
 include/llama.h                               |    3 +-
 scripts/get_hf_chat_template.py               |   77 +
 src/CMakeLists.txt                            |    2 +-
 src/llama-arch.cpp                            |    6 +-
 src/llama-arch.h                              |    4 +-
 src/llama-model.cpp                           |    6 +-
 tests/test-chat-template.cpp                  |  167 +-
 22 files changed, 3563 insertions(+), 133 deletions(-)
 create mode 100644 common/chat-template.hpp
 create mode 100644 common/minja.hpp
 create mode 100755 scripts/get_hf_chat_template.py

diff --git a/Makefile b/Makefile
index 19ae0d5f1..295522ba3 100644
--- a/Makefile
+++ b/Makefile
@@ -1361,7 +1361,9 @@ llama-server: \
 	examples/server/httplib.h \
 	examples/server/index.html.hpp \
 	examples/server/loading.html.hpp \
+	common/chat-template.hpp \
 	common/json.hpp \
+	common/minja.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index df1cdf9a5..24b7f8741 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -56,6 +56,7 @@ add_library(${TARGET} STATIC
     arg.cpp
     arg.h
     base64.hpp
+    chat-template.hpp
     common.cpp
     common.h
     console.cpp
@@ -64,6 +65,7 @@ add_library(${TARGET} STATIC
     json.hpp
     log.cpp
     log.h
+    minja.hpp
     ngram-cache.cpp
     ngram-cache.h
     sampling.cpp
diff --git a/common/arg.cpp b/common/arg.cpp
index 126970950..a6226a34b 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -325,6 +325,14 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
     }
 
+    if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
+        throw std::runtime_error(string_format(
+            "error: the supplied chat template is not supported: %s%s\n",
+            params.chat_template.c_str(),
+            params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
+        ));
+    }
+
     return true;
 }
 
@@ -1947,24 +1955,44 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--jinja"},
+        "use jinja template for chat (default: disabled)",
+        [](common_params & params) {
+            params.use_jinja = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
             "set custom jinja chat template (default: template taken from model's metadata)\n"
             "if suffix/prefix are specified, template will be disabled\n"
+            "only commonly used templates are accepted (unless --jinja is set before this flag):\n"
             "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
         ),
         [](common_params & params, const std::string & value) {
-            if (!common_chat_verify_template(value)) {
-                throw std::runtime_error(string_format(
-                    "error: the supplied chat template is not supported: %s\n"
-                    "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
-                    value.c_str()
-                ));
-            }
             params.chat_template = value;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+    add_opt(common_arg(
+        {"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
+        string_format(
+            "set custom jinja chat template file (default: template taken from model's metadata)\n"
+            "if suffix/prefix are specified, template will be disabled\n"
+            "only commonly used templates are accepted (unless --jinja is set before this flag):\n"
+            "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
+        ),
+        [](common_params & params, const std::string & value) {
+            std::ifstream file(value);
+            if (!file) {
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            }
+            std::copy(
+                std::istreambuf_iterator<char>(file),
+                std::istreambuf_iterator<char>(),
+                std::back_inserter(params.chat_template));
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
     add_opt(common_arg(
         {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
         string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
diff --git a/common/chat-template.hpp b/common/chat-template.hpp
new file mode 100644
index 000000000..b4a90145c
--- /dev/null
+++ b/common/chat-template.hpp
@@ -0,0 +1,249 @@
+/*
+    Copyright 2024 Google LLC
+
+    Use of this source code is governed by an MIT-style
+    license that can be found in the LICENSE file or at
+    https://opensource.org/licenses/MIT.
+*/
+// SPDX-License-Identifier: MIT
+#pragma once
+
+#include "minja.hpp"
+#include <json.hpp>
+#include <string>
+#include <vector>
+
+using json = nlohmann::ordered_json;
+
+namespace minja {
+
+class chat_template {
+  public:
+
+  private:
+    bool supports_tools_ = true;
+    // Meta-Llama-3.1-8B-Instruct's template expects arguments to be an object.
+    // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
+    bool requires_object_arguments_ = false;
+    bool supports_system_role_ = true;
+    bool supports_parallel_tool_calls_ = false;
+    std::string source_;
+    std::string bos_token_;
+    std::string eos_token_;
+    std::shared_ptr<minja::TemplateNode> template_root_;
+
+    std::string try_render(
+        const nlohmann::ordered_json & messages,
+        const nlohmann::ordered_json & tools,
+        bool add_generation_prompt,
+        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
+    {
+        try {
+            auto prompt = apply(messages, tools, add_generation_prompt, extra_context);
+            // fprintf(stderr, "Prompt: %s\n", prompt.c_str());
+            return prompt;
+        } catch (const std::exception & e) {
+            // fprintf(stderr, "Error: %s\n", e.what());
+            return "";
+        }
+    }
+
+  public:
+    chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
+        : source_(source), bos_token_(bos_token), eos_token_(eos_token)
+    {
+        template_root_ = minja::Parser::parse(source_, {
+            /* .trim_blocks = */ true,
+            /* .lstrip_blocks = */ true,
+            /* .keep_trailing_newline = */ false,
+        });
+        supports_tools_ = source.find("tools") != std::string::npos;
+
+        auto renders_string_arguments =
+            try_render({
+                {
+                    {"role", "user"},
+                    {"content", "Hey"}
+                },
+                {
+                    {"role", "assistant"},
+                    {"tool_calls", json::array({
+                        {
+                            {"id", "call_1___"},
+                            {"type", "function"},
+                            {"function", {
+                                {"arguments", "{\"code\": \"print('Hello, World!')\"}"},
+                                {"name", "ipython"},
+                            }},
+                        },
+                    })},
+                }
+            }, {}, false).find("{\"code\": \"print") != std::string::npos;
+        if (!renders_string_arguments) {
+            auto renders_object_arguments =
+                try_render({
+                    {
+                        {"role", "user"},
+                        {"content", "Hey"}
+                    },
+                    {
+                        {"role", "assistant"},
+                        {"tool_calls", json::array({
+                            {
+                                {"id", "call_1___"},
+                                {"type", "function"},
+                                {"function", {
+                                    {"arguments", {
+                                        {"code", "print('Hello, World!')"},
+                                    }},
+                                    {"name", "ipython"},
+                                }},
+                            },
+                        })},
+                    }
+                }, {}, false).find("{\"code\": \"print") != std::string::npos;
+            requires_object_arguments_ = renders_object_arguments;
+        }
+        supports_parallel_tool_calls_ = source.find("tool_call_id") != std::string::npos;
+
+        supports_system_role_ = try_render({
+            {{"role", "system"}, {"content", "<System Needle>"}},
+            {{"role", "user"},   {"content", "Hey"}}
+        }, {}, false).find("<System Needle>") != std::string::npos;
+    }
+
+    const std::string & source() const { return source_; }
+    const std::string & bos_token() const { return bos_token_; }
+    const std::string & eos_token() const { return eos_token_; }
+    bool supports_tools() const { return supports_tools_; }
+    bool supports_parallel_tool_calls() const { return supports_parallel_tool_calls_; }
+
+    std::string apply(
+        const nlohmann::ordered_json & messages,
+        const nlohmann::ordered_json & tools,
+        bool add_generation_prompt,
+        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
+    {
+        json actual_messages;
+
+        // First, "fix" messages so they have a chance to be rendered correctly by the template
+
+        if (requires_object_arguments_ || !supports_system_role_ || !supports_tools_) {
+            actual_messages = json::array();
+
+            std::string pending_system;
+            auto flush_sys = [&]() {
+                if (!pending_system.empty()) {
+                    actual_messages.push_back({
+                        {"role", "user"},
+                        {"content", pending_system},
+                    });
+                    pending_system.clear();
+                }
+            };
+            for (const auto & message_ : messages) {
+                auto message = message_;
+                if (!message.contains("role") || !message.contains("content")) {
+                    throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
+                }
+                std::string role = message.at("role");
+
+                if (message.contains("tool_calls")) {
+                    if (requires_object_arguments_ || !supports_tools_) {
+                        for (auto & tool_call : message.at("tool_calls")) {
+                            if (tool_call["type"] == "function") {
+                                auto & function = tool_call.at("function");
+                                std::string arguments = function.at("arguments");
+                                function["arguments"] = json::parse(arguments);
+                            }
+                        }
+                    }
+                    if (!supports_tools_) {
+                        auto content = message.at("content");
+                        auto tool_calls = json::array();
+                        for (const auto & tool_call : message.at("tool_calls")) {
+                            if (tool_call.at("type") != "function") {
+                                continue;
+                            }
+                            const auto & function = tool_call.at("function");
+                            auto tc = json {
+                                {"name", function.at("name")},
+                                {"arguments", function.at("arguments")},
+                            };
+                            if (tool_call.contains("id")) {
+                                tc["id"] = tool_call["id"];
+                            }
+                            tool_calls.push_back(tc);
+                        }
+                        auto obj = json {
+                            {"tool_calls", tool_calls},
+                        };
+                        if (!content.is_null() && content != "") {
+                            obj["content"] = content;
+                        }
+                        message["content"] = obj.dump(2);
+                        message.erase("tool_calls");
+                    }
+                }
+                if (!supports_tools_ && role == "tool") {
+                    message["role"] = "user";
+                    auto obj = json {
+                        {"tool_response", {
+                            {"tool", message.at("name")},
+                            {"content", message.at("content")},
+                        }},
+                    };
+                    if (message.contains("tool_call_id")) {
+                        obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
+                    }
+                    message["content"] = obj.dump(2);
+                    message.erase("name");
+                }
+
+                if (!message["content"].is_null() && !supports_system_role_) {
+                    std::string content = message.at("content");
+                    if (role == "system") {
+                        if (!pending_system.empty()) pending_system += "\n";
+                        pending_system += content;
+                        continue;
+                    } else {
+                        if (role == "user") {
+                            if (!pending_system.empty()) {
+                                message["content"] = pending_system + (content.empty() ? "" : "\n" + content);
+                                pending_system.clear();
+                            }
+                        } else {
+                            flush_sys();
+                        }
+                    }
+                }
+                actual_messages.push_back(message);
+            }
+            flush_sys();
+        } else {
+            actual_messages = messages;
+        }
+
+        auto context = minja::Context::make(json({
+            {"messages", actual_messages},
+            {"add_generation_prompt", add_generation_prompt},
+            {"bos_token", bos_token_},
+            {"eos_token", eos_token_},
+        }));
+
+        if (!tools.is_null()) {
+            auto tools_val = minja::Value(tools);
+            context->set("tools", tools_val);
+        }
+        if (!extra_context.is_null()) {
+            for (auto & kv : extra_context.items()) {
+                minja::Value val(kv.value());
+                context->set(kv.key(), val);
+            }
+        }
+
+        return template_root_->render(context);
+    }
+};
+
+}  // namespace minja
diff --git a/common/common.cpp b/common/common.cpp
index 451826d5d..727ab0a10 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -12,6 +12,7 @@
 #include "json.hpp"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
+#include "chat-template.hpp"
 
 #include <algorithm>
 #include <cinttypes>
@@ -1728,67 +1729,75 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
 // Chat template utils
 //
 
-std::string common_get_builtin_chat_template(const struct llama_model * model) {
-    const char * ptr_tmpl = llama_model_chat_template(model);
-    return ptr_tmpl == nullptr ? "" : ptr_tmpl;
-}
-
-bool common_chat_verify_template(const std::string & tmpl) {
+bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
+    if (use_jinja) {
+        try {
+            auto chat_template = minja::chat_template(tmpl, "<s>", "</s>");
+            chat_template.apply({{
+                {"role", "user"},
+                {"content", "test"},
+            }}, json(), true);
+            return true;
+        } catch (const std::exception & e) {
+            LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
+            return false;
+        }
+    }
     llama_chat_message chat[] = {{"user", "test"}};
     const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
     return res >= 0;
 }
 
-std::string common_chat_apply_template(const struct llama_model * model,
-        const std::string & tmpl,
+std::string common_chat_apply_template(
+        const common_chat_template & tmpl,
         const std::vector<common_chat_msg> & msgs,
-        bool add_ass) {
+        bool add_ass,
+        bool use_jinja) {
+    if (use_jinja) {
+        auto messages = json::array();
+        for (const auto & msg : msgs) {
+            messages.push_back({{"role", msg.role}, {"content", msg.content}});
+        }
+        return tmpl.apply(messages, /* tools= */ json(), add_ass);
+    }
+
     int alloc_size = 0;
-    bool fallback = false; // indicate if we must fallback to default chatml
     std::vector<llama_chat_message> chat;
     for (const auto & msg : msgs) {
         chat.push_back({msg.role.c_str(), msg.content.c_str()});
         alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
     }
 
-    const char * ptr_tmpl = tmpl.empty() ? llama_model_chat_template(model) : tmpl.c_str();
     std::vector<char> buf(alloc_size);
 
     // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+    int32_t res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
 
     // error: chat template is not supported
     if (res < 0) {
-        if (ptr_tmpl != nullptr) {
-            // if the custom "tmpl" is not supported, we throw an error
-            // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
-            throw std::runtime_error("this custom template is not supported");
-        }
-
-        // If the built-in template is not supported, we default to chatml
-        res = llama_chat_apply_template("chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
-        fallback = true;
+        // if the custom "tmpl" is not supported, we throw an error
+        // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
+        throw std::runtime_error("this custom template is not supported");
     }
 
     // if it turns out that our buffer is too small, we resize it
     if ((size_t) res > buf.size()) {
         buf.resize(res);
-        res = llama_chat_apply_template(
-            fallback ? "chatml" : ptr_tmpl,
-            chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+        res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     }
 
     std::string formatted_chat(buf.data(), res);
     return formatted_chat;
 }
 
-std::string common_chat_format_single(const struct llama_model * model,
-        const std::string & tmpl,
+std::string common_chat_format_single(
+        const common_chat_template & tmpl,
         const std::vector<common_chat_msg> & past_msg,
         const common_chat_msg & new_msg,
-        bool add_ass) {
+        bool add_ass,
+        bool use_jinja) {
     std::ostringstream ss;
-    auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
+    auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(tmpl, past_msg, false, use_jinja);
     std::vector<common_chat_msg> chat_new(past_msg);
     // if the past_msg ends with a newline, we must preserve it in the formatted version
     if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
@@ -1796,21 +1805,74 @@ std::string common_chat_format_single(const struct llama_model * model,
     };
     // format chat with new_msg
     chat_new.push_back(new_msg);
-    auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
+    auto fmt_new_msg = common_chat_apply_template(tmpl, chat_new, add_ass, use_jinja);
     // get the diff part
     ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
     return ss.str();
 }
 
-std::string common_chat_format_example(const struct llama_model * model,
-        const std::string & tmpl) {
+std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) {
     std::vector<common_chat_msg> msgs = {
         {"system",    "You are a helpful assistant"},
         {"user",      "Hello"},
         {"assistant", "Hi there"},
         {"user",      "How are you?"},
     };
-    return common_chat_apply_template(model, tmpl, msgs, true);
+    return common_chat_apply_template(tmpl, msgs, true, use_jinja);
+}
+
+common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override)
+{
+    auto vocab = llama_model_get_vocab(model);
+    std::string default_template_src = chat_template_override;
+    std::string template_tool_use_src = chat_template_override;
+    bool has_explicit_template = !chat_template_override.empty();
+    if (chat_template_override.empty()) {
+        auto str = llama_model_chat_template(model, /* name */ nullptr);
+        if (str) {
+            default_template_src = str;
+            has_explicit_template = true;
+        }
+        str = llama_model_chat_template(model, /* name */ "tool_use");
+        if (str) {
+            template_tool_use_src = str;
+            has_explicit_template = true;
+        }
+    }
+    if (default_template_src.empty() || default_template_src == "chatml") {
+        if (!template_tool_use_src.empty()) {
+            default_template_src = template_tool_use_src;
+        } else {
+            default_template_src = R"(
+                {%- for message in messages -%}
+                    {{- "<|im_start|>" + message.role + "\n" + message.content + "<|im_end|>\n" -}}
+                {%- endfor -%}
+                {%- if add_generation_prompt -%}
+                    {{- "<|im_start|>assistant\n" -}}
+                {%- endif -%}
+            )";
+        }
+    }
+    const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
+        if (token == LLAMA_TOKEN_NULL) {
+            if (default_template_src.find(jinja_variable_name) != std::string::npos
+                || template_tool_use_src.find(jinja_variable_name) != std::string::npos) {
+                LOG_WRN("%s: warning: vocab does not have a %s token, jinja template won't work as intended.\n", __func__, name);
+            }
+            return std::string();
+        } else {
+            return common_token_to_piece(vocab, token, true);
+        }
+    };
+    auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
+    auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
+    return {
+        has_explicit_template,
+        std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
+        template_tool_use_src.empty()
+            ? nullptr
+            : std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos)
+    };
 }
 
 //
diff --git a/common/common.h b/common/common.h
index b2709c044..7c9d73ce1 100644
--- a/common/common.h
+++ b/common/common.h
@@ -334,6 +334,7 @@ struct common_params {
     std::string hostname      = "127.0.0.1";
     std::string public_path   = "";                                                                         // NOLINT
     std::string chat_template = "";                                                                         // NOLINT
+    bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
 
     std::vector<std::string> api_keys;
@@ -603,30 +604,43 @@ struct common_chat_msg {
     std::string content;
 };
 
-// Get the built-in chat template for the model. Return empty string if not present.
-std::string common_get_builtin_chat_template(const struct llama_model * model);
-
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-bool common_chat_verify_template(const std::string & tmpl);
+bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
+
+namespace minja {
+    class chat_template;
+}
+
+typedef minja::chat_template common_chat_template;
+
+struct common_chat_templates {
+    bool has_explicit_template; // Model had builtin template or template overridde was specified.
+    std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
+    std::unique_ptr<common_chat_template> template_tool_use;
+};
 
 // CPP wrapper for llama_chat_apply_template
 // If the built-in template is not supported, we default to chatml
 // If the custom "tmpl" is not supported, we throw an error
-std::string common_chat_apply_template(const struct llama_model * model,
-        const std::string & tmpl,
+std::string common_chat_apply_template(
+        const common_chat_template & tmpl,
         const std::vector<common_chat_msg> & chat,
-        bool add_ass);
+        bool add_ass,
+        bool use_jinja);
 
 // Format single message, while taking into account the position of that message in chat history
-std::string common_chat_format_single(const struct llama_model * model,
-        const std::string & tmpl,
+std::string common_chat_format_single(
+        const common_chat_template & tmpl,
         const std::vector<common_chat_msg> & past_msg,
         const common_chat_msg & new_msg,
-        bool add_ass);
+        bool add_ass,
+        bool use_jinja);
 
 // Returns an example of formatted chat
-std::string common_chat_format_example(const struct llama_model * model,
-        const std::string & tmpl);
+std::string common_chat_format_example(
+    const common_chat_template & tmpl, bool use_jinja);
+
+common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override);
 
 //
 // KV cache utils
diff --git a/common/minja.hpp b/common/minja.hpp
new file mode 100644
index 000000000..f0ee7a49a
--- /dev/null
+++ b/common/minja.hpp
@@ -0,0 +1,2788 @@
+/*
+    Copyright 2024 Google LLC
+
+    Use of this source code is governed by an MIT-style
+    license that can be found in the LICENSE file or at
+    https://opensource.org/licenses/MIT.
+*/
+// SPDX-License-Identifier: MIT
+#pragma once
+
+#include <iostream>
+#include <string>
+#include <vector>
+#include <regex>
+#include <memory>
+#include <stdexcept>
+#include <sstream>
+#include <unordered_set>
+#include <json.hpp>
+
+using json = nlohmann::ordered_json;
+
+namespace minja {
+
+class Context;
+
+struct Options {
+    bool trim_blocks;  // removes the first newline after a block
+    bool lstrip_blocks;  // removes leading whitespace on the line of the block
+    bool keep_trailing_newline;  // don't remove last newline
+};
+
+struct ArgumentsValue;
+
+inline std::string normalize_newlines(const std::string & s) {
+#ifdef _WIN32
+  static const std::regex nl_regex("\r\n");
+  return std::regex_replace(s, nl_regex, "\n");
+#else
+  return s;
+#endif
+}
+
+/* Values that behave roughly like in Python. */
+class Value : public std::enable_shared_from_this<Value> {
+public:
+  using CallableType = std::function<Value(const std::shared_ptr<Context> &, ArgumentsValue &)>;
+  using FilterType = std::function<Value(const std::shared_ptr<Context> &, ArgumentsValue &)>;
+
+private:
+  using ObjectType = nlohmann::ordered_map<json, Value>;  // Only contains primitive keys
+  using ArrayType = std::vector<Value>;
+
+  std::shared_ptr<ArrayType> array_;
+  std::shared_ptr<ObjectType> object_;
+  std::shared_ptr<CallableType> callable_;
+  json primitive_;
+
+  Value(const std::shared_ptr<ArrayType> & array) : array_(array) {}
+  Value(const std::shared_ptr<ObjectType> & object) : object_(object) {}
+  Value(const std::shared_ptr<CallableType> & callable) : object_(std::make_shared<ObjectType>()), callable_(callable) {}
+
+  /* Python-style string repr */
+  static void dump_string(const json & primitive, std::ostringstream & out, char string_quote = '\'') {
+    if (!primitive.is_string()) throw std::runtime_error("Value is not a string: " + primitive.dump());
+    auto s = primitive.dump();
+    if (string_quote == '"' || s.find('\'') != std::string::npos) {
+      out << s;
+      return;
+    }
+    // Reuse json dump, just changing string quotes
+    out << string_quote;
+    for (size_t i = 1, n = s.size() - 1; i < n; ++i) {
+      if (s[i] == '\\' && s[i + 1] == '"') {
+        out << '"';
+        i++;
+      } else if (s[i] == string_quote) {
+        out << '\\' << string_quote;
+      } else {
+        out << s[i];
+      }
+    }
+    out << string_quote;
+  }
+  void dump(std::ostringstream & out, int indent = -1, int level = 0, bool to_json = false) const {
+    auto print_indent = [&](int level) {
+      if (indent > 0) {
+          out << "\n";
+          for (int i = 0, n = level * indent; i < n; ++i) out << ' ';
+      }
+    };
+    auto print_sub_sep = [&]() {
+      out << ',';
+      if (indent < 0) out << ' ';
+      else print_indent(level + 1);
+    };
+
+    auto string_quote = to_json ? '"' : '\'';
+
+    if (is_null()) out << "null";
+    else if (array_) {
+      out << "[";
+      print_indent(level + 1);
+      for (size_t i = 0; i < array_->size(); ++i) {
+        if (i) print_sub_sep();
+        (*array_)[i].dump(out, indent, level + 1, to_json);
+      }
+      print_indent(level);
+      out << "]";
+    } else if (object_) {
+      out << "{";
+      print_indent(level + 1);
+      for (auto begin = object_->begin(), it = begin; it != object_->end(); ++it) {
+        if (it != begin) print_sub_sep();
+        if (it->first.is_string()) {
+          dump_string(it->first, out, string_quote);
+        } else {
+          out << string_quote << it->first.dump() << string_quote;
+        }
+        out << ": ";
+        it->second.dump(out, indent, level + 1, to_json);
+      }
+      print_indent(level);
+      out << "}";
+    } else if (callable_) {
+      throw std::runtime_error("Cannot dump callable to JSON");
+    } else if (is_boolean() && !to_json) {
+      out << (this->to_bool() ? "True" : "False");
+    } else if (is_string() && !to_json) {
+      dump_string(primitive_, out, string_quote);
+    } else {
+      out << primitive_.dump();
+    }
+  }
+
+public:
+  Value() {}
+  Value(const bool& v) : primitive_(v) {}
+  Value(const int64_t & v) : primitive_(v) {}
+  Value(const double& v) : primitive_(v) {}
+  Value(const std::nullptr_t &) {}
+  Value(const std::string & v) : primitive_(v) {}
+  Value(const char * v) : primitive_(std::string(v)) {}
+
+  Value(const json & v) {
+    if (v.is_object()) {
+      auto object = std::make_shared<ObjectType>();
+      for (auto it = v.begin(); it != v.end(); ++it) {
+        (*object)[it.key()] = it.value();
+      }
+      object_ = std::move(object);
+    } else if (v.is_array()) {
+      auto array = std::make_shared<ArrayType>();
+      for (const auto& item : v) {
+        array->push_back(Value(item));
+      }
+      array_ = array;
+    } else {
+      primitive_ = v;
+    }
+  }
+
+  std::vector<Value> keys() {
+    if (!object_) throw std::runtime_error("Value is not an object: " + dump());
+    std::vector<Value> res;
+    for (const auto& item : *object_) {
+      res.push_back(item.first);
+    }
+    return res;
+  }
+
+  size_t size() const {
+    if (is_object()) return object_->size();
+    if (is_array()) return array_->size();
+    if (is_string()) return primitive_.get<std::string>().length();
+    throw std::runtime_error("Value is not an array or object: " + dump());
+  }
+
+  static Value array(const std::vector<Value> values = {}) {
+    auto array = std::make_shared<ArrayType>();
+    for (const auto& item : values) {
+      array->push_back(item);
+    }
+    return Value(array);
+  }
+  static Value object(const std::shared_ptr<ObjectType> object = std::make_shared<ObjectType>()) {
+    return Value(object);
+  }
+  static Value callable(const CallableType & callable) {
+    return Value(std::make_shared<CallableType>(callable));
+  }
+
+  void insert(size_t index, const Value& v) {
+    if (!array_)
+      throw std::runtime_error("Value is not an array: " + dump());
+    array_->insert(array_->begin() + index, v);
+  }
+  void push_back(const Value& v) {
+    if (!array_)
+      throw std::runtime_error("Value is not an array: " + dump());
+    array_->push_back(v);
+  }
+  Value pop(const Value& index) {
+    if (is_array()) {
+      if (array_->empty())
+        throw std::runtime_error("pop from empty list");
+      if (index.is_null()) {
+        auto ret = array_->back();
+        array_->pop_back();
+        return ret;
+      } else if (!index.is_number_integer()) {
+        throw std::runtime_error("pop index must be an integer: " + index.dump());
+      } else {
+        auto i = index.get<int>();
+        if (i < 0 || i >= static_cast<int>(array_->size()))
+          throw std::runtime_error("pop index out of range: " + index.dump());
+        auto it = array_->begin() + (i < 0 ? array_->size() + i : i);
+        auto ret = *it;
+        array_->erase(it);
+        return ret;
+      }
+    } else if (is_object()) {
+      if (!index.is_hashable())
+        throw std::runtime_error("Unashable type: " + index.dump());
+      auto it = object_->find(index.primitive_);
+      if (it == object_->end())
+        throw std::runtime_error("Key not found: " + index.dump());
+      auto ret = it->second;
+      object_->erase(it);
+      return ret;
+    } else {
+      throw std::runtime_error("Value is not an array or object: " + dump());
+    }
+  }
+  Value get(const Value& key) {
+    if (array_) {
+      if (!key.is_number_integer()) {
+        return Value();
+      }
+      auto index = key.get<int>();
+      return array_->at(index < 0 ? array_->size() + index : index);
+    } else if (object_) {
+      if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+      auto it = object_->find(key.primitive_);
+      if (it == object_->end()) return Value();
+      return it->second;
+    }
+    return Value();
+  }
+  void set(const Value& key, const Value& value) {
+    if (!object_) throw std::runtime_error("Value is not an object: " + dump());
+    if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+    (*object_)[key.primitive_] = value;
+  }
+  Value call(const std::shared_ptr<Context> & context, ArgumentsValue & args) const {
+    if (!callable_) throw std::runtime_error("Value is not callable: " + dump());
+    return (*callable_)(context, args);
+  }
+
+  bool is_object() const { return !!object_; }
+  bool is_array() const { return !!array_; }
+  bool is_callable() const { return !!callable_; }
+  bool is_null() const { return !object_ && !array_ && primitive_.is_null() && !callable_; }
+  bool is_boolean() const { return primitive_.is_boolean(); }
+  bool is_number_integer() const { return primitive_.is_number_integer(); }
+  bool is_number_float() const { return primitive_.is_number_float(); }
+  bool is_number() const { return primitive_.is_number(); }
+  bool is_string() const { return primitive_.is_string(); }
+  bool is_iterable() const { return is_array() || is_object() || is_string(); }
+
+  bool is_primitive() const { return !array_ && !object_ && !callable_; }
+  bool is_hashable() const { return is_primitive(); }
+
+  bool empty() const {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (is_string()) return primitive_.empty();
+    if (is_array()) return array_->empty();
+    if (is_object()) return object_->empty();
+    return false;
+  }
+
+  void for_each(const std::function<void(Value &)> & callback) const {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (array_) {
+      for (auto& item : *array_) {
+        callback(item);
+      }
+    } else if (object_) {
+      for (auto & item : *object_) {
+        Value key(item.first);
+        callback(key);
+      }
+    } else if (is_string()) {
+      for (char c : primitive_.get<std::string>()) {
+        auto val = Value(std::string(1, c));
+        callback(val);
+      }
+    } else {
+      throw std::runtime_error("Value is not iterable: " + dump());
+    }
+  }
+
+  bool to_bool() const {
+    if (is_null()) return false;
+    if (is_boolean()) return get<bool>();
+    if (is_number()) return get<double>() != 0;
+    if (is_string()) return !get<std::string>().empty();
+    if (is_array()) return !empty();
+    return true;
+  }
+
+  int64_t to_int() const {
+    if (is_null()) return 0;
+    if (is_boolean()) return get<bool>() ? 1 : 0;
+    if (is_number()) return static_cast<int64_t>(get<double>());
+    if (is_string()) {
+      try {
+        return std::stol(get<std::string>());
+      } catch (const std::exception &) {
+        return 0;
+      }
+    }
+    return 0;
+  }
+
+  bool operator<(const Value & other) const {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (is_number() && other.is_number()) return get<double>() < other.get<double>();
+    if (is_string() && other.is_string()) return get<std::string>() < other.get<std::string>();
+    throw std::runtime_error("Cannot compare values: " + dump() + " < " + other.dump());
+  }
+  bool operator>=(const Value & other) const { return !(*this < other); }
+
+  bool operator>(const Value & other) const {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (is_number() && other.is_number()) return get<double>() > other.get<double>();
+    if (is_string() && other.is_string()) return get<std::string>() > other.get<std::string>();
+    throw std::runtime_error("Cannot compare values: " + dump() + " > " + other.dump());
+  }
+  bool operator<=(const Value & other) const { return !(*this > other); }
+
+  bool operator==(const Value & other) const {
+    if (callable_ || other.callable_) {
+      if (callable_.get() != other.callable_.get()) return false;
+    }
+    if (array_) {
+      if (!other.array_) return false;
+      if (array_->size() != other.array_->size()) return false;
+      for (size_t i = 0; i < array_->size(); ++i) {
+        if (!(*array_)[i].to_bool() || !(*other.array_)[i].to_bool() || (*array_)[i] != (*other.array_)[i]) return false;
+      }
+      return true;
+    } else if (object_) {
+      if (!other.object_) return false;
+      if (object_->size() != other.object_->size()) return false;
+      for (const auto& item : *object_) {
+        if (!item.second.to_bool() || !other.object_->count(item.first) || item.second != other.object_->at(item.first)) return false;
+      }
+      return true;
+    } else {
+      return primitive_ == other.primitive_;
+    }
+  }
+  bool operator!=(const Value & other) const { return !(*this == other); }
+
+  bool contains(const char * key) const { return contains(std::string(key)); }
+  bool contains(const std::string & key) const {
+    if (array_) {
+      return false;
+    } else if (object_) {
+      return object_->find(key) != object_->end();
+    } else {
+      throw std::runtime_error("contains can only be called on arrays and objects: " + dump());
+    }
+  }
+  bool contains(const Value & value) const {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (array_) {
+      for (const auto& item : *array_) {
+        if (item.to_bool() && item == value) return true;
+      }
+      return false;
+    } else if (object_) {
+      if (!value.is_hashable()) throw std::runtime_error("Unashable type: " + value.dump());
+      return object_->find(value.primitive_) != object_->end();
+    } else {
+      throw std::runtime_error("contains can only be called on arrays and objects: " + dump());
+    }
+  }
+  void erase(size_t index) {
+    if (!array_) throw std::runtime_error("Value is not an array: " + dump());
+    array_->erase(array_->begin() + index);
+  }
+  void erase(const std::string & key) {
+    if (!object_) throw std::runtime_error("Value is not an object: " + dump());
+    object_->erase(key);
+  }
+  const Value& at(const Value & index) const {
+    return const_cast<Value*>(this)->at(index);
+  }
+  Value& at(const Value & index) {
+    if (!index.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+    if (is_array()) return array_->at(index.get<int>());
+    if (is_object()) return object_->at(index.primitive_);
+    throw std::runtime_error("Value is not an array or object: " + dump());
+  }
+  const Value& at(size_t index) const {
+    return const_cast<Value*>(this)->at(index);
+  }
+  Value& at(size_t index) {
+    if (is_null())
+      throw std::runtime_error("Undefined value or reference");
+    if (is_array()) return array_->at(index);
+    if (is_object()) return object_->at(index);
+    throw std::runtime_error("Value is not an array or object: " + dump());
+  }
+
+  template <typename T>
+  T get(const std::string & key, T default_value) const {
+    if (!contains(key)) return default_value;
+    return at(key).get<T>();
+  }
+
+  template <typename T>
+  T get() const {
+    if (is_primitive()) return primitive_.get<T>();
+    throw std::runtime_error("get<T> not defined for this value type: " + dump());
+  }
+
+  std::string dump(int indent=-1, bool to_json=false) const {
+    std::ostringstream out;
+    dump(out, indent, 0, to_json);
+    return out.str();
+  }
+
+  Value operator-() const {
+      if (is_number_integer())
+        return -get<int64_t>();
+      else
+        return -get<double>();
+  }
+  std::string to_str() const {
+    if (is_string()) return get<std::string>();
+    if (is_number_integer()) return std::to_string(get<int64_t>());
+    if (is_number_float()) return std::to_string(get<double>());
+    if (is_boolean()) return get<bool>() ? "True" : "False";
+    if (is_null()) return "None";
+    return dump();
+  }
+  Value operator+(const Value& rhs) const {
+      if (is_string() || rhs.is_string()) {
+        return to_str() + rhs.to_str();
+      } else if (is_number_integer() && rhs.is_number_integer()) {
+        return get<int64_t>() + rhs.get<int64_t>();
+      } else if (is_array() && rhs.is_array()) {
+        auto res = Value::array();
+        for (const auto& item : *array_) res.push_back(item);
+        for (const auto& item : *rhs.array_) res.push_back(item);
+        return res;
+      } else {
+        return get<double>() + rhs.get<double>();
+      }
+  }
+  Value operator-(const Value& rhs) const {
+      if (is_number_integer() && rhs.is_number_integer())
+        return get<int64_t>() - rhs.get<int64_t>();
+      else
+        return get<double>() - rhs.get<double>();
+  }
+  Value operator*(const Value& rhs) const {
+      if (is_string() && rhs.is_number_integer()) {
+        std::ostringstream out;
+        for (int64_t i = 0, n = rhs.get<int64_t>(); i < n; ++i) {
+          out << to_str();
+        }
+        return out.str();
+      }
+      else if (is_number_integer() && rhs.is_number_integer())
+        return get<int64_t>() * rhs.get<int64_t>();
+      else
+        return get<double>() * rhs.get<double>();
+  }
+  Value operator/(const Value& rhs) const {
+      if (is_number_integer() && rhs.is_number_integer())
+        return get<int64_t>() / rhs.get<int64_t>();
+      else
+        return get<double>() / rhs.get<double>();
+  }
+  Value operator%(const Value& rhs) const {
+    return get<int64_t>() % rhs.get<int64_t>();
+  }
+};
+
+struct ArgumentsValue {
+  std::vector<Value> args;
+  std::vector<std::pair<std::string, Value>> kwargs;
+
+  bool has_named(const std::string & name) {
+    for (const auto & p : kwargs) {
+      if (p.first == name) return true;
+    }
+    return false;
+  }
+
+  Value get_named(const std::string & name) {
+    for (const auto & [key, value] : kwargs) {
+      if (key == name) return value;
+    }
+    return Value();
+  }
+
+  bool empty() {
+    return args.empty() && kwargs.empty();
+  }
+
+  void expectArgs(const std::string & method_name, const std::pair<size_t, size_t> & pos_count, const std::pair<size_t, size_t> & kw_count) {
+    if (args.size() < pos_count.first || args.size() > pos_count.second || kwargs.size() < kw_count.first || kwargs.size() > kw_count.second) {
+      std::ostringstream out;
+      out << method_name << " must have between " << pos_count.first << " and " << pos_count.second << " positional arguments and between " << kw_count.first << " and " << kw_count.second << " keyword arguments";
+      throw std::runtime_error(out.str());
+    }
+  }
+};
+
+template <>
+inline json Value::get<json>() const {
+  if (is_primitive()) return primitive_;
+  if (is_null()) return json();
+  if (array_) {
+    std::vector<json> res;
+    for (const auto& item : *array_) {
+      res.push_back(item.get<json>());
+    }
+    return res;
+  }
+  if (object_) {
+    json res = json::object();
+    for (const auto& [key, value] : *object_) {
+      if (key.is_string()) {
+        res[key.get<std::string>()] = value.get<json>();
+      } else if (key.is_primitive()) {
+        res[key.dump()] = value.get<json>();
+      } else {
+        throw std::runtime_error("Invalid key type for conversion to JSON: " + key.dump());
+      }
+    }
+    if (is_callable()) {
+      res["__callable__"] = true;
+    }
+    return res;
+  }
+  throw std::runtime_error("get<json> not defined for this value type: " + dump());
+}
+
+} // namespace minja
+
+namespace std {
+  template <>
+  struct hash<minja::Value> {
+    size_t operator()(const minja::Value & v) const {
+      if (!v.is_hashable())
+        throw std::runtime_error("Unsupported type for hashing: " + v.dump());
+      return std::hash<json>()(v.get<json>());
+    }
+  };
+} // namespace std
+
+namespace minja {
+
+static std::string error_location_suffix(const std::string & source, size_t pos) {
+  auto get_line = [&](size_t line) {
+    auto start = source.begin();
+    for (size_t i = 1; i < line; ++i) {
+      start = std::find(start, source.end(), '\n') + 1;
+    }
+    auto end = std::find(start, source.end(), '\n');
+    return std::string(start, end);
+  };
+  auto start = source.begin();
+  auto end = source.end();
+  auto it = start + pos;
+  auto line = std::count(start, it, '\n') + 1;
+  auto max_line = std::count(start, end, '\n') + 1;
+  auto col = pos - std::string(start, it).rfind('\n');
+  std::ostringstream out;
+  out << " at row " << line << ", column " << col << ":\n";
+  if (line > 1) out << get_line(line - 1) << "\n";
+  out << get_line(line) << "\n";
+  out << std::string(col - 1, ' ') << "^\n";
+  if (line < max_line) out << get_line(line + 1) << "\n";
+
+  return out.str();
+}
+
+class Context : public std::enable_shared_from_this<Context> {
+  protected:
+    Value values_;
+    std::shared_ptr<Context> parent_;
+  public:
+    Context(Value && values, const std::shared_ptr<Context> & parent = nullptr) : values_(std::move(values)), parent_(parent) {
+        if (!values_.is_object()) throw std::runtime_error("Context values must be an object: " + values_.dump());
+    }
+    virtual ~Context() {}
+
+    static std::shared_ptr<Context> builtins();
+    static std::shared_ptr<Context> make(Value && values, const std::shared_ptr<Context> & parent = builtins());
+
+    std::vector<Value> keys() {
+        return values_.keys();
+    }
+    virtual Value get(const Value & key) {
+        if (values_.contains(key)) return values_.at(key);
+        if (parent_) return parent_->get(key);
+        return Value();
+    }
+    virtual Value & at(const Value & key) {
+        if (values_.contains(key)) return values_.at(key);
+        if (parent_) return parent_->at(key);
+        throw std::runtime_error("Undefined variable: " + key.dump());
+    }
+    virtual bool contains(const Value & key) {
+        if (values_.contains(key)) return true;
+        if (parent_) return parent_->contains(key);
+        return false;
+    }
+    virtual void set(const Value & key, Value & value) {
+        values_.set(key, value);
+    }
+};
+
+struct Location {
+    std::shared_ptr<std::string> source;
+    size_t pos;
+};
+
+class Expression {
+protected:
+    virtual Value do_evaluate(const std::shared_ptr<Context> & context) const = 0;
+public:
+    using Parameters = std::vector<std::pair<std::string, std::shared_ptr<Expression>>>;
+
+    Location location;
+
+    Expression(const Location & location) : location(location) {}
+    virtual ~Expression() = default;
+
+    Value evaluate(const std::shared_ptr<Context> & context) const {
+        try {
+            return do_evaluate(context);
+        } catch (const std::exception & e) {
+            std::ostringstream out;
+            out << e.what();
+            if (location.source) out << error_location_suffix(*location.source, location.pos);
+            throw std::runtime_error(out.str());
+        }
+    }
+};
+
+class VariableExpr : public Expression {
+    std::string name;
+public:
+    VariableExpr(const Location & location, const std::string& n)
+      : Expression(location), name(n) {}
+    std::string get_name() const { return name; }
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!context->contains(name)) {
+            return Value();
+        }
+        return context->at(name);
+    }
+};
+
+static void destructuring_assign(const std::vector<std::string> & var_names, const std::shared_ptr<Context> & context, Value& item) {
+  if (var_names.size() == 1) {
+      Value name(var_names[0]);
+      context->set(name, item);
+  } else {
+      if (!item.is_array() || item.size() != var_names.size()) {
+          throw std::runtime_error("Mismatched number of variables and items in destructuring assignment");
+      }
+      for (size_t i = 0; i < var_names.size(); ++i) {
+          context->set(var_names[i], item.at(i));
+      }
+  }
+}
+
+enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline };
+
+class TemplateToken {
+public:
+    enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter };
+
+    static std::string typeToString(Type t) {
+        switch (t) {
+            case Type::Text: return "text";
+            case Type::Expression: return "expression";
+            case Type::If: return "if";
+            case Type::Else: return "else";
+            case Type::Elif: return "elif";
+            case Type::EndIf: return "endif";
+            case Type::For: return "for";
+            case Type::EndFor: return "endfor";
+            case Type::Set: return "set";
+            case Type::EndSet: return "endset";
+            case Type::Comment: return "comment";
+            case Type::Macro: return "macro";
+            case Type::EndMacro: return "endmacro";
+            case Type::Filter: return "filter";
+            case Type::EndFilter: return "endfilter";
+        }
+        return "Unknown";
+    }
+
+    TemplateToken(Type type, const Location & location, SpaceHandling pre, SpaceHandling post) : type(type), location(location), pre_space(pre), post_space(post) {}
+    virtual ~TemplateToken() = default;
+
+    Type type;
+    Location location;
+    SpaceHandling pre_space = SpaceHandling::Keep;
+    SpaceHandling post_space = SpaceHandling::Keep;
+};
+
+struct TextTemplateToken : public TemplateToken {
+    std::string text;
+    TextTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Text, location, pre, post), text(t) {}
+};
+
+struct ExpressionTemplateToken : public TemplateToken {
+    std::shared_ptr<Expression> expr;
+    ExpressionTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr<Expression> && e) : TemplateToken(Type::Expression, location, pre, post), expr(std::move(e)) {}
+};
+
+struct IfTemplateToken : public TemplateToken {
+    std::shared_ptr<Expression> condition;
+    IfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr<Expression> && c) : TemplateToken(Type::If, location, pre, post), condition(std::move(c)) {}
+};
+
+struct ElifTemplateToken : public TemplateToken {
+    std::shared_ptr<Expression> condition;
+    ElifTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr<Expression> && c) : TemplateToken(Type::Elif, location, pre, post), condition(std::move(c)) {}
+};
+
+struct ElseTemplateToken : public TemplateToken {
+    ElseTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Else, location, pre, post) {}
+};
+
+struct EndIfTemplateToken : public TemplateToken {
+    EndIfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndIf, location, pre, post) {}
+};
+
+struct MacroTemplateToken : public TemplateToken {
+    std::shared_ptr<VariableExpr> name;
+    Expression::Parameters params;
+    MacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr<VariableExpr> && n, Expression::Parameters && p)
+      : TemplateToken(Type::Macro, location, pre, post), name(std::move(n)), params(std::move(p)) {}
+};
+
+struct EndMacroTemplateToken : public TemplateToken {
+    EndMacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndMacro, location, pre, post) {}
+};
+
+struct FilterTemplateToken : public TemplateToken {
+    std::shared_ptr<Expression> filter;
+    FilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr<Expression> && filter)
+      : TemplateToken(Type::Filter, location, pre, post), filter(std::move(filter)) {}
+};
+
+struct EndFilterTemplateToken : public TemplateToken {
+    EndFilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFilter, location, pre, post) {}
+};
+
+struct ForTemplateToken : public TemplateToken {
+    std::vector<std::string> var_names;
+    std::shared_ptr<Expression> iterable;
+    std::shared_ptr<Expression> condition;
+    bool recursive;
+    ForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::vector<std::string> & vns, std::shared_ptr<Expression> && iter,
+      std::shared_ptr<Expression> && c, bool r)
+      : TemplateToken(Type::For, location, pre, post), var_names(vns), iterable(std::move(iter)), condition(std::move(c)), recursive(r) {}
+};
+
+struct EndForTemplateToken : public TemplateToken {
+    EndForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, location, pre, post) {}
+};
+
+struct SetTemplateToken : public TemplateToken {
+    std::string ns;
+    std::vector<std::string> var_names;
+    std::shared_ptr<Expression> value;
+    SetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string & ns, const std::vector<std::string> & vns, std::shared_ptr<Expression> && v)
+      : TemplateToken(Type::Set, location, pre, post), ns(ns), var_names(vns), value(std::move(v)) {}
+};
+
+struct EndSetTemplateToken : public TemplateToken {
+    EndSetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndSet, location, pre, post) {}
+};
+
+struct CommentTemplateToken : public TemplateToken {
+    std::string text;
+    CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {}
+};
+
+class TemplateNode {
+    Location location_;
+protected:
+    virtual void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const = 0;
+
+public:
+    TemplateNode(const Location & location) : location_(location) {}
+    void render(std::ostringstream & out, const std::shared_ptr<Context> & context) const {
+        try {
+            do_render(out, context);
+        } catch (const std::exception & e) {
+            std::ostringstream err;
+            err << e.what();
+            if (location_.source) err << error_location_suffix(*location_.source, location_.pos);
+            throw std::runtime_error(err.str());
+        }
+    }
+    const Location & location() const { return location_; }
+    virtual ~TemplateNode() = default;
+    std::string render(const std::shared_ptr<Context> & context) const {
+        std::ostringstream out;
+        render(out, context);
+        return out.str();
+    }
+};
+
+class SequenceNode : public TemplateNode {
+    std::vector<std::shared_ptr<TemplateNode>> children;
+public:
+    SequenceNode(const Location & location, std::vector<std::shared_ptr<TemplateNode>> && c)
+      : TemplateNode(location), children(std::move(c)) {}
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const override {
+        for (const auto& child : children) child->render(out, context);
+    }
+};
+
+class TextNode : public TemplateNode {
+    std::string text;
+public:
+    TextNode(const Location & location, const std::string& t) : TemplateNode(location), text(t) {}
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> &) const override {
+      out << text;
+    }
+};
+
+class ExpressionNode : public TemplateNode {
+    std::shared_ptr<Expression> expr;
+public:
+    ExpressionNode(const Location & location, std::shared_ptr<Expression> && e) : TemplateNode(location), expr(std::move(e)) {}
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const override {
+      if (!expr) throw std::runtime_error("ExpressionNode.expr is null");
+      auto result = expr->evaluate(context);
+      if (result.is_string()) {
+          out << result.get<std::string>();
+      } else if (result.is_boolean()) {
+          out << (result.get<bool>() ? "True" : "False");
+      } else if (!result.is_null()) {
+          out << result.dump();
+      }
+  }
+};
+
+class IfNode : public TemplateNode {
+    std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<TemplateNode>>> cascade;
+public:
+    IfNode(const Location & location, std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<TemplateNode>>> && c)
+        : TemplateNode(location), cascade(std::move(c)) {}
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const override {
+      for (const auto& branch : cascade) {
+          auto enter_branch = true;
+          if (branch.first) {
+            enter_branch = branch.first->evaluate(context).to_bool();
+          }
+          if (enter_branch) {
+            if (!branch.second) throw std::runtime_error("IfNode.cascade.second is null");
+              branch.second->render(out, context);
+              return;
+          }
+      }
+    }
+};
+
+class ForNode : public TemplateNode {
+    std::vector<std::string> var_names;
+    std::shared_ptr<Expression> iterable;
+    std::shared_ptr<Expression> condition;
+    std::shared_ptr<TemplateNode> body;
+    bool recursive;
+    std::shared_ptr<TemplateNode> else_body;
+public:
+    ForNode(const Location & location, std::vector<std::string> && var_names, std::shared_ptr<Expression> && iterable,
+      std::shared_ptr<Expression> && condition, std::shared_ptr<TemplateNode> && body, bool recursive, std::shared_ptr<TemplateNode> && else_body)
+            : TemplateNode(location), var_names(var_names), iterable(std::move(iterable)), condition(std::move(condition)), body(std::move(body)), recursive(recursive), else_body(std::move(else_body)) {}
+
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const override {
+      // https://jinja.palletsprojects.com/en/3.0.x/templates/#for
+      if (!iterable) throw std::runtime_error("ForNode.iterable is null");
+      if (!body) throw std::runtime_error("ForNode.body is null");
+
+      auto iterable_value = iterable->evaluate(context);
+      Value::CallableType loop_function;
+
+      std::function<void(Value&)> visit = [&](Value& iter) {
+          auto filtered_items = Value::array();
+          if (!iter.is_null()) {
+            if (!iterable_value.is_iterable()) {
+              throw std::runtime_error("For loop iterable must be iterable: " + iterable_value.dump());
+            }
+            iterable_value.for_each([&](Value & item) {
+                destructuring_assign(var_names, context, item);
+                if (!condition || condition->evaluate(context).to_bool()) {
+                  filtered_items.push_back(item);
+                }
+            });
+          }
+          if (filtered_items.empty()) {
+            if (else_body) {
+              else_body->render(out, context);
+            }
+          } else {
+              auto loop = recursive ? Value::callable(loop_function) : Value::object();
+              loop.set("length", (int64_t) filtered_items.size());
+
+              size_t cycle_index = 0;
+              loop.set("cycle", Value::callable([&](const std::shared_ptr<Context> &, ArgumentsValue & args) {
+                  if (args.args.empty() || !args.kwargs.empty()) {
+                      throw std::runtime_error("cycle() expects at least 1 positional argument and no named arg");
+                  }
+                  auto item = args.args[cycle_index];
+                  cycle_index = (cycle_index + 1) % args.args.size();
+                  return item;
+              }));
+              auto loop_context = Context::make(Value::object(), context);
+              loop_context->set("loop", loop);
+              for (size_t i = 0, n = filtered_items.size(); i < n; ++i) {
+                  auto & item = filtered_items.at(i);
+                  destructuring_assign(var_names, loop_context, item);
+                  loop.set("index", (int64_t) i + 1);
+                  loop.set("index0", (int64_t) i);
+                  loop.set("revindex", (int64_t) (n - i));
+                  loop.set("revindex0", (int64_t) (n - i - 1));
+                  loop.set("length", (int64_t) n);
+                  loop.set("first", i == 0);
+                  loop.set("last", i == (n - 1));
+                  loop.set("previtem", i > 0 ? filtered_items.at(i - 1) : Value());
+                  loop.set("nextitem", i < n - 1 ? filtered_items.at(i + 1) : Value());
+                  body->render(out, loop_context);
+              }
+          }
+      };
+
+      if (recursive) {
+        loop_function = [&](const std::shared_ptr<Context> &, ArgumentsValue & args) {
+            if (args.args.size() != 1 || !args.kwargs.empty() || !args.args[0].is_array()) {
+                throw std::runtime_error("loop() expects exactly 1 positional iterable argument");
+            }
+            auto & items = args.args[0];
+            visit(items);
+            return Value();
+        };
+      }
+
+      visit(iterable_value);
+  }
+};
+
+class MacroNode : public TemplateNode {
+    std::shared_ptr<VariableExpr> name;
+    Expression::Parameters params;
+    std::shared_ptr<TemplateNode> body;
+    std::unordered_map<std::string, size_t> named_param_positions;
+public:
+    MacroNode(const Location & location, std::shared_ptr<VariableExpr> && n, Expression::Parameters && p, std::shared_ptr<TemplateNode> && b)
+        : TemplateNode(location), name(std::move(n)), params(std::move(p)), body(std::move(b)) {
+        for (size_t i = 0; i < params.size(); ++i) {
+          const auto & name = params[i].first;
+          if (!name.empty()) {
+            named_param_positions[name] = i;
+          }
+        }
+    }
+    void do_render(std::ostringstream &, const std::shared_ptr<Context> & macro_context) const override {
+        if (!name) throw std::runtime_error("MacroNode.name is null");
+        if (!body) throw std::runtime_error("MacroNode.body is null");
+        auto callable = Value::callable([&](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+            auto call_context = macro_context;
+            std::vector<bool> param_set(params.size(), false);
+            for (size_t i = 0, n = args.args.size(); i < n; i++) {
+                auto & arg = args.args[i];
+                if (i >= params.size()) throw std::runtime_error("Too many positional arguments for macro " + name->get_name());
+                param_set[i] = true;
+                auto & param_name = params[i].first;
+                call_context->set(param_name, arg);
+            }
+            for (auto & [arg_name, value] : args.kwargs) {
+                auto it = named_param_positions.find(arg_name);
+                if (it == named_param_positions.end()) throw std::runtime_error("Unknown parameter name for macro " + name->get_name() + ": " + arg_name);
+
+                call_context->set(arg_name, value);
+                param_set[it->second] = true;
+            }
+            // Set default values for parameters that were not passed
+            for (size_t i = 0, n = params.size(); i < n; i++) {
+                if (!param_set[i] && params[i].second != nullptr) {
+                    auto val = params[i].second->evaluate(context);
+                    call_context->set(params[i].first, val);
+                }
+            }
+            return body->render(call_context);
+        });
+        macro_context->set(name->get_name(), callable);
+    }
+};
+
+class FilterNode : public TemplateNode {
+    std::shared_ptr<Expression> filter;
+    std::shared_ptr<TemplateNode> body;
+
+public:
+    FilterNode(const Location & location, std::shared_ptr<Expression> && f, std::shared_ptr<TemplateNode> && b)
+        : TemplateNode(location), filter(std::move(f)), body(std::move(b)) {}
+
+    void do_render(std::ostringstream & out, const std::shared_ptr<Context> & context) const override {
+        if (!filter) throw std::runtime_error("FilterNode.filter is null");
+        if (!body) throw std::runtime_error("FilterNode.body is null");
+        auto filter_value = filter->evaluate(context);
+        if (!filter_value.is_callable()) {
+            throw std::runtime_error("Filter must be a callable: " + filter_value.dump());
+        }
+        std::string rendered_body = body->render(context);
+
+        ArgumentsValue filter_args = {{Value(rendered_body)}, {}};
+        auto result = filter_value.call(context, filter_args);
+        out << result.to_str();
+    }
+};
+
+class SetNode : public TemplateNode {
+    std::string ns;
+    std::vector<std::string> var_names;
+    std::shared_ptr<Expression> value;
+public:
+    SetNode(const Location & location, const std::string & ns, const std::vector<std::string> & vns, std::shared_ptr<Expression> && v)
+        : TemplateNode(location), ns(ns), var_names(vns), value(std::move(v)) {}
+    void do_render(std::ostringstream &, const std::shared_ptr<Context> & context) const override {
+      if (!value) throw std::runtime_error("SetNode.value is null");
+      if (!ns.empty()) {
+        if (var_names.size() != 1) {
+          throw std::runtime_error("Namespaced set only supports a single variable name");
+        }
+        auto & name = var_names[0];
+        auto ns_value = context->get(ns);
+        if (!ns_value.is_object()) throw std::runtime_error("Namespace '" + ns + "' is not an object");
+        ns_value.set(name, this->value->evaluate(context));
+      } else {
+        auto val = value->evaluate(context);
+        destructuring_assign(var_names, context, val);
+      }
+    }
+};
+
+class SetTemplateNode : public TemplateNode {
+    std::string name;
+    std::shared_ptr<TemplateNode> template_value;
+public:
+    SetTemplateNode(const Location & location, const std::string & name, std::shared_ptr<TemplateNode> && tv)
+        : TemplateNode(location), name(name), template_value(std::move(tv)) {}
+    void do_render(std::ostringstream &, const std::shared_ptr<Context> & context) const override {
+      if (!template_value) throw std::runtime_error("SetTemplateNode.template_value is null");
+      Value value { template_value->render(context) };
+      context->set(name, value);
+    }
+};
+
+class IfExpr : public Expression {
+    std::shared_ptr<Expression> condition;
+    std::shared_ptr<Expression> then_expr;
+    std::shared_ptr<Expression> else_expr;
+public:
+    IfExpr(const Location & location, std::shared_ptr<Expression> && c, std::shared_ptr<Expression> && t, std::shared_ptr<Expression> && e)
+        : Expression(location), condition(std::move(c)), then_expr(std::move(t)), else_expr(std::move(e)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+      if (!condition) throw std::runtime_error("IfExpr.condition is null");
+      if (!then_expr) throw std::runtime_error("IfExpr.then_expr is null");
+      if (condition->evaluate(context).to_bool()) {
+        return then_expr->evaluate(context);
+      }
+      if (else_expr) {
+        return else_expr->evaluate(context);
+      }
+      return nullptr;
+    }
+};
+
+class LiteralExpr : public Expression {
+    Value value;
+public:
+    LiteralExpr(const Location & location, const Value& v)
+      : Expression(location), value(v) {}
+    Value do_evaluate(const std::shared_ptr<Context> &) const override { return value; }
+};
+
+class ArrayExpr : public Expression {
+    std::vector<std::shared_ptr<Expression>> elements;
+public:
+    ArrayExpr(const Location & location, std::vector<std::shared_ptr<Expression>> && e)
+      : Expression(location), elements(std::move(e)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        auto result = Value::array();
+        for (const auto& e : elements) {
+            if (!e) throw std::runtime_error("Array element is null");
+            result.push_back(e->evaluate(context));
+        }
+        return result;
+    }
+};
+
+class DictExpr : public Expression {
+    std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<Expression>>> elements;
+public:
+    DictExpr(const Location & location, std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<Expression>>> && e)
+      : Expression(location), elements(std::move(e)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        auto result = Value::object();
+        for (const auto& [key, value] : elements) {
+            if (!key) throw std::runtime_error("Dict key is null");
+            if (!value) throw std::runtime_error("Dict value is null");
+            result.set(key->evaluate(context), value->evaluate(context));
+        }
+        return result;
+    }
+};
+
+class SliceExpr : public Expression {
+public:
+    std::shared_ptr<Expression> start, end;
+    SliceExpr(const Location & location, std::shared_ptr<Expression> && s, std::shared_ptr<Expression> && e)
+      : Expression(location), start(std::move(s)), end(std::move(e)) {}
+    Value do_evaluate(const std::shared_ptr<Context> &) const override {
+        throw std::runtime_error("SliceExpr not implemented");
+    }
+};
+
+class SubscriptExpr : public Expression {
+    std::shared_ptr<Expression> base;
+    std::shared_ptr<Expression> index;
+public:
+    SubscriptExpr(const Location & location, std::shared_ptr<Expression> && b, std::shared_ptr<Expression> && i)
+        : Expression(location), base(std::move(b)), index(std::move(i)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!base) throw std::runtime_error("SubscriptExpr.base is null");
+        if (!index) throw std::runtime_error("SubscriptExpr.index is null");
+        auto target_value = base->evaluate(context);
+        if (auto slice = dynamic_cast<SliceExpr*>(index.get())) {
+          auto start = slice->start ? slice->start->evaluate(context).get<int64_t>() : 0;
+          auto end = slice->end ? slice->end->evaluate(context).get<int64_t>() : (int64_t) target_value.size();
+          if (target_value.is_string()) {
+            std::string s = target_value.get<std::string>();
+            if (start < 0) start = s.size() + start;
+            if (end < 0) end = s.size() + end;
+            return s.substr(start, end - start);
+          } else if (target_value.is_array()) {
+            if (start < 0) start = target_value.size() + start;
+            if (end < 0) end = target_value.size() + end;
+            auto result = Value::array();
+            for (auto i = start; i < end; ++i) {
+              result.push_back(target_value.at(i));
+            }
+            return result;
+          } else {
+            throw std::runtime_error(target_value.is_null() ? "Cannot subscript null" : "Subscripting only supported on arrays and strings");
+          }
+        } else {
+          auto index_value = index->evaluate(context);
+          if (target_value.is_null()) {
+            if (auto t = dynamic_cast<VariableExpr*>(base.get())) {
+              throw std::runtime_error("'" + t->get_name() + "' is " + (context->contains(t->get_name()) ? "null" : "not defined"));
+            }
+            throw std::runtime_error("Trying to access property '" +  index_value.dump() + "' on null!");
+          }
+          return target_value.get(index_value);
+        }
+    }
+};
+
+class UnaryOpExpr : public Expression {
+public:
+    enum class Op { Plus, Minus, LogicalNot, Expansion, ExpansionDict };
+    std::shared_ptr<Expression> expr;
+    Op op;
+    UnaryOpExpr(const Location & location, std::shared_ptr<Expression> && e, Op o)
+      : Expression(location), expr(std::move(e)), op(o) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!expr) throw std::runtime_error("UnaryOpExpr.expr is null");
+        auto e = expr->evaluate(context);
+        switch (op) {
+            case Op::Plus: return e;
+            case Op::Minus: return -e;
+            case Op::LogicalNot: return !e.to_bool();
+            case Op::Expansion:
+            case Op::ExpansionDict:
+                throw std::runtime_error("Expansion operator is only supported in function calls and collections");
+
+        }
+        throw std::runtime_error("Unknown unary operator");
+    }
+};
+
+class BinaryOpExpr : public Expression {
+public:
+    enum class Op { StrConcat, Add, Sub, Mul, MulMul, Div, DivDiv, Mod, Eq, Ne, Lt, Gt, Le, Ge, And, Or, In, NotIn, Is, IsNot };
+private:
+    std::shared_ptr<Expression> left;
+    std::shared_ptr<Expression> right;
+    Op op;
+public:
+    BinaryOpExpr(const Location & location, std::shared_ptr<Expression> && l, std::shared_ptr<Expression> && r, Op o)
+        : Expression(location), left(std::move(l)), right(std::move(r)), op(o) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!left) throw std::runtime_error("BinaryOpExpr.left is null");
+        if (!right) throw std::runtime_error("BinaryOpExpr.right is null");
+        auto l = left->evaluate(context);
+
+        auto do_eval = [&](const Value & l) -> Value {
+          if (op == Op::Is || op == Op::IsNot) {
+            auto t = dynamic_cast<VariableExpr*>(right.get());
+            if (!t) throw std::runtime_error("Right side of 'is' operator must be a variable");
+
+            auto eval = [&]() {
+              const auto & name = t->get_name();
+              if (name == "none") return l.is_null();
+              if (name == "boolean") return l.is_boolean();
+              if (name == "integer") return l.is_number_integer();
+              if (name == "float") return l.is_number_float();
+              if (name == "number") return l.is_number();
+              if (name == "string") return l.is_string();
+              if (name == "mapping") return l.is_object();
+              if (name == "iterable") return l.is_iterable();
+              if (name == "sequence") return l.is_array();
+              if (name == "defined") return !l.is_null();
+              throw std::runtime_error("Unknown type for 'is' operator: " + name);
+            };
+            auto value = eval();
+            return Value(op == Op::Is ? value : !value);
+          }
+
+          if (op == Op::And) {
+            if (!l.to_bool()) return Value(false);
+            return right->evaluate(context).to_bool();
+          } else if (op == Op::Or) {
+            if (l.to_bool()) return l;
+            return right->evaluate(context);
+          }
+
+          auto r = right->evaluate(context);
+          switch (op) {
+              case Op::StrConcat: return l.to_str() + r.to_str();
+              case Op::Add:       return l + r;
+              case Op::Sub:       return l - r;
+              case Op::Mul:       return l * r;
+              case Op::Div:       return l / r;
+              case Op::MulMul:    return std::pow(l.get<double>(), r.get<double>());
+              case Op::DivDiv:    return l.get<int64_t>() / r.get<int64_t>();
+              case Op::Mod:       return l.get<int64_t>() % r.get<int64_t>();
+              case Op::Eq:        return l == r;
+              case Op::Ne:        return l != r;
+              case Op::Lt:        return l < r;
+              case Op::Gt:        return l > r;
+              case Op::Le:        return l <= r;
+              case Op::Ge:        return l >= r;
+              case Op::In:        return (r.is_array() || r.is_object()) && r.contains(l);
+              case Op::NotIn:     return !(r.is_array() && r.contains(l));
+              default:            break;
+          }
+          throw std::runtime_error("Unknown binary operator");
+        };
+
+        if (l.is_callable()) {
+          return Value::callable([l, do_eval](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+            auto ll = l.call(context, args);
+            return do_eval(ll); //args[0].second);
+          });
+        } else {
+          return do_eval(l);
+        }
+    }
+};
+
+struct ArgumentsExpression {
+    std::vector<std::shared_ptr<Expression>> args;
+    std::vector<std::pair<std::string, std::shared_ptr<Expression>>> kwargs;
+
+    ArgumentsValue evaluate(const std::shared_ptr<Context> & context) const {
+        ArgumentsValue vargs;
+        for (const auto& arg : this->args) {
+            if (auto un_expr = std::dynamic_pointer_cast<UnaryOpExpr>(arg)) {
+                if (un_expr->op == UnaryOpExpr::Op::Expansion) {
+                    auto array = un_expr->expr->evaluate(context);
+                    if (!array.is_array()) {
+                        throw std::runtime_error("Expansion operator only supported on arrays");
+                    }
+                    array.for_each([&](Value & value) {
+                        vargs.args.push_back(value);
+                    });
+                    continue;
+                } else if (un_expr->op == UnaryOpExpr::Op::ExpansionDict) {
+                    auto dict = un_expr->expr->evaluate(context);
+                    if (!dict.is_object()) {
+                        throw std::runtime_error("ExpansionDict operator only supported on objects");
+                    }
+                    dict.for_each([&](const Value & key) {
+                        vargs.kwargs.push_back({key.get<std::string>(), dict.at(key)});
+                    });
+                    continue;
+                }
+            }
+            vargs.args.push_back(arg->evaluate(context));
+        }
+        for (const auto& [name, value] : this->kwargs) {
+            vargs.kwargs.push_back({name, value->evaluate(context)});
+        }
+        return vargs;
+    }
+};
+
+static std::string strip(const std::string & s) {
+  auto start = s.find_first_not_of(" \t\n\r");
+  if (start == std::string::npos) return "";
+  auto end = s.find_last_not_of(" \t\n\r");
+  return s.substr(start, end - start + 1);
+}
+
+static std::string html_escape(const std::string & s) {
+  std::string result;
+  result.reserve(s.size());
+  for (const auto & c : s) {
+    switch (c) {
+      case '&': result += "&amp;"; break;
+      case '<': result += "&lt;"; break;
+      case '>': result += "&gt;"; break;
+      case '"': result += "&#34;"; break;
+      case '\'': result += "&apos;"; break;
+      default: result += c; break;
+    }
+  }
+  return result;
+}
+
+class MethodCallExpr : public Expression {
+    std::shared_ptr<Expression> object;
+    std::shared_ptr<VariableExpr> method;
+    ArgumentsExpression args;
+public:
+    MethodCallExpr(const Location & location, std::shared_ptr<Expression> && obj, std::shared_ptr<VariableExpr> && m, ArgumentsExpression && a)
+        : Expression(location), object(std::move(obj)), method(std::move(m)), args(std::move(a)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!object) throw std::runtime_error("MethodCallExpr.object is null");
+        if (!method) throw std::runtime_error("MethodCallExpr.method is null");
+        auto obj = object->evaluate(context);
+        auto vargs = args.evaluate(context);
+        if (obj.is_null()) {
+          throw std::runtime_error("Trying to call method '" + method->get_name() + "' on null");
+        }
+        if (obj.is_array()) {
+          if (method->get_name() == "append") {
+              vargs.expectArgs("append method", {1, 1}, {0, 0});
+              obj.push_back(vargs.args[0]);
+              return Value();
+          } else if (method->get_name() == "pop") {
+              vargs.expectArgs("pop method", {0, 1}, {0, 0});
+              return obj.pop(vargs.args.empty() ? Value() : vargs.args[0]);
+          } else if (method->get_name() == "insert") {
+              vargs.expectArgs("insert method", {2, 2}, {0, 0});
+              auto index = vargs.args[0].get<int64_t>();
+              if (index < 0 || index > (int64_t) obj.size()) throw std::runtime_error("Index out of range for insert method");
+              obj.insert(index, vargs.args[1]);
+              return Value();
+          }
+        } else if (obj.is_object()) {
+          if (method->get_name() == "items") {
+            vargs.expectArgs("items method", {0, 0}, {0, 0});
+            auto result = Value::array();
+            for (const auto& key : obj.keys()) {
+              result.push_back(Value::array({key, obj.at(key)}));
+            }
+            return result;
+          } else if (method->get_name() == "pop") {
+            vargs.expectArgs("pop method", {1, 1}, {0, 0});
+            return obj.pop(vargs.args[0]);
+          } else if (method->get_name() == "get") {
+            vargs.expectArgs("get method", {1, 2}, {0, 0});
+            auto key = vargs.args[0];
+            if (vargs.args.size() == 1) {
+              return obj.contains(key) ? obj.at(key) : Value();
+            } else {
+              return obj.contains(key) ? obj.at(key) : vargs.args[1];
+            }
+          } else if (obj.contains(method->get_name())) {
+            auto callable = obj.at(method->get_name());
+            if (!callable.is_callable()) {
+              throw std::runtime_error("Property '" + method->get_name() + "' is not callable");
+            }
+            return callable.call(context, vargs);
+          }
+        } else if (obj.is_string()) {
+          auto str = obj.get<std::string>();
+          if (method->get_name() == "strip") {
+            vargs.expectArgs("strip method", {0, 0}, {0, 0});
+            return Value(strip(str));
+          } else if (method->get_name() == "endswith") {
+            vargs.expectArgs("endswith method", {1, 1}, {0, 0});
+            auto suffix = vargs.args[0].get<std::string>();
+            return suffix.length() <= str.length() && std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
+          } else if (method->get_name() == "title") {
+            vargs.expectArgs("title method", {0, 0}, {0, 0});
+            auto res = str;
+            for (size_t i = 0, n = res.size(); i < n; ++i) {
+              if (i == 0 || std::isspace(res[i - 1])) res[i] = std::toupper(res[i]);
+              else res[i] = std::tolower(res[i]);
+            }
+            return res;
+          }
+        }
+        throw std::runtime_error("Unknown method: " + method->get_name());
+    }
+};
+
+class CallExpr : public Expression {
+public:
+    std::shared_ptr<Expression> object;
+    ArgumentsExpression args;
+    CallExpr(const Location & location, std::shared_ptr<Expression> && obj, ArgumentsExpression && a)
+        : Expression(location), object(std::move(obj)), args(std::move(a)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        if (!object) throw std::runtime_error("CallExpr.object is null");
+        auto obj = object->evaluate(context);
+        if (!obj.is_callable()) {
+          throw std::runtime_error("Object is not callable: " + obj.dump(2));
+        }
+        auto vargs = args.evaluate(context);
+        return obj.call(context, vargs);
+    }
+};
+
+class FilterExpr : public Expression {
+    std::vector<std::shared_ptr<Expression>> parts;
+public:
+    FilterExpr(const Location & location, std::vector<std::shared_ptr<Expression>> && p)
+      : Expression(location), parts(std::move(p)) {}
+    Value do_evaluate(const std::shared_ptr<Context> & context) const override {
+        Value result;
+        bool first = true;
+        for (const auto& part : parts) {
+          if (!part) throw std::runtime_error("FilterExpr.part is null");
+          if (first) {
+            first = false;
+            result = part->evaluate(context);
+          } else {
+            if (auto ce = dynamic_cast<CallExpr*>(part.get())) {
+              auto target = ce->object->evaluate(context);
+              ArgumentsValue args = ce->args.evaluate(context);
+              args.args.insert(args.args.begin(), result);
+              result = target.call(context, args);
+            } else {
+              auto callable = part->evaluate(context);
+              ArgumentsValue args;
+              args.args.insert(args.args.begin(), result);
+              result = callable.call(context, args);
+            }
+          }
+        }
+        return result;
+    }
+
+    void prepend(std::shared_ptr<Expression> && e) {
+        parts.insert(parts.begin(), std::move(e));
+    }
+};
+
+class Parser {
+private:
+    using CharIterator = std::string::const_iterator;
+
+    std::shared_ptr<std::string> template_str;
+    CharIterator start, end, it;
+    Options options;
+
+    Parser(const std::shared_ptr<std::string>& template_str, const Options & options) : template_str(template_str), options(options) {
+      if (!template_str) throw std::runtime_error("Template string is null");
+      start = it = this->template_str->begin();
+      end = this->template_str->end();
+    }
+
+    bool consumeSpaces(SpaceHandling space_handling = SpaceHandling::Strip) {
+      if (space_handling == SpaceHandling::Strip) {
+        while (it != end && std::isspace(*it)) ++it;
+      }
+      return true;
+    }
+
+    std::unique_ptr<std::string> parseString() {
+      auto doParse = [&](char quote) -> std::unique_ptr<std::string> {
+        if (it == end || *it != quote) return nullptr;
+        std::string result;
+        bool escape = false;
+        for (++it; it != end; ++it) {
+          if (escape) {
+            escape = false;
+            switch (*it) {
+              case 'n': result += '\n'; break;
+              case 'r': result += '\r'; break;
+              case 't': result += '\t'; break;
+              case 'b': result += '\b'; break;
+              case 'f': result += '\f'; break;
+              case '\\': result += '\\'; break;
+              default:
+                if (*it == quote) {
+                  result += quote;
+                } else {
+                  result += *it;
+                }
+                break;
+            }
+          } else if (*it == '\\') {
+            escape = true;
+          } else if (*it == quote) {
+              ++it;
+            return std::make_unique<std::string>(std::move(result));
+          } else {
+            result += *it;
+          }
+        }
+        return nullptr;
+      };
+
+      consumeSpaces();
+      if (it == end) return nullptr;
+      if (*it == '"') return doParse('"');
+      if (*it == '\'') return doParse('\'');
+      return nullptr;
+    }
+
+    json parseNumber(CharIterator& it, const CharIterator& end) {
+        auto before = it;
+        consumeSpaces();
+        auto start = it;
+        bool hasDecimal = false;
+        bool hasExponent = false;
+
+        if (it != end && (*it == '-' || *it == '+')) ++it;
+
+        while (it != end) {
+          if (std::isdigit(*it)) {
+            ++it;
+          } else if (*it == '.') {
+            if (hasDecimal) throw std::runtime_error("Multiple decimal points");
+            hasDecimal = true;
+            ++it;
+          } else if (it != start && (*it == 'e' || *it == 'E')) {
+            if (hasExponent) throw std::runtime_error("Multiple exponents");
+            hasExponent = true;
+            ++it;
+          } else {
+            break;
+          }
+        }
+        if (start == it) {
+          it = before;
+          return json(); // No valid characters found
+        }
+
+        std::string str(start, it);
+        try {
+          return json::parse(str);
+        } catch (json::parse_error& e) {
+          throw std::runtime_error("Failed to parse number: '" + str + "' (" + std::string(e.what()) + ")");
+          return json();
+        }
+    }
+
+    /** integer, float, bool, string */
+    std::shared_ptr<Value> parseConstant() {
+      auto start = it;
+      consumeSpaces();
+      if (it == end) return nullptr;
+      if (*it == '"' || *it == '\'') {
+        auto str = parseString();
+        if (str) return std::make_shared<Value>(*str);
+      }
+      static std::regex prim_tok(R"(true\b|True\b|false\b|False\b|None\b)");
+      auto token = consumeToken(prim_tok);
+      if (!token.empty()) {
+        if (token == "true" || token == "True") return std::make_shared<Value>(true);
+        if (token == "false" || token == "False") return std::make_shared<Value>(false);
+        if (token == "None") return std::make_shared<Value>(nullptr);
+        throw std::runtime_error("Unknown constant token: " + token);
+      }
+
+      auto number = parseNumber(it, end);
+      if (!number.is_null()) return std::make_shared<Value>(number);
+
+      it = start;
+      return nullptr;
+    }
+
+    class expression_parsing_error : public std::runtime_error {
+        const CharIterator it;
+      public:
+        expression_parsing_error(const std::string & message, const CharIterator & it)
+            : std::runtime_error(message), it(it) {}
+        size_t get_pos(const CharIterator & begin) const {
+            return std::distance(begin, it);
+      }
+    };
+
+    bool peekSymbols(const std::vector<std::string> & symbols) const {
+        for (const auto & symbol : symbols) {
+            if (std::distance(it, end) >= (int64_t) symbol.size() && std::string(it, it + symbol.size()) == symbol) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    std::vector<std::string> consumeTokenGroups(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) {
+        auto start = it;
+        consumeSpaces(space_handling);
+        std::smatch match;
+        if (std::regex_search(it, end, match, regex) && match.position() == 0) {
+            it += match[0].length();
+            std::vector<std::string> ret;
+            for (size_t i = 0, n = match.size(); i < n; ++i) {
+                ret.push_back(match[i].str());
+            }
+            return ret;
+        }
+        it = start;
+        return {};
+    }
+    std::string consumeToken(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) {
+        auto start = it;
+        consumeSpaces(space_handling);
+        std::smatch match;
+        if (std::regex_search(it, end, match, regex) && match.position() == 0) {
+            it += match[0].length();
+            return match[0].str();
+        }
+        it = start;
+        return "";
+    }
+
+    std::string consumeToken(const std::string & token, SpaceHandling space_handling = SpaceHandling::Strip) {
+        auto start = it;
+        consumeSpaces(space_handling);
+        if (std::distance(it, end) >= (int64_t) token.size() && std::string(it, it + token.size()) == token) {
+            it += token.size();
+            return token;
+        }
+        it = start;
+        return "";
+    }
+
+    std::shared_ptr<Expression> parseExpression(bool allow_if_expr = true) {
+        auto left = parseLogicalOr();
+        if (it == end) return left;
+
+        if (!allow_if_expr) return left;
+
+        static std::regex if_tok(R"(if\b)");
+        if (consumeToken(if_tok).empty()) {
+          return left;
+        }
+
+        auto location = get_location();
+        auto [condition, else_expr] = parseIfExpression();
+        return std::make_shared<IfExpr>(location, std::move(condition), std::move(left), std::move(else_expr));
+    }
+
+    Location get_location() const {
+        return {template_str, (size_t) std::distance(start, it)};
+    }
+
+    std::pair<std::shared_ptr<Expression>, std::shared_ptr<Expression>> parseIfExpression() {
+        auto condition = parseLogicalOr();
+        if (!condition) throw std::runtime_error("Expected condition expression");
+
+        static std::regex else_tok(R"(else\b)");
+        std::shared_ptr<Expression> else_expr;
+        if (!consumeToken(else_tok).empty()) {
+          else_expr = parseExpression();
+          if (!else_expr) throw std::runtime_error("Expected 'else' expression");
+        }
+        return std::pair(std::move(condition), std::move(else_expr));
+    }
+
+    std::shared_ptr<Expression> parseLogicalOr() {
+        auto left = parseLogicalAnd();
+        if (!left) throw std::runtime_error("Expected left side of 'logical or' expression");
+
+        static std::regex or_tok(R"(or\b)");
+        auto location = get_location();
+        while (!consumeToken(or_tok).empty()) {
+            auto right = parseLogicalAnd();
+            if (!right) throw std::runtime_error("Expected right side of 'or' expression");
+            left = std::make_shared<BinaryOpExpr>(location, std::move(left), std::move(right), BinaryOpExpr::Op::Or);
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> parseLogicalNot() {
+        static std::regex not_tok(R"(not\b)");
+        auto location = get_location();
+
+        if (!consumeToken(not_tok).empty()) {
+          auto sub = parseLogicalNot();
+          if (!sub) throw std::runtime_error("Expected expression after 'not' keyword");
+          return std::make_shared<UnaryOpExpr>(location, std::move(sub), UnaryOpExpr::Op::LogicalNot);
+        }
+        return parseLogicalCompare();
+    }
+
+    std::shared_ptr<Expression> parseLogicalAnd() {
+        auto left = parseLogicalNot();
+        if (!left) throw std::runtime_error("Expected left side of 'logical and' expression");
+
+        static std::regex and_tok(R"(and\b)");
+        auto location = get_location();
+        while (!consumeToken(and_tok).empty()) {
+            auto right = parseLogicalNot();
+            if (!right) throw std::runtime_error("Expected right side of 'and' expression");
+            left = std::make_shared<BinaryOpExpr>(location, std::move(left), std::move(right), BinaryOpExpr::Op::And);
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> parseLogicalCompare() {
+        auto left = parseStringConcat();
+        if (!left) throw std::runtime_error("Expected left side of 'logical compare' expression");
+
+        static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not[\r\n\s]+in\b)");
+        static std::regex not_tok(R"(not\b)");
+        std::string op_str;
+        while (!(op_str = consumeToken(compare_tok)).empty()) {
+            auto location = get_location();
+            if (op_str == "is") {
+              auto negated = !consumeToken(not_tok).empty();
+
+              auto identifier = parseIdentifier();
+              if (!identifier) throw std::runtime_error("Expected identifier after 'is' keyword");
+
+              return std::make_shared<BinaryOpExpr>(
+                  left->location,
+                  std::move(left), std::move(identifier),
+                  negated ? BinaryOpExpr::Op::IsNot : BinaryOpExpr::Op::Is);
+            }
+            auto right = parseStringConcat();
+            if (!right) throw std::runtime_error("Expected right side of 'logical compare' expression");
+            BinaryOpExpr::Op op;
+            if (op_str == "==") op = BinaryOpExpr::Op::Eq;
+            else if (op_str == "!=") op = BinaryOpExpr::Op::Ne;
+            else if (op_str == "<") op = BinaryOpExpr::Op::Lt;
+            else if (op_str == ">") op = BinaryOpExpr::Op::Gt;
+            else if (op_str == "<=") op = BinaryOpExpr::Op::Le;
+            else if (op_str == ">=") op = BinaryOpExpr::Op::Ge;
+            else if (op_str == "in") op = BinaryOpExpr::Op::In;
+            else if (op_str.substr(0, 3) == "not") op = BinaryOpExpr::Op::NotIn;
+            else throw std::runtime_error("Unknown comparison operator: " + op_str);
+            left = std::make_shared<BinaryOpExpr>(get_location(), std::move(left), std::move(right), op);
+        }
+        return left;
+    }
+
+    Expression::Parameters parseParameters() {
+        consumeSpaces();
+        if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in param list");
+
+        Expression::Parameters result;
+
+        while (it != end) {
+            if (!consumeToken(")").empty()) {
+                return result;
+            }
+            auto expr = parseExpression();
+            if (!expr) throw std::runtime_error("Expected expression in call args");
+
+            if (auto ident = dynamic_cast<VariableExpr*>(expr.get())) {
+                if (!consumeToken("=").empty()) {
+                    auto value = parseExpression();
+                    if (!value) throw std::runtime_error("Expected expression in for named arg");
+                    result.emplace_back(ident->get_name(), std::move(value));
+                } else {
+                    result.emplace_back(ident->get_name(), nullptr);
+                }
+            } else {
+                result.emplace_back(std::string(), std::move(expr));
+            }
+            if (consumeToken(",").empty()) {
+              if (consumeToken(")").empty()) {
+                throw std::runtime_error("Expected closing parenthesis in call args");
+              }
+              return result;
+            }
+        }
+        throw std::runtime_error("Expected closing parenthesis in call args");
+    }
+
+    ArgumentsExpression parseCallArgs() {
+        consumeSpaces();
+        if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in call args");
+
+        ArgumentsExpression result;
+
+        while (it != end) {
+            if (!consumeToken(")").empty()) {
+                return result;
+            }
+            auto expr = parseExpression();
+            if (!expr) throw std::runtime_error("Expected expression in call args");
+
+            if (auto ident = dynamic_cast<VariableExpr*>(expr.get())) {
+                if (!consumeToken("=").empty()) {
+                    auto value = parseExpression();
+                    if (!value) throw std::runtime_error("Expected expression in for named arg");
+                    result.kwargs.emplace_back(ident->get_name(), std::move(value));
+                } else {
+                    result.args.emplace_back(std::move(expr));
+                }
+            } else {
+                result.args.emplace_back(std::move(expr));
+            }
+            if (consumeToken(",").empty()) {
+              if (consumeToken(")").empty()) {
+                throw std::runtime_error("Expected closing parenthesis in call args");
+              }
+              return result;
+            }
+        }
+        throw std::runtime_error("Expected closing parenthesis in call args");
+    }
+
+    std::shared_ptr<VariableExpr> parseIdentifier() {
+        static std::regex ident_regex(R"((?!(?:not|is|and|or|del)\b)[a-zA-Z_]\w*)");
+        auto location = get_location();
+        auto ident = consumeToken(ident_regex);
+        if (ident.empty())
+          return nullptr;
+        return std::make_shared<VariableExpr>(location, ident);
+    }
+
+    std::shared_ptr<Expression> parseStringConcat() {
+        auto left = parseMathPow();
+        if (!left) throw std::runtime_error("Expected left side of 'string concat' expression");
+
+        static std::regex concat_tok(R"(~(?!\}))");
+        if (!consumeToken(concat_tok).empty()) {
+            auto right = parseLogicalAnd();
+            if (!right) throw std::runtime_error("Expected right side of 'string concat' expression");
+            left = std::make_shared<BinaryOpExpr>(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::StrConcat);
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> parseMathPow() {
+        auto left = parseMathPlusMinus();
+        if (!left) throw std::runtime_error("Expected left side of 'math pow' expression");
+
+        while (!consumeToken("**").empty()) {
+            auto right = parseMathPlusMinus();
+            if (!right) throw std::runtime_error("Expected right side of 'math pow' expression");
+            left = std::make_shared<BinaryOpExpr>(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::MulMul);
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> parseMathPlusMinus() {
+        static std::regex plus_minus_tok(R"(\+|-(?![}%#]\}))");
+
+        auto left = parseMathMulDiv();
+        if (!left) throw std::runtime_error("Expected left side of 'math plus/minus' expression");
+        std::string op_str;
+        while (!(op_str = consumeToken(plus_minus_tok)).empty()) {
+            auto right = parseMathMulDiv();
+            if (!right) throw std::runtime_error("Expected right side of 'math plus/minus' expression");
+            auto op = op_str == "+" ? BinaryOpExpr::Op::Add : BinaryOpExpr::Op::Sub;
+            left = std::make_shared<BinaryOpExpr>(get_location(), std::move(left), std::move(right), op);
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> parseMathMulDiv() {
+        auto left = parseMathUnaryPlusMinus();
+        if (!left) throw std::runtime_error("Expected left side of 'math mul/div' expression");
+
+        static std::regex mul_div_tok(R"(\*\*?|//?|%(?!\}))");
+        std::string op_str;
+        while (!(op_str = consumeToken(mul_div_tok)).empty()) {
+            auto right = parseMathUnaryPlusMinus();
+            if (!right) throw std::runtime_error("Expected right side of 'math mul/div' expression");
+            auto op = op_str == "*" ? BinaryOpExpr::Op::Mul
+                : op_str == "**" ? BinaryOpExpr::Op::MulMul
+                : op_str == "/" ? BinaryOpExpr::Op::Div
+                : op_str == "//" ? BinaryOpExpr::Op::DivDiv
+                : BinaryOpExpr::Op::Mod;
+            left = std::make_shared<BinaryOpExpr>(get_location(), std::move(left), std::move(right), op);
+        }
+
+        if (!consumeToken("|").empty()) {
+            auto expr = parseMathMulDiv();
+            if (auto filter = dynamic_cast<FilterExpr*>(expr.get())) {
+                filter->prepend(std::move(left));
+                return expr;
+            } else {
+                std::vector<std::shared_ptr<Expression>> parts;
+                parts.emplace_back(std::move(left));
+                parts.emplace_back(std::move(expr));
+                return std::make_shared<FilterExpr>(get_location(), std::move(parts));
+            }
+        }
+        return left;
+    }
+
+    std::shared_ptr<Expression> call_func(const std::string & name, ArgumentsExpression && args) const {
+        return std::make_shared<CallExpr>(get_location(), std::make_shared<VariableExpr>(get_location(), name), std::move(args));
+    }
+
+    std::shared_ptr<Expression> parseMathUnaryPlusMinus() {
+        static std::regex unary_plus_minus_tok(R"(\+|-(?![}%#]\}))");
+        auto op_str = consumeToken(unary_plus_minus_tok);
+        auto expr = parseExpansion();
+        if (!expr) throw std::runtime_error("Expected expr of 'unary plus/minus/expansion' expression");
+
+        if (!op_str.empty()) {
+            auto op = op_str == "+" ? UnaryOpExpr::Op::Plus : UnaryOpExpr::Op::Minus;
+            return std::make_shared<UnaryOpExpr>(get_location(), std::move(expr), op);
+        }
+        return expr;
+    }
+
+    std::shared_ptr<Expression> parseExpansion() {
+      static std::regex expansion_tok(R"(\*\*?)");
+      auto op_str = consumeToken(expansion_tok);
+      auto expr = parseValueExpression();
+      if (op_str.empty()) return expr;
+      if (!expr) throw std::runtime_error("Expected expr of 'expansion' expression");
+      return std::make_shared<UnaryOpExpr>(get_location(), std::move(expr), op_str == "*" ? UnaryOpExpr::Op::Expansion : UnaryOpExpr::Op::ExpansionDict);
+    }
+
+    std::shared_ptr<Expression> parseValueExpression() {
+      auto parseValue = [&]() -> std::shared_ptr<Expression> {
+        auto location = get_location();
+        auto constant = parseConstant();
+        if (constant) return std::make_shared<LiteralExpr>(location, *constant);
+
+        static std::regex null_regex(R"(null\b)");
+        if (!consumeToken(null_regex).empty()) return std::make_shared<LiteralExpr>(location, Value());
+
+        auto identifier = parseIdentifier();
+        if (identifier) return identifier;
+
+        auto braced = parseBracedExpressionOrArray();
+        if (braced) return braced;
+
+        auto array = parseArray();
+        if (array) return array;
+
+        auto dictionary = parseDictionary();
+        if (dictionary) return dictionary;
+
+        throw std::runtime_error("Expected value expression");
+      };
+
+      auto value = parseValue();
+
+      while (it != end && consumeSpaces() && peekSymbols({ "[", "." })) {
+        if (!consumeToken("[").empty()) {
+            std::shared_ptr<Expression> index;
+            if (!consumeToken(":").empty()) {
+              auto slice_end = parseExpression();
+              index = std::make_shared<SliceExpr>(slice_end->location, nullptr, std::move(slice_end));
+            } else {
+              auto slice_start = parseExpression();
+              if (!consumeToken(":").empty()) {
+                consumeSpaces();
+                if (peekSymbols({ "]" })) {
+                  index = std::make_shared<SliceExpr>(slice_start->location, std::move(slice_start), nullptr);
+                } else {
+                  auto slice_end = parseExpression();
+                  index = std::make_shared<SliceExpr>(slice_start->location, std::move(slice_start), std::move(slice_end));
+                }
+              } else {
+                index = std::move(slice_start);
+              }
+            }
+            if (!index) throw std::runtime_error("Empty index in subscript");
+            if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript");
+
+            value = std::make_shared<SubscriptExpr>(value->location, std::move(value), std::move(index));
+        } else if (!consumeToken(".").empty()) {
+            auto identifier = parseIdentifier();
+            if (!identifier) throw std::runtime_error("Expected identifier in subscript");
+
+            consumeSpaces();
+            if (peekSymbols({ "(" })) {
+              auto callParams = parseCallArgs();
+              value = std::make_shared<MethodCallExpr>(identifier->location, std::move(value), std::move(identifier), std::move(callParams));
+            } else {
+              auto key = std::make_shared<LiteralExpr>(identifier->location, Value(identifier->get_name()));
+              value = std::make_shared<SubscriptExpr>(identifier->location, std::move(value), std::move(key));
+            }
+        }
+        consumeSpaces();
+      }
+
+      if (peekSymbols({ "(" })) {
+        auto location = get_location();
+        auto callParams = parseCallArgs();
+        value = std::make_shared<CallExpr>(location, std::move(value), std::move(callParams));
+      }
+      return value;
+    }
+
+    std::shared_ptr<Expression> parseBracedExpressionOrArray() {
+        if (consumeToken("(").empty()) return nullptr;
+
+        auto expr = parseExpression();
+        if (!expr) throw std::runtime_error("Expected expression in braced expression");
+
+        if (!consumeToken(")").empty()) {
+            return expr;  // Drop the parentheses
+        }
+
+        std::vector<std::shared_ptr<Expression>> tuple;
+        tuple.emplace_back(std::move(expr));
+
+        while (it != end) {
+          if (consumeToken(",").empty()) throw std::runtime_error("Expected comma in tuple");
+          auto next = parseExpression();
+          if (!next) throw std::runtime_error("Expected expression in tuple");
+          tuple.push_back(std::move(next));
+
+          if (!consumeToken(")").empty()) {
+              return std::make_shared<ArrayExpr>(get_location(), std::move(tuple));
+          }
+        }
+        throw std::runtime_error("Expected closing parenthesis");
+    }
+
+    std::shared_ptr<Expression> parseArray() {
+        if (consumeToken("[").empty()) return nullptr;
+
+        std::vector<std::shared_ptr<Expression>> elements;
+        if (!consumeToken("]").empty()) {
+            return std::make_shared<ArrayExpr>(get_location(), std::move(elements));
+        }
+        auto first_expr = parseExpression();
+        if (!first_expr) throw std::runtime_error("Expected first expression in array");
+        elements.push_back(std::move(first_expr));
+
+        while (it != end) {
+            if (!consumeToken(",").empty()) {
+              auto expr = parseExpression();
+              if (!expr) throw std::runtime_error("Expected expression in array");
+              elements.push_back(std::move(expr));
+            } else if (!consumeToken("]").empty()) {
+                return std::make_shared<ArrayExpr>(get_location(), std::move(elements));
+            } else {
+                throw std::runtime_error("Expected comma or closing bracket in array");
+            }
+        }
+        throw std::runtime_error("Expected closing bracket");
+    }
+
+    std::shared_ptr<Expression> parseDictionary() {
+        if (consumeToken("{").empty()) return nullptr;
+
+        std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<Expression>>> elements;
+        if (!consumeToken("}").empty()) {
+            return std::make_shared<DictExpr>(get_location(), std::move(elements));
+        }
+
+        auto parseKeyValuePair = [&]() {
+            auto key = parseExpression();
+            if (!key) throw std::runtime_error("Expected key in dictionary");
+            if (consumeToken(":").empty()) throw std::runtime_error("Expected colon betweek key & value in dictionary");
+            auto value = parseExpression();
+            if (!value) throw std::runtime_error("Expected value in dictionary");
+            elements.emplace_back(std::pair(std::move(key), std::move(value)));
+        };
+
+        parseKeyValuePair();
+
+        while (it != end) {
+            if (!consumeToken(",").empty()) {
+                parseKeyValuePair();
+            } else if (!consumeToken("}").empty()) {
+                return std::make_shared<DictExpr>(get_location(), std::move(elements));
+            } else {
+                throw std::runtime_error("Expected comma or closing brace in dictionary");
+            }
+        }
+        throw std::runtime_error("Expected closing brace");
+    }
+
+    SpaceHandling parsePreSpace(const std::string& s) const {
+        if (s == "-")
+          return SpaceHandling::Strip;
+        return SpaceHandling::Keep;
+    }
+
+    SpaceHandling parsePostSpace(const std::string& s) const {
+        if (s == "-") return SpaceHandling::Strip;
+        return SpaceHandling::Keep;
+    }
+
+    using TemplateTokenVector = std::vector<std::unique_ptr<TemplateToken>>;
+    using TemplateTokenIterator = TemplateTokenVector::const_iterator;
+
+    std::vector<std::string> parseVarNames() {
+      static std::regex varnames_regex(R"(((?:\w+)(?:[\r\n\s]*,[\r\n\s]*(?:\w+))*)[\r\n\s]*)");
+
+      std::vector<std::string> group;
+      if ((group = consumeTokenGroups(varnames_regex)).empty()) throw std::runtime_error("Expected variable names");
+      std::vector<std::string> varnames;
+      std::istringstream iss(group[1]);
+      std::string varname;
+      while (std::getline(iss, varname, ',')) {
+        varnames.push_back(strip(varname));
+      }
+      return varnames;
+    }
+
+    std::runtime_error unexpected(const TemplateToken & token) const {
+      return std::runtime_error("Unexpected " + TemplateToken::typeToString(token.type)
+        + error_location_suffix(*template_str, token.location.pos));
+    }
+    std::runtime_error unterminated(const TemplateToken & token) const {
+      return std::runtime_error("Unterminated " + TemplateToken::typeToString(token.type)
+        + error_location_suffix(*template_str, token.location.pos));
+    }
+
+    TemplateTokenVector tokenize() {
+      static std::regex comment_tok(R"(\{#([-~]?)(.*?)([-~]?)#\})");
+      static std::regex expr_open_regex(R"(\{\{([-~])?)");
+      static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
+      static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|set|endset|block|endblock|macro|endmacro|filter|endfilter)\b)");
+      static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
+      static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
+      static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
+
+      TemplateTokenVector tokens;
+      std::vector<std::string> group;
+      std::string text;
+      std::smatch match;
+
+      try {
+        while (it != end) {
+          auto location = get_location();
+
+          if (!(group = consumeTokenGroups(comment_tok, SpaceHandling::Keep)).empty()) {
+            auto pre_space = parsePreSpace(group[1]);
+            auto content = group[2];
+            auto post_space = parsePostSpace(group[3]);
+            tokens.push_back(std::make_unique<CommentTemplateToken>(location, pre_space, post_space, content));
+          } else if (!(group = consumeTokenGroups(expr_open_regex, SpaceHandling::Keep)).empty()) {
+            auto pre_space = parsePreSpace(group[1]);
+            auto expr = parseExpression();
+
+            if ((group = consumeTokenGroups(expr_close_regex)).empty()) {
+              throw std::runtime_error("Expected closing expression tag");
+            }
+
+            auto post_space = parsePostSpace(group[1]);
+            tokens.push_back(std::make_unique<ExpressionTemplateToken>(location, pre_space, post_space, std::move(expr)));
+          } else if (!(group = consumeTokenGroups(block_open_regex, SpaceHandling::Keep)).empty()) {
+            auto pre_space = parsePreSpace(group[1]);
+
+            std::string keyword;
+
+            auto parseBlockClose = [&]() -> SpaceHandling {
+              if ((group = consumeTokenGroups(block_close_regex)).empty()) throw std::runtime_error("Expected closing block tag");
+              return parsePostSpace(group[1]);
+            };
+
+            if ((keyword = consumeToken(block_keyword_tok)).empty()) throw std::runtime_error("Expected block keyword");
+
+            if (keyword == "if") {
+              auto condition = parseExpression();
+              if (!condition) throw std::runtime_error("Expected condition in if block");
+
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<IfTemplateToken>(location, pre_space, post_space, std::move(condition)));
+            } else if (keyword == "elif") {
+              auto condition = parseExpression();
+              if (!condition) throw std::runtime_error("Expected condition in elif block");
+
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<ElifTemplateToken>(location, pre_space, post_space, std::move(condition)));
+            } else if (keyword == "else") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<ElseTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "endif") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndIfTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "for") {
+              static std::regex recursive_tok(R"(recursive\b)");
+              static std::regex if_tok(R"(if\b)");
+
+              auto varnames = parseVarNames();
+              static std::regex in_tok(R"(in\b)");
+              if (consumeToken(in_tok).empty()) throw std::runtime_error("Expected 'in' keyword in for block");
+              auto iterable = parseExpression(/* allow_if_expr = */ false);
+              if (!iterable) throw std::runtime_error("Expected iterable in for block");
+
+              std::shared_ptr<Expression> condition;
+              if (!consumeToken(if_tok).empty()) {
+                condition = parseExpression();
+              }
+              auto recursive = !consumeToken(recursive_tok).empty();
+
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<ForTemplateToken>(location, pre_space, post_space, std::move(varnames), std::move(iterable), std::move(condition), recursive));
+            } else if (keyword == "endfor") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndForTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "set") {
+              static std::regex namespaced_var_regex(R"((\w+)[\s\n\r]*\.[\s\n\r]*(\w+))");
+
+              std::string ns;
+              std::vector<std::string> var_names;
+              std::shared_ptr<Expression> value;
+              if (!(group = consumeTokenGroups(namespaced_var_regex)).empty()) {
+                ns = group[1];
+                var_names.push_back(group[2]);
+
+                if (consumeToken("=").empty()) throw std::runtime_error("Expected equals sign in set block");
+
+                value = parseExpression();
+                if (!value) throw std::runtime_error("Expected value in set block");
+              } else {
+                var_names = parseVarNames();
+
+                if (!consumeToken("=").empty()) {
+                  value = parseExpression();
+                  if (!value) throw std::runtime_error("Expected value in set block");
+                }
+              }
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<SetTemplateToken>(location, pre_space, post_space, ns, var_names, std::move(value)));
+            } else if (keyword == "endset") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndSetTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "macro") {
+              auto macroname = parseIdentifier();
+              if (!macroname) throw std::runtime_error("Expected macro name in macro block");
+              auto params = parseParameters();
+
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<MacroTemplateToken>(location, pre_space, post_space, std::move(macroname), std::move(params)));
+            } else if (keyword == "endmacro") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndMacroTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "filter") {
+              auto filter = parseExpression();
+              if (!filter) throw std::runtime_error("Expected expression in filter block");
+
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<FilterTemplateToken>(location, pre_space, post_space, std::move(filter)));
+            } else if (keyword == "endfilter") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndFilterTemplateToken>(location, pre_space, post_space));
+            } else {
+              throw std::runtime_error("Unexpected block: " + keyword);
+            }
+          } else if (std::regex_search(it, end, match, non_text_open_regex)) {
+            auto text_end = it + match.position();
+            text = std::string(it, text_end);
+            it = text_end;
+            tokens.push_back(std::make_unique<TextTemplateToken>(location, SpaceHandling::Keep, SpaceHandling::Keep, text));
+          } else {
+            text = std::string(it, end);
+            it = end;
+            tokens.push_back(std::make_unique<TextTemplateToken>(location, SpaceHandling::Keep, SpaceHandling::Keep, text));
+          }
+        }
+        return tokens;
+      } catch (const std::exception & e) {
+        throw std::runtime_error(e.what() + error_location_suffix(*template_str, std::distance(start, it)));
+      }
+    }
+
+    std::shared_ptr<TemplateNode> parseTemplate(
+          const TemplateTokenIterator & begin,
+          TemplateTokenIterator & it,
+          const TemplateTokenIterator & end,
+          bool fully = false) const {
+        std::vector<std::shared_ptr<TemplateNode>> children;
+        while (it != end) {
+          const auto start = it;
+          const auto & token = *(it++);
+          if (auto if_token = dynamic_cast<IfTemplateToken*>(token.get())) {
+              std::vector<std::pair<std::shared_ptr<Expression>, std::shared_ptr<TemplateNode>>> cascade;
+              cascade.emplace_back(std::move(if_token->condition), parseTemplate(begin, it, end));
+
+              while (it != end && (*it)->type == TemplateToken::Type::Elif) {
+                  auto elif_token = dynamic_cast<ElifTemplateToken*>((*(it++)).get());
+                  cascade.emplace_back(std::move(elif_token->condition), parseTemplate(begin, it, end));
+              }
+
+              if (it != end && (*it)->type == TemplateToken::Type::Else) {
+                cascade.emplace_back(nullptr, parseTemplate(begin, ++it, end));
+              }
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndIf) {
+                  throw unterminated(**start);
+              }
+              children.emplace_back(std::make_shared<IfNode>(token->location, std::move(cascade)));
+          } else if (auto for_token = dynamic_cast<ForTemplateToken*>(token.get())) {
+              auto body = parseTemplate(begin, it, end);
+              auto else_body = std::shared_ptr<TemplateNode>();
+              if (it != end && (*it)->type == TemplateToken::Type::Else) {
+                else_body = parseTemplate(begin, ++it, end);
+              }
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndFor) {
+                  throw unterminated(**start);
+              }
+              children.emplace_back(std::make_shared<ForNode>(token->location, std::move(for_token->var_names), std::move(for_token->iterable), std::move(for_token->condition), std::move(body), for_token->recursive, std::move(else_body)));
+          } else if (auto text_token = dynamic_cast<TextTemplateToken*>(token.get())) {
+              SpaceHandling pre_space = (it - 1) != begin ? (*(it - 2))->post_space : SpaceHandling::Keep;
+              SpaceHandling post_space = it != end ? (*it)->pre_space : SpaceHandling::Keep;
+
+              auto text = text_token->text;
+              if (post_space == SpaceHandling::Strip) {
+                static std::regex trailing_space_regex(R"((\s|\r|\n)+$)");
+                text = std::regex_replace(text, trailing_space_regex, "");
+              } else if (options.lstrip_blocks && it != end) {
+                auto i = text.size();
+                while (i > 0 && (text[i - 1] == ' ' || text[i - 1] == '\t')) i--;
+                if ((i == 0 && (it - 1) == begin) || (i > 0 && text[i - 1] == '\n')) {
+                  text.resize(i);
+                }
+              }
+              if (pre_space == SpaceHandling::Strip) {
+                static std::regex leading_space_regex(R"(^(\s|\r|\n)+)");
+                text = std::regex_replace(text, leading_space_regex, "");
+              } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast<ExpressionTemplateToken*>((*(it - 2)).get())) {
+                if (text.length() > 0 && text[0] == '\n') {
+                  text.erase(0, 1);
+                }
+              }
+              if (it == end && !options.keep_trailing_newline) {
+                auto i = text.size();
+                if (i > 0 && text[i - 1] == '\n') {
+                  i--;
+                  if (i > 0 && text[i - 1] == '\r') i--;
+                  text.resize(i);
+                }
+              }
+              children.emplace_back(std::make_shared<TextNode>(token->location, text));
+          } else if (auto expr_token = dynamic_cast<ExpressionTemplateToken*>(token.get())) {
+              children.emplace_back(std::make_shared<ExpressionNode>(token->location, std::move(expr_token->expr)));
+          } else if (auto set_token = dynamic_cast<SetTemplateToken*>(token.get())) {
+            if (set_token->value) {
+              children.emplace_back(std::make_shared<SetNode>(token->location, set_token->ns, set_token->var_names, std::move(set_token->value)));
+            } else {
+              auto value_template = parseTemplate(begin, it, end);
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndSet) {
+                  throw unterminated(**start);
+              }
+              if (!set_token->ns.empty()) throw std::runtime_error("Namespaced set not supported in set with template value");
+              if (set_token->var_names.size() != 1) throw std::runtime_error("Structural assignment not supported in set with template value");
+              auto & name = set_token->var_names[0];
+              children.emplace_back(std::make_shared<SetTemplateNode>(token->location, name, std::move(value_template)));
+            }
+          } else if (auto macro_token = dynamic_cast<MacroTemplateToken*>(token.get())) {
+              auto body = parseTemplate(begin, it, end);
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndMacro) {
+                  throw unterminated(**start);
+              }
+              children.emplace_back(std::make_shared<MacroNode>(token->location, std::move(macro_token->name), std::move(macro_token->params), std::move(body)));
+          } else if (auto filter_token = dynamic_cast<FilterTemplateToken*>(token.get())) {
+              auto body = parseTemplate(begin, it, end);
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndFilter) {
+                  throw unterminated(**start);
+              }
+              children.emplace_back(std::make_shared<FilterNode>(token->location, std::move(filter_token->filter), std::move(body)));
+          } else if (dynamic_cast<CommentTemplateToken*>(token.get())) {
+              // Ignore comments
+          } else if (dynamic_cast<EndForTemplateToken*>(token.get())
+                  || dynamic_cast<EndSetTemplateToken*>(token.get())
+                  || dynamic_cast<EndMacroTemplateToken*>(token.get())
+                  || dynamic_cast<EndFilterTemplateToken*>(token.get())
+                  || dynamic_cast<EndIfTemplateToken*>(token.get())
+                  || dynamic_cast<ElseTemplateToken*>(token.get())
+                  || dynamic_cast<ElifTemplateToken*>(token.get())) {
+              it--;  // unconsume the token
+              break;  // exit the loop
+          } else {
+              throw unexpected(**(it-1));
+          }
+        }
+        if (fully && it != end) {
+            throw unexpected(**it);
+        }
+        if (children.empty()) {
+          return std::make_shared<TextNode>(Location { template_str, 0 }, std::string());
+        } else if (children.size() == 1) {
+          return std::move(children[0]);
+        } else {
+          return std::make_shared<SequenceNode>(children[0]->location(), std::move(children));
+        }
+    }
+
+public:
+
+    static std::shared_ptr<TemplateNode> parse(const std::string& template_str, const Options & options) {
+        Parser parser(std::make_shared<std::string>(normalize_newlines(template_str)), options);
+        auto tokens = parser.tokenize();
+        TemplateTokenIterator begin = tokens.begin();
+        auto it = begin;
+        TemplateTokenIterator end = tokens.end();
+        return parser.parseTemplate(begin, it, end, /* full= */ true);
+    }
+};
+
+static Value simple_function(const std::string & fn_name, const std::vector<std::string> & params, const std::function<Value(const std::shared_ptr<Context> &, Value & args)> & fn) {
+  std::map<std::string, size_t> named_positions;
+  for (size_t i = 0, n = params.size(); i < n; i++) named_positions[params[i]] = i;
+
+  return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) -> Value {
+    auto args_obj = Value::object();
+    std::vector<bool> provided_args(params.size());
+    for (size_t i = 0, n = args.args.size(); i < n; i++) {
+      auto & arg = args.args[i];
+      if (i < params.size()) {
+        args_obj.set(params[i], arg);
+        provided_args[i] = true;
+      } else {
+        throw std::runtime_error("Too many positional params for " + fn_name);
+      }
+    }
+    for (auto & [name, value] : args.kwargs) {
+      auto named_pos_it = named_positions.find(name);
+      if (named_pos_it == named_positions.end()) {
+        throw std::runtime_error("Unknown argument " + name + " for function " + fn_name);
+      }
+      provided_args[named_pos_it->second] = true;
+      args_obj.set(name, value);
+    }
+    return fn(context, args_obj);
+  });
+}
+
+inline std::shared_ptr<Context> Context::builtins() {
+  auto globals = Value::object();
+
+  globals.set("raise_exception", simple_function("raise_exception", { "message" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+    throw std::runtime_error(args.at("message").get<std::string>());
+  }));
+  globals.set("tojson", simple_function("tojson", { "value", "indent" }, [](const std::shared_ptr<Context> &, Value & args) {
+    return Value(args.at("value").dump(args.get<int64_t>("indent", -1), /* tojson= */ true));
+  }));
+  globals.set("items", simple_function("items", { "object" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto items = Value::array();
+    if (args.contains("object")) {
+      auto & obj = args.at("object");
+      if (obj.is_string()) {
+        auto json_obj = json::parse(obj.get<std::string>());
+        for (const auto & kv : json_obj.items()) {
+          items.push_back(Value::array({kv.key(), kv.value()}));
+        }
+      } else if (!obj.is_null()) {
+        for (auto & key : obj.keys()) {
+          items.push_back(Value::array({key, obj.at(key)}));
+        }
+      }
+    }
+    return items;
+  }));
+  globals.set("last", simple_function("last", { "items" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto items = args.at("items");
+    if (!items.is_array()) throw std::runtime_error("object is not a list");
+    if (items.size() == 0) return Value();
+    return items.at(items.size() - 1);
+  }));
+  globals.set("trim", simple_function("trim", { "text" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto & text = args.at("text");
+    return text.is_null() ? text : Value(strip(text.get<std::string>()));
+  }));
+  globals.set("lower", simple_function("lower", { "text" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto text = args.at("text");
+    if (text.is_null()) return text;
+    std::string res;
+    auto str = text.get<std::string>();
+    std::transform(str.begin(), str.end(), std::back_inserter(res), ::tolower);
+    return Value(res);
+  }));
+  globals.set("default", Value::callable([=](const std::shared_ptr<Context> &, ArgumentsValue & args) {
+    args.expectArgs("default", {2, 3}, {0, 1});
+    auto & value = args.args[0];
+    auto & default_value = args.args[1];
+    bool boolean = false;
+    if (args.args.size() == 3) {
+      boolean = args.args[2].get<bool>();
+    } else {
+      Value bv = args.get_named("boolean");
+      if (!bv.is_null()) {
+        boolean = bv.get<bool>();
+      }
+    }
+    return boolean ? (value.to_bool() ? value : default_value) : value.is_null() ? default_value : value;
+  }));
+  auto escape = simple_function("escape", { "text" }, [](const std::shared_ptr<Context> &, Value & args) {
+    return Value(html_escape(args.at("text").get<std::string>()));
+  });
+  globals.set("e", escape);
+  globals.set("escape", escape);
+  globals.set("joiner", simple_function("joiner", { "sep" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto sep = args.get<std::string>("sep", "");
+    auto first = std::make_shared<bool>(true);
+    return simple_function("", {}, [sep, first](const std::shared_ptr<Context> &, const Value &) -> Value {
+      if (*first) {
+        *first = false;
+        return "";
+      }
+      return sep;
+    });
+    return Value(html_escape(args.at("text").get<std::string>()));
+  }));
+  globals.set("count", simple_function("count", { "items" }, [](const std::shared_ptr<Context> &, Value & args) {
+    return Value((int64_t) args.at("items").size());
+  }));
+  globals.set("dictsort", simple_function("dictsort", { "value" }, [](const std::shared_ptr<Context> &, Value & args) {
+    if (args.size() != 1) throw std::runtime_error("dictsort expects exactly 1 argument (TODO: fix implementation)");
+    auto & value = args.at("value");
+    auto keys = value.keys();
+    std::sort(keys.begin(), keys.end());
+    auto res = Value::array();
+    for (auto & key : keys) {
+      res.push_back(Value::array({key, value.at(key)}));
+    }
+    return res;
+  }));
+  globals.set("join", simple_function("join", { "items", "d" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto do_join = [](Value & items, const std::string & sep) {
+      std::ostringstream oss;
+      auto first = true;
+      for (size_t i = 0, n = items.size(); i < n; ++i) {
+        if (first) first = false;
+        else oss << sep;
+        oss << items.at(i).to_str();
+      }
+      return Value(oss.str());
+    };
+    auto sep = args.get<std::string>("d", "");
+    if (args.contains("items")) {
+        auto & items = args.at("items");
+        return do_join(items, sep);
+    } else {
+      return simple_function("", {"items"}, [sep, do_join](const std::shared_ptr<Context> &, Value & args) {
+        auto & items = args.at("items");
+        if (!items.to_bool() || !items.is_array()) throw std::runtime_error("join expects an array for items, got: " + items.dump());
+        return do_join(items, sep);
+      });
+    }
+  }));
+  globals.set("namespace", Value::callable([=](const std::shared_ptr<Context> &, ArgumentsValue & args) {
+    auto ns = Value::object();
+    args.expectArgs("namespace", {0, 0}, {0, (std::numeric_limits<size_t>::max)()});
+    for (auto & [name, value] : args.kwargs) {
+      ns.set(name, value);
+    }
+    return ns;
+  }));
+  auto equalto = simple_function("equalto", { "expected", "actual" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      return args.at("actual") == args.at("expected");
+  });
+  globals.set("equalto", equalto);
+  globals.set("==", equalto);
+  globals.set("length", simple_function("length", { "items" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      auto & items = args.at("items");
+      return (int64_t) items.size();
+  }));
+  globals.set("safe", simple_function("safe", { "value" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      return args.at("value").to_str();
+  }));
+  globals.set("string", simple_function("string", { "value" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      return args.at("value").to_str();
+  }));
+  globals.set("int", simple_function("int", { "value" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      return args.at("value").to_int();
+  }));
+  globals.set("list", simple_function("list", { "items" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      auto & items = args.at("items");
+      if (!items.is_array()) throw std::runtime_error("object is not iterable");
+      return items;
+  }));
+  globals.set("unique", simple_function("unique", { "items" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      auto & items = args.at("items");
+      if (!items.is_array()) throw std::runtime_error("object is not iterable");
+      std::unordered_set<Value> seen;
+      auto result = Value::array();
+      for (size_t i = 0, n = items.size(); i < n; i++) {
+        auto pair = seen.insert(items.at(i));
+        if (pair.second) {
+          result.push_back(items.at(i));
+        }
+      }
+      return result;
+  }));
+  auto make_filter = [](const Value & filter, Value & extra_args) -> Value {
+    return simple_function("", { "value" }, [=](const std::shared_ptr<Context> & context, Value & args) {
+      auto & value = args.at("value");
+      ArgumentsValue actual_args;
+      actual_args.args.emplace_back(value);
+      for (size_t i = 0, n = extra_args.size(); i < n; i++) {
+        actual_args.args.emplace_back(extra_args.at(i));
+      }
+      return filter.call(context, actual_args);
+    });
+  };
+  // https://jinja.palletsprojects.com/en/3.0.x/templates/#jinja-filters.reject
+  globals.set("reject", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+    args.expectArgs("reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
+    auto & items = args.args[0];
+    auto filter_fn = context->get(args.args[1]);
+    if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
+
+    auto filter_args = Value::array();
+    for (size_t i = 2, n = args.args.size(); i < n; i++) {
+      filter_args.push_back(args.args[i]);
+    }
+    auto filter = make_filter(filter_fn, filter_args);
+
+    auto res = Value::array();
+    for (size_t i = 0, n = items.size(); i < n; i++) {
+      auto & item = items.at(i);
+      ArgumentsValue filter_args;
+      filter_args.args.emplace_back(item);
+      auto pred_res = filter.call(context, filter_args);
+      if (!pred_res.to_bool()) {
+        res.push_back(item);
+      }
+    }
+    return res;
+  }));
+  globals.set("map", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+    auto res = Value::array();
+    if (args.args.size() == 1 &&
+      ((args.has_named("attribute") && args.kwargs.size() == 1) || (args.has_named("default") && args.kwargs.size() == 2))) {
+      auto & items = args.args[0];
+      auto attr_name = args.get_named("attribute");
+      auto default_value = args.get_named("default");
+      for (size_t i = 0, n = items.size(); i < n; i++) {
+        auto & item = items.at(i);
+        auto attr = item.get(attr_name);
+        res.push_back(attr.is_null() ? default_value : attr);
+      }
+    } else if (args.kwargs.empty() && args.args.size() >= 2) {
+      auto fn = context->get(args.args[1]);
+      if (fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
+      ArgumentsValue filter_args { {Value()}, {} };
+      for (size_t i = 2, n = args.args.size(); i < n; i++) {
+        filter_args.args.emplace_back(args.args[i]);
+      }
+      for (size_t i = 0, n = args.args[0].size(); i < n; i++) {
+        auto & item = args.args[0].at(i);
+        filter_args.args[0] = item;
+        res.push_back(fn.call(context, filter_args));
+      }
+    } else {
+      throw std::runtime_error("Invalid or unsupported arguments for map");
+    }
+    return res;
+  }));
+  globals.set("indent", simple_function("indent", { "text", "indent", "first" }, [](const std::shared_ptr<Context> &, Value & args) {
+    auto text = args.at("text").get<std::string>();
+    auto first = args.get<bool>("first", false);
+    std::string out;
+    std::string indent(args.get<int64_t>("indent", 0), ' ');
+    std::istringstream iss(text);
+    std::string line;
+    auto is_first = true;
+    while (std::getline(iss, line, '\n')) {
+      auto needs_indent = !is_first || first;
+      if (is_first) is_first = false;
+      else out += "\n";
+      if (needs_indent) out += indent;
+      out += line;
+    }
+    if (!text.empty() && text.back() == '\n') out += "\n";
+    return out;
+  }));
+  globals.set("selectattr", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+    args.expectArgs("selectattr", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
+    auto & items = args.args[0];
+    if (items.is_null())
+      return Value::array();
+    auto attr_name = args.args[1].get<std::string>();
+
+    bool has_test = false;
+    Value test_fn;
+    ArgumentsValue test_args {{Value()}, {}};
+    if (args.args.size() >= 3) {
+      has_test = true;
+      test_fn = context->get(args.args[2]);
+      if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump());
+      for (size_t i = 3, n = args.args.size(); i < n; i++) {
+        test_args.args.emplace_back(args.args[i]);
+      }
+      test_args.kwargs = args.kwargs;
+    }
+
+    auto res = Value::array();
+    for (size_t i = 0, n = items.size(); i < n; i++) {
+      auto & item = items.at(i);
+      auto attr = item.get(attr_name);
+      if (has_test) {
+        test_args.args[0] = attr;
+        if (test_fn.call(context, test_args).to_bool()) {
+          res.push_back(item);
+        }
+      } else {
+        res.push_back(attr);
+      }
+    }
+    return res;
+  }));
+  globals.set("range", Value::callable([=](const std::shared_ptr<Context> &, ArgumentsValue & args) {
+    std::vector<int64_t> startEndStep(3);
+    std::vector<bool> param_set(3);
+    if (args.args.size() == 1) {
+      startEndStep[1] = args.args[0].get<int64_t>();
+      param_set[1] = true;
+    } else {
+      for (size_t i = 0; i < args.args.size(); i++) {
+        auto & arg = args.args[i];
+        auto v = arg.get<int64_t>();
+        startEndStep[i] = v;
+        param_set[i] = true;
+        }
+      }
+      for (auto & [name, value] : args.kwargs) {
+        size_t i;
+        if (name == "start") i = 0;
+        else if (name == "end") i = 1;
+        else if (name == "step") i = 2;
+        else throw std::runtime_error("Unknown argument " + name + " for function range");
+
+        if (param_set[i]) {
+          throw std::runtime_error("Duplicate argument " + name + " for function range");
+        }
+        startEndStep[i] = value.get<int64_t>();
+        param_set[i] = true;
+    }
+    if (!param_set[1]) {
+      throw std::runtime_error("Missing required argument 'end' for function range");
+    }
+    int64_t start = param_set[0] ? startEndStep[0] : 0;
+    int64_t end = startEndStep[1];
+    int64_t step = param_set[2] ? startEndStep[2] : 1;
+
+    auto res = Value::array();
+    if (step > 0) {
+      for (int64_t i = start; i < end; i += step) {
+        res.push_back(Value(i));
+      }
+    } else {
+      for (int64_t i = start; i > end; i += step) {
+        res.push_back(Value(i));
+      }
+    }
+    return res;
+  }));
+
+  return std::make_shared<Context>(std::move(globals));
+}
+
+inline std::shared_ptr<Context> Context::make(Value && values, const std::shared_ptr<Context> & parent) {
+  return std::make_shared<Context>(values.is_null() ? Value::object() : std::move(values), parent);
+}
+
+}  // namespace minja
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 39666a0e8..da2a03ab9 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -4,6 +4,7 @@
 #include "log.h"
 #include "sampling.h"
 #include "llama.h"
+#include "chat-template.hpp"
 
 #include <cstdio>
 #include <cstring>
@@ -84,14 +85,6 @@ static void sigint_handler(int signo) {
 }
 #endif
 
-static std::string chat_add_and_format(struct llama_model * model, std::vector<common_chat_msg> & chat_msgs, const std::string & role, const std::string & content) {
-    common_chat_msg new_msg{role, content};
-    auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
-    chat_msgs.push_back({role, content});
-    LOG_DBG("formatted: '%s'\n", formatted.c_str());
-    return formatted;
-}
-
 int main(int argc, char ** argv) {
     common_params params;
     g_params = &params;
@@ -165,6 +158,7 @@ int main(int argc, char ** argv) {
     }
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
+    auto chat_templates = common_chat_templates_from_model(model, params.chat_template);
 
     LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
 
@@ -207,7 +201,7 @@ int main(int argc, char ** argv) {
     }
 
     // auto enable conversation mode if chat template is available
-    const bool has_chat_template = !common_get_builtin_chat_template(model).empty() || !params.chat_template.empty();
+    const bool has_chat_template = chat_templates.has_explicit_template && chat_templates.template_default;
     if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
         if (has_chat_template) {
             LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
@@ -225,7 +219,7 @@ int main(int argc, char ** argv) {
     // print chat template example in conversation mode
     if (params.conversation_mode) {
         if (params.enable_chat_template) {
-            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str());
+            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(*chat_templates.template_default, params.use_jinja).c_str());
         } else {
             LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
         }
@@ -269,10 +263,18 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd_inp;
 
+    auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
+        common_chat_msg new_msg{role, content};
+        auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja);
+        chat_msgs.push_back({role, content});
+        LOG_DBG("formatted: '%s'\n", formatted.c_str());
+        return formatted;
+    };
+
     {
         auto prompt = (params.conversation_mode && params.enable_chat_template)
             // format the system prompt in conversation mode (fallback to default if empty)
-            ? chat_add_and_format(model, chat_msgs, "system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt)
+            ? chat_add_and_format("system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt)
             // otherwise use the prompt as is
             : params.prompt;
         if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
@@ -779,7 +781,7 @@ int main(int argc, char ** argv) {
                     }
 
                     if (params.enable_chat_template) {
-                        chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str());
+                        chat_add_and_format("assistant", assistant_ss.str());
                     }
                     is_interacting = true;
                     LOG("\n");
@@ -844,7 +846,7 @@ int main(int argc, char ** argv) {
 
                     bool format_chat = params.conversation_mode && params.enable_chat_template;
                     std::string user_inp = format_chat
-                        ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
+                        ? chat_add_and_format("user", std::move(buffer))
                         : std::move(buffer);
                     // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
                     const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index d04108e71..e567ad716 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -28,6 +28,7 @@
 #include "json.hpp"
 #include "linenoise.cpp/linenoise.h"
 #include "llama-cpp.h"
+#include "chat-template.hpp"
 
 #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
 [[noreturn]] static void sigint_handler(int) {
@@ -105,6 +106,7 @@ class Opt {
     llama_model_params   model_params;
     std::string model_;
     std::string          user;
+    bool                 use_jinja   = false;
     int                  context_size = -1, ngl = -1;
     float                temperature = -1;
     bool                 verbose     = false;
@@ -156,6 +158,8 @@ class Opt {
             } else if (options_parsing &&
                        (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
                 verbose = true;
+            } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
+                use_jinja = true;
             } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
                 help = true;
                 return 0;
@@ -713,13 +717,31 @@ static void add_message(const char * role, const std::string & text, LlamaData &
 }
 
 // Function to apply the chat template and resize `formatted` if needed
-static int apply_chat_template(LlamaData & llama_data, const bool append) {
+static int apply_chat_template(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, bool use_jinja) {
+    if (use_jinja) {
+        json messages = json::array();
+        for (const auto & msg : llama_data.messages) {
+            messages.push_back({
+                {"role", msg.role},
+                {"content", msg.content},
+            });
+        }
+        try {
+            auto result = tmpl.apply(messages, /* tools= */ json(), append);
+            llama_data.fmtted.resize(result.size() + 1);
+            memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
+            return result.size();
+        } catch (const std::exception & e) {
+            printe("failed to render the chat template: %s\n", e.what());
+            return -1;
+        }
+    }
     int result = llama_chat_apply_template(
-        llama_model_chat_template(llama_data.model.get()), llama_data.messages.data(), llama_data.messages.size(), append,
+        tmpl.source().c_str(), llama_data.messages.data(), llama_data.messages.size(), append,
         append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
     if (append && result > static_cast<int>(llama_data.fmtted.size())) {
         llama_data.fmtted.resize(result);
-        result = llama_chat_apply_template(llama_model_chat_template(llama_data.model.get()), llama_data.messages.data(),
+        result = llama_chat_apply_template(tmpl.source().c_str(), llama_data.messages.data(),
                                            llama_data.messages.size(), append, llama_data.fmtted.data(),
                                            llama_data.fmtted.size());
     }
@@ -871,8 +893,8 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
 }
 
 // Helper function to apply the chat template and handle errors
-static int apply_chat_template_with_error_handling(LlamaData & llama_data, const bool append, int & output_length) {
-    const int new_len = apply_chat_template(llama_data, append);
+static int apply_chat_template_with_error_handling(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) {
+    const int new_len = apply_chat_template(tmpl, llama_data, append, use_jinja);
     if (new_len < 0) {
         printe("failed to apply the chat template\n");
         return -1;
@@ -931,9 +953,11 @@ static int get_user_input(std::string & user_input, const std::string & user) {
 }
 
 // Main chat loop function
-static int chat_loop(LlamaData & llama_data, const std::string & user) {
+static int chat_loop(LlamaData & llama_data, const std::string & user, bool use_jinja) {
     int prev_len = 0;
     llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
+    auto chat_templates = common_chat_templates_from_model(llama_data.model.get(), "");
+    GGML_ASSERT(chat_templates.template_default);
     static const bool stdout_a_terminal = is_stdout_a_terminal();
     while (true) {
         // Get user input
@@ -944,7 +968,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user) {
 
         add_message("user", user.empty() ? user_input : user, llama_data);
         int new_len;
-        if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
+        if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, true, new_len, use_jinja) < 0) {
             return 1;
         }
 
@@ -959,7 +983,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user) {
         }
 
         add_message("assistant", response, llama_data);
-        if (apply_chat_template_with_error_handling(llama_data, false, prev_len) < 0) {
+        if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, false, prev_len, use_jinja) < 0) {
             return 1;
         }
     }
@@ -1019,7 +1043,7 @@ int main(int argc, const char ** argv) {
         return 1;
     }
 
-    if (chat_loop(llama_data, opt.user)) {
+    if (chat_loop(llama_data, opt.user, opt.use_jinja)) {
         return 1;
     }
 
diff --git a/examples/server/README.md b/examples/server/README.md
index 1f0a27d96..5022de672 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -126,7 +126,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') |
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
-
+| `--jinja` | Enable experimental Jinja templating engine (needed for tool use) |
 
 **Example-specific params**
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index f35206d7b..5f08c4ecc 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1688,6 +1688,8 @@ struct server_context {
     // Necessary similarity of prompt for slot selection
     float slot_prompt_similarity = 0.0f;
 
+    common_chat_templates chat_templates;
+
     ~server_context() {
         // Clear any sampling context
         for (server_slot & slot : slots) {
@@ -1767,14 +1769,39 @@ struct server_context {
             cparams_dft.type_v = GGML_TYPE_F16;
         }
 
+        chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
+        GGML_ASSERT(chat_templates.template_default.get() != nullptr);
+
         return true;
     }
 
-    bool validate_builtin_chat_template() const {
+    bool validate_builtin_chat_template(bool use_jinja) const {
         llama_chat_message chat[] = {{"user", "test"}};
-        const char * tmpl = llama_model_chat_template(model);
-        const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0);
-        return chat_res > 0;
+
+        if (use_jinja) {
+            auto templates = common_chat_templates_from_model(model, "");
+            GGML_ASSERT(templates.template_default);
+            try {
+                templates.template_default->apply({{
+                    {"role", "user"},
+                    {"content", "test"},
+                }}, json(), true);
+                if (templates.template_tool_use) {
+                    templates.template_tool_use->apply({{
+                        {"role", "user"},
+                        {"content", "test"},
+                    }}, json(), true);
+                }
+                return true;
+            } catch (const std::exception & e) {
+                SRV_ERR("failed to apply template: %s\n", e.what());
+                return false;
+            }
+        } else {
+            const char * tmpl = llama_model_chat_template(model, /* name */ nullptr);
+            const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0);
+            return chat_res > 0;
+        }
     }
 
     void init() {
@@ -3659,9 +3686,12 @@ int main(int argc, char ** argv) {
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model },
-            { "chat_template",               common_get_builtin_chat_template(ctx_server.model) },
+            { "chat_template",               ctx_server.chat_templates.template_default->source() },
             { "build_info",                  build_info },
         };
+        if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) {
+            data["chat_template_tool_use"] = ctx_server.chat_templates.template_tool_use->source();
+        }
 
         res_ok(res, data);
     };
@@ -3889,7 +3919,10 @@ int main(int argc, char ** argv) {
             return;
         }
 
-        json data = oaicompat_chat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
+        auto body = json::parse(req.body);
+        const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
+        json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
+
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
             data,
@@ -4299,7 +4332,7 @@ int main(int argc, char ** argv) {
 
     // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
     if (params.chat_template.empty()) {
-        if (!ctx_server.validate_builtin_chat_template()) {
+        if (!ctx_server.validate_builtin_chat_template(params.use_jinja)) {
             LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
             params.chat_template = "chatml";
         }
@@ -4307,8 +4340,8 @@ int main(int argc, char ** argv) {
 
     // print sample chat example to make it clear which template is used
     LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
-        params.chat_template.empty() ? "(built-in)" : params.chat_template.c_str(),
-        common_chat_format_example(ctx_server.model, params.chat_template).c_str());
+        ctx_server.chat_templates.template_default->source().c_str(),
+        common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str());
 
     ctx_server.queue_tasks.on_new_task(std::bind(
                 &server_context::process_single_task, &ctx_server, std::placeholders::_1));
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index b15dba6eb..2e15348dc 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -4,22 +4,26 @@ from utils import *
 
 server = ServerPreset.tinyllama2()
 
-
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
 
 
 @pytest.mark.parametrize(
-    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
+    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
     [
-        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", False, None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", True, None),
+        (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
     ]
 )
-def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
+def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
     global server
+    server.jinja = jinja
+    server.chat_template = chat_template
     server.start()
     res = server.make_request("POST", "/chat/completions", data={
         "model": model,
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index 9d1a7a5b0..9964db2f9 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -72,13 +72,14 @@ class ServerProcess:
     pooling: str | None = None
     draft: int | None = None
     api_key: str | None = None
-    response_format: str | None = None
     lora_files: List[str] | None = None
     disable_ctx_shift: int | None = False
     draft_min: int | None = None
     draft_max: int | None = None
     no_webui: bool | None = None
+    jinja: bool | None = None
     chat_template: str | None = None
+    chat_template_file: str | None = None
 
     # session variables
     process: subprocess.Popen | None = None
@@ -169,8 +170,12 @@ class ServerProcess:
             server_args.extend(["--draft-min", self.draft_min])
         if self.no_webui:
             server_args.append("--no-webui")
+        if self.jinja:
+            server_args.append("--jinja")
         if self.chat_template:
             server_args.extend(["--chat-template", self.chat_template])
+        if self.chat_template_file:
+            server_args.extend(["--chat-template-file", self.chat_template_file])
 
         args = [str(arg) for arg in [server_path, *server_args]]
         print(f"bench: starting server with: {' '.join(args)}")
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 699480f90..c5987250c 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -16,6 +16,8 @@
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
+#include "minja.hpp"
+#include "chat-template.hpp"
 
 #include <random>
 #include <sstream>
@@ -349,7 +351,7 @@ static llama_tokens format_infill(
 }
 
 // Format given chat. If tmpl is empty, we take the template from model metadata
-inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
+inline std::string format_chat(const common_chat_template & tmpl, const std::vector<json> & messages) {
     std::vector<common_chat_msg> chat;
 
     for (size_t i = 0; i < messages.size(); ++i) {
@@ -377,7 +379,7 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
         chat.push_back({role, content});
     }
 
-    const auto formatted_chat = common_chat_apply_template(model, tmpl, chat, true);
+    const auto formatted_chat = common_chat_apply_template(tmpl, chat, true, /* use_jinja= */ false);
     LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str());
 
     return formatted_chat;
@@ -576,14 +578,23 @@ static json oaicompat_completion_params_parse(const json & body) {
     return llama_params;
 }
 
-static json oaicompat_chat_completion_params_parse(
-        const struct llama_model * model,
-        const json & body, /* openai api json semantics */
-        const std::string & chat_template) {
+static json oaicompat_completion_params_parse(
+    const json & body, /* openai api json semantics */
+    const common_chat_template & tmpl,
+    bool use_jinja)
+{
     json llama_params;
 
-    // Apply chat template to the list of messages
-    llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
+    auto tools = json_value(body, "tools", json());
+    auto has_tools = tools.is_array() && !tools.empty();
+
+    if (has_tools) {
+        if (use_jinja) {
+            LOG_WRN("tools param is not fully supported yet\n");
+        } else {
+            throw std::runtime_error("tools param requires --jinja flag");
+        }
+    }
 
     // Handle "stop" field
     if (body.contains("stop") && body.at("stop").is_string()) {
@@ -606,6 +617,13 @@ static json oaicompat_chat_completion_params_parse(
         }
     }
 
+    // Apply chat template to the list of messages
+    if (use_jinja) {
+        llama_params["prompt"] = tmpl.apply(body.at("messages"), tools, /* add_generation_prompt= */ true);
+    } else {
+        llama_params["prompt"] = format_chat(tmpl, body.at("messages"));
+    }
+
     // Handle "n" field
     int n_choices = json_value(body, "n", 1);
     if (n_choices != 1) {
@@ -621,7 +639,7 @@ static json oaicompat_chat_completion_params_parse(
     }
 
     // Params supported by OAI but unsupported by llama.cpp
-    static const std::vector<std::string> unsupported_params { "tools", "tool_choice" };
+    static const std::vector<std::string> unsupported_params { "tool_choice" };
     for (const auto & param : unsupported_params) {
         if (body.contains(param)) {
             throw std::runtime_error("Unsupported param: " + param);
diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index 212b3fd79..c5534cc13 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -163,7 +163,7 @@ int main(int argc, char ** argv) {
             break;
         }
 
-        const char * tmpl = llama_model_chat_template(model);
+        const char * tmpl = llama_model_chat_template(model, /* name */ nullptr);
 
         // add the user input to the message list and format it
         messages.push_back({"user", strdup(user.c_str())});
diff --git a/include/llama.h b/include/llama.h
index 298b8d1bc..3b75e7607 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -510,7 +510,8 @@ extern "C" {
     LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
 
     // Get the default chat template. Returns nullptr if not available
-    LLAMA_API const char * llama_model_chat_template(const struct llama_model * model);
+    // If name is NULL, returns the default chat template
+    LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name);
 
     // Returns the total number of parameters in the model
     LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
diff --git a/scripts/get_hf_chat_template.py b/scripts/get_hf_chat_template.py
new file mode 100755
index 000000000..23bb1de59
--- /dev/null
+++ b/scripts/get_hf_chat_template.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+'''
+  Fetches the Jinja chat template of a HuggingFace model.
+  If a model has multiple chat templates, you can specify the variant name.
+
+  Syntax:
+    ./scripts/get_hf_chat_template.py model_id [variant]
+
+  Examples:
+    ./scripts/get_hf_chat_template.py NousResearch/Meta-Llama-3-8B-Instruct
+    ./scripts/get_hf_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use
+    ./scripts/get_hf_chat_template.py meta-llama/Llama-3.2-3B-Instruct
+'''
+
+import json
+import re
+import sys
+
+
+def get_hf_chat_template(model_id, variant=None):
+    try:
+        # Use huggingface_hub library if available.
+        # Allows access to gated models if the user has access and ran `huggingface-cli login`.
+        from huggingface_hub import hf_hub_download
+        with open(hf_hub_download(repo_id=model_id, filename="tokenizer_config.json")) as f:
+            config_str = f.read()
+    except ImportError:
+        import requests
+        assert re.match(r"^[\w.-]+/[\w.-]+$", model_id), f"Invalid model ID: {model_id}"
+        response = requests.get(f"https://huggingface.co/{model_id}/resolve/main/tokenizer_config.json")
+        if response.status_code == 401:
+            raise Exception('Access to this model is gated, please request access, authenticate with `huggingface-cli login` and make sure to run `pip install huggingface_hub`')
+        response.raise_for_status()
+        config_str = response.text
+
+    try:
+        config = json.loads(config_str)
+    except json.JSONDecodeError:
+        # Fix https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
+        # (Remove extra '}' near the end of the file)
+        config = json.loads(re.sub(r'\}([\n\s]*\}[\n\s]*\],[\n\s]*"clean_up_tokenization_spaces")', r'\1', config_str))
+
+    chat_template = config['chat_template']
+    if isinstance(chat_template, str):
+        return chat_template
+    else:
+        variants = {
+            ct['name']: ct['template']
+            for ct in chat_template
+        }
+
+        def format_variants():
+            return ', '.join(f'"{v}"' for v in variants.keys())
+
+        if variant is None:
+            if 'default' not in variants:
+                raise Exception(f'Please specify a chat template variant (one of {format_variants()})')
+            variant = 'default'
+            sys.stderr.write(f'Note: picked "default" chat template variant (out of {format_variants()})\n')
+        elif variant not in variants:
+            raise Exception(f"Variant {variant} not found in chat template (found {format_variants()})")
+
+        return variants[variant]
+
+
+def main(args):
+    if len(args) < 1:
+        raise ValueError("Please provide a model ID and an optional variant name")
+    model_id = args[0]
+    variant = None if len(args) < 2 else args[1]
+
+    template = get_hf_chat_template(model_id, variant)
+    sys.stdout.write(template)
+
+
+if __name__ == '__main__':
+    main(sys.argv[1:])
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index aeb75bf3e..e1b02e4c0 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -29,7 +29,7 @@ add_library(llama
             unicode-data.cpp
             )
 
-target_include_directories(llama PUBLIC . ../include)
+target_include_directories(llama PUBLIC . ../include ../common)
 target_compile_features   (llama PUBLIC cxx_std_17) # don't bump
 
 target_link_libraries(llama PUBLIC ggml)
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index d7d277e72..a7260f495 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -179,6 +179,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
     { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
     { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat_template"                 },
+    { LLM_KV_TOKENIZER_CHAT_TEMPLATE_N,      "tokenizer.chat_template.%s"              },
     { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
     { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
     { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },
@@ -1443,10 +1444,11 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_CONVNEXT_GAMMA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
 };
 
-LLM_KV::LLM_KV(llm_arch arch) : arch(arch) {}
+LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
 
 std::string LLM_KV::operator()(llm_kv kv) const {
-    return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
+    return suffix ? ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch), suffix)
+        : ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
 }
 
 std::string LLM_TN_IMPL::str() const {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 349844790..122fdcebe 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -177,6 +177,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
     LLM_KV_TOKENIZER_CHAT_TEMPLATE,
+    LLM_KV_TOKENIZER_CHAT_TEMPLATE_N,
     LLM_KV_TOKENIZER_FIM_PRE_ID,
     LLM_KV_TOKENIZER_FIM_SUF_ID,
     LLM_KV_TOKENIZER_FIM_MID_ID,
@@ -335,9 +336,10 @@ enum llm_tensor_layer {
 };
 
 struct LLM_KV {
-    LLM_KV(llm_arch arch);
+    LLM_KV(llm_arch arch, const char * suffix = nullptr);
 
     llm_arch arch;
+    const char * suffix;
 
     std::string operator()(llm_kv kv) const;
 };
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 590386e64..031b4c30b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -3955,8 +3955,10 @@ uint64_t llama_model_size(const struct llama_model * model) {
     return model->size();
 }
 
-const char * llama_model_chat_template(const struct llama_model * model) {
-    const auto & it = model->gguf_kv.find(LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE));
+const char * llama_model_chat_template(const struct llama_model * model, const char * name) {
+    const auto key = name ? LLM_KV(model->arch, name)(LLM_KV_TOKENIZER_CHAT_TEMPLATE_N)
+        : LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE);
+    const auto & it = model->gguf_kv.find(key);
     if (it == model->gguf_kv.end()) {
         return nullptr;
     }
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 5ec318bd4..190643136 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -7,6 +7,16 @@
 
 #include "llama.h"
 #include "common.h"
+#include "chat-template.hpp"
+
+static std::string normalize_newlines(const std::string & s) {
+#ifdef _WIN32
+  static const std::regex nl_regex("\r\n");
+  return std::regex_replace(s, nl_regex, "\n");
+#else
+  return s;
+#endif
+}
 
 int main(void) {
     std::vector<llama_chat_message> conversation {
@@ -21,156 +31,228 @@ int main(void) {
         std::string name;
         std::string template_str;
         std::string expected_output;
+        std::string expected_output_jinja;
+        std::string bos_token = "";
+        std::string eos_token = "";
+        bool supported_with_jinja = true;
     };
     std::vector<TestCase> test_cases {
         {
             /* .name= */ "teknium/OpenHermes-2.5-Mistral-7B",
             /* .template_str= */ "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
             /* .expected_output= */ "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt)",
             /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
             /* .expected_output= */ "[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "TheBloke/FusionNet_34Bx2_MoE-AWQ",
-            /* .template_str= */ "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
-            /* .expected_output= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s><s>[INST] Who are you [/INST]   I am an assistant   </s><s>[INST] Another question [/INST]",
+            /* .template_str= */ "{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}",
+            /* .expected_output= */       "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s><s>[INST] Who are you [/INST]   I am an assistant   </s><s>[INST] Another question [/INST]",
+            /* .expected_output_jinja= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST]    I am an assistant    </s><s>[INST] Another question [/INST]",
+            /* .bos_token= */ "<s>",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "bofenghuang/vigogne-2-70b-chat",
-            /* .template_str= */ "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
-            /* .expected_output= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s>[INST] Who are you [/INST]I am an assistant</s>[INST] Another question [/INST]",
+            /* .template_str= */ "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+            /* .expected_output= */       "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s>[INST] Who are you [/INST]I am an assistant</s>[INST] Another question [/INST]",
+            /* .expected_output_jinja= */ "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "mlabonne/AlphaMonarch-7B",
             /* .template_str= */ "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
-            /* .expected_output= */ "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+            /* .expected_output= */          "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+            /* .expected_output_jinja= */ "<s>system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+            /* .bos_token= */ "<s>",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "google/gemma-7b-it",
             /* .template_str= */ "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
-            /* .expected_output= */ "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+            /* .expected_output= */       "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+            /* .expected_output_jinja= */ "<start_of_turn>user\nYou are a helpful assistant\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
         },
         {
             /* .name= */ "OrionStarAI/Orion-14B-Chat",
             /* .template_str= */ "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
-            /* .expected_output= */ "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+            /* .expected_output= */       "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+            /* .expected_output_jinja= */ "Human: You are a helpful assistant\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "openchat/openchat-3.5-0106",
             // The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
             // So we match against the included template but implement the suggested version.
             /* .template_str= */ "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
-            /* .expected_output= */ "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
+            /* .expected_output= */                            "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
+            /* .expected_output_jinja= */ "GPT4 Correct System: You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
         },
         {
             /* .name= */ "deepseek-ai/deepseek-coder-33b-instruct",
             /* .template_str= */ "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
             /* .expected_output= */ "You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n   I am an assistant   \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
+            /* .expected_output_jinja= */ "",
         },
         {
             /* .name= */ "eachadea/vicuna-13b-1.1",
             // No template included in tokenizer_config.json, so this template likely needs to be manually set.
             /* .template_str= */ "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
             /* .expected_output= */ "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "Orca-Vicuna",
             // No template included in tokenizer_config.json, so this template likely needs to be manually set.
             /* .template_str= */ "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
             /* .expected_output= */ "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "CohereForAI/c4ai-command-r-plus",
             /* .template_str= */ "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
             /* .expected_output= */ "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
+            /* .expected_output_jinja= */ "",
         },
         {
             /* .name= */ "Llama-3",
             /* .template_str= */ "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
             /* .expected_output= */ "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+            /* .expected_output_jinja= */ "",
         },
         {
             /* .name= */ "Phi-3-mini",
             /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
-            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output= */     "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output_jinja= */ "<|user|>\nYou are a helpful assistant\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
         },
         {
             /* .name= */ "Phi-3-small",
             /* .template_str= */ "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
             /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output_jinja= */ "",
         },
         {
             /* .name= */ "Phi-3-medium",
             /* .template_str= */ "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
-            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output= */     "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output_jinja= */ "<|user|>\nYou are a helpful assistant\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
         },
         {
             /* .name= */ "Phi-3-vision",
             /* .template_str= */ "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
             /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "ChatGLM3",
             /* .template_str= */ "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
-            /* .expected_output= */ "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
+            /* .expected_output= */       "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
+            /* .expected_output_jinja= */ "[gMASK]sop<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
         },
         {
             /* .name= */ "ChatGLM4",
             /* .template_str= */ u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
             /* .expected_output= */ "[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "MiniCPM-3B-OpenHermes-2.5-v2-GGUF",
             /* .template_str= */ u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
             /* .expected_output= */ u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "DeepSeek-V2",
             /* .template_str= */ "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
             /* .expected_output= */ u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant:    I am an assistant   <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "<｜end▁of▁sentence｜>",
         },
         {
             /* .name= */ "ibm-granite/granite-3.0-8b-instruct",
             /* .template_str= */ "{%- if tools %}\n    {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n    {%- for tool in tools %}\n    {{- tool | tojson(indent=4) }}\n    {%- if not loop.last %}\n        {{- '\n\n' }}\n    {%- endif %}\n    {%- endfor %}\n    {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n    {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'user' %}\n    {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>'  + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'assistant_tool_call' %}\n    {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- elif message['role'] == 'tool_response' %}\n    {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n    {%- endif %}\n    {%- if loop.last and add_generation_prompt %}\n    {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n    {%- endif %}\n{%- endfor %}",
-            /* .expected_output= */ "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>   I am an assistant   <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n",
+            /* .expected_output= */       "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>   I am an assistant   <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n",
+            /* .expected_output_jinja= */ "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>   I am an assistant   <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>",
         },
         {
             /* .name= */ "mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt)",
             /* .template_str= */ "{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif %}\n    {%- if message['role'] == 'user' %}\n        {%- if loop.first and system_message is defined %}\n            {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n        {%- else %}\n            {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n        {%- endif %}\n    {%- elif message['role'] == 'assistant' %}\n        {{- ' ' + message['content'] + eos_token}}\n    {%- else %}\n        {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n    {%- endif %}\n{%- endfor %}\n",
             /* .expected_output= */ " [INST] You are a helpful assistant\n\nHello [/INST] Hi there</s> [INST] Who are you [/INST]    I am an assistant   </s> [INST] Another question [/INST]",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start)",
             /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS] [\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n        {{- \"[TOOL_CALLS] [\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- \" \" + message[\"content\"]|trim + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
-            /* .expected_output= */ "[INST] You are a helpful assistant\n\nHello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant</s>[INST] Another question[/INST]",
+            /* .expected_output= */       "[INST] You are a helpful assistant\n\nHello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant</s>[INST] Another question[/INST]",
+            /* .expected_output_jinja= */ "[INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant</s>[INST] You are a helpful assistant\n\nAnother question[/INST]",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start)",
             /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS][\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n        {{- \"[TOOL_CALLS][\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
-            /* .expected_output= */ "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
+            /* .expected_output= */       "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
+            /* .expected_output_jinja= */ "[INST]Hello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]You are a helpful assistant\n\nAnother question[/INST]",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)",
             /* .template_str= */ "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
             /* .expected_output= */ "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "</s>",
         },
         {
             /* .name= */ "ai-sage/GigaChat-20B-A3B-instruct",
             /* .template_str= */ "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
             /* .expected_output= */ "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
+            /* .supported_with_jinja= */ false, // Requires additional_special_tokens as extra context
         },
         {
             /* .name= */ "Infinigence/Megrez-3B-Instruct",
             /* .template_str= */ u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}",
             /* .expected_output= */ "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|>   I am an assistant   <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
         {
             /* .name= */ "phi-4",
             /* .template_str= */ "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|><|im_start|>assistant<|im_sep|>'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}",
             /* .expected_output= */ "<|im_start|>system<|im_sep|>You are a helpful assistant<|im_end|><|im_start|>user<|im_sep|>Hello<|im_end|><|im_start|>assistant<|im_sep|>Hi there<|im_end|><|im_start|>user<|im_sep|>Who are you<|im_end|><|im_start|>assistant<|im_sep|>   I am an assistant   <|im_end|><|im_start|>user<|im_sep|>Another question<|im_end|><|im_start|>assistant<|im_sep|>",
+            /* .expected_output_jinja= */ "",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
         },
     };
     std::vector<char> formatted_chat(1024);
@@ -190,6 +272,7 @@ int main(void) {
     // test invalid chat template
     res = llama_chat_apply_template("INVALID TEMPLATE", conversation.data(), conversation.size(), true, formatted_chat.data(), formatted_chat.size());
     assert(res < 0);
+    const auto add_generation_prompt = true;
 
     for (const auto & test_case : test_cases) {
         printf("\n\n=== %s ===\n\n", test_case.name.c_str());
@@ -198,26 +281,59 @@ int main(void) {
             test_case.template_str.c_str(),
             conversation.data(),
             conversation.size(),
-            true,
+            add_generation_prompt,
             formatted_chat.data(),
             formatted_chat.size()
         );
         formatted_chat.resize(res);
         std::string output(formatted_chat.data(), formatted_chat.size());
-        printf("%s\n", output.c_str());
-        printf("-------------------------\n");
-        assert(output == test_case.expected_output);
+        if (output != test_case.expected_output) {
+            printf("Expected:\n%s\n", test_case.expected_output.c_str());
+            printf("-------------------------\n");
+            printf("Actual:\n%s\n", output.c_str());
+            fflush(stdout);
+            assert(output == test_case.expected_output);
+        }
     }
 
+    json messages = json::array();
+    for (const auto & msg : conversation) {
+        messages.push_back({
+            {"role", msg.role},
+            {"content", msg.content},
+        });
+    }
+    for (const auto & test_case : test_cases) {
+        if (!test_case.supported_with_jinja) {
+            continue;
+        }
+        printf("\n\n=== %s (jinja) ===\n\n", test_case.name.c_str());
+        try {
+            minja::chat_template tmpl(test_case.template_str, test_case.bos_token, test_case.eos_token);
+            auto output = normalize_newlines(tmpl.apply(messages, json(), add_generation_prompt));
+            auto expected_output = normalize_newlines(test_case.expected_output_jinja.empty() ? test_case.expected_output : test_case.expected_output_jinja);
+            if (output != expected_output) {
+                printf("Expected:\n%s\n", expected_output.c_str());
+                printf("-------------------------\n");
+                printf("Actual:\n%s\n", output.c_str());
+                fflush(stdout);
+                assert(output == expected_output);
+            }
+        } catch (const std::exception & e) {
+            printf("ERROR: %s\n", e.what());
+            assert(false);
+        }
+    }
 
     // test llama_chat_format_single for system message
     printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
     std::vector<common_chat_msg> chat2;
     common_chat_msg sys_msg{"system", "You are a helpful assistant"};
 
-    auto fmt_sys = [&](std::string tmpl) {
-        auto output = common_chat_format_single(nullptr, tmpl, chat2, sys_msg, false);
-        printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str());
+    auto fmt_sys = [&](std::string tmpl_str) {
+        minja::chat_template tmpl(tmpl_str, "", "");
+        auto output = common_chat_format_single(tmpl, chat2, sys_msg, false, /* use_jinja= */ false);
+        printf("fmt_sys(%s) : %s\n", tmpl_str.c_str(), output.c_str());
         printf("-------------------------\n");
         return output;
     };
@@ -241,9 +357,10 @@ int main(void) {
     chat2.push_back({"assistant", "I am assistant"});
     common_chat_msg new_msg{"user", "How are you"};
 
-    auto fmt_single = [&](std::string tmpl) {
-        auto output = common_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
-        printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str());
+    auto fmt_single = [&](std::string tmpl_str) {
+        minja::chat_template tmpl(tmpl_str, "", "");
+        auto output = common_chat_format_single(tmpl, chat2, new_msg, true, /* use_jinja= */ false);
+        printf("fmt_single(%s) : %s\n", tmpl_str.c_str(), output.c_str());
         printf("-------------------------\n");
         return output;
     };
@@ -258,7 +375,5 @@ int main(void) {
     assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
     assert(fmt_single("gigachat") == "user<|role_sep|>How are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>");
 
-    printf("Test chat templates: OK\n");
-
     return 0;
 }

From 3e3357fd77bcf5bd8cfcc53ca53d4a5532e67e1b Mon Sep 17 00:00:00 2001
From: tc-mb <157115220+tc-mb@users.noreply.github.com>
Date: Wed, 22 Jan 2025 15:35:48 +0800
Subject: [PATCH 220/376] llava : support Minicpm-omni (#11289)

* init

* add readme

* update readme

* no use make

* update readme

* update fix code

* fix editorconfig-checker

* no change convert py

* use clip_image_u8_free
---
 examples/llava/README-minicpmo2.6.md          | 46 +++++++++++++++++++
 examples/llava/clip.cpp                       | 29 +++++++++++-
 examples/llava/llava.cpp                      | 13 ++----
 examples/llava/minicpmv-cli.cpp               | 10 +++-
 .../minicpmv-convert-image-encoder-to-gguf.py | 15 ++++--
 examples/llava/minicpmv-surgery.py            |  2 +-
 6 files changed, 100 insertions(+), 15 deletions(-)
 create mode 100644 examples/llava/README-minicpmo2.6.md

diff --git a/examples/llava/README-minicpmo2.6.md b/examples/llava/README-minicpmo2.6.md
new file mode 100644
index 000000000..8713a43d6
--- /dev/null
+++ b/examples/llava/README-minicpmo2.6.md
@@ -0,0 +1,46 @@
+## MiniCPM-o 2.6
+Currently, this readme only supports minicpm-omni's image capabilities, and we will update the full-mode support as soon as possible.
+
+### Prepare models and code
+
+Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder.
+
+Clone llama.cpp:
+```bash
+git clone git@github.com:OpenBMB/llama.cpp.git
+cd llama.cpp
+git checkout minicpm-omni
+```
+
+### Usage of MiniCPM-o 2.6
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
+
+```bash
+python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6
+python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
+python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
+
+# quantize int4 version
+./llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+Build llama.cpp using `CMake`:
+https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md
+
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+Inference on Linux or Mac
+```
+# run f16 version
+./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run quantized int4 version
+./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+
+# or run in interactive mode
+./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
+```
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 7a8a3156b..24073c5a9 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -718,6 +718,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         else if (ctx->minicpmv_version == 3) {
             pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
         }
+        else if (ctx->minicpmv_version == 4) {
+            pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
+        }
         ggml_set_name(pos_embed, "pos_embed");
         ggml_set_input(pos_embed);
     }
@@ -1053,6 +1056,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                     n_head = hidden_size/d_head;
                     num_query = 64;
                 }
+                else if (ctx->minicpmv_version == 4) {
+                    hidden_size = 3584;
+                    n_head = hidden_size/d_head;
+                    num_query = 64;
+                }
 
                 struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b);
                 Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
@@ -2041,6 +2049,7 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
                 images[images.size()-1].push_back(patch);
             }
         }
+        clip_image_u8_free(refine_image);
     }
     return images;
 }
@@ -2079,6 +2088,13 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
                 clip_image_f32_free(res);
             }
         }
+        for (size_t i = 0; i < imgs.size(); ++i) {
+            for (size_t j = 0; j < imgs[i].size(); ++j) {
+                if (imgs[i][j] != nullptr) {
+                    clip_image_u8_free(imgs[i][j]);
+                }
+            }
+        }
         return true;
     }
     else if (ctx->has_qwen2vl_merger) {
@@ -2335,6 +2351,9 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
         else if (ctx->minicpmv_version == 3) {
             n_patches = 64;
         }
+        else if (ctx->minicpmv_version == 4) {
+            n_patches = 64;
+        }
     } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
         int patch_size = params.patch_size * 2;
         int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
@@ -2514,8 +2533,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             //    -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
             struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
             int* positions_data = (int*)malloc(ggml_nbytes(positions));
-            int bucket_coords_h[70];
-            int bucket_coords_w[70];
+            int bucket_coords_h[1024];
+            int bucket_coords_w[1024];
             for (int i = 0; i < pos_h; i++){
                 bucket_coords_h[i] = std::floor(70.0*i/pos_h);
             }
@@ -2543,6 +2562,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             else if (ctx->minicpmv_version == 3) {
                 embed_dim = 3584;
             }
+            else if (ctx->minicpmv_version == 4) {
+                embed_dim = 3584;
+            }
             auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
 
             float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
@@ -2786,6 +2808,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         else if (ctx->minicpmv_version == 3) {
             return 3584;
         }
+        else if (ctx->minicpmv_version == 4) {
+            return 3584;
+        }
     }
     if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
         return ctx->vision_model.mm_1_b->ne[0];
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index c598caf3d..2cac7933d 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -216,7 +216,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     return true;
 }
 
-static clip_image_f32 * only_v2_5_reshape_by_patch(clip_image_f32 * image, int patch_size) {
+static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) {
     int width = image->nx;
     int height = image->ny;
     int num_patches = (height / patch_size) * (width / patch_size);
@@ -277,13 +277,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
                 encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
             }
             else {
-                int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
-                if (has_minicpmv_projector == 2) {
-                    encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
-                }
-                else if (has_minicpmv_projector == 3) {
-                    encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
-                }
+                encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
             }
 
             if (!encoded) {
@@ -313,6 +307,9 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         load_image_size->height = img->ny;
         clip_add_load_image_size(ctx_clip, load_image_size);
         LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
+        delete[] img_res_v.data;
+        img_res_v.size = 0;
+        img_res_v.data = nullptr;
     }
     else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp
index 38c44e130..53d902d61 100644
--- a/examples/llava/minicpmv-cli.cpp
+++ b/examples/llava/minicpmv-cli.cpp
@@ -140,6 +140,9 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
     else if (has_minicpmv_projector == 3) {
         system_prompt = "<|im_start|>user\n";
     }
+    else if (has_minicpmv_projector == 4) {
+        system_prompt = "<|im_start|>user\n";
+    }
     LOG_INF("%s: image token past: %d\n", __func__, n_past);
     eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, false);
     process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
@@ -227,6 +230,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
         else if (has_minicpmv_projector == 3) {
             user_prompt = "<|im_start|>user\n" + prompt;
         }
+        else if (has_minicpmv_projector == 4) {
+            user_prompt = "<|im_start|>user\n" + prompt;
+        }
     }
 
     eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
@@ -236,6 +242,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
     else if (has_minicpmv_projector == 3) {
         eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
     }
+    else if (has_minicpmv_projector == 4) {
+        eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
+    }
 
     // generate the response
 
@@ -308,7 +317,6 @@ int main(int argc, char ** argv) {
                     const auto * tmp = llama_loop(ctx_llava, smpl, n_past);
                     response += tmp;
                     if (strcmp(tmp, "</s>") == 0) break;
-                    if (strstr(tmp, "###")) break; // Yi-VL behavior
                     printf("%s", tmp);// mistral llava-1.6
                     if (strstr(response.c_str(), "<user>")) break; // minicpm-v
                     fflush(stdout);
diff --git a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py
index ea773742a..9b196757f 100644
--- a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py
+++ b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py
@@ -501,7 +501,7 @@ default_image_mean = [0.48145466, 0.4578275, 0.40821073]
 default_image_std = [0.26862954, 0.26130258, 0.27577711]
 ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
 ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
-ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3', default=2)
+ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2)
 
 # with proper
 args = ap.parse_args()
@@ -545,12 +545,19 @@ if args.use_f32:
 
 minicpmv_version = args.minicpmv_version
 emb_dim = 4096
+block_count = 26
 if minicpmv_version == 1:
     emb_dim = 2304
+    block_count = 26
 elif minicpmv_version == 2:
     emb_dim = 4096
+    block_count = 27
 elif minicpmv_version == 3:
     emb_dim = 3584
+    block_count = 27
+elif minicpmv_version == 4:
+    emb_dim = 3584
+    block_count = 27
 
 default_vision_config = {
         "hidden_size": 1152,
@@ -567,6 +574,9 @@ model = Idefics2VisionTransformer(vision_config)
 if minicpmv_version == 3:
     vision_config = SiglipVisionConfig(**default_vision_config)
     model = SiglipVisionTransformer(vision_config)
+elif minicpmv_version == 4:
+    vision_config = SiglipVisionConfig(**default_vision_config)
+    model = SiglipVisionTransformer(vision_config)
 
 processor = None
 # if model.attn_pool is not None:
@@ -587,7 +597,7 @@ elif args.minicpmv_projector is not None:
     fname_middle = "mmproj-"
     has_text_encoder = False
     has_minicpmv_projector = True
-    minicpmv_version = 3
+    minicpmv_version = 4
 elif args.vision_only:
     fname_middle = "vision-"
     has_text_encoder = False
@@ -625,7 +635,6 @@ if has_vision_encoder:
     fout.add_uint32("clip.vision.projection_dim", 0)
     fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
     fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
-    block_count = 26
     fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
 
     if processor is not None:
diff --git a/examples/llava/minicpmv-surgery.py b/examples/llava/minicpmv-surgery.py
index 748ff5c57..ba8211658 100644
--- a/examples/llava/minicpmv-surgery.py
+++ b/examples/llava/minicpmv-surgery.py
@@ -8,7 +8,7 @@ ap.add_argument("-m", "--model", help="Path to MiniCPM-V model")
 args = ap.parse_args()
 
 # find the model part that includes the the multimodal projector weights
-model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
+model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True, torch_dtype=torch.bfloat16)
 checkpoint = model.state_dict()
 
 # get a list of mm tensor names

From a94f3b2727e97eb6c904006eb786960c069282bc Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Wed, 22 Jan 2025 09:51:44 +0000
Subject: [PATCH 221/376] `common`: utils to split / join / repeat strings
 (from json converter) (#11342)

* Factor string_join, string_split, string_repeat into common

* json: refactor to surface a versatile builder

* Update common.cpp
---
 common/common.cpp                 | 42 +++++++++++++
 common/common.h                   |  4 ++
 common/json-schema-to-grammar.cpp | 98 +++++++++++--------------------
 common/json-schema-to-grammar.h   | 10 +++-
 4 files changed, 90 insertions(+), 64 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 727ab0a10..6dea8e3d2 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -484,6 +484,48 @@ void string_replace_all(std::string & s, const std::string & search, const std::
     s = std::move(builder);
 }
 
+std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
+    std::ostringstream result;
+    for (size_t i = 0; i < values.size(); ++i) {
+        if (i > 0) {
+            result << separator;
+        }
+        result << values[i];
+    }
+    return result.str();
+}
+
+std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) {
+    std::vector<std::string> parts;
+    size_t start = 0;
+    size_t end = str.find(delimiter);
+
+    while (end != std::string::npos) {
+        parts.push_back(str.substr(start, end - start));
+        start = end + delimiter.length();
+        end = str.find(delimiter, start);
+    }
+
+    parts.push_back(str.substr(start));
+
+    return parts;
+}
+
+std::string string_repeat(const std::string & str, size_t n) {
+    if (n == 0) {
+        return "";
+    }
+
+    std::string result;
+    result.reserve(str.length() * n);
+
+    for (size_t i = 0; i < n; ++i) {
+        result += str;
+    }
+
+    return result;
+}
+
 std::string string_from(bool value) {
     return value ? "true" : "false";
 }
diff --git a/common/common.h b/common/common.h
index 7c9d73ce1..571260372 100644
--- a/common/common.h
+++ b/common/common.h
@@ -429,6 +429,10 @@ std::string string_format(const char * fmt, ...);
 std::string string_strip(const std::string & str);
 std::string string_get_sortable_timestamp();
 
+std::string string_join(const std::vector<std::string> & values, const std::string & separator);
+std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
+std::string string_repeat(const std::string & str, size_t n);
+
 void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
 
 template<class T>
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index dadc18c8b..4d426b6bd 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -1,4 +1,6 @@
 #include "json-schema-to-grammar.h"
+#include "common.h"
+
 #include <algorithm>
 #include <fstream>
 #include <map>
@@ -11,11 +13,6 @@
 
 using json = nlohmann::ordered_json;
 
-template <typename Iterator>
-static std::string join(Iterator begin, Iterator end, const std::string & separator);
-
-static std::string repeat(const std::string & str, size_t n);
-
 static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
     auto has_max = max_items != std::numeric_limits<int>::max();
 
@@ -128,8 +125,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
                 if (sub_len > 0) {
                     auto from_sub = from.substr(i + 1);
                     auto to_sub = to.substr(i + 1);
-                    auto sub_zeros = repeat("0", sub_len);
-                    auto sub_nines = repeat("9", sub_len);
+                    auto sub_zeros = string_repeat("0", sub_len);
+                    auto sub_nines = string_repeat("9", sub_len);
 
                     auto to_reached = false;
                     out << "(";
@@ -188,8 +185,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
         auto max_digits = max_s.length();
 
         for (auto digits = min_digits; digits < max_digits; digits++) {
-            uniform_range(min_s, repeat("9", digits));
-            min_s = "1" + repeat("0", digits);
+            uniform_range(min_s, string_repeat("9", digits));
+            min_s = "1" + string_repeat("0", digits);
             out << " | ";
         }
         uniform_range(min_s, max_s);
@@ -318,49 +315,6 @@ std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
 std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
 
-template <typename Iterator>
-std::string join(Iterator begin, Iterator end, const std::string & separator) {
-    std::ostringstream result;
-    if (begin != end) {
-        result << *begin;
-        for (Iterator it = begin + 1; it != end; ++it) {
-            result << separator << *it;
-        }
-    }
-    return result.str();
-}
-
-static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
-    std::vector<std::string> tokens;
-    size_t start = 0;
-    size_t end = str.find(delimiter);
-
-    while (end != std::string::npos) {
-        tokens.push_back(str.substr(start, end - start));
-        start = end + delimiter.length();
-        end = str.find(delimiter, start);
-    }
-
-    tokens.push_back(str.substr(start));
-
-    return tokens;
-}
-
-static std::string repeat(const std::string & str, size_t n) {
-    if (n == 0) {
-        return "";
-    }
-
-    std::string result;
-    result.reserve(str.length() * n);
-
-    for (size_t i = 0; i < n; ++i) {
-        result += str;
-    }
-
-    return result;
-}
-
 static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch  &)> & replacement) {
     std::smatch match;
     std::string result;
@@ -389,6 +343,7 @@ static std::string format_literal(const std::string & literal) {
 
 class SchemaConverter {
 private:
+    friend std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);
     std::function<json(const std::string &)> _fetch_json;
     bool _dotall;
     std::map<std::string, std::string> _rules;
@@ -418,7 +373,7 @@ private:
         for (size_t i = 0; i < alt_schemas.size(); i++) {
             rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
         }
-        return join(rules.begin(), rules.end(), " | ");
+        return string_join(rules, " | ");
     }
 
     std::string _visit_pattern(const std::string & pattern, const std::string & name) {
@@ -481,7 +436,7 @@ private:
                 for (const auto & item : ret) {
                     results.push_back(to_rule(item));
                 }
-                return std::make_pair(join(results.begin(), results.end(), " "), false);
+                return std::make_pair(string_join(results, " "), false);
             };
 
             while (i < length) {
@@ -539,7 +494,7 @@ private:
                     }
                     curly_brackets += '}';
                     i++;
-                    auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
+                    auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
                     int min_times = 0;
                     int max_times = std::numeric_limits<int>::max();
                     try {
@@ -854,7 +809,7 @@ public:
                             return;
                         }
                         std::string pointer = ref.substr(ref.find('#') + 1);
-                        std::vector<std::string> tokens = split(pointer, "/");
+                        std::vector<std::string> tokens = string_split(pointer, "/");
                         for (size_t i = 1; i < tokens.size(); ++i) {
                             std::string sel = tokens[i];
                             if (target.is_null() || !target.contains(sel)) {
@@ -905,7 +860,7 @@ public:
             for (const auto & v : schema["enum"]) {
                 enum_values.push_back(_generate_constant_rule(v));
             }
-            return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space");
+            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space");
         } else if ((schema_type.is_null() || schema_type == "object")
                 && (schema.contains("properties") ||
                     (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
@@ -1019,10 +974,10 @@ public:
 
     void check_errors() {
         if (!_errors.empty()) {
-            throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
+            throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
         }
         if (!_warnings.empty()) {
-            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
+            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str());
         }
     }
 
@@ -1036,10 +991,27 @@ public:
 };
 
 std::string json_schema_to_grammar(const json & schema) {
-    SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
-    auto copy = schema;
-    converter.resolve_refs(copy, "input");
-    converter.visit(copy, "");
+    return build_grammar([&](const llama_grammar_builder & callbacks) {
+        auto copy = schema;
+        callbacks.resolve_refs(copy);
+        callbacks.add_schema("", copy);
+    });
+}
+
+std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb) {
+    SchemaConverter converter([&](const std::string &) { return json(); }, /* dotall= */ false);
+    llama_grammar_builder builder {
+        /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
+            return converter._add_rule(name, rule);
+        },
+        /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) {
+            return converter.visit(schema, name == "root" ? "" : name);
+        },
+        /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) {
+            converter.resolve_refs(schema, "");
+        }
+    };
+    cb(builder);
     converter.check_errors();
     return converter.format_grammar();
 }
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
index 41623b346..4f43ab3a5 100644
--- a/common/json-schema-to-grammar.h
+++ b/common/json-schema-to-grammar.h
@@ -5,4 +5,12 @@
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
 
-std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);
+std::string json_schema_to_grammar(const nlohmann::ordered_json & schema);
+
+struct llama_grammar_builder {
+    std::function<std::string(const std::string &, const std::string &)> add_rule;
+    std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
+    std::function<void(nlohmann::ordered_json &)> resolve_refs;
+};
+
+std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);

From 96f405393461062450692430e4916809bf71c3c4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?=
 <66251151+jpodivin@users.noreply.github.com>
Date: Wed, 22 Jan 2025 12:51:32 +0100
Subject: [PATCH 222/376] Adding logprobs to /v1/completions (#11344)

Signed-off-by: Jiri Podivin <jpodivin@redhat.com>
---
 examples/server/server.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 5f08c4ecc..412908aa8 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -267,6 +267,11 @@ struct server_task {
         params.speculative.n_min = std::max(params.speculative.n_min, 2);
         params.speculative.n_max = std::max(params.speculative.n_max, 0);
 
+        // Use OpenAI API logprobs only if n_probs wasn't provided
+        if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
+            params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
+        }
+
         if (data.contains("lora")) {
             if (data.at("lora").is_array()) {
                 params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora"));

From c64d2becb13f2a7c0145b516a05f028d949a046b Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Wed, 22 Jan 2025 16:16:27 +0000
Subject: [PATCH 223/376] `minja`: sync at
 https://github.com/google/minja/commit/0f5f7f2b3770eb682fbc11763266d45204173686
 (#11352)

---
 common/chat-template.hpp | 37 ++++++++++++++++++++++++++++---------
 common/minja.hpp         | 28 ++++++++++++++++++++++++++--
 2 files changed, 54 insertions(+), 11 deletions(-)

diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index b4a90145c..42ee0b615 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -25,6 +25,7 @@ class chat_template {
     // Meta-Llama-3.1-8B-Instruct's template expects arguments to be an object.
     // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
     bool requires_object_arguments_ = false;
+    bool requires_typed_content_ = false;
     bool supports_system_role_ = true;
     bool supports_parallel_tool_calls_ = false;
     std::string source_;
@@ -32,14 +33,14 @@ class chat_template {
     std::string eos_token_;
     std::shared_ptr<minja::TemplateNode> template_root_;
 
-    std::string try_render(
+    std::string try_raw_render(
         const nlohmann::ordered_json & messages,
         const nlohmann::ordered_json & tools,
         bool add_generation_prompt,
         const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
     {
         try {
-            auto prompt = apply(messages, tools, add_generation_prompt, extra_context);
+            auto prompt = apply(messages, tools, add_generation_prompt, extra_context, /* adjust_inputs= */ false);
             // fprintf(stderr, "Prompt: %s\n", prompt.c_str());
             return prompt;
         } catch (const std::exception & e) {
@@ -60,7 +61,7 @@ class chat_template {
         supports_tools_ = source.find("tools") != std::string::npos;
 
         auto renders_string_arguments =
-            try_render({
+            try_raw_render({
                 {
                     {"role", "user"},
                     {"content", "Hey"}
@@ -81,7 +82,7 @@ class chat_template {
             }, {}, false).find("{\"code\": \"print") != std::string::npos;
         if (!renders_string_arguments) {
             auto renders_object_arguments =
-                try_render({
+                try_raw_render({
                     {
                         {"role", "user"},
                         {"content", "Hey"}
@@ -106,10 +107,13 @@ class chat_template {
         }
         supports_parallel_tool_calls_ = source.find("tool_call_id") != std::string::npos;
 
-        supports_system_role_ = try_render({
+        supports_system_role_ = try_raw_render({
             {{"role", "system"}, {"content", "<System Needle>"}},
             {{"role", "user"},   {"content", "Hey"}}
         }, {}, false).find("<System Needle>") != std::string::npos;
+
+        requires_typed_content_ = try_raw_render({{{"role", "user"},   {"content", "Hey"}}}, {}, false).find("Hey") == std::string::npos
+            && try_raw_render({{{"role", "user"},   {"content", {{{"type", "text"}, {"text", "Hey"}}}}}}, {}, false).find("Hey") != std::string::npos;
     }
 
     const std::string & source() const { return source_; }
@@ -122,19 +126,34 @@ class chat_template {
         const nlohmann::ordered_json & messages,
         const nlohmann::ordered_json & tools,
         bool add_generation_prompt,
-        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
+        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
+        bool adjust_inputs = true) const
     {
         json actual_messages;
 
         // First, "fix" messages so they have a chance to be rendered correctly by the template
 
-        if (requires_object_arguments_ || !supports_system_role_ || !supports_tools_) {
+        if (adjust_inputs && (requires_object_arguments_ || !supports_system_role_ || !supports_tools_ || requires_typed_content_)) {
             actual_messages = json::array();
 
+            auto add_message = [&](const json & msg) {
+                if (requires_typed_content_ && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
+                    actual_messages.push_back({
+                        {"role", msg.at("role")},
+                        {"content", {{
+                            {"type", "text"},
+                            {"text", msg.at("content")},
+                        }}},
+                    });
+                } else {
+                    actual_messages.push_back(msg);
+                }
+            };
+
             std::string pending_system;
             auto flush_sys = [&]() {
                 if (!pending_system.empty()) {
-                    actual_messages.push_back({
+                    add_message({
                         {"role", "user"},
                         {"content", pending_system},
                     });
@@ -217,7 +236,7 @@ class chat_template {
                         }
                     }
                 }
-                actual_messages.push_back(message);
+                add_message(message);
             }
             flush_sys();
         } else {
diff --git a/common/minja.hpp b/common/minja.hpp
index f0ee7a49a..80bdd4b41 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -693,7 +693,7 @@ enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline };
 
 class TemplateToken {
 public:
-    enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter };
+    enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter };
 
     static std::string typeToString(Type t) {
         switch (t) {
@@ -712,6 +712,8 @@ public:
             case Type::EndMacro: return "endmacro";
             case Type::Filter: return "filter";
             case Type::EndFilter: return "endfilter";
+            case Type::Generation: return "generation";
+            case Type::EndGeneration: return "endgeneration";
         }
         return "Unknown";
     }
@@ -788,6 +790,14 @@ struct EndForTemplateToken : public TemplateToken {
     EndForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, location, pre, post) {}
 };
 
+struct GenerationTemplateToken : public TemplateToken {
+    GenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Generation, location, pre, post) {}
+};
+
+struct EndGenerationTemplateToken : public TemplateToken {
+    EndGenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndGeneration, location, pre, post) {}
+};
+
 struct SetTemplateToken : public TemplateToken {
     std::string ns;
     std::vector<std::string> var_names;
@@ -2149,7 +2159,7 @@ private:
       static std::regex comment_tok(R"(\{#([-~]?)(.*?)([-~]?)#\})");
       static std::regex expr_open_regex(R"(\{\{([-~])?)");
       static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
-      static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|set|endset|block|endblock|macro|endmacro|filter|endfilter)\b)");
+      static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter)\b)");
       static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
       static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
       static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
@@ -2229,6 +2239,12 @@ private:
             } else if (keyword == "endfor") {
               auto post_space = parseBlockClose();
               tokens.push_back(std::make_unique<EndForTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "generation") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<GenerationTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "endgeneration") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<EndGenerationTemplateToken>(location, pre_space, post_space));
             } else if (keyword == "set") {
               static std::regex namespaced_var_regex(R"((\w+)[\s\n\r]*\.[\s\n\r]*(\w+))");
 
@@ -2330,6 +2346,13 @@ private:
                   throw unterminated(**start);
               }
               children.emplace_back(std::make_shared<ForNode>(token->location, std::move(for_token->var_names), std::move(for_token->iterable), std::move(for_token->condition), std::move(body), for_token->recursive, std::move(else_body)));
+          } else if (dynamic_cast<GenerationTemplateToken*>(token.get())) {
+              auto body = parseTemplate(begin, it, end);
+              if (it == end || (*(it++))->type != TemplateToken::Type::EndGeneration) {
+                  throw unterminated(**start);
+              }
+              // Treat as a no-op, as our scope is templates for inference, not training (`{% generation %}` wraps generated tokens for masking).
+              children.emplace_back(std::move(body));
           } else if (auto text_token = dynamic_cast<TextTemplateToken*>(token.get())) {
               SpaceHandling pre_space = (it - 1) != begin ? (*(it - 2))->post_space : SpaceHandling::Keep;
               SpaceHandling post_space = it != end ? (*it)->pre_space : SpaceHandling::Keep;
@@ -2397,6 +2420,7 @@ private:
                   || dynamic_cast<EndFilterTemplateToken*>(token.get())
                   || dynamic_cast<EndIfTemplateToken*>(token.get())
                   || dynamic_cast<ElseTemplateToken*>(token.get())
+                  || dynamic_cast<EndGenerationTemplateToken*>(token.get())
                   || dynamic_cast<ElifTemplateToken*>(token.get())) {
               it--;  // unconsume the token
               break;  // exit the loop

From 12c2bdf2de34f747d13b270fc9d3b52490bf194f Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 22 Jan 2025 17:44:40 +0100
Subject: [PATCH 224/376] server : fix draft context not being released
 (#11354)

---
 examples/server/server.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 412908aa8..4cfb3c9bb 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1772,6 +1772,9 @@ struct server_context {
             // force F16 KV cache for the draft model for extra performance
             cparams_dft.type_k = GGML_TYPE_F16;
             cparams_dft.type_v = GGML_TYPE_F16;
+
+            // the context is not needed - we will create one for each slot
+            llama_init_dft.context.reset();
         }
 
         chat_templates = common_chat_templates_from_model(model, params_base.chat_template);

From 16d3df7ab0bb9def78047eab4d9b15393997f495 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 22 Jan 2025 19:44:26 +0200
Subject: [PATCH 225/376] readme : add plugin links (#11355)

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 784669ce1..97d028670 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,9 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
-- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123
+- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
+- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
+- Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
 - Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
 - Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
 

From 6152129d05870cb38162c422c6ba80434e021e9f Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 22 Jan 2025 19:22:20 +0100
Subject: [PATCH 226/376] main : update README documentation for batch size
 (#11353)

* main : update README documentation for batch size

* fix formatting

* minor
---
 examples/main/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/main/README.md b/examples/main/README.md
index 17d80a622..46f92eb7a 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -310,9 +310,9 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 ### Batch Size
 
--   `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+- `-ub N`, `--ubatch-size N`: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: `512`.
 
-- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.
+- `-b N`, `--batch-size N`: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: `2048`.
 
 ### Prompt Caching
 

From 5245729e3317064959faefc5e5cbc63f4e9cfbea Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 23 Jan 2025 01:01:17 -0600
Subject: [PATCH 227/376] vulkan: fix diag_mask_inf (#11323)

With robustbufferaccess disabled, this shader was showing OOB stores. There
is a bounds check in the code, but the workgrouop dimensions were reversed vs
CUDA and it was running the wrong number of threads. So fix the workgroup
dimensions and disable robustness for this pipeline.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp                   | 2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 78c2f5c45..eae4f0739 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2012,7 +2012,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_tanh_f32, "tanh_f32", tanh_f32_len, tanh_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp
index 4e68742b5..26d8bc22a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp
@@ -12,7 +12,7 @@ layout (push_constant) uniform parameter
 
 #include "types.comp"
 
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+layout(local_size_x = 1, local_size_y = 512, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};

From 1971adf55e3ebbfab83771d6174d37b77c352c71 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 23 Jan 2025 01:07:50 -0600
Subject: [PATCH 228/376] vulkan: sort shaders for more deterministic binary
 (#11315)

Fixes #11306.
---
 ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 8bcb64101..e9c6cb9d4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -17,13 +17,13 @@
 #include <cstring>
 #include <cstdlib>
 #include <cassert>
+#include <algorithm>
 #include <sys/stat.h>
 #include <sys/types.h>
 
 #ifdef _WIN32
     #include <windows.h>
     #include <direct.h> // For _mkdir on Windows
-    #include <algorithm> // For std::replace on w64devkit
 #else
     #include <unistd.h>
     #include <sys/wait.h>
@@ -502,6 +502,7 @@ void write_output_files() {
     fprintf(hdr, "#include <cstdint>\n\n");
     fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str());
 
+    std::sort(shader_fnames.begin(), shader_fnames.end());
     for (const auto& pair : shader_fnames) {
         const std::string& name = pair.first;
         #ifdef _WIN32

From 955a6c2d91480954e90469517c4b91c4052c6a59 Mon Sep 17 00:00:00 2001
From: amd-dwang <dong.wang@amd.com>
Date: Thu, 23 Jan 2025 15:14:28 +0800
Subject: [PATCH 229/376] Vulkan-run-test: fix mmq_wg_denoms (#11343)

There should be a copy-and-paste error here.

*mmq_wg_denoms should be used together with *warptile_mmq, instead of
wg_denoms.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 88 ++++++++++++++--------------
 1 file changed, 44 insertions(+), 44 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index eae4f0739..c325416d1 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1673,31 +1673,31 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
 
         if (device->coopmat_acc_f16_support) {
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         } else {
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
@@ -1707,31 +1707,31 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM2(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 
             if (device->coopmat_acc_f16_support) {
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             } else {
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, , wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             }
         }
 #undef CREATE_MM2

From f211d1dc10332b7e89a4abd1041903fa63bfed27 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Thu, 23 Jan 2025 10:38:20 +0000
Subject: [PATCH 230/376] Treat hf.co/ prefix the same as hf:// (#11350)

ollama uses hf.co/ to specify huggingface prefix, like RamaLama
uses hf://

Treat them similarly.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index e567ad716..c710d4326 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -634,20 +634,20 @@ class LlamaData {
         return path.substr(pos + 1);
     }
 
-    int remove_proto(std::string & model_) {
-        const std::string::size_type pos = model_.find("://");
+    int rm_until_substring(std::string & model_, const std::string & substring) {
+        const std::string::size_type pos = model_.find(substring);
         if (pos == std::string::npos) {
             return 1;
         }
 
-        model_ = model_.substr(pos + 3);  // Skip past "://"
+        model_ = model_.substr(pos + substring.size());  // Skip past the substring
         return 0;
     }
 
     int resolve_model(std::string & model_) {
         int                            ret     = 0;
         if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) {
-            remove_proto(model_);
+            rm_until_substring(model_, "://");
 
             return ret;
         }
@@ -656,13 +656,16 @@ class LlamaData {
         const std::vector<std::string> headers = { "--header",
                                                    "Accept: application/vnd.docker.distribution.manifest.v2+json" };
         if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
-            remove_proto(model_);
+            rm_until_substring(model_, "://");
+            ret = huggingface_dl(model_, headers, bn);
+        } else if (string_starts_with(model_, "hf.co/")) {
+            rm_until_substring(model_, "hf.co/");
             ret = huggingface_dl(model_, headers, bn);
         } else if (string_starts_with(model_, "ollama://")) {
-            remove_proto(model_);
+            rm_until_substring(model_, "://");
             ret = ollama_dl(model_, headers, bn);
         } else if (string_starts_with(model_, "https://")) {
-            download(model_, headers, bn, true);
+            ret = download(model_, headers, bn, true);
         } else {
             ret = ollama_dl(model_, headers, bn);
         }

From 58456616408c828a1ce6d2481940fd1c97bce3b5 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 23 Jan 2025 13:56:05 +0100
Subject: [PATCH 231/376] server : add more clean up when cancel_tasks is
 called (#11340)

* server : add more clean up when cancel_tasks is called

* fix recv_with_timeout

* std::remove_if

* fix std::remove_if
---
 examples/server/server.cpp | 43 +++++++++++++++++++++++++++++++-------
 1 file changed, 35 insertions(+), 8 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 4cfb3c9bb..a94c3822c 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1433,6 +1433,10 @@ struct server_queue {
         } else {
             queue_tasks.push_back(std::move(task));
         }
+        // if this is cancel task make sure to clean up pending tasks
+        if (task.type == SERVER_TASK_TYPE_CANCEL) {
+            cleanup_pending_task(task.id_target);
+        }
         condition_tasks.notify_one();
         return task.id;
     }
@@ -1450,6 +1454,10 @@ struct server_queue {
             } else {
                 queue_tasks.push_back(std::move(task));
             }
+            // if this is cancel task make sure to clean up pending tasks
+            if (task.type == SERVER_TASK_TYPE_CANCEL) {
+                cleanup_pending_task(task.id_target);
+            }
         }
         condition_tasks.notify_one();
         return 0;
@@ -1544,6 +1552,20 @@ struct server_queue {
             }
         }
     }
+
+private:
+    void cleanup_pending_task(int id_task) {
+        // no need lock because this is called exclusively by post()
+        auto rm_func = [id_task](const server_task & task) {
+            return task.id_target == id_task;
+        };
+        queue_tasks.erase(
+            std::remove_if(queue_tasks.begin(),          queue_tasks.end(),          rm_func),
+            queue_tasks.end());
+        queue_tasks_deferred.erase(
+            std::remove_if(queue_tasks_deferred.begin(), queue_tasks_deferred.end(), rm_func),
+            queue_tasks_deferred.end());
+    }
 };
 
 struct server_response {
@@ -1579,6 +1601,12 @@ struct server_response {
 
         std::unique_lock<std::mutex> lock(mutex_results);
         waiting_task_ids.erase(id_task);
+        // make sure to clean up all pending results
+        queue_results.erase(
+            std::remove_if(queue_results.begin(), queue_results.end(), [id_task](const server_task_result_ptr & res) {
+                return res->id == id_task;
+            }),
+            queue_results.end());
     }
 
     void remove_waiting_task_ids(const std::unordered_set<int> & id_tasks) {
@@ -1598,7 +1626,7 @@ struct server_response {
                 return !queue_results.empty();
             });
 
-            for (int i = 0; i < (int) queue_results.size(); i++) {
+            for (size_t i = 0; i < queue_results.size(); i++) {
                 if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) {
                     server_task_result_ptr res = std::move(queue_results[i]);
                     queue_results.erase(queue_results.begin() + i);
@@ -1615,12 +1643,6 @@ struct server_response {
     server_task_result_ptr recv_with_timeout(const std::unordered_set<int> & id_tasks, int timeout) {
         while (true) {
             std::unique_lock<std::mutex> lock(mutex_results);
-            bool cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout), [&]{
-                return !queue_results.empty();
-            });
-            if (!cr_res) {
-                return nullptr;
-            }
 
             for (int i = 0; i < (int) queue_results.size(); i++) {
                 if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) {
@@ -1629,6 +1651,11 @@ struct server_response {
                     return res;
                 }
             }
+
+            std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout));
+            if (cr_res == std::cv_status::timeout) {
+                return nullptr;
+            }
         }
 
         // should never reach here
@@ -2376,8 +2403,8 @@ struct server_context {
 
             server_task task(SERVER_TASK_TYPE_CANCEL);
             task.id_target = id_task;
-            cancel_tasks.push_back(task);
             queue_results.remove_waiting_task_id(id_task);
+            cancel_tasks.push_back(task);
         }
         // push to beginning of the queue, so it has highest priority
         queue_tasks.post(cancel_tasks, true);

From f7fb43cd0b84280c261f440dc8e85eafad4a0ca6 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Thu, 23 Jan 2025 16:16:18 +0000
Subject: [PATCH 232/376] Add -ngl (#11372)

Most other llama.cpp cli tools accept -ngl with a single dash.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index c710d4326..715f21b5d 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -147,7 +147,8 @@ class Opt {
                 if (handle_option_with_value(argc, argv, i, context_size) == 1) {
                     return 1;
                 }
-            } else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) {
+            } else if (options_parsing &&
+                       (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
                 if (handle_option_with_value(argc, argv, i, ngl) == 1) {
                     return 1;
                 }

From 05f63cc9ee859de07f585f7b12939345f39ada8b Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Thu, 23 Jan 2025 20:04:31 +0000
Subject: [PATCH 233/376] Update documentation (#11373)

To show -n, -ngl, --ngl is acceptable.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 715f21b5d..92a49eb74 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -195,7 +195,7 @@ class Opt {
             "Options:\n"
             "  -c, --context-size <value>\n"
             "      Context size (default: %d)\n"
-            "  -n, --ngl <value>\n"
+            "  -n, -ngl, --ngl <value>\n"
             "      Number of GPU layers (default: %d)\n"
             "  --temp <value>\n"
             "      Temperature (default: %.1f)\n"

From 564804b79b78df1469ec8646869972de5e885ec4 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 23 Jan 2025 14:51:24 -0600
Subject: [PATCH 234/376] tests: fix some mul_mat test gaps (#11375)

Now that we have batched mat-vec mul Vulkan shaders for up to n==8,
these tests weren't actually exercising the mat-mat mul path. Test
n==9 as well. Also, change to use all_types.
---
 tests/test-backend-ops.cpp | 15 ++++-----------
 1 file changed, 4 insertions(+), 11 deletions(-)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 74d1bee39..381956a04 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3909,17 +3909,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 4));
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 128, 4));
 
-    for (int i = 1; i < 9; ++i) {
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16,    GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_1,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_0,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_1,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_K,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_K,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q6_K,   GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
-        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_IQ4_NL, GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
+    for (ggml_type type_a : all_types) {
+        for (int i = 1; i < 10; ++i) {
+            test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
+        }
     }
 
 #if 1

From c07e87f38bd0c22ec6dbc852ae50aaa1c64632d4 Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Fri, 24 Jan 2025 09:02:38 +0100
Subject: [PATCH 235/376] server : (webui) put DeepSeek R1 CoT in a collapsible
 <details> element (#11364)

* webui : put DeepSeek R1 CoT in a collapsible <details> element

* webui: refactor split

* webui: don't use regex to split cot and response

* webui: format+qol

* webui: no loading icon if the model isn't generating

* ui fix, add configs

* add jsdoc types

* only filter </think> for assistant msg

* build

* update build

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/public/index.html.gz | Bin 1206492 -> 1207150 bytes
 examples/server/webui/index.html     |  27 ++++-
 examples/server/webui/src/main.js    | 165 +++++++++++++++++++++------
 3 files changed, 159 insertions(+), 33 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 26f3583bdf844ccdb67fb197a56991a25d4b2ecc..582ccc0d3f8d07ce79dd9e978772f9c5ea85c426 100644
GIT binary patch
delta 1136744
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z*1abpX(%}?NK{^bLOZ7HTEK`u;FXIRIATvYSi5<J#60|348(5bQ`@U4BD7L1Ev=a)
zaRlMmM)JcEKVM&@gurG3oH{^%fNf8RBp-<tN$l6FT{NmS!cy%t4Nj%#P2^^G5Z-L%
zW7KO^JJ9>Z`_w$!U%6{N1lP4`P+v#FL|fFoD)zvE6}*Li<bW7-XR!2;&~*JsEDO@H
zPvtKBb{BlxVuyv^!qsexr&do4TI@k@TxO9?4sZv)5gSpgp(v|IOdEPCwRFX@zYp{`
zAS+V5$ycyYxZNzGfOh#-d)-+dKnVD@R+=znBRB>OCIF|pnN5_NOFbJ&Z2B%v%;&2r
z3Fo3Thia04Ea(=0*{SjBL*%n%bu(!PgyL(fiaYpv;SOC}T16<kH=F2wLvYo8mM5Fc
zBZA-{N4?ViB%+#tG>F(kWw1mfX7TtvX$bAJPwRxuy5&bmX5fR)Ra;X3)1T0u^;O48
zd-$M~x5@G(!)X$*WgvfB6Nv&A_-0;~M^!qM1=|UKDIxw0HjfLbV3WlUU?2ZOO0o}p
zxhB)(bNH6|WAy}m`h@5tA2`+Bd#VP!{UcTt#Pe9IgEU<UsNg-9-(*RYf>T^JeHVT(
z;%aO30GfP=A$hF3O%Rc;7Eunpi}LPPU8-Wu_UfsF)cN9ax9c*pXXV(8>Rj1qeE$wI
zg%-7c;S(B7Kx?cR%FErdjKddky>$z_#98-<Svkuc>Y23BQI+t1G(+_ryHAxLL3&_;
z|Mh1KCJ<2R08nu!u!He#KhX7Ibwgtu1#!TT8Ycy6?3rrnz;-|y!@vHo|MqYH=f9&@
zen4MzAF$%;-E+8LUze(OhxMy5j|#^mj-z~kb#h3T{_p?(pZ|<@hcWuP^}9*!CFvud
zs}B<PfBXx&O47$pF)J&t`@-IZ@#1k8?Bx5bT?^y>XjZvM79OXI7c#)_-O3DJn1vyn
zP)jxyq6;fG?eq-xtiL8<TG>((Bc@1LEF|n`9;$5)^uDp7b3&CG&tt>0VJ)8Z^i;5a
z{HK3HyO=3t7mLWWZw?#C<T_+h<EhR?WU>~B1hI~e<3!Y+d+!DzSlNkiNUQHzs~CmX
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zV%WN>PIYTiP-cg27&*K<g<c=qdejM8;JJP{X}f=G_E?DWHZ=zCJUaq!EY3JGalV6V
z3H_OIy^#KaL;*cM+N8cq$!u5S=oI|}J5D0=c#6zu>`pA!f(wF=o=^DORp50i@aC5n
N{}2B1ryi>KA^_G@EiV87

diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index 2180ef4ad..d3893ea4e 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -141,6 +141,7 @@
               :msg="pendingMsg"
               :key="pendingMsg.id"
               :is-generating="isGenerating"
+              :show-thought-in-progress="config.showThoughtInProgress"
               :edit-user-msg-and-regenerate="() => {}"
               :regenerate-msg="() => {}"></message-bubble>
           </div>
@@ -202,6 +203,20 @@
               </template>
             </div>
           </details>
+          <!-- Section: Reasoning models -->
+          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary class="collapse-title font-bold">Reasoning models</summary>
+            <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.showThoughtInProgress" />
+                <span class="ml-4">Expand though process by default for generating message</span>
+              </div>
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.excludeThoughtOnReq" />
+                <span class="ml-4">Exclude thought process when sending request to API (Recommended for DeepSeek-R1)</span>
+              </div>
+            </div>
+          </details>
           <!-- Section: Advanced config -->
           <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
             <summary class="collapse-title font-bold">Advanced config</summary>
@@ -261,7 +276,17 @@
           <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
           <!-- render message as markdown -->
           <div v-else dir="auto">
-            <vue-markdown :source="msg.content"></vue-markdown>
+            <details v-if="msg.role === 'assistant' && splitMsgContent.cot" class="collapse bg-base-200 collapse-arrow mb-4" :open="splitMsgContent.isThinking && showThoughtInProgress">
+              <summary class="collapse-title">
+                <span v-if="splitMsgContent.isThinking">
+                  <span v-if="isGenerating" class="loading loading-spinner loading-md mr-2" style="vertical-align: middle;"></span>
+                  <b>Thinking</b>
+                </span>
+                <b v-else>Thought Process</b>
+              </summary>
+              <vue-markdown :source="splitMsgContent.cot" dir="auto" class="collapse-content"></vue-markdown>
+            </details>
+            <vue-markdown :source="splitMsgContent.content"></vue-markdown>
           </div>
           <!-- render timings if enabled -->
           <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index feb741a4e..90f4ca368 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -17,6 +17,11 @@ import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
 
 const isDev = import.meta.env.MODE === 'development';
 
+// types
+/** @typedef {{ id: number, role: 'user' | 'assistant', content: string, timings: any }} Message */
+/** @typedef {{ role: 'user' | 'assistant', content: string }} APIMessage */
+/** @typedef {{ id: string, lastModified: number, messages: Array<Message> }} Conversation */
+
 // utility functions
 const isString = (x) => !!x.toLowerCase;
 const isBoolean = (x) => x === true || x === false;
@@ -50,6 +55,8 @@ const CONFIG_DEFAULT = {
   apiKey: '',
   systemMessage: 'You are a helpful assistant.',
   showTokensPerSecond: false,
+  showThoughtInProgress: false,
+  excludeThoughtOnReq: true,
   // make sure these default values are in sync with `common.h`
   samplers: 'edkypmxt',
   temperature: 0.8,
@@ -172,6 +179,7 @@ const MessageBubble = defineComponent({
     config: Object,
     msg: Object,
     isGenerating: Boolean,
+    showThoughtInProgress: Boolean,
     editUserMsgAndRegenerate: Function,
     regenerateMsg: Function,
   },
@@ -188,7 +196,31 @@ const MessageBubble = defineComponent({
         prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
         predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
       };
-    }
+    },
+    splitMsgContent() {
+      const content = this.msg.content;
+      if (this.msg.role !== 'assistant') {
+        return { content };
+      }
+      let actualContent = '';
+      let cot = '';
+      let isThinking = false;
+      let thinkSplit = content.split('<think>', 2);
+      actualContent += thinkSplit[0];
+      while (thinkSplit[1] !== undefined) {
+        // <think> tag found
+        thinkSplit = thinkSplit[1].split('</think>', 2);
+        cot += thinkSplit[0];
+        isThinking = true;
+        if (thinkSplit[1] !== undefined) {
+          // </think> closing tag found
+          isThinking = false;
+          thinkSplit = thinkSplit[1].split('<think>', 2);
+          actualContent += thinkSplit[0];
+        }
+      }
+      return { content: actualContent, cot, isThinking };
+    },
   },
   methods: {
     copyMsg() {
@@ -208,7 +240,10 @@ const MessageBubble = defineComponent({
 // format: { [convId]: { id: string, lastModified: number, messages: [...] } }
 // convId is a string prefixed with 'conv-'
 const StorageUtils = {
-  // manage conversations
+  /**
+   * manage conversations
+   * @returns {Array<Conversation>}
+   */
   getAllConversations() {
     const res = [];
     for (const key in localStorage) {
@@ -219,11 +254,19 @@ const StorageUtils = {
     res.sort((a, b) => b.lastModified - a.lastModified);
     return res;
   },
-  // can return null if convId does not exist
+  /**
+   * can return null if convId does not exist
+   * @param {string} convId
+   * @returns {Conversation | null}
+   */
   getOneConversation(convId) {
     return JSON.parse(localStorage.getItem(convId) || 'null');
   },
-  // if convId does not exist, create one
+  /**
+   * if convId does not exist, create one
+   * @param {string} convId
+   * @param {Message} msg
+   */
   appendMsg(convId, msg) {
     if (msg.content === null) return;
     const conv = StorageUtils.getOneConversation(convId) || {
@@ -235,12 +278,24 @@ const StorageUtils = {
     conv.lastModified = Date.now();
     localStorage.setItem(convId, JSON.stringify(conv));
   },
+  /**
+   * Get new conversation id
+   * @returns {string}
+   */
   getNewConvId() {
     return `conv-${Date.now()}`;
   },
+  /**
+   * remove conversation by id
+   * @param {string} convId
+   */
   remove(convId) {
     localStorage.removeItem(convId);
   },
+  /**
+   * remove all conversations
+   * @param {string} convId
+   */
   filterAndKeepMsgs(convId, predicate) {
     const conv = StorageUtils.getOneConversation(convId);
     if (!conv) return;
@@ -248,6 +303,11 @@ const StorageUtils = {
     conv.lastModified = Date.now();
     localStorage.setItem(convId, JSON.stringify(conv));
   },
+  /**
+   * remove last message from conversation
+   * @param {string} convId
+   * @returns {Message | undefined}
+   */
   popMsg(convId) {
     const conv = StorageUtils.getOneConversation(convId);
     if (!conv) return;
@@ -322,10 +382,12 @@ const mainApp = createApp({
   data() {
     return {
       conversations: StorageUtils.getAllConversations(),
-      messages: [], // { id: number, role: 'user' | 'assistant', content: string }
+      /** @type {Array<Message>} */
+      messages: [],
       viewingConvId: StorageUtils.getNewConvId(),
       inputMsg: '',
       isGenerating: false,
+      /** @type {Array<Message> | null} */
       pendingMsg: null, // the on-going message from assistant
       stopGeneration: () => {},
       selectedTheme: StorageUtils.getTheme(),
@@ -333,6 +395,7 @@ const mainApp = createApp({
       showConfigDialog: false,
       // const
       themes: THEMES,
+      /** @type {CONFIG_DEFAULT} */
       configDefault: {...CONFIG_DEFAULT},
       configInfo: {...CONFIG_INFO},
       isDev,
@@ -425,42 +488,50 @@ const mainApp = createApp({
       this.isGenerating = true;
 
       try {
+        /** @type {CONFIG_DEFAULT} */
+        const config = this.config;
         const abortController = new AbortController();
         this.stopGeneration = () => abortController.abort();
+        /** @type {Array<APIMessage>} */
+        let messages = [
+          { role: 'system', content: config.systemMessage },
+          ...normalizeMsgsForAPI(this.messages),
+        ];
+        if (config.excludeThoughtOnReq) {
+          messages = filterThoughtFromMsgs(messages);
+        }
+        if (isDev) console.log({messages});
         const params = {
-          messages: [
-            { role: 'system', content: this.config.systemMessage },
-            ...this.messages,
-          ],
+          messages,
           stream: true,
           cache_prompt: true,
-          samplers: this.config.samplers,
-          temperature: this.config.temperature,
-          dynatemp_range: this.config.dynatemp_range,
-          dynatemp_exponent: this.config.dynatemp_exponent,
-          top_k: this.config.top_k,
-          top_p: this.config.top_p,
-          min_p: this.config.min_p,
-          typical_p: this.config.typical_p,
-          xtc_probability: this.config.xtc_probability,
-          xtc_threshold: this.config.xtc_threshold,
-          repeat_last_n: this.config.repeat_last_n,
-          repeat_penalty: this.config.repeat_penalty,
-          presence_penalty: this.config.presence_penalty,
-          frequency_penalty: this.config.frequency_penalty,
-          dry_multiplier: this.config.dry_multiplier,
-          dry_base: this.config.dry_base,
-          dry_allowed_length: this.config.dry_allowed_length,
-          dry_penalty_last_n: this.config.dry_penalty_last_n,
-          max_tokens: this.config.max_tokens,
-          timings_per_token: !!this.config.showTokensPerSecond,
-          ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}),
+          samplers: config.samplers,
+          temperature: config.temperature,
+          dynatemp_range: config.dynatemp_range,
+          dynatemp_exponent: config.dynatemp_exponent,
+          top_k: config.top_k,
+          top_p: config.top_p,
+          min_p: config.min_p,
+          typical_p: config.typical_p,
+          xtc_probability: config.xtc_probability,
+          xtc_threshold: config.xtc_threshold,
+          repeat_last_n: config.repeat_last_n,
+          repeat_penalty: config.repeat_penalty,
+          presence_penalty: config.presence_penalty,
+          frequency_penalty: config.frequency_penalty,
+          dry_multiplier: config.dry_multiplier,
+          dry_base: config.dry_base,
+          dry_allowed_length: config.dry_allowed_length,
+          dry_penalty_last_n: config.dry_penalty_last_n,
+          max_tokens: config.max_tokens,
+          timings_per_token: !!config.showTokensPerSecond,
+          ...(config.custom.length ? JSON.parse(config.custom) : {}),
         };
         const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
           method: 'POST',
           headers: {
             'Content-Type': 'application/json',
-            ...(this.config.apiKey ? {'Authorization': `Bearer ${this.config.apiKey}`} : {})
+            ...(config.apiKey ? {'Authorization': `Bearer ${config.apiKey}`} : {})
           },
           body: JSON.stringify(params),
           signal: abortController.signal,
@@ -477,7 +548,7 @@ const mainApp = createApp({
             };
           }
           const timings = chunk.timings;
-          if (timings && this.config.showTokensPerSecond) {
+          if (timings && config.showTokensPerSecond) {
             // only extract what's really needed, to save some space
             this.pendingMsg.timings = {
               prompt_n: timings.prompt_n,
@@ -598,3 +669,33 @@ try {
     <button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
   </div>`;
 }
+
+/**
+ * filter out redundant fields upon sending to API
+ * @param {Array<APIMessage>} messages
+ * @returns {Array<APIMessage>}
+ */
+function normalizeMsgsForAPI(messages) {
+  return messages.map((msg) => {
+    return {
+      role: msg.role,
+      content: msg.content,
+    };
+  });
+}
+
+/**
+ * recommended for DeepsSeek-R1, filter out content between <think> and </think> tags
+ * @param {Array<APIMessage>} messages
+ * @returns {Array<APIMessage>}
+ */
+function filterThoughtFromMsgs(messages) {
+  return messages.map((msg) => {
+    return {
+      role: msg.role,
+      content: msg.role === 'assistant'
+        ? msg.content.split('</think>').at(-1).trim()
+        : msg.content,
+    };
+  });
+}

From 01f37edf1a6fae76fd9e2e02109aae6995a914f0 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Fri, 24 Jan 2025 09:39:24 +0000
Subject: [PATCH 236/376] Update llama-run README.md (#11386)

For consistency

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/run/README.md b/examples/run/README.md
index a06805441..89a552079 100644
--- a/examples/run/README.md
+++ b/examples/run/README.md
@@ -3,11 +3,10 @@
 The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models.
 
 ```bash
-llama-run granite-code
+llama-run granite3-moe
 ```
 
 ```bash
-llama-run -h
 Description:
   Runs a llm
 
@@ -17,7 +16,7 @@ Usage:
 Options:
   -c, --context-size <value>
       Context size (default: 2048)
-  -n, --ngl <value>
+  -n, -ngl, --ngl <value>
       Number of GPU layers (default: 0)
   --temp <value>
       Temperature (default: 0.8)

From 1af6945eb0d0e97525dc0ec18167abf05c28f482 Mon Sep 17 00:00:00 2001
From: "Bernhard M. Wiedemann" <githubbmwprimary@lsmod.de>
Date: Fri, 24 Jan 2025 12:21:35 +0100
Subject: [PATCH 237/376] cmake : avoid -march=native when reproducible build
 is wanted (#11366)

See https://reproducible-builds.org/ for why this is good
and https://reproducible-builds.org/specs/source-date-epoch/
for the definition of this variable.

Without this patch, compiling on different machines produced different binaries, which made verification of results difficult.

Fixes: #11317

This patch was done while working on reproducible builds for openSUSE.
---
 ggml/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 185079aa4..ff68ddc21 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -58,7 +58,8 @@ else()
     set(GGML_BLAS_VENDOR_DEFAULT "Generic")
 endif()
 
-if (CMAKE_CROSSCOMPILING)
+if (CMAKE_CROSSCOMPILING OR DEFINED ENV{SOURCE_DATE_EPOCH})
+    message(STATUS "Setting GGML_NATIVE_DEFAULT to OFF")
     set(GGML_NATIVE_DEFAULT OFF)
 else()
     set(GGML_NATIVE_DEFAULT ON)

From 8137b4bb2b5fd4cb4a752bfe69ccfd915a313d58 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 24 Jan 2025 12:38:31 +0100
Subject: [PATCH 238/376] CPU/CUDA: fix (GQA) mul mat back, add CUDA support
 (#11380)

---
 ggml/src/ggml-cpu/ggml-cpu.c    |   4 +-
 ggml/src/ggml-cpu/ggml-cpu.cpp  |   3 +-
 ggml/src/ggml-cuda/binbcast.cu  |  54 ++++++++-------
 ggml/src/ggml-cuda/ggml-cuda.cu |   2 +-
 ggml/src/ggml-cuda/out-prod.cu  |   7 +-
 ggml/src/ggml.c                 |  32 +++++----
 tests/test-backend-ops.cpp      | 115 +++++++++++++++++++++++++++-----
 7 files changed, 156 insertions(+), 61 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 0ed92b3ff..9e627da8c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -7883,7 +7883,7 @@ static void ggml_compute_forward_out_prod_f32(
 
                     float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
                     float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
-                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1   + i2*nb2   + i3*nb3));
 
                     ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1);
                 }
@@ -7892,7 +7892,7 @@ static void ggml_compute_forward_out_prod_f32(
 
                     float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
                     float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
-                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1   + i2*nb2   + i3*nb3));
 
                     ggml_vec_mad_f32(ne0, d, s0, *s1);
                 }
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 35a1c876c..2ccb4b472 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -416,7 +416,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         case GGML_OP_IM2COL_BACK:
             return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
         case GGML_OP_OUT_PROD:
-            return (src0->type == GGML_TYPE_F32 || ggml_is_quantized(src0->type)) && src1->type == GGML_TYPE_F32;
+            return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
+                src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         default:
             return true;
     }
diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index c7b6be4e2..ce4b9cfb5 100644
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -93,26 +93,31 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
 
 template <typename T>
 static __global__ void k_repeat_back(
-    const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
-    const int64_t ne0, const int64_t ne1, const int64_t ne2) {
+    const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+    const size_t s00, const size_t s01, const size_t s02, const size_t s03,
+    const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3) {
 
-    const int64_t tid0 = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
-    const int64_t tid1 = (int64_t) blockIdx.y*blockDim.y + threadIdx.y;
-    const int64_t tid2 = (int64_t) blockIdx.z*blockDim.z + threadIdx.z;
+    const int64_t tid0  = int64_t(blockIdx.x)*blockDim.x + threadIdx.x;
+    const int64_t tid1  = int64_t(blockIdx.y)*blockDim.y + threadIdx.y;
+    const int64_t tid23 = int64_t(blockIdx.z)*blockDim.z + threadIdx.z;
+    const int64_t tid2  = tid23 % ne2;
+    const int64_t tid3  = tid23 / ne2;
 
     if (tid0 >= ne0) {
         return;
     }
 
     T sum = 0;
-    for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
-        for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
-            for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
-                sum += src[i2*ne01*ne00 + i1*ne00 + i0];
+    for (int64_t i3 = tid3; i3 < ne03; i3 += ne3) {
+        for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
+            for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
+                for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
+                    sum += src[i3*s03 + i2*s02 + i1*s01 + i0*s00];
+                }
             }
         }
     }
-    dst[tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
+    dst[tid3*ne2*ne1*ne0 + tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
 }
 
 template<float (*bin_op)(const float, const float)>
@@ -274,12 +279,14 @@ struct bin_bcast_cuda {
 
 template <typename T>
 static void repeat_back_cuda(
-    const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
-    const int64_t ne0, const int64_t ne1, const int64_t ne2, cudaStream_t stream) {
+    const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+    const size_t s00, const size_t s01, const size_t s02, const size_t s03,
+    const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
 
     const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2);
-    k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>(src, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+    const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2*ne3);
+    k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>
+        (src, dst, ne00, ne01, ne02, ne03, s00, s01, s02, s03, ne0, ne1, ne2, ne3);
 }
 
 template<class op>
@@ -326,27 +333,26 @@ void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const ggml_tensor * src0 = dst->src[0];
 
     GGML_ASSERT(src0->type == dst->type);
-    GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_can_repeat(dst, src0));
 
     cudaStream_t stream = ctx.stream();
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_ASSERT(ne2*ne3 <= (1 << 15));
+
+    const size_t ts = ggml_type_size(src0->type);
+    const size_t s00 = nb00 / ts;
+    const size_t s01 = nb01 / ts;
+    const size_t s02 = nb02 / ts;
+    const size_t s03 = nb03 / ts;
 
     switch (dst->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0->data;
             float       * dst_d  = (float       *) dst->data;
-            repeat_back_cuda<float>(src0_d, dst_d, ne00, ne01, ne02, ne0, ne1, ne2, stream);
+            repeat_back_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s00, s01, s02, s03, ne0, ne1, ne2, ne3, stream);
         } break;
         default: {
             GGML_ASSERT(false);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 7fd1fc853..e602419bc 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3002,7 +3002,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
             } break;
         case GGML_OP_REPEAT_BACK:
-                return op->type == GGML_TYPE_F32 && op->src[0]->ne[3] == 1;
+                return op->type == GGML_TYPE_F32 && (op->src[0]->ne[2]*op->src[0]->ne[3]) <= (1 << 15);
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
diff --git a/ggml/src/ggml-cuda/out-prod.cu b/ggml/src/ggml-cuda/out-prod.cu
index 73e3e2c47..c9b2b699c 100644
--- a/ggml/src/ggml-cuda/out-prod.cu
+++ b/ggml/src/ggml-cuda/out-prod.cu
@@ -34,6 +34,9 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     CUBLAS_CHECK(cublasSetStream(handle, stream));
 
+    const int64_t lda = nb01 / sizeof(float);
+    const int64_t ldc = nb1  / sizeof(float);
+
     const bool src1_T = ggml_is_transposed(src1);
     const cublasOperation_t src1_cublas_op =  src1_T ? CUBLAS_OP_N : CUBLAS_OP_T;
     const int64_t           ldb            = (src1_T ?        nb10 :        nb11) /  sizeof(float);
@@ -57,9 +60,9 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             CUBLAS_CHECK(
                 cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
                         ne0, ne1, ne01,
-                        &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, ne00,
+                        &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
                                 src1_d +  i3      *s13 +  i2      *s12, ldb,
-                        &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ne0));
+                        &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ldc));
         }
     }
 }
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index b1d0d4913..92c4294c5 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -5339,7 +5339,7 @@ static void ggml_compute_backward(
         } break;
         case GGML_OP_MUL: {
             if (src0_needs_grads) {
-                ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, src1, grad));
+                ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, src1));
             }
             if (src1_needs_grads) {
                 struct ggml_tensor * tmp = ggml_mul(ctx, src0, grad);
@@ -5431,21 +5431,25 @@ static void ggml_compute_backward(
             // src1.shape   [n,p,qq,rr]
 
             if (src0_needs_grads) {
-                struct ggml_tensor * s1_tg =
+                GGML_ASSERT(grad->ne[2] == src1->ne[2]);
+                GGML_ASSERT(grad->ne[3] == src1->ne[3]);
+                struct ggml_tensor * tmp =
                     ggml_out_prod(ctx, // [n,m,qq,rr]
                         src1,          // [n,p,qq,rr]
                         grad);         // [m,p,qq,rr]
-                const int64_t qq = s1_tg->ne[2];
-                const int64_t rr = s1_tg->ne[3];
-                const int64_t q1 = src0->ne[2];
-                const int64_t r1 = src0->ne[3];
-                const bool ne2_broadcasted = qq > q1;
-                const bool ne3_broadcasted = rr > r1;
-                if (ne2_broadcasted || ne3_broadcasted) {
-                    // sum broadcast repetitions of s1_tg into shape of src0
-                    s1_tg = ggml_repeat_back(ctx, s1_tg, src0);
+                if (!ggml_are_same_shape(tmp, src0)) {
+                    GGML_ASSERT(tmp->ne[0] == src0->ne[0]);
+                    GGML_ASSERT(tmp->ne[1] == src0->ne[1]);
+                    GGML_ASSERT(tmp->ne[3] == 1);
+
+                    const int64_t nr2 = tmp->ne[2] / src0->ne[2];
+                    const size_t nb2 = tmp->nb[2] * nr2;
+                    const size_t nb3 = tmp->nb[2];
+
+                    tmp = ggml_view_4d(ctx, tmp, src0->ne[0], src0->ne[1], src0->ne[2], nr2, tmp->nb[1], nb2, nb3, 0);
+                    tmp = ggml_repeat_back(ctx, tmp, src0);
                 }
-                ggml_add_or_set(ctx, cgraph, isrc0, s1_tg /*= [n,m,q1,r1]*/);
+                ggml_add_or_set(ctx, cgraph, isrc0, tmp);
             }
             if (src1_needs_grads) {
                 ggml_add_or_set(ctx, cgraph, isrc1,
@@ -5514,7 +5518,9 @@ static void ggml_compute_backward(
             if (src0_needs_grads) {
                 GGML_ASSERT(!cgraph->grads[isrc0] || ggml_is_contiguous(cgraph->grads[isrc0]));
                 GGML_ASSERT(ggml_is_contiguous(grad));
-                ggml_add_or_set(ctx, cgraph, isrc0, grad);
+                GGML_ASSERT(ggml_nelements(tensor) == ggml_nelements(src0));
+                ggml_add_or_set(ctx, cgraph, isrc0,
+                    ggml_are_same_shape(tensor, src0) ? grad : ggml_reshape(ctx, grad, src0));
             }
         } break;
         case GGML_OP_RESHAPE: {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 381956a04..468016403 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1302,6 +1302,59 @@ struct test_repeat : public test_case {
     }
 };
 
+// GGML_OP_REPEAT_BACK
+struct test_repeat_back : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    const std::array<int, 4> nr;
+    const bool v; // whether src is a noncontiguous view
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne, nr, v);
+    }
+
+    size_t op_size(ggml_tensor * t) override {
+        return ggml_nbytes(t) * 2;
+    }
+
+    test_repeat_back(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {8, 6, 4, 2},
+            std::array<int, 4> nr = {2, 2, 2, 2},
+            bool v = false)
+        : type(type), ne(ne), nr(nr), v(v) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * src = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
+        ggml_set_name(src, "src");
+
+        if (v) {
+            GGML_ASSERT(ne[0] % 2 == 0);
+            GGML_ASSERT(ne[1] % 2 == 0);
+            GGML_ASSERT(ne[2] % 2 == 0);
+            GGML_ASSERT(ne[3] % 2 == 0);
+            GGML_ASSERT(nr[0] % 2 == 0 || nr[0] == 1);
+            GGML_ASSERT(nr[1] % 2 == 0 || nr[1] == 1);
+            GGML_ASSERT(nr[2] % 2 == 0 || nr[2] == 1);
+            GGML_ASSERT(nr[3] % 2 == 0 || nr[3] == 1);
+
+            const int64_t ne00 = nr[0] == 1 ? src->ne[0] : src->ne[0] / 2;
+            const int64_t ne01 = nr[1] == 1 ? src->ne[1] : src->ne[1] / 2;
+            const int64_t ne02 = nr[2] == 1 ? src->ne[2] : src->ne[2] / 2;
+            const int64_t ne03 = nr[3] == 1 ? src->ne[3] : src->ne[3] / 2;
+
+            src = ggml_view_4d(ctx, src, ne00, ne01, ne02, ne03, src->nb[1], src->nb[2], src->nb[3], 0);
+        }
+
+        ggml_tensor * target = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(target, "target");
+
+        ggml_tensor * out = ggml_repeat_back(ctx, src, target);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // GGML_OP_DUP
 struct test_dup : public test_case {
     const ggml_type type;
@@ -1849,6 +1902,10 @@ struct test_mul_mat : public test_case {
         return 5e-4;
     }
 
+    int64_t grad_nmax() override {
+        return 20000;
+    }
+
     uint64_t op_flops(ggml_tensor * t) override {
         GGML_UNUSED(t);
         return 2 * m * n * k * bs[0] * nr[0] * bs[1] * nr[1];
@@ -1878,8 +1935,12 @@ struct test_mul_mat : public test_case {
 
             a = ggml_new_tensor_4d(ctx, type_a, ne_a[per[0]], ne_a[per[1]], ne_a[per[2]], ne_a[per[3]]);
             b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]);
-            ggml_set_param(ctx, a);
-            ggml_set_param(ctx, b);
+            if (!ggml_is_quantized(type_a)) {
+                if (bs[1] == 1 && nr[1] == 1) {
+                    ggml_set_param(ctx, a);
+                }
+                ggml_set_param(ctx, b);
+            }
             ggml_set_name(a, "a");
             ggml_set_name(b, "b");
 
@@ -1890,8 +1951,12 @@ struct test_mul_mat : public test_case {
         } else {
             a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0],       bs[1]);
             b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
-            ggml_set_param(ctx, a);
-            ggml_set_param(ctx, b);
+            if (!ggml_is_quantized(type_a)) {
+                if (bs[1] == 1 && nr[1] == 1) {
+                    ggml_set_param(ctx, a);
+                }
+                ggml_set_param(ctx, b);
+            }
             ggml_set_name(a, "a");
             ggml_set_name(b, "b");
         }
@@ -3798,6 +3863,16 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 5, 4, ne3}, {1, 1, 1, 2}));
     }
 
+    for (bool view : {false, true}) {
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_F32, {8, 6, 4, 2}, {1, 1, 1, 1}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_F32, {8, 6, 4, 2}, {2, 1, 1, 1}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_F32, {8, 6, 4, 2}, {1, 2, 1, 1}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_F32, {8, 6, 4, 2}, {1, 1, 2, 1}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_F32, {8, 6, 4, 2}, {1, 1, 1, 2}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_I32, {8, 6, 4, 2}, {2, 1, 1, 1}, view));
+        test_cases.emplace_back(new test_repeat_back(GGML_TYPE_I16, {8, 6, 4, 2}, {1, 1, 1, 2}, view));
+    }
+
     test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
     test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
@@ -3919,21 +3994,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
             // test cases without permutation
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, { 1,  1}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10,  1}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10,  1}, {2, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10, 10}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10, 10}, {2, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10, 10}, {1, 2}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {10, 10}, {2, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {1, 1}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {1, 1}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {1, 1}, {1, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 1}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 1}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 2}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 2}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 2}, {1, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {3, 2}, {2, 2}));
 
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1,  1}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10,  1}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10,  1}, {2, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2}));
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {1, 1}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {1, 1}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {1, 1}, {1, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 1}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 1}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 2}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 2}, {2, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 2}, {1, 2}));
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {3, 2}, {2, 2}));
 
             // test cases with permutation
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));

From a07c2c8a52464646ce13040e62c1ea04459f721e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jafar=20Uru=C3=A7?= <jafar.uruc@gmail.com>
Date: Fri, 24 Jan 2025 13:30:13 +0000
Subject: [PATCH 239/376] docs : Update readme to build targets for local
 docker build (#11368)

---
 docs/backend/SYCL.md |  2 +-
 docs/build.md        |  2 +-
 docs/docker.md       | 12 ++++++------
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 8d8312e91..89ddbd669 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -133,7 +133,7 @@ The docker build option is currently limited to *intel GPU* targets.
 ### Build image
 ```sh
 # Using FP16
-docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
+docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
 ```
 
 *Notes*:
diff --git a/docs/build.md b/docs/build.md
index 3b0d2211d..dd6495028 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -286,7 +286,7 @@ You don't need to install Vulkan SDK. It will be installed inside the container.
 
 ```sh
 # Build the image
-docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
+docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
 
 # Then, use it:
 docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
diff --git a/docs/docker.md b/docs/docker.md
index 8d90e6ded..dac9a9ec1 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -60,9 +60,9 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia
 ## Building Docker locally
 
 ```bash
-docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
-docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
-docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
+docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
+docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile .
+docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
 ```
 
 You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
@@ -95,9 +95,9 @@ Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/
 ## Building Docker locally
 
 ```bash
-docker build -t local/llama.cpp:full-musa -f .devops/full-musa.Dockerfile .
-docker build -t local/llama.cpp:light-musa -f .devops/llama-cli-musa.Dockerfile .
-docker build -t local/llama.cpp:server-musa -f .devops/llama-server-musa.Dockerfile .
+docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
+docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
+docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
 ```
 
 You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.

From 9755129c27da76d768bd7e47e206bac61b40cf18 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 24 Jan 2025 18:41:30 +0200
Subject: [PATCH 240/376] release : pack /lib in the packages (#11392)

* release : pack /lib and /include in the packages

* cmake : put libs in /bin

* TMP : push artifacts

* Revert "TMP : push artifacts"

This reverts commit 4decf2c4dfc5cdf5d96ea44c03c8f9801ab41262.

* ci : fix HIP cmake compiler options to be on first line

* ci : restore the original HIP commands

* ci : change ubuntu build from latest to 20.04

* ci : try to fix macos build rpaths

* ci : remove obsolete MacOS build

* TMP : push artifacts

* ci : change back to ubuntu latest

* ci : macos set build rpath to "@loader_path"

* ci : fix typo

* ci : change ubuntu package to 22.04

* Revert "TMP : push artifacts"

This reverts commit 537b09e70ffc604c414ee78acf3acb4c940ec597.
---
 .github/workflows/build.yml | 101 +++++++++++++++++++-----------------
 CMakeLists.txt              |   1 +
 2 files changed, 53 insertions(+), 49 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index fe3b2cdfa..fda726955 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -56,6 +56,7 @@ jobs:
           mkdir build
           cd build
           cmake .. \
+            -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_CURL=ON \
             -DGGML_METAL_USE_BF16=ON \
@@ -120,6 +121,7 @@ jobs:
           # Metal is disabled due to intermittent failures with Github runners not having a GPU:
           # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
           cmake -B build \
+            -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_CURL=ON \
             -DGGML_METAL=OFF \
@@ -160,8 +162,8 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
           name: llama-bin-macos-x64.zip
 
-  ubuntu-latest-cmake:
-    runs-on: ubuntu-latest
+  ubuntu-cpu-cmake:
+    runs-on: ubuntu-22.04
 
     steps:
       - name: Clone
@@ -181,7 +183,10 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON
+          cmake .. \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=ON \
+            -DGGML_RPC=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -256,7 +261,10 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+          cmake .. \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Build (no OpenMP)
@@ -265,7 +273,11 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DGGML_OPENMP=OFF
+          cmake .. \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+            -DGGML_OPENMP=OFF
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Test
@@ -295,7 +307,8 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake -DGGML_RPC=ON ..
+          cmake .. \
+            -DGGML_RPC=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -325,7 +338,8 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake -DGGML_VULKAN=ON ..
+          cmake .. \
+            -DGGML_VULKAN=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -352,13 +366,18 @@ jobs:
       - name: Build with native CMake HIP support
         id: cmake_build
         run: |
-          cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON
+          cmake -B build -S . \
+            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
+            -DGGML_HIP=ON
           cmake --build build --config Release -j $(nproc)
 
       - name: Build with legacy HIP support
         id: cmake_build_legacy_hip
         run: |
-          cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON
+          cmake -B build2 -S . \
+            -DCMAKE_C_COMPILER=hipcc \
+            -DCMAKE_CXX_COMPILER=hipcc \
+            -DGGML_HIP=ON
           cmake --build build2 --config Release -j $(nproc)
 
   ubuntu-22-cmake-musa:
@@ -379,7 +398,8 @@ jobs:
       - name: Build with native CMake MUSA support
         id: cmake_build
         run: |
-          cmake -B build -S . -DGGML_MUSA=ON
+          cmake -B build -S . \
+            -DGGML_MUSA=ON
           cmake --build build --config Release -j $(nproc)
 
   ubuntu-22-cmake-sycl:
@@ -420,7 +440,10 @@ jobs:
           source /opt/intel/oneapi/setvars.sh
           mkdir build
           cd build
-          cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
+          cmake .. \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx
           cmake --build . --config Release -j $(nproc)
 
   ubuntu-22-cmake-sycl-fp16:
@@ -461,42 +484,13 @@ jobs:
           source /opt/intel/oneapi/setvars.sh
           mkdir build
           cd build
-          cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON ..
+          cmake .. \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx \
+            -DGGML_SYCL_F16=ON
           cmake --build . --config Release -j $(nproc)
 
-  # TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
-  #       how to debug it.
-  #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
-  #       would be great if we fix these
-  macOS-latest-cmake:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Dependencies
-        id: depends
-        continue-on-error: true
-        run: |
-          brew update
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          mkdir build
-          cd build
-          cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF ..
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900
-
   macOS-latest-cmake-ios:
     runs-on: macos-latest
 
@@ -827,7 +821,13 @@ jobs:
 
         - name: Build with CMake
           run: |
-            cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=89-real -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined -DLLAMA_FATAL_WARNINGS=ON
+            cmake -S . -B build -G Ninja \
+              -DCMAKE_BUILD_TYPE=Release \
+              -DCMAKE_CUDA_ARCHITECTURES=89-real \
+              -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \
+              -DLLAMA_FATAL_WARNINGS=ON \
+              -DGGML_NATIVE=OFF \
+              -DGGML_CUDA=ON
             cmake --build build
 
   windows-2019-cmake-cuda:
@@ -916,7 +916,11 @@ jobs:
         shell: cmd
         run: |
           call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
-          cmake -S . -B build -G "Ninja Multi-Config" -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DGGML_RPC=ON
+          cmake -S . -B build -G "Ninja Multi-Config" \
+            -DLLAMA_BUILD_SERVER=ON \
+            -DGGML_NATIVE=OFF \
+            -DGGML_CUDA=ON \
+            -DGGML_RPC=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% -t ggml
           cmake --build build --config Release
@@ -1201,8 +1205,7 @@ jobs:
     runs-on: ubuntu-latest
 
     needs:
-      - ubuntu-latest-cmake
-      - macOS-latest-cmake
+      - ubuntu-cpu-cmake
       - windows-latest-cmake
       - windows-2019-cmake-cuda
       - windows-latest-cmake-hip-release
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 42caed486..7e41a44d2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -16,6 +16,7 @@ endif()
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
 
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
 if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(LLAMA_STANDALONE ON)

From 9fbadaef4f0903c64895ba9c70f02ac6e6a4b41c Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Fri, 24 Jan 2025 17:50:49 +0100
Subject: [PATCH 241/376] rocBLAS: Avoid fp32->fp16->fp32 conversion on cdna
 (#11356)

---
 ggml/src/ggml-cuda/ggml-cuda.cu | 62 ++++++++++++++++++++-------------
 ggml/src/ggml-cuda/mmvq.cu      |  3 +-
 2 files changed, 40 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index e602419bc..fb3d9e2d9 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1082,7 +1082,9 @@ static void ggml_cuda_op_mul_mat_cublas(
 
     const int compute_capability = ggml_cuda_info().devices[id].cc;
 
-    if (compute_capability >= GGML_CUDA_CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
+    const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT;
+
+    if (compute_capability >= GGML_CUDA_CC_VOLTA && use_fp16) {
         // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
         ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
         if (src0->type != GGML_TYPE_F16) {
@@ -1103,28 +1105,38 @@ static void ggml_cuda_op_mul_mat_cublas(
             to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get();
-        ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols);
-
-        const half alpha_f16 = 1.0f;
-        const half beta_f16 = 0.0f;
-
-        cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
-        if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
-            cu_compute_type = CUBLAS_COMPUTE_32F;
-        }
 
         CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
-        CUBLAS_CHECK(
-            cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
-                    row_diff, src1_ncols, ne10,
-                    &alpha_f16, src0_ptr,       CUDA_R_16F, ne00,
-                                src1_ptr,       CUDA_R_16F, ne10,
-                    &beta_f16,   dst_f16.get(), CUDA_R_16F, ldc,
-                    cu_compute_type,
-                    CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
-        const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
-        to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+        if (compute_capability == GGML_CUDA_CC_CDNA) {
+            const float alpha = 1.0f;
+            const float beta = 0.0f;
+            CUBLAS_CHECK(
+                cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
+                        row_diff, src1_ncols, ne10,
+                        &alpha, src0_ptr,       CUDA_R_16F, ne00,
+                                    src1_ptr,       CUDA_R_16F, ne10,
+                        &beta,   dst_dd_i, CUDA_R_32F, ldc,
+                        CUBLAS_COMPUTE_32F,
+                        CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+        } else {
+            ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols);
+
+            const half alpha_f16 = 1.0f;
+            const half beta_f16 = 0.0f;
+
+            CUBLAS_CHECK(
+                cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
+                        row_diff, src1_ncols, ne10,
+                        &alpha_f16, src0_ptr,       CUDA_R_16F, ne00,
+                                    src1_ptr,       CUDA_R_16F, ne10,
+                        &beta_f16,   dst_dd_i, CUDA_R_16F, ldc,
+                        CUBLAS_COMPUTE_16F,
+                        CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+
+            const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
+            to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+        }
     } else {
         ggml_cuda_pool_alloc<float> src0_ddq_as_f32(ctx.pool(id));
         ggml_cuda_pool_alloc<float> src1_ddq_as_f32(ctx.pool(id));
@@ -1613,10 +1625,6 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
     cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
     cudaDataType_t      cu_data_type    = CUDA_R_16F;
 
-    if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
-        cu_compute_type = CUBLAS_COMPUTE_32F;
-    }
-
     // dst strides
     size_t nbd2 = dst->nb[2];
     size_t nbd3 = dst->nb[3];
@@ -1645,6 +1653,12 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
         beta  = &beta_f32;
     }
 
+    if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
+        cu_compute_type = CUBLAS_COMPUTE_32F;
+        alpha = &alpha_f32;
+        beta  = &beta_f32;
+    }
+
     GGML_ASSERT(ne12 % ne02 == 0);
     GGML_ASSERT(ne13 % ne03 == 0);
 
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index e3b912d87..4fb466ca0 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -142,7 +142,7 @@ static void mul_mat_vec_q_cuda(
     int64_t nwarps = 1;
     int64_t rows_per_cuda_block = 1;
 
-    if (ggml_cuda_info().devices[id].cc < GGML_CUDA_CC_CDNA || ggml_cuda_info().devices[id].cc == GGML_CUDA_CC_RDNA1) { // NVIDIA and AMD older than RDNA2 but not CDNA
+    if (ggml_cuda_info().devices[id].cc < GGML_CUDA_CC_RDNA2) { // NVIDIA and AMD older than RDNA2
         switch(ncols_y) {
             case 1:
                 nwarps = 4;
@@ -166,6 +166,7 @@ static void mul_mat_vec_q_cuda(
                 break;
         }
     }
+
     const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
     const dim3 block_nums(nblocks, 1, 1);
     const dim3 block_dims(WARP_SIZE, nwarps, 1);

From c5d9effb49649db80a52caf5c0626de6f342f526 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 24 Jan 2025 21:02:43 +0100
Subject: [PATCH 242/376] CUDA: fix FP16 cuBLAS GEMM (#11396)

---
 ggml/src/ggml-cuda/ggml-cuda.cu | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index fb3d9e2d9..fbe889a01 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1114,8 +1114,8 @@ static void ggml_cuda_op_mul_mat_cublas(
             CUBLAS_CHECK(
                 cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                         row_diff, src1_ncols, ne10,
-                        &alpha, src0_ptr,       CUDA_R_16F, ne00,
-                                    src1_ptr,       CUDA_R_16F, ne10,
+                        &alpha, src0_ptr,  CUDA_R_16F, ne00,
+                                src1_ptr,  CUDA_R_16F, ne10,
                         &beta,   dst_dd_i, CUDA_R_32F, ldc,
                         CUBLAS_COMPUTE_32F,
                         CUBLAS_GEMM_DEFAULT_TENSOR_OP));
@@ -1128,9 +1128,9 @@ static void ggml_cuda_op_mul_mat_cublas(
             CUBLAS_CHECK(
                 cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                         row_diff, src1_ncols, ne10,
-                        &alpha_f16, src0_ptr,       CUDA_R_16F, ne00,
-                                    src1_ptr,       CUDA_R_16F, ne10,
-                        &beta_f16,   dst_dd_i, CUDA_R_16F, ldc,
+                        &alpha_f16, src0_ptr,      CUDA_R_16F, ne00,
+                                    src1_ptr,      CUDA_R_16F, ne10,
+                        &beta_f16,  dst_f16.get(), CUDA_R_16F, ldc,
                         CUBLAS_COMPUTE_16F,
                         CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 

From 5f0db9522f347b095f84c3033d6c1c1895402e25 Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Sat, 25 Jan 2025 00:02:23 +0100
Subject: [PATCH 243/376] hip : Add hipGraph and VMM support to ROCM (#11362)

* Add hipGraph support

* Enable VMM on rocm
---
 ggml/CMakeLists.txt              |  1 +
 ggml/src/ggml-cuda/common.cuh    |  2 +-
 ggml/src/ggml-cuda/ggml-cuda.cu  | 58 +++++++++++++++++++++-----------
 ggml/src/ggml-cuda/vendors/hip.h | 43 +++++++++++++++++++++++
 ggml/src/ggml-hip/CMakeLists.txt |  8 +++++
 5 files changed, 92 insertions(+), 20 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index ff68ddc21..123c755ac 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -154,6 +154,7 @@ option(GGML_CUDA_FA_ALL_QUANTS              "ggml: compile all quants for FlashA
 option(GGML_CUDA_GRAPHS                     "ggml: use CUDA graphs (llama.cpp only)"          ${GGML_CUDA_GRAPHS_DEFAULT})
 
 option(GGML_HIP                             "ggml: use HIP"                                   OFF)
+option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
 option(GGML_HIP_UMA                         "ggml: use HIP unified memory architecture"       OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
 option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 2c0a56226..a79fa83c5 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -588,7 +588,7 @@ struct ggml_tensor_extra_gpu {
 };
 
 
-#if (CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS)
+#if ((CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS)) || defined(GGML_HIP_GRAPHS)
 #define USE_CUDA_GRAPH
 #endif
 
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index fbe889a01..a53a1bbd0 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -62,7 +62,7 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 [[noreturn]]
 void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
     int id = -1; // in case cudaGetDevice fails
-    cudaGetDevice(&id);
+    (void)cudaGetDevice(&id);
 
     GGML_LOG_ERROR(GGML_CUDA_NAME " error: %s\n", msg);
     GGML_LOG_ERROR("  current device: %d, in function %s at %s:%d\n", id, func, file, line);
@@ -152,7 +152,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_CUDA_NO_VMM)
         CUdevice device;
         CU_CHECK(cuDeviceGet(&device, id));
         CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
@@ -164,7 +164,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
             alloc_prop.location.id = id;
             CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
         }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_CUDA_NO_VMM)
         info.devices[id].vmm = !!device_vmm;
 
         cudaDeviceProp prop;
@@ -300,7 +300,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
 };
 
 // pool with virtual memory
-#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_CUDA_NO_VMM)
 struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
@@ -309,6 +309,9 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     size_t pool_used = 0;
     size_t pool_size = 0;
     size_t granularity;
+#if defined(GGML_USE_HIP)
+    std::vector<std::pair<CUdeviceptr, size_t>> mappings;
+#endif
 
     explicit ggml_cuda_pool_vmm(int device) :
         device(device),
@@ -317,7 +320,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
 
     ~ggml_cuda_pool_vmm() {
         if (pool_addr != 0) {
+#if defined(GGML_USE_HIP)
+            // Workaround for https://github.com/ROCm/ROCR-Runtime/issues/285
+            for (std::pair<CUdeviceptr, size_t> & mapping : mappings) {
+                CU_CHECK(cuMemUnmap(mapping.first, mapping.second));
+            }
+#else
             CU_CHECK(cuMemUnmap(pool_addr, pool_size));
+#endif
             CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE));
         }
     }
@@ -350,7 +360,11 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
             }
 
             // map at the end of the pool
-            CU_CHECK(cuMemMap(pool_addr + pool_size, reserve_size, 0, handle, 0));
+            CUdeviceptr start_ptr = (CUdeviceptr)((char *)(pool_addr) + pool_size);
+            CU_CHECK(cuMemMap(start_ptr, reserve_size, 0, handle, 0));
+#if defined(GGML_USE_HIP)
+            mappings.push_back({start_ptr, reserve_size});
+#endif
 
             // the memory allocation handle is no longer needed after mapping
             CU_CHECK(cuMemRelease(handle));
@@ -360,7 +374,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
             access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
             access.location.id = device;
             access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
-            CU_CHECK(cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1));
+            CU_CHECK(cuMemSetAccess((CUdeviceptr)((char *)(pool_addr) + pool_size), reserve_size, &access, 1));
 
             // add to the pool
             pool_size += reserve_size;
@@ -372,7 +386,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
 
         GGML_ASSERT(pool_addr != 0);
 
-        void * ptr = (void *) (pool_addr + pool_used);
+        void * ptr = (void *) ((CUdeviceptr)((char *)(pool_addr) + pool_used));
         *actual_size = size;
         pool_used += size;
 
@@ -391,17 +405,17 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
         pool_used -= size;
 
         // all deallocations must be in reverse order of the allocations
-        GGML_ASSERT(ptr == (void *) (pool_addr + pool_used));
+        GGML_ASSERT(ptr == (void *) ((char *)(pool_addr) + pool_used));
     }
 };
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_CUDA_NO_VMM)
 
 std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
-#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_CUDA_NO_VMM)
     if (ggml_cuda_info().devices[device].vmm) {
         return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
     }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
+#endif // !defined(GGML_CUDA_NO_VMM)
     return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
@@ -547,7 +561,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
     cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device);
     if (err != cudaSuccess) {
         // clear the error
-        cudaGetLastError();
+        (void)cudaGetLastError();
         GGML_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err));
         return nullptr;
     }
@@ -962,7 +976,7 @@ static void * ggml_cuda_host_malloc(size_t size) {
     cudaError_t err = cudaMallocHost((void **) &ptr, size);
     if (err != cudaSuccess) {
         // clear the error
-        cudaGetLastError();
+        (void)cudaGetLastError();
         GGML_LOG_DEBUG("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
                            size / 1024.0 / 1024.0, cudaGetErrorString(err));
         return nullptr;
@@ -1209,7 +1223,7 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
                         CUDA_CHECK(err);
                     } else {
                         // reset the error
-                        cudaGetLastError();
+                        (void)cudaGetLastError();
                     }
                 } else {
                     cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
@@ -1217,7 +1231,7 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
                         CUDA_CHECK(err);
                     } else {
                         // reset the error
-                        cudaGetLastError();
+                        (void)cudaGetLastError();
                     }
                 }
             }
@@ -2452,7 +2466,7 @@ static void maintain_cuda_graph(ggml_backend_cuda_context * cuda_ctx, std::vecto
                     if (stat == cudaErrorInvalidDeviceFunction) {
                         // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
                         // We don't need to update blas nodes, so clear error and move on.
-                        cudaGetLastError();
+                        (void)cudaGetLastError();
                     } else {
                         GGML_ASSERT(stat == cudaSuccess);
                     }
@@ -2507,14 +2521,20 @@ static bool is_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx,
 static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
 
     cudaGraphExecUpdateResultInfo result_info;
+#ifdef __HIP_PLATFORM_AMD__
+    hipGraphNode_t errorNode;
+    hipError_t stat = hipGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &errorNode, &result_info);
+#else
     cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
+#endif
     if (stat == cudaErrorGraphExecUpdateFailure) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
 #endif
+
         // The pre-existing graph exec cannot be updated due to violated constraints
         // so instead clear error and re-instantiate
-        cudaGetLastError();
+        (void)cudaGetLastError();
         CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
         cuda_ctx->cuda_graph->instance = nullptr;
         CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
@@ -2742,7 +2762,7 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
     cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
     if (err != cudaSuccess) {
         // clear the error
-        cudaGetLastError();
+        (void)cudaGetLastError();
 
         GGML_LOG_DEBUG("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
                            size / 1024.0 / 1024.0, cudaGetErrorString(err));
@@ -2762,7 +2782,7 @@ void ggml_backend_cuda_unregister_host_buffer(void * buffer) {
     cudaError_t err = cudaHostUnregister(buffer);
     if (err != cudaSuccess) {
         // clear the error
-        cudaGetLastError();
+        (void)cudaGetLastError();
     }
 }
 
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index c905b15d7..8594093f0 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -19,6 +19,12 @@
 #define CUBLAS_TF32_TENSOR_OP_MATH 0
 #define CUDA_R_16F  HIPBLAS_R_16F
 #define CUDA_R_32F  HIPBLAS_R_32F
+#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED hipDeviceAttributeVirtualMemoryManagementSupported
+#define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED hipMemAllocationGranularityRecommended
+#define CU_MEM_ALLOCATION_TYPE_PINNED hipMemAllocationTypePinned
+#define CU_MEM_LOCATION_TYPE_DEVICE hipMemLocationTypeDevice
+#define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
+#define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
 #define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
 #define cublasCreate hipblasCreate
@@ -74,6 +80,21 @@
 #define cudaMemGetInfo hipMemGetInfo
 #define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
 #define cudaSetDevice hipSetDevice
+#define cuDeviceGet hipDeviceGet
+#define CUdevice hipDevice_t
+#define CUdeviceptr hipDeviceptr_t
+#define cuMemUnmap hipMemUnmap
+#define CUmemAccessDesc hipMemAccessDesc
+#define cuMemAddressFree hipMemAddressFree
+#define cuMemRelease hipMemRelease
+#define CUmemGenericAllocationHandle hipMemGenericAllocationHandle_t
+#define cuMemCreate hipMemCreate
+#define cuMemAddressReserve hipMemAddressReserve
+#define cuMemMap hipMemMap
+#define cuMemSetAccess hipMemSetAccess
+#define cuMemGetAllocationGranularity hipMemGetAllocationGranularity
+#define CUmemAllocationProp hipMemAllocationProp
+#define cuDeviceGetAttribute hipDeviceGetAttribute
 #define cudaStreamCreateWithFlags hipStreamCreateWithFlags
 #define cudaStreamDestroy hipStreamDestroy
 #define cudaStreamFireAndForget hipStreamFireAndForget
@@ -81,6 +102,28 @@
 #define cudaStreamPerThread hipStreamPerThread
 #define cudaStreamSynchronize hipStreamSynchronize
 #define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaGraphExec_t hipGraphExec_t
+#define cudaGraphNode_t hipGraphNode_t
+#define cudaKernelNodeParams hipKernelNodeParams
+#define cudaKernelNodeParams hipKernelNodeParams
+#define cudaGraphExecDestroy hipGraphExecDestroy
+#define cudaGraphLaunch hipGraphLaunch
+#define cudaErrorGraphExecUpdateFailure hipErrorGraphExecUpdateFailure
+#define cudaGraphExecUpdateResultInfo hipGraphExecUpdateResult
+#define cudaGraphNodeType hipGraphNodeType
+#define cudaGraphNodeTypeKernel hipGraphNodeTypeKernel
+#define cudaGraphInstantiate hipGraphInstantiate
+#define cudaStreamEndCapture hipStreamEndCapture
+#define cudaGraphDestroy hipGraphDestroy
+#define cudaGraphKernelNodeSetParams hipGraphKernelNodeSetParams
+#define cudaErrorInvalidDeviceFunction hipErrorInvalidDeviceFunction
+#define cudaGraphKernelNodeGetParams hipGraphKernelNodeGetParams
+#define cudaGraphNodeGetType hipGraphNodeGetType
+#define cudaGraphGetNodes hipGraphGetNodes
+#define cudaGraphExecUpdate hipGraphExecUpdate
+#define cudaStreamCaptureModeRelaxed hipStreamCaptureModeRelaxed
+#define cudaStreamBeginCapture hipStreamBeginCapture
+#define cudaGraph_t hipGraph_t
 #define cudaStream_t hipStream_t
 #define cudaSuccess hipSuccess
 #define __trap() do { abort(); __builtin_unreachable(); } while(0)
diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index d090ba9bd..77994a698 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -92,6 +92,14 @@ if (GGML_CUDA_NO_PEER_COPY)
     add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
 endif()
 
+if (GGML_HIP_GRAPHS)
+    add_compile_definitions(GGML_HIP_GRAPHS)
+endif()
+
+if (GGML_CUDA_NO_VMM)
+    add_compile_definitions(GGML_CUDA_NO_VMM)
+endif()
+
 if (CXX_IS_HIPCC)
     set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
     target_link_libraries(ggml-hip PRIVATE hip::device)

From 466ea66f338d63109540dae1df97ccfdbf4cd08f Mon Sep 17 00:00:00 2001
From: jiahao su <damow890@gmail.com>
Date: Sat, 25 Jan 2025 07:26:01 +0800
Subject: [PATCH 244/376] CANN: Add Ascend CANN build ci (#10217)

* CANN: Add Ascend CANN build ci

* Update build.yml

* Modify cann image version

* Update build.yml

* Change to run on x86 system

* Update build.yml

* Update build.yml

* Modify format error

* Update build.yml

* Add 'Ascend NPU' label restrictions

* Exclude non PR event

Co-authored-by: Yuanhao Ji <jiyuanhao@apache.org>

* Update build.yml

---------

Co-authored-by: Yuanhao Ji <jiyuanhao@apache.org>
---
 .github/workflows/build.yml | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index fda726955..7d08574f5 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1464,3 +1464,37 @@ jobs:
 #          popd
 #          emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}
 #          make
+
+  openEuler-latest-cmake-cann:
+    if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }}
+    defaults:
+      run:
+       shell: bash -el {0}
+    runs-on: ubuntu-24.04-arm
+    strategy:
+      matrix:
+        cann:
+          - '8.0.rc3.beta1-910b-openeuler22.03-py3.10'
+        device:
+          - 'ascend910b3'
+        build:
+          - 'Release'
+    container: ascendai/cann:${{ matrix.cann }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        run: |
+          yum update -y
+          yum install -y git gcc gcc-c++ make cmake
+
+      - name: Build
+        run: |
+          export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+
+          cmake -S . -B build \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build }} \
+              -DGGML_CANN=on \
+              -DSOC_TYPE=${{ matrix.device }}
+          cmake --build build -j $(nproc)

From 00c24acb2ac49d9f8318e808b6ada2f5649f253f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 25 Jan 2025 13:36:48 +0200
Subject: [PATCH 245/376] ci : fix line breaks on windows builds (#11409)

* ci : fix line breaks on windows builds

* cont : another try

* ci : fix powershell line breaks
---
 .github/workflows/build.yml | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7d08574f5..37cb6b1e7 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -916,10 +916,10 @@ jobs:
         shell: cmd
         run: |
           call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
-          cmake -S . -B build -G "Ninja Multi-Config" \
-            -DLLAMA_BUILD_SERVER=ON \
-            -DGGML_NATIVE=OFF \
-            -DGGML_CUDA=ON \
+          cmake -S . -B build -G "Ninja Multi-Config" ^
+            -DLLAMA_BUILD_SERVER=ON ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_CUDA=ON ^
             -DGGML_RPC=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% -t ggml
@@ -1073,7 +1073,12 @@ jobs:
         run: |
           $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
           $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
-          cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON
+          cmake -G "Unix Makefiles" -B build -S . `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DGGML_HIP=ON `
+            -DGGML_RPC=ON
           cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
 
   windows-latest-cmake-hip-release:
@@ -1111,7 +1116,13 @@ jobs:
         run: |
           $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
           $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
-          cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON
+          cmake -G "Unix Makefiles" -B build -S . `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DAMDGPU_TARGETS=${{ matrix.gpu_target }} `
+            -DGGML_HIP=ON `
+            -DGGML_RPC=ON
           cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
           cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"

From 20a758155bc5f37290b20ea44d76ba99c4e7f2cb Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Sat, 25 Jan 2025 15:22:29 +0100
Subject: [PATCH 246/376] docker : fix CPU ARM build (#11403)

* docker : fix CPU ARM build

* add CURL to other builds
---
 .devops/cpu.Dockerfile | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index 8d020f16c..ab0e951bc 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -2,6 +2,8 @@ ARG UBUNTU_VERSION=22.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
+ARG TARGETARCH
+
 RUN apt-get update && \
     apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
@@ -9,7 +11,11 @@ WORKDIR /app
 
 COPY . .
 
-RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON; \
+    else \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON; \
+    fi && \
     cmake --build build -j $(nproc)
 
 RUN mkdir -p /app/lib && \

From 49b0e3cec4b67dc9f4debe3a16acd4c819f751d6 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Sat, 25 Jan 2025 16:36:44 +0100
Subject: [PATCH 247/376] server : fix cleaning up stream task (#11418)

* server : fix cleaning up stream task

* one more spot
---
 examples/server/server.cpp | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index a94c3822c..b1cde2d7f 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1427,16 +1427,16 @@ struct server_queue {
     int post(server_task task, bool front = false) {
         std::unique_lock<std::mutex> lock(mutex_tasks);
         GGML_ASSERT(task.id != -1);
+        // if this is cancel task make sure to clean up pending tasks
+        if (task.type == SERVER_TASK_TYPE_CANCEL) {
+            cleanup_pending_task(task.id_target);
+        }
         QUE_DBG("new task, id = %d, front = %d\n", task.id, front);
         if (front) {
             queue_tasks.push_front(std::move(task));
         } else {
             queue_tasks.push_back(std::move(task));
         }
-        // if this is cancel task make sure to clean up pending tasks
-        if (task.type == SERVER_TASK_TYPE_CANCEL) {
-            cleanup_pending_task(task.id_target);
-        }
         condition_tasks.notify_one();
         return task.id;
     }
@@ -1448,16 +1448,16 @@ struct server_queue {
             if (task.id == -1) {
                 task.id = id++;
             }
+            // if this is cancel task make sure to clean up pending tasks
+            if (task.type == SERVER_TASK_TYPE_CANCEL) {
+                cleanup_pending_task(task.id_target);
+            }
             QUE_DBG("new task, id = %d/%d, front = %d\n", task.id, (int) tasks.size(), front);
             if (front) {
                 queue_tasks.push_front(std::move(task));
             } else {
                 queue_tasks.push_back(std::move(task));
             }
-            // if this is cancel task make sure to clean up pending tasks
-            if (task.type == SERVER_TASK_TYPE_CANCEL) {
-                cleanup_pending_task(task.id_target);
-            }
         }
         condition_tasks.notify_one();
         return 0;
@@ -1554,10 +1554,10 @@ struct server_queue {
     }
 
 private:
-    void cleanup_pending_task(int id_task) {
+    void cleanup_pending_task(int id_target) {
         // no need lock because this is called exclusively by post()
-        auto rm_func = [id_task](const server_task & task) {
-            return task.id_target == id_task;
+        auto rm_func = [id_target](const server_task & task) {
+            return task.id_target == id_target;
         };
         queue_tasks.erase(
             std::remove_if(queue_tasks.begin(),          queue_tasks.end(),          rm_func),

From 6e264a905bec9e4c0111eb4c91379c88accef7c6 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Sat, 25 Jan 2025 17:22:41 +0100
Subject: [PATCH 248/376] docker : add GGML_CPU_ARM_ARCH arg to select ARM
 architecture to build for (#11419)

---
 .devops/cpu.Dockerfile | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index ab0e951bc..522ee8147 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -4,6 +4,8 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 
 ARG TARGETARCH
 
+ARG GGML_CPU_ARM_ARCH=armv8-a
+
 RUN apt-get update && \
     apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
@@ -12,9 +14,12 @@ WORKDIR /app
 COPY . .
 
 RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON; \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
+    elif [ "$TARGETARCH" = "arm64" ]; then \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
     else \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON; \
+        echo "Unsupported architecture"; \
+        exit 1; \
     fi && \
     cmake --build build -j $(nproc)
 

From ca6baf76c1a7adb9134b08d2bc4c65557297ff87 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 25 Jan 2025 11:26:37 -0600
Subject: [PATCH 249/376] build: add /bigobj to MSVC build (#11407)

---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7e41a44d2..e7f520582 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,6 +50,7 @@ endif()
 if (MSVC)
     add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
+    add_compile_options(/bigobj)
 endif()
 
 #

From 26771a1491f3a4c3d5b99c4c267b81aca9a7dfa0 Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Sat, 25 Jan 2025 21:01:12 +0100
Subject: [PATCH 250/376] Hip: disable VMM on hip as it seams that it dosent
 work in some configurations (#11420)

---
 ggml/CMakeLists.txt              |  1 +
 ggml/src/ggml-cuda/common.cuh    |  4 ++++
 ggml/src/ggml-cuda/ggml-cuda.cu  | 14 +++++++-------
 ggml/src/ggml-hip/CMakeLists.txt |  4 ++--
 4 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 123c755ac..bbabb14de 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -155,6 +155,7 @@ option(GGML_CUDA_GRAPHS                     "ggml: use CUDA graphs (llama.cpp on
 
 option(GGML_HIP                             "ggml: use HIP"                                   OFF)
 option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
+option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_UMA                         "ggml: use HIP unified memory architecture"       OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
 option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index a79fa83c5..bb6120568 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -131,6 +131,10 @@ typedef float dfloat; // dequantize float
 typedef float2 dfloat2;
 #endif // GGML_CUDA_F16
 
+#if (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
+#define GGML_USE_VMM
+#endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
+
 #if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 #define FP16_AVAILABLE
 #endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index a53a1bbd0..85178abd2 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -152,7 +152,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
-#if !defined(GGML_CUDA_NO_VMM)
+#if defined(GGML_USE_VMM)
         CUdevice device;
         CU_CHECK(cuDeviceGet(&device, id));
         CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
@@ -164,7 +164,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
             alloc_prop.location.id = id;
             CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
         }
-#endif // !defined(GGML_CUDA_NO_VMM)
+#endif // defined(GGML_USE_VMM)
         info.devices[id].vmm = !!device_vmm;
 
         cudaDeviceProp prop;
@@ -300,7 +300,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
 };
 
 // pool with virtual memory
-#if !defined(GGML_CUDA_NO_VMM)
+#if defined(GGML_USE_VMM)
 struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
@@ -408,14 +408,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
         GGML_ASSERT(ptr == (void *) ((char *)(pool_addr) + pool_used));
     }
 };
-#endif // !defined(GGML_CUDA_NO_VMM)
+#endif // defined(GGML_USE_VMM)
 
 std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
-#if !defined(GGML_CUDA_NO_VMM)
+#if defined(GGML_USE_VMM)
     if (ggml_cuda_info().devices[device].vmm) {
         return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
     }
-#endif // !defined(GGML_CUDA_NO_VMM)
+#endif // defined(GGML_USE_VMM)
     return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
@@ -3250,7 +3250,7 @@ static ggml_backend_feature * ggml_backend_cuda_get_features(ggml_backend_reg_t
         features.push_back({ "FORCE_CUBLAS", "1" });
     #endif
 
-    #ifdef GGML_CUDA_NO_VMM
+    #ifndef GGML_USE_VMM
         features.push_back({ "NO_VMM", "1" });
     #endif
 
diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index 77994a698..ecc3bc66d 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -96,8 +96,8 @@ if (GGML_HIP_GRAPHS)
     add_compile_definitions(GGML_HIP_GRAPHS)
 endif()
 
-if (GGML_CUDA_NO_VMM)
-    add_compile_definitions(GGML_CUDA_NO_VMM)
+if (GGML_HIP_NO_VMM)
+    add_compile_definitions(GGML_HIP_NO_VMM)
 endif()
 
 if (CXX_IS_HIPCC)

From 4a75d19376f2f00dbae6c266eb9c4f3001872b52 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 25 Jan 2025 15:29:57 -0600
Subject: [PATCH 251/376] vulkan: compile shaders on-demand (#11406)

Reduce first-run startup time and memory consumption.

Should fix #11339.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 64 ++++++++++++++++++----------
 1 file changed, 41 insertions(+), 23 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c325416d1..a9d6b923c 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -85,6 +85,10 @@ struct vk_pipeline_struct {
     uint32_t parameter_count;
     std::array<uint32_t, 3> wg_denoms;
     uint32_t align;
+    // set to true to request the pipeline is compiled after the dryrun
+    bool needed {};
+    // set to true when the shader has been compiled
+    bool compiled {};
 };
 
 typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
@@ -186,8 +190,11 @@ struct vk_device_struct {
     bool mul_mat_id_m;
     bool mul_mat_id_s;
 
-    vk_matmul_pipeline pipeline_matmul_f32;
-    vk_matmul_pipeline pipeline_matmul_f32_f16;
+    // set to true to indicate that some shaders need to be compiled after the dryrun
+    bool need_compiles {};
+
+    vk_matmul_pipeline pipeline_matmul_f32 {};
+    vk_matmul_pipeline pipeline_matmul_f32_f16 {};
     vk_matmul_pipeline2 pipeline_matmul_f16;
     vk_matmul_pipeline2 pipeline_matmul_f16_f32;
     vk_pipeline pipeline_matmul_split_k_reduce;
@@ -195,7 +202,7 @@ struct vk_device_struct {
     vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_COUNT];
     vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT];
 
-    vk_matmul_pipeline pipeline_matmul_id_f32;
+    vk_matmul_pipeline pipeline_matmul_id_f32 {};
     vk_matmul_pipeline2 pipeline_matmul_id_f16;
     vk_matmul_pipeline2 pipeline_matmul_id_f16_f32;
 
@@ -776,13 +783,6 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
     GGML_ASSERT(parameter_count > 0);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
-    pipeline = std::make_shared<vk_pipeline_struct>();
-    pipeline->name = name;
-    pipeline->parameter_count = parameter_count;
-    pipeline->push_constant_size = push_constant_size;
-    pipeline->wg_denoms = wg_denoms;
-    pipeline->align = align;
-
     vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
     pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);
 
@@ -865,6 +865,7 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
     }
 
     pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+    pipeline->compiled = true;
 
     {
         std::lock_guard<std::mutex> guard(device->mutex);
@@ -875,12 +876,6 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         std::lock_guard<std::mutex> guard(compile_count_mutex);
         assert(compile_count > 0);
         compile_count--;
-
-        // "Progress bar" for shader compiles
-        static uint32_t total_compile_count = 0;
-        if ((total_compile_count++ % 10) == 0) {
-            std::cerr << ".";
-        }
     }
     compile_count_cond.notify_all();
 }
@@ -906,6 +901,10 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline)
 static void ggml_pipeline_request_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) {
     VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")");
     device->pipeline_descriptor_set_requirements[pipeline->name] += n;
+    if (!pipeline->compiled) {
+        pipeline->needed = true;
+        device->need_compiles = true;
+    }
 }
 
 static void ggml_pipeline_allocate_descriptor_sets(vk_device& device) {
@@ -1388,8 +1387,6 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
 static void ggml_vk_load_shaders(vk_device& device) {
     VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");
 
-    std::cerr << "ggml_vulkan: Compiling shaders";
-
     // some shaders have a minimum subgroup size
     const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
     const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);
@@ -1527,15 +1524,33 @@ static void ggml_vk_load_shaders(vk_device& device) {
         }
     }
 
-    device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
-    device->pipeline_matmul_f32_f16 = std::make_shared<vk_matmul_pipeline_struct>();
-
-    device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    if (!device->pipeline_matmul_f32) {
+        device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    }
+    if (!device->pipeline_matmul_f32_f16) {
+        device->pipeline_matmul_f32_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    }
+    if (!device->pipeline_matmul_id_f32) {
+        device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    }
 
     std::vector<std::future<void>> compiles;
     auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint,
                                               uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
                                               uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
+
+        if (!pipeline) {
+            pipeline = std::make_shared<vk_pipeline_struct>();
+            pipeline->name = name;
+            pipeline->parameter_count = parameter_count;
+            pipeline->push_constant_size = push_constant_size;
+            pipeline->wg_denoms = wg_denoms;
+            pipeline->align = align;
+        }
+
+        if (!pipeline->needed || pipeline->compiled) {
+            return;
+        }
         {
             // wait until fewer than N compiles are in progress
             uint32_t N = std::max(1u, std::thread::hardware_concurrency());
@@ -2050,7 +2065,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     for (auto &c : compiles) {
         c.wait();
     }
-    std::cerr << "Done!" << std::endl;
+    device->need_compiles = false;
 }
 
 static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props);
@@ -7656,6 +7671,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false);
     }
+    if (ctx->device->need_compiles) {
+        ggml_vk_load_shaders(ctx->device);
+    }
     ggml_vk_preallocate_buffers(ctx);
     ggml_pipeline_allocate_descriptor_sets(ctx->device);
 

From f35726c2fb0a824246e004ab4bedcde37f3f0dd0 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 25 Jan 2025 20:10:03 -0600
Subject: [PATCH 252/376] build: apply MSVC /bigobj option to c/cpp files only
 (#11423)

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e7f520582..2f2b1a201 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,7 +50,8 @@ endif()
 if (MSVC)
     add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
-    add_compile_options(/bigobj)
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
 endif()
 
 #

From 2cc9b8c32c78d09cd1b4df0aaa605ab2d0176243 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 26 Jan 2025 14:30:15 +0200
Subject: [PATCH 253/376] readme : update hot topics

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 97d028670..ff8536773 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
+- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
 - **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
 - Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123

From 1d8ee06000ecdd274e7f0a0465d6bf26ad2b3491 Mon Sep 17 00:00:00 2001
From: Frank Mai <thxcode0824@gmail.com>
Date: Sun, 26 Jan 2025 23:20:34 +0800
Subject: [PATCH 254/376] rpc: fix register position (#11424)

Signed-off-by: thxCode <thxcode0824@gmail.com>
---
 src/llama-model.cpp |  2 ++
 src/llama.cpp       | 12 +++++++++++-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 031b4c30b..18bd0b071 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1303,10 +1303,12 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
     const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
     auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
         if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
+            LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s\n", il, ggml_backend_dev_name(cpu_dev));
             return {cpu_dev, &pimpl->cpu_buft_list};
         }
         const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
         auto * dev = devices.at(layer_gpu);
+        LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s\n", il, ggml_backend_dev_name(dev));
         return {dev, &pimpl->gpu_buft_list.at(dev)};
     };
 
diff --git a/src/llama.cpp b/src/llama.cpp
index e8cfe5012..094157ccf 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -9405,6 +9405,7 @@ static struct llama_model * llama_model_load_from_file_impl(
             model->devices.push_back(*dev);
         }
     } else {
+        std::vector<ggml_backend_dev_t> rpc_servers;
         // use all available devices
         for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
             ggml_backend_dev_t dev = ggml_backend_dev_get(i);
@@ -9415,10 +9416,19 @@ static struct llama_model * llama_model_load_from_file_impl(
                     break;
 
                 case GGML_BACKEND_DEVICE_TYPE_GPU:
-                    model->devices.push_back(dev);
+                    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+                    if (ggml_backend_reg_name(reg) == std::string("RPC")) {
+                        rpc_servers.push_back(dev);
+                    } else {
+                        model->devices.push_back(dev);
+                    }
                     break;
             }
         }
+        // add RPC servers at the front of the list
+        if (!rpc_servers.empty()) {
+            model->devices.insert(model->devices.begin(), rpc_servers.begin(), rpc_servers.end());
+        }
     }
 
     // if using single GPU mode, remove all except the main GPU

From 19f65187cbf009801288861133267ee5573ceead Mon Sep 17 00:00:00 2001
From: bandoti <141645996+bandoti@users.noreply.github.com>
Date: Sun, 26 Jan 2025 12:07:48 -0400
Subject: [PATCH 255/376] cmake: add ggml find package (#11369)

* Add initial ggml cmake package

* Add build numbers to ggml find-package

* Expand variables with GGML_ prefix

* Guard against adding to cache variable twice

* Add git to msys2 workflow

* Handle ggml-cpu-* variants

* Link ggml/ggml-base libraries to their targets

* Replace main-cmake-pkg with simple-cmake-pkg

* Interface features require c_std_90

* Fix typo

* Removed unnecessary bracket from status message

* Update examples/simple-cmake-pkg/README.md

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update examples/simple-cmake-pkg/README.md

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 .github/workflows/build.yml                   |   1 +
 CMakeLists.txt                                |  23 +--
 cmake/llama-config.cmake.in                   | 156 +-----------------
 examples/main-cmake-pkg/CMakeLists.txt        |  32 ----
 examples/main-cmake-pkg/README.md             |  31 ----
 .../.gitignore                                |   0
 examples/simple-cmake-pkg/CMakeLists.txt      |  11 ++
 examples/simple-cmake-pkg/README.md           |  34 ++++
 ggml/CMakeLists.txt                           |  71 ++++++++
 ggml/cmake/ggml-config.cmake.in               | 147 +++++++++++++++++
 ggml/src/CMakeLists.txt                       |  11 ++
 11 files changed, 284 insertions(+), 233 deletions(-)
 delete mode 100644 examples/main-cmake-pkg/CMakeLists.txt
 delete mode 100644 examples/main-cmake-pkg/README.md
 rename examples/{main-cmake-pkg => simple-cmake-pkg}/.gitignore (100%)
 create mode 100644 examples/simple-cmake-pkg/CMakeLists.txt
 create mode 100644 examples/simple-cmake-pkg/README.md
 create mode 100644 ggml/cmake/ggml-config.cmake.in

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 37cb6b1e7..cd8422f8a 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -613,6 +613,7 @@ jobs:
           msystem: ${{matrix.sys}}
           install: >-
             base-devel
+            git
             mingw-w64-${{matrix.env}}-toolchain
             mingw-w64-${{matrix.env}}-cmake
             mingw-w64-${{matrix.env}}-openblas
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2f2b1a201..4c62d1788 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -188,27 +188,14 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
 set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
 set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
 
-# At the moment some compile definitions are placed within the ggml/src
-# directory but not exported on the `ggml` target. This could be improved by
-# determining _precisely_ which defines are necessary for the llama-config
-# package.
-#
-set(GGML_TRANSIENT_DEFINES)
-get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
-get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
-if (GGML_DIR_DEFINES)
-    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_DIR_DEFINES})
-endif()
-get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
-if (GGML_TARGET_DEFINES)
-    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES})
-endif()
-get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
-# all public headers
 set(LLAMA_PUBLIC_HEADERS
     ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h
     ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h)
-set_target_properties(llama PROPERTIES PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
+
+set_target_properties(llama
+    PROPERTIES
+        PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
+
 install(TARGETS llama LIBRARY PUBLIC_HEADER)
 
 configure_package_config_file(
diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in
index 5c55bc6b8..40ade96e5 100644
--- a/cmake/llama-config.cmake.in
+++ b/cmake/llama-config.cmake.in
@@ -3,159 +3,13 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
 set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)
 
-set(GGML_STATIC @GGML_STATIC@)
-set(GGML_NATIVE @GGML_NATIVE@)
-set(GGML_LTO    @GGML_LTO@)
-set(GGML_CCACHE @GGML_CCACHE@)
-set(GGML_AVX    @GGML_AVX@)
-set(GGML_AVX2   @GGML_AVX2@)
-set(GGML_AVX512 @GGML_AVX512@)
-set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@)
-set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@)
-set(GGML_AVX512_BF16 @GGML_AVX512_BF16@)
-set(GGML_AMX_TILE @GGML_AMX_TILE@)
-set(GGML_AMX_INT8 @GGML_AMX_INT8@)
-set(GGML_AMX_BF16 @GGML_AMX_BF16@)
-set(GGML_FMA  @GGML_FMA@)
-set(GGML_LASX @GGML_LASX@)
-set(GGML_LSX  @GGML_LSX@)
-set(GGML_RVV  @GGML_RVV@)
-set(GGML_SVE  @GGML_SVE@)
-
-set(GGML_ACCELERATE @GGML_ACCELERATE@)
-set(GGML_OPENMP  @GGML_OPENMP@)
-set(GGML_CPU_HBM @GGML_CPU_HBM@)
-set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@)
-
-set(GGML_CUDA_FORCE_MMQ    @GGML_CUDA_FORCE_MMQ@)
-set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@)
-set(GGML_CUDA_F16          @GGML_CUDA_F16@)
-set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@)
-set(GGML_CUDA_NO_PEER_COPY  @GGML_CUDA_NO_PEER_COPY@)
-set(GGML_CUDA_NO_VMM        @GGML_CUDA_NO_VMM@)
-set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@)
-set(GGML_CUDA_GRAPHS        @GGML_CUDA_GRAPHS@)
-
-set(GGML_HIP_UMA @GGML_HIP_UMA@)
-
-set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
-set(GGML_VULKAN_DEBUG         @GGML_VULKAN_DEBUG@)
-set(GGML_VULKAN_MEMORY_DEBUG  @GGML_VULKAN_MEMORY_DEBUG@)
-set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@)
-set(GGML_VULKAN_PERF      @GGML_VULKAN_PERF@)
-set(GGML_VULKAN_VALIDATE  @GGML_VULKAN_VALIDATE@)
-set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@)
-
-set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@)
-set(GGML_METAL_NDEBUG   @GGML_METAL_NDEBUG@)
-set(GGML_METAL_SHADER_DEBUG  @GGML_METAL_SHADER_DEBUG@)
-set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@)
-set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@)
-set(GGML_METAL_STD @GGML_METAL_STD@)
-
-set(GGML_SYCL_F16    @GGML_SYCL_F16@)
-set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@)
-set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@)
-
-
 @PACKAGE_INIT@
 
 set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
 set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
 set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
 
-find_package(Threads REQUIRED)
-
-set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
-set(_llama_link_deps "")
-set(_llama_link_opts "")
-foreach(_ggml_lib ggml ggml-base)
-    string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
-    find_library(${_ggml_lib_var} ${_ggml_lib}
-        REQUIRED
-        HINTS ${LLAMA_LIB_DIR}
-        NO_CMAKE_FIND_ROOT_PATH
-    )
-    list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
-    message(STATUS "Found ${${_ggml_lib_var}}")
-endforeach()
-
-foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan)
-    string(TOUPPER "GGML_${backend}" backend_id)
-    set(_ggml_lib "ggml-${backend}")
-    string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
-
-    find_library(${_ggml_lib_var} ${_ggml_lib}
-        HINTS ${LLAMA_LIB_DIR}
-        NO_CMAKE_FIND_ROOT_PATH
-    )
-    if(${_ggml_lib_var})
-        list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
-        set(${backend_id} ON)
-        message(STATUS "Found backend ${${_ggml_lib_var}}")
-    else()
-        set(${backend_id} OFF)
-    endif()
-endforeach()
-
-if (NOT LLAMA_SHARED_LIB)
-    if (APPLE AND GGML_ACCELERATE)
-        find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
-        list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK})
-    endif()
-
-    if (GGML_OPENMP)
-        find_package(OpenMP REQUIRED)
-        list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
-    endif()
-
-    if (GGML_CPU_HBM)
-        find_library(memkind memkind REQUIRED)
-        list(APPEND _llama_link_deps memkind)
-    endif()
-
-    if (GGML_BLAS)
-        find_package(BLAS REQUIRED)
-        list(APPEND _llama_link_deps ${BLAS_LIBRARIES})
-        list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS})
-    endif()
-
-    if (GGML_CUDA)
-        find_package(CUDAToolkit REQUIRED)
-    endif()
-
-    if (GGML_METAL)
-        find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
-        find_library(METAL_FRAMEWORK    Metal REQUIRED)
-        find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
-        list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY}
-                                     ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
-    endif()
-
-    if (GGML_VULKAN)
-        find_package(Vulkan REQUIRED)
-        list(APPEND _llama_link_deps Vulkan::Vulkan)
-    endif()
-
-    if (GGML_HIP)
-        find_package(hip     REQUIRED)
-        find_package(hipblas REQUIRED)
-        find_package(rocblas REQUIRED)
-        list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas)
-    endif()
-
-    if (GGML_SYCL)
-        find_package(DNNL)
-        if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
-            list(APPEND _llama_link_deps DNNL::dnnl)
-        endif()
-        if (WIN32)
-            find_package(IntelSYCL REQUIRED)
-            find_package(MKL       REQUIRED)
-            list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
-        endif()
-    endif()
-endif()
+find_package(ggml REQUIRED)
 
 find_library(llama_LIBRARY llama
     REQUIRED
@@ -167,12 +21,10 @@ add_library(llama UNKNOWN IMPORTED)
 set_target_properties(llama
     PROPERTIES
         INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
-        INTERFACE_LINK_LIBRARIES "${_llama_link_deps}"
-        INTERFACE_LINK_OPTIONS   "${_llama_link_opts}"
-        INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}"
+        INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
         IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
         IMPORTED_LOCATION "${llama_LIBRARY}"
-        INTERFACE_COMPILE_FEATURES cxx_std_11
-        POSITION_INDEPENDENT_CODE ON )
+        INTERFACE_COMPILE_FEATURES c_std_90
+        POSITION_INDEPENDENT_CODE ON)
 
 check_required_components(Llama)
diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt
deleted file mode 100644
index 5563f4de0..000000000
--- a/examples/main-cmake-pkg/CMakeLists.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-cmake_minimum_required(VERSION 3.12)
-project("llama-cli-cmake-pkg" C CXX)
-set(TARGET llama-cli-cmake-pkg)
-
-find_package(Llama 0.0.1 REQUIRED)
-
-# Bake common functionality in with target. Because applications
-# using the relocatable Llama package should be outside of the
-# source tree, llama-cli-cmake-pkg pretends the dependencies are built-in.
-set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common")
-add_library(common OBJECT)
-file(GLOB _common_files
-    "${_common_path}/*.h"
-    "${_common_path}/*.cpp"
-)
-target_sources(common PRIVATE ${_common_files})
-
-# If the common project was part of "llama-cli-cmake-pkg" the transient
-# defines would automatically be attached. Because the common func-
-# tionality is separate, but dependent upon the defines, it must be
-# explicitly extracted from the "llama" target.
-#
-get_target_property(_llama_transient_defines llama
-    INTERFACE_COMPILE_DEFINITIONS)
-
-target_compile_definitions(common PRIVATE "${_llama_transient_defines}")
-
-add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp)
-target_include_directories(${TARGET} PRIVATE ${_common_path})
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md
deleted file mode 100644
index 08d83dd08..000000000
--- a/examples/main-cmake-pkg/README.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# llama.cpp/example/main-cmake-pkg
-
-This program builds [llama-cli](../main) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.
-
-## Building
-
-Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions.
-
-### Considerations
-
-When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package.
-
-### Build llama.cpp and install to C:\LlamaCPP directory
-
-```cmd
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64
-cmake --build build --config Release
-cmake --install build --prefix C:/LlamaCPP
-```
-
-### Build llama-cli-cmake-pkg
-
-
-```cmd
-cd ..\examples\main-cmake-pkg
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
-cmake --build build --config Release
-cmake --install build --prefix C:/MyLlamaApp
-```
diff --git a/examples/main-cmake-pkg/.gitignore b/examples/simple-cmake-pkg/.gitignore
similarity index 100%
rename from examples/main-cmake-pkg/.gitignore
rename to examples/simple-cmake-pkg/.gitignore
diff --git a/examples/simple-cmake-pkg/CMakeLists.txt b/examples/simple-cmake-pkg/CMakeLists.txt
new file mode 100644
index 000000000..128e38c8f
--- /dev/null
+++ b/examples/simple-cmake-pkg/CMakeLists.txt
@@ -0,0 +1,11 @@
+cmake_minimum_required(VERSION 3.12)
+project(llama-simple-cmake-pkg)
+
+set(TARGET llama-simple-cmake-pkg)
+
+find_package(Llama REQUIRED)
+
+add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../simple/simple.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama ggml::all ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/simple-cmake-pkg/README.md b/examples/simple-cmake-pkg/README.md
new file mode 100644
index 000000000..8b30049e2
--- /dev/null
+++ b/examples/simple-cmake-pkg/README.md
@@ -0,0 +1,34 @@
+# llama.cpp/example/simple-cmake-pkg
+
+This program builds [simple](../simple) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.
+
+## Building
+
+Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions.
+
+### Considerations
+
+When hardware acceleration libraries are used (e.g. CUDA, Metal, Vulkan, etc.), the appropriate dependencies will be searched for automatically. So, for example, when finding a package
+
+### Build llama.cpp and install to llama.cpp/inst
+
+```sh
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+cmake -S . -B build
+cmake --build build
+cmake --install build --prefix inst
+
+### Build simple-cmake-pkg
+
+```sh
+cd examples/simple-cmake-pkg
+cmake -S . -B build -DCMAKE_PREFIX_PATH=../../inst/lib/cmake
+cmake --build build
+```
+
+### Run simple-cmake-pkg
+
+```sh
+./build/llama-simple-cmake-pkg -m ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is"
+```
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index bbabb14de..7c069e420 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -267,3 +267,74 @@ if (GGML_STANDALONE)
     install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
         DESTINATION share/pkgconfig)
 endif()
+
+#
+# Create CMake package
+#
+
+# Generate version info based on git commit.
+
+find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
+execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
+    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+    OUTPUT_VARIABLE GGML_BUILD_NUMBER
+    OUTPUT_STRIP_TRAILING_WHITESPACE
+)
+
+if(GGML_BUILD_NUMBER EQUAL 1)
+    message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
+endif()
+
+execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
+    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+    OUTPUT_VARIABLE GGML_BUILD_COMMIT
+    OUTPUT_STRIP_TRAILING_WHITESPACE
+)
+
+# Capture variables prefixed with GGML_.
+
+set(variable_set_statements
+"
+####### Expanded from @GGML_VARIABLES_EXPANED@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run        #######
+
+")
+
+set(GGML_SHARED_LIB ${BUILD_SHARED_LIBS})
+
+get_cmake_property(all_variables VARIABLES)
+foreach(variable_name IN LISTS all_variables)
+    if(variable_name MATCHES "^GGML_")
+        string(REPLACE ";" "\\;"
+               variable_value "${${variable_name}}")
+
+        set(variable_set_statements
+            "${variable_set_statements}set(${variable_name} \"${variable_value}\")\n")
+    endif()
+endforeach()
+
+set(GGML_VARIABLES_EXPANDED ${variable_set_statements})
+
+# Create the CMake package and set install location.
+
+set(GGML_INSTALL_VERSION 0.0.${GGML_BUILD_NUMBER})
+set(GGML_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header  files")
+set(GGML_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
+set(GGML_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
+
+configure_package_config_file(
+        ${CMAKE_CURRENT_SOURCE_DIR}/cmake/ggml-config.cmake.in
+        ${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake
+    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml
+    PATH_VARS GGML_INCLUDE_INSTALL_DIR
+              GGML_LIB_INSTALL_DIR
+              GGML_BIN_INSTALL_DIR)
+
+write_basic_package_version_file(
+        ${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
+    VERSION ${GGML_INSTALL_VERSION}
+    COMPATIBILITY SameMajorVersion)
+
+install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake
+              ${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
+        DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml)
diff --git a/ggml/cmake/ggml-config.cmake.in b/ggml/cmake/ggml-config.cmake.in
new file mode 100644
index 000000000..bf39f9c00
--- /dev/null
+++ b/ggml/cmake/ggml-config.cmake.in
@@ -0,0 +1,147 @@
+
+@GGML_VARIABLES_EXPANDED@
+
+@PACKAGE_INIT@
+
+set_and_check(GGML_INCLUDE_DIR "@PACKAGE_GGML_INCLUDE_INSTALL_DIR@")
+set_and_check(GGML_LIB_DIR "@PACKAGE_GGML_LIB_INSTALL_DIR@")
+set_and_check(GGML_BIN_DIR "@PACKAGE_GGML_BIN_INSTALL_DIR@")
+
+find_package(Threads REQUIRED)
+
+find_library(GGML_LIBRARY ggml
+    REQUIRED
+    HINTS ${GGML_LIB_DIR}
+    NO_CMAKE_FIND_ROOT_PATH)
+
+add_library(ggml::ggml UNKNOWN IMPORTED)
+set_target_properties(ggml::ggml
+    PROPERTIES
+        IMPORTED_LOCATION "${GGML_LIBRARY}")
+
+find_library(GGML_BASE_LIBRARY ggml-base
+    REQUIRED
+    HINTS ${GGML_LIB_DIR}
+    NO_CMAKE_FIND_ROOT_PATH)
+
+add_library(ggml::ggml-base UNKNOWN IMPORTED)
+set_target_properties(ggml::ggml-base
+    PROPERTIES
+        IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
+
+if (NOT GGML_SHARED_LIB)
+    if (APPLE AND GGML_ACCELERATE)
+        find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${ACCELERATE_FRAMEWORK})
+    endif()
+
+    if (GGML_OPENMP)
+        find_package(OpenMP REQUIRED)
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
+    endif()
+
+    if (GGML_CPU_HBM)
+        find_library(memkind memkind REQUIRED)
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES memkind)
+    endif()
+
+    if (GGML_BLAS)
+        find_package(BLAS REQUIRED)
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
+        list(APPEND GGML_CPU_INTERFACE_LINK_OPTIONS   ${BLAS_LINKER_FLAGS})
+    endif()
+
+    if (GGML_CUDA)
+        find_package(CUDAToolkit REQUIRED)
+    endif()
+
+    if (GGML_METAL)
+        find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
+        find_library(METAL_FRAMEWORK    Metal REQUIRED)
+        find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
+
+        list(APPEND GGML_METAL_INTERFACE_LINK_LIBRARIES
+                    ${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
+    endif()
+
+    if (GGML_VULKAN)
+        find_package(Vulkan REQUIRED)
+        list(APPEND GGML_VULKAN_INTERFACE_LINK_LIBRARIES Vulkan::Vulkan)
+    endif()
+
+    if (GGML_HIP)
+        find_package(hip     REQUIRED)
+        find_package(hipblas REQUIRED)
+        find_package(rocblas REQUIRED)
+        list(APPEND GGML_HIP_INTERFACE_LINK_LIBRARIES hip::host roc::rocblas roc::hipblas)
+    endif()
+
+    if (GGML_SYCL)
+        find_package(DNNL)
+        if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
+            list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES DNNL::dnnl)
+        endif()
+        if (WIN32)
+            find_package(IntelSYCL REQUIRED)
+            find_package(MKL       REQUIRED)
+            list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
+        endif()
+    endif()
+endif()
+
+set(_ggml_all_targets "")
+foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
+    string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
+    string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+
+    find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+        REQUIRED
+        HINTS ${GGML_LIB_DIR}
+        NO_CMAKE_FIND_ROOT_PATH)
+
+    message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
+
+    add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
+    set_target_properties(ggml::${_ggml_backend}
+        PROPERTIES
+            INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
+            IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+            IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
+            INTERFACE_COMPILE_FEATURES c_std_90
+            POSITION_INDEPENDENT_CODE ON)
+
+    string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
+    if(is_cpu_variant)
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml" "ggml::ggml-base")
+        set_target_properties(ggml::${_ggml_backend}
+           PROPERTIES
+               INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
+
+        if(GGML_CPU_INTERFACE_LINK_OPTIONS)
+            set_target_properties(ggml::${_ggml_backend}
+                PROPERTIES
+                    INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
+        endif()
+
+    else()
+        list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml" "ggml::ggml-base")
+        set_target_properties(ggml::${_ggml_backend}
+            PROPERTIES
+                INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
+
+        if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+            set_target_properties(ggml::${_ggml_backend}
+                PROPERTIES
+                    INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
+        endif()
+    endif()
+
+    list(APPEND _ggml_all_targets ggml::${_ggml_backend})
+endforeach()
+
+add_library(ggml::all INTERFACE IMPORTED)
+set_target_properties(ggml::all
+    PROPERTIES
+        INTERFACE_LINK_LIBRARIES "${_ggml_all_targets}")
+
+check_required_components(ggml)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index ae1cd2337..8d2b948fb 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -250,6 +250,17 @@ function(ggml_add_backend_library backend)
         target_compile_definitions(${backend} PRIVATE GGML_BACKEND_BUILD)
         target_compile_definitions(${backend} PUBLIC  GGML_BACKEND_SHARED)
     endif()
+
+    if(NOT GGML_AVAILABLE_BACKENDS)
+        set(GGML_AVAILABLE_BACKENDS "${backend}"
+            CACHE INTERNAL "List of backends for cmake package")
+    else()
+        list(FIND GGML_AVAILABLE_BACKENDS "${backend}" has_backend)
+        if(has_backend EQUAL -1)
+            set(GGML_AVAILABLE_BACKENDS "${GGML_AVAILABLE_BACKENDS};${backend}"
+                CACHE INTERNAL "List of backends for cmake package")
+        endif()
+    endif()
 endfunction()
 
 function(ggml_add_backend backend)

From 6f53d8a6b41e48c73b345fc6c712c3b00ea4fb93 Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Sun, 26 Jan 2025 18:22:43 +0100
Subject: [PATCH 256/376] docker: add missing vulkan library to base layer and
 update to 24.04 (#11422)

Signed-off-by: rare-magma <rare-magma@posteo.eu>
---
 .devops/vulkan.Dockerfile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index cfc2162e3..ad5dcd374 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=jammy
+ARG UBUNTU_VERSION=24.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget
 
 # Install Vulkan SDK and cURL
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
     apt update -y && \
     apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
 
@@ -34,7 +34,7 @@ RUN mkdir -p /app/full \
 FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl libvulkan-dev \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

From 178a7eb952d211b8d4232d5e50ae1b64519172a9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 26 Jan 2025 20:06:16 +0200
Subject: [PATCH 257/376] metal : use residency sets (#11427)

* metal : use residency sets

ggml-ci

* metal : restore commandBufferWithUnretainedReferences calls [no ci]

* metal : release descriptors

ggml-ci

* metal : check env GGML_METAL_NO_RESIDENCY

ggml-ci

* metal : fix build + clean-up

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal.m | 136 +++++++++++++++++++++++++++----
 1 file changed, 119 insertions(+), 17 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index a85502ee0..c9474345d 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -19,7 +19,10 @@
 // max number of MTLCommandBuffer used to submit a graph for processing
 #define GGML_METAL_MAX_COMMAND_BUFFERS 8
 
-#define UNUSED(x) (void)(x)
+// create residency sets only on macOS >= 15.0
+#if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000
+#define GGML_METAL_HAS_RESIDENCY_SETS 1
+#endif
 
 // globals
 
@@ -39,6 +42,7 @@ static struct ggml_backend_metal_device_context {
 
     bool has_simdgroup_reduction;
     bool has_simdgroup_mm;
+    bool has_residency_sets;
     bool has_bfloat;
     bool use_bfloat;
 
@@ -48,6 +52,7 @@ static struct ggml_backend_metal_device_context {
     /*.mtl_device_ref_count    =*/ 0,
     /*.has_simdgroup_reduction =*/ false,
     /*.has_simdgroup_mm        =*/ false,
+    /*.has_residency_sets      =*/ false,
     /*.has_bfloat              =*/ false,
     /*.use_bfloat              =*/ false,
     /*.name                    =*/ "",
@@ -65,6 +70,10 @@ static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_dev
 
         ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
 
+#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
+        ctx->has_residency_sets = getenv("GGML_METAL_NO_RESIDENCY") == NULL;
+#endif
+
         ctx->has_bfloat  = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
         ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6];
 
@@ -483,6 +492,11 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
     GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
 
     ctx->queue  = [device newCommandQueue];
+    if (ctx->queue == nil) {
+        GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__);
+        return NULL;
+    }
+
     ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
 
     id<MTLLibrary> metal_library;
@@ -649,6 +663,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
 
     GGML_LOG_INFO("%s: simdgroup reduction   = %s\n", __func__, ctx_dev->has_simdgroup_reduction     ? "true" : "false");
     GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, ctx_dev->has_simdgroup_mm            ? "true" : "false");
+    GGML_LOG_INFO("%s: has residency sets    = %s\n", __func__, ctx_dev->has_residency_sets          ? "true" : "false");
     GGML_LOG_INFO("%s: has bfloat            = %s\n", __func__, ctx_dev->has_bfloat                  ? "true" : "false");
     GGML_LOG_INFO("%s: use bfloat            = %s\n", __func__, ctx_dev->use_bfloat                  ? "true" : "false");
     GGML_LOG_INFO("%s: hasUnifiedMemory      = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false");
@@ -1035,8 +1050,70 @@ struct ggml_backend_metal_buffer_context {
     // multiple buffers are used only to avoid the maximum buffer size limitation when using mmap
     int n_buffers;
     struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
+
+    // optional MTLResidencySet
+    id rset;
 };
 
+// rset init
+static bool ggml_backend_metal_buffer_rset_init(
+        struct ggml_backend_metal_buffer_context * ctx,
+        struct ggml_backend_metal_device_context * ctx_dev,
+        id<MTLDevice> device) {
+    ctx->rset = nil;
+
+    if (!ctx_dev->has_residency_sets) {
+        return true;
+    }
+
+#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
+    if (@available(macOS 15.0, *)) {
+        MTLResidencySetDescriptor * desc = [[MTLResidencySetDescriptor alloc] init];
+        desc.label = @"ggml_backend_metal";
+        desc.initialCapacity = ctx->n_buffers;
+
+        NSError * error;
+        ctx->rset = [device newResidencySetWithDescriptor:desc error:&error];
+        if (error) {
+            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+            [desc release];
+            return false;
+        }
+
+        [desc release];
+
+        for (int i = 0; i < ctx->n_buffers; i++) {
+            [ctx->rset addAllocation:ctx->buffers[i].metal];
+        }
+
+        [ctx->rset commit];
+        [ctx->rset requestResidency];
+
+        return true;
+    }
+#else
+    GGML_UNUSED(ctx_dev);
+    GGML_UNUSED(device);
+#endif
+
+    return true;
+}
+
+// rset free
+static void ggml_backend_metal_buffer_rset_free(struct ggml_backend_metal_buffer_context * ctx) {
+#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
+    if (@available(macOS 15.0, *)) {
+        if (ctx->rset) {
+            [ctx->rset endResidency];
+            [ctx->rset removeAllAllocations];
+            [ctx->rset release];
+        }
+    }
+#else
+    GGML_UNUSED(ctx);
+#endif
+}
+
 // finds the Metal buffer that contains the tensor data on the GPU device
 // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
 // Metal buffer based on the host memory pointer
@@ -4176,6 +4253,8 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
     for (int i = 0; i < ctx->n_buffers; i++) {
         [ctx->buffers[i].metal release];
     }
+
+    ggml_backend_metal_buffer_rset_free(ctx);
     ggml_backend_metal_device_rel(buffer->buft->device->context);
 
     if (ctx->owned) {
@@ -4198,19 +4277,19 @@ static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
 static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
     memset((char *)tensor->data + offset, value, size);
 
-    UNUSED(buffer);
+    GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     memcpy((char *)tensor->data + offset, data, size);
 
-    UNUSED(buffer);
+    GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     memcpy(data, (const char *)tensor->data + offset, size);
 
-    UNUSED(buffer);
+    GGML_UNUSED(buffer);
 }
 
 static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
@@ -4220,7 +4299,7 @@ static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, c
     }
     return false;
 
-    UNUSED(buffer);
+    GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
@@ -4246,7 +4325,7 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
 static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
     return "Metal";
 
-    UNUSED(buft);
+    GGML_UNUSED(buft);
 }
 
 static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
@@ -4270,8 +4349,8 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t s
     }
 #endif
 #endif
-    UNUSED(device);
-    UNUSED(size_aligned);
+    GGML_UNUSED(device);
+    GGML_UNUSED(size_aligned);
 }
 
 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -4284,7 +4363,8 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
         size_aligned += (size_page - (size_aligned % size_page));
     }
 
-    id<MTLDevice> device = ggml_backend_metal_device_acq(buft->device->context);
+    struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)buft->device->context;
+    id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
 
     ctx->all_data = ggml_metal_host_malloc(size_aligned);
     ctx->all_size = size_aligned;
@@ -4307,7 +4387,14 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
     if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) {
         GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
         free(ctx);
-        ggml_backend_metal_device_rel(buft->device->context);
+        ggml_backend_metal_device_rel(ctx_dev);
+        return NULL;
+    }
+
+    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
+        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
+        free(ctx);
+        ggml_backend_metal_device_rel(ctx_dev);
         return NULL;
     }
 
@@ -4318,7 +4405,7 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
 
 static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return 32;
-    UNUSED(buft);
+    GGML_UNUSED(buft);
 }
 
 static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
@@ -4328,13 +4415,13 @@ static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_ty
 
     return max_size;
 
-    UNUSED(buft);
+    GGML_UNUSED(buft);
 }
 
 static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
-    UNUSED(buft);
+    GGML_UNUSED(buft);
 }
 
 ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
@@ -4357,7 +4444,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
 static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
     return "Metal_Mapped";
 
-    UNUSED(buft);
+    GGML_UNUSED(buft);
 }
 
 static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) {
@@ -4400,7 +4487,8 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
         size_aligned += (size_page - (size_aligned % size_page));
     }
 
-    id<MTLDevice> device = ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main);
+    struct ggml_backend_metal_device_context * ctx_dev = &g_ggml_ctx_dev_main;
+    id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
 
     // the buffer fits into the max buffer size allowed by the device
     if (size_aligned <= device.maxBufferLength) {
@@ -4453,6 +4541,13 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
         }
     }
 
+    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
+        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
+        free(ctx);
+        ggml_backend_metal_device_rel(ctx_dev);
+        return NULL;
+    }
+
     return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
 }
 
@@ -4461,7 +4556,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
 static const char * ggml_backend_metal_name(ggml_backend_t backend) {
     return "Metal";
 
-    UNUSED(backend);
+    GGML_UNUSED(backend);
 }
 
 static void ggml_backend_metal_free(ggml_backend_t backend) {
@@ -4766,6 +4861,13 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back
         }
     }
 
+    if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
+        GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
+        free(ctx);
+        ggml_backend_metal_device_rel(ctx_dev);
+        return NULL;
+    }
+
     return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
 }
 
@@ -4779,7 +4881,7 @@ static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml
     return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name ||
             buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name;
 
-    UNUSED(dev);
+    GGML_UNUSED(dev);
 }
 
 static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {

From caf773f249aa267c78d3da5567b8ab156080ea59 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Sun, 26 Jan 2025 22:45:32 +0100
Subject: [PATCH 258/376] docker : fix ARM build and Vulkan build (#11434)

* ci : do not fail-fast for docker

* build arm64/amd64 separatedly

* fix pip

* no fast fail

* vulkan: try jammy
---
 .devops/vulkan.Dockerfile    | 4 ++--
 .github/workflows/docker.yml | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index ad5dcd374..b5bd3b6d2 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=24.04
+ARG UBUNTU_VERSION=22.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget
 
 # Install Vulkan SDK and cURL
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
     apt update -y && \
     apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
 
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index d71f1eb38..6bf22eb66 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -32,10 +32,12 @@ jobs:
     env:
       COMMIT_SHA: ${{ github.sha }}
     strategy:
+      fail-fast: false
       matrix:
         config:
           # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}

From acd38efee316f3a5ed2e6afcbc5814807c347053 Mon Sep 17 00:00:00 2001
From: Ihar Hrachyshka <ihrachys@redhat.com>
Date: Mon, 27 Jan 2025 02:41:59 -0500
Subject: [PATCH 259/376] metal: Handle null returned from
 MTLCreateSystemDefaultDevice() (#11441)

This fixes segmentation fault error when running tests when no metal
devices are available (for example, when not linked with Core Graphics
framework or otherwise).
---
 ggml/src/ggml-metal/ggml-metal.m | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index c9474345d..76f8e4291 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -64,7 +64,9 @@ static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_dev
 
     if (ctx->mtl_device == nil) {
         ctx->mtl_device = MTLCreateSystemDefaultDevice();
+    }
 
+    if (ctx->mtl_device) {
         ctx->has_simdgroup_reduction  = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
         ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
 
@@ -99,8 +101,10 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
     ctx->mtl_device_ref_count--;
 
     if (ctx->mtl_device_ref_count == 0) {
-        [ctx->mtl_device release];
-        ctx->mtl_device = nil;
+        if (ctx->mtl_device) {
+            [ctx->mtl_device release];
+            ctx->mtl_device = nil;
+        }
     }
 }
 

From df984e014714cba4c99ef894b20b51cbcef31b16 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 27 Jan 2025 12:07:12 +0100
Subject: [PATCH 260/376] llama: refactor llama_decode_impl (#11381)

---
 src/llama.cpp | 243 +++++++++++++++++++++++++++++---------------------
 1 file changed, 140 insertions(+), 103 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 094157ccf..12e8f41fc 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8432,13 +8432,141 @@ static enum ggml_status llama_graph_compute(
     return status;
 }
 
+static int llama_prepare_sbatch(
+        llama_context     & lctx,
+        const llama_batch & batch,
+        uint32_t          & n_outputs) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const uint32_t n_tokens_all = batch.n_tokens;
+    const  int64_t n_embd       = hparams.n_embd;
+
+    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
+
+    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+    if (batch.token) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            if (batch.token[i] < 0 || uint32_t(batch.token[i]) >= model.vocab.n_tokens()) {
+                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
+                return -1;
+            }
+        }
+    }
+    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
+    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
+
+    lctx.n_queued_tokens += n_tokens_all;
+    lctx.embd_seq.clear();
+
+    // count outputs
+    if (batch.logits && !embd_pooled) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            n_outputs += batch.logits[i] != 0;
+        }
+    } else if (lctx.logits_all || embd_pooled) {
+        n_outputs = n_tokens_all;
+    } else {
+        // keep last output only
+        n_outputs = 1;
+    }
+
+    lctx.sbatch.from_batch(batch, n_embd,
+        /* simple_split */ !lctx.kv_self.recurrent,
+        /* logits_all   */ n_outputs == n_tokens_all);
+
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
+        return -2;
+    };
+
+    return 0;
+}
+
+static int llama_prepare_ubatch(
+        llama_context          & lctx,
+        llama_kv_slot_restorer & kv_slot_restorer,
+        llama_ubatch           & ubatch,
+        const uint32_t           n_outputs,
+        const uint32_t           n_tokens_all) {
+    GGML_ASSERT(lctx.sbatch.n_tokens > 0);
+
+    auto       & kv_self = lctx.kv_self;
+    const auto & cparams = lctx.cparams;
+    const auto & hparams = lctx.model.hparams;
+
+    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
+
+    if (lctx.kv_self.recurrent) {
+        if (embd_pooled) {
+            // Pooled embeddings cannot be split across ubatches (yet)
+            ubatch = lctx.sbatch.split_seq(cparams.n_ubatch);
+        } else {
+            // recurrent model architectures are easier to implement
+            // with equal-length sequences
+            ubatch = lctx.sbatch.split_equal(cparams.n_ubatch);
+        }
+    } else {
+        ubatch = lctx.sbatch.split_simple(cparams.n_ubatch);
+    }
+
+    // count the outputs in this u_batch
+    {
+        int32_t n_outputs_new = 0;
+
+        if (n_outputs == n_tokens_all) {
+            n_outputs_new = ubatch.n_tokens;
+        } else {
+            GGML_ASSERT(ubatch.output);
+            for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+                n_outputs_new += int32_t(ubatch.output[i] != 0);
+            }
+        }
+
+        // needs to happen before the graph is built
+        lctx.n_outputs = n_outputs_new;
+    }
+
+    // non-causal masks do not use the KV cache
+    if (hparams.causal_attn) {
+        llama_kv_cache_update(&lctx);
+
+        // if we have enough unused cells before the current head ->
+        //   better to start searching from the beginning of the cache, hoping to fill it
+        if (kv_self.head > kv_self.used + 2*ubatch.n_tokens) {
+            kv_self.head = 0;
+        }
+
+        const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
+        if (!slot) {
+            return 1;
+        }
+        kv_slot_restorer.save(slot);
+
+        if (!kv_self.recurrent) {
+            // a heuristic, to avoid attending the full cache if it is not yet utilized
+            // after enough generations, the benefit from this heuristic disappears
+            // if we start defragmenting the cache, the benefit from this will be more important
+            const uint32_t pad = llama_kv_cache_get_padding(cparams);
+            kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
+            //kv_self.n = llama_kv_cache_cell_max(kv_self);
+        }
+    }
+
+    return 0;
+}
+
 // decode a batch of tokens by evaluating the transformer
 // in case of unsuccessful decoding (error or warning),
 // the kv_cache state will be returned to its original state
 // (for non-recurrent models) or cleaned (for recurrent models)
 //
 //   - lctx:      llama context
-//   - batch:     batch to evaluate
+//   - inp_batch: batch to evaluate
 //
 // return 0 on success
 // return positive int on warning
@@ -8455,37 +8583,18 @@ static int llama_decode_impl(
         return -1;
     }
 
-    // temporary allocate memory for the input batch if needed
+    // temporarily allocate memory for the input batch if needed
     llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.kv_self.max_pos() + 1);
-
     const llama_batch & batch = batch_allocr.batch;
-    const uint32_t n_tokens_all = batch.n_tokens;
 
     const auto & model   = lctx.model;
     const auto & vocab   = model.vocab;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
-    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
-
-    if (batch.token) {
-        for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
-                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
-                return -1;
-            }
-        }
-    }
-
-    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
-
-    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
-
     if (lctx.t_compute_start_us == 0) {
         lctx.t_compute_start_us = ggml_time_us();
     }
-    lctx.n_queued_tokens += n_tokens_all;
-
     auto & kv_self = lctx.kv_self;
     llama_kv_slot_restorer kv_slot_restorer(kv_self);
 
@@ -8495,99 +8604,27 @@ static int llama_decode_impl(
     uint32_t n_outputs = 0;
     uint32_t n_outputs_prev = 0;
 
-    const auto n_ubatch = cparams.n_ubatch;
-
-    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
-    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
-
-    lctx.embd_seq.clear();
-
-    // count outputs
-    if (batch.logits && !embd_pooled) {
-        for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            n_outputs += batch.logits[i] != 0;
+    {
+        const int ret = llama_prepare_sbatch(lctx, batch, n_outputs);
+        if (ret != 0) {
+            return ret;
         }
-    } else if (lctx.logits_all || embd_pooled) {
-        n_outputs = n_tokens_all;
-    } else {
-        // keep last output only
-        n_outputs = 1;
     }
 
-    lctx.sbatch.from_batch(batch, n_embd,
-        /* simple_split */ !kv_self.recurrent,
-        /* logits_all   */ n_outputs == n_tokens_all);
-
-    // reserve output buffer
-    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
-        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
-        return -2;
-    };
-
     while (lctx.sbatch.n_tokens > 0) {
         llama_ubatch ubatch;
-        if (kv_self.recurrent) {
-            if (embd_pooled) {
-                // Pooled embeddings cannot be split across ubatches (yet)
-                ubatch = lctx.sbatch.split_seq(n_ubatch);
-            } else {
-                // recurrent model architectures are easier to implement
-                // with equal-length sequences
-                ubatch = lctx.sbatch.split_equal(n_ubatch);
-            }
-        } else {
-            ubatch = lctx.sbatch.split_simple(n_ubatch);
-        }
-        const uint32_t n_tokens = ubatch.n_tokens;
-
-        // count the outputs in this u_batch
         {
-            int32_t n_outputs_new = 0;
-
-            if (n_outputs == n_tokens_all) {
-                n_outputs_new = n_tokens;
-            } else {
-                GGML_ASSERT(ubatch.output);
-                for (uint32_t i = 0; i < n_tokens; i++) {
-                    n_outputs_new += (int32_t) (ubatch.output[i] != 0);
-                }
+            const int ret = llama_prepare_ubatch(lctx, kv_slot_restorer, ubatch, n_outputs, batch.n_tokens);
+            if (ret != 0) {
+                return ret;
             }
-
-            // needs to happen before the graph is built
-            lctx.n_outputs = n_outputs_new;
         }
 
-        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
-        ggml_threadpool_t threadpool = n_tokens == 1 ? lctx.threadpool : lctx.threadpool_batch;
+        const int         n_threads  = ubatch.n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+        ggml_threadpool_t threadpool = ubatch.n_tokens == 1 ? lctx.threadpool   : lctx.threadpool_batch;
 
         GGML_ASSERT(n_threads > 0);
 
-        // non-causal masks do not use the KV cache
-        if (hparams.causal_attn) {
-            llama_kv_cache_update(&lctx);
-
-            // if we have enough unused cells before the current head ->
-            //   better to start searching from the beginning of the cache, hoping to fill it
-            if (kv_self.head > kv_self.used + 2*n_tokens) {
-                kv_self.head = 0;
-            }
-
-            const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
-            if (!slot) {
-                return 1;
-            }
-            kv_slot_restorer.save(slot);
-
-            if (!kv_self.recurrent) {
-                // a heuristic, to avoid attending the full cache if it is not yet utilized
-                // after enough generations, the benefit from this heuristic disappears
-                // if we start defragmenting the cache, the benefit from this will be more important
-                const uint32_t pad = llama_kv_cache_get_padding(cparams);
-                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
-                //kv_self.n = llama_kv_cache_cell_max(kv_self);
-            }
-        }
-
         //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
 
         ggml_backend_sched_reset(lctx.sched.get());
@@ -8640,7 +8677,7 @@ static int llama_decode_impl(
 
         // update the kv ring buffer
         {
-            kv_self.head += n_tokens;
+            kv_self.head += ubatch.n_tokens;
 
             // Ensure kv cache head points to a valid index.
             if (kv_self.head >= kv_self.size) {

From a5203b4465c5c87813936bde98170e25bb09024f Mon Sep 17 00:00:00 2001
From: lexasub <lexakopp2212@gmail.com>
Date: Mon, 27 Jan 2025 17:42:09 +0400
Subject: [PATCH 261/376] llama : minor fixes for up llama load model speed
 (#11448)

* impl::load change map bpe_ranks to onordered map for reduce time of impl::load on 30%

* llama_model_loader::init_mapping - replace new llama_mmap to std::make_unique<llama_mmap> for clean code & reduce (/2) time of running init_mappings

* Update src/llama-vocab.cpp

---------

Co-authored-by: lexasub <empty@empty.ru>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 src/llama-model-loader.cpp | 2 +-
 src/llama-vocab.cpp        | 9 +++++++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 75073bf61..05d58ad90 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -819,7 +819,7 @@ void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps
         for (const auto & file : files) {
             auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
             auto * is_numa_fn = (decltype(ggml_is_numa) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_is_numa");
-            std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, is_numa_fn()));
+            std::unique_ptr<llama_mmap> mapping = std::make_unique<llama_mmap>(file.get(), prefetch ? -1 : 0, is_numa_fn());
             mmaps_used.emplace_back(mapping->size(), 0);
             if (mlock_mmaps) {
                 std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 0782d3a41..561f8bdb8 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1245,8 +1245,13 @@ struct llama_vocab::impl {
 
     std::vector<llama_token> cache_special_tokens;
     std::vector<std::string> cache_token_to_piece; // llama_token_to_piece(special = true);
-
-    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
+    struct pair_hash {
+        size_t operator()(const std::pair<std::string, std::string> & p) const {
+            return std::hash<std::string>{}(p.first) ^  //create some hash for pair
+                   (std::hash<std::string>{}(p.second) << 1);
+        }
+    };
+    std::unordered_map<std::pair<std::string, std::string>, int, pair_hash> bpe_ranks;
 
     // set of all tokens that cause "end of generation"
     std::set<llama_token> special_eog_ids;

From d6d24cd9ed6d0b9558643dcc28f2124bef488c52 Mon Sep 17 00:00:00 2001
From: Haus1 <haus.xda@gmail.com>
Date: Mon, 27 Jan 2025 08:58:17 -0500
Subject: [PATCH 262/376] AMD: parse the architecture as supplied by
 gcnArchName (#11244)

The value provided by minor doesn't include stepping for AMD, parse the value returned by gcnArchName instead to retrieve an accurate ID.
---
 ggml/src/ggml-cuda/common.cuh   | 20 +++++-----
 ggml/src/ggml-cuda/ggml-cuda.cu | 67 ++++++++++++++++++++++++++++++++-
 2 files changed, 75 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index bb6120568..a66322da0 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -46,20 +46,20 @@
 #define GGML_CUDA_CC_VOLTA      700
 #define GGML_CUDA_CC_TURING     750
 #define GGML_CUDA_CC_AMPERE     800
-#define GGML_CUDA_CC_OFFSET_AMD 1000000
+#define GGML_CUDA_CC_OFFSET_AMD 0x1000000
 
 // GCN/CNDA, wave size is 64
-#define GGML_CUDA_CC_GCN4       (GGML_CUDA_CC_OFFSET_AMD + 803)  // Tonga, Fiji, Polaris, minimum for fast fp16
-#define GGML_CUDA_CC_VEGA       (GGML_CUDA_CC_OFFSET_AMD + 900)  // Vega56/64, minimum for fp16 dual issue
-#define GGML_CUDA_CC_VEGA20     (GGML_CUDA_CC_OFFSET_AMD + 906)  // MI50/Radeon VII, minimum for dp4a
-#define GGML_CUDA_CC_CDNA       (GGML_CUDA_CC_OFFSET_AMD + 908)  // MI100, minimum for MFMA, acc registers
-#define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 910)  // MI210, minimum acc register renameing
-#define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 942)  // MI300
+#define GGML_CUDA_CC_GCN4       (GGML_CUDA_CC_OFFSET_AMD + 0x803)  // Tonga, Fiji, Polaris, minimum for fast fp16
+#define GGML_CUDA_CC_VEGA       (GGML_CUDA_CC_OFFSET_AMD + 0x900)  // Vega56/64, minimum for fp16 dual issue
+#define GGML_CUDA_CC_VEGA20     (GGML_CUDA_CC_OFFSET_AMD + 0x906)  // MI50/Radeon VII, minimum for dp4a
+#define GGML_CUDA_CC_CDNA       (GGML_CUDA_CC_OFFSET_AMD + 0x908)  // MI100, minimum for MFMA, acc registers
+#define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x910)  // MI210, minimum acc register renameing
+#define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x942)  // MI300
 
 // RNDA removes MFMA, dp4a, xnack, acc registers, wave size is 32
-#define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 1010) // RX 5000
-#define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 1030) // RX 6000, minimum for dp4a
-#define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 1100) // RX 7000, minimum for WMMA
+#define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
+#define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
+#define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
 
 #define GGML_CUDA_CC_QY1        210
 #define GGML_CUDA_CC_QY2        220
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 85178abd2..402f37e85 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -119,6 +119,55 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device)
 #endif
 }
 
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+static int ggml_cuda_parse_id(char devName[]) {
+    // A list of possible Target IDs can be found under the rocclr/clr repo in device.cpp
+    // these values are not stable so this is susceptible to breakage
+    // https://github.com/ROCm/clr/blob/amd-staging/rocclr/device/device.cpp
+    int archMajor = 0x0;
+    int archMinor = 0x0;
+    int archNum = GGML_CUDA_CC_OFFSET_AMD;
+    int archLen = strlen(devName);
+    char archName[archLen + 1];
+
+    // strip leading 'gfx' while copying into our buffer
+    if (archLen > 3) {
+        strcpy(archName, &devName[3]);
+        archLen -= 3;
+    }
+
+    // trim trailing :xnack- or :sramecc- statuses
+    archLen = strcspn(archName, ":");
+    archName[archLen] = '\0';
+
+    // tease out the version information
+    if (archLen > 8) {
+        // versions labeled generic use '-' as delimiter
+        // strip the trailing "-generic" then iterate through what remains
+        if ((strstr(archName, "-generic"))) {
+            archName[archLen - 8] = '\0';
+            char * pch;
+            if ((pch = strtok(archName, "-"))) {
+                archMajor = (int)strtoul(pch, 0, 16);
+                if ((pch = strtok(NULL, "-"))) {
+                    archMinor = 0x10 * (int)strtoul(pch, 0, 16);
+                }
+            }
+        }
+    } else if (archLen >= 3) {
+        // last two digits should be the minor * 0x10 + stepping
+        archMinor = (int)strtoul(&archName[archLen - 2], 0, 16);
+        archName[archLen - 2] = '\0';
+
+        // only the major version remains
+        archMajor = (int)strtoul(archName, 0, 16);
+    }
+    archNum += archMajor * 0x100;
+    archNum += archMinor;
+    return archNum;
+}
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+
 static ggml_cuda_device_info ggml_cuda_init() {
 #ifdef __HIP_PLATFORM_AMD__
     // Workaround for a rocBLAS bug when using multiple graphics cards:
@@ -169,7 +218,6 @@ static ggml_cuda_device_info ggml_cuda_init() {
 
         cudaDeviceProp prop;
         CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
 
         info.default_tensor_split[id] = total_vram;
         total_vram += prop.totalGlobalMem;
@@ -178,10 +226,25 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.devices[id].smpb  = prop.sharedMemPerBlock;
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
         info.devices[id].smpbo = prop.sharedMemPerBlock;
-        info.devices[id].cc = 100*prop.major + 10*prop.minor + GGML_CUDA_CC_OFFSET_AMD;
+
+        info.devices[id].cc = ggml_cuda_parse_id(prop.gcnArchName);
+        if ((info.devices[id].cc & 0xff00) == 0x0) {
+            GGML_LOG_WARN("invalid architecture ID received for device %d %s: %s  cc %d.%d\n",
+                            id, prop.name, prop.gcnArchName, prop.major, prop.minor);
+
+            // Fallback to prop.major and prop.minor
+            if (prop.major > 0) {
+                info.devices[id].cc = GGML_CUDA_CC_OFFSET_AMD + prop.major * 0x100;
+                info.devices[id].cc += prop.minor * 0x10;
+            }
+        }
+        GGML_LOG_INFO("  Device %d: %s, %s (0x%x), VMM: %s\n",
+                        id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, device_vmm ? "yes" : "no");
 #else
         info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
+        GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
+                        id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
 #endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     }
 

From a4417ddda98fd0558fb4d802253e68a933704b59 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Mon, 27 Jan 2025 19:36:10 +0100
Subject: [PATCH 263/376] Add new hf protocol for ollama (#11449)

https://huggingface.co/docs/hub/en/ollama

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 121 ++++++++++++++++++++++++++++---------------
 1 file changed, 80 insertions(+), 41 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 92a49eb74..8a0db74b6 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -319,6 +319,10 @@ class HttpClient {
   public:
     int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
              const bool progress, std::string * response_str = nullptr) {
+        if (std::filesystem::exists(output_file)) {
+            return 0;
+        }
+
         std::string output_file_partial;
         curl = curl_easy_init();
         if (!curl) {
@@ -558,13 +562,14 @@ class LlamaData {
         }
 
         sampler = initialize_sampler(opt);
+
         return 0;
     }
 
   private:
 #ifdef LLAMA_USE_CURL
-    int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
-                 const bool progress, std::string * response_str = nullptr) {
+    int download(const std::string & url, const std::string & output_file, const bool progress,
+                 const std::vector<std::string> & headers = {}, std::string * response_str = nullptr) {
         HttpClient http;
         if (http.init(url, headers, output_file, progress, response_str)) {
             return 1;
@@ -573,48 +578,85 @@ class LlamaData {
         return 0;
     }
 #else
-    int download(const std::string &, const std::vector<std::string> &, const std::string &, const bool,
+    int download(const std::string &, const std::string &, const bool, const std::vector<std::string> & = {},
                  std::string * = nullptr) {
         printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+
         return 1;
     }
 #endif
 
-    int huggingface_dl(const std::string & model, const std::vector<std::string> headers, const std::string & bn) {
-        // Find the second occurrence of '/' after protocol string
-        size_t pos = model.find('/');
-        pos        = model.find('/', pos + 1);
-        if (pos == std::string::npos) {
-            return 1;
-        }
-
-        const std::string hfr = model.substr(0, pos);
-        const std::string hff = model.substr(pos + 1);
-        const std::string url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
-        return download(url, headers, bn, true);
-    }
-
-    int ollama_dl(std::string & model, const std::vector<std::string> headers, const std::string & bn) {
-        if (model.find('/') == std::string::npos) {
-            model = "library/" + model;
-        }
-
-        std::string model_tag = "latest";
-        size_t      colon_pos = model.find(':');
+    // Helper function to handle model tag extraction and URL construction
+    std::pair<std::string, std::string> extract_model_and_tag(std::string & model, const std::string & base_url) {
+        std::string  model_tag = "latest";
+        const size_t colon_pos = model.find(':');
         if (colon_pos != std::string::npos) {
             model_tag = model.substr(colon_pos + 1);
             model     = model.substr(0, colon_pos);
         }
 
-        std::string manifest_url = "https://registry.ollama.ai/v2/" + model + "/manifests/" + model_tag;
+        std::string url = base_url + model + "/manifests/" + model_tag;
+
+        return { model, url };
+    }
+
+    // Helper function to download and parse the manifest
+    int download_and_parse_manifest(const std::string & url, const std::vector<std::string> & headers,
+                                    nlohmann::json & manifest) {
         std::string manifest_str;
-        const int   ret = download(manifest_url, headers, "", false, &manifest_str);
+        int         ret = download(url, "", false, headers, &manifest_str);
         if (ret) {
             return ret;
         }
 
-        nlohmann::json manifest = nlohmann::json::parse(manifest_str);
-        std::string    layer;
+        manifest = nlohmann::json::parse(manifest_str);
+
+        return 0;
+    }
+
+    int huggingface_dl(std::string & model, const std::string & bn) {
+        // Find the second occurrence of '/' after protocol string
+        size_t pos = model.find('/');
+        pos        = model.find('/', pos + 1);
+        std::string              hfr, hff;
+        std::vector<std::string> headers = { "User-Agent: llama-cpp", "Accept: application/json" };
+        std::string              url;
+
+        if (pos == std::string::npos) {
+            auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/");
+            hfr                             = model_name;
+
+            nlohmann::json manifest;
+            int            ret = download_and_parse_manifest(manifest_url, headers, manifest);
+            if (ret) {
+                return ret;
+            }
+
+            hff = manifest["ggufFile"]["rfilename"];
+        } else {
+            hfr = model.substr(0, pos);
+            hff = model.substr(pos + 1);
+        }
+
+        url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
+
+        return download(url, bn, true, headers);
+    }
+
+    int ollama_dl(std::string & model, const std::string & bn) {
+        const std::vector<std::string> headers = { "Accept: application/vnd.docker.distribution.manifest.v2+json" };
+        if (model.find('/') == std::string::npos) {
+            model = "library/" + model;
+        }
+
+        auto [model_name, manifest_url] = extract_model_and_tag(model, "https://registry.ollama.ai/v2/");
+        nlohmann::json manifest;
+        int            ret = download_and_parse_manifest(manifest_url, {}, manifest);
+        if (ret) {
+            return ret;
+        }
+
+        std::string layer;
         for (const auto & l : manifest["layers"]) {
             if (l["mediaType"] == "application/vnd.ollama.image.model") {
                 layer = l["digest"];
@@ -622,8 +664,9 @@ class LlamaData {
             }
         }
 
-        std::string blob_url = "https://registry.ollama.ai/v2/" + model + "/blobs/" + layer;
-        return download(blob_url, headers, bn, true);
+        std::string blob_url = "https://registry.ollama.ai/v2/" + model_name + "/blobs/" + layer;
+
+        return download(blob_url, bn, true, headers);
     }
 
     std::string basename(const std::string & path) {
@@ -653,22 +696,18 @@ class LlamaData {
             return ret;
         }
 
-        const std::string              bn      = basename(model_);
-        const std::vector<std::string> headers = { "--header",
-                                                   "Accept: application/vnd.docker.distribution.manifest.v2+json" };
+        const std::string bn = basename(model_);
         if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
             rm_until_substring(model_, "://");
-            ret = huggingface_dl(model_, headers, bn);
+            ret = huggingface_dl(model_, bn);
         } else if (string_starts_with(model_, "hf.co/")) {
             rm_until_substring(model_, "hf.co/");
-            ret = huggingface_dl(model_, headers, bn);
-        } else if (string_starts_with(model_, "ollama://")) {
-            rm_until_substring(model_, "://");
-            ret = ollama_dl(model_, headers, bn);
+            ret = huggingface_dl(model_, bn);
         } else if (string_starts_with(model_, "https://")) {
-            ret = download(model_, headers, bn, true);
-        } else {
-            ret = ollama_dl(model_, headers, bn);
+            ret = download(model_, bn, true);
+        } else {  // ollama:// or nothing
+            rm_until_substring(model_, "://");
+            ret = ollama_dl(model_, bn);
         }
 
         model_ = bn;

From 2b8525d5c89b124c4578a2621cbeb64354ff3d9c Mon Sep 17 00:00:00 2001
From: Michael Engel <mengel@redhat.com>
Date: Tue, 28 Jan 2025 09:32:40 +0100
Subject: [PATCH 264/376] Handle missing model in CLI parameters for llama-run
 (#11399)

The HTTP client in llama-run only prints an error in case the download of
a resource failed. If the model name in the CLI parameter list is missing,
this causes the application to crash.
In order to prevent this, a check for the required model parameter has been
added and errors for resource downloads get propagated to the caller.

Signed-off-by: Michael Engel <mengel@redhat.com>
---
 examples/run/run.cpp | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 8a0db74b6..5980a786f 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -181,6 +181,10 @@ class Opt {
             }
         }
 
+        if (model_.empty()){
+            return 1;
+        }
+
         return 0;
     }
 
@@ -350,7 +354,11 @@ class HttpClient {
         data.file_size = set_resume_point(output_file_partial);
         set_progress_options(progress, data);
         set_headers(headers);
-        perform(url);
+        CURLcode res = perform(url);
+        if (res != CURLE_OK){
+            printe("Fetching resource '%s' failed: %s\n", url.c_str(), curl_easy_strerror(res));
+            return 1;
+        }
         if (!output_file.empty()) {
             std::filesystem::rename(output_file_partial, output_file);
         }
@@ -415,16 +423,12 @@ class HttpClient {
         }
     }
 
-    void perform(const std::string & url) {
-        CURLcode res;
+    CURLcode perform(const std::string & url) {
         curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
         curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
         curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
         curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
-        res = curl_easy_perform(curl);
-        if (res != CURLE_OK) {
-            printe("curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
-        }
+        return curl_easy_perform(curl);
     }
 
     static std::string human_readable_time(double seconds) {

From 6e84b0ab8e10b8f6f99a32855f976ebcd35b0353 Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Tue, 28 Jan 2025 15:26:58 +0530
Subject: [PATCH 265/376] SYCL : SOFTMAX F16 mask support and other fixes
 (#11261)

Implemented ggml_sycl_op_soft_max() F16 src1(mask) support for which a pragma deprecation warning was added during #5021.
To do this, had to decouple it from ggml_sycl_op_flatten which always considered src1 to be of fp32 type(many OP functions are dependent on it).

* SYCL: SOFTMAX F16 mask support and other fixes

* test-backend-ops: Add F16 mask test cases
---
 ggml/src/ggml-sycl/ggml-sycl.cpp |  6 +---
 ggml/src/ggml-sycl/softmax.cpp   | 56 +++++++++++++++++++-------------
 ggml/src/ggml-sycl/softmax.hpp   |  6 +---
 tests/test-backend-ops.cpp       | 45 ++++++++++++++++---------
 4 files changed, 64 insertions(+), 49 deletions(-)

diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index ed4d8bb8b..2984ed82e 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -3878,10 +3878,6 @@ static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_diag_mask_inf);
 }
 
-static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_soft_max);
-}
-
 static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT(ggml_is_contiguous(dst->src[0])); // TODO: this restriction is temporary until non-cont support is implemented
     ggml_sycl_op_flatten(ctx, dst->src[0], dst->src[1], dst, ggml_sycl_op_rope);
@@ -4090,7 +4086,7 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
             ggml_sycl_diag_mask_inf(ctx, dst);
             break;
         case GGML_OP_SOFT_MAX:
-            ggml_sycl_soft_max(ctx, dst);
+            ggml_sycl_op_soft_max(ctx, dst);
             break;
         case GGML_OP_ROPE:
             ggml_sycl_rope(ctx, dst);
diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index a9b3fce0d..563e0655f 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -1,7 +1,7 @@
-#include "norm.hpp"
+#include "softmax.hpp"
 
-template <bool vals_smem, int ncols_template, int block_size_template>
-static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par,
+template <bool vals_smem, int ncols_template, int block_size_template, typename T>
+static void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par,
                          const int nrows_y, const float scale, const float max_bias, const float m0,
                          const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
     const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
@@ -29,7 +29,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         slope = sycl::pow(base, float(exp));
     }
 
-    float *vals = vals_smem ? buf + std::max(nwarps, WARP_SIZE) : dst + rowx * ncols;
+    float *vals = vals_smem ? buf + sycl::max(nwarps, WARP_SIZE) : dst + rowx * ncols;
     float max_val = -INFINITY;
 
     for (int col0 = 0; col0 < ncols; col0 += block_size) {
@@ -42,7 +42,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         const int ix = rowx*ncols + col;
         const int iy = rowy*ncols + col;
 
-        const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f);
+        const float val = x[ix]*scale + (mask ? slope*static_cast<float>(mask[iy]) : 0.0f);
 
         vals[col] = val;
         max_val = sycl::max(max_val, val);
@@ -65,7 +65,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
         item_ct1.barrier(sycl::access::fence_space::local_space);
         max_val = buf[lane_id];
         for (size_t i = 1; i < nreduce; i += 1) {
-            max_val = std::max(max_val, buf[lane_id + i * WARP_SIZE]);
+            max_val = sycl::max(max_val, buf[lane_id + i * WARP_SIZE]);
         }
         max_val = warp_reduce_max(max_val, item_ct1);
     }
@@ -122,8 +122,8 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
     }
 }
 
-template <bool vals_smem, int ncols_template, int block_size_template>
-static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par,
+template <bool vals_smem, int ncols_template, int block_size_template, typename T>
+static void soft_max_f32_submitter(const float * x, const T * mask, float * dst, const int ncols_par,
                                    const int nrows_y, const float scale, const float max_bias, const float m0,
                                    const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
                                    const size_t n_local_scratch, queue_ptr stream) {
@@ -141,7 +141,8 @@ static void soft_max_f32_submitter(const float * x, const float * mask, float *
     });
 }
 
-static void soft_max_f32_sycl(const float * x, const float * mask,
+template<typename T>
+static void soft_max_f32_sycl(const float * x, const T * mask,
                               float * dst, const int ncols_x, const int nrows_x,
                               const int nrows_y, const float scale, const float max_bias,
                               queue_ptr stream, int device) {
@@ -223,22 +224,16 @@ static void soft_max_f32_sycl(const float * x, const float * mask,
     }
 }
 
-void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_dd, const float *src1_dd,
-                                  float *dst_dd,
-                                  const queue_ptr &main_stream) {
+void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support")
-#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+    GGML_ASSERT(!dst->src[1] || dst->src[1]->type == GGML_TYPE_F16 || dst->src[1]->type == GGML_TYPE_F32); // src1 contains mask and it is optional
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src0->ne[1];
+    const int64_t ne00 = dst->src[0]->ne[0];
+    const int64_t nrows_x = ggml_nrows(dst->src[0]);
+    const int64_t nrows_y = dst->src[0]->ne[1];
 
     float scale = 1.0f;
     float max_bias = 0.0f;
@@ -246,6 +241,21 @@ void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *s
     memcpy(&scale, dst->op_params + 0, sizeof(float));
     memcpy(&max_bias, dst->op_params + 1, sizeof(float));
 
-    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00,
-        nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
+    const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
+    float * dst_dd = static_cast<float *>(dst->data);
+
+    ggml_sycl_set_device(ctx.device);
+    dpct::queue_ptr main_stream = ctx.stream();
+
+    if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F16) {
+        const sycl::half * src1_dd = static_cast<sycl::half *>(dst->src[1]->data);
+        soft_max_f32_sycl<sycl::half>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias,
+                          main_stream, ctx.device);
+    } else if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F32) {
+        const float * src1_dd = static_cast<const float *>(dst->src[1]->data);
+        soft_max_f32_sycl<float>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
+    } else {
+        /* mask unavailable */
+        soft_max_f32_sycl<float>(src0_dd, nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
+    }
 }
diff --git a/ggml/src/ggml-sycl/softmax.hpp b/ggml/src/ggml-sycl/softmax.hpp
index bdb8f712e..2cf8582ec 100644
--- a/ggml/src/ggml-sycl/softmax.hpp
+++ b/ggml/src/ggml-sycl/softmax.hpp
@@ -15,10 +15,6 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_soft_max(ggml_backend_sycl_context &ctx, const ggml_tensor *src0,
-    const ggml_tensor *src1, ggml_tensor *dst,
-    const float *src0_dd, const float *src1_dd,
-    float *dst_dd,
-    const queue_ptr &main_stream);
+void ggml_sycl_op_soft_max(ggml_backend_sycl_context &ctx, ggml_tensor *dst);
 
 #endif // GGML_SYCL_SOFTMAX_HPP
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 468016403..4c5c4dd9c 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2347,11 +2347,12 @@ struct test_soft_max : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
     const bool mask;
+    const ggml_type m_prec;
     const float scale;
     const float max_bias;
 
     std::string vars() override {
-        return VARS_TO_STR5(type, ne, mask, scale, max_bias);
+        return VARS_TO_STR6(type, ne, mask, m_prec, scale, max_bias);
     }
 
     // the 1024 test with bias occasionally fails:
@@ -2363,9 +2364,10 @@ struct test_soft_max : public test_case {
     test_soft_max(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {10, 5, 4, 3},
             bool mask = false,
+            ggml_type m_prec = GGML_TYPE_F32,
             float scale = 1.0f,
             float max_bias = 0.0f)
-        : type(type), ne(ne), mask(mask), scale(scale), max_bias(max_bias) {}
+        : type(type), ne(ne), mask(mask), m_prec(m_prec), scale(scale), max_bias(max_bias) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
@@ -2374,7 +2376,7 @@ struct test_soft_max : public test_case {
 
         ggml_tensor * mask = nullptr;
         if (this->mask) {
-            mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]);
+            mask = ggml_new_tensor_2d(ctx, m_prec, ne[0], ne[1]);
             ggml_set_name(mask, "mask");
         }
 
@@ -4150,17 +4152,28 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             for (float scale : {1.0f, 0.1f}) {
                 for (int64_t ne0 : {16, 1024}) {
                     for (int64_t ne1 : {16, 1024}) {
-                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0,   ne1,   1, 1}, mask, scale, max_bias));
-                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
+                        if (mask) {
+                            for (ggml_type m_prec : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+                                test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0,   ne1,   1, 1}, mask, m_prec, scale, max_bias));
+                                test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, m_prec, scale, max_bias));
+                            }
+                        } else {
+                            /* The precision of mask here doesn't matter as boolean mask is false */
+                            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0,   ne1,   1, 1}, mask, GGML_TYPE_F32, scale, max_bias));
+                            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, GGML_TYPE_F32, scale, max_bias));
+                        }
                     }
                 }
             }
         }
     }
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true,  0.1f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true, GGML_TYPE_F32,  0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true, GGML_TYPE_F16,  0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, GGML_TYPE_F32, 0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, GGML_TYPE_F32,  0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, GGML_TYPE_F16,  0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, GGML_TYPE_F32,  0.1f, 8.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, GGML_TYPE_F16,  0.1f, 8.0f));
 
     for (float max_bias : {0.0f, 8.0f}) {
         for (float scale : {1.0f, 0.1f}) {
@@ -4296,13 +4309,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {8192, 512, 2, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {3072, 512, 2, 1}, {0, 2, 1, 3}));
 
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {1024, 1024, 10, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 1024, 10, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {256, 256, 20, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, 1.0f, 0.0f));
-    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {1024, 1024, 10, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 1024, 10, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {256, 256, 20, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, GGML_TYPE_F32, 1.0f, 0.0f));
 
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 10, 1, 1}));
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));

From f643120bad8ab3a753daa64aaac8288ee5800e06 Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Tue, 28 Jan 2025 11:42:32 +0100
Subject: [PATCH 266/376] docker: add perplexity and bench commands to full
 image (#11438)

Signed-off-by: rare-magma <rare-magma@posteo.eu>
---
 .devops/tools.sh | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/.devops/tools.sh b/.devops/tools.sh
index 9a86e6ea0..41a6b1e55 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -13,9 +13,13 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
     exec ./llama-cli "$@"
+elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
+    exec ./llama-bench "$@"
+elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
+    exec ./llama-perplexity "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
-    for i in `ls $1/$2/ggml-model-f16.bin*`; do
+    for i in $(ls $1/$2/ggml-model-f16.bin*); do
         if [ -f "${i/f16/q4_0}" ]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
@@ -30,6 +34,10 @@ else
     echo "Available commands: "
     echo "  --run (-r): Run a model previously converted into ggml"
     echo "              ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
+    echo "  --bench (-b): Benchmark the performance of the inference for various parameters."
+    echo "              ex: -m model.gguf"
+    echo "  --perplexity (-p): Measure the perplexity of a model over a given text."
+    echo "              ex: -m model.gguf -f file.txt"
     echo "  --convert (-c): Convert a llama model into ggml"
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"

From 4bf3119d61c1de5660025fd5a611effe503e3d2b Mon Sep 17 00:00:00 2001
From: someone13574 <81528246+someone13574@users.noreply.github.com>
Date: Tue, 28 Jan 2025 09:15:34 -0500
Subject: [PATCH 267/376] cmake : don't fail on `GGML_CPU=OFF` (#11457)

---
 ggml/src/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 8d2b948fb..566709135 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -308,7 +308,7 @@ if (GGML_CPU_ALL_VARIANTS)
         # MSVC doesn't support AMX
         ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
     endif()
-else ()
+elseif (GGML_CPU)
     ggml_add_cpu_backend_variant_impl("")
 endif()
 

From d7d1eccacccaa698c9232014b96a82b359595d6e Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Tue, 28 Jan 2025 15:17:25 +0100
Subject: [PATCH 268/376] docker: allow installing pip packages system-wide
 (#11437)

Signed-off-by: rare-magma <rare-magma@posteo.eu>
---
 .devops/vulkan.Dockerfile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index b5bd3b6d2..eabf832f8 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -55,8 +55,9 @@ RUN apt-get update \
     git \
     python3 \
     python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
+    python3-wheel \
+    && pip install --break-system-packages --upgrade setuptools \
+    && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

From 7fee2889e6565830631fbe76d47ef85cf8fd946a Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Tue, 28 Jan 2025 15:45:41 +0100
Subject: [PATCH 269/376] Add github protocol pulling and http:// (#11465)

As pulling protocols to llama-run

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 40 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 39 insertions(+), 1 deletion(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 5980a786f..40f2bcb00 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -673,6 +673,40 @@ class LlamaData {
         return download(blob_url, bn, true, headers);
     }
 
+    int github_dl(const std::string & model, const std::string & bn) {
+        std::string repository = model;
+        std::string branch     = "main";
+        size_t      at_pos     = model.find('@');
+        if (at_pos != std::string::npos) {
+            repository = model.substr(0, at_pos);
+            branch     = model.substr(at_pos + 1);
+        }
+
+        std::vector<std::string> repo_parts;
+        size_t                   start = 0;
+        for (size_t end = 0; (end = repository.find('/', start)) != std::string::npos; start = end + 1) {
+            repo_parts.push_back(repository.substr(start, end - start));
+        }
+
+        repo_parts.push_back(repository.substr(start));
+        if (repo_parts.size() < 3) {
+            printe("Invalid GitHub repository format\n");
+            return 1;
+        }
+
+        const std::string org          = repo_parts[0];
+        const std::string project      = repo_parts[1];
+        std::string       project_path = repo_parts[2];
+        for (size_t i = 3; i < repo_parts.size(); ++i) {
+            project_path += "/" + repo_parts[i];
+        }
+
+        const std::string url =
+            "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch + "/" + project_path;
+
+        return download(url, bn, true);
+    }
+
     std::string basename(const std::string & path) {
         const size_t pos = path.find_last_of("/\\");
         if (pos == std::string::npos) {
@@ -707,8 +741,12 @@ class LlamaData {
         } else if (string_starts_with(model_, "hf.co/")) {
             rm_until_substring(model_, "hf.co/");
             ret = huggingface_dl(model_, bn);
-        } else if (string_starts_with(model_, "https://")) {
+        } else if (string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) {
             ret = download(model_, bn, true);
+        } else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) {
+            rm_until_substring(model_, "github://");
+            rm_until_substring(model_, "github:");
+            ret = github_dl(model_, bn);
         } else {  // ollama:// or nothing
             rm_until_substring(model_, "://");
             ret = ollama_dl(model_, bn);

From cae9fb4361138b937464524eed907328731b81f6 Mon Sep 17 00:00:00 2001
From: Nikita Sarychev <42014488+sARY77@users.noreply.github.com>
Date: Tue, 28 Jan 2025 07:42:20 -0800
Subject: [PATCH 270/376] HIP: Only call rocblas_initialize on rocblas versions
 with the multiple instantation bug (#11080)

This disables the workaround on rocblas fixed versions (>=4.0.0) to eliminate the runtime cost and unnecessary VRAM allocation of loading all tensile objects.
---
 ggml/src/ggml-cuda/ggml-cuda.cu | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 402f37e85..de3f9c2ca 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -42,6 +42,7 @@
 #include <algorithm>
 #include <array>
 #include <atomic>
+#include <charconv>
 #include <cinttypes>
 #include <cstddef>
 #include <cstdint>
@@ -172,8 +173,25 @@ static ggml_cuda_device_info ggml_cuda_init() {
 #ifdef __HIP_PLATFORM_AMD__
     // Workaround for a rocBLAS bug when using multiple graphics cards:
     // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
-    rocblas_initialize();
-    CUDA_CHECK(cudaDeviceSynchronize());
+    {
+        int major_version = 0;
+        size_t version_length = 0;
+        if (rocblas_get_version_string_size(&version_length) == rocblas_status_success) {
+            std::string version(version_length, '\0');
+            if (rocblas_get_version_string(version.data(), version.size()) == rocblas_status_success) {
+                version.resize(::strlen(version.c_str()));
+                int parsed_value = 0;
+                if (std::from_chars(version.c_str(), version.c_str() + version.length(), parsed_value).ec == std::errc()) {
+                    major_version = parsed_value;
+                }
+            }
+        }
+        if (major_version < 4) {
+            GGML_LOG_DEBUG(GGML_CUDA_NAME " calling rocblas_initialize as a workaround for a rocBLAS bug\n");
+            rocblas_initialize();
+            CUDA_CHECK(cudaDeviceSynchronize());
+        }
+    }
 #endif
 
     ggml_cuda_device_info info = {};

From be5ef7963fcf14a9c77c963fdd3f7b606eacb498 Mon Sep 17 00:00:00 2001
From: uvos <philipp@uvos.xyz>
Date: Tue, 28 Jan 2025 23:06:32 +0100
Subject: [PATCH 271/376] HIP: Supress transformation warning in softmax.cu

loops with bounds not known at compile time can not be unrolled.
when ncols_template == 0, the bounds of the loop are not constexpr, thus llvm cant unroll the loops here.
---
 ggml/src/ggml-cuda/softmax.cu | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu
index 9aa4b8489..da377200e 100644
--- a/ggml/src/ggml-cuda/softmax.cu
+++ b/ggml/src/ggml-cuda/softmax.cu
@@ -13,6 +13,12 @@ __device__ float __forceinline__ t2f32<half>(half val) {
     return __half2float(val);
 }
 
+// When ncols_template == 0 the bounds for the loops in this function are not known and can't be unrolled.
+// As we want to keep pragma unroll for all other cases we supress the clang transformation warning here.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpass-failed"
+#endif
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
         const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y,
@@ -118,6 +124,9 @@ static __global__ void soft_max_f32(
         dst[col] = vals[col] * inv_sum;
     }
 }
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
 
 static __global__ void soft_max_back_f32(
         const float * grad, const float * dstf, float * dst, const int ncols, const float scale) {

From d0c08040b6c8bebeade7b8d5764df6cf901678d5 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Wed, 29 Jan 2025 00:02:56 +0100
Subject: [PATCH 272/376] ci : fix build CPU arm64 (#11472)

* ci : fix build CPU arm64

* failed, trying ubuntu 22

* vulkan: ubuntu 24

* vulkan : jammy --> noble
---
 .devops/vulkan.Dockerfile    | 4 ++--
 .github/workflows/docker.yml | 5 ++---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index eabf832f8..9064f3838 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget
 
 # Install Vulkan SDK and cURL
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
     apt update -y && \
     apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
 
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 6bf22eb66..6955a7dc8 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -28,7 +28,7 @@ jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
 
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     env:
       COMMIT_SHA: ${{ github.sha }}
     strategy:
@@ -36,8 +36,7 @@ jobs:
       matrix:
         config:
           # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
           - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}

From cf8cc856d7d02165bd08593b4757e1256a62d501 Mon Sep 17 00:00:00 2001
From: peidaqi <peidaqi@gmail.com>
Date: Tue, 28 Jan 2025 16:03:42 -0700
Subject: [PATCH 273/376] server : Fixed wrong function name in llamacpp server
 unit test (#11473)

The test_completion_stream_with_openai_library() function is actually with stream=False by default, and test_completion_with_openai_library() with stream=True
---
 examples/server/tests/unit/test_completion.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py
index c1fc12462..0ed5b99be 100644
--- a/examples/server/tests/unit/test_completion.py
+++ b/examples/server/tests/unit/test_completion.py
@@ -87,7 +87,7 @@ def test_completion_stream_vs_non_stream():
     assert content_stream == res_non_stream.body["content"]
 
 
-def test_completion_stream_with_openai_library():
+def test_completion_with_openai_library():
     global server
     server.start()
     client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
@@ -102,7 +102,7 @@ def test_completion_stream_with_openai_library():
     assert match_regex("(going|bed)+", res.choices[0].text)
 
 
-def test_completion_with_openai_library():
+def test_completion_stream_with_openai_library():
     global server
     server.start()
     client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")

From 794fe23f29fb40104975c91fe19f23798f7c726e Mon Sep 17 00:00:00 2001
From: Emreerdog <34742675+Emreerdog@users.noreply.github.com>
Date: Wed, 29 Jan 2025 02:22:06 +0300
Subject: [PATCH 274/376] cmake: add hints for locating ggml on Windows using
 Llama find-package (#11466)

---
 cmake/llama-config.cmake.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in
index 40ade96e5..90cbec5b6 100644
--- a/cmake/llama-config.cmake.in
+++ b/cmake/llama-config.cmake.in
@@ -9,7 +9,7 @@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
 set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
 set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
 
-find_package(ggml REQUIRED)
+find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
 
 find_library(llama_LIBRARY llama
     REQUIRED

From 325afb370a1a7b32b5fe46a749bc840c66db9765 Mon Sep 17 00:00:00 2001
From: Molly Sophia <mollysophia379@gmail.com>
Date: Wed, 29 Jan 2025 12:07:21 +0800
Subject: [PATCH 275/376] llama: fix missing k_cache store for rwkv6qwen2
 (#11445)

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
---
 src/llama.cpp | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 12e8f41fc..192b20a27 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -7700,17 +7700,13 @@ struct llm_build_context {
                 1
             );
 
+            struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
             ggml_build_forward_expand(
                 gf,
                 ggml_cpy(
                     ctx0,
-                    wkv_states,
-                    ggml_view_1d(
-                        ctx0,
-                        kv_self.v_l[il],
-                        hparams.n_embd_v_s() * n_seqs,
-                        hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
-                    )
+                    ggml_view_1d(ctx0, last_norm_att, n_embd * n_seqs, 0),
+                    ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
                 )
             );
 

From b636228c0ad0db95bf73008094c6145a05615cf6 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 29 Jan 2025 09:38:54 +0100
Subject: [PATCH 276/376] embedding : enable --no-warmup option (#11475)

This commit enables the `--no-warmup` option for the llama-embeddings.

The motivation for this change is to allow the user to disable the
warmup when running the the program.
---
 common/arg.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index a6226a34b..f5e9b294f 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -877,7 +877,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(

From d2e518e9b4231077013ffb0e5c4cc24a0c4c1b7d Mon Sep 17 00:00:00 2001
From: issixx <46835150+issixx@users.noreply.github.com>
Date: Fri, 17 Jan 2025 21:29:08 +0900
Subject: [PATCH 277/376] ggml-cpu : fix ggml_graph_compute_thread did not
 terminate on abort. (ggml/1065)

some threads kept looping and failed to terminate properly after an abort during CPU execution.

Co-authored-by: issi <issi@gmail.com>
---
 ggml/src/ggml-cpu/ggml-cpu.c | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 9e627da8c..e809f05d2 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1302,7 +1302,7 @@ struct ggml_threadpool {
     // these are atomic as an annotation for thread-sanitizer
     atomic_bool stop;         // Used for stopping the threadpool altogether
     atomic_bool pause;        // Used for pausing the threadpool or individual threads
-    atomic_bool abort;        // Used for aborting processing of a graph
+    atomic_int abort;         // Used for aborting processing of a graph
 
     struct ggml_compute_state * workers;   // per thread state
     int          n_threads_max; // number of threads in the pool
@@ -13851,14 +13851,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         /*.threadpool=*/ tp,
     };
 
-    for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) {
+    for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
         struct ggml_tensor * node = cgraph->nodes[node_n];
 
         ggml_compute_forward(&params, node);
 
         if (state->ith == 0 && cplan->abort_callback &&
                 cplan->abort_callback(cplan->abort_callback_data)) {
-            tp->abort = true;
+            atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);
             tp->ec    = GGML_STATUS_ABORTED;
         }
 
@@ -14031,7 +14031,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
         threadpool->current_chunk    = 0;
         threadpool->stop             = false;
         threadpool->pause            = tpp->paused;
-        threadpool->abort            = false;
+        threadpool->abort            = -1;
         threadpool->workers          = NULL;
         threadpool->n_threads_max    = tpp->n_threads;
         threadpool->n_threads_cur    = tpp->n_threads;
@@ -14110,7 +14110,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
         threadpool->cgraph           = cgraph;
         threadpool->cplan            = cplan;
         threadpool->current_chunk    = 0;
-        threadpool->abort            = false;
+        threadpool->abort            = -1;
         threadpool->ec               = GGML_STATUS_SUCCESS;
     }
 

From 1a0e87d29152cb9d4ce13b4ad64b0382c9ba1ab6 Mon Sep 17 00:00:00 2001
From: William Tambellini <wtambellini@sdl.com>
Date: Thu, 23 Jan 2025 11:59:08 -0800
Subject: [PATCH 278/376] ggml : add option to not print stack on abort
 (ggml/1081)

* Add option to not print stack on abort

Add option/envvar to disable stack printing on abort.
Also link some unittests with Threads to fix link errors on
ubuntu/g++11.

* Update ggml/src/ggml.c

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 ggml/src/ggml.c | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 92c4294c5..3b4861542 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -128,6 +128,10 @@ static void ggml_print_backtrace_symbols(void) {
 #endif
 
 static void ggml_print_backtrace(void) {
+    const char * GGML_NO_BACKTRACE = getenv("GGML_NO_BACKTRACE");
+    if (GGML_NO_BACKTRACE) {
+        return;
+    }
     char attach[32];
     snprintf(attach, sizeof(attach), "attach %d", getpid());
     int pid = fork();

From 815857791d3639a4d544d0a8cf25a49b0325c08c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 29 Jan 2025 11:25:29 +0200
Subject: [PATCH 279/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index cfba59d32..ddb9d817e 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-d92321c0d151fe73a47d89738c7c3091ac904297
+32f0b85987396945afea2291d5f4c5862434292b

From f0d4b29edfc633ec3ba6442482a6b43a5cd80a01 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Wed, 29 Jan 2025 12:23:10 +0100
Subject: [PATCH 280/376] Parse https://ollama.com/library/ syntax (#11480)

People search for ollama models using the web ui, this change
allows one to copy the url from the browser and for it to be
compatible with llama-run.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 40 ++++++++++++++++------------------------
 1 file changed, 16 insertions(+), 24 deletions(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 40f2bcb00..9cecae48c 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -674,36 +674,27 @@ class LlamaData {
     }
 
     int github_dl(const std::string & model, const std::string & bn) {
-        std::string repository = model;
-        std::string branch     = "main";
-        size_t      at_pos     = model.find('@');
+        std::string  repository = model;
+        std::string  branch     = "main";
+        const size_t at_pos     = model.find('@');
         if (at_pos != std::string::npos) {
             repository = model.substr(0, at_pos);
             branch     = model.substr(at_pos + 1);
         }
 
-        std::vector<std::string> repo_parts;
-        size_t                   start = 0;
-        for (size_t end = 0; (end = repository.find('/', start)) != std::string::npos; start = end + 1) {
-            repo_parts.push_back(repository.substr(start, end - start));
-        }
-
-        repo_parts.push_back(repository.substr(start));
+        const std::vector<std::string> repo_parts = string_split(repository, "/");
         if (repo_parts.size() < 3) {
             printe("Invalid GitHub repository format\n");
             return 1;
         }
 
-        const std::string org          = repo_parts[0];
-        const std::string project      = repo_parts[1];
-        std::string       project_path = repo_parts[2];
-        for (size_t i = 3; i < repo_parts.size(); ++i) {
-            project_path += "/" + repo_parts[i];
+        const std::string & org          = repo_parts[0];
+        const std::string & project      = repo_parts[1];
+        std::string         url          = "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch;
+        for (size_t i = 2; i < repo_parts.size(); ++i) {
+            url += "/" + repo_parts[i];
         }
 
-        const std::string url =
-            "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch + "/" + project_path;
-
         return download(url, bn, true);
     }
 
@@ -735,19 +726,20 @@ class LlamaData {
         }
 
         const std::string bn = basename(model_);
-        if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
+        if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://") ||
+            string_starts_with(model_, "hf.co/")) {
+            rm_until_substring(model_, "hf.co/");
             rm_until_substring(model_, "://");
             ret = huggingface_dl(model_, bn);
-        } else if (string_starts_with(model_, "hf.co/")) {
-            rm_until_substring(model_, "hf.co/");
-            ret = huggingface_dl(model_, bn);
-        } else if (string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) {
+        } else if ((string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) &&
+                   !string_starts_with(model_, "https://ollama.com/library/")) {
             ret = download(model_, bn, true);
         } else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) {
-            rm_until_substring(model_, "github://");
             rm_until_substring(model_, "github:");
+            rm_until_substring(model_, "://");
             ret = github_dl(model_, bn);
         } else {  // ollama:// or nothing
+            rm_until_substring(model_, "ollama.com/library/");
             rm_until_substring(model_, "://");
             ret = ollama_dl(model_, bn);
         }

From 2711d0215ff6a1ecb2202ac8c1f135bceed7057b Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Wed, 29 Jan 2025 09:26:50 -0600
Subject: [PATCH 281/376] vulkan: Catch pipeline creation failure and print an
 error message (#11436)

* vulkan: Catch pipeline creation failure and print an error message

Also, fix some warnings from my on-demand compile change.

* vulkan: fix pipeline creation logging
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a9d6b923c..6c7e60650 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -774,12 +774,12 @@ static uint32_t compile_count = 0;
 static std::mutex compile_count_mutex;
 static std::condition_variable compile_count_cond;
 
-static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint,
-                                         uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants,
-                                         uint32_t align, bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) {
-    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size <<
-                 ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align <<
-                 ", " << disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")");
+static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, size_t spv_size, const void* spv_data, const std::string entrypoint,
+                                         uint32_t parameter_count, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants,
+                                         bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) {
+    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << pipeline->name << ", " << entrypoint << ", " << parameter_count <<
+                 ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " <<
+                 disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")");
     GGML_ASSERT(parameter_count > 0);
     GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
 
@@ -864,7 +864,13 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         compute_pipeline_create_info.setPNext(&rci);
     }
 
-    pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+    try {
+        pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+    } catch (const vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Compute pipeline creation failed for " << pipeline->name << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        throw e;
+    }
     pipeline->compiled = true;
 
     {
@@ -1560,8 +1566,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
             }
             compile_count++;
         }
-        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint,
-                                      parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness, require_full_subgroups, required_subgroup_size));
+        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), spv_size, spv_data, entrypoint,
+                                      parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
     };
 
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)

From e51c47b401f8cb5f21630a05171e2529cde4d186 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 29 Jan 2025 16:34:18 +0100
Subject: [PATCH 282/376] server : update auto gen files comments [no ci]
 (#11484)

* server : update auto gen files comments

This commit updates the 'auto generated files' comments in server.cpp
and removes `deps.sh` from the comment.

The motivation for this change is that `deps.sh` was removed in
Commit 91c36c269bca75b2d08119c653512cd20b4ea2ba ("server : (web ui)
Various improvements, now use vite as bundler (#10599)").

* squash! server : update auto gen files comments [no ci]

Move comments about file generation to README.md.

* squash! server : update auto gen files comments [no ci]

Remove the comments in server.cpp that mention that information
can be found in the README.md file.
---
 examples/server/README.md  | 6 +++++-
 examples/server/server.cpp | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 5022de672..e788d8b59 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -236,9 +236,13 @@ npm i
 # to run the dev server
 npm run dev
 
-# to build the public/index.html
+# to build the public/index.html.gz
 npm run build
 ```
+After `public/index.html.gz` has been generated we need to generate the c++
+headers (like build/examples/server/index.html.gz.hpp) that will be included
+by server.cpp. This is done by building `llama-server` as described in the
+[build](#build) section above.
 
 NOTE: if you are using the vite dev server, you can change the API base URL to llama.cpp. To do that, run this code snippet in browser's console:
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b1cde2d7f..c5efbdb09 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -14,7 +14,7 @@
 // mime type for sending response
 #define MIMETYPE_JSON "application/json; charset=utf-8"
 
-// auto generated files (update with ./deps.sh)
+// auto generated files (see README.md for details)
 #include "index.html.gz.hpp"
 #include "loading.html.hpp"
 

From 66ee4f297cff3c7ce98b31dbc0ce909d41b9e408 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?R=C3=A9my=20Oudompheng?= <oudomphe@phare.normalesup.org>
Date: Wed, 29 Jan 2025 18:29:39 +0100
Subject: [PATCH 283/376] vulkan: implement initial support for IQ2 and IQ3
 quantizations (#11360)

* vulkan: initial support for IQ3_S

* vulkan: initial support for IQ3_XXS

* vulkan: initial support for IQ2_XXS

* vulkan: initial support for IQ2_XS

* vulkan: optimize Q3_K by removing branches

* vulkan: implement dequantize variants for coopmat2

* vulkan: initial support for IQ2_S

* vulkan: vertically realign code

* port failing dequant callbacks from mul_mm

* Fix array length mismatches

* vulkan: avoid using workgroup size before it is referenced

* tests: increase timeout for Vulkan llvmpipe backend

---------

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
---
 .github/workflows/build.yml                   |   3 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 157 +++-
 .../vulkan-shaders/copy_from_quant.comp       |   4 +-
 .../vulkan-shaders/copy_to_quant.comp         |   4 +-
 .../vulkan-shaders/dequant_funcs.comp         | 218 +++++-
 .../vulkan-shaders/dequant_funcs_cm2.comp     | 164 ++++
 .../vulkan-shaders/dequant_iq2_s.comp         |  44 ++
 .../vulkan-shaders/dequant_iq2_xs.comp        |  43 +
 .../vulkan-shaders/dequant_iq2_xxs.comp       |  48 ++
 .../vulkan-shaders/dequant_iq3_s.comp         |  39 +
 .../vulkan-shaders/dequant_iq3_xxs.comp       |  49 ++
 .../vulkan-shaders/dequant_iq4_nl.comp        |   2 +-
 .../vulkan-shaders/flash_attn_cm2.comp        |   4 +-
 .../vulkan-shaders/get_rows_quant.comp        |   4 +-
 .../vulkan-shaders/mul_mat_vec.comp           |   4 +-
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    | 122 ++-
 .../vulkan-shaders/mul_mm_cm2.comp            |   4 +-
 .../src/ggml-vulkan/vulkan-shaders/types.comp | 738 +++++++++++++++++-
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   5 +
 19 files changed, 1616 insertions(+), 40 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index cd8422f8a..7eaf9c460 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -346,7 +346,8 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          # This is using llvmpipe and runs slower than other backends
+          ctest -L main --verbose --timeout 1800
 
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 6c7e60650..9ca3959ab 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1616,6 +1616,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
         //CREATE_FA(GGML_TYPE_Q4_K, q4_k)
         //CREATE_FA(GGML_TYPE_Q5_K, q5_k)
         //CREATE_FA(GGML_TYPE_Q6_K, q6_k)
+        //CREATE_FA(GGML_TYPE_IQ2_XXS, iq2_xxs)
+        //CREATE_FA(GGML_TYPE_IQ2_XS, iq2_xs)
+        //CREATE_FA(GGML_TYPE_IQ2_S, iq2_s)
+        //CREATE_FA(GGML_TYPE_IQ3_XXS, iq3_xxs)
+        //CREATE_FA(GGML_TYPE_IQ3_S, iq3_s)
         CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl)
 #undef CREATE_FA
 
@@ -1644,7 +1649,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f16,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f16,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f16,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f16,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
 
         CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
@@ -1657,7 +1667,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
     } else
@@ -1705,7 +1720,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         } else {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
@@ -1718,7 +1738,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
@@ -1739,7 +1764,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             } else {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
@@ -1752,7 +1782,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             }
         }
 #undef CREATE_MM2
@@ -1796,7 +1831,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
         if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
@@ -1815,7 +1855,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc, matmul_id_iq4_nl_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM2
 #undef CREATE_MM
@@ -1851,7 +1896,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
         if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
@@ -1870,7 +1920,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM
     }
@@ -1901,7 +1956,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq2_xxs_f32_f32_len, mul_mat_vec_iq2_xxs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq2_xs_f32_f32_len,  mul_mat_vec_iq2_xs_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f32_f32_len,   mul_mat_vec_iq2_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f32_f32_len, mul_mat_vec_iq3_xxs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f32_f32_len,   mul_mat_vec_iq3_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f32_f32_len,  mul_mat_vec_iq4_nl_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
 
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
@@ -1915,7 +1975,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq2_xxs_f16_f32_len, mul_mat_vec_iq2_xxs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq2_xs_f16_f32_len,  mul_mat_vec_iq2_xs_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f16_f32_len,   mul_mat_vec_iq2_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f16_f32_len, mul_mat_vec_iq3_xxs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f16_f32_len,   mul_mat_vec_iq3_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f16_f32_len,  mul_mat_vec_iq4_nl_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -1930,7 +1995,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", mul_mat_vec_id_iq2_xxs_f32_len, mul_mat_vec_id_iq2_xxs_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  mul_mat_vec_id_iq2_xs_f32_len,  mul_mat_vec_id_iq2_xs_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   mul_mat_vec_id_iq2_s_f32_len,   mul_mat_vec_id_iq2_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", mul_mat_vec_id_iq3_xxs_f32_len, mul_mat_vec_id_iq3_xxs_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -1944,7 +2014,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL], "dequant_iq4_nl", dequant_iq4_nl_len, dequant_iq4_nl_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XXS], "dequant_iq2_xxs", dequant_iq2_xxs_len, dequant_iq2_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XS],  "dequant_iq2_xs",  dequant_iq2_xs_len,  dequant_iq2_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_S],   "dequant_iq2_s",   dequant_iq2_s_len,   dequant_iq2_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_XXS], "dequant_iq3_xxs", dequant_iq3_xxs_len, dequant_iq3_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S],   "dequant_iq3_s",   dequant_iq3_s_len,   dequant_iq3_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
 
     // get_rows
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -1954,7 +2029,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs", get_rows_iq2_xxs_len, get_rows_iq2_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XS],  "get_rows_iq2_xs",  get_rows_iq2_xs_len,  get_rows_iq2_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_S],   "get_rows_iq2_s",   get_rows_iq2_s_len,   get_rows_iq2_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs", get_rows_iq3_xxs_len, get_rows_iq3_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S],   "get_rows_iq3_s",   get_rows_iq3_s_len,   get_rows_iq3_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -1963,7 +2043,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs_f32", get_rows_iq2_xxs_f32_len, get_rows_iq2_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XS],  "get_rows_iq2_xs_f32",  get_rows_iq2_xs_f32_len,  get_rows_iq2_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_S],   "get_rows_iq2_s_f32",   get_rows_iq2_s_f32_len,   get_rows_iq2_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs_f32", get_rows_iq3_xxs_f32_len, get_rows_iq3_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S],   "get_rows_iq3_s_f32",   get_rows_iq3_s_f32_len,   get_rows_iq3_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
 
@@ -2890,6 +2975,11 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -2938,6 +3028,11 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -2969,6 +3064,11 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3012,6 +3112,11 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3038,6 +3143,11 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -7907,6 +8017,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_Q4_K:
                     case GGML_TYPE_Q5_K:
                     case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_IQ2_XXS:
+                    case GGML_TYPE_IQ2_XS:
+                    case GGML_TYPE_IQ2_S:
+                    case GGML_TYPE_IQ3_XXS:
+                    case GGML_TYPE_IQ3_S:
                     case GGML_TYPE_IQ4_NL:
                         break;
                     default:
@@ -7975,6 +8090,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 //case GGML_TYPE_Q4_K:
                 //case GGML_TYPE_Q5_K:
                 //case GGML_TYPE_Q6_K:
+                //case GGML_TYPE_IQ2_XXS:
+                //case GGML_TYPE_IQ2_XS:
+                //case GGML_TYPE_IQ2_S:
+                //case GGML_TYPE_IQ3_XXS:
+                //case GGML_TYPE_IQ3_S:
                 case GGML_TYPE_IQ4_NL:
                     break;
                 default:
@@ -7992,6 +8112,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_IQ2_XXS:
+                    case GGML_TYPE_IQ2_XS:
+                    case GGML_TYPE_IQ2_S:
+                    case GGML_TYPE_IQ3_XXS:
+                    case GGML_TYPE_IQ3_S:
                     case GGML_TYPE_IQ4_NL:
                         return true;
                     default:
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
index c09bf496b..aeae5400d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
@@ -12,8 +12,8 @@ layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
 #endif
 
 void main() {
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
     if (gl_LocalInvocationIndex.x != 0) {
         return;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
index ccf5b980a..d4b068e61 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
@@ -217,8 +217,8 @@ void quantize(uint dst_idx, uint src_idx)
 #endif
 
 void main() {
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
     if (gl_LocalInvocationIndex.x != 0) {
         return;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
index 91bb8f8db..ee6877531 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
@@ -88,6 +88,222 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
 }
 #endif
 
+#if defined(DATA_A_IQ2_XXS)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint ib8 = (iqs / 8) % 4;
+    const uint qs = data_a[a_offset + ib].qs[8 * ib32 + ib8];
+    // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale)
+    const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[4 * ib32 + 2],
+        data_a_packed16[a_offset + ib].qs[4 * ib32 + 3]));
+    const float db = 0.25 * (0.5 + (signs >> 28));
+    const uint sign7 = bitfieldExtract(signs, 7 * int(ib8), 7);
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq2xxs_grid[qs][(iqs % 8) / 4] >> (8 * (iqs % 4)));
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    return db * vec2(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0)
+    );
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint ib8 = (iqs / 8) % 4;
+    const uint qs = data_a[a_offset + ib].qs[8 * ib32 + ib8];
+    // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale)
+    const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[4 * ib32 + 2],
+        data_a_packed16[a_offset + ib].qs[4 * ib32 + 3]));
+    const float db = 0.25 * (0.5 + (signs >> 28));
+    const uint sign7 = bitfieldExtract(signs, 7 * int(ib8), 7);
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq2xxs_grid[qs][(iqs % 8) / 4] >> (8 * (iqs % 4)));
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    bool sign2 = (sign & 4) != 0;
+    bool sign3 = (sign & 8) != 0;
+    return db * vec4(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0),
+        grid.z * (sign2 ? -1.0 : 1.0),
+        grid.w * (sign3 ? -1.0 : 1.0)
+    );
+}
+#endif
+
+#if defined(DATA_A_IQ2_XS)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint scale = (data_a[a_offset + ib].scales[iqs / 32] >> (4 * ((iqs / 16) & 1))) & 0xf;
+    const uint qs = data_a[a_offset + ib].qs[iqs / 8];
+    const float db = 0.25 * (0.5 + scale);
+    const uint sign7 = qs >> 9;
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq2xs_grid[qs & 511][(iqs % 8) / 4] >> (8 * (iqs % 4)));
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    return db * vec2(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0)
+    );
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint scale = (data_a[a_offset + ib].scales[iqs / 32] >> (4 * ((iqs / 16) & 1))) & 0xf;
+    const uint qs = data_a[a_offset + ib].qs[iqs / 8];
+    const float db = 0.25 * (0.5 + scale);
+    const uint sign7 = qs >> 9;
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq2xs_grid[qs & 511][(iqs % 8) / 4] >> (8 * (iqs % 4)));
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    bool sign2 = (sign & 4) != 0;
+    bool sign3 = (sign & 8) != 0;
+    return db * vec4(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0),
+        grid.z * (sign2 ? -1.0 : 1.0),
+        grid.w * (sign3 ? -1.0 : 1.0)
+    );
+}
+#endif
+
+#if defined(DATA_A_IQ2_S)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint ib8 = iqs / 8;
+
+    const uint scale = (data_a[a_offset + ib].scales[ib32] >> (4 * ((iqs / 16) & 1))) & 0xf;
+    const uint qs = data_a[a_offset + ib].qs[ib8];
+    const uint qh = data_a[a_offset + ib].qh[ib32];
+    const uint qhshift = 2 * (ib8 % 4);
+    const uint sign = data_a[a_offset + ib].qs[QUANT_K / 8 + ib8] >> (iqs % 8);
+
+    const float db = 0.25 * (0.5 + scale);
+    const u8vec4 grid = unpack8(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(iqs % 8) / 4]);
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    return db * vec2(
+        grid[iqs % 4] * (sign0 ? -1.0 : 1.0),
+        grid[(iqs % 4) + 1] * (sign1 ? -1.0 : 1.0)
+    );
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint ib8 = iqs / 8;
+
+    const uint scale = (data_a[a_offset + ib].scales[ib32] >> (4 * ((iqs / 16) & 1))) & 0xf;
+    const uint qs = data_a[a_offset + ib].qs[ib8];
+    const uint qh = data_a[a_offset + ib].qh[ib32];
+    const uint qhshift = 2 * (ib8 % 4);
+    const uint sign = data_a[a_offset + ib].qs[QUANT_K / 8 + ib8] >> (iqs % 8);
+
+    const float db = 0.25 * (0.5 + scale);
+    const u8vec4 grid = unpack8(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(iqs % 8) / 4]);
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    bool sign2 = (sign & 4) != 0;
+    bool sign3 = (sign & 8) != 0;
+    return db * vec4(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0),
+        grid.z * (sign2 ? -1.0 : 1.0),
+        grid.w * (sign3 ? -1.0 : 1.0)
+    );
+}
+#endif
+
+#if defined(DATA_A_IQ3_XXS)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint ib4 = iqs / 4;
+    const uint ib32 = iqs / 32;
+    const uint is = QUANT_K / 4 + 4 * ib32;
+    const uint qs = data_a[a_offset + ib].qs[ib4];
+    // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale)
+    const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[is / 2],
+        data_a_packed16[a_offset + ib].qs[is / 2 + 1]));
+    const float db = 0.5 * (0.5 + (signs >> 28));
+    const uint sign7 = bitfieldExtract(signs, 7 * (int(ib4 / 2) % 4), 7);
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq3xxs_grid[qs] >> (8 * (iqs % 4)));
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    return db * vec2(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0)
+    );
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint ib4 = iqs / 4;
+    const uint ib32 = iqs / 32;
+    const uint is = QUANT_K / 4 + 4 * ib32;
+    const uint qs = data_a[a_offset + ib].qs[ib4];
+    const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[is / 2],
+        data_a_packed16[a_offset + ib].qs[is / 2 + 1]));
+    const float db = 0.5 * (0.5 + (signs >> 28));
+    const uint sign7 = bitfieldExtract(signs, 7 * (int(ib4 / 2) % 4), 7);
+    // Add parity bit
+    const uint sign8 = sign7 | (bitCount(sign7) << 7);
+    const uint sign = sign8 >> (iqs % 8);
+    const u8vec4 grid = unpack8(iq3xxs_grid[qs]);
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    bool sign2 = (sign & 4) != 0;
+    bool sign3 = (sign & 8) != 0;
+    return db * vec4(
+        grid.x * (sign0 ? -1.0 : 1.0),
+        grid.y * (sign1 ? -1.0 : 1.0),
+        grid.z * (sign2 ? -1.0 : 1.0),
+        grid.w * (sign3 ? -1.0 : 1.0)
+    );
+}
+#endif
+
+#if defined(DATA_A_IQ3_S)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint qs = data_a[a_offset + ib].qs[iqs / 4];
+    const uint qh = data_a[a_offset + ib].qh[iqs / 32];
+    const uint sign = data_a[a_offset + ib].signs[iqs / 8] >> (iqs % 8);
+    const uint scale = data_a[a_offset + ib].scales[iqs / 64];
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    const float db = 1 + 2 * ((scale >> (4 * ((iqs / 32) & 1))) & 0xf);
+    const uint32_t grid = iq3s_grid[qs | ((qh << (8 - ((iqs / 4) % 8))) & 256)] >> (8 * (iqs % 4));
+    return db * vec2(
+        int(grid & 0xFF) * (sign0 ? -1.0 : 1.0),
+        int((grid >> 8) & 0xFF) * (sign1 ? -1.0 : 1.0)
+    );
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint ib4 = iqs / 4;
+    const uint ib32 = iqs / 32;
+    const uint qs = data_a[a_offset + ib].qs[ib4];
+    const uint qh = data_a[a_offset + ib].qh[ib32];
+    const uint sign = data_a[a_offset + ib].signs[iqs / 8] >> (iqs % 8);
+    const uint scale = data_a[a_offset + ib].scales[ib32 / 2];
+    bool sign0 = (sign & 1) != 0;
+    bool sign1 = (sign & 2) != 0;
+    bool sign2 = (sign & 4) != 0;
+    bool sign3 = (sign & 8) != 0;
+    const float db = 1 + 2 * ((scale >> (4 * (ib32 & 1))) & 0xf);
+    const uint32_t grid = iq3s_grid[qs | ((qh << (8 - ib4 % 8)) & 256)] >> (8 * (iqs % 4));
+    return db * vec4(
+        int(grid & 0xFF) * (sign0 ? -1.0 : 1.0),
+        int((grid >> 8) & 0xFF) * (sign1 ? -1.0 : 1.0),
+        int((grid >> 16) & 0xFF) * (sign2 ? -1.0 : 1.0),
+        int((grid >> 24) & 0xFF) * (sign3 ? -1.0 : 1.0)
+    );
+}
+#endif
+
 #if defined(DATA_A_IQ4_NL)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
@@ -105,7 +321,7 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif
 
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
 vec2 get_dm(uint ib, uint a_offset) {
     return vec2(float(data_a[a_offset + ib].d), 0);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index 175e31fa7..974efd3f9 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -301,6 +301,160 @@ float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2
     return ret;
 }
 
+#if defined(DATA_A_IQ2_XXS)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XXS {
+   block_iq2_xxs block;
+};
+
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XXS_packed16 {
+   block_iq2_xxs_packed16 block;
+};
+
+float16_t dequantFuncIQ2_XXS(const in decodeBufIQ2_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    decodeBufIQ2_XXS_packed16 bl16 = decodeBufIQ2_XXS_packed16(bl);
+    const float16_t d = bl.block.d;
+    const uint idx = coordInBlock[1];
+
+    const uint ib32 = (idx & 0xE0) >> 5; // 0..7
+    const uint ib8 = (idx & 0x18) >> 3;  // 0..3
+    const uint iqs = 8 * ib32 + ib8;
+
+    const uint8_t qs = bl.block.qs[iqs];
+    const uint signscale = pack32(u16vec2(bl16.block.qs[4*ib32+2], bl16.block.qs[4*ib32+3]));
+
+    const float16_t dscale = bl.block.d * 0.25hf * (0.5hf + float16_t(signscale >> 28));
+    uint sign = bitfieldExtract(signscale, 7 * int(ib8), 7);
+    sign |= bitCount(sign) << 7;
+
+    const uint8_t g = unpack8(iq2xxs_grid[qs][(idx & 4) >> 2])[idx & 3];
+
+    float16_t ret = dscale * float16_t(g) * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
+
+    return ret;
+}
+#endif
+
+#if defined(DATA_A_IQ2_XS)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XS {
+   block_iq2_xs block;
+};
+
+float16_t dequantFuncIQ2_XS(const in decodeBufIQ2_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    const float16_t d = bl.block.d;
+    const uint idx = coordInBlock[1];
+
+    const uint is = (idx & 0xE0) >> 5;     // 0..8
+    const uint sshift = (idx & 0x10) >> 2; // 0,4
+    const uint iqs = (idx & 0xF8) >> 3;    // 0..63
+
+    const uint16_t qs = bl.block.qs[iqs];
+    const float16_t dscale = bl.block.d * 0.25hf * (0.5hf + float16_t((bl.block.scales[is] >> sshift) & 0xF));
+
+    uint sign = uint(qs >> 9);
+    sign |= bitCount(sign) << 7;
+    const uint8_t g = unpack8(iq2xs_grid[qs & 0x1FF][(idx & 4) >> 2])[idx & 3];
+
+    float16_t ret = dscale * float16_t(g) * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
+    return ret;
+}
+#endif
+
+#if defined(DATA_A_IQ2_S)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_S {
+   block_iq2_s block;
+};
+
+float16_t dequantFuncIQ2_S(const in decodeBufIQ2_S bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    uint idx = coordInBlock[1];
+    uint lsb = idx & 1;
+    idx /= 2;
+
+    const uint ib8 = (idx % 128) / 4; // 0..31
+    const uint ib32 = ib8 / 4;        // 0..7
+
+    const uint scale = (bl.block.scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+    const uint qs = bl.block.qs[ib8];
+    const uint qh = bl.block.qh[ib32];
+    const uint qhshift = 2 * (ib8 % 4);
+    const uint sign = bl.block.qs[QUANT_K / 8 + ib8] >> (2 * (idx % 4));
+
+    const float d = float(bl.block.d);
+    const float db = d * 0.25 * (0.5 + scale);
+    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+    const uint16_t grid = unpack16(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 2) >> 1])[idx & 1];
+    const vec2 v = db * vec2(sign01) * vec2(unpack8(grid));
+    return float16_t(v[lsb]);
+}
+#endif
+
+#if defined(DATA_A_IQ3_XXS)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_XXS {
+   block_iq3_xxs block;
+};
+
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_XXS_packed16 {
+   block_iq3_xxs_packed16 block;
+};
+
+float16_t dequantFuncIQ3_XXS(const in decodeBufIQ3_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    uint idx = coordInBlock[1];
+    uint lsb = idx & 1;
+    idx /= 2;
+
+    const uint iqs = (idx % 128) / 2;           // 0..63
+    const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values
+
+    const float d = float(bl.block.d);
+    const uint qs = bl.block.qs[iqs];
+    const uint signs = pack32(u8vec4(
+        bl.block.qs[is+0],
+        bl.block.qs[is+1],
+        bl.block.qs[is+2],
+        bl.block.qs[is+3]
+    ));
+    const float db = d * 0.5 * (0.5 + (signs >> 28));
+    const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7);
+    const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (2 * (idx % 4));
+    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+    const uint grid = iq3xxs_grid[qs] >> (16 * (idx & 1));
+    const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+    return float16_t(v[lsb]);
+}
+#endif
+
+#if defined(DATA_A_IQ3_S)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_S {
+   block_iq3_s block;
+};
+
+float16_t dequantFuncIQ3_S(const in decodeBufIQ3_S bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    uint idx = coordInBlock[1];
+    uint lsb = idx & 1;
+    idx /= 2;
+
+    const uint iqs = (idx % 128) / 2;           // 0..63
+    const uint iqh = iqs / 8;
+
+    const float d = float(bl.block.d);
+    const uint qs = bl.block.qs[iqs];
+    const uint qh = bl.block.qh[iqh];
+    const int8_t sign = int8_t(bl.block.signs[iqs / 2] >> (2 * (idx % 4)));
+    const uint scale = bl.block.scales[iqs / 16];
+    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign)));
+    const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf));
+    const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> (16 * (idx % 2));
+    const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+
+    return float16_t(v[lsb]);
+}
+#endif
+
+
 #if defined(DATA_A_IQ4_NL)
 layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL {
    block_iq4_nl block;
@@ -340,6 +494,16 @@ float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoor
 #define dequantFuncA dequantFuncQ5_K
 #elif defined(DATA_A_Q6_K)
 #define dequantFuncA dequantFuncQ6_K
+#elif defined(DATA_A_IQ2_XXS)
+#define dequantFuncA dequantFuncIQ2_XXS
+#elif defined(DATA_A_IQ2_XS)
+#define dequantFuncA dequantFuncIQ2_XS
+#elif defined(DATA_A_IQ2_S)
+#define dequantFuncA dequantFuncIQ2_S
+#elif defined(DATA_A_IQ3_XXS)
+#define dequantFuncA dequantFuncIQ3_XXS
+#elif defined(DATA_A_IQ3_S)
+#define dequantFuncA dequantFuncIQ3_S
 #elif defined(DATA_A_IQ4_NL)
 #define dequantFuncA dequantFuncIQ4_NL
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp
new file mode 100644
index 000000000..48f6b65bc
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp
@@ -0,0 +1,44 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq2_s data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 subblock (32 values with 2 scales)
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint ib32 = gl_LocalInvocationID.x % 8;
+    const uint b_idx = 256 * ib + 32 * ib32;
+
+    const float d = float(data_a[ib].d);
+    const vec2 scale = vec2(data_a[ib].scales[ib32] & 0xf, data_a[ib].scales[ib32] >> 4);
+    const vec2 db = d * (0.5 + scale) * 0.25;
+
+    uint qh = data_a[ib].qh[ib32];
+    [[unroll]] for (uint l = 0; l < 4; ++l) {
+        uint qs = data_a[ib].qs[4 * ib32 + l];
+        const uint8_t sign = data_a[ib].qs[QUANT_K / 8 + 4 * ib32 + l];
+        qs |= (qh << (8 - 2 * l)) & 0x300;
+        const uvec2 grid = iq2s_grid[qs & 511];
+        const u8vec4 grid0 = unpack8(grid.x);
+        const u8vec4 grid1 = unpack8(grid.y);
+        data_b[b_idx + 8 * l + 0] = D_TYPE(db[l/2] * grid0.x * ((sign & 1) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 1] = D_TYPE(db[l/2] * grid0.y * ((sign & 2) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 2] = D_TYPE(db[l/2] * grid0.z * ((sign & 4) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 3] = D_TYPE(db[l/2] * grid0.w * ((sign & 8) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 4] = D_TYPE(db[l/2] * grid1.x * ((sign & 16) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 5] = D_TYPE(db[l/2] * grid1.y * ((sign & 32) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 6] = D_TYPE(db[l/2] * grid1.z * ((sign & 64) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 7] = D_TYPE(db[l/2] * grid1.w * ((sign & 128) != 0 ? -1.0 : 1.0));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp
new file mode 100644
index 000000000..a08331c40
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp
@@ -0,0 +1,43 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq2_xs data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 subblock (32 values with 2 scales)
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint ib32 = gl_LocalInvocationID.x % 8;
+    const uint b_idx = 256 * ib + 32 * ib32;
+
+    const float d = float(data_a[ib].d);
+    const vec2 scale = vec2(data_a[ib].scales[ib32] & 0xf, data_a[ib].scales[ib32] >> 4);
+    const vec2 db = d * (0.5 + scale) * 0.25;
+
+    [[unroll]] for (uint l = 0; l < 4; ++l) {
+        uint16_t qs = data_a[ib].qs[4 * ib32 + l];
+        const uint sign7 = qs >> 9;
+        const uint sign8 = sign7 | (bitCount(sign7) << 7); // parity bit
+        const uvec2 grid = iq2xs_grid[qs & 511];
+        const u8vec4 grid0 = unpack8(grid.x);
+        const u8vec4 grid1 = unpack8(grid.y);
+        data_b[b_idx + 8 * l + 0] = D_TYPE(db[l/2] * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 1] = D_TYPE(db[l/2] * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 2] = D_TYPE(db[l/2] * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 3] = D_TYPE(db[l/2] * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 4] = D_TYPE(db[l/2] * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 5] = D_TYPE(db[l/2] * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 6] = D_TYPE(db[l/2] * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 7] = D_TYPE(db[l/2] * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp
new file mode 100644
index 000000000..e370690bc
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp
@@ -0,0 +1,48 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq2_xxs data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 scale block (32 values)
+    // Each block is described by 4 lattice indices, 4x7 sign bits and 4 scale bits
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint is = gl_LocalInvocationID.x % 8;
+    const uint b_idx = 256 * ib + 32 * is;
+
+    const float d = float(data_a[ib].d);
+    uint signscale = pack32(u8vec4(
+        data_a[ib].qs[8*is + 4],
+        data_a[ib].qs[8*is + 5],
+        data_a[ib].qs[8*is + 6],
+        data_a[ib].qs[8*is + 7]
+    ));
+    const float db = d * (0.5 + (signscale >> 28)) * 0.25;
+
+    [[unroll]] for (uint l = 0; l < 4; ++l) {
+        const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7);
+        const uint sign8 = sign7 | (bitCount(sign7) << 7); // parity bit
+        const uvec2 grid = iq2xxs_grid[data_a[ib].qs[8 * is + l]];
+        const u8vec4 grid0 = unpack8(grid.x);
+        const u8vec4 grid1 = unpack8(grid.y);
+        data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 1] = D_TYPE(db * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 2] = D_TYPE(db * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 3] = D_TYPE(db * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 4] = D_TYPE(db * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 5] = D_TYPE(db * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 6] = D_TYPE(db * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 7] = D_TYPE(db * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp
new file mode 100644
index 000000000..c3f4bca5d
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp
@@ -0,0 +1,39 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq3_s data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 scale nibble.
+    // Each block contains 4 scale bytes (8 scales) for 256 output values.
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint is = gl_LocalInvocationID.x % 8;
+    const uint b_idx = 256 * ib + 32 * is;
+
+    const float d = float(data_a[ib].d);
+    const float db = d * (1 + 2 * ((data_a[ib].scales[is] >> (4 * (is % 2))) & 0xf));
+
+    // We must produce 32 values using 4 sign bytes, 1 qh byte, 8 qs bytes.
+    uint qh = data_a[ib].qh[is];
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        uint qs = data_a[ib].qs[8 * is + l];
+        uint gidx = qs | ((qh << (8 - l)) & 256);
+        uint8_t signs = data_a[ib].signs[8 * is + l / 2] >> (4 * (l & 1));
+        u8vec4 grid = unpack8(iq3s_grid[gidx]);
+        data_b[b_idx + 4 * l + 0] = D_TYPE(db * grid.x * ((signs & 1) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 4 * l + 1] = D_TYPE(db * grid.y * ((signs & 2) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 4 * l + 2] = D_TYPE(db * grid.z * ((signs & 4) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 4 * l + 3] = D_TYPE(db * grid.w * ((signs & 8) != 0 ? -1.0 : 1.0));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp
new file mode 100644
index 000000000..a92b82961
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp
@@ -0,0 +1,49 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq3_xxs data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 scale block (32 values)
+    // 8 threads handle 1 superblock
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint is = gl_LocalInvocationID.x % 8;
+    const uint b_idx = 256 * ib + 32 * is;
+    const uint s_idx = QUANT_K / 4 + 4 * is;
+
+    const float d = float(data_a[ib].d);
+    uint signscale = pack32(u8vec4(
+        data_a[ib].qs[s_idx + 0],
+        data_a[ib].qs[s_idx + 1],
+        data_a[ib].qs[s_idx + 2],
+        data_a[ib].qs[s_idx + 3]
+    ));
+    const float db = d * (0.5 + (signscale >> 28)) * 0.5;
+
+    [[unroll]] for (uint l = 0; l < 4; ++l) {
+        const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7);
+        // Restore parity bit.
+        const uint sign8 = sign7 | (bitCount(sign7) << 7);
+        const u8vec4 grid0 = unpack8(iq3xxs_grid[data_a[ib].qs[8 * is + 2 * l]]);
+        const u8vec4 grid1 = unpack8(iq3xxs_grid[data_a[ib].qs[8 * is + 2 * l + 1]]);
+        data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 1] = D_TYPE(db * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 2] = D_TYPE(db * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 3] = D_TYPE(db * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 4] = D_TYPE(db * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 5] = D_TYPE(db * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 6] = D_TYPE(db * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0));
+        data_b[b_idx + 8 * l + 7] = D_TYPE(db * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp
index 8de14fc03..46d9ad15e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp
@@ -10,7 +10,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
 void main() {
     const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
 
-    init_iq4nl_shmem();
+    init_iq_shmem(gl_WorkGroupSize);
 
     const uint tid = gl_LocalInvocationID.x % 64;
     const uint il  = tid/32;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index 3735d0dbb..043a53023 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -104,8 +104,8 @@ ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE ele
 #endif
 
 void main() {
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
 #endif
 
     const uint32_t N = p.N;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
index 1426fde65..09dc43d8d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
@@ -12,8 +12,8 @@ void main() {
     const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
     const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
 
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
 #endif
 
     if (i00 >= p.ne00) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
index 53902858d..48156e7ba 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
@@ -133,8 +133,8 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 void main() {
     const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
 
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
 #endif
 
     // do NUM_ROWS at a time, unless there aren't enough remaining rows
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index 48122cbef..d0559aac8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -95,8 +95,8 @@ shared ACC_TYPE coopmat_stage[TM * TN * NUM_WARPS];
 #endif
 
 void main() {
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
 #endif
 
 #ifdef MUL_MAT_ID
@@ -343,10 +343,8 @@ void main() {
             const uint qsshift = halfsplit * 2;          // 0,2,4,6
             const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
 
-            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
-                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
-                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
-                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
+            const int8_t us = int8_t(((data_a[ib].scales[is % 8] >> (4 * int(is / 8))) & 0xF)
+                                  | (((data_a[ib].scales[8 + (is % 4)] >> (2 * int(is / 4))) & 3) << 4));
             const float dl = float(data_a[ib].d) * float(us - 32);
 
             buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
@@ -439,6 +437,118 @@ void main() {
 
             buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
             buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+#elif defined(DATA_A_IQ2_XXS)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint ib32 = (idx % 128) / 16;         // 0..7
+            const uint ib8 = (idx / 4) % 4;
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[8 * ib32 + ib8];
+            const uint signs = pack32(u8vec4(
+                data_a[ib].qs[8*ib32 + 4],
+                data_a[ib].qs[8*ib32 + 5],
+                data_a[ib].qs[8*ib32 + 6],
+                data_a[ib].qs[8*ib32 + 7]
+            ));
+            const float db = d * 0.25 * (0.5 + (signs >> 28));
+            const uint32_t sign7 = bitfieldExtract(signs, 7 * int(ib8), 7);
+            const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (2 * (idx % 4));
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+            const uint grid = iq2xxs_grid[qs][(idx % 4) / 2] >> (16 * (idx & 1));
+            const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ2_XS)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint ib32 = (idx % 128) / 16;         // 0..7
+            const uint ib8 = (idx / 4) % 4;             // 0..3
+
+            const float d = float(data_a[ib].d);
+            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+            const float db = d * 0.25 * (0.5 + scale);
+            const uint qs = data_a[ib].qs[4 * ib32 + ib8];
+            const uint sign7 = qs >> 9;
+            const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (2 * (idx % 4));
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+            const uint grid = iq2xs_grid[qs & 511][(idx % 4) / 2] >> (16 * (idx & 1));
+            const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ2_S)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;        // 2 values per idx
+            const uint ib8 = (idx % 128) / 4; // 0..31
+            const uint ib32 = ib8 / 4;        // 0..7
+
+            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+            const uint qs = data_a[ib].qs[ib8];
+            const uint qh = data_a[ib].qh[ib32];
+            const uint qhshift = 2 * (ib8 % 4);
+            const uint sign = data_a[ib].qs[QUANT_K / 8 + ib8] >> (2 * (idx % 4));
+
+            const float d = float(data_a[ib].d);
+            const float db = d * 0.25 * (0.5 + scale);
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+            const uint16_t grid = unpack16(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 2) >> 1])[idx & 1];
+            const vec2 v = db * vec2(sign01) * vec2(unpack8(grid));
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ3_XXS)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = (idx % 128) / 2;           // 0..63
+            const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[iqs];
+            const uint signs = pack32(u8vec4(
+                data_a[ib].qs[is+0],
+                data_a[ib].qs[is+1],
+                data_a[ib].qs[is+2],
+                data_a[ib].qs[is+3]
+            ));
+            const float db = d * 0.5 * (0.5 + (signs >> 28));
+            const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7);
+            const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (2 * (idx % 4));
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
+            const uint grid = iq3xxs_grid[qs] >> (16 * (idx & 1));
+            const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ3_S)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = (idx % 128) / 2;           // 0..63
+            const uint iqh = iqs / 8;
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[iqs];
+            const uint qh = data_a[ib].qh[iqh];
+            const int8_t sign = int8_t(data_a[ib].signs[iqs / 2] >> (2 * (idx % 4)));
+            const uint scale = data_a[ib].scales[iqs / 16];
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign)));
+            const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf));
+            const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> (16 * (idx % 2));
+            const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
 #elif defined(DATA_A_IQ4_NL)
             const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
             const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 57f9e7245..27c5d68b3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -106,8 +106,8 @@ D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem
 #endif
 
 void main() {
-#if defined(DATA_A_IQ4_NL)
-    init_iq4nl_shmem();
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+    init_iq_shmem(gl_WorkGroupSize);
 #endif
 
 #ifdef MUL_MAT_ID
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
index 1e35b6652..9e56a3530 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
@@ -294,6 +294,738 @@ struct block_q6_K_packed16
 
 // IQuants
 
+#define QUANT_K_IQ2_XXS 256
+#define QUANT_R_IQ2_XXS 1
+
+struct block_iq2_xxs
+{
+    float16_t d;
+    uint8_t qs[QUANT_K_IQ2_XXS/4];
+};
+
+struct block_iq2_xxs_packed16
+{
+    float16_t d;
+    uint16_t qs[QUANT_K_IQ2_XXS/8];
+};
+
+#if defined(DATA_A_IQ2_XXS)
+
+const uvec2[256] iq2xxs_grid_const = {
+    uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808),
+    uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x082b0808, 0x08080808),
+    uvec2(0x082b082b, 0x08080808), uvec2(0x082b2b08, 0x08080808), uvec2(0x082b2b2b, 0x08080808), uvec2(0x19080819, 0x08080808),
+    uvec2(0x19081908, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808),
+    uvec2(0x192b1908, 0x08080808), uvec2(0x2b080808, 0x08080808), uvec2(0x2b08082b, 0x08080808), uvec2(0x2b082b2b, 0x08080808),
+    uvec2(0x2b2b082b, 0x08080808), uvec2(0x08080819, 0x08080819), uvec2(0x08081908, 0x08080819), uvec2(0x08190808, 0x08080819),
+    uvec2(0x08191919, 0x08080819), uvec2(0x19080808, 0x08080819), uvec2(0x2b081908, 0x08080819), uvec2(0x2b192b08, 0x08080819),
+    uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b), uvec2(0x082b082b, 0x0808082b), uvec2(0x2b08082b, 0x0808082b),
+    uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x082b0819, 0x08081908),
+    uvec2(0x082b1908, 0x08081908), uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19082b08, 0x08081908),
+    uvec2(0x192b0808, 0x08081908), uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b190808, 0x08081908),
+    uvec2(0x2b2b1908, 0x08081908), uvec2(0x08080808, 0x08081919), uvec2(0x0808082b, 0x08081919), uvec2(0x08082b08, 0x08081919),
+    uvec2(0x082b0808, 0x08081919), uvec2(0x1908192b, 0x08081919), uvec2(0x192b2b19, 0x08081919), uvec2(0x2b080808, 0x08081919),
+    uvec2(0x2b190819, 0x08081919), uvec2(0x08082b19, 0x0808192b), uvec2(0x08190808, 0x0808192b), uvec2(0x19080808, 0x0808192b),
+    uvec2(0x2b081908, 0x0808192b), uvec2(0x2b2b1908, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x08081919, 0x08082b08),
+    uvec2(0x08082b08, 0x08082b08), uvec2(0x08191908, 0x08082b08), uvec2(0x082b2b08, 0x08082b08), uvec2(0x19080819, 0x08082b08),
+    uvec2(0x19081908, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x1919082b, 0x08082b08), uvec2(0x2b082b08, 0x08082b08),
+    uvec2(0x08081908, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x0808082b, 0x08082b2b), uvec2(0x08191908, 0x08082b2b),
+    uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x082b0819, 0x08190808),
+    uvec2(0x19080808, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x2b081908, 0x08190808), uvec2(0x2b190808, 0x08190808),
+    uvec2(0x2b191919, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x08082b08, 0x08190819), uvec2(0x082b0808, 0x08190819),
+    uvec2(0x19190808, 0x08190819), uvec2(0x19192b2b, 0x08190819), uvec2(0x2b080808, 0x08190819), uvec2(0x082b1908, 0x0819082b),
+    uvec2(0x19081919, 0x0819082b), uvec2(0x08080808, 0x08191908), uvec2(0x08082b08, 0x08191908), uvec2(0x082b0808, 0x08191908),
+    uvec2(0x082b1919, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x08192b08, 0x08191919),
+    uvec2(0x192b082b, 0x08191919), uvec2(0x08080808, 0x0819192b), uvec2(0x0819192b, 0x0819192b), uvec2(0x08080819, 0x08192b08),
+    uvec2(0x08081908, 0x08192b08), uvec2(0x08190808, 0x08192b08), uvec2(0x19080808, 0x08192b08), uvec2(0x2b080819, 0x08192b08),
+    uvec2(0x08080808, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x2b2b0808, 0x08192b19), uvec2(0x19190819, 0x08192b2b),
+    uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08082b2b, 0x082b0808), uvec2(0x19081908, 0x082b0808),
+    uvec2(0x192b0819, 0x082b0808), uvec2(0x2b080808, 0x082b0808), uvec2(0x2b08082b, 0x082b0808), uvec2(0x082b2b19, 0x082b0819),
+    uvec2(0x19082b08, 0x082b0819), uvec2(0x08080808, 0x082b082b), uvec2(0x0808082b, 0x082b082b), uvec2(0x08080819, 0x082b1908),
+    uvec2(0x08081908, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x19080808, 0x082b1908), uvec2(0x1919192b, 0x082b1908),
+    uvec2(0x08080808, 0x082b1919), uvec2(0x19080819, 0x082b1919), uvec2(0x192b1908, 0x082b1919), uvec2(0x2b190808, 0x082b192b),
+    uvec2(0x08082b08, 0x082b2b08), uvec2(0x082b0808, 0x082b2b08), uvec2(0x2b191908, 0x082b2b08), uvec2(0x19081908, 0x082b2b2b),
+    uvec2(0x08080819, 0x19080808), uvec2(0x08081908, 0x19080808), uvec2(0x08190808, 0x19080808), uvec2(0x08192b08, 0x19080808),
+    uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808), uvec2(0x19080808, 0x19080808), uvec2(0x19082b08, 0x19080808),
+    uvec2(0x1919192b, 0x19080808), uvec2(0x192b0808, 0x19080808), uvec2(0x2b080819, 0x19080808), uvec2(0x2b081908, 0x19080808),
+    uvec2(0x2b190808, 0x19080808), uvec2(0x08080808, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x192b0819, 0x19080819),
+    uvec2(0x2b080808, 0x19080819), uvec2(0x2b081919, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08190808, 0x1908082b),
+    uvec2(0x19082b08, 0x1908082b), uvec2(0x1919192b, 0x1908082b), uvec2(0x192b2b08, 0x1908082b), uvec2(0x08080808, 0x19081908),
+    uvec2(0x08082b08, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x2b080808, 0x19081908), uvec2(0x2b192b19, 0x19081908),
+    uvec2(0x0819082b, 0x19081919), uvec2(0x082b1908, 0x19081919), uvec2(0x08080808, 0x1908192b), uvec2(0x08080819, 0x19082b08),
+    uvec2(0x08081908, 0x19082b08), uvec2(0x08190808, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x19081919, 0x19082b08),
+    uvec2(0x08080808, 0x19082b19), uvec2(0x19192b08, 0x19082b19), uvec2(0x192b0819, 0x19082b19), uvec2(0x2b08082b, 0x19082b19),
+    uvec2(0x19081919, 0x19082b2b), uvec2(0x2b190808, 0x19082b2b), uvec2(0x08080808, 0x19190808), uvec2(0x08082b08, 0x19190808),
+    uvec2(0x08190819, 0x19190808), uvec2(0x08192b19, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x2b080808, 0x19190808),
+    uvec2(0x2b082b08, 0x19190808), uvec2(0x08081908, 0x19190819), uvec2(0x1908082b, 0x19190819), uvec2(0x2b2b1908, 0x19190819),
+    uvec2(0x2b190819, 0x1919082b), uvec2(0x2b190808, 0x19191908), uvec2(0x2b19082b, 0x19191908), uvec2(0x08082b2b, 0x19191919),
+    uvec2(0x08080819, 0x1919192b), uvec2(0x19191908, 0x1919192b), uvec2(0x08080808, 0x19192b08), uvec2(0x08190819, 0x19192b08),
+    uvec2(0x08192b19, 0x19192b08), uvec2(0x192b1908, 0x19192b08), uvec2(0x19080808, 0x19192b19), uvec2(0x08082b08, 0x19192b2b),
+    uvec2(0x08081908, 0x192b0808), uvec2(0x08190808, 0x192b0808), uvec2(0x19080808, 0x192b0808), uvec2(0x192b2b08, 0x192b0808),
+    uvec2(0x08080808, 0x192b0819), uvec2(0x19191919, 0x192b0819), uvec2(0x08192b08, 0x192b082b), uvec2(0x192b0808, 0x192b082b),
+    uvec2(0x08080808, 0x192b1908), uvec2(0x08081919, 0x192b1908), uvec2(0x08190808, 0x192b1919), uvec2(0x0819082b, 0x192b1919),
+    uvec2(0x2b081908, 0x192b1919), uvec2(0x1908082b, 0x192b2b08), uvec2(0x08080808, 0x2b080808), uvec2(0x0808082b, 0x2b080808),
+    uvec2(0x08082b2b, 0x2b080808), uvec2(0x19080819, 0x2b080808), uvec2(0x2b08082b, 0x2b080808), uvec2(0x08081908, 0x2b080819),
+    uvec2(0x08192b08, 0x2b080819), uvec2(0x19080808, 0x2b080819), uvec2(0x08190819, 0x2b08082b), uvec2(0x08080819, 0x2b081908),
+    uvec2(0x08081908, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x08191919, 0x2b081908), uvec2(0x19080808, 0x2b081908),
+    uvec2(0x192b0808, 0x2b081908), uvec2(0x08080808, 0x2b081919), uvec2(0x1908192b, 0x2b081919), uvec2(0x2b191908, 0x2b081919),
+    uvec2(0x08082b19, 0x2b08192b), uvec2(0x19080808, 0x2b08192b), uvec2(0x192b0808, 0x2b08192b), uvec2(0x0808082b, 0x2b082b08),
+    uvec2(0x08081908, 0x2b082b19), uvec2(0x08190819, 0x2b082b2b), uvec2(0x08081908, 0x2b190808), uvec2(0x08190808, 0x2b190808),
+    uvec2(0x082b1908, 0x2b190808), uvec2(0x19080808, 0x2b190808), uvec2(0x2b2b0819, 0x2b190808), uvec2(0x0819192b, 0x2b190819),
+    uvec2(0x2b080808, 0x2b190819), uvec2(0x19081919, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x082b082b, 0x2b191908),
+    uvec2(0x19081908, 0x2b191908), uvec2(0x19190819, 0x2b191919), uvec2(0x2b080819, 0x2b192b08), uvec2(0x082b0808, 0x2b192b19),
+    uvec2(0x0808082b, 0x2b2b0808), uvec2(0x19190808, 0x2b2b0808), uvec2(0x2b081919, 0x2b2b0808), uvec2(0x08082b19, 0x2b2b0819),
+    uvec2(0x08080808, 0x2b2b082b), uvec2(0x08192b08, 0x2b2b1908), uvec2(0x19190808, 0x2b2b2b08), uvec2(0x08081908, 0x2b2b2b19)
+};
+
+shared uvec2 iq2xxs_grid[256];
+
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < iq2xxs_grid.length(); i += wgsize.x) {
+        iq2xxs_grid[i] = iq2xxs_grid_const[i];
+    }
+    barrier();
+}
+
+#define QUANT_K QUANT_K_IQ2_XXS
+#define QUANT_R QUANT_R_IQ2_XXS
+#define A_TYPE block_iq2_xxs
+#define A_TYPE_PACKED16 block_iq2_xxs_packed16
+#endif
+
+#define QUANT_K_IQ2_XS 256
+#define QUANT_R_IQ2_XS 1
+
+struct block_iq2_xs
+{
+    float16_t d;
+    uint16_t qs[QUANT_K_IQ2_XS/8];
+    uint8_t scales[QUANT_K_IQ2_XS/32];
+};
+
+struct block_iq2_xs_packed16
+{
+    float16_t d;
+    uint16_t qs[QUANT_K_IQ2_XS/8];
+    uint16_t scales[QUANT_K_IQ2_XS/64];
+};
+
+#if defined(DATA_A_IQ2_XS)
+
+const uvec2 iq2xs_grid_const[512] = {
+    uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808),
+    uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x0819192b, 0x08080808),
+    uvec2(0x08192b19, 0x08080808), uvec2(0x082b0808, 0x08080808), uvec2(0x082b082b, 0x08080808), uvec2(0x082b1919, 0x08080808),
+    uvec2(0x082b2b08, 0x08080808), uvec2(0x19080819, 0x08080808), uvec2(0x19081908, 0x08080808), uvec2(0x1908192b, 0x08080808),
+    uvec2(0x19082b19, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x1919082b, 0x08080808), uvec2(0x19191919, 0x08080808),
+    uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808), uvec2(0x192b1908, 0x08080808), uvec2(0x2b080808, 0x08080808),
+    uvec2(0x2b08082b, 0x08080808), uvec2(0x2b081919, 0x08080808), uvec2(0x2b082b08, 0x08080808), uvec2(0x2b190819, 0x08080808),
+    uvec2(0x2b191908, 0x08080808), uvec2(0x2b192b19, 0x08080808), uvec2(0x2b2b0808, 0x08080808), uvec2(0x08080819, 0x08080819),
+    uvec2(0x08081908, 0x08080819), uvec2(0x0808192b, 0x08080819), uvec2(0x08082b19, 0x08080819), uvec2(0x08190808, 0x08080819),
+    uvec2(0x0819082b, 0x08080819), uvec2(0x08191919, 0x08080819), uvec2(0x08192b08, 0x08080819), uvec2(0x08192b2b, 0x08080819),
+    uvec2(0x082b0819, 0x08080819), uvec2(0x082b1908, 0x08080819), uvec2(0x19080808, 0x08080819), uvec2(0x1908082b, 0x08080819),
+    uvec2(0x19081919, 0x08080819), uvec2(0x19082b08, 0x08080819), uvec2(0x19190819, 0x08080819), uvec2(0x19191908, 0x08080819),
+    uvec2(0x192b0808, 0x08080819), uvec2(0x192b2b08, 0x08080819), uvec2(0x2b080819, 0x08080819), uvec2(0x2b081908, 0x08080819),
+    uvec2(0x2b190808, 0x08080819), uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b), uvec2(0x08081919, 0x0808082b),
+    uvec2(0x08082b08, 0x0808082b), uvec2(0x08190819, 0x0808082b), uvec2(0x08191908, 0x0808082b), uvec2(0x082b0808, 0x0808082b),
+    uvec2(0x19080819, 0x0808082b), uvec2(0x19081908, 0x0808082b), uvec2(0x19190808, 0x0808082b), uvec2(0x19191919, 0x0808082b),
+    uvec2(0x2b080808, 0x0808082b), uvec2(0x2b082b2b, 0x0808082b), uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908),
+    uvec2(0x0808192b, 0x08081908), uvec2(0x08082b19, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x0819082b, 0x08081908),
+    uvec2(0x08191919, 0x08081908), uvec2(0x08192b08, 0x08081908), uvec2(0x082b0819, 0x08081908), uvec2(0x082b1908, 0x08081908),
+    uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19081919, 0x08081908), uvec2(0x19082b08, 0x08081908),
+    uvec2(0x19190819, 0x08081908), uvec2(0x19191908, 0x08081908), uvec2(0x1919192b, 0x08081908), uvec2(0x192b0808, 0x08081908),
+    uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b190808, 0x08081908), uvec2(0x08080808, 0x08081919),
+    uvec2(0x0808082b, 0x08081919), uvec2(0x08081919, 0x08081919), uvec2(0x08082b08, 0x08081919), uvec2(0x08190819, 0x08081919),
+    uvec2(0x08191908, 0x08081919), uvec2(0x082b0808, 0x08081919), uvec2(0x19080819, 0x08081919), uvec2(0x19081908, 0x08081919),
+    uvec2(0x19190808, 0x08081919), uvec2(0x192b0819, 0x08081919), uvec2(0x2b080808, 0x08081919), uvec2(0x08080819, 0x0808192b),
+    uvec2(0x08081908, 0x0808192b), uvec2(0x08190808, 0x0808192b), uvec2(0x082b192b, 0x0808192b), uvec2(0x19080808, 0x0808192b),
+    uvec2(0x1908082b, 0x0808192b), uvec2(0x2b081908, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x0808082b, 0x08082b08),
+    uvec2(0x08081919, 0x08082b08), uvec2(0x08082b08, 0x08082b08), uvec2(0x08082b2b, 0x08082b08), uvec2(0x08190819, 0x08082b08),
+    uvec2(0x08191908, 0x08082b08), uvec2(0x082b0808, 0x08082b08), uvec2(0x082b1919, 0x08082b08), uvec2(0x19080819, 0x08082b08),
+    uvec2(0x19081908, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x19192b08, 0x08082b08), uvec2(0x2b080808, 0x08082b08),
+    uvec2(0x2b2b0808, 0x08082b08), uvec2(0x2b2b2b2b, 0x08082b08), uvec2(0x08080819, 0x08082b19), uvec2(0x08081908, 0x08082b19),
+    uvec2(0x08190808, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x2b080819, 0x08082b19), uvec2(0x2b082b19, 0x08082b19),
+    uvec2(0x08080808, 0x08082b2b), uvec2(0x082b0808, 0x08082b2b), uvec2(0x082b2b08, 0x08082b2b), uvec2(0x2b19192b, 0x08082b2b),
+    uvec2(0x2b2b0808, 0x08082b2b), uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808), uvec2(0x0808192b, 0x08190808),
+    uvec2(0x08082b19, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x0819082b, 0x08190808), uvec2(0x08191919, 0x08190808),
+    uvec2(0x08192b08, 0x08190808), uvec2(0x082b0819, 0x08190808), uvec2(0x082b1908, 0x08190808), uvec2(0x19080808, 0x08190808),
+    uvec2(0x1908082b, 0x08190808), uvec2(0x19081919, 0x08190808), uvec2(0x19082b08, 0x08190808), uvec2(0x19190819, 0x08190808),
+    uvec2(0x19191908, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x192b2b2b, 0x08190808), uvec2(0x2b080819, 0x08190808),
+    uvec2(0x2b081908, 0x08190808), uvec2(0x2b190808, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x0808082b, 0x08190819),
+    uvec2(0x08081919, 0x08190819), uvec2(0x08082b08, 0x08190819), uvec2(0x08190819, 0x08190819), uvec2(0x08191908, 0x08190819),
+    uvec2(0x082b0808, 0x08190819), uvec2(0x19080819, 0x08190819), uvec2(0x19081908, 0x08190819), uvec2(0x19190808, 0x08190819),
+    uvec2(0x2b080808, 0x08190819), uvec2(0x2b191908, 0x08190819), uvec2(0x2b19192b, 0x08190819), uvec2(0x08080819, 0x0819082b),
+    uvec2(0x08081908, 0x0819082b), uvec2(0x0808192b, 0x0819082b), uvec2(0x08190808, 0x0819082b), uvec2(0x19080808, 0x0819082b),
+    uvec2(0x192b0808, 0x0819082b), uvec2(0x08080808, 0x08191908), uvec2(0x0808082b, 0x08191908), uvec2(0x08081919, 0x08191908),
+    uvec2(0x08082b08, 0x08191908), uvec2(0x08190819, 0x08191908), uvec2(0x08191908, 0x08191908), uvec2(0x082b0808, 0x08191908),
+    uvec2(0x19080819, 0x08191908), uvec2(0x19081908, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x19190808, 0x08191908),
+    uvec2(0x192b1908, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x08080819, 0x08191919), uvec2(0x08081908, 0x08191919),
+    uvec2(0x08190808, 0x08191919), uvec2(0x19080808, 0x08191919), uvec2(0x08080808, 0x0819192b), uvec2(0x08191908, 0x0819192b),
+    uvec2(0x19082b19, 0x0819192b), uvec2(0x08080819, 0x08192b08), uvec2(0x08081908, 0x08192b08), uvec2(0x08190808, 0x08192b08),
+    uvec2(0x0819082b, 0x08192b08), uvec2(0x19080808, 0x08192b08), uvec2(0x19191908, 0x08192b08), uvec2(0x2b08192b, 0x08192b08),
+    uvec2(0x08080808, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x192b192b, 0x08192b19), uvec2(0x19190819, 0x08192b2b),
+    uvec2(0x2b2b2b19, 0x08192b2b), uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08081919, 0x082b0808),
+    uvec2(0x08082b08, 0x082b0808), uvec2(0x08082b2b, 0x082b0808), uvec2(0x08190819, 0x082b0808), uvec2(0x08191908, 0x082b0808),
+    uvec2(0x082b0808, 0x082b0808), uvec2(0x19080819, 0x082b0808), uvec2(0x19081908, 0x082b0808), uvec2(0x19190808, 0x082b0808),
+    uvec2(0x2b080808, 0x082b0808), uvec2(0x2b2b0808, 0x082b0808), uvec2(0x08080819, 0x082b0819), uvec2(0x08081908, 0x082b0819),
+    uvec2(0x08190808, 0x082b0819), uvec2(0x19080808, 0x082b0819), uvec2(0x19082b08, 0x082b0819), uvec2(0x192b1919, 0x082b0819),
+    uvec2(0x08080808, 0x082b082b), uvec2(0x082b082b, 0x082b082b), uvec2(0x2b080808, 0x082b082b), uvec2(0x2b2b2b08, 0x082b082b),
+    uvec2(0x08080819, 0x082b1908), uvec2(0x08081908, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x082b2b19, 0x082b1908),
+    uvec2(0x19080808, 0x082b1908), uvec2(0x08080808, 0x082b1919), uvec2(0x19080819, 0x082b1919), uvec2(0x1919082b, 0x082b1919),
+    uvec2(0x2b192b19, 0x082b1919), uvec2(0x08080819, 0x082b192b), uvec2(0x08192b2b, 0x082b192b), uvec2(0x2b2b192b, 0x082b192b),
+    uvec2(0x08080808, 0x082b2b08), uvec2(0x08082b08, 0x082b2b08), uvec2(0x08082b2b, 0x082b2b08), uvec2(0x082b0808, 0x082b2b08),
+    uvec2(0x19191919, 0x082b2b08), uvec2(0x2b082b08, 0x082b2b08), uvec2(0x2b2b082b, 0x082b2b08), uvec2(0x192b2b08, 0x082b2b19),
+    uvec2(0x2b190808, 0x082b2b19), uvec2(0x08082b08, 0x082b2b2b), uvec2(0x082b0808, 0x082b2b2b), uvec2(0x2b08082b, 0x082b2b2b),
+    uvec2(0x2b082b08, 0x082b2b2b), uvec2(0x2b082b2b, 0x082b2b2b), uvec2(0x08080819, 0x19080808), uvec2(0x08081908, 0x19080808),
+    uvec2(0x0808192b, 0x19080808), uvec2(0x08082b19, 0x19080808), uvec2(0x08190808, 0x19080808), uvec2(0x0819082b, 0x19080808),
+    uvec2(0x08191919, 0x19080808), uvec2(0x08192b08, 0x19080808), uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808),
+    uvec2(0x19080808, 0x19080808), uvec2(0x1908082b, 0x19080808), uvec2(0x19081919, 0x19080808), uvec2(0x19082b08, 0x19080808),
+    uvec2(0x19082b2b, 0x19080808), uvec2(0x19190819, 0x19080808), uvec2(0x19191908, 0x19080808), uvec2(0x192b0808, 0x19080808),
+    uvec2(0x192b1919, 0x19080808), uvec2(0x2b080819, 0x19080808), uvec2(0x2b081908, 0x19080808), uvec2(0x2b190808, 0x19080808),
+    uvec2(0x08080808, 0x19080819), uvec2(0x0808082b, 0x19080819), uvec2(0x08081919, 0x19080819), uvec2(0x08082b08, 0x19080819),
+    uvec2(0x08190819, 0x19080819), uvec2(0x08191908, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x19080819, 0x19080819),
+    uvec2(0x19081908, 0x19080819), uvec2(0x19190808, 0x19080819), uvec2(0x2b080808, 0x19080819), uvec2(0x2b081919, 0x19080819),
+    uvec2(0x2b2b082b, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08081908, 0x1908082b), uvec2(0x08190808, 0x1908082b),
+    uvec2(0x0819082b, 0x1908082b), uvec2(0x082b2b19, 0x1908082b), uvec2(0x19080808, 0x1908082b), uvec2(0x08080808, 0x19081908),
+    uvec2(0x0808082b, 0x19081908), uvec2(0x08081919, 0x19081908), uvec2(0x08082b08, 0x19081908), uvec2(0x08190819, 0x19081908),
+    uvec2(0x08191908, 0x19081908), uvec2(0x08192b19, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x19080819, 0x19081908),
+    uvec2(0x19081908, 0x19081908), uvec2(0x19190808, 0x19081908), uvec2(0x2b080808, 0x19081908), uvec2(0x2b191908, 0x19081908),
+    uvec2(0x08080819, 0x19081919), uvec2(0x08081908, 0x19081919), uvec2(0x08190808, 0x19081919), uvec2(0x082b1908, 0x19081919),
+    uvec2(0x19080808, 0x19081919), uvec2(0x2b192b2b, 0x19081919), uvec2(0x08080808, 0x1908192b), uvec2(0x08082b2b, 0x1908192b),
+    uvec2(0x19081908, 0x1908192b), uvec2(0x19190808, 0x1908192b), uvec2(0x08080819, 0x19082b08), uvec2(0x08081908, 0x19082b08),
+    uvec2(0x08190808, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x19081919, 0x19082b08), uvec2(0x19191908, 0x19082b08),
+    uvec2(0x192b082b, 0x19082b08), uvec2(0x08080808, 0x19082b19), uvec2(0x08190819, 0x19082b19), uvec2(0x19081908, 0x19082b19),
+    uvec2(0x19190808, 0x19082b19), uvec2(0x192b2b19, 0x19082b19), uvec2(0x08081908, 0x19082b2b), uvec2(0x08080808, 0x19190808),
+    uvec2(0x0808082b, 0x19190808), uvec2(0x08081919, 0x19190808), uvec2(0x08082b08, 0x19190808), uvec2(0x08190819, 0x19190808),
+    uvec2(0x08191908, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x082b2b08, 0x19190808), uvec2(0x19080819, 0x19190808),
+    uvec2(0x19081908, 0x19190808), uvec2(0x19190808, 0x19190808), uvec2(0x2b080808, 0x19190808), uvec2(0x08080819, 0x19190819),
+    uvec2(0x08081908, 0x19190819), uvec2(0x08190808, 0x19190819), uvec2(0x08191919, 0x19190819), uvec2(0x19080808, 0x19190819),
+    uvec2(0x1908082b, 0x19190819), uvec2(0x08080808, 0x1919082b), uvec2(0x19081908, 0x1919082b), uvec2(0x2b2b2b2b, 0x1919082b),
+    uvec2(0x08080819, 0x19191908), uvec2(0x08081908, 0x19191908), uvec2(0x08190808, 0x19191908), uvec2(0x082b0819, 0x19191908),
+    uvec2(0x19080808, 0x19191908), uvec2(0x192b0808, 0x19191908), uvec2(0x2b080819, 0x19191908), uvec2(0x2b2b0819, 0x19191908),
+    uvec2(0x08080808, 0x19191919), uvec2(0x08082b08, 0x19191919), uvec2(0x2b080808, 0x19191919), uvec2(0x2b082b08, 0x19191919),
+    uvec2(0x082b0819, 0x1919192b), uvec2(0x192b2b08, 0x1919192b), uvec2(0x2b2b0819, 0x1919192b), uvec2(0x08080808, 0x19192b08),
+    uvec2(0x08191908, 0x19192b08), uvec2(0x19080819, 0x19192b08), uvec2(0x19190808, 0x19192b08), uvec2(0x2b192b19, 0x19192b08),
+    uvec2(0x08192b2b, 0x19192b19), uvec2(0x19080808, 0x19192b19), uvec2(0x1908082b, 0x19192b19), uvec2(0x2b081919, 0x19192b2b),
+    uvec2(0x08080819, 0x192b0808), uvec2(0x08081908, 0x192b0808), uvec2(0x08190808, 0x192b0808), uvec2(0x19080808, 0x192b0808),
+    uvec2(0x19191908, 0x192b0808), uvec2(0x192b082b, 0x192b0808), uvec2(0x2b08192b, 0x192b0808), uvec2(0x2b2b2b19, 0x192b0808),
+    uvec2(0x08080808, 0x192b0819), uvec2(0x082b1908, 0x192b082b), uvec2(0x19082b2b, 0x192b082b), uvec2(0x2b19082b, 0x192b082b),
+    uvec2(0x08080808, 0x192b1908), uvec2(0x0819192b, 0x192b1908), uvec2(0x08190808, 0x192b1919), uvec2(0x19080808, 0x192b1919),
+    uvec2(0x19081919, 0x192b1919), uvec2(0x2b2b1908, 0x192b1919), uvec2(0x08080819, 0x192b2b08), uvec2(0x192b2b2b, 0x192b2b08),
+    uvec2(0x082b1919, 0x192b2b19), uvec2(0x0808192b, 0x192b2b2b), uvec2(0x19191908, 0x192b2b2b), uvec2(0x192b082b, 0x192b2b2b),
+    uvec2(0x08080808, 0x2b080808), uvec2(0x0808082b, 0x2b080808), uvec2(0x08081919, 0x2b080808), uvec2(0x08082b08, 0x2b080808),
+    uvec2(0x08190819, 0x2b080808), uvec2(0x08191908, 0x2b080808), uvec2(0x082b0808, 0x2b080808), uvec2(0x082b2b2b, 0x2b080808),
+    uvec2(0x19080819, 0x2b080808), uvec2(0x19081908, 0x2b080808), uvec2(0x19190808, 0x2b080808), uvec2(0x2b080808, 0x2b080808),
+    uvec2(0x2b08082b, 0x2b080808), uvec2(0x2b2b2b08, 0x2b080808), uvec2(0x2b2b2b2b, 0x2b080808), uvec2(0x08080819, 0x2b080819),
+    uvec2(0x08081908, 0x2b080819), uvec2(0x0808192b, 0x2b080819), uvec2(0x08190808, 0x2b080819), uvec2(0x19080808, 0x2b080819),
+    uvec2(0x19190819, 0x2b080819), uvec2(0x19192b19, 0x2b080819), uvec2(0x08080808, 0x2b08082b), uvec2(0x082b0808, 0x2b08082b),
+    uvec2(0x2b080808, 0x2b08082b), uvec2(0x2b08082b, 0x2b08082b), uvec2(0x2b2b0808, 0x2b08082b), uvec2(0x2b2b2b08, 0x2b08082b),
+    uvec2(0x08080819, 0x2b081908), uvec2(0x08081908, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x0819082b, 0x2b081908),
+    uvec2(0x08191919, 0x2b081908), uvec2(0x19080808, 0x2b081908), uvec2(0x192b0808, 0x2b081908), uvec2(0x2b082b19, 0x2b081908),
+    uvec2(0x08080808, 0x2b081919), uvec2(0x19081908, 0x2b081919), uvec2(0x2b2b1919, 0x2b081919), uvec2(0x08192b08, 0x2b08192b),
+    uvec2(0x192b2b2b, 0x2b08192b), uvec2(0x08080808, 0x2b082b08), uvec2(0x08082b08, 0x2b082b08), uvec2(0x082b1919, 0x2b082b08),
+    uvec2(0x19192b2b, 0x2b082b08), uvec2(0x2b080808, 0x2b082b08), uvec2(0x2b08082b, 0x2b082b08), uvec2(0x2b2b2b08, 0x2b082b08),
+    uvec2(0x0808192b, 0x2b082b19), uvec2(0x082b082b, 0x2b082b2b), uvec2(0x2b080808, 0x2b082b2b), uvec2(0x2b082b08, 0x2b082b2b),
+    uvec2(0x2b19192b, 0x2b082b2b), uvec2(0x2b2b2b08, 0x2b082b2b), uvec2(0x08080819, 0x2b190808), uvec2(0x08081908, 0x2b190808),
+    uvec2(0x08190808, 0x2b190808), uvec2(0x19080808, 0x2b190808), uvec2(0x1919192b, 0x2b190808), uvec2(0x2b081908, 0x2b190808),
+    uvec2(0x08080808, 0x2b190819), uvec2(0x082b082b, 0x2b190819), uvec2(0x192b1908, 0x2b190819), uvec2(0x1919192b, 0x2b19082b),
+    uvec2(0x2b082b19, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x08081919, 0x2b191908), uvec2(0x19081908, 0x2b191908),
+    uvec2(0x19190808, 0x2b191908), uvec2(0x19192b08, 0x2b191908), uvec2(0x082b2b19, 0x2b191919), uvec2(0x2b190808, 0x2b191919),
+    uvec2(0x2b19082b, 0x2b191919), uvec2(0x19080819, 0x2b19192b), uvec2(0x19190819, 0x2b192b08), uvec2(0x2b2b192b, 0x2b192b08),
+    uvec2(0x19082b19, 0x2b192b19), uvec2(0x08191919, 0x2b192b2b), uvec2(0x192b0808, 0x2b192b2b), uvec2(0x08080808, 0x2b2b0808),
+    uvec2(0x0808082b, 0x2b2b0808), uvec2(0x08082b08, 0x2b2b0808), uvec2(0x08082b2b, 0x2b2b0808), uvec2(0x082b0808, 0x2b2b0808),
+    uvec2(0x082b2b2b, 0x2b2b0808), uvec2(0x2b2b0808, 0x2b2b0808), uvec2(0x19190819, 0x2b2b0819), uvec2(0x19192b19, 0x2b2b0819),
+    uvec2(0x2b2b192b, 0x2b2b0819), uvec2(0x08080808, 0x2b2b082b), uvec2(0x0808082b, 0x2b2b082b), uvec2(0x08082b08, 0x2b2b082b),
+    uvec2(0x082b2b2b, 0x2b2b082b), uvec2(0x2b080808, 0x2b2b082b), uvec2(0x2b2b0808, 0x2b2b082b), uvec2(0x19080808, 0x2b2b1908),
+    uvec2(0x2b191919, 0x2b2b1908), uvec2(0x192b1919, 0x2b2b192b), uvec2(0x2b192b08, 0x2b2b192b), uvec2(0x08082b2b, 0x2b2b2b08),
+    uvec2(0x082b0808, 0x2b2b2b08), uvec2(0x082b082b, 0x2b2b2b08), uvec2(0x082b2b08, 0x2b2b2b08), uvec2(0x2b2b0808, 0x2b2b2b08),
+    uvec2(0x2b2b2b08, 0x2b2b2b08), uvec2(0x08081908, 0x2b2b2b19), uvec2(0x2b081908, 0x2b2b2b19), uvec2(0x2b08192b, 0x2b2b2b19),
+    uvec2(0x082b2b08, 0x2b2b2b2b), uvec2(0x082b2b2b, 0x2b2b2b2b), uvec2(0x2b190819, 0x2b2b2b2b), uvec2(0x2b2b2b2b, 0x2b2b2b2b),
+};
+
+shared uvec2 iq2xs_grid[512];
+
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < iq2xs_grid.length(); i += wgsize.x) {
+        iq2xs_grid[i] = iq2xs_grid_const[i];
+    }
+    barrier();
+}
+
+#define QUANT_K QUANT_K_IQ2_XS
+#define QUANT_R QUANT_R_IQ2_XS
+#define A_TYPE block_iq2_xs
+#define A_TYPE_PACKED16 block_iq2_xs_packed16
+#endif
+
+#define QUANT_K_IQ2_S 256
+#define QUANT_R_IQ2_S 1
+
+struct block_iq2_s
+{
+    float16_t d;
+    uint8_t qs[QUANT_K_IQ2_S/4];
+    uint8_t qh[QUANT_K_IQ2_S/32];
+    uint8_t scales[QUANT_K_IQ2_S/32];
+};
+
+#if defined(DATA_A_IQ2_S)
+
+const uvec2 iq2s_grid_const[1024] = {
+    uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808),
+    uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x0819192b, 0x08080808),
+    uvec2(0x08192b19, 0x08080808), uvec2(0x082b0808, 0x08080808), uvec2(0x082b082b, 0x08080808), uvec2(0x082b1919, 0x08080808),
+    uvec2(0x082b2b08, 0x08080808), uvec2(0x19080819, 0x08080808), uvec2(0x19081908, 0x08080808), uvec2(0x1908192b, 0x08080808),
+    uvec2(0x19082b19, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x1919082b, 0x08080808), uvec2(0x19191919, 0x08080808),
+    uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808), uvec2(0x192b1908, 0x08080808), uvec2(0x192b192b, 0x08080808),
+    uvec2(0x192b2b19, 0x08080808), uvec2(0x2b080808, 0x08080808), uvec2(0x2b08082b, 0x08080808), uvec2(0x2b081919, 0x08080808),
+    uvec2(0x2b082b08, 0x08080808), uvec2(0x2b190819, 0x08080808), uvec2(0x2b191908, 0x08080808), uvec2(0x2b2b0808, 0x08080808),
+    uvec2(0x2b2b1919, 0x08080808), uvec2(0x2b2b2b2b, 0x08080808), uvec2(0x08080819, 0x08080819), uvec2(0x08081908, 0x08080819),
+    uvec2(0x0808192b, 0x08080819), uvec2(0x08082b19, 0x08080819), uvec2(0x08190808, 0x08080819), uvec2(0x0819082b, 0x08080819),
+    uvec2(0x08191919, 0x08080819), uvec2(0x08192b08, 0x08080819), uvec2(0x082b0819, 0x08080819), uvec2(0x082b1908, 0x08080819),
+    uvec2(0x19080808, 0x08080819), uvec2(0x1908082b, 0x08080819), uvec2(0x19081919, 0x08080819), uvec2(0x19082b08, 0x08080819),
+    uvec2(0x19190819, 0x08080819), uvec2(0x19191908, 0x08080819), uvec2(0x1919192b, 0x08080819), uvec2(0x19192b19, 0x08080819),
+    uvec2(0x192b0808, 0x08080819), uvec2(0x192b1919, 0x08080819), uvec2(0x192b2b08, 0x08080819), uvec2(0x2b080819, 0x08080819),
+    uvec2(0x2b081908, 0x08080819), uvec2(0x2b190808, 0x08080819), uvec2(0x2b19082b, 0x08080819), uvec2(0x2b191919, 0x08080819),
+    uvec2(0x2b2b0819, 0x08080819), uvec2(0x2b2b1908, 0x08080819), uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b),
+    uvec2(0x08081919, 0x0808082b), uvec2(0x08082b08, 0x0808082b), uvec2(0x08190819, 0x0808082b), uvec2(0x08191908, 0x0808082b),
+    uvec2(0x082b0808, 0x0808082b), uvec2(0x082b2b2b, 0x0808082b), uvec2(0x19080819, 0x0808082b), uvec2(0x19081908, 0x0808082b),
+    uvec2(0x1908192b, 0x0808082b), uvec2(0x19082b19, 0x0808082b), uvec2(0x19190808, 0x0808082b), uvec2(0x19191919, 0x0808082b),
+    uvec2(0x2b080808, 0x0808082b), uvec2(0x2b081919, 0x0808082b), uvec2(0x2b082b2b, 0x0808082b), uvec2(0x2b191908, 0x0808082b),
+    uvec2(0x2b2b082b, 0x0808082b), uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908), uvec2(0x0808192b, 0x08081908),
+    uvec2(0x08082b19, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x0819082b, 0x08081908), uvec2(0x08191919, 0x08081908),
+    uvec2(0x08192b08, 0x08081908), uvec2(0x082b0819, 0x08081908), uvec2(0x082b1908, 0x08081908), uvec2(0x082b192b, 0x08081908),
+    uvec2(0x082b2b19, 0x08081908), uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19081919, 0x08081908),
+    uvec2(0x19082b08, 0x08081908), uvec2(0x19082b2b, 0x08081908), uvec2(0x19190819, 0x08081908), uvec2(0x19191908, 0x08081908),
+    uvec2(0x1919192b, 0x08081908), uvec2(0x19192b19, 0x08081908), uvec2(0x192b0808, 0x08081908), uvec2(0x192b082b, 0x08081908),
+    uvec2(0x192b1919, 0x08081908), uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b08192b, 0x08081908),
+    uvec2(0x2b082b19, 0x08081908), uvec2(0x2b190808, 0x08081908), uvec2(0x2b191919, 0x08081908), uvec2(0x2b192b08, 0x08081908),
+    uvec2(0x2b2b0819, 0x08081908), uvec2(0x2b2b1908, 0x08081908), uvec2(0x08080808, 0x08081919), uvec2(0x0808082b, 0x08081919),
+    uvec2(0x08081919, 0x08081919), uvec2(0x08082b08, 0x08081919), uvec2(0x08082b2b, 0x08081919), uvec2(0x08190819, 0x08081919),
+    uvec2(0x08191908, 0x08081919), uvec2(0x0819192b, 0x08081919), uvec2(0x08192b19, 0x08081919), uvec2(0x082b0808, 0x08081919),
+    uvec2(0x082b1919, 0x08081919), uvec2(0x082b2b08, 0x08081919), uvec2(0x19080819, 0x08081919), uvec2(0x19081908, 0x08081919),
+    uvec2(0x1908192b, 0x08081919), uvec2(0x19082b19, 0x08081919), uvec2(0x19190808, 0x08081919), uvec2(0x1919082b, 0x08081919),
+    uvec2(0x19191919, 0x08081919), uvec2(0x19192b08, 0x08081919), uvec2(0x192b0819, 0x08081919), uvec2(0x192b1908, 0x08081919),
+    uvec2(0x2b080808, 0x08081919), uvec2(0x2b08082b, 0x08081919), uvec2(0x2b081919, 0x08081919), uvec2(0x2b082b08, 0x08081919),
+    uvec2(0x2b190819, 0x08081919), uvec2(0x2b191908, 0x08081919), uvec2(0x2b2b0808, 0x08081919), uvec2(0x08080819, 0x0808192b),
+    uvec2(0x08081908, 0x0808192b), uvec2(0x0808192b, 0x0808192b), uvec2(0x08082b19, 0x0808192b), uvec2(0x08190808, 0x0808192b),
+    uvec2(0x08191919, 0x0808192b), uvec2(0x19080808, 0x0808192b), uvec2(0x19081919, 0x0808192b), uvec2(0x19082b08, 0x0808192b),
+    uvec2(0x19190819, 0x0808192b), uvec2(0x19191908, 0x0808192b), uvec2(0x192b0808, 0x0808192b), uvec2(0x2b080819, 0x0808192b),
+    uvec2(0x2b081908, 0x0808192b), uvec2(0x2b190808, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x0808082b, 0x08082b08),
+    uvec2(0x08081919, 0x08082b08), uvec2(0x08082b08, 0x08082b08), uvec2(0x08190819, 0x08082b08), uvec2(0x08191908, 0x08082b08),
+    uvec2(0x0819192b, 0x08082b08), uvec2(0x08192b19, 0x08082b08), uvec2(0x082b0808, 0x08082b08), uvec2(0x082b1919, 0x08082b08),
+    uvec2(0x082b2b2b, 0x08082b08), uvec2(0x19080819, 0x08082b08), uvec2(0x19081908, 0x08082b08), uvec2(0x1908192b, 0x08082b08),
+    uvec2(0x19082b19, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x1919082b, 0x08082b08), uvec2(0x19191919, 0x08082b08),
+    uvec2(0x19192b08, 0x08082b08), uvec2(0x192b0819, 0x08082b08), uvec2(0x192b1908, 0x08082b08), uvec2(0x2b080808, 0x08082b08),
+    uvec2(0x2b081919, 0x08082b08), uvec2(0x2b191908, 0x08082b08), uvec2(0x2b2b2b2b, 0x08082b08), uvec2(0x08080819, 0x08082b19),
+    uvec2(0x08081908, 0x08082b19), uvec2(0x08190808, 0x08082b19), uvec2(0x0819082b, 0x08082b19), uvec2(0x08191919, 0x08082b19),
+    uvec2(0x08192b08, 0x08082b19), uvec2(0x082b0819, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x19081919, 0x08082b19),
+    uvec2(0x19082b08, 0x08082b19), uvec2(0x19190819, 0x08082b19), uvec2(0x19191908, 0x08082b19), uvec2(0x192b0808, 0x08082b19),
+    uvec2(0x2b080819, 0x08082b19), uvec2(0x2b190808, 0x08082b19), uvec2(0x08080808, 0x08082b2b), uvec2(0x08190819, 0x08082b2b),
+    uvec2(0x08191908, 0x08082b2b), uvec2(0x082b082b, 0x08082b2b), uvec2(0x082b2b08, 0x08082b2b), uvec2(0x082b2b2b, 0x08082b2b),
+    uvec2(0x19190808, 0x08082b2b), uvec2(0x2b192b19, 0x08082b2b), uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808),
+    uvec2(0x0808192b, 0x08190808), uvec2(0x08082b19, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x0819082b, 0x08190808),
+    uvec2(0x08191919, 0x08190808), uvec2(0x08192b08, 0x08190808), uvec2(0x082b0819, 0x08190808), uvec2(0x082b1908, 0x08190808),
+    uvec2(0x082b192b, 0x08190808), uvec2(0x19080808, 0x08190808), uvec2(0x1908082b, 0x08190808), uvec2(0x19081919, 0x08190808),
+    uvec2(0x19082b08, 0x08190808), uvec2(0x19190819, 0x08190808), uvec2(0x19191908, 0x08190808), uvec2(0x1919192b, 0x08190808),
+    uvec2(0x19192b19, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x192b082b, 0x08190808), uvec2(0x192b1919, 0x08190808),
+    uvec2(0x192b2b08, 0x08190808), uvec2(0x2b080819, 0x08190808), uvec2(0x2b081908, 0x08190808), uvec2(0x2b08192b, 0x08190808),
+    uvec2(0x2b190808, 0x08190808), uvec2(0x2b191919, 0x08190808), uvec2(0x2b192b08, 0x08190808), uvec2(0x2b2b0819, 0x08190808),
+    uvec2(0x2b2b1908, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x0808082b, 0x08190819), uvec2(0x08081919, 0x08190819),
+    uvec2(0x08082b08, 0x08190819), uvec2(0x08082b2b, 0x08190819), uvec2(0x08190819, 0x08190819), uvec2(0x08191908, 0x08190819),
+    uvec2(0x0819192b, 0x08190819), uvec2(0x08192b19, 0x08190819), uvec2(0x082b0808, 0x08190819), uvec2(0x082b082b, 0x08190819),
+    uvec2(0x082b1919, 0x08190819), uvec2(0x082b2b08, 0x08190819), uvec2(0x19080819, 0x08190819), uvec2(0x19081908, 0x08190819),
+    uvec2(0x1908192b, 0x08190819), uvec2(0x19082b19, 0x08190819), uvec2(0x19190808, 0x08190819), uvec2(0x1919082b, 0x08190819),
+    uvec2(0x19191919, 0x08190819), uvec2(0x19192b08, 0x08190819), uvec2(0x192b0819, 0x08190819), uvec2(0x192b1908, 0x08190819),
+    uvec2(0x2b080808, 0x08190819), uvec2(0x2b08082b, 0x08190819), uvec2(0x2b081919, 0x08190819), uvec2(0x2b082b08, 0x08190819),
+    uvec2(0x2b190819, 0x08190819), uvec2(0x2b191908, 0x08190819), uvec2(0x08080819, 0x0819082b), uvec2(0x08081908, 0x0819082b),
+    uvec2(0x08082b19, 0x0819082b), uvec2(0x08190808, 0x0819082b), uvec2(0x08191919, 0x0819082b), uvec2(0x082b0819, 0x0819082b),
+    uvec2(0x082b1908, 0x0819082b), uvec2(0x19080808, 0x0819082b), uvec2(0x19081919, 0x0819082b), uvec2(0x19190819, 0x0819082b),
+    uvec2(0x19191908, 0x0819082b), uvec2(0x2b080819, 0x0819082b), uvec2(0x2b081908, 0x0819082b), uvec2(0x2b190808, 0x0819082b),
+    uvec2(0x08080808, 0x08191908), uvec2(0x0808082b, 0x08191908), uvec2(0x08081919, 0x08191908), uvec2(0x08082b08, 0x08191908),
+    uvec2(0x08190819, 0x08191908), uvec2(0x08191908, 0x08191908), uvec2(0x0819192b, 0x08191908), uvec2(0x08192b19, 0x08191908),
+    uvec2(0x082b0808, 0x08191908), uvec2(0x082b1919, 0x08191908), uvec2(0x082b2b08, 0x08191908), uvec2(0x19080819, 0x08191908),
+    uvec2(0x19081908, 0x08191908), uvec2(0x1908192b, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x19190808, 0x08191908),
+    uvec2(0x1919082b, 0x08191908), uvec2(0x19191919, 0x08191908), uvec2(0x19192b08, 0x08191908), uvec2(0x192b0819, 0x08191908),
+    uvec2(0x192b1908, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x2b08082b, 0x08191908), uvec2(0x2b081919, 0x08191908),
+    uvec2(0x2b082b08, 0x08191908), uvec2(0x2b190819, 0x08191908), uvec2(0x2b191908, 0x08191908), uvec2(0x2b2b0808, 0x08191908),
+    uvec2(0x08080819, 0x08191919), uvec2(0x08081908, 0x08191919), uvec2(0x0808192b, 0x08191919), uvec2(0x08082b19, 0x08191919),
+    uvec2(0x08190808, 0x08191919), uvec2(0x0819082b, 0x08191919), uvec2(0x08191919, 0x08191919), uvec2(0x08192b08, 0x08191919),
+    uvec2(0x082b0819, 0x08191919), uvec2(0x082b1908, 0x08191919), uvec2(0x19080808, 0x08191919), uvec2(0x1908082b, 0x08191919),
+    uvec2(0x19081919, 0x08191919), uvec2(0x19082b08, 0x08191919), uvec2(0x19190819, 0x08191919), uvec2(0x19191908, 0x08191919),
+    uvec2(0x192b0808, 0x08191919), uvec2(0x2b080819, 0x08191919), uvec2(0x2b081908, 0x08191919), uvec2(0x2b190808, 0x08191919),
+    uvec2(0x08080808, 0x0819192b), uvec2(0x08081919, 0x0819192b), uvec2(0x08082b08, 0x0819192b), uvec2(0x08190819, 0x0819192b),
+    uvec2(0x08191908, 0x0819192b), uvec2(0x082b0808, 0x0819192b), uvec2(0x19080819, 0x0819192b), uvec2(0x19081908, 0x0819192b),
+    uvec2(0x19190808, 0x0819192b), uvec2(0x2b080808, 0x0819192b), uvec2(0x2b2b2b2b, 0x0819192b), uvec2(0x08080819, 0x08192b08),
+    uvec2(0x08081908, 0x08192b08), uvec2(0x0808192b, 0x08192b08), uvec2(0x08082b19, 0x08192b08), uvec2(0x08190808, 0x08192b08),
+    uvec2(0x08191919, 0x08192b08), uvec2(0x08192b08, 0x08192b08), uvec2(0x082b0819, 0x08192b08), uvec2(0x19080808, 0x08192b08),
+    uvec2(0x1908082b, 0x08192b08), uvec2(0x19081919, 0x08192b08), uvec2(0x19082b08, 0x08192b08), uvec2(0x19190819, 0x08192b08),
+    uvec2(0x19191908, 0x08192b08), uvec2(0x192b0808, 0x08192b08), uvec2(0x2b080819, 0x08192b08), uvec2(0x2b081908, 0x08192b08),
+    uvec2(0x08080808, 0x08192b19), uvec2(0x0808082b, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x08082b08, 0x08192b19),
+    uvec2(0x08190819, 0x08192b19), uvec2(0x08191908, 0x08192b19), uvec2(0x082b0808, 0x08192b19), uvec2(0x19080819, 0x08192b19),
+    uvec2(0x19081908, 0x08192b19), uvec2(0x19190808, 0x08192b19), uvec2(0x192b2b19, 0x08192b19), uvec2(0x2b2b082b, 0x08192b19),
+    uvec2(0x08081908, 0x08192b2b), uvec2(0x08190808, 0x08192b2b), uvec2(0x19080808, 0x08192b2b), uvec2(0x1919192b, 0x08192b2b),
+    uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08081919, 0x082b0808), uvec2(0x08082b08, 0x082b0808),
+    uvec2(0x08190819, 0x082b0808), uvec2(0x08191908, 0x082b0808), uvec2(0x0819192b, 0x082b0808), uvec2(0x08192b19, 0x082b0808),
+    uvec2(0x082b0808, 0x082b0808), uvec2(0x082b1919, 0x082b0808), uvec2(0x082b2b2b, 0x082b0808), uvec2(0x19080819, 0x082b0808),
+    uvec2(0x19081908, 0x082b0808), uvec2(0x19190808, 0x082b0808), uvec2(0x1919082b, 0x082b0808), uvec2(0x19191919, 0x082b0808),
+    uvec2(0x192b1908, 0x082b0808), uvec2(0x2b080808, 0x082b0808), uvec2(0x2b082b2b, 0x082b0808), uvec2(0x2b191908, 0x082b0808),
+    uvec2(0x2b2b2b2b, 0x082b0808), uvec2(0x08080819, 0x082b0819), uvec2(0x08081908, 0x082b0819), uvec2(0x08190808, 0x082b0819),
+    uvec2(0x0819082b, 0x082b0819), uvec2(0x08191919, 0x082b0819), uvec2(0x082b0819, 0x082b0819), uvec2(0x19080808, 0x082b0819),
+    uvec2(0x1908082b, 0x082b0819), uvec2(0x19081919, 0x082b0819), uvec2(0x19190819, 0x082b0819), uvec2(0x19191908, 0x082b0819),
+    uvec2(0x192b0808, 0x082b0819), uvec2(0x2b080819, 0x082b0819), uvec2(0x2b081908, 0x082b0819), uvec2(0x2b190808, 0x082b0819),
+    uvec2(0x08080808, 0x082b082b), uvec2(0x08082b2b, 0x082b082b), uvec2(0x082b082b, 0x082b082b), uvec2(0x082b2b08, 0x082b082b),
+    uvec2(0x082b2b2b, 0x082b082b), uvec2(0x19081908, 0x082b082b), uvec2(0x19190808, 0x082b082b), uvec2(0x2b082b08, 0x082b082b),
+    uvec2(0x2b082b2b, 0x082b082b), uvec2(0x2b2b2b08, 0x082b082b), uvec2(0x08080819, 0x082b1908), uvec2(0x08081908, 0x082b1908),
+    uvec2(0x0808192b, 0x082b1908), uvec2(0x08082b19, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x08191919, 0x082b1908),
+    uvec2(0x08192b08, 0x082b1908), uvec2(0x082b0819, 0x082b1908), uvec2(0x082b1908, 0x082b1908), uvec2(0x19080808, 0x082b1908),
+    uvec2(0x1908082b, 0x082b1908), uvec2(0x19081919, 0x082b1908), uvec2(0x19082b08, 0x082b1908), uvec2(0x19190819, 0x082b1908),
+    uvec2(0x19191908, 0x082b1908), uvec2(0x192b0808, 0x082b1908), uvec2(0x2b080819, 0x082b1908), uvec2(0x2b081908, 0x082b1908),
+    uvec2(0x2b190808, 0x082b1908), uvec2(0x08080808, 0x082b1919), uvec2(0x08081919, 0x082b1919), uvec2(0x08082b08, 0x082b1919),
+    uvec2(0x08190819, 0x082b1919), uvec2(0x08191908, 0x082b1919), uvec2(0x082b0808, 0x082b1919), uvec2(0x19080819, 0x082b1919),
+    uvec2(0x19081908, 0x082b1919), uvec2(0x19190808, 0x082b1919), uvec2(0x192b192b, 0x082b1919), uvec2(0x2b080808, 0x082b1919),
+    uvec2(0x08080819, 0x082b192b), uvec2(0x08081908, 0x082b192b), uvec2(0x08190808, 0x082b192b), uvec2(0x19080808, 0x082b192b),
+    uvec2(0x19192b19, 0x082b192b), uvec2(0x08080808, 0x082b2b08), uvec2(0x08081919, 0x082b2b08), uvec2(0x08190819, 0x082b2b08),
+    uvec2(0x08191908, 0x082b2b08), uvec2(0x19080819, 0x082b2b08), uvec2(0x19081908, 0x082b2b08), uvec2(0x19190808, 0x082b2b08),
+    uvec2(0x2b082b2b, 0x082b2b08), uvec2(0x2b2b2b2b, 0x082b2b08), uvec2(0x08080819, 0x082b2b19), uvec2(0x08081908, 0x082b2b19),
+    uvec2(0x08190808, 0x082b2b19), uvec2(0x2b191919, 0x082b2b19), uvec2(0x08082b2b, 0x082b2b2b), uvec2(0x082b082b, 0x082b2b2b),
+    uvec2(0x192b1908, 0x082b2b2b), uvec2(0x2b082b08, 0x082b2b2b), uvec2(0x2b082b2b, 0x082b2b2b), uvec2(0x08080819, 0x19080808),
+    uvec2(0x08081908, 0x19080808), uvec2(0x0808192b, 0x19080808), uvec2(0x08082b19, 0x19080808), uvec2(0x08190808, 0x19080808),
+    uvec2(0x0819082b, 0x19080808), uvec2(0x08191919, 0x19080808), uvec2(0x08192b08, 0x19080808), uvec2(0x08192b2b, 0x19080808),
+    uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808), uvec2(0x082b192b, 0x19080808), uvec2(0x19080808, 0x19080808),
+    uvec2(0x1908082b, 0x19080808), uvec2(0x19081919, 0x19080808), uvec2(0x19082b08, 0x19080808), uvec2(0x19082b2b, 0x19080808),
+    uvec2(0x19190819, 0x19080808), uvec2(0x19191908, 0x19080808), uvec2(0x1919192b, 0x19080808), uvec2(0x19192b19, 0x19080808),
+    uvec2(0x192b0808, 0x19080808), uvec2(0x192b082b, 0x19080808), uvec2(0x192b1919, 0x19080808), uvec2(0x2b080819, 0x19080808),
+    uvec2(0x2b081908, 0x19080808), uvec2(0x2b190808, 0x19080808), uvec2(0x2b191919, 0x19080808), uvec2(0x2b192b08, 0x19080808),
+    uvec2(0x2b2b0819, 0x19080808), uvec2(0x2b2b1908, 0x19080808), uvec2(0x08080808, 0x19080819), uvec2(0x0808082b, 0x19080819),
+    uvec2(0x08081919, 0x19080819), uvec2(0x08082b08, 0x19080819), uvec2(0x08190819, 0x19080819), uvec2(0x08191908, 0x19080819),
+    uvec2(0x0819192b, 0x19080819), uvec2(0x08192b19, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x082b082b, 0x19080819),
+    uvec2(0x082b1919, 0x19080819), uvec2(0x19080819, 0x19080819), uvec2(0x19081908, 0x19080819), uvec2(0x1908192b, 0x19080819),
+    uvec2(0x19082b19, 0x19080819), uvec2(0x19190808, 0x19080819), uvec2(0x1919082b, 0x19080819), uvec2(0x19191919, 0x19080819),
+    uvec2(0x19192b08, 0x19080819), uvec2(0x192b0819, 0x19080819), uvec2(0x192b1908, 0x19080819), uvec2(0x2b080808, 0x19080819),
+    uvec2(0x2b08082b, 0x19080819), uvec2(0x2b081919, 0x19080819), uvec2(0x2b082b08, 0x19080819), uvec2(0x2b190819, 0x19080819),
+    uvec2(0x2b191908, 0x19080819), uvec2(0x2b2b0808, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08081908, 0x1908082b),
+    uvec2(0x08190808, 0x1908082b), uvec2(0x0819082b, 0x1908082b), uvec2(0x08191919, 0x1908082b), uvec2(0x08192b08, 0x1908082b),
+    uvec2(0x082b1908, 0x1908082b), uvec2(0x19080808, 0x1908082b), uvec2(0x19081919, 0x1908082b), uvec2(0x19082b08, 0x1908082b),
+    uvec2(0x19190819, 0x1908082b), uvec2(0x19191908, 0x1908082b), uvec2(0x192b0808, 0x1908082b), uvec2(0x2b080819, 0x1908082b),
+    uvec2(0x2b081908, 0x1908082b), uvec2(0x08080808, 0x19081908), uvec2(0x0808082b, 0x19081908), uvec2(0x08081919, 0x19081908),
+    uvec2(0x08082b08, 0x19081908), uvec2(0x08082b2b, 0x19081908), uvec2(0x08190819, 0x19081908), uvec2(0x08191908, 0x19081908),
+    uvec2(0x0819192b, 0x19081908), uvec2(0x08192b19, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x082b082b, 0x19081908),
+    uvec2(0x082b1919, 0x19081908), uvec2(0x082b2b08, 0x19081908), uvec2(0x19080819, 0x19081908), uvec2(0x19081908, 0x19081908),
+    uvec2(0x1908192b, 0x19081908), uvec2(0x19082b19, 0x19081908), uvec2(0x19190808, 0x19081908), uvec2(0x1919082b, 0x19081908),
+    uvec2(0x19191919, 0x19081908), uvec2(0x19192b08, 0x19081908), uvec2(0x192b0819, 0x19081908), uvec2(0x192b1908, 0x19081908),
+    uvec2(0x2b080808, 0x19081908), uvec2(0x2b08082b, 0x19081908), uvec2(0x2b081919, 0x19081908), uvec2(0x2b082b08, 0x19081908),
+    uvec2(0x2b190819, 0x19081908), uvec2(0x2b191908, 0x19081908), uvec2(0x2b2b0808, 0x19081908), uvec2(0x08080819, 0x19081919),
+    uvec2(0x08081908, 0x19081919), uvec2(0x0808192b, 0x19081919), uvec2(0x08082b19, 0x19081919), uvec2(0x08190808, 0x19081919),
+    uvec2(0x0819082b, 0x19081919), uvec2(0x08191919, 0x19081919), uvec2(0x08192b08, 0x19081919), uvec2(0x082b0819, 0x19081919),
+    uvec2(0x082b1908, 0x19081919), uvec2(0x19080808, 0x19081919), uvec2(0x1908082b, 0x19081919), uvec2(0x19081919, 0x19081919),
+    uvec2(0x19082b08, 0x19081919), uvec2(0x19190819, 0x19081919), uvec2(0x19191908, 0x19081919), uvec2(0x192b0808, 0x19081919),
+    uvec2(0x192b2b2b, 0x19081919), uvec2(0x2b080819, 0x19081919), uvec2(0x2b081908, 0x19081919), uvec2(0x2b190808, 0x19081919),
+    uvec2(0x08080808, 0x1908192b), uvec2(0x0808082b, 0x1908192b), uvec2(0x08081919, 0x1908192b), uvec2(0x08082b08, 0x1908192b),
+    uvec2(0x08190819, 0x1908192b), uvec2(0x08191908, 0x1908192b), uvec2(0x082b0808, 0x1908192b), uvec2(0x19080819, 0x1908192b),
+    uvec2(0x19081908, 0x1908192b), uvec2(0x19190808, 0x1908192b), uvec2(0x2b080808, 0x1908192b), uvec2(0x2b2b1919, 0x1908192b),
+    uvec2(0x08080819, 0x19082b08), uvec2(0x08081908, 0x19082b08), uvec2(0x08082b19, 0x19082b08), uvec2(0x08190808, 0x19082b08),
+    uvec2(0x0819082b, 0x19082b08), uvec2(0x08191919, 0x19082b08), uvec2(0x08192b08, 0x19082b08), uvec2(0x082b0819, 0x19082b08),
+    uvec2(0x082b1908, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x1908082b, 0x19082b08), uvec2(0x19081919, 0x19082b08),
+    uvec2(0x19082b08, 0x19082b08), uvec2(0x19190819, 0x19082b08), uvec2(0x19191908, 0x19082b08), uvec2(0x192b0808, 0x19082b08),
+    uvec2(0x2b081908, 0x19082b08), uvec2(0x2b190808, 0x19082b08), uvec2(0x08080808, 0x19082b19), uvec2(0x0808082b, 0x19082b19),
+    uvec2(0x08081919, 0x19082b19), uvec2(0x08082b08, 0x19082b19), uvec2(0x08190819, 0x19082b19), uvec2(0x08191908, 0x19082b19),
+    uvec2(0x082b0808, 0x19082b19), uvec2(0x19080819, 0x19082b19), uvec2(0x19081908, 0x19082b19), uvec2(0x19190808, 0x19082b19),
+    uvec2(0x2b080808, 0x19082b19), uvec2(0x2b19192b, 0x19082b19), uvec2(0x08080819, 0x19082b2b), uvec2(0x08081908, 0x19082b2b),
+    uvec2(0x08190808, 0x19082b2b), uvec2(0x19080808, 0x19082b2b), uvec2(0x08080808, 0x19190808), uvec2(0x0808082b, 0x19190808),
+    uvec2(0x08081919, 0x19190808), uvec2(0x08082b08, 0x19190808), uvec2(0x08190819, 0x19190808), uvec2(0x08191908, 0x19190808),
+    uvec2(0x0819192b, 0x19190808), uvec2(0x08192b19, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x082b082b, 0x19190808),
+    uvec2(0x082b1919, 0x19190808), uvec2(0x082b2b08, 0x19190808), uvec2(0x19080819, 0x19190808), uvec2(0x19081908, 0x19190808),
+    uvec2(0x1908192b, 0x19190808), uvec2(0x19082b19, 0x19190808), uvec2(0x19190808, 0x19190808), uvec2(0x1919082b, 0x19190808),
+    uvec2(0x19191919, 0x19190808), uvec2(0x19192b08, 0x19190808), uvec2(0x192b0819, 0x19190808), uvec2(0x192b1908, 0x19190808),
+    uvec2(0x2b080808, 0x19190808), uvec2(0x2b08082b, 0x19190808), uvec2(0x2b081919, 0x19190808), uvec2(0x2b082b08, 0x19190808),
+    uvec2(0x2b190819, 0x19190808), uvec2(0x2b191908, 0x19190808), uvec2(0x08080819, 0x19190819), uvec2(0x08081908, 0x19190819),
+    uvec2(0x0808192b, 0x19190819), uvec2(0x08082b19, 0x19190819), uvec2(0x08190808, 0x19190819), uvec2(0x0819082b, 0x19190819),
+    uvec2(0x08191919, 0x19190819), uvec2(0x08192b08, 0x19190819), uvec2(0x082b0819, 0x19190819), uvec2(0x082b1908, 0x19190819),
+    uvec2(0x19080808, 0x19190819), uvec2(0x1908082b, 0x19190819), uvec2(0x19081919, 0x19190819), uvec2(0x19082b08, 0x19190819),
+    uvec2(0x19190819, 0x19190819), uvec2(0x19191908, 0x19190819), uvec2(0x192b0808, 0x19190819), uvec2(0x2b080819, 0x19190819),
+    uvec2(0x2b081908, 0x19190819), uvec2(0x2b190808, 0x19190819), uvec2(0x08080808, 0x1919082b), uvec2(0x08081919, 0x1919082b),
+    uvec2(0x08082b08, 0x1919082b), uvec2(0x08190819, 0x1919082b), uvec2(0x08191908, 0x1919082b), uvec2(0x082b0808, 0x1919082b),
+    uvec2(0x19080819, 0x1919082b), uvec2(0x19081908, 0x1919082b), uvec2(0x19190808, 0x1919082b), uvec2(0x192b2b19, 0x1919082b),
+    uvec2(0x2b080808, 0x1919082b), uvec2(0x08080819, 0x19191908), uvec2(0x08081908, 0x19191908), uvec2(0x0808192b, 0x19191908),
+    uvec2(0x08082b19, 0x19191908), uvec2(0x08190808, 0x19191908), uvec2(0x0819082b, 0x19191908), uvec2(0x08191919, 0x19191908),
+    uvec2(0x08192b08, 0x19191908), uvec2(0x082b0819, 0x19191908), uvec2(0x082b1908, 0x19191908), uvec2(0x19080808, 0x19191908),
+    uvec2(0x1908082b, 0x19191908), uvec2(0x19081919, 0x19191908), uvec2(0x19082b08, 0x19191908), uvec2(0x19190819, 0x19191908),
+    uvec2(0x19191908, 0x19191908), uvec2(0x192b0808, 0x19191908), uvec2(0x2b080819, 0x19191908), uvec2(0x2b081908, 0x19191908),
+    uvec2(0x2b190808, 0x19191908), uvec2(0x08080808, 0x19191919), uvec2(0x0808082b, 0x19191919), uvec2(0x08081919, 0x19191919),
+    uvec2(0x08082b08, 0x19191919), uvec2(0x08190819, 0x19191919), uvec2(0x08191908, 0x19191919), uvec2(0x082b0808, 0x19191919),
+    uvec2(0x19080819, 0x19191919), uvec2(0x19081908, 0x19191919), uvec2(0x19190808, 0x19191919), uvec2(0x2b080808, 0x19191919),
+    uvec2(0x08080819, 0x1919192b), uvec2(0x08081908, 0x1919192b), uvec2(0x08190808, 0x1919192b), uvec2(0x082b192b, 0x1919192b),
+    uvec2(0x19080808, 0x1919192b), uvec2(0x08080808, 0x19192b08), uvec2(0x0808082b, 0x19192b08), uvec2(0x08081919, 0x19192b08),
+    uvec2(0x08082b08, 0x19192b08), uvec2(0x08190819, 0x19192b08), uvec2(0x08191908, 0x19192b08), uvec2(0x082b0808, 0x19192b08),
+    uvec2(0x19080819, 0x19192b08), uvec2(0x19081908, 0x19192b08), uvec2(0x19190808, 0x19192b08), uvec2(0x19192b2b, 0x19192b08),
+    uvec2(0x2b080808, 0x19192b08), uvec2(0x08080819, 0x19192b19), uvec2(0x08081908, 0x19192b19), uvec2(0x08190808, 0x19192b19),
+    uvec2(0x19080808, 0x19192b19), uvec2(0x08080808, 0x19192b2b), uvec2(0x08192b19, 0x19192b2b), uvec2(0x2b081919, 0x19192b2b),
+    uvec2(0x2b2b2b08, 0x19192b2b), uvec2(0x08080819, 0x192b0808), uvec2(0x08081908, 0x192b0808), uvec2(0x0808192b, 0x192b0808),
+    uvec2(0x08190808, 0x192b0808), uvec2(0x0819082b, 0x192b0808), uvec2(0x08191919, 0x192b0808), uvec2(0x08192b08, 0x192b0808),
+    uvec2(0x082b0819, 0x192b0808), uvec2(0x082b1908, 0x192b0808), uvec2(0x19080808, 0x192b0808), uvec2(0x19081919, 0x192b0808),
+    uvec2(0x19082b08, 0x192b0808), uvec2(0x19190819, 0x192b0808), uvec2(0x19191908, 0x192b0808), uvec2(0x192b0808, 0x192b0808),
+    uvec2(0x2b081908, 0x192b0808), uvec2(0x2b190808, 0x192b0808), uvec2(0x08080808, 0x192b0819), uvec2(0x0808082b, 0x192b0819),
+    uvec2(0x08081919, 0x192b0819), uvec2(0x08082b08, 0x192b0819), uvec2(0x08190819, 0x192b0819), uvec2(0x08191908, 0x192b0819),
+    uvec2(0x082b0808, 0x192b0819), uvec2(0x19080819, 0x192b0819), uvec2(0x19081908, 0x192b0819), uvec2(0x19190808, 0x192b0819),
+    uvec2(0x2b080808, 0x192b0819), uvec2(0x2b192b19, 0x192b0819), uvec2(0x08081908, 0x192b082b), uvec2(0x08190808, 0x192b082b),
+    uvec2(0x19080808, 0x192b082b), uvec2(0x1919192b, 0x192b082b), uvec2(0x2b2b0819, 0x192b082b), uvec2(0x08080808, 0x192b1908),
+    uvec2(0x08081919, 0x192b1908), uvec2(0x08082b08, 0x192b1908), uvec2(0x08190819, 0x192b1908), uvec2(0x08191908, 0x192b1908),
+    uvec2(0x082b0808, 0x192b1908), uvec2(0x19080819, 0x192b1908), uvec2(0x19081908, 0x192b1908), uvec2(0x19190808, 0x192b1908),
+    uvec2(0x2b080808, 0x192b1908), uvec2(0x08080819, 0x192b1919), uvec2(0x08081908, 0x192b1919), uvec2(0x08190808, 0x192b1919),
+    uvec2(0x19080808, 0x192b1919), uvec2(0x19082b2b, 0x192b1919), uvec2(0x192b2b08, 0x192b1919), uvec2(0x2b19082b, 0x192b1919),
+    uvec2(0x08080808, 0x192b192b), uvec2(0x2b191908, 0x192b192b), uvec2(0x08080819, 0x192b2b08), uvec2(0x08081908, 0x192b2b08),
+    uvec2(0x08190808, 0x192b2b08), uvec2(0x192b1919, 0x192b2b08), uvec2(0x2b192b08, 0x192b2b08), uvec2(0x08080808, 0x192b2b19),
+    uvec2(0x082b2b2b, 0x192b2b19), uvec2(0x1908082b, 0x192b2b2b), uvec2(0x2b2b0819, 0x192b2b2b), uvec2(0x08080808, 0x2b080808),
+    uvec2(0x0808082b, 0x2b080808), uvec2(0x08081919, 0x2b080808), uvec2(0x08082b08, 0x2b080808), uvec2(0x08190819, 0x2b080808),
+    uvec2(0x08191908, 0x2b080808), uvec2(0x08192b19, 0x2b080808), uvec2(0x082b0808, 0x2b080808), uvec2(0x082b1919, 0x2b080808),
+    uvec2(0x19080819, 0x2b080808), uvec2(0x19081908, 0x2b080808), uvec2(0x19190808, 0x2b080808), uvec2(0x1919082b, 0x2b080808),
+    uvec2(0x19191919, 0x2b080808), uvec2(0x19192b08, 0x2b080808), uvec2(0x192b0819, 0x2b080808), uvec2(0x2b080808, 0x2b080808),
+    uvec2(0x2b081919, 0x2b080808), uvec2(0x2b190819, 0x2b080808), uvec2(0x2b191908, 0x2b080808), uvec2(0x08080819, 0x2b080819),
+    uvec2(0x08081908, 0x2b080819), uvec2(0x08082b19, 0x2b080819), uvec2(0x08190808, 0x2b080819), uvec2(0x0819082b, 0x2b080819),
+    uvec2(0x08191919, 0x2b080819), uvec2(0x08192b08, 0x2b080819), uvec2(0x082b0819, 0x2b080819), uvec2(0x082b1908, 0x2b080819),
+    uvec2(0x19080808, 0x2b080819), uvec2(0x1908082b, 0x2b080819), uvec2(0x19081919, 0x2b080819), uvec2(0x19082b08, 0x2b080819),
+    uvec2(0x19190819, 0x2b080819), uvec2(0x19191908, 0x2b080819), uvec2(0x2b080819, 0x2b080819), uvec2(0x2b081908, 0x2b080819),
+    uvec2(0x2b190808, 0x2b080819), uvec2(0x2b2b2b19, 0x2b080819), uvec2(0x08080808, 0x2b08082b), uvec2(0x08081919, 0x2b08082b),
+    uvec2(0x08082b2b, 0x2b08082b), uvec2(0x08190819, 0x2b08082b), uvec2(0x08191908, 0x2b08082b), uvec2(0x19080819, 0x2b08082b),
+    uvec2(0x19081908, 0x2b08082b), uvec2(0x19190808, 0x2b08082b), uvec2(0x08080819, 0x2b081908), uvec2(0x08081908, 0x2b081908),
+    uvec2(0x0808192b, 0x2b081908), uvec2(0x08082b19, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x0819082b, 0x2b081908),
+    uvec2(0x08191919, 0x2b081908), uvec2(0x08192b08, 0x2b081908), uvec2(0x082b0819, 0x2b081908), uvec2(0x19080808, 0x2b081908),
+    uvec2(0x1908082b, 0x2b081908), uvec2(0x19081919, 0x2b081908), uvec2(0x19082b08, 0x2b081908), uvec2(0x19190819, 0x2b081908),
+    uvec2(0x19191908, 0x2b081908), uvec2(0x192b0808, 0x2b081908), uvec2(0x2b080819, 0x2b081908), uvec2(0x2b081908, 0x2b081908),
+    uvec2(0x2b190808, 0x2b081908), uvec2(0x08080808, 0x2b081919), uvec2(0x0808082b, 0x2b081919), uvec2(0x08081919, 0x2b081919),
+    uvec2(0x08082b08, 0x2b081919), uvec2(0x08190819, 0x2b081919), uvec2(0x08191908, 0x2b081919), uvec2(0x082b0808, 0x2b081919),
+    uvec2(0x19080819, 0x2b081919), uvec2(0x19081908, 0x2b081919), uvec2(0x19190808, 0x2b081919), uvec2(0x2b080808, 0x2b081919),
+    uvec2(0x2b082b2b, 0x2b081919), uvec2(0x08080819, 0x2b08192b), uvec2(0x08081908, 0x2b08192b), uvec2(0x08190808, 0x2b08192b),
+    uvec2(0x082b2b19, 0x2b08192b), uvec2(0x19080808, 0x2b08192b), uvec2(0x08080808, 0x2b082b08), uvec2(0x08081919, 0x2b082b08),
+    uvec2(0x08190819, 0x2b082b08), uvec2(0x08191908, 0x2b082b08), uvec2(0x19080819, 0x2b082b08), uvec2(0x19081908, 0x2b082b08),
+    uvec2(0x19190808, 0x2b082b08), uvec2(0x2b2b082b, 0x2b082b08), uvec2(0x08080819, 0x2b082b19), uvec2(0x08081908, 0x2b082b19),
+    uvec2(0x19080808, 0x2b082b19), uvec2(0x192b1919, 0x2b082b19), uvec2(0x082b082b, 0x2b082b2b), uvec2(0x19192b08, 0x2b082b2b),
+    uvec2(0x19192b2b, 0x2b082b2b), uvec2(0x2b08082b, 0x2b082b2b), uvec2(0x2b2b082b, 0x2b082b2b), uvec2(0x08080819, 0x2b190808),
+    uvec2(0x08081908, 0x2b190808), uvec2(0x08082b19, 0x2b190808), uvec2(0x08190808, 0x2b190808), uvec2(0x0819082b, 0x2b190808),
+    uvec2(0x08191919, 0x2b190808), uvec2(0x08192b08, 0x2b190808), uvec2(0x082b1908, 0x2b190808), uvec2(0x19080808, 0x2b190808),
+    uvec2(0x1908082b, 0x2b190808), uvec2(0x19081919, 0x2b190808), uvec2(0x19082b08, 0x2b190808), uvec2(0x19190819, 0x2b190808),
+    uvec2(0x19191908, 0x2b190808), uvec2(0x192b0808, 0x2b190808), uvec2(0x2b080819, 0x2b190808), uvec2(0x2b081908, 0x2b190808),
+    uvec2(0x2b190808, 0x2b190808), uvec2(0x08080808, 0x2b190819), uvec2(0x08081919, 0x2b190819), uvec2(0x08190819, 0x2b190819),
+    uvec2(0x08191908, 0x2b190819), uvec2(0x19080819, 0x2b190819), uvec2(0x19081908, 0x2b190819), uvec2(0x19190808, 0x2b190819),
+    uvec2(0x19192b2b, 0x2b190819), uvec2(0x08080819, 0x2b19082b), uvec2(0x08081908, 0x2b19082b), uvec2(0x08190808, 0x2b19082b),
+    uvec2(0x19080808, 0x2b19082b), uvec2(0x2b2b192b, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x0808082b, 0x2b191908),
+    uvec2(0x08081919, 0x2b191908), uvec2(0x08082b08, 0x2b191908), uvec2(0x08190819, 0x2b191908), uvec2(0x08191908, 0x2b191908),
+    uvec2(0x082b0808, 0x2b191908), uvec2(0x19080819, 0x2b191908), uvec2(0x19081908, 0x2b191908), uvec2(0x19190808, 0x2b191908),
+    uvec2(0x2b080808, 0x2b191908), uvec2(0x2b19192b, 0x2b191908), uvec2(0x08080819, 0x2b191919), uvec2(0x08081908, 0x2b191919),
+    uvec2(0x08190808, 0x2b191919), uvec2(0x19080808, 0x2b191919), uvec2(0x2b192b08, 0x2b191919), uvec2(0x2b2b0819, 0x2b191919),
+    uvec2(0x08080808, 0x2b19192b), uvec2(0x1908192b, 0x2b19192b), uvec2(0x192b1908, 0x2b19192b), uvec2(0x08080819, 0x2b192b08),
+    uvec2(0x08081908, 0x2b192b08), uvec2(0x08190808, 0x2b192b08), uvec2(0x082b192b, 0x2b192b08), uvec2(0x19080808, 0x2b192b08),
+    uvec2(0x2b2b2b19, 0x2b192b08), uvec2(0x08080808, 0x2b192b19), uvec2(0x19082b19, 0x2b192b19), uvec2(0x1919082b, 0x2b192b19),
+    uvec2(0x2b190808, 0x2b192b2b), uvec2(0x08080808, 0x2b2b0808), uvec2(0x08081919, 0x2b2b0808), uvec2(0x08082b2b, 0x2b2b0808),
+    uvec2(0x08191908, 0x2b2b0808), uvec2(0x082b082b, 0x2b2b0808), uvec2(0x082b2b2b, 0x2b2b0808), uvec2(0x19080819, 0x2b2b0808),
+    uvec2(0x19081908, 0x2b2b0808), uvec2(0x19190808, 0x2b2b0808), uvec2(0x2b2b082b, 0x2b2b0808), uvec2(0x2b2b2b2b, 0x2b2b0808),
+    uvec2(0x19080808, 0x2b2b0819), uvec2(0x192b1919, 0x2b2b0819), uvec2(0x0808082b, 0x2b2b082b), uvec2(0x08082b2b, 0x2b2b082b),
+    uvec2(0x082b082b, 0x2b2b082b), uvec2(0x082b2b08, 0x2b2b082b), uvec2(0x082b2b2b, 0x2b2b082b), uvec2(0x2b08082b, 0x2b2b082b),
+    uvec2(0x2b082b08, 0x2b2b082b), uvec2(0x2b082b2b, 0x2b2b082b), uvec2(0x2b2b2b08, 0x2b2b082b), uvec2(0x08080819, 0x2b2b1908),
+    uvec2(0x08081908, 0x2b2b1908), uvec2(0x08190808, 0x2b2b1908), uvec2(0x19080808, 0x2b2b1908), uvec2(0x2b082b19, 0x2b2b1908),
+    uvec2(0x2b2b1908, 0x2b2b1908), uvec2(0x08080808, 0x2b2b1919), uvec2(0x08192b19, 0x2b2b1919), uvec2(0x19190819, 0x2b2b192b),
+    uvec2(0x08082b2b, 0x2b2b2b08), uvec2(0x082b2b08, 0x2b2b2b08), uvec2(0x2b2b082b, 0x2b2b2b08), uvec2(0x19191908, 0x2b2b2b19),
+    uvec2(0x2b08192b, 0x2b2b2b19), uvec2(0x08082b08, 0x2b2b2b2b), uvec2(0x08082b2b, 0x2b2b2b2b), uvec2(0x082b0808, 0x2b2b2b2b),
+    uvec2(0x082b082b, 0x2b2b2b2b), uvec2(0x082b2b08, 0x2b2b2b2b), uvec2(0x2b082b08, 0x2b2b2b2b), uvec2(0x2b2b2b2b, 0x2b2b2b2b)
+};
+
+shared uvec2 iq2s_grid[1024];
+
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < iq2s_grid.length(); i += wgsize.x) {
+        iq2s_grid[i] = iq2s_grid_const[i];
+    }
+    barrier();
+}
+
+#define QUANT_K QUANT_K_IQ2_S
+#define QUANT_R QUANT_R_IQ2_S
+#define A_TYPE block_iq2_s
+#endif
+
+#define QUANT_K_IQ3_XXS 256
+#define QUANT_R_IQ3_XXS 1
+
+struct block_iq3_xxs
+{
+    float16_t d;
+    uint8_t qs[QUANT_K_IQ3_XXS/4 + QUANT_K_IQ3_XXS/8];
+};
+
+struct block_iq3_xxs_packed16
+{
+    float16_t d;
+    uint16_t qs[QUANT_K_IQ3_XXS/8 + QUANT_K_IQ3_XXS/16];
+};
+
+#if defined(DATA_A_IQ3_XXS)
+
+const uint32_t iq3xxs_grid_const[256] = {
+    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
+    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
+    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
+    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
+    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
+    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
+    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
+    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
+    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
+    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
+    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
+    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
+    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
+    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
+    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
+    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
+    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
+    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
+    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
+    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
+    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
+    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
+    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
+    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
+    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
+    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
+    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
+    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
+    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
+    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
+    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
+    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
+};
+
+shared uint32_t iq3xxs_grid[256];
+
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < iq3xxs_grid.length(); i += wgsize.x) {
+        iq3xxs_grid[i] = iq3xxs_grid_const[i];
+    }
+    barrier();
+}
+
+#define QUANT_K QUANT_K_IQ3_XXS
+#define QUANT_R QUANT_R_IQ3_XXS
+#define A_TYPE block_iq3_xxs
+#define A_TYPE_PACKED16 block_iq3_xxs_packed16
+#endif
+
+#define QUANT_K_IQ3_S 256
+#define QUANT_R_IQ3_S 1
+
+struct block_iq3_s
+{
+    float16_t d;
+    uint8_t qs[QUANT_K_IQ3_S/4];
+    uint8_t qh[QUANT_K_IQ3_S/32];
+    uint8_t signs[QUANT_K_IQ3_S/8];
+    uint8_t scales[QUANT_K_IQ3_S/64];
+};
+
+struct block_iq3_s_packed16
+{
+    float16_t d;
+    uint16_t qs[QUANT_K_IQ3_S/4/2];
+    uint16_t qh[QUANT_K_IQ3_S/32/2];
+    uint16_t signs[QUANT_K_IQ3_S/8/2];
+    uint16_t scales[QUANT_K_IQ3_S/64/2];
+};
+
+#if defined(DATA_A_IQ3_S)
+
+const uint32_t iq3s_grid_const[512] = {
+    0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
+    0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
+    0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
+    0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
+    0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
+    0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
+    0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
+    0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
+    0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
+    0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
+    0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
+    0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
+    0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
+    0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
+    0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
+    0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
+    0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
+    0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
+    0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
+    0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
+    0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
+    0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
+    0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
+    0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
+    0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
+    0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
+    0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
+    0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
+    0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
+    0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
+    0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
+    0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
+    0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
+    0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
+    0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
+    0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
+    0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
+    0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
+    0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
+    0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
+    0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
+    0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
+    0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
+    0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
+    0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
+    0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
+    0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
+    0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
+    0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
+    0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
+    0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
+    0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
+    0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
+    0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
+    0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
+    0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
+    0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
+    0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
+    0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
+    0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
+    0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
+    0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
+    0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
+    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
+};
+
+shared uint32_t iq3s_grid[512];
+
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < iq3s_grid.length(); i += wgsize.x) {
+        iq3s_grid[i] = iq3s_grid_const[i];
+    }
+    barrier();
+}
+
+#define QUANT_K QUANT_K_IQ3_S
+#define QUANT_R QUANT_R_IQ3_S
+#define A_TYPE block_iq3_s
+#define A_TYPE_PACKED16 block_iq3_s_packed16
+#endif
+
 #define QUANT_K_IQ4_NL 32
 #define QUANT_R_IQ4_NL 2
 
@@ -318,11 +1050,11 @@ const int8_t kvalues_iq4nl_const[16] = {
 
 shared FLOAT_TYPE kvalues_iq4nl[16];
 
-void init_iq4nl_shmem()
+void init_iq_shmem(uvec3 wgsize)
 {
     // copy the table into shared memory and sync
-    if (gl_LocalInvocationIndex.x < 16) {
-        kvalues_iq4nl[gl_LocalInvocationIndex.x] = FLOAT_TYPE(kvalues_iq4nl_const[gl_LocalInvocationIndex.x]);
+    for (uint i = gl_LocalInvocationIndex.x; i < kvalues_iq4nl.length(); i += wgsize.x) {
+        kvalues_iq4nl[i] = FLOAT_TYPE(kvalues_iq4nl_const[i]);
     }
     barrier();
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index e9c6cb9d4..93ddbfadc 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -55,6 +55,11 @@ const std::vector<std::string> type_names = {
     "q4_k",
     "q5_k",
     "q6_k",
+    "iq2_xxs",
+    "iq2_xs",
+    "iq2_s",
+    "iq3_xxs",
+    "iq3_s",
     "iq4_nl"
 };
 

From eb7cf15a808d4d7a71eef89cc6a9b96fe82989dc Mon Sep 17 00:00:00 2001
From: Nigel Bosch <pnigelb@gmail.com>
Date: Wed, 29 Jan 2025 12:45:44 -0600
Subject: [PATCH 284/376] server : add /apply-template endpoint for additional
 use cases of Minja functionality (#11489)

* add /apply-template endpoint to server

* remove unnecessary line

* add /apply-template documentation

* return only "prompt" field in /apply-template

* use suggested idea instead of my overly verbose way
---
 examples/server/README.md                         |  8 ++++++++
 examples/server/server.cpp                        |  9 +++++++++
 .../server/tests/unit/test_chat_completion.py     | 15 +++++++++++++++
 3 files changed, 32 insertions(+)

diff --git a/examples/server/README.md b/examples/server/README.md
index e788d8b59..cedae0b6d 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -576,6 +576,14 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
 
 `tokens`: Set the tokens to detokenize.
 
+### POST `/apply-template`: Apply chat template to a conversation
+
+Uses the server's prompt template formatting functionality to convert chat messages to a single string expected by a chat model as input, but does not perform inference. Instead, the prompt string is returned in the `prompt` field of the JSON response. The prompt can then be modified as desired (for example, to insert "Sure!" at the beginning of the model's response) before sending to `/completion` to generate the chat response.
+
+*Options:*
+
+`messages`: (Required) Chat turns in the same format as `/v1/chat/completions`.
+
 ### POST `/embedding`: Generate embedding of a given text
 
 > [!IMPORTANT]
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c5efbdb09..6e28d283a 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -4124,6 +4124,14 @@ int main(int argc, char ** argv) {
         res_ok(res, root);
     };
 
+    const auto handle_apply_template = [&ctx_server, &params, &res_ok](const httplib::Request & req, httplib::Response & res) {
+        auto body = json::parse(req.body);
+        const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
+        json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
+
+        res_ok(res, {{ "prompt", data.at("prompt") }});
+    };
+
     const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
         handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE);
     };
@@ -4300,6 +4308,7 @@ int main(int argc, char ** argv) {
     svr->Post("/v1/reranking",        handle_rerank);
     svr->Post("/tokenize",            handle_tokenize);
     svr->Post("/detokenize",          handle_detokenize);
+    svr->Post("/apply-template",      handle_apply_template);
     // LoRA adapters hotswap
     svr->Get ("/lora-adapters",       handle_lora_adapters_list);
     svr->Post("/lora-adapters",       handle_lora_adapters_apply);
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 2e15348dc..add3f810f 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -121,6 +121,21 @@ def test_chat_template():
     assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
 
 
+def test_apply_chat_template():
+    global server
+    server.chat_template = "command-r"
+    server.start()
+    res = server.make_request("POST", "/apply-template", data={
+        "messages": [
+            {"role": "system", "content": "You are a test."},
+            {"role": "user", "content":"Hi there"},
+        ]
+    })
+    assert res.status_code == 200
+    assert "prompt" in res.body
+    assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+
+
 @pytest.mark.parametrize("response_format,n_predicted,re_content", [
     ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
     ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),

From e0449763a4f335cca374254a72892141f41eaa59 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 30 Jan 2025 05:48:14 +0100
Subject: [PATCH 285/376] server : update json snippets in README.md [no ci]
 (#11492)

This commit updates some of JSON snippets in README.md file and
removes the `json` language tag from the code blocks.

The motivation for this changes is that if there is invalid json in a
code snippet these are highlighted in red which can make it somewhat
difficult to read and can be a little distracting.
---
 examples/server/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index cedae0b6d..d1c9be589 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -460,7 +460,7 @@ These words will not be included in the completion, so make sure to add them to
 - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements:
-  ```json
+  ```
   {
     "content": "<the generated completion text>",
     "tokens": [ generated token ids if requested ],
@@ -561,7 +561,7 @@ If `with_pieces` is `true`:
 ```
 
 With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
-```json
+```
 {
   "tokens": [
     {"id": 198, "piece": [195]}, // hex C3
@@ -776,7 +776,7 @@ Same as the `/v1/embeddings` endpoint.
 
 **Response format**
 
-```json
+```
 [
   {
     "index": 0,

From 7919256c57f05c09b0b50ec9abb37ff62dab7251 Mon Sep 17 00:00:00 2001
From: Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com>
Date: Thu, 30 Jan 2025 12:58:02 +0700
Subject: [PATCH 286/376] readme : reference examples relative links (#11505)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ff8536773..382c67041 100644
--- a/README.md
+++ b/README.md
@@ -422,7 +422,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
-[^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
+[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
 ## [`llama-bench`](examples/llama-bench)

From 496e5bf46bab757d05e18227cb4e17055ae42f42 Mon Sep 17 00:00:00 2001
From: Isaac McFadyen <isaac@imcf.me>
Date: Thu, 30 Jan 2025 04:11:53 -0500
Subject: [PATCH 287/376] server : (docs) added response format for
 /apply-template [no ci] (#11503)

---
 examples/server/README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/examples/server/README.md b/examples/server/README.md
index d1c9be589..44da503df 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -584,6 +584,10 @@ Uses the server's prompt template formatting functionality to convert chat messa
 
 `messages`: (Required) Chat turns in the same format as `/v1/chat/completions`.
 
+**Response format**
+
+Returns a JSON object with a field `prompt` containing a string of the input messages formatted according to the model's chat template format.
+
 ### POST `/embedding`: Generate embedding of a given text
 
 > [!IMPORTANT]

From 4314e56c4f8c5091f45732f39bd94c0c6c323798 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 30 Jan 2025 11:05:00 +0100
Subject: [PATCH 288/376] server : use lambda instead of std::bind (#11507)

This commit replaces the two usages of `std::bind` in favor of lambdas for
the callback functions for `callback_new_task` and
`callback_update_slots`.

The motivation for this changes is consistency with the rest of the code
in server.cpp (lambdas are used for all other callbacks/handlers). Also
lambdas are more readable (perhaps this is subjective) but also they are
recommended over `std::bind` in modern C++.

Ref: https://github.com/LithoCoders/dailycpp/blob/master/EffectiveModernC%2B%2B/chapter6/Item34_Prefer_lambdas_to_std::bind.md
---
 examples/server/server.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 6e28d283a..b9aa5c81c 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -4387,11 +4387,13 @@ int main(int argc, char ** argv) {
         ctx_server.chat_templates.template_default->source().c_str(),
         common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str());
 
-    ctx_server.queue_tasks.on_new_task(std::bind(
-                &server_context::process_single_task, &ctx_server, std::placeholders::_1));
+    ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) {
+        ctx_server.process_single_task(task);
+    });
 
-    ctx_server.queue_tasks.on_update_slots(std::bind(
-                &server_context::update_slots, &ctx_server));
+    ctx_server.queue_tasks.on_update_slots([&ctx_server]() {
+        ctx_server.update_slots();
+    });
 
     shutdown_handler = [&](int) {
         ctx_server.queue_tasks.terminate();

From ffd0821c57edc7e5d04338ab0c6b1461198df15f Mon Sep 17 00:00:00 2001
From: mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
Date: Thu, 30 Jan 2025 11:10:59 +0100
Subject: [PATCH 289/376] vocab : correctly identify LF token for GPT-2 style
 BPE tokenizer (#11496)

---
 src/llama-vocab.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 561f8bdb8..ad9ffe66a 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1692,7 +1692,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
         linefeed_id = ids[0];
     } else {
-        const std::vector<int> ids = tokenize("\xC4\x8A", false); // U+010A
+        const std::vector<int> ids = tokenize("\n", false);
 
         //GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
         if (ids.empty()) {

From 3d804dec7661fbb7de9b7f93267e2fc3ca0193c1 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Thu, 30 Jan 2025 10:30:27 +0000
Subject: [PATCH 290/376] sync: minja (#11499)

---
 common/chat-template.hpp | 252 +++++++++++++++++++++++++++------------
 common/minja.hpp         | 119 +++++++++---------
 2 files changed, 238 insertions(+), 133 deletions(-)

diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index 42ee0b615..75ba5d938 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -17,17 +17,26 @@ using json = nlohmann::ordered_json;
 
 namespace minja {
 
+struct chat_template_caps {
+    bool supports_tools = false;
+    bool supports_tool_calls = false;
+    bool supports_tool_responses = false;
+    bool supports_system_role = false;
+    bool supports_parallel_tool_calls = false;
+    bool supports_tool_call_id = false;
+    // meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object.
+    // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
+    bool requires_object_arguments = false;
+    // CohereForAI/c4ai-command-r-plus simple variant
+    bool requires_non_null_content = false;
+    // MiniMaxAI/MiniMax-Text-01 special
+    bool requires_typed_content = false;
+};
+
 class chat_template {
-  public:
 
   private:
-    bool supports_tools_ = true;
-    // Meta-Llama-3.1-8B-Instruct's template expects arguments to be an object.
-    // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
-    bool requires_object_arguments_ = false;
-    bool requires_typed_content_ = false;
-    bool supports_system_role_ = true;
-    bool supports_parallel_tool_calls_ = false;
+    chat_template_caps caps_;
     std::string source_;
     std::string bos_token_;
     std::string eos_token_;
@@ -41,15 +50,16 @@ class chat_template {
     {
         try {
             auto prompt = apply(messages, tools, add_generation_prompt, extra_context, /* adjust_inputs= */ false);
-            // fprintf(stderr, "Prompt: %s\n", prompt.c_str());
+            // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
             return prompt;
         } catch (const std::exception & e) {
-            // fprintf(stderr, "Error: %s\n", e.what());
+            // fprintf(stderr, "try_raw_render error: %s\n", e.what());
             return "";
         }
     }
 
   public:
+
     chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
         : source_(source), bos_token_(bos_token), eos_token_(eos_token)
     {
@@ -58,69 +68,120 @@ class chat_template {
             /* .lstrip_blocks = */ true,
             /* .keep_trailing_newline = */ false,
         });
-        supports_tools_ = source.find("tools") != std::string::npos;
 
-        auto renders_string_arguments =
-            try_raw_render({
-                {
-                    {"role", "user"},
-                    {"content", "Hey"}
-                },
-                {
-                    {"role", "assistant"},
-                    {"tool_calls", json::array({
-                        {
-                            {"id", "call_1___"},
-                            {"type", "function"},
-                            {"function", {
-                                {"arguments", "{\"code\": \"print('Hello, World!')\"}"},
-                                {"name", "ipython"},
+        auto contains = [](const std::string & haystack, const std::string & needle) {
+            return haystack.find(needle) != std::string::npos;
+        };
+
+        const std::string user_needle = "<User Needle>";
+        const std::string sys_needle = "<System Needle>";
+        const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}};
+        const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}};
+
+        caps_.requires_typed_content =
+            !contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle)
+            && contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle);
+
+        const auto dummy_user_msg = caps_.requires_typed_content
+            ? dummy_typed_user_msg
+            : dummy_str_user_msg;
+        const json needle_system_msg = {
+            {"role", "system"},
+            {"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)},
+        };
+
+        caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle);
+
+        auto out = try_raw_render(json::array({
+            dummy_user_msg
+        }), json::array({
+            {
+                {"name", "some_tool"},
+                {"type", "function"},
+                {"function", {
+                    {"name", "some_tool"},
+                    {"description", "Some tool."},
+                    {"parameters", {
+                        {"type", "object"},
+                        {"properties", {
+                            {"arg", {
+                                {"type", "string"},
+                                {"description", "Some argument."},
                             }},
-                        },
-                    })},
+                        }},
+                        {"required", json::array({ "arg" })},
+                    }},
+                }},
+            },
+        }), false);
+        caps_.supports_tools = contains(out, "some_tool");
+
+        auto make_tool_calls_msg = [&](const json & tool_calls) {
+            return json {
+                {"role", "assistant"},
+                {"content", nullptr},
+                {"tool_calls", tool_calls},
+            };
+        };
+        auto make_tool_call = [](const std::string & tool_name, const json & arguments) {
+            return json {
+                {"id", "call_1___"},
+                {"type", "function"},
+                {"function", {
+                    {"arguments", arguments},
+                    {"name", tool_name},
+                }},
+            };
+        };
+        const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}};
+
+        // Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want.
+        out = try_raw_render(json::array({
+            dummy_user_msg,
+            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
+        }), {}, false);
+        auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
+        out = try_raw_render(json::array({
+            dummy_user_msg,
+            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
+        }), {}, false);
+        auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
+
+        caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
+        caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
+        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
+        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
+        caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
+
+        if (caps_.supports_tool_calls) {
+            auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
+            auto tc1 = make_tool_call("test_tool1", dummy_args);
+            auto tc2 = make_tool_call("test_tool2", dummy_args);
+            auto out = try_raw_render(json::array({
+                dummy_user_msg,
+                make_tool_calls_msg(json::array({tc1, tc2})),
+            }), {}, false);
+            caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2");
+
+            out = try_raw_render(json::array({
+                dummy_user_msg,
+                make_tool_calls_msg(json::array({tc1})),
+                {
+                    {"role", "tool"},
+                    {"name", "test_tool1"},
+                    {"content", "Some response!"},
+                    {"tool_call_id", "call_911_"},
                 }
-            }, {}, false).find("{\"code\": \"print") != std::string::npos;
-        if (!renders_string_arguments) {
-            auto renders_object_arguments =
-                try_raw_render({
-                    {
-                        {"role", "user"},
-                        {"content", "Hey"}
-                    },
-                    {
-                        {"role", "assistant"},
-                        {"tool_calls", json::array({
-                            {
-                                {"id", "call_1___"},
-                                {"type", "function"},
-                                {"function", {
-                                    {"arguments", {
-                                        {"code", "print('Hello, World!')"},
-                                    }},
-                                    {"name", "ipython"},
-                                }},
-                            },
-                        })},
-                    }
-                }, {}, false).find("{\"code\": \"print") != std::string::npos;
-            requires_object_arguments_ = renders_object_arguments;
+            }), {}, false);
+            caps_.supports_tool_responses = contains(out, "Some response!");
+            caps_.supports_tool_call_id = contains(out, "call_911_");
         }
-        supports_parallel_tool_calls_ = source.find("tool_call_id") != std::string::npos;
-
-        supports_system_role_ = try_raw_render({
-            {{"role", "system"}, {"content", "<System Needle>"}},
-            {{"role", "user"},   {"content", "Hey"}}
-        }, {}, false).find("<System Needle>") != std::string::npos;
-
-        requires_typed_content_ = try_raw_render({{{"role", "user"},   {"content", "Hey"}}}, {}, false).find("Hey") == std::string::npos
-            && try_raw_render({{{"role", "user"},   {"content", {{{"type", "text"}, {"text", "Hey"}}}}}}, {}, false).find("Hey") != std::string::npos;
     }
 
     const std::string & source() const { return source_; }
     const std::string & bos_token() const { return bos_token_; }
     const std::string & eos_token() const { return eos_token_; }
-    bool supports_tools() const { return supports_tools_; }
-    bool supports_parallel_tool_calls() const { return supports_parallel_tool_calls_; }
+    const chat_template_caps & original_caps() const { return caps_; }
 
     std::string apply(
         const nlohmann::ordered_json & messages,
@@ -131,13 +192,19 @@ class chat_template {
     {
         json actual_messages;
 
-        // First, "fix" messages so they have a chance to be rendered correctly by the template
-
-        if (adjust_inputs && (requires_object_arguments_ || !supports_system_role_ || !supports_tools_ || requires_typed_content_)) {
+        auto needs_adjustments = adjust_inputs && (false
+            || !caps_.supports_system_role
+            || !caps_.supports_tools
+            || !caps_.supports_tool_responses
+            || !caps_.supports_tool_calls
+            || caps_.requires_object_arguments
+            || caps_.requires_typed_content
+        );
+        if (needs_adjustments) {
             actual_messages = json::array();
 
             auto add_message = [&](const json & msg) {
-                if (requires_typed_content_ && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
+                if (caps_.requires_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
                     actual_messages.push_back({
                         {"role", msg.at("role")},
                         {"content", {{
@@ -160,7 +227,9 @@ class chat_template {
                     pending_system.clear();
                 }
             };
-            for (const auto & message_ : messages) {
+            auto needs_tools_in_system = !tools.is_null() && tools.size() > 0 && !caps_.supports_tools;
+
+            for (const auto & message_ : needs_tools_in_system ? add_system(messages, "Available tools: " + tools.dump(2)) : messages) {
                 auto message = message_;
                 if (!message.contains("role") || !message.contains("content")) {
                     throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
@@ -168,16 +237,22 @@ class chat_template {
                 std::string role = message.at("role");
 
                 if (message.contains("tool_calls")) {
-                    if (requires_object_arguments_ || !supports_tools_) {
+                    if (caps_.requires_object_arguments || !caps_.supports_tool_calls) {
                         for (auto & tool_call : message.at("tool_calls")) {
                             if (tool_call["type"] == "function") {
                                 auto & function = tool_call.at("function");
-                                std::string arguments = function.at("arguments");
-                                function["arguments"] = json::parse(arguments);
+                                auto & arguments = function.at("arguments");
+                                if (arguments.is_string()) {
+                                    try {
+                                        arguments = json::parse(arguments.get<std::string>());
+                                    } catch (const std::exception & ecvt) {
+                                        fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what());
+                                    }
+                                }
                             }
                         }
                     }
-                    if (!supports_tools_) {
+                    if (!caps_.supports_tool_calls) {
                         auto content = message.at("content");
                         auto tool_calls = json::array();
                         for (const auto & tool_call : message.at("tool_calls")) {
@@ -204,7 +279,7 @@ class chat_template {
                         message.erase("tool_calls");
                     }
                 }
-                if (!supports_tools_ && role == "tool") {
+                if (!caps_.supports_tool_responses && role == "tool") {
                     message["role"] = "user";
                     auto obj = json {
                         {"tool_response", {
@@ -219,7 +294,7 @@ class chat_template {
                     message.erase("name");
                 }
 
-                if (!message["content"].is_null() && !supports_system_role_) {
+                if (!message["content"].is_null() && !caps_.supports_system_role) {
                     std::string content = message.at("content");
                     if (role == "system") {
                         if (!pending_system.empty()) pending_system += "\n";
@@ -238,7 +313,9 @@ class chat_template {
                 }
                 add_message(message);
             }
-            flush_sys();
+            if (!caps_.supports_system_role) {
+                flush_sys();
+            }
         } else {
             actual_messages = messages;
         }
@@ -261,7 +338,28 @@ class chat_template {
             }
         }
 
-        return template_root_->render(context);
+        auto ret = template_root_->render(context);
+        // fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str());
+        // fprintf(stderr, "apply: %s\n\n", ret.c_str());
+        return ret;
+    }
+
+    static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) {
+        json messages_with_system = messages;
+
+        if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") {
+            std::string existing_system = messages_with_system.at(0).at("content");
+            messages_with_system[0] = json {
+                {"role", "system"},
+                {"content", existing_system + "\n" + system_prompt},
+            };
+        } else {
+            messages_with_system.insert(messages_with_system.begin(), json {
+                {"role", "system"},
+                {"content", system_prompt},
+            });
+        }
+        return messages_with_system;
     }
 };
 
diff --git a/common/minja.hpp b/common/minja.hpp
index 80bdd4b41..f0e80fd7c 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -628,7 +628,7 @@ class Context : public std::enable_shared_from_this<Context> {
         if (parent_) return parent_->contains(key);
         return false;
     }
-    virtual void set(const Value & key, Value & value) {
+    virtual void set(const Value & key, const Value & value) {
         values_.set(key, value);
     }
 };
@@ -2648,31 +2648,34 @@ inline std::shared_ptr<Context> Context::builtins() {
       return filter.call(context, actual_args);
     });
   };
-  // https://jinja.palletsprojects.com/en/3.0.x/templates/#jinja-filters.reject
-  globals.set("reject", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
-    args.expectArgs("reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
-    auto & items = args.args[0];
-    auto filter_fn = context->get(args.args[1]);
-    if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
+  auto select_or_reject = [make_filter](bool is_select) {
+    return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+      args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
+      auto & items = args.args[0];
+      auto filter_fn = context->get(args.args[1]);
+      if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
 
-    auto filter_args = Value::array();
-    for (size_t i = 2, n = args.args.size(); i < n; i++) {
-      filter_args.push_back(args.args[i]);
-    }
-    auto filter = make_filter(filter_fn, filter_args);
-
-    auto res = Value::array();
-    for (size_t i = 0, n = items.size(); i < n; i++) {
-      auto & item = items.at(i);
-      ArgumentsValue filter_args;
-      filter_args.args.emplace_back(item);
-      auto pred_res = filter.call(context, filter_args);
-      if (!pred_res.to_bool()) {
-        res.push_back(item);
+      auto filter_args = Value::array();
+      for (size_t i = 2, n = args.args.size(); i < n; i++) {
+        filter_args.push_back(args.args[i]);
       }
-    }
-    return res;
-  }));
+      auto filter = make_filter(filter_fn, filter_args);
+
+      auto res = Value::array();
+      for (size_t i = 0, n = items.size(); i < n; i++) {
+        auto & item = items.at(i);
+        ArgumentsValue filter_args;
+        filter_args.args.emplace_back(item);
+        auto pred_res = filter.call(context, filter_args);
+        if (pred_res.to_bool() == (is_select ? true : false)) {
+          res.push_back(item);
+        }
+      }
+      return res;
+    });
+  };
+  globals.set("select", select_or_reject(/* is_select= */ true));
+  globals.set("reject", select_or_reject(/* is_select= */ false));
   globals.set("map", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
     auto res = Value::array();
     if (args.args.size() == 1 &&
@@ -2720,41 +2723,45 @@ inline std::shared_ptr<Context> Context::builtins() {
     if (!text.empty() && text.back() == '\n') out += "\n";
     return out;
   }));
-  globals.set("selectattr", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
-    args.expectArgs("selectattr", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
-    auto & items = args.args[0];
-    if (items.is_null())
-      return Value::array();
-    auto attr_name = args.args[1].get<std::string>();
+  auto select_or_reject_attr = [](bool is_select) {
+    return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
+      args.expectArgs(is_select ? "selectattr" : "rejectattr", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
+      auto & items = args.args[0];
+      if (items.is_null())
+        return Value::array();
+      auto attr_name = args.args[1].get<std::string>();
 
-    bool has_test = false;
-    Value test_fn;
-    ArgumentsValue test_args {{Value()}, {}};
-    if (args.args.size() >= 3) {
-      has_test = true;
-      test_fn = context->get(args.args[2]);
-      if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump());
-      for (size_t i = 3, n = args.args.size(); i < n; i++) {
-        test_args.args.emplace_back(args.args[i]);
-      }
-      test_args.kwargs = args.kwargs;
-    }
-
-    auto res = Value::array();
-    for (size_t i = 0, n = items.size(); i < n; i++) {
-      auto & item = items.at(i);
-      auto attr = item.get(attr_name);
-      if (has_test) {
-        test_args.args[0] = attr;
-        if (test_fn.call(context, test_args).to_bool()) {
-          res.push_back(item);
+      bool has_test = false;
+      Value test_fn;
+      ArgumentsValue test_args {{Value()}, {}};
+      if (args.args.size() >= 3) {
+        has_test = true;
+        test_fn = context->get(args.args[2]);
+        if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump());
+        for (size_t i = 3, n = args.args.size(); i < n; i++) {
+          test_args.args.emplace_back(args.args[i]);
         }
-      } else {
-        res.push_back(attr);
+        test_args.kwargs = args.kwargs;
       }
-    }
-    return res;
-  }));
+
+      auto res = Value::array();
+      for (size_t i = 0, n = items.size(); i < n; i++) {
+        auto & item = items.at(i);
+        auto attr = item.get(attr_name);
+        if (has_test) {
+          test_args.args[0] = attr;
+          if (test_fn.call(context, test_args).to_bool() == (is_select ? true : false)) {
+            res.push_back(item);
+          }
+        } else {
+          res.push_back(attr);
+        }
+      }
+      return res;
+    });
+  };
+  globals.set("selectattr", select_or_reject_attr(/* is_select= */ true));
+  globals.set("rejectattr", select_or_reject_attr(/* is_select= */ false));
   globals.set("range", Value::callable([=](const std::shared_ptr<Context> &, ArgumentsValue & args) {
     std::vector<int64_t> startEndStep(3);
     std::vector<bool> param_set(3);

From c300e68ef490e6cf6c04ed96fd27a6a53ab8a422 Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Wed, 29 Jan 2025 17:46:23 +0100
Subject: [PATCH 291/376] CUDA/HIP: add warp_size to cuda_device_info

---
 ggml/src/ggml-cuda/common.cuh   | 1 +
 ggml/src/ggml-cuda/ggml-cuda.cu | 6 ++++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index a66322da0..eec227dce 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -520,6 +520,7 @@ struct ggml_cuda_device_info {
         bool    vmm;                // virtual memory support
         size_t  vmm_granularity;    // granularity of virtual memory
         size_t  total_vram;
+        int     warp_size;          // Number of threads in a dispatch
     };
 
     cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {};
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index de3f9c2ca..ecf06fec4 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -242,6 +242,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
 
         info.devices[id].nsm   = prop.multiProcessorCount;
         info.devices[id].smpb  = prop.sharedMemPerBlock;
+        info.devices[id].warp_size = prop.warpSize;
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
         info.devices[id].smpbo = prop.sharedMemPerBlock;
 
@@ -256,8 +257,9 @@ static ggml_cuda_device_info ggml_cuda_init() {
                 info.devices[id].cc += prop.minor * 0x10;
             }
         }
-        GGML_LOG_INFO("  Device %d: %s, %s (0x%x), VMM: %s\n",
-                        id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, device_vmm ? "yes" : "no");
+        GGML_LOG_INFO("  Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d\n",
+                      id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff,
+                      device_vmm ? "yes" : "no", prop.warpSize);
 #else
         info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
         info.devices[id].cc = 100*prop.major + 10*prop.minor;

From 6af1ca48cbdf9a438438afd0a9a549a272bc95bf Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Wed, 29 Jan 2025 19:12:42 +0100
Subject: [PATCH 292/376] HIP: Prepare reduction operators for wave 64

---
 ggml/src/ggml-cuda/common.cuh   | 59 +++++++++++++++------------------
 ggml/src/ggml-cuda/ggml-cuda.cu |  4 +--
 2 files changed, 28 insertions(+), 35 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index eec227dce..8d8d3932e 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -190,53 +190,46 @@ static __device__ void no_device_code(
 #define NO_DEVICE_CODE //GGML_ABORT("NO_DEVICE_CODE not valid in host code.")
 #endif // __CUDA_ARCH__
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_sum(int x) {
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
     return __reduce_add_sync(0xffffffff, x);
 #else
 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        x += __shfl_xor_sync(0xffffffff, x, offset, 32);
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, offset, width);
     }
     return x;
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 }
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ float warp_reduce_sum(float x) {
 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        x += __shfl_xor_sync(0xffffffff, x, offset, 32);
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, offset, width);
     }
     return x;
 }
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32);
-        a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32);
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, offset, width);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, offset, width);
     }
     return a;
 }
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 #ifdef FP16_AVAILABLE
-
-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32);
-        reinterpret_cast<half&>(a.x) +=  __low2half(a_other);
-        reinterpret_cast<half&>(a.y) += __high2half(a_other);
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, width));
     }
     return a;
-#else
-#pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32));
-    }
-    return a;
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 
 #else
     NO_DEVICE_CODE;
@@ -244,10 +237,11 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 #endif // FP16_AVAILABLE
 }
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ float warp_reduce_max(float x) {
 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
-        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, width));
     }
     return x;
 }
@@ -269,35 +263,34 @@ static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b
 }
 
 static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
-
-#if CUDART_VERSION >= CUDART_HMAX
+#if defined(GGML_USE_HIP) && HIP_VERSION >= 50700000
+    return half2(__hmax(a.x, b.x), __hmax(a.y, b.y));
+#elif !defined(GGML_USE_HIP) && CUDART_VERSION >= CUDART_HMAX
     return __hmax2(a, b);
-#else
+#elif !defined(GGML_USE_HIP)
     half2 ret;
     reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a),  __low2float(b)));
     reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
     return ret;
-#endif // CUDART_VERSION >= CUDART_HMAX
-
 #else
     GGML_UNUSED(a);
     GGML_UNUSED(b);
     NO_DEVICE_CODE;
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif
 }
 
+template<int width = WARP_SIZE>
 static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 #pragma unroll
-   for (int offset = 16; offset > 0; offset >>= 1) {
-       x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
+   for (int offset = width/2; offset > 0; offset >>= 1) {
+       x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
    }
    return x;
 #else
    GGML_UNUSED(x);
    NO_DEVICE_CODE;
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 }
 
 #if CUDART_VERSION < CUDART_HMASK
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index ecf06fec4..383131c77 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -240,8 +240,8 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.default_tensor_split[id] = total_vram;
         total_vram += prop.totalGlobalMem;
 
-        info.devices[id].nsm   = prop.multiProcessorCount;
-        info.devices[id].smpb  = prop.sharedMemPerBlock;
+        info.devices[id].nsm       = prop.multiProcessorCount;
+        info.devices[id].smpb      = prop.sharedMemPerBlock;
         info.devices[id].warp_size = prop.warpSize;
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
         info.devices[id].smpbo = prop.sharedMemPerBlock;

From 27d135c970c00f655d486f870edacded792bef5c Mon Sep 17 00:00:00 2001
From: uvos <devnull@uvos.xyz>
Date: Wed, 29 Jan 2025 19:36:00 +0100
Subject: [PATCH 293/376] HIP: require at least HIP 5.5

---
 ggml/src/ggml-hip/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index ecc3bc66d..7a877bdc1 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -40,6 +40,10 @@ find_package(hip     REQUIRED)
 find_package(hipblas REQUIRED)
 find_package(rocblas REQUIRED)
 
+if (${hip_VERSION} VERSION_LESS 5.5)
+    message(FATAL_ERROR "At least ROCM/HIP V5.5 is required")
+endif()
+
 message(STATUS "HIP and hipBLAS found")
 
 file(GLOB   GGML_HEADERS_ROCM "../ggml-cuda/*.cuh")

From 8b576b6c55bc4e6be898b47522f0ef402b93ef62 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Thu, 30 Jan 2025 19:13:58 +0000
Subject: [PATCH 294/376] Tool call support (generic + native for Llama,
 Functionary, Hermes, Mistral, Firefunction, DeepSeek) w/ lazy grammars
 (#9639)

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 .editorconfig                                 |   8 +
 .github/workflows/server.yml                  |   2 +-
 Makefile                                      |   9 +
 README.md                                     |   1 +
 common/CMakeLists.txt                         |   2 +
 common/chat.cpp                               | 848 ++++++++++++++++++
 common/chat.hpp                               |  50 ++
 common/common.cpp                             |  22 +-
 common/common.h                               |  17 +-
 common/json-schema-to-grammar.cpp             |  15 +-
 common/json-schema-to-grammar.h               |   9 +-
 common/sampling.cpp                           |  11 +-
 examples/gbnf-validator/gbnf-validator.cpp    |   2 +-
 examples/main/main.cpp                        |  27 +-
 examples/server/README.md                     |  76 ++
 examples/server/server.cpp                    | 187 ++--
 examples/server/tests/README.md               |  13 +-
 examples/server/tests/pytest.ini              |   4 +
 examples/server/tests/tests.sh                |  11 +-
 .../server/tests/unit/test_chat_completion.py |  11 +-
 examples/server/tests/unit/test_tool_call.py  | 352 ++++++++
 examples/server/tests/utils.py                |   6 +-
 examples/server/utils.hpp                     |  80 +-
 include/llama.h                               |  12 +
 ...reForAI-c4ai-command-r-plus-tool_use.jinja | 202 +++++
 ...rch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | 152 ++++
 ...earch-Hermes-3-Llama-3.1-8B-tool_use.jinja | 152 ++++
 .../templates/Qwen-Qwen2.5-7B-Instruct.jinja  |  54 ++
 ...seek-ai-DeepSeek-R1-Distill-Llama-8B.jinja |   1 +
 ...seek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja |  56 ++
 ...fireworks-ai-llama-3-firefunction-v2.jinja |  57 ++
 models/templates/google-gemma-2-2b-it.jinja   |   4 +
 .../meetkai-functionary-medium-v3.1.jinja     |  58 ++
 .../meetkai-functionary-medium-v3.2.jinja     | 287 ++++++
 .../meta-llama-Llama-3.1-8B-Instruct.jinja    | 109 +++
 .../meta-llama-Llama-3.2-3B-Instruct.jinja    |  93 ++
 .../meta-llama-Llama-3.3-70B-Instruct.jinja   | 109 +++
 .../microsoft-Phi-3.5-mini-instruct.jinja     |   8 +
 ...mistralai-Mistral-Nemo-Instruct-2407.jinja |  87 ++
 scripts/fetch_server_test_models.py           | 105 +++
 ..._chat_template.py => get_chat_template.py} |  12 +-
 src/llama-grammar.cpp                         |  88 +-
 src/llama-grammar.h                           |  23 +-
 src/llama-sampling.cpp                        |  51 +-
 tests/CMakeLists.txt                          |   1 +
 tests/test-chat-template.cpp                  |  10 +-
 tests/test-chat.cpp                           | 521 +++++++++++
 tests/test-grammar-integration.cpp            |   2 +-
 48 files changed, 3861 insertions(+), 156 deletions(-)
 create mode 100644 common/chat.cpp
 create mode 100644 common/chat.hpp
 create mode 100644 examples/server/tests/pytest.ini
 create mode 100644 examples/server/tests/unit/test_tool_call.py
 create mode 100644 models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja
 create mode 100644 models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja
 create mode 100644 models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja
 create mode 100644 models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
 create mode 100644 models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja
 create mode 100644 models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja
 create mode 100644 models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
 create mode 100644 models/templates/google-gemma-2-2b-it.jinja
 create mode 100644 models/templates/meetkai-functionary-medium-v3.1.jinja
 create mode 100644 models/templates/meetkai-functionary-medium-v3.2.jinja
 create mode 100644 models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja
 create mode 100644 models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja
 create mode 100644 models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja
 create mode 100644 models/templates/microsoft-Phi-3.5-mini-instruct.jinja
 create mode 100644 models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja
 create mode 100755 scripts/fetch_server_test_models.py
 rename scripts/{get_hf_chat_template.py => get_chat_template.py} (86%)
 mode change 100755 => 100644
 create mode 100644 tests/test-chat.cpp

diff --git a/.editorconfig b/.editorconfig
index eac38a15f..5d63d0a51 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -40,3 +40,11 @@ indent_style = tab
 [examples/cvector-generator/*.txt]
 trim_trailing_whitespace = unset
 insert_final_newline = unset
+
+[models/templates/*.jinja]
+indent_style = unset
+indent_size = unset
+end_of_line = unset
+charset = unset
+trim_trailing_whitespace = unset
+insert_final_newline = unset
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index ed1c357a5..0cbc3d640 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -205,7 +205,7 @@ jobs:
         run: |
           cd examples/server/tests
           $env:PYTHONIOENCODING = ":replace"
-          pytest -v -x
+          pytest -v -x -m "not slow"
 
       - name: Slow tests
         id: server_integration_tests_slow
diff --git a/Makefile b/Makefile
index 295522ba3..ef152d246 100644
--- a/Makefile
+++ b/Makefile
@@ -52,6 +52,7 @@ TEST_TARGETS = \
 	tests/test-arg-parser \
 	tests/test-autorelease \
 	tests/test-backend-ops \
+	tests/test-chat \
 	tests/test-chat-template \
 	tests/test-double-float \
 	tests/test-grammar-integration \
@@ -983,6 +984,7 @@ OBJ_COMMON = \
 	$(DIR_COMMON)/ngram-cache.o \
 	$(DIR_COMMON)/sampling.o \
 	$(DIR_COMMON)/speculative.o \
+	$(DIR_COMMON)/chat.o \
 	$(DIR_COMMON)/build-info.o \
 	$(DIR_COMMON)/json-schema-to-grammar.o
 
@@ -1361,6 +1363,8 @@ llama-server: \
 	examples/server/httplib.h \
 	examples/server/index.html.hpp \
 	examples/server/loading.html.hpp \
+	common/chat.cpp \
+	common/chat.hpp \
 	common/chat-template.hpp \
 	common/json.hpp \
 	common/minja.hpp \
@@ -1471,6 +1475,11 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
 	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+tests/test-chat: tests/test-chat.cpp \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 tests/test-opt: tests/test-opt.cpp \
 	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
diff --git a/README.md b/README.md
index 382c67041..d40309875 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 - **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
 - **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
+- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639
 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
 - Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
 - Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 24b7f8741..72f0915c1 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -56,6 +56,8 @@ add_library(${TARGET} STATIC
     arg.cpp
     arg.h
     base64.hpp
+    chat.cpp
+    chat.hpp
     chat-template.hpp
     common.cpp
     common.h
diff --git a/common/chat.cpp b/common/chat.cpp
new file mode 100644
index 000000000..d9a654892
--- /dev/null
+++ b/common/chat.cpp
@@ -0,0 +1,848 @@
+#include "chat.hpp"
+#include "chat-template.hpp"
+#include "json-schema-to-grammar.h"
+#include "log.h"
+#include "minja.hpp"
+
+std::string common_chat_format_name(common_chat_format format) {
+    switch (format) {
+        case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
+        case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
+        case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo";
+        case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x";
+        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools";
+        case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1";
+        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
+        case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
+        default:
+            throw std::runtime_error("Unknown chat format");
+    }
+}
+
+const common_grammar_options grammar_options {
+    /* .dotall = */ false,
+    /* .compact_spaces = */ false,
+    // /* .compact_spaces = */ true,
+};
+
+static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) {
+    // // https://json.nlohmann.me/features/parsing/sax_interface/
+    struct json_error_locator : public nlohmann::json_sax<json> {
+        std::size_t position;
+        bool found_error;
+
+        json_error_locator() : position(0), found_error(false) {}
+
+        bool parse_error(std::size_t position, const std::string &, const json::exception &) override {
+            this->position = position - 1;
+            this->found_error = true;
+            return false;
+        }
+        bool null() override { return true; }
+        bool boolean(bool) override { return true; }
+        bool number_integer(number_integer_t) override { return true; }
+        bool number_unsigned(number_unsigned_t) override { return true; }
+        bool number_float(number_float_t, const string_t &) override { return true; }
+        bool string(string_t &) override { return true; }
+        bool binary(binary_t &) override { return true; }
+        bool start_object(std::size_t) override { return true; }
+        bool key(string_t &) override { return true; }
+        bool end_object() override { return true; }
+        bool start_array(std::size_t) override { return true; }
+        bool end_array() override { return true; }
+    };
+    json_error_locator err_loc;
+    json::sax_parse(it, end, &err_loc);
+
+    std::string::const_iterator temptative_end;
+    if (err_loc.found_error) {
+        temptative_end = it + err_loc.position;
+    } else {
+        temptative_end = end;
+    }
+    std::string json_sub {it, temptative_end};
+    try {
+        out = json::parse(json_sub);
+        it = temptative_end;
+        return true;
+    } catch (const std::exception &) {
+        return false;
+    }
+}
+
+
+/**
+ * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
+ * Aggregates the prefix, suffix and in-between text into the content.
+ */
+static common_chat_msg parse_json_tool_calls(
+    const std::string& input,
+    const std::optional<std::regex> & trigger_opt,
+    const std::regex & function_regex,
+    const std::regex & close_regex) {
+    std::smatch match;
+
+    common_chat_msg result;
+    result.role = "assistant";
+
+
+    auto end = input.end();
+    auto it = input.begin();
+
+    if (trigger_opt) {
+        if (!std::regex_search(it, end, match, *trigger_opt)) {
+            result.content = input;
+            return result;
+        }
+        result.content = match.prefix().str();
+        it = match.suffix().first;
+    }
+
+    while (it != end) {
+        std::sregex_iterator rend;
+        std::sregex_iterator rit(it, end, function_regex);
+        if (rit == rend) {
+            fprintf(stderr, "No more tool calls found\n");
+            result.content += std::string(it, end);
+            break;
+        }
+        auto name = rit->str(1);
+        result.content += std::string(it, rit->prefix().second);
+        it = rit->suffix().first;
+
+        json arguments;
+        if (!parse_json(it, end, arguments)) {
+            throw std::runtime_error("Failed to parse json tool call arguments");
+        }
+        if (!std::regex_search(it, end, match, close_regex)) {
+            throw std::runtime_error("Malformed input, missing closing pattern");
+        }
+        it = match.suffix().first;
+        result.tool_calls.push_back({name, arguments.is_string() ? arguments.get<std::string>() : arguments.dump(), /* id= */ ""});
+    }
+    return result;
+}
+
+static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) {
+    auto content_end = input.find(prefix);
+    size_t tc_start = std::string::npos;
+
+    common_chat_msg result;
+    result.role = "assistant";
+    const auto process_tool_calls = [&](const json & tool_calls) {
+        for (const auto & tool_call : tool_calls) {
+            const auto & arguments = tool_call["arguments"];
+            result.tool_calls.push_back({
+                tool_call["name"],
+                arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
+                tool_call.contains("id") ? tool_call["id"] : "",
+            });
+        }
+    };
+    if (content_end == std::string::npos) {
+        result.content = input;
+    } else {
+        tc_start = content_end + prefix.size() - rstrip_prefix;
+        result.content = input.substr(0, content_end);
+        auto tool_calls = json::parse(input.substr(tc_start));
+        process_tool_calls(tool_calls);
+    }
+    return result;
+}
+
+static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
+    for (const auto & tool : tools) {
+        if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) {
+            LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
+            continue;
+        }
+        fn(tool);
+    }
+}
+
+static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+
+    auto tool_call_schemas = json::array();
+    foreach_function(inputs.tools, [&](const json & tool) {
+        const auto & function = tool["function"];
+        auto tool_schema = json {
+            {"type", "object"},
+            {"properties", {
+                {"name", {
+                    {"type", "string"},
+                    {"const", function["name"]},
+                }},
+                {"arguments", function["parameters"]},
+            }},
+            {"required", json::array({"name", "arguments"})},
+        };
+        if (function.contains("description")) {
+            tool_schema["description"] = function["description"];
+        }
+        if (inputs.parallel_tool_calls) {
+            tool_schema["properties"]["id"] = {
+                {"type", "string"},
+                {"minLength", 4},
+            };
+            tool_schema["required"].push_back("id");
+        }
+        tool_call_schemas.emplace_back(tool_schema);
+    });
+    const auto tool_call =
+        inputs.parallel_tool_calls
+            ? json {
+                {"type", "object"},
+                {"properties", {
+                    {"tool_calls", {
+                        {"type", "array"},
+                        {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
+                            {"anyOf", tool_call_schemas},
+                        }},
+                        {"minItems", 1},
+                    }},
+                }},
+                {"required", json::array({"tool_calls"})},
+            }
+            : json {
+                {"type", "object"},
+                {"properties", {
+                    {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
+                        {"anyOf", tool_call_schemas},
+                    }},
+                }},
+                {"required", json::array({"tool_call"})},
+            };
+    const auto schema =
+        inputs.tool_choice != "required"
+            ? json {
+                {"anyOf", json::array({
+                    tool_call,
+                    {
+                        {"type", "object"},
+                        {"properties", {
+                            {"response", inputs.json_schema.is_null()
+                                ? json {{"type", "string"}}
+                                : inputs.json_schema
+                            },
+                        }},
+                        {"required", json::array({"response"})},
+                    },
+                })}
+            }
+            : tool_call;
+
+    data.grammar_lazy = false;
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        builder.add_schema("root", schema);
+    }, grammar_options);
+
+    auto tweaked_messages = common_chat_template::add_system(
+        inputs.messages,
+        "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
+
+    data.prompt = tmpl.apply(tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_GENERIC;
+    return data;
+}
+static common_chat_msg common_chat_parse_generic(const std::string & input) {
+    json data = json::parse(input);
+    common_chat_msg result;
+    result.role = "assistant";
+    if (data.contains("tool_calls")) {
+        for (const auto & tool_call : data["tool_calls"]) {
+            result.tool_calls.push_back({
+                tool_call["name"],
+                tool_call["arguments"].dump(),
+                tool_call.contains("id") ? tool_call["id"] : "",
+            });
+        }
+    } else if (data.contains("tool_call")) {
+        result.tool_calls.push_back({
+            data["tool_call"]["name"],
+            data["tool_call"]["arguments"].dump(),
+            /* id= */ "",
+        });
+    } else if (data.contains("response")) {
+        const auto & response = data["response"];
+        result.content = response.is_string() ? response.get<std::string>() : response.dump(2);
+    }
+    return result;
+}
+
+static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        auto schemas = json::array();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            schemas.push_back({
+                {"type", "object"},
+                {"properties", {
+                    // Important note: the model is probably trained to take a JSON stringified arguments value.
+                    // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object.
+                    {"name", {
+                        {"type", "string"},
+                        {"const", function["name"]},
+                    }},
+                    {"arguments", function["parameters"]},
+                    {"id", {
+                        {"type", "string"},
+                        // Nemo's template expects a 9-character alphanumeric ID.
+                        {"pattern", "^[a-zA-Z0-9]{9}$"},
+                    }},
+                }},
+                {"required", json::array({"name", "arguments", "id"})},
+            });
+        });
+        auto schema = json {
+            {"type", "array"},
+            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
+            {"minItems", 1},
+        };
+        if (!inputs.parallel_tool_calls) {
+            schema["maxItems"] = 1;
+        }
+        builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
+    }, grammar_options);
+    data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
+    return data;
+}
+static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) {
+    return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
+}
+
+static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
+    if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
+        throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
+    }
+    const auto & parameters_properties = parameters.at("properties");
+    const auto & parameters_required = parameters.at("required");
+    for (const auto & prop : expected_properties) {
+        if (!parameters_properties.contains(prop)) {
+            throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop);
+        }
+        if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) {
+            throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop);
+        }
+    }
+    if (parameters_properties.size() != expected_properties.size()) {
+        throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", "));
+    }
+}
+
+static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) {
+    auto builtin_tools = json::array();
+    common_chat_params data;
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        std::vector<std::string> tool_rules;
+
+        auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
+            if (name == "wolfram_alpha") {
+                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
+                expect_tool_parameters(name, parameters, {"query"});
+            } else if (name == "web_search" || name == "brave_search") {
+                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
+                expect_tool_parameters(name, parameters, {"query"});
+            } else if (name == "python" || name == "code_interpreter") {
+                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
+                expect_tool_parameters(name, parameters, {"code"});
+            } else {
+                return false;
+            }
+
+            std::vector<std::string> kvs;
+            for (const auto & [key, value] : parameters.at("properties").items()) {
+                kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value));
+            }
+
+            tool_rules.push_back(
+                builder.add_rule(
+                    name + "-call",
+                    "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
+            builtin_tools.push_back(name);
+
+            return true;
+        };
+
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            std::string name = function["name"];
+            auto parameters = function["parameters"];
+            builder.resolve_refs(parameters);
+
+            // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
+            if (allow_python_tag_builtin_tools) {
+                handle_builtin_tool(name, parameters);
+            }
+            tool_rules.push_back(
+                builder.add_rule(
+                    name + "-call",
+                    "\"{\" ( \"\\\"type\\\": \\\"function\\\", \" | space ) "
+                    "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
+                        builder.add_schema(name + "-args", parameters) +
+                    " \"}\""));
+            data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
+        });
+        data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
+        data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
+        if (!builtin_tools.empty()) {
+            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
+        }
+        builder.add_rule("root", string_join(tool_rules, " | "));
+    }, grammar_options);
+    data.additional_stops.push_back("<|eom_id|>");
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
+        {"tools_in_user_message", false},
+        {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
+    });
+    data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
+        ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
+        : COMMON_CHAT_FORMAT_LLAMA_3_X;
+    return data;
+}
+static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
+    // TODO: tighten & simplify the parser, don't accept leading text context.
+    static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": ");
+    static std::regex close_regex("\\}");
+    static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)");
+
+    if (with_builtin_tools) {
+        std::smatch match;
+        if (std::regex_match(input, match, builtin_call_regex)) {
+            auto name = match[1].str();
+            auto raw_args = match[2].str();
+
+            // TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing.
+            auto it_eq = raw_args.find('=');
+            auto arg_name = raw_args.substr(0, it_eq);
+            auto arg_value_str = raw_args.substr(it_eq + 1);
+            auto arg_value = json::parse(arg_value_str);
+
+            return {
+                /* .role = */ "assistant",
+                /* .content = */ match.prefix().str(),
+                /* .tool_calls = */ {
+                    {
+                        /* .name = */ match[1],
+                        /* .arguments = */ (json {
+                            {arg_name, arg_value},
+                        }).dump(),
+                        /* .id = */ "",
+                    },
+                },
+            };
+        }
+    }
+    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
+}
+
+static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        std::vector<std::string> tool_rules;
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            std::string name = function["name"];
+            auto parameters = function["parameters"];
+            auto args_rule = builder.add_schema(name + "-args", parameters);
+            tool_rules.push_back(builder.add_rule(name + "-call",
+                "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
+        });
+        data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
+        builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
+    }, grammar_options);
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
+    return data;
+}
+static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) {
+    static std::regex trigger_regex("<｜tool▁calls▁begin｜>");
+    static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
+    static std::regex close_regex("```<｜tool▁call▁end｜>");
+    return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex);
+}
+
+static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    fprintf(stderr, "%s\n", __func__);
+    common_chat_params data;
+    data.prompt = tmpl.apply(inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
+        {"datetime", "Jan 29 2025 13:00:00 GMT"},
+        {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
+    }, /* adjust_inputs= */ false);
+    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
+        data.grammar_lazy = inputs.tool_choice != "required";
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            auto schemas = json::array();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool["function"];
+                schemas.push_back({
+                    {"type", "object"},
+                    {"properties", {
+                        {"name", {
+                            {"type", "string"},
+                            {"const", function["name"]},
+                        }},
+                        {"arguments", function["parameters"]},
+                    }},
+                    {"required", json::array({"name", "arguments", "id"})},
+                });
+            });
+            auto schema = json {
+                {"type", "array"},
+                {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
+                {"minItems", 1},
+            };
+            if (!inputs.parallel_tool_calls) {
+                schema["maxItems"] = 1;
+            }
+            builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema));
+        }, grammar_options);
+        data.grammar_triggers.push_back({" functools[", /* .at_start = */ false});
+        data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2;
+    } else {
+        data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    }
+    return data;
+}
+static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) {
+    return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1);
+}
+
+static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
+    // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
+    common_chat_params data;
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
+    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
+        data.grammar_lazy = inputs.tool_choice != "required";
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> first_tool_rules;
+            std::vector<std::string> subsequent_tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool["function"];
+                std::string name = function["name"];
+                auto parameters = function["parameters"];
+                auto args_rule = builder.add_schema(name + "-args", parameters);
+                first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule));
+                subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule));
+                data.grammar_triggers.push_back({name, /* .at_start = */ true});
+                data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false});
+            });
+            auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space";
+            if (inputs.parallel_tool_calls) {
+                auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space";
+                builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*");
+            } else {
+                builder.add_rule("root", first_rule);
+            }
+
+        }, grammar_options);
+    }
+    return data;
+}
+
+static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) {
+    auto expected_it = expected.begin();
+    auto tmp_it = it;
+    while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) {
+        ++tmp_it;
+        ++expected_it;
+    }
+    if (expected_it == expected.end()) {
+        it = tmp_it;
+        return true;
+    }
+    return false;
+}
+
+static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
+    static std::regex function_regex(R"((?:>>>)?(\w+)\n)");
+    static std::regex close_regex(R"($|(?=>>>))");
+
+    std::string content;
+    auto it = input.begin();
+    const auto end = input.end();
+
+    if (consume(it, end, "all\n")) {
+        std::smatch match;
+        if (std::regex_search(it, end, match, function_regex)) {
+            auto fun_it = match.prefix().second;
+            content = std::string(it, fun_it);
+            it = fun_it;
+        } else {
+            common_chat_msg res;
+            res.role = "assistant";
+            res.content = std::string(it, end);
+            return res;
+        }
+    }
+    // TODO: tighten & simplify.
+    auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
+    res.content = content;
+    return res;
+}
+
+static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
+    common_chat_params data;
+    json tools = inputs.tools.is_null() ? inputs.tools : json::array();
+    std::string python_code_argument_name;
+    auto has_raw_python = false;
+
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        std::vector<std::string> tool_rules;
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            const auto & parameters = function["parameters"];
+            std::string name = function["name"];
+            if (name == "python" || name == "ipython") {
+                if (!parameters.contains("type")) {
+                    throw std::runtime_error("Missing type in python tool");
+                }
+                has_raw_python = true;
+                auto type = parameters.at("type");
+                if (type == "object") {
+                    auto properties = parameters.at("properties");
+                    for (auto it = properties.begin(); it != properties.end(); ++it) {
+                        if (it.value().at("type") == "string") {
+                            if (!python_code_argument_name.empty()) {
+                                throw std::runtime_error("Multiple string arguments found in python tool");
+                            }
+                            python_code_argument_name = it.key();
+                        }
+                    }
+                    if (python_code_argument_name.empty()) {
+                        throw std::runtime_error("No string argument found in python tool");
+                    }
+                } else if (type != "string") {
+                    throw std::runtime_error("Invalid type in python tool: " + type.dump());
+                }
+            }
+            tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
+        });
+        if (has_raw_python) {
+            tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
+            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
+        }
+        auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
+        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
+        data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
+    }, grammar_options);
+
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    // TODO: if (has_raw_python)
+    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
+    return data;
+}
+static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
+    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
+    static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
+    std::smatch match;
+    if (std::regex_search(input, match, python_tag_regex)) {
+        auto code = match[1].str();
+        return {
+            /* .role = */ "assistant",
+            /* .content = */ match.prefix().str(),
+            /* .tool_calls = */ {
+                {
+                    /* .name = */ "python",
+                    /* .arguments = */ (json {{"code", code}}).dump(),
+                    /* .id = */ "",
+                },
+            }
+        };
+    }
+    static std::regex function_regex(R"(<function=(\w+)>)");
+    static std::regex close_regex(R"(</function>)");
+    // TODO: tighten & simplify.
+    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
+}
+
+static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+    // (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        std::vector<std::string> tool_rules;
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            std::string name = function["name"];
+            auto parameters = function["parameters"];
+            builder.resolve_refs(parameters);
+            tool_rules.push_back(builder.add_schema(name + "-call", {
+                {"type", "object"},
+                {"properties", json {
+                    {"name", json {{"const", name}}},
+                    {"arguments", parameters},
+                }},
+                {"required", json::array({"name", "arguments"})},
+            }));
+        });
+        auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
+        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
+        data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
+        // Not really a trigger but need to print this special token to get a successful parse.
+        data.grammar_triggers.push_back({"</tool_call>", /* .at_start = */ false});
+    }, grammar_options);
+
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
+    return data;
+}
+static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) {
+    try {
+        std::regex start_pattern(R"([\n\s]*<tool_call>)");
+        std::regex middle_pattern(R"([\n\s]*</tool_call>[\n\s]*<tool_call>)");
+        std::regex end_pattern(R"([\n\s]*</tool_call>[\n\s]*$)");
+
+        auto end = input.end();
+        std::sregex_iterator rend;
+        std::sregex_iterator rit(input.begin(), end, start_pattern);
+        if (rit == rend) {
+            return {
+                /* .role = */ "assistant",
+                /* .content = */ input,
+                /* .tool_calls = */ {},
+            };
+        }
+
+        common_chat_msg result;
+        result.role = "assistant";
+        result.content = rit->prefix();
+
+        auto it = rit->suffix().first;
+        while (it != end) {
+            json call;
+            if (!parse_json(it, end, call)) {
+                throw std::runtime_error("Failed to parse json tool call");
+            }
+            const auto & arguments = call["arguments"];
+            result.tool_calls.push_back({
+                call["name"],
+                arguments.dump(),
+                // arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
+                /* id= */ "",
+            });
+            rit = {it, end, middle_pattern};
+            if (rit != rend) {
+                it = rit->suffix().first;
+            } else {
+                rit = {it, end, end_pattern};
+                if (rit == rend) {
+                    throw std::runtime_error("Malformed input, missing </tool_call>");
+                }
+                break;
+            }
+        }
+        return result;
+    } catch (const std::exception & e) {
+        return {
+            /* .role = */ "assistant",
+            /* .content = */ input,
+            /* .tool_calls = */ {},
+        };
+    }
+}
+
+static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    data.grammar_lazy = false;
+    if (!inputs.json_schema.is_null()) {
+        if (!inputs.grammar.empty()) {
+            throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
+        }
+        data.grammar = json_schema_to_grammar(inputs.json_schema);
+    } else {
+        data.grammar = inputs.grammar.empty();
+    }
+    return data;
+}
+
+common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none";
+    LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false");
+
+    if (has_tools && !inputs.grammar.empty()) {
+        throw std::runtime_error("Cannot specify grammar with tools");
+    }
+
+    const auto & src = tmpl.source();
+    if (src.find(">>>all") != std::string::npos) {
+        // Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when
+        return common_chat_params_init_functionary_v3_2(tmpl, inputs);
+    }
+    if (src.find(" functools[") != std::string::npos) {
+        // Firefunction v2 requires datetime and functions in the context, even w/o tools.
+        return common_chat_params_init_firefunction_v2(tmpl, inputs);
+    }
+
+    if (!has_tools) {
+        return common_chat_params_init_without_tools(tmpl, inputs);
+    }
+
+    if (src.find("<tool_call>") != std::string::npos) {
+        return common_chat_params_init_hermes_2_pro(tmpl, inputs);
+    }
+    if (src.find("<|start_header_id|>") != std::string::npos
+        && src.find("<function=") != std::string::npos) {
+        return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, inputs);
+    }
+    if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
+        auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
+        return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools);
+    }
+    if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) {
+        return common_chat_params_init_deepseek_r1(tmpl, inputs);
+    }
+    if (src.find("[TOOL_CALLS]") != std::string::npos) {
+        return common_chat_params_init_mistral_nemo(tmpl, inputs);
+    }
+    return common_chat_params_init_generic(tmpl, inputs);
+}
+
+static common_chat_msg common_chat_parse_content_only(const std::string & input) {
+    return {
+        /* .role = */ "assistant",
+        /* .content = */ input,
+        /* .tool_calls = */ {},
+    };
+}
+
+common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) {
+    switch (format) {
+        case COMMON_CHAT_FORMAT_CONTENT_ONLY:
+            return common_chat_parse_content_only(input);
+        case COMMON_CHAT_FORMAT_GENERIC:
+            return common_chat_parse_generic(input);
+        case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
+            return common_chat_parse_mistral_nemo(input);
+        case COMMON_CHAT_FORMAT_LLAMA_3_X:
+            return common_chat_parse_llama_3_1(input);
+        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
+            return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true);
+        case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
+            return common_chat_parse_deepseek_r1(input);
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
+            return common_chat_parse_functionary_v3_2(input);
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
+            return common_chat_parse_functionary_v3_1_llama_3_1(input);
+        case COMMON_CHAT_FORMAT_HERMES_2_PRO:
+            return common_chat_parse_hermes_2_pro(input);
+        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
+            return common_chat_parse_firefunction_v2(input);
+        default:
+            throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
+    }
+}
diff --git a/common/chat.hpp b/common/chat.hpp
new file mode 100644
index 000000000..ca165aa13
--- /dev/null
+++ b/common/chat.hpp
@@ -0,0 +1,50 @@
+// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
+
+#pragma once
+
+#include "common.h"
+#include <json.hpp>
+#include <optional>
+#include <string>
+#include <vector>
+
+using json = nlohmann::ordered_json;
+
+struct common_chat_inputs {
+    json messages;
+    json tools;
+    json tool_choice;
+    json json_schema;
+    bool parallel_tool_calls;
+    bool stream;
+    std::string grammar;
+    bool add_generation_prompt = true;
+};
+
+enum common_chat_format {
+    COMMON_CHAT_FORMAT_CONTENT_ONLY,
+    COMMON_CHAT_FORMAT_GENERIC,
+    COMMON_CHAT_FORMAT_MISTRAL_NEMO,
+    COMMON_CHAT_FORMAT_LLAMA_3_X,
+    COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
+    COMMON_CHAT_FORMAT_DEEPSEEK_R1,
+    COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
+    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
+    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
+    COMMON_CHAT_FORMAT_HERMES_2_PRO,
+
+    COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
+};
+
+struct common_chat_params {
+    common_chat_format                  format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    json                                prompt;
+    std::string                         grammar;
+    bool                                grammar_lazy = false;
+    std::vector<common_grammar_trigger> grammar_triggers;
+    std::vector<std::string>            additional_stops;
+};
+
+struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params);
+std::string               common_chat_format_name(common_chat_format format);
+common_chat_msg           common_chat_parse(      const std::string & input, common_chat_format format);
diff --git a/common/common.cpp b/common/common.cpp
index 6dea8e3d2..6c81d18f9 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -12,6 +12,7 @@
 #include "json.hpp"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
+#include "chat.hpp"
 #include "chat-template.hpp"
 
 #include <algorithm>
@@ -1774,11 +1775,13 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
 bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
     if (use_jinja) {
         try {
-            auto chat_template = minja::chat_template(tmpl, "<s>", "</s>");
-            chat_template.apply({{
+            auto chat_template = common_chat_template(tmpl, "<s>", "</s>");
+            common_chat_inputs inputs;
+            inputs.messages = json::array({{
                 {"role", "user"},
                 {"content", "test"},
-            }}, json(), true);
+            }});
+            common_chat_params_init(chat_template, inputs);
             return true;
         } catch (const std::exception & e) {
             LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
@@ -1800,7 +1803,10 @@ std::string common_chat_apply_template(
         for (const auto & msg : msgs) {
             messages.push_back({{"role", msg.role}, {"content", msg.content}});
         }
-        return tmpl.apply(messages, /* tools= */ json(), add_ass);
+        common_chat_inputs inputs;
+        inputs.messages = messages;
+        inputs.add_generation_prompt = add_ass;
+        return common_chat_params_init(tmpl, inputs).prompt;
     }
 
     int alloc_size = 0;
@@ -1855,10 +1861,10 @@ std::string common_chat_format_single(
 
 std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) {
     std::vector<common_chat_msg> msgs = {
-        {"system",    "You are a helpful assistant"},
-        {"user",      "Hello"},
-        {"assistant", "Hi there"},
-        {"user",      "How are you?"},
+        {"system",    "You are a helpful assistant", {}},
+        {"user",      "Hello", {}},
+        {"assistant", "Hi there", {}},
+        {"user",      "How are you?", {}},
     };
     return common_chat_apply_template(tmpl, msgs, true, use_jinja);
 }
diff --git a/common/common.h b/common/common.h
index 571260372..6c1809277 100644
--- a/common/common.h
+++ b/common/common.h
@@ -109,6 +109,11 @@ enum common_conversation_mode {
     COMMON_CONVERSATION_MODE_AUTO     = 2,
 };
 
+struct common_grammar_trigger {
+    std::string word;
+    bool at_start;
+};
+
 // sampling parameters
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@@ -154,7 +159,10 @@ struct common_params_sampling {
         COMMON_SAMPLER_TYPE_TEMPERATURE,
     };
 
-    std::string grammar; // optional BNF-like grammar to constrain sampling
+    std::string                         grammar; // optional BNF-like grammar to constrain sampling
+    bool                                grammar_lazy = false;
+    std::vector<common_grammar_trigger> grammar_trigger_words;  // optional trigger words to trigger lazy grammar
+    std::vector<llama_token>            grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
 
     std::vector<llama_logit_bias> logit_bias; // logit biases to apply
 
@@ -602,10 +610,17 @@ std::string common_detokenize(
 // Chat template utils
 //
 
+struct common_tool_call {
+    std::string name;
+    std::string arguments;
+    std::string id;
+};
+
 // same with llama_chat_message, but uses std::string
 struct common_chat_msg {
     std::string role;
     std::string content;
+    std::vector<common_tool_call> tool_calls;
 };
 
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 4d426b6bd..1f47e313e 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -343,7 +343,7 @@ static std::string format_literal(const std::string & literal) {
 
 class SchemaConverter {
 private:
-    friend std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);
+    friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
     std::function<json(const std::string &)> _fetch_json;
     bool _dotall;
     std::map<std::string, std::string> _rules;
@@ -764,10 +764,11 @@ private:
 public:
     SchemaConverter(
         const std::function<json(const std::string &)> & fetch_json,
-        bool dotall)
+        bool dotall,
+        bool compact_spaces)
           : _fetch_json(fetch_json), _dotall(dotall)
     {
-        _rules["space"] = SPACE_RULE;
+        _rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE;
     }
 
     void resolve_refs(json & schema, const std::string & url) {
@@ -991,16 +992,16 @@ public:
 };
 
 std::string json_schema_to_grammar(const json & schema) {
-    return build_grammar([&](const llama_grammar_builder & callbacks) {
+    return build_grammar([&](const common_grammar_builder & callbacks) {
         auto copy = schema;
         callbacks.resolve_refs(copy);
         callbacks.add_schema("", copy);
     });
 }
 
-std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb) {
-    SchemaConverter converter([&](const std::string &) { return json(); }, /* dotall= */ false);
-    llama_grammar_builder builder {
+std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
+    SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces);
+    common_grammar_builder builder {
         /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
             return converter._add_rule(name, rule);
         },
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
index 4f43ab3a5..ba4112cb9 100644
--- a/common/json-schema-to-grammar.h
+++ b/common/json-schema-to-grammar.h
@@ -7,10 +7,15 @@
 
 std::string json_schema_to_grammar(const nlohmann::ordered_json & schema);
 
-struct llama_grammar_builder {
+struct common_grammar_builder {
     std::function<std::string(const std::string &, const std::string &)> add_rule;
     std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
     std::function<void(nlohmann::ordered_json &)> resolve_refs;
 };
 
-std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);
+struct common_grammar_options {
+    bool dotall = false;
+    bool compact_spaces = false;
+};
+
+std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 7241ac321..bc7e49fdb 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -151,9 +151,18 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     lparams.no_perf = params.no_perf;
 
+    std::vector<const char *> trigger_words;
+    trigger_words.reserve(params.grammar_trigger_words.size());
+    for (const auto & str : params.grammar_trigger_words) {
+        trigger_words.push_back(str.word.c_str());
+    }
     auto * result = new common_sampler {
         /* .params = */ params,
-        /* .grmr   = */ llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
+        /* .grmr   = */ params.grammar_lazy
+            ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
+                                              trigger_words.data(), trigger_words.size(),
+                                              params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
+            :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
         /* .chain  = */ llama_sampler_chain_init(lparams),
         /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur    = */ {},
diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
index 17a0e27c4..a610e6a0b 100644
--- a/examples/gbnf-validator/gbnf-validator.cpp
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -76,7 +76,7 @@ int main(int argc, char** argv) {
         grammar_str = buffer.str();
     }
 
-    llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root");
+    llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0);
     if (grammar == nullptr) {
         fprintf(stdout, "Failed to initialize llama_grammar\n");
         return 1;
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index da2a03ab9..e654d3542 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -254,7 +254,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const bool add_bos = llama_vocab_get_add_bos(vocab);
+    const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja;
     if (!llama_model_has_encoder(model)) {
         GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
     }
@@ -264,9 +264,9 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd_inp;
 
     auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
-        common_chat_msg new_msg{role, content};
+        common_chat_msg new_msg{role, content, {}};
         auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja);
-        chat_msgs.push_back({role, content});
+        chat_msgs.push_back({role, content, {}});
         LOG_DBG("formatted: '%s'\n", formatted.c_str());
         return formatted;
     };
@@ -503,12 +503,14 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd;
 
-    // tokenized antiprompts
-    std::vector<std::vector<llama_token>> antiprompt_ids;
+    // single-token antiprompts
+    std::vector<llama_token> antiprompt_token;
 
-    antiprompt_ids.reserve(params.antiprompt.size());
     for (const std::string & antiprompt : params.antiprompt) {
-        antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true));
+        auto ids = ::common_tokenize(ctx, antiprompt, false, true);
+        if (ids.size() == 1) {
+            antiprompt_token.push_back(ids[0]);
+        }
     }
 
     if (llama_model_has_encoder(model)) {
@@ -753,14 +755,11 @@ int main(int argc, char ** argv) {
 
                 // check for reverse prompt using special tokens
                 llama_token last_token = common_sampler_last(smpl);
-                for (std::vector<llama_token> ids : antiprompt_ids) {
-                    if (ids.size() == 1 && last_token == ids[0]) {
-                        if (params.interactive) {
-                            is_interacting = true;
-                        }
-                        is_antiprompt = true;
-                        break;
+                if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) {
+                    if (params.interactive) {
+                        is_interacting = true;
                     }
+                    is_antiprompt = true;
                 }
 
                 if (is_antiprompt) {
diff --git a/examples/server/README.md b/examples/server/README.md
index 44da503df..ce1ae8858 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -1117,6 +1117,82 @@ curl http://localhost:8080/v1/chat/completions \
 }'
 ```
 
+... and even tool usage (needs `--jinja` flag):
+
+  ```shell
+  llama-server --jinja -hfr lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF -hff Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf -fa
+
+  # https://huggingface.co/meetkai/functionary-medium-v3.2
+  llama-server --jinja -hfr bartowski/functionary-medium-v3.2-GGUF -hff functionary-medium-v3.2-IQ4_XS.gguf -fa
+
+  # https://huggingface.co/meetkai/functionary-medium-v3.1
+  llama-server --jinja -hfr meetkai/functionary-medium-v3.1-GGUF -hff functionary-medium-llama-3.1.Q4_0.gguf -fa
+
+  curl http://localhost:8080/v1/chat/completions -d '{
+    "model": "gpt-3.5-turbo",
+    "tools": [
+      {
+        "type":"function",
+        "function":{
+          "name":"get_current_weather",
+          "description":"Get the current weather in a given location",
+          "parameters":{
+            "type":"object",
+            "properties":{
+              "location":{
+                "type":"string",
+                "description":"The city and state, e.g. San Francisco, CA"
+              }
+            },
+            "required":["location"]
+          }
+        }
+      }
+    ],
+    "messages": [
+      {
+        "role": "user",
+        "content": "What is the weather like in Istanbul?."
+      }
+    ]
+  }'
+  ```
+
+  <details>
+  <summary>Show output</summary>
+
+  ```json
+  {
+    "choices": [
+      {
+        "finish_reason": "tool",
+        "index": 0,
+        "message": {
+          "content": null,
+          "tool_calls": [
+            {
+              "name": "python",
+              "arguments": "{\"code\":\" \\nprint(\\\"Hello, World!\\\")\"}"
+            }
+          ],
+          "role": "assistant"
+        }
+      }
+    ],
+    "created": 1727287211,
+    "model": "gpt-3.5-turbo",
+    "object": "chat.completion",
+    "usage": {
+      "completion_tokens": 16,
+      "prompt_tokens": 44,
+      "total_tokens": 60
+    },
+    "id": "chatcmpl-Htbgh9feMmGM0LEH2hmQvwsCxq3c6Ni8"
+  }
+  ```
+
+  </details>
+
 ### POST `/v1/embeddings`: OpenAI-compatible embeddings API
 
 This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b9aa5c81c..d1ea343dd 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -113,10 +113,11 @@ struct slot_params {
     struct common_params_speculative speculative;
 
     // OAI-compat fields
-    bool           verbose        = false;
-    oaicompat_type oaicompat      = OAICOMPAT_TYPE_NONE;
-    std::string    oaicompat_model;
-    std::string    oaicompat_cmpl_id;
+    bool                  verbose                   = false;
+    oaicompat_type        oaicompat                 = OAICOMPAT_TYPE_NONE;
+    std::string           oaicompat_model;
+    std::string           oaicompat_cmpl_id;
+    common_chat_format    oaicompat_chat_format     = COMMON_CHAT_FORMAT_CONTENT_ONLY;
 
     json to_json() const {
         std::vector<std::string> samplers;
@@ -164,6 +165,8 @@ struct slot_params {
             {"n_probs",                   sampling.n_probs},
             {"min_keep",                  sampling.min_keep},
             {"grammar",                   sampling.grammar},
+            // {"grammar_trigger_words",     sampling.grammar_trigger_words},
+            {"grammar_trigger_tokens",    sampling.grammar_trigger_tokens},
             {"samplers",                  samplers},
             {"speculative.n_max",         speculative.n_max},
             {"speculative.n_min",         speculative.n_min},
@@ -325,12 +328,50 @@ struct server_task {
         if (data.contains("json_schema") && !data.contains("grammar")) {
             try {
                 auto schema                  = json_value(data, "json_schema", json::object());
-                params.sampling.grammar = json_schema_to_grammar(schema);
+                LOG_DBG("JSON schema: %s\n", schema.dump(2).c_str());
+                params.sampling.grammar      = json_schema_to_grammar(schema);
+                LOG_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
             } catch (const std::exception & e) {
                 throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
             }
         } else {
-            params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
+            params.sampling.grammar      = json_value(data, "grammar", defaults.sampling.grammar);
+            LOG_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
+            params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
+            LOG_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
+        }
+
+        {
+            auto it = data.find("chat_format");
+            if (it != data.end()) {
+                params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
+                LOG_DBG("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
+            } else {
+                params.oaicompat_chat_format = defaults.oaicompat_chat_format;
+            }
+        }
+
+        {
+            const auto grammar_triggers = data.find("grammar_triggers");
+            if (grammar_triggers != data.end()) {
+                for (const auto & t : *grammar_triggers) {
+                    common_grammar_trigger trigger;
+                    trigger.word = t.at("word");
+                    trigger.at_start = t.at("at_start");
+
+                    auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
+                    if (ids.size() == 1) {
+                        LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
+                        params.sampling.grammar_trigger_tokens.push_back(ids[0]);
+                        continue;
+                    }
+                    LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
+                    params.sampling.grammar_trigger_words.push_back(trigger);
+                }
+            }
+            if (params.sampling.grammar_lazy) {
+                GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0);
+            }
         }
 
         {
@@ -382,22 +423,12 @@ struct server_task {
         }
 
         {
-            const auto & samplers = data.find("samplers");
+            const auto samplers = data.find("samplers");
             if (samplers != data.end()) {
                 if (samplers->is_array()) {
-                    std::vector<std::string> sampler_names;
-                    for (const auto & name : *samplers) {
-                        if (name.is_string()) {
-                            sampler_names.emplace_back(name);
-                        }
-                    }
-                    params.sampling.samplers = common_sampler_types_from_names(sampler_names, false);
+                    params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
                 } else if (samplers->is_string()){
-                    std::string sampler_string;
-                    for (const auto & name : *samplers) {
-                        sampler_string += name;
-                    }
-                    params.sampling.samplers = common_sampler_types_from_chars(sampler_string);
+                    params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
                 }
             } else {
                 params.sampling.samplers = defaults.sampling.samplers;
@@ -544,7 +575,7 @@ struct completion_token_output {
 struct server_task_result_cmpl_final : server_task_result {
     int index = 0;
 
-    std::string  content;
+    std::string content;
     llama_tokens tokens;
 
     bool stream;
@@ -566,10 +597,11 @@ struct server_task_result_cmpl_final : server_task_result {
     slot_params generation_params;
 
     // OAI-compat fields
-    bool           verbose        = false;
-    oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
-    std::string    oaicompat_model;
-    std::string    oaicompat_cmpl_id;
+    bool                  verbose                  = false;
+    oaicompat_type        oaicompat                = OAICOMPAT_TYPE_NONE;
+    std::string           oaicompat_model;
+    std::string           oaicompat_cmpl_id;
+    common_chat_format    oaicompat_chat_format    = COMMON_CHAT_FORMAT_CONTENT_ONLY;
 
     virtual int get_index() override {
         return index;
@@ -663,18 +695,38 @@ struct server_task_result_cmpl_final : server_task_result {
 
     json to_json_oaicompat_chat() {
         std::string finish_reason = "length";
+        common_chat_msg message;
         if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
-            finish_reason = "stop";
+            message = common_chat_parse(content, oaicompat_chat_format);
+            finish_reason = message.tool_calls.empty() ? "stop" : "tool_calls";
+        } else {
+            message.content = content;
         }
 
-        json choice = json{
+        json tool_calls;
+        if (!message.tool_calls.empty()) {
+            tool_calls = json::array();
+            for (const auto & tc : message.tool_calls) {
+                tool_calls.push_back({
+                    {"type", "function"},
+                    {"function", {
+                        {"name", tc.name},
+                        {"arguments", tc.arguments},
+                    }},
+                    {"id", tc.id.empty() ? json() : json(tc.id)},
+                });
+            }
+        }
+
+        json choice {
             {"finish_reason", finish_reason},
             {"index", 0},
             {"message", json {
-                {"content", content},
-                {"role",    "assistant"}
-            }
-        }};
+                {"content", message.content},
+                {"tool_calls", tool_calls},
+                {"role", "assistant"},
+            }},
+        };
 
         if (!stream && probs_output.size() > 0) {
             choice["logprobs"] = json{
@@ -716,7 +768,7 @@ struct server_task_result_cmpl_final : server_task_result {
             finish_reason = "stop";
         }
 
-        json choice = json{
+        json choice = json {
             {"finish_reason", finish_reason},
             {"index", 0},
             {"delta", json::object()}
@@ -1191,6 +1243,8 @@ struct server_slot {
 
     llama_token sampled;
 
+    common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+
     // stats
     size_t n_sent_text        = 0; // number of sent text character
 
@@ -1815,17 +1869,16 @@ struct server_context {
 
         if (use_jinja) {
             auto templates = common_chat_templates_from_model(model, "");
+            common_chat_inputs inputs;
+            inputs.messages = json::array({{
+                {"role", "user"},
+                {"content", "test"},
+            }});
             GGML_ASSERT(templates.template_default);
             try {
-                templates.template_default->apply({{
-                    {"role", "user"},
-                    {"content", "test"},
-                }}, json(), true);
+                common_chat_params_init(*templates.template_default, inputs);
                 if (templates.template_tool_use) {
-                    templates.template_tool_use->apply({{
-                        {"role", "user"},
-                        {"content", "test"},
-                    }}, json(), true);
+                    common_chat_params_init(*templates.template_tool_use, inputs);
                 }
                 return true;
             } catch (const std::exception & e) {
@@ -2275,11 +2328,11 @@ struct server_context {
         res->id_slot         = slot.id;
 
         res->index           = slot.index;
-        res->content         = slot.generated_text;
-        res->tokens          = slot.generated_tokens;
+        res->content         = std::move(slot.generated_text);
+        res->tokens          = std::move(slot.generated_tokens);
         res->timings         = slot.get_timings();
         res->prompt          = common_detokenize(ctx, slot.prompt_tokens, true);
-        res->response_fields = slot.params.response_fields;
+        res->response_fields = std::move(slot.params.response_fields);
 
         res->truncated           = slot.truncated;
         res->n_decoded           = slot.n_decoded;
@@ -2290,12 +2343,12 @@ struct server_context {
         res->stop                = slot.stop;
         res->post_sampling_probs = slot.params.post_sampling_probs;
 
-        res->verbose           = slot.params.verbose;
-        res->stream            = slot.params.stream;
-        res->oaicompat         = slot.params.oaicompat;
-        res->oaicompat_model   = slot.params.oaicompat_model;
-        res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
-
+        res->verbose               = slot.params.verbose;
+        res->stream                = slot.params.stream;
+        res->oaicompat             = slot.params.oaicompat;
+        res->oaicompat_model       = slot.params.oaicompat_model;
+        res->oaicompat_cmpl_id     = slot.params.oaicompat_cmpl_id;
+        res->oaicompat_chat_format = slot.params.oaicompat_chat_format;
         // populate res.probs_output
         if (slot.params.sampling.n_probs > 0) {
             if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) {
@@ -2773,6 +2826,11 @@ struct server_context {
         // track if given slot can be batched with slots already in the batch
         server_slot * slot_batched = nullptr;
 
+        auto accept_special_token = [&](server_slot & slot, llama_token token) {
+            const auto & trigger_tokens = slot.params.sampling.grammar_trigger_tokens;
+            return params_base.special || std::find(trigger_tokens.begin(), trigger_tokens.end(), token) != trigger_tokens.end();
+        };
+
         // frist, add sampled tokens from any ongoing sequences
         for (auto & slot : slots) {
             if (slot.state != SLOT_STATE_GENERATING) {
@@ -3136,7 +3194,7 @@ struct server_context {
 
                 completion_token_output result;
                 result.tok          = id;
-                result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
+                result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
                 result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
 
                 if (slot.params.sampling.n_probs > 0) {
@@ -3225,7 +3283,7 @@ struct server_context {
                     completion_token_output result;
 
                     result.tok          = ids[i];
-                    result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
+                    result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
                     result.prob         = 1.0f; // set later
 
                     // TODO: set result.probs
@@ -3722,6 +3780,8 @@ int main(int argc, char ** argv) {
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model },
             { "chat_template",               ctx_server.chat_templates.template_default->source() },
+            { "bos_token",                   ctx_server.chat_templates.template_default->bos_token() },
+            { "eos_token",                   ctx_server.chat_templates.template_default->eos_token() },
             { "build_info",                  build_info },
         };
         if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) {
@@ -3763,7 +3823,9 @@ int main(int argc, char ** argv) {
         std::vector<server_task> tasks;
 
         try {
-            std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, data.at("prompt"), true, true);
+            const auto & prompt = data.at("prompt");
+            LOG_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
+            std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
             tasks.reserve(tokenized_prompts.size());
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {
                 server_task task = server_task(type);
@@ -3779,8 +3841,8 @@ int main(int argc, char ** argv) {
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
                 // OAI-compat
-                task.params.oaicompat         = oaicompat;
-                task.params.oaicompat_cmpl_id = completion_id;
+                task.params.oaicompat                 = oaicompat;
+                task.params.oaicompat_cmpl_id         = completion_id;
                 // oaicompat_model is already populated by params_from_json_cmpl
 
                 tasks.push_back(task);
@@ -3949,14 +4011,14 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_chat_completions = [&ctx_server, &params, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
+        LOG_DBG("request: %s\n", req.body.c_str());
         if (ctx_server.params_base.embedding) {
             res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
         auto body = json::parse(req.body);
-        const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
-        json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
+        json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
 
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
@@ -3966,6 +4028,13 @@ int main(int argc, char ** argv) {
             OAICOMPAT_TYPE_CHAT);
     };
 
+    // same with handle_chat_completions, but without inference part
+    const auto handle_apply_template = [&ctx_server, &params, &res_ok](const httplib::Request & req, httplib::Response & res) {
+        auto body = json::parse(req.body);
+        json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
+        res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
+    };
+
     const auto handle_models = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
         json models = {
             {"object", "list"},
@@ -4124,14 +4193,6 @@ int main(int argc, char ** argv) {
         res_ok(res, root);
     };
 
-    const auto handle_apply_template = [&ctx_server, &params, &res_ok](const httplib::Request & req, httplib::Response & res) {
-        auto body = json::parse(req.body);
-        const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
-        json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
-
-        res_ok(res, {{ "prompt", data.at("prompt") }});
-    };
-
     const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
         handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE);
     };
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
index 5787276ab..1de0eb30e 100644
--- a/examples/server/tests/README.md
+++ b/examples/server/tests/README.md
@@ -31,8 +31,9 @@ It's possible to override some scenario steps values with environment variables:
 | `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/llama-server`                         |
 | `DEBUG`                  | to enable steps and server verbose mode `--verbose`                                       |
 | `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
+| `LLAMA_CACHE`            | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this |
 
-To run slow tests:
+To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed):
 
 ```shell
 SLOW_TESTS=1 ./tests.sh
@@ -44,10 +45,16 @@ To run with stdout/stderr display in real time (verbose output, but useful for d
 DEBUG=1 ./tests.sh -s -v -x
 ```
 
-To run single test unit:
+To run all the tests in a file:
 
 ```shell
-./tests.sh unit/test_{name of test case here}.py -v -x
+./tests.sh unit/test_chat_completion.py.py -v -x
+```
+
+To run a single test:
+
+```shell
+./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req
 ```
 
 Hint: You can compile and run test in single command, useful for local developement:
diff --git a/examples/server/tests/pytest.ini b/examples/server/tests/pytest.ini
new file mode 100644
index 000000000..6df308df7
--- /dev/null
+++ b/examples/server/tests/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+markers =
+    slow: marks tests as slow (deselect with '-m "not slow"')
+    serial
diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh
index 1e0777de3..33fa8cc64 100755
--- a/examples/server/tests/tests.sh
+++ b/examples/server/tests/tests.sh
@@ -6,9 +6,18 @@ cd $SCRIPT_DIR
 
 set -eu
 
+if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
+    # Slow tests for tool calls need quite a few models ahead of time to avoid timing out.
+    python $SCRIPT_DIR/../../../scripts/fetch_server_test_models.py
+fi
+
 if [ $# -lt 1 ]
 then
-    pytest -v -x
+    if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
+        pytest -v -x
+    else
+        pytest -v -x -m "not slow"
+    fi
 else
     pytest "$@"
 fi
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index add3f810f..0be04bab5 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -2,7 +2,7 @@ import pytest
 from openai import OpenAI
 from utils import *
 
-server = ServerPreset.tinyllama2()
+server: ServerProcess
 
 @pytest.fixture(autouse=True)
 def create_server():
@@ -13,11 +13,12 @@ def create_server():
 @pytest.mark.parametrize(
     "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
     [
-        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", False, None),
-        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", True, None),
-        (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
+        # TODO: fix testing of non-tool jinja mode
+        # (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
+        # (None, "Book", "What is the best book", 8, "I want to play with", 23, 8, "length", True, "This is not a chat template, it is"),
+        # ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
     ]
 )
 def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
diff --git a/examples/server/tests/unit/test_tool_call.py b/examples/server/tests/unit/test_tool_call.py
new file mode 100644
index 000000000..e6ed9c9be
--- /dev/null
+++ b/examples/server/tests/unit/test_tool_call.py
@@ -0,0 +1,352 @@
+import pytest
+from utils import *
+
+server: ServerProcess
+
+TIMEOUT_SERVER_START = 15*60
+TIMEOUT_HTTP_REQUEST = 60
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.model_alias = "tinyllama-2-tool-call"
+    server.server_port = 8081
+
+
+TEST_TOOL = {
+    "type":"function",
+    "function": {
+        "name": "test",
+        "description": "",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "success": {"type": "boolean", "const": True},
+            },
+            "required": ["success"]
+        }
+    }
+}
+
+PYTHON_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "python",
+        "description": "Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "The code to run in the ipython interpreter."
+                }
+            },
+            "required": ["code"]
+        }
+    }
+}
+
+WEATHER_TOOL = {
+  "type":"function",
+  "function":{
+    "name":"get_current_weather",
+    "description":"Get the current weather in a given location",
+    "parameters":{
+      "type":"object",
+      "properties":{
+        "location":{
+          "type":"string",
+          "description":"The city and country/state, e.g. 'San Francisco, CA', or 'Paris, France'"
+        }
+      },
+      "required":["location"]
+    }
+  }
+}
+
+
+def do_test_completion_with_required_tool_tiny(template_name: str, tool: dict, argument_key: str | None):
+    n_predict = 512
+    global server
+    # server = ServerPreset.stories15m_moe()
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "tool_choice": "required",
+        "tools": [tool],
+        "parallel_tool_calls": False,
+        "temperature": 0.0,
+        "top_k": 1,
+        "top_p": 1.0,
+    })
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
+    assert expected_function_name == tool_call["function"]["name"]
+    actual_arguments = tool_call["function"]["arguments"]
+    assert isinstance(actual_arguments, str)
+    if argument_key is not None:
+        actual_arguments = json.loads(actual_arguments)
+        assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
+
+
+@pytest.mark.parametrize("template_name,tool,argument_key", [
+    ("google-gemma-2-2b-it",                          TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
+])
+def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None):
+    do_test_completion_with_required_tool_tiny(template_name, tool, argument_key)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("template_name,tool,argument_key", [
+    ("meta-llama-Llama-3.1-8B-Instruct",              TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.1-8B-Instruct",              PYTHON_TOOL,          "code"),
+    ("meetkai-functionary-medium-v3.1",               TEST_TOOL,            "success"),
+    ("meetkai-functionary-medium-v3.1",               PYTHON_TOOL,          "code"),
+    ("meetkai-functionary-medium-v3.2",               TEST_TOOL,            "success"),
+    ("meetkai-functionary-medium-v3.2",               PYTHON_TOOL,          "code"),
+    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL,            "success"),
+    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL,          "code"),
+    ("meta-llama-Llama-3.2-3B-Instruct",              TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.2-3B-Instruct",              PYTHON_TOOL,          "code"),
+    ("mistralai-Mistral-Nemo-Instruct-2407",          TEST_TOOL,            "success"),
+    ("mistralai-Mistral-Nemo-Instruct-2407",          PYTHON_TOOL,          "code"),
+    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   TEST_TOOL,            "success"),
+    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   PYTHON_TOOL,          "code"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      TEST_TOOL,            "success"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      PYTHON_TOOL,          "code"),
+    ("fireworks-ai-llama-3-firefunction-v2",          TEST_TOOL,            "success"),
+    ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "code"),
+])
+def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None):
+    do_test_completion_with_required_tool_tiny(template_name, tool, argument_key)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [
+    (TEST_TOOL,    "success",  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (TEST_TOOL,    "success",  "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    (PYTHON_TOOL,  "code",     "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    (TEST_TOOL,    "success",  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (TEST_TOOL,    "success",  "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (TEST_TOOL,    "success",  "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (TEST_TOOL,    "success",  "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (TEST_TOOL,    "success",  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    (TEST_TOOL,    "success",  "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    (TEST_TOOL,    "success",  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (TEST_TOOL,    "success",  "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    # TODO: fix these
+    # (TEST_TOOL,    "success",  "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+    # (PYTHON_TOOL,  "code",     "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+])
+def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: Tuple[str, str | None] | None):
+    n_predict = 512
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if template_override:
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "tool_choice": "required",
+        "tools": [tool],
+        "parallel_tool_calls": False,
+        "temperature": 0.0,
+        "top_k": 1,
+        "top_p": 1.0,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
+    assert expected_function_name == tool_call["function"]["name"]
+    actual_arguments = tool_call["function"]["arguments"]
+    assert isinstance(actual_arguments, str)
+    if argument_key is not None:
+        actual_arguments = json.loads(actual_arguments)
+        assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
+
+
+def do_test_completion_without_tool_call(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
+    global server
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "say hello world with python"},
+        ],
+        "tools": tools if tools else None,
+        "tool_choice": tool_choice,
+        "temperature": 0.0,
+        "top_k": 1,
+        "top_p": 1.0,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}'
+
+
+@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [],            None),
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [TEST_TOOL],   None),
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [PYTHON_TOOL], 'none'),
+])
+def test_completion_without_tool_call_fast(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
+    do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
+    ("meetkai-functionary-medium-v3.2",               256, [],            None),
+    ("meetkai-functionary-medium-v3.2",               256, [TEST_TOOL],   None),
+    ("meetkai-functionary-medium-v3.2",               256, [PYTHON_TOOL], 'none'),
+    ("meetkai-functionary-medium-v3.1",               256, [],            None),
+    ("meetkai-functionary-medium-v3.1",               256, [TEST_TOOL],   None),
+    ("meetkai-functionary-medium-v3.1",               256, [PYTHON_TOOL], 'none'),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [],            None),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [TEST_TOOL],   None),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [PYTHON_TOOL], 'none'),
+])
+def test_completion_without_tool_call_slow(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
+    do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("hf_repo,template_override", [
+    ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    # ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+])
+def test_weather_tool_call(hf_repo: str, template_override: Tuple[str, str | None] | None):
+    global server
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = 512
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if template_override:
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 256,
+        "messages": [
+            {"role": "user", "content": "What is the weather in Istanbul?"},
+        ],
+        "tools": [WEATHER_TOOL],
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"]
+    actual_arguments = json.loads(tool_call["function"]["arguments"])
+    assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}"
+    location = actual_arguments["location"]
+    assert isinstance(location, str), f"Expected location to be a string, got {type(location)}: {json.dumps(location)}"
+    assert re.match('^Istanbul(, (TR|Turkey|Türkiye))?$', location), f'Expected Istanbul for location, got {location}'
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("expected_arguments_override,hf_repo,template_override", [
+    (None,                 "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    (None,                 "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (None,                 "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai-functionary-medium-v3.2", None)),
+    ('{"code":"print("}',  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (None,                 "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    ('{"code":"print("}',  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    (None,                 "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (None,                 "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",      ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (None,                 "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")),
+    (None,                 "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    # (None,                 "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+])
+def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo: str, template_override: Tuple[str, str | None] | None):
+    global server
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = 128
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if template_override:
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 256,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "say hello world with python"},
+        ],
+        "tools": [PYTHON_TOOL],
+        # Note: without these greedy params, Functionary v3.2 writes `def hello_world():\n    print("Hello, World!")\nhello_world()` which is correct but a pain to test.
+        "temperature": 0.0,
+        "top_k": 1,
+        "top_p": 1.0,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"]
+    actual_arguments = tool_call["function"]["arguments"]
+    if expected_arguments_override is not None:
+        assert actual_arguments == expected_arguments_override
+    else:
+        actual_arguments = json.loads(actual_arguments)
+        assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}"
+        code = actual_arguments["code"]
+        assert isinstance(code, str), f"Expected code to be a string, got {type(code)}: {json.dumps(code)}"
+        assert re.match(r'''print\(("[Hh]ello,? [Ww]orld!?"|'[Hh]ello,? [Ww]orld!?')\)''', code), f'Expected hello world, got {code}'
diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py
index 9964db2f9..ce0680662 100644
--- a/examples/server/tests/utils.py
+++ b/examples/server/tests/utils.py
@@ -26,7 +26,7 @@ from re import RegexFlag
 import wget
 
 
-DEFAULT_HTTP_TIMEOUT = 10 if "LLAMA_SANITIZE" not in os.environ else 30
+DEFAULT_HTTP_TIMEOUT = 12 if "LLAMA_SANITIZE" not in os.environ else 30
 
 
 class ServerResponse:
@@ -41,7 +41,7 @@ class ServerProcess:
     server_port: int = 8080
     server_host: str = "127.0.0.1"
     model_hf_repo: str = "ggml-org/models"
-    model_hf_file: str = "tinyllamas/stories260K.gguf"
+    model_hf_file: str | None = "tinyllamas/stories260K.gguf"
     model_alias: str = "tinyllama-2"
     temperature: float = 0.8
     seed: int = 42
@@ -191,7 +191,7 @@ class ServerProcess:
             creationflags=flags,
             stdout=sys.stdout,
             stderr=sys.stdout,
-            env={**os.environ, "LLAMA_CACHE": "tmp"},
+            env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None,
         )
         server_instances.add(self)
 
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index c5987250c..3d2c04666 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -17,6 +17,7 @@
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
 #include "minja.hpp"
+#include "chat.hpp"
 #include "chat-template.hpp"
 
 #include <random>
@@ -376,7 +377,7 @@ inline std::string format_chat(const common_chat_template & tmpl, const std::vec
             throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
         }
 
-        chat.push_back({role, content});
+        chat.push_back({role, content, /* tool_calls= */ {}});
     }
 
     const auto formatted_chat = common_chat_apply_template(tmpl, chat, true, /* use_jinja= */ false);
@@ -483,14 +484,13 @@ static bool ends_with(const std::string & str, const std::string & suffix) {
 
 static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
     if (!text.empty() && !stop.empty()) {
-        const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
-            if (stop[char_index] == text_last_char) {
-                const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial)) {
-                    return text.size() - char_index - 1;
-                }
+        auto it = std::find(stop.rbegin(), stop.rend(), text.back());
+        while (it != stop.rend()) {
+            size_t length = std::distance(it, stop.rend());
+            if (text.length() >= length && 0 == text.compare(text.length() - length, length, stop)) {
+                return text.length() - length;
             }
+            it = std::find(std::next(it), stop.rend(), text.back());
         }
     }
 
@@ -580,21 +580,30 @@ static json oaicompat_completion_params_parse(const json & body) {
 
 static json oaicompat_completion_params_parse(
     const json & body, /* openai api json semantics */
-    const common_chat_template & tmpl,
-    bool use_jinja)
+    bool use_jinja,
+    const common_chat_templates & chat_templates)
 {
     json llama_params;
+    const auto & tmpl = body.contains("tools") && chat_templates.template_tool_use
+        ? *chat_templates.template_tool_use
+        : *chat_templates.template_default;
 
     auto tools = json_value(body, "tools", json());
-    auto has_tools = tools.is_array() && !tools.empty();
+    auto stream = json_value(body, "stream", false);
 
-    if (has_tools) {
-        if (use_jinja) {
-            LOG_WRN("tools param is not fully supported yet\n");
-        } else {
+    if (tools.is_array() && !tools.empty()) {
+        if (stream) {
+            throw std::runtime_error("Cannot use tools with stream");
+        }
+        if (!use_jinja) {
             throw std::runtime_error("tools param requires --jinja flag");
         }
     }
+    if (!use_jinja) {
+        if (body.contains("tool_choice") && !body.at("tool_choice").is_null()) {
+            throw std::runtime_error("Unsupported param: tool_choice");
+        }
+    }
 
     // Handle "stop" field
     if (body.contains("stop") && body.at("stop").is_string()) {
@@ -619,7 +628,38 @@ static json oaicompat_completion_params_parse(
 
     // Apply chat template to the list of messages
     if (use_jinja) {
-        llama_params["prompt"] = tmpl.apply(body.at("messages"), tools, /* add_generation_prompt= */ true);
+        auto tool_choice = json_value(body, "tool_choice", std::string("auto"));
+        if (tool_choice != "none" && tool_choice != "auto" && tool_choice != "required") {
+            throw std::runtime_error("Invalid tool_choice: " + tool_choice);
+        }
+        if (tool_choice != "none" && llama_params.contains("grammar")) {
+            throw std::runtime_error("Cannot use custom grammar constraints with tools.");
+        }
+        common_chat_inputs inputs;
+        inputs.messages = body.at("messages");
+        inputs.tools = tools;
+        inputs.tool_choice = tool_choice;
+        inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
+        inputs.stream = stream;
+        // TODO: support mixing schema w/ tools beyond generic format.
+        inputs.json_schema = json_value(llama_params, "json_schema", json::object());
+        auto chat_params = common_chat_params_init(tmpl, inputs);
+
+        llama_params["chat_format"] = static_cast<int>(chat_params.format);
+        llama_params["prompt"] = chat_params.prompt;
+        llama_params["grammar"] = chat_params.grammar;
+        llama_params["grammar_lazy"] = chat_params.grammar_lazy;
+        auto grammar_triggers = json::array();
+        for (const auto & trigger : chat_params.grammar_triggers) {
+            grammar_triggers.push_back({
+                {"word", trigger.word},
+                {"at_start", trigger.at_start},
+            });
+        }
+        llama_params["grammar_triggers"] = grammar_triggers;
+        for (const auto & stop : chat_params.additional_stops) {
+            llama_params["stop"].push_back(stop);
+        }
     } else {
         llama_params["prompt"] = format_chat(tmpl, body.at("messages"));
     }
@@ -638,14 +678,6 @@ static json oaicompat_completion_params_parse(
         throw std::runtime_error("top_logprobs requires logprobs to be set to true");
     }
 
-    // Params supported by OAI but unsupported by llama.cpp
-    static const std::vector<std::string> unsupported_params { "tool_choice" };
-    for (const auto & param : unsupported_params) {
-        if (body.contains(param)) {
-            throw std::runtime_error("Unsupported param: " + param);
-        }
-    }
-
     // Copy remaining properties to llama_params
     // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint.
     // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
diff --git a/include/llama.h b/include/llama.h
index 3b75e7607..61907ed40 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1199,6 +1199,18 @@ extern "C" {
                           const char * grammar_str,
                           const char * grammar_root);
 
+    /// @details Lazy grammar sampler, introduced in https://github.com/ggerganov/llama.cpp/pull/9639
+    /// @param trigger_words A list of words that will trigger the grammar sampler. This may be updated to a loose regex syntax (w/ ^) in a near future.
+    /// @param trigger_tokens A list of tokens that will trigger the grammar sampler.
+    LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy(
+            const struct llama_vocab * vocab,
+                          const char * grammar_str,
+                          const char * grammar_root,
+                         const char ** trigger_words,
+                                size_t num_trigger_words,
+                   const llama_token * trigger_tokens,
+                                size_t num_trigger_tokens);
+
     /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
                              int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
diff --git a/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja b/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja
new file mode 100644
index 000000000..f5baef30b
--- /dev/null
+++ b/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja
@@ -0,0 +1,202 @@
+
+{%- macro json_to_python_type(json_spec) %}
+{%- set basic_type_map = {
+    "string": "str",
+    "number": "float",
+    "integer": "int",
+    "boolean": "bool"
+} %}
+
+{%- if basic_type_map[json_spec.type] is defined %}
+    {{- basic_type_map[json_spec.type] }}
+{%- elif json_spec.type == "array" %}
+    {{- "List[" +  json_to_python_type(json_spec.items) + "]"}}
+{%- elif json_spec.type == "object" %}
+    {{- "Dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
+{%- elif json_spec.type is iterable %}
+    {{- "Union[" }}
+    {%- for t in json_spec.type %}
+      {{- json_to_python_type({"type": t}) }}
+      {%- if not loop.last %}
+        {{- "," }} 
+    {%- endif %}
+    {%- endfor %}
+    {{- "]" }}
+{%- else %}
+    {{- "Any" }}
+{%- endif %}
+{%- endmacro %}
+
+{%- macro old_tool_parser(tools) %}
+{%- for tool in tools %}
+    {%- if loop.index0 != 0 %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- '```python\ndef ' + tool.name + '(' }}
+    {%- for param_name, param_fields in tool.parameter_definitions|items %}
+        {%- if loop.index0 != 0 %}
+            {{- ', '}}
+        {%- endif %}
+        {{- param_name + ': ' }}
+        {%- if not param_fields.required %}
+            {{- 'Optional[' + param_fields.type + '] = None'}}
+        {%- else %}
+            {{- param_fields.type }}
+        {%- endif %}
+    {%- endfor %}
+    {{- ') -> List[Dict]:\n    """'}}
+    {{- tool.description }}
+    {%- if tool.parameter_definitions|length != 0 %}
+        {{- '\n\n    Args:\n        '}}
+        {%- for param_name, param_fields in tool.parameter_definitions|items %}
+            {%- if loop.index0 != 0 %}
+                {{- '\n        ' }}
+            {%- endif %}
+            {{- param_name + ' ('}}
+            {%- if not param_fields.required %}
+                {{- 'Optional[' + param_fields.type + ']'}}
+            {%- else %}
+                {{- param_fields.type }}
+            {%- endif %}
+            {{- '): ' + param_fields.description }}
+        {%- endfor %}
+    {%- endif %}
+    {{- '\n    """\n    pass\n```' }}
+{%- endfor %}
+{%- endmacro %}
+
+{%- macro new_tool_parser(tools) %}
+{%- for tool in tools %}
+  {%- if loop.index0 != 0 %}
+    {{- '\n\n'}}
+  {%- endif %}
+  {%- if tool.function is defined %}
+    {%- set tool = tool.function %}
+  {%- endif %}
+  {{-'```python
+def ' + tool.name + '('}}
+  {%- for param_name, param_fields in tool.parameters.properties|items %}
+    {%- if loop.index0 != 0 %}
+      {{- ', '}}
+    {%- endif %}
+    {{-param_name + ": "}} 
+    {%- if not param_name in tool.parameters.required %}
+      {{-'Optional[' + json_to_python_type(param_fields) + '] = None'}}
+    {%- else %}
+      {{- json_to_python_type(param_fields) }}
+    {%- endif %}
+  {%- endfor %}
+  {{- ') -> List[Dict]:
+    """'}}
+  {{- tool.description }}
+  {%- if tool.parameters.properties|length != 0 %}
+    {{- '\n\n    Args:\n        '}}
+    {%- for param_name, param_fields in tool.parameters.properties|items %}
+      {%- if loop.index0 != 0 %}
+        {{- '\n        ' }}
+      {%- endif %}
+      {{- param_name + ' ('}}
+      {%- if not param_name in tool.parameters.required %}
+        {{-'Optional[' + json_to_python_type(param_fields) + ']'}}
+      {%- else %}
+        {{- json_to_python_type(param_fields) }}
+      {%- endif %}
+      {{- '): ' + param_fields.description }}
+    {%- endfor %}
+    {%- endif %}
+    {{- '\n    """\n    pass\n```' }}
+{%- endfor %}
+{%- endmacro %}
+
+{{- bos_token }}
+{%- if messages[0]['role'] == 'system' %}
+  {%- set loop_messages = messages[1:] %}
+  {%- set system_message = messages[0]['content'] %}
+{%- else %}
+  {%- set loop_messages = messages %}
+  {%- set system_message = '## Task and Context\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user\'s needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.' %}
+{%- endif %}
+{{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }}
+{{- '# Safety Preamble' }}
+{{- '
+The instructions in this section override those in the task description and style guide sections. Don\'t answer questions that are harmful or immoral.' }}
+{{- '
+
+# System Preamble' }}
+{{- '
+## Basic Rules' }}
+{{- '
+You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user\'s requests, you cite your sources in your answers, according to those instructions.' }}
+{{- '
+
+# User Preamble' }}
+{{- '
+' + system_message }}
+{{-'
+
+## Available Tools
+Here is a list of tools that you have available to you:
+
+'}}
+{%- set ns = namespace(new_tools=true) %}
+{%- for tool in tools %}
+    {%- if tool.parameter_definitions is defined %}
+        {%- set ns.new_tools = false %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.new_tools %}
+    {{- new_tool_parser(tools) }}
+{%- else %}
+    {{- old_tool_parser(tools) }}
+{%- endif %}
+{{- '<|END_OF_TURN_TOKEN|>'}}
+{%- for message in loop_messages %}
+  {%- set content = message['content'] %}
+  {%- if message.role == 'user' %}
+    {{- '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }}
+  {%- elif message.role == 'system' %}
+    {{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }}
+  {%- elif message.role == 'assistant' and message.tool_calls is defined %}
+    {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
+    {%- if message.content is defined %}
+        {{- message.content|trim }}
+    {%- endif %}
+    {{- '\nAction:\n```json\n[\n' }}
+    {%- for tool_call in message.tool_calls %}
+        {%- if tool_call.function is defined %}
+            {%- set tool_call = tool_call.function %}
+        {%- endif %}
+        {{- '{\n'|indent(4, first=true) }}
+        {{- '"tool_name": "'|indent(8, first=true) + tool_call.name + '",\n' }}
+        {{- '"parameters": '|indent(8, first=true) }}
+        {%- if tool_call.arguments is defined and tool_call.arguments|length > 0 %}    
+            {{- tool_call.arguments|tojson(indent=4)|indent(8) }}
+            {{- '\n' }}
+        {%- else %}
+            {{- '{}\n' }}
+        {%- endif %}
+        {{- '}'|indent(4, first=true) }}
+        {%- if not loop.last %}
+            {{- ',\n' }}
+        {%- endif %}
+    {%- endfor %}
+    {{- "\n]```\n" }}
+  {%- elif message.role == 'assistant' %}
+    {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content|trim + '<|END_OF_TURN_TOKEN|>' }}
+  {%- elif message.role == 'tool' %}
+    {{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>\n' }}
+    {{- message.content|trim }}
+    {{- '</results><|END_OF_TURN_TOKEN|>' }}
+  {%- endif %}
+{%- endfor %}
+{{-'<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write \'Action:\' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user\'s last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
+```json
+[
+    {
+        "tool_name": title of the tool in the specification,
+        "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
+    }
+]```<|END_OF_TURN_TOKEN|>'}}
+{%- if add_generation_prompt %}
+  {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
+{%- endif %}
diff --git a/models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja b/models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja
new file mode 100644
index 000000000..149250bd5
--- /dev/null
+++ b/models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja
@@ -0,0 +1,152 @@
+{%- macro json_to_python_type(json_spec) %}
+{%- set basic_type_map = {
+    "string": "str",
+    "number": "float",
+    "integer": "int",
+    "boolean": "bool"
+} %}
+
+{%- if basic_type_map[json_spec.type] is defined %}
+    {{- basic_type_map[json_spec.type] }}
+{%- elif json_spec.type == "array" %}
+    {{- "list[" +  json_to_python_type(json_spec|items) + "]"}}
+{%- elif json_spec.type == "object" %}
+    {%- if json_spec.additionalProperties is defined %}
+        {{- "dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
+    {%- else %}
+        {{- "dict" }}
+    {%- endif %}
+{%- elif json_spec.type is iterable %}
+    {{- "Union[" }}
+    {%- for t in json_spec.type %}
+      {{- json_to_python_type({"type": t}) }}
+      {%- if not loop.last %}
+        {{- "," }} 
+    {%- endif %}
+    {%- endfor %}
+    {{- "]" }}
+{%- else %}
+    {{- "Any" }}
+{%- endif %}
+{%- endmacro %}
+
+
+{{- bos_token }}
+{{- '<|im_start|>system
+' }}
+{{- "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> " }}
+{%- for tool in tools %}
+    {%- if tool.function is defined %}
+        {%- set tool = tool.function %}
+    {%- endif %}
+    {{- '{"type": "function", "function": ' }}
+    {{- '{"name": "' + tool.name + '", ' }}
+    {{- '"description": "' + tool.name + '(' }}
+    {%- for param_name, param_fields in tool.parameters.properties|items %}
+        {{- param_name + ": " + json_to_python_type(param_fields) }}
+        {%- if not loop.last %}
+            {{- ", " }}
+        {%- endif %}
+    {%- endfor %}
+    {{- ")" }}
+    {%- if tool.return is defined %}
+        {{- " -> " + json_to_python_type(tool.return) }}
+    {%- endif %}
+    {{- " - " + tool.description + "
+
+" }}
+    {%- for param_name, param_fields in tool.parameters.properties|items %}
+        {%- if loop.first %}
+            {{- "    Args:
+" }}
+        {%- endif %}
+        {{- "        " + param_name + "(" + json_to_python_type(param_fields) + "): " + param_fields.description|trim }}
+    {%- endfor %}
+    {%- if tool.return is defined and tool.return.description is defined %}
+        {{- "
+    Returns:
+        " + tool.return.description }}
+    {%- endif %}
+    {{- '"' }}
+    {{- ', "parameters": ' }}
+    {%- if tool.parameters.properties | length == 0 %}
+        {{- "{}" }}
+    {%- else %}
+        {{- tool.parameters|tojson }}
+    {%- endif %}
+    {{- "}" }}
+    {%- if not loop.last %}
+        {{- "
+" }}
+    {%- endif %}
+{%- endfor %}
+{{- " </tools>" }}
+{{- 'Use the following pydantic model json schema for each tool call you will make: {"properties": {"name": {"title": "Name", "type": "string"}, "arguments": {"title": "Arguments", "type": "object"}}, "required": ["name", "arguments"], "title": "FunctionCall", "type": "object"}}
+' }}
+{{- "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
+" }}
+{{- "<tool_call>
+" }}
+{{- '{"name": <function-name>, "arguments": <args-dict>}
+' }}
+{{- '</tool_call><|im_end|>
+' }}
+{%- for message in messages %}
+    {%- if message.role == "user" or message.role == "system" or (message.role == "assistant" and message.tool_calls is not defined) %}
+        {{- '<|im_start|>' + message.role + '
+' + message.content + '<|im_end|>' + '
+' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+    {%- for tool_call in message.tool_calls %}
+       {{- '
+<tool_call>
+' }}           {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '{' }}
+            {{- '"name": "' }}
+            {{- tool_call.name }}
+            {{- '"' }}
+            {{- ', '}}
+            {%- if tool_call.arguments is defined %}
+                {{- '"arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments|tojson }}
+                {%- endif %}
+            {%- endif %}
+             {{- '}' }}
+            {{- '
+</tool_call>' }}
+    {%- endfor %}
+        {{- '<|im_end|>
+' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>tool
+' }}
+        {%- endif %}
+        {{- '<tool_response>
+' }}
+        {{- message.content }}
+        {%- if not loop.last %}
+            {{- '
+</tool_response>
+' }}
+        {%- else %}
+            {{- '
+</tool_response>' }}
+        {%- endif %}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant
+' }}
+{%- endif %}
diff --git a/models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja b/models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja
new file mode 100644
index 000000000..149250bd5
--- /dev/null
+++ b/models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja
@@ -0,0 +1,152 @@
+{%- macro json_to_python_type(json_spec) %}
+{%- set basic_type_map = {
+    "string": "str",
+    "number": "float",
+    "integer": "int",
+    "boolean": "bool"
+} %}
+
+{%- if basic_type_map[json_spec.type] is defined %}
+    {{- basic_type_map[json_spec.type] }}
+{%- elif json_spec.type == "array" %}
+    {{- "list[" +  json_to_python_type(json_spec|items) + "]"}}
+{%- elif json_spec.type == "object" %}
+    {%- if json_spec.additionalProperties is defined %}
+        {{- "dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
+    {%- else %}
+        {{- "dict" }}
+    {%- endif %}
+{%- elif json_spec.type is iterable %}
+    {{- "Union[" }}
+    {%- for t in json_spec.type %}
+      {{- json_to_python_type({"type": t}) }}
+      {%- if not loop.last %}
+        {{- "," }} 
+    {%- endif %}
+    {%- endfor %}
+    {{- "]" }}
+{%- else %}
+    {{- "Any" }}
+{%- endif %}
+{%- endmacro %}
+
+
+{{- bos_token }}
+{{- '<|im_start|>system
+' }}
+{{- "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> " }}
+{%- for tool in tools %}
+    {%- if tool.function is defined %}
+        {%- set tool = tool.function %}
+    {%- endif %}
+    {{- '{"type": "function", "function": ' }}
+    {{- '{"name": "' + tool.name + '", ' }}
+    {{- '"description": "' + tool.name + '(' }}
+    {%- for param_name, param_fields in tool.parameters.properties|items %}
+        {{- param_name + ": " + json_to_python_type(param_fields) }}
+        {%- if not loop.last %}
+            {{- ", " }}
+        {%- endif %}
+    {%- endfor %}
+    {{- ")" }}
+    {%- if tool.return is defined %}
+        {{- " -> " + json_to_python_type(tool.return) }}
+    {%- endif %}
+    {{- " - " + tool.description + "
+
+" }}
+    {%- for param_name, param_fields in tool.parameters.properties|items %}
+        {%- if loop.first %}
+            {{- "    Args:
+" }}
+        {%- endif %}
+        {{- "        " + param_name + "(" + json_to_python_type(param_fields) + "): " + param_fields.description|trim }}
+    {%- endfor %}
+    {%- if tool.return is defined and tool.return.description is defined %}
+        {{- "
+    Returns:
+        " + tool.return.description }}
+    {%- endif %}
+    {{- '"' }}
+    {{- ', "parameters": ' }}
+    {%- if tool.parameters.properties | length == 0 %}
+        {{- "{}" }}
+    {%- else %}
+        {{- tool.parameters|tojson }}
+    {%- endif %}
+    {{- "}" }}
+    {%- if not loop.last %}
+        {{- "
+" }}
+    {%- endif %}
+{%- endfor %}
+{{- " </tools>" }}
+{{- 'Use the following pydantic model json schema for each tool call you will make: {"properties": {"name": {"title": "Name", "type": "string"}, "arguments": {"title": "Arguments", "type": "object"}}, "required": ["name", "arguments"], "title": "FunctionCall", "type": "object"}}
+' }}
+{{- "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
+" }}
+{{- "<tool_call>
+" }}
+{{- '{"name": <function-name>, "arguments": <args-dict>}
+' }}
+{{- '</tool_call><|im_end|>
+' }}
+{%- for message in messages %}
+    {%- if message.role == "user" or message.role == "system" or (message.role == "assistant" and message.tool_calls is not defined) %}
+        {{- '<|im_start|>' + message.role + '
+' + message.content + '<|im_end|>' + '
+' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+    {%- for tool_call in message.tool_calls %}
+       {{- '
+<tool_call>
+' }}           {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '{' }}
+            {{- '"name": "' }}
+            {{- tool_call.name }}
+            {{- '"' }}
+            {{- ', '}}
+            {%- if tool_call.arguments is defined %}
+                {{- '"arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments|tojson }}
+                {%- endif %}
+            {%- endif %}
+             {{- '}' }}
+            {{- '
+</tool_call>' }}
+    {%- endfor %}
+        {{- '<|im_end|>
+' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>tool
+' }}
+        {%- endif %}
+        {{- '<tool_response>
+' }}
+        {{- message.content }}
+        {%- if not loop.last %}
+            {{- '
+</tool_response>
+' }}
+        {%- else %}
+            {{- '
+</tool_response>' }}
+        {%- endif %}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant
+' }}
+{%- endif %}
diff --git a/models/templates/Qwen-Qwen2.5-7B-Instruct.jinja b/models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
new file mode 100644
index 000000000..bdf7919a9
--- /dev/null
+++ b/models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
diff --git a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja
new file mode 100644
index 000000000..02a1c3bce
--- /dev/null
+++ b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja
@@ -0,0 +1 @@
+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}
\ No newline at end of file
diff --git a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja
new file mode 100644
index 000000000..2ebfe7c1e
--- /dev/null
+++ b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja
@@ -0,0 +1,56 @@
+{% if not add_generation_prompt is defined %}
+{% set add_generation_prompt = false %}
+{% endif %}
+{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}
+{%- for message in messages %}
+{%- if message['role'] == 'system' %}
+{% set ns.system_prompt = message['content'] %}
+{%- endif %}
+{%- endfor %}
+{{bos_token}}
+{{ns.system_prompt}}
+{%- for message in messages %}
+{%- if message['role'] == 'user' %}
+{%- set ns.is_tool = false -%}
+{{'<｜User｜>' + message['content']}}
+{%- endif %}
+{%- if message['role'] == 'assistant' and message['content'] is none %}
+{%- set ns.is_tool = false -%}
+{%- for tool in message['tool_calls']%}
+{%- if not ns.is_first %}
+{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}
+{%- set ns.is_first = true -%}
+{%- else %}
+{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}
+{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{%- if message['role'] == 'assistant' and message['content'] is not none %}
+{%- if ns.is_tool %}
+{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}
+{%- set ns.is_tool = false -%}
+{%- else %}
+{% set content = message['content'] %}
+{% if '</think>' in content %}
+{% set content = content.split('</think>')[-1] %}
+{% endif %}
+{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}
+{%- endif %}
+{%- endif %}
+{%- if message['role'] == 'tool' %}
+{%- set ns.is_tool = true -%}
+{%- if ns.is_output_first %}
+{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
+{%- set ns.is_output_first = false %}
+{%- else %}
+{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
+{%- endif %}
+{%- endif %}
+{%- endfor -%}
+{% if ns.is_tool %}
+{{'<｜tool▁outputs▁end｜>'}}
+{% endif %}
+{% if add_generation_prompt and not ns.is_tool %}
+{{'<｜Assistant｜>'}}
+{% endif %}
\ No newline at end of file
diff --git a/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja b/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
new file mode 100644
index 000000000..9b8136df7
--- /dev/null
+++ b/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
@@ -0,0 +1,57 @@
+{%- set loop_messages = messages -%}
+{%- set message_roles = ['system', 'user', 'assistant', 'tool'] -%}
+{%- set system_prompt_suffix -%}
+{%- filter trim -%}
+In addition to plain text responses, you can chose to call one or more of the provided functions.
+
+Use the following rule to decide when to call a function:
+  * if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so
+  * if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls
+
+If you decide to call functions:
+  * prefix function calls with functools marker (no closing marker required)
+  * all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]}, ...]
+  * follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples
+  * respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0
+  * make sure you pick the right functions that match the user intent
+
+Available functions as JSON spec:
+{%- endfilter -%}
+{%- endset -%}
+{%- set system_prompt_suffix = system_prompt_suffix + "\n" + functions -%}
+{%- set system_prompt_suffix = system_prompt_suffix + '\nToday is ' + datetime + '.' -%}
+{%- set ns = namespace(role='', content='') -%}
+{#- Basic consistency checks -#}
+{%- if not loop_messages -%}
+  {{ raise_exception('Expected non-empty messages') }}
+{%- endif -%}
+{%- for message in loop_messages -%}
+  {%- set ns.role = message['role'] | lower -%}
+  {%- if ns.role not in message_roles -%}
+    {%- set message_roles_string = message_roles | join(', ') -%}
+    {{ raise_exception('Invalid role ' + message['role'] + '. Only ' + message_roles_string + ' are supported.') }}
+  {%- endif -%}
+  {%- set msg_content = message['content'] | default('', true) | trim -%}
+  {%- if loop.index0 == 0 -%}
+    {%- if ns.role == 'system' -%}
+      {%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\n' + message['content'] | trim + '\n' + system_prompt_suffix + '<|eot_id|>' -%}
+    {%- else -%}
+      {%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\nYou are a helpful assistant with access to functions.\n' + system_prompt_suffix + '<|eot_id|>' -%}
+    {%- endif -%}
+    {%- set ns.content = bos_token + system_prompt -%}
+    {{- ns.content -}}
+  {%- endif -%}
+  {%- if loop.index0 > 0 or ns.role != 'system' -%}
+    {%- set ns.content = '<|start_header_id|>' + ns.role + '<|end_header_id|>\n\n' + msg_content -%}
+    {%- if 'tool_calls' in message and message['tool_calls'] -%}
+      {%- set tool = namespace(calls=[]) -%}
+      {%- for call in message['tool_calls'] -%}
+        {%- set tool.calls = tool.calls + ['{"name": "' + call['function']['name'] + '", "arguments": ' + call['function']['arguments'] + '}'] -%}
+      {%- endfor -%}
+      {%- set ns.content = ns.content + ' functools[' + tool.calls | join(', ') + ']' -%}
+    {%- endif -%}
+    {%- set ns.content = ns.content + '<|eot_id|>' -%}
+    {{- ns.content -}}
+  {%- endif -%}
+{%- endfor -%}
+{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
diff --git a/models/templates/google-gemma-2-2b-it.jinja b/models/templates/google-gemma-2-2b-it.jinja
new file mode 100644
index 000000000..923ec253c
--- /dev/null
+++ b/models/templates/google-gemma-2-2b-it.jinja
@@ -0,0 +1,4 @@
+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}
\ No newline at end of file
diff --git a/models/templates/meetkai-functionary-medium-v3.1.jinja b/models/templates/meetkai-functionary-medium-v3.1.jinja
new file mode 100644
index 000000000..29d64a215
--- /dev/null
+++ b/models/templates/meetkai-functionary-medium-v3.1.jinja
@@ -0,0 +1,58 @@
+{# version=v3-llama3.1 #}{%- if not tools is defined -%}
+    {%- set tools = none -%}
+{%- endif -%}
+
+{%- set has_code_interpreter = tools | selectattr("type", "equalto", "code_interpreter") | list | length > 0 -%}
+{%- if has_code_interpreter -%}
+    {%- set tools = tools | rejectattr("type", "equalto", "code_interpreter") | list -%}
+{%- endif -%}
+
+{#- System message + builtin tools #}
+{{- bos_token + "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if has_code_interpreter %}
+    {{- "Environment: ipython\n\n" }}
+{%- else -%}
+    {{ "\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n\n" }}
+{%- if tools %}
+    {{- "\nYou have access to the following functions:\n\n" }}
+    {%- for t in tools %}
+        {%- if "type" in t -%}
+            {{ "Use the function '"|safe + t["function"]["name"] + "' to '"|safe + t["function"]["description"] + "'\n"|safe + t["function"] | tojson() }}
+        {%- else -%}
+            {{ "Use the function '"|safe + t["name"] + "' to '"|safe + t["description"] + "'\n"|safe + t | tojson() }}
+        {%- endif -%}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- '\nThink very carefully before calling functions.\nIf a you choose to call a function ONLY reply in the following format:\n<{start_tag}={function_name}>{parameters}{end_tag}\nwhere\n\nstart_tag => `<function`\nparameters => a JSON dict with the function argument name as key and function argument value as value.\nend_tag => `</function>`\n\nHere is an example,\n<function=example_function_name>{"example_name": "example_value"}</function>\n\nReminder:\n- If looking for real time information use relevant functions before falling back to brave_search\n- Function calls MUST follow the specified format, start with <function= and end with </function>\n- Required parameters MUST be specified\n- Only call one function at a time\n- Put the entire function call reply on one line\n\n' -}}
+{%- endif %}
+{{- "<|eot_id|>" -}}
+
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' or message['role'] == 'system' -%}
+        {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
+    {%- elif message['role'] == 'tool' -%}
+        {{ '<|start_header_id|>ipython<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
+    {%- else -%}
+        {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}
+        {%- if message['content'] -%}
+            {{ message['content'] }}
+        {%- endif -%}
+        {%- if 'tool_calls' in message and message['tool_calls'] -%}
+            {%- for tool_call in message['tool_calls'] -%}
+                {%- if tool_call["function"]["name"] == "python" -%}
+                    {{ '<|python_tag|>' + tool_call['function']['arguments'] }}
+                {%- else -%}
+                    {{ '<function=' + tool_call['function']['name'] + '>' + tool_call['function']['arguments'] + '</function>' }}
+                {%- endif -%}
+            {%- endfor -%}
+            {{ '<|eom_id|>' }}
+        {%- else -%}
+            {{ '<|eot_id|>' }}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif -%}
\ No newline at end of file
diff --git a/models/templates/meetkai-functionary-medium-v3.2.jinja b/models/templates/meetkai-functionary-medium-v3.2.jinja
new file mode 100644
index 000000000..74fd1e7af
--- /dev/null
+++ b/models/templates/meetkai-functionary-medium-v3.2.jinja
@@ -0,0 +1,287 @@
+{# version=v3.llama3 #}{%- macro append_new_param_info(param_declaration, comment_info, examples_info, depth) -%}
+    {%- set offset = "" -%}
+    {%- if depth >= 1 -%}
+        {%- set offset = "    " * depth -%}
+    {%- endif -%}
+    {%- if comment_info != "<|NONE|>" -%}
+        {{ "\n" + offset + comment_info }}
+        {%- if examples_info | length > 0 -%}
+            {# Append each example info #}
+            {%- for example in examples_info -%}
+                {{ "\n" + offset + "// " + example|string|replace("'", '"') }}
+            {%- endfor -%}
+        {%- endif -%}
+    {%- endif -%}
+    {{ "\n" + offset + param_declaration }}
+{%- endmacro -%}
+
+{%- macro convert_data_type(param_type) -%}
+    {%- if param_type == "integer" or param_type == "float" -%}
+        {{ "number" }}
+    {%- else -%}
+        {{ param_type }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro get_param_type(param) -%}
+    {%- set param_type = "any" -%}
+
+    {%- if "type" in param -%}
+        {%- set raw_param_type = param["type"] -%}
+        {%- if raw_param_type is iterable and raw_param_type is not string -%}
+            {%- set param_type = raw_param_type | join(" | ") -%}
+        {%- else -%}
+            {%- set param_type = raw_param_type -%}
+        {%- endif -%}
+        {{ convert_data_type(param_type) }}
+    {%- elif "oneOf" in param -%}
+        {%- set one_of_types = param["oneOf"]|selectattr("type", "defined")|list -%}
+        {%- set one_of_types = one_of_types|map(attribute="type")|unique|list -%}
+        {{ convert_data_type(one_of_types | join(" | ")) }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro get_format_param(param) -%}
+    {%- if "format" in param -%}
+        {{ param["format"] }}
+    {%- elif "oneOf" in param -%}
+        {%- set formats = [] -%}
+        {%- for item in param["oneOf"] -%}
+            {%- if "format" in item -%}
+                {%- if item["format"] == param["oneOf"][-1]["format"] -%}
+                    {{ item["format"] }}
+                {%- else -%}
+                    {{ item["format"] + " or "}}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ "<|NONE|>" }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro get_param_info(param) -%}
+    {%- set param_type = param.get("type", "any") -%}
+    {%- set format_param = get_format_param(param) -%}
+
+    {%- if "description" in param or "default" in param or format_param != "<|NONE|>" or param["maximum"] or param["minimum"] or param["maxLength"] or param["minLength"] -%}
+        {{ "//" }}
+        {%- if "description" in param -%}
+            {%- set desc = param["description"] -%}
+            {%- if not desc.endswith(".") -%}
+                {%- set desc = desc + "." -%}
+            {%- endif -%}
+            {{ " " + desc }}
+        {%- endif -%}
+
+        {%- if "default" in param -%}
+            {%- set default_value = param["default"] -%}
+            {%- if param_type == "string" -%}
+                {%- set default_value = '"' ~ default_value ~ '"' -%}
+            {%- endif -%}
+            {{ " Default=" ~ default_value ~ "." }}
+        {%- endif -%}
+
+        {%- set format_param = get_format_param(param) -%}
+        {%- if format_param != "<|NONE|>" -%}
+            {{ " Format=" ~ format_param }}
+        {%- endif -%}
+
+        {%- for field, field_name in [("maximum", "Maximum"), ("minimum", "Minimum"), ("maxLength", "Maximum length"), ("minLength", "Minimum length")] -%}
+            {%- if field in param -%}
+                {{ " " + field_name ~ "=" ~ param[field] }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ "<|NONE|>"}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro get_enum_option_str(enum_options) -%}
+    {%- for v in enum_options -%}
+        {%- if v is string -%}
+            {{ '"' + v + '"' }}
+        {%- else -%}
+            {{ v }}
+        {%- endif -%}
+        {%- if enum_options|length > 0 and v != enum_options[-1] -%}
+            {{ " | " }}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro -%}
+
+{%- macro get_array_typescript(param_name, param_dic, depth) -%}
+    {%- set offset = '' -%}
+    {%- if depth >= 1 -%}
+        {%- set offset = "    " * depth -%}
+    {%- endif -%}
+    {%- set items_info = param_dic.get('items', {}) -%}
+
+    {%- if items_info|length == 0 -%}
+        {%- if param_name -%}
+            {{ "\n" + offset + param_name + ": []" }}
+        {%- else -%}
+            {{ "\n" + offset + "[]" }}
+        {%- endif -%}
+    {%- else -%}
+        {%- set array_type = get_param_type(items_info) -%}
+        {%- if array_type == 'object' -%}
+            {%- if param_name -%}
+                {{ "\n" + offset + param_name + ": {" }}
+            {%- else -%}
+                {{ "\n" + offset + "{" }}
+            {%- endif -%}
+            {{ get_parameter_typescript(items_info.get('properties', {}), items_info.get('required', []), depth + 1) -}}
+            {{- "\n" + offset + "}[]" }}
+        {%- elif array_type == 'array' -%}
+            {%- set item_info = get_array_typescript(None, items_info, depth + 1) -%}
+            {%- if not param_name -%}
+                {{ "\n" + item_info + "[]" }}
+            {%- else -%}
+                {{ "\n" + offset + param_name + ": " + item_info|trim + "[]" }}
+            {%- endif -%}
+        {%- else -%}
+            {%- if 'enum' in items_info -%}
+                {%- set item_type = get_enum_option_str(items_info['enum']) -%}
+                {%- if param_name is none -%}
+                    {{ "(" + item_type + ")[]"}}
+                {%- else -%}
+                    {{ "\n" + offset + param_name + ": (" + item_type + ")[]" }}
+                {%- endif -%}
+            {%- else -%}
+                {%- if param_name is none -%}
+                    {{ "\n" + array_type + "[]" }}
+                {%- else -%}
+                    {{ "\n" + offset + param_name + ": " + array_type + "[]," }}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro get_parameter_typescript(properties, required_params, depth=0) -%}
+    {%- set res = "" -%}
+    {%- for param_name, param in properties.items() -%}
+        {%- if param is mapping -%}
+            {%- set comment_info = get_param_info(param) -%}
+            {# Param Examples #}
+            {%- set examples_info = [] -%}
+            {%- if "examples" in param -%}
+                {%- set examples_info = ["Example " + param_name + ":"] -%}
+                {%- set examples_info = examples_info + param["examples"] -%}
+            {%- endif -%}
+
+            {# Param Name declaration #}
+            {%- set param_declaration = param_name -%}
+            {%- if required_params is iterable and param_name not in required_params -%}
+                {%- set param_declaration = param_declaration + "?" -%}
+            {%- endif -%}
+
+            {%- set param_type = get_param_type(param) -%}
+
+            {# Handle indentation based on depth #}
+            {%- set offset = "" -%}
+            {%- if depth >= 1 -%}
+                {%- set offset = "    " * depth -%}
+            {%- endif -%}
+
+            {%- if param_type == "object" -%}
+                {%- if comment_info != "<|NONE|>" -%}
+                    {{ "\n" + offset + comment_info }}
+                {%- endif -%}
+                {%- if examples_info|length > 0 -%}
+                    {%- for example in examples_info -%}
+                        {{ "\n" + offset + "// " + example|string|replace("'", '"') }}
+                    {%- endfor -%}
+                {%- endif -%}
+                {%- set param_declaration = param_declaration + ": {" -%}
+                {{ "\n" + offset + param_declaration -}}
+                {{- get_parameter_typescript(param.get("properties", {}), param.get("required", []), depth + 1) -}}
+                {{- "\n" + offset + "}," }}
+            {%- elif param_type == "array" -%}
+                {%- set item_info = param.get("items", {}) -%}
+                {%- if "type" not in item_info -%}
+                    {%- set param_declaration = param_declaration + ": []," -%}
+                    {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}
+                {%- else -%}
+                    {%- if comment_info != "<|NONE|>" -%}
+                        {{ "\n" + offset + comment_info }}
+                    {%- endif -%}
+                    {%- if examples_info|length > 0 -%}
+                        {%- for example in examples_info -%}
+                            {{ "\n" + offset + "// " + example|string|replace("'", '"') }}
+                        {%- endfor -%}
+                    {%- endif -%}
+                    {%- set array_declaration = get_array_typescript(param_declaration, param, depth) -%}
+                    {%- if not array_declaration.endswith(",") -%}
+                        {%- set array_declaration = array_declaration + "," -%}
+                    {%- endif -%}
+                    {{ array_declaration}}
+                {%- endif -%}
+            {%- else -%}
+                {%- if "enum" in param -%}
+                    {%- set param_type = get_enum_option_str(param["enum"]) -%}
+                {%- endif -%}
+                {%- if "nullable" in param and param["nullable"] -%}
+                    {%- set param_type = param_type + " | null" -%}
+                {%- endif -%}
+                {%- set param_declaration = param_declaration + ": " + param_type + "," -%}
+                {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro -%}
+
+{%- macro generate_schema_from_functions(functions, namespace='functions') -%}
+    {{ "// Supported function definitions that should be called when necessary.\n" -}}
+    {{- "namespace " + namespace + " {\n\n" -}}
+
+    {%- for function in functions -%}
+        {%- if function.get("function") -%}
+            {%- set function = function.get("function") -%}
+        {%- endif -%}
+
+        {%- set function_name = function.get("name") -%}
+        {%- if function_name -%}
+            {%- set description = function.get('description', '') -%}
+            {%- set parameters = function.get('parameters', {}) -%}
+            {{- "// " + description + "\n" -}}
+            {{- "type " + function_name -}}
+            {%- if parameters and parameters.get("properties") -%}
+                {{- " = (_: {" -}}
+                {%- set required_params = parameters.get("required", []) -%}
+                {{ get_parameter_typescript(parameters.get("properties"), required_params, 0) -}}
+                {{- "\n}) => any;\n\n" }}
+            {%- else -%}
+                {{ " = () => any;\n\n" }}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {{ "} // namespace " + namespace }}
+{%- endmacro -%}
+{%- if not tools -%}
+    {%- set tools = [] -%}
+{%- endif -%}
+{{ bos_token + '<|start_header_id|>system<|end_header_id|>\n\nYou are capable of executing available function(s) if required.\nOnly execute function(s) when absolutely necessary.\nAsk for the required input to:recipient==all\nUse JSON for function arguments.\nRespond in this format:\n>>>${recipient}\n${content}\nAvailable functions:\n' + generate_schema_from_functions(tools) + '<|eot_id|>' -}}
+{%- if tools|length > 0 and tools|selectattr("type", "equalto", "code_interpreter")|list|length > 0 -%}
+    {{ '<|start_header_id|>system<|end_header_id|>\n\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at \'/mnt/data\' can be used to save and persist user files.<|eot_id|>' }}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' or message['role'] == 'system' -%}
+        {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
+    {%- elif message['role'] == 'tool' -%}
+        {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
+    {%- else -%}
+        {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}
+        {%- if message['content'] -%}
+            {{ '>>>all\n' + message['content'] }}
+        {%- endif -%}
+        {%- if 'tool_calls' in message and message['tool_calls'] -%}
+            {%- for tool_call in message['tool_calls'] -%}
+                {{ '>>>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments'] }}
+            {%- endfor -%}
+        {%- endif -%}
+        {{ '<|eot_id|>' }}
+    {%- endif -%}
+{%- endfor -%}
+{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n>>>' }}{% endif %}
\ No newline at end of file
diff --git a/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja b/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja
new file mode 100644
index 000000000..33089ace1
--- /dev/null
+++ b/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja
@@ -0,0 +1,109 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
diff --git a/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja b/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja
new file mode 100644
index 000000000..1bad6a0f6
--- /dev/null
+++ b/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja
@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
diff --git a/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja b/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja
new file mode 100644
index 000000000..33089ace1
--- /dev/null
+++ b/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja
@@ -0,0 +1,109 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
diff --git a/models/templates/microsoft-Phi-3.5-mini-instruct.jinja b/models/templates/microsoft-Phi-3.5-mini-instruct.jinja
new file mode 100644
index 000000000..d1533d152
--- /dev/null
+++ b/models/templates/microsoft-Phi-3.5-mini-instruct.jinja
@@ -0,0 +1,8 @@
+{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'user' %}{{'<|user|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
+' + message['content'] + '<|end|>
+'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
+' }}{% else %}{{ eos_token }}{% endif %}
\ No newline at end of file
diff --git a/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja b/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja
new file mode 100644
index 000000000..9c21a3f13
--- /dev/null
+++ b/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja
@@ -0,0 +1,87 @@
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}
+
+{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}
+{%- set ns = namespace() %}
+{%- set ns.index = 0 %}
+{%- for message in loop_messages %}
+    {%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
+        {%- if (message["role"] == "user") != (ns.index % 2 == 0) %}
+            {{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}
+        {%- endif %}
+        {%- set ns.index = ns.index + 1 %}
+    {%- endif %}
+{%- endfor %}
+
+{{- bos_token }}
+{%- for message in loop_messages %}
+    {%- if message["role"] == "user" %}
+        {%- if tools is not none and (message == user_messages[-1]) %}
+            {{- "[AVAILABLE_TOOLS][" }}
+            {%- for tool in tools %}
+                {%- set tool = tool.function %}
+                {{- '{"type": "function", "function": {' }}
+                {%- for key, val in tool.items() if key != "return" %}
+                    {%- if val is string %}
+                        {{- '"' + key + '": "' + val + '"' }}
+                    {%- else %}
+                        {{- '"' + key + '": ' + val|tojson }}
+                    {%- endif %}
+                    {%- if not loop.last %}
+                        {{- ", " }}
+                    {%- endif %}
+                {%- endfor %}
+                {{- "}}" }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- else %}
+                    {{- "]" }}
+                {%- endif %}
+            {%- endfor %}
+            {{- "[/AVAILABLE_TOOLS]" }}
+            {%- endif %}
+        {%- if loop.last and system_message is defined %}
+            {{- "[INST]" + system_message + "\n\n" + message["content"] + "[/INST]" }}
+        {%- else %}
+            {{- "[INST]" + message["content"] + "[/INST]" }}
+        {%- endif %}
+    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}
+        {{- "[TOOL_CALLS][" }}
+        {%- for tool_call in message.tool_calls %}
+            {%- set out = tool_call.function|tojson %}
+            {{- out[:-1] }}
+            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}
+                {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
+            {%- endif %}
+            {{- ', "id": "' + tool_call.id + '"}' }}
+            {%- if not loop.last %}
+                {{- ", " }}
+            {%- else %}
+                {{- "]" + eos_token }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif message["role"] == "assistant" %}
+        {{- message["content"] + eos_token}}
+    {%- elif message["role"] == "tool_results" or message["role"] == "tool" %}
+        {%- if message.content is defined and message.content.content is defined %}
+            {%- set content = message.content.content %}
+        {%- else %}
+            {%- set content = message.content %}
+        {%- endif %}
+        {{- '[TOOL_RESULTS]{"content": ' + content|string + ", " }}
+        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}
+            {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
+        {%- endif %}
+        {{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }}
+    {%- else %}
+        {{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}
+    {%- endif %}
+{%- endfor %}
diff --git a/scripts/fetch_server_test_models.py b/scripts/fetch_server_test_models.py
new file mode 100755
index 000000000..05690b138
--- /dev/null
+++ b/scripts/fetch_server_test_models.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+'''
+    This script fetches all the models used in the server tests.
+
+    This is useful for slow tests that use larger models, to avoid them timing out on the model downloads.
+
+    It is meant to be run from the root of the repository.
+
+    Example:
+        python scripts/fetch_server_test_models.py
+        ( cd examples/server/tests && ./tests.sh -v -x -m slow )
+'''
+import ast
+import glob
+import logging
+import os
+from typing import Generator
+from pydantic import BaseModel
+from typing import Optional
+import subprocess
+
+
+class HuggingFaceModel(BaseModel):
+    hf_repo: str
+    hf_file: Optional[str] = None
+
+    class Config:
+        frozen = True
+
+
+def collect_hf_model_test_parameters(test_file) -> Generator[HuggingFaceModel, None, None]:
+    try:
+        with open(test_file) as f:
+            tree = ast.parse(f.read())
+    except Exception as e:
+        logging.error(f'collect_hf_model_test_parameters failed on {test_file}: {e}')
+        return
+
+    for node in ast.walk(tree):
+        if isinstance(node, ast.FunctionDef):
+            for dec in node.decorator_list:
+                if isinstance(dec, ast.Call) and isinstance(dec.func, ast.Attribute) and dec.func.attr == 'parametrize':
+                    param_names = ast.literal_eval(dec.args[0]).split(",")
+                    if "hf_repo" not in param_names:
+                        continue
+
+                    raw_param_values = dec.args[1]
+                    if not isinstance(raw_param_values, ast.List):
+                        logging.warning(f'Skipping non-list parametrize entry at {test_file}:{node.lineno}')
+                        continue
+
+                    hf_repo_idx = param_names.index("hf_repo")
+                    hf_file_idx = param_names.index("hf_file") if "hf_file" in param_names else None
+
+                    for t in raw_param_values.elts:
+                        if not isinstance(t, ast.Tuple):
+                            logging.warning(f'Skipping non-tuple parametrize entry at {test_file}:{node.lineno}')
+                            continue
+                        yield HuggingFaceModel(
+                            hf_repo=ast.literal_eval(t.elts[hf_repo_idx]),
+                            hf_file=ast.literal_eval(t.elts[hf_file_idx]) if hf_file_idx is not None else None)
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+
+    models = sorted(list(set([
+        model
+        for test_file in glob.glob('examples/server/tests/unit/test_*.py')
+        for model in collect_hf_model_test_parameters(test_file)
+    ])), key=lambda m: (m.hf_repo, m.hf_file))
+
+    logging.info(f'Found {len(models)} models in parameterized tests:')
+    for m in models:
+        logging.info(f'  - {m.hf_repo} / {m.hf_file}')
+
+    cli_path = os.environ.get(
+        'LLAMA_SERVER_BIN_PATH',
+        os.path.join(
+            os.path.dirname(__file__),
+            '../build/bin/Release/llama-cli.exe' if os.name == 'nt' else '../build/bin/llama-cli'))
+
+    for m in models:
+        if '<' in m.hf_repo or (m.hf_file is not None and '<' in m.hf_file):
+            continue
+        if m.hf_file is not None and '-of-' in m.hf_file:
+            logging.warning(f'Skipping model at {m.hf_repo} / {m.hf_file} because it is a split file')
+            continue
+        logging.info(f'Using llama-cli to ensure model {m.hf_repo}/{m.hf_file} was fetched')
+        cmd = [
+            cli_path,
+            '-hfr', m.hf_repo,
+            *([] if m.hf_file is None else ['-hff', m.hf_file]),
+            '-n', '1',
+            '-p', 'Hey',
+            '--no-warmup',
+            '--log-disable',
+            '-no-cnv']
+        if m.hf_file != 'tinyllamas/stories260K.gguf' and 'Mistral-Nemo' not in m.hf_repo:
+            cmd.append('-fa')
+        try:
+            subprocess.check_call(cmd)
+        except subprocess.CalledProcessError:
+            logging.error(f'Failed to fetch model at {m.hf_repo} / {m.hf_file} with command:\n  {" ".join(cmd)}')
+            exit(1)
diff --git a/scripts/get_hf_chat_template.py b/scripts/get_chat_template.py
old mode 100755
new mode 100644
similarity index 86%
rename from scripts/get_hf_chat_template.py
rename to scripts/get_chat_template.py
index 23bb1de59..e8982d11a
--- a/scripts/get_hf_chat_template.py
+++ b/scripts/get_chat_template.py
@@ -4,12 +4,12 @@
   If a model has multiple chat templates, you can specify the variant name.
 
   Syntax:
-    ./scripts/get_hf_chat_template.py model_id [variant]
+    ./scripts/get_chat_template.py model_id [variant]
 
   Examples:
-    ./scripts/get_hf_chat_template.py NousResearch/Meta-Llama-3-8B-Instruct
-    ./scripts/get_hf_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use
-    ./scripts/get_hf_chat_template.py meta-llama/Llama-3.2-3B-Instruct
+    ./scripts/get_chat_template.py NousResearch/Meta-Llama-3-8B-Instruct
+    ./scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use
+    ./scripts/get_chat_template.py meta-llama/Llama-3.2-3B-Instruct
 '''
 
 import json
@@ -17,7 +17,7 @@ import re
 import sys
 
 
-def get_hf_chat_template(model_id, variant=None):
+def get_chat_template(model_id, variant=None):
     try:
         # Use huggingface_hub library if available.
         # Allows access to gated models if the user has access and ran `huggingface-cli login`.
@@ -69,7 +69,7 @@ def main(args):
     model_id = args[0]
     variant = None if len(args) < 2 else args[1]
 
-    template = get_hf_chat_template(model_id, variant)
+    template = get_chat_template(model_id, variant)
     sys.stdout.write(template)
 
 
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index bebe4e9a3..6be5cbe0e 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -560,7 +560,7 @@ bool llama_grammar_parser::parse(const char * src) {
             }
         }
     } catch (const std::exception & err) {
-        fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
+        fprintf(stderr, "%s: error parsing grammar: %s\n\n%s\n", __func__, err.what(), src);
         rules.clear();
         return false;
     }
@@ -960,10 +960,28 @@ struct llama_grammar * llama_grammar_init_impl(
     // Important: vec_rules has to be moved here, not copied, because stacks contains
     // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
     // then the pointers would be invalidated when the local vec_rules goes out of scope.
-    return new llama_grammar { vocab, std::move(vec_rules), std::move(stacks), {}, };
+    return new llama_grammar {
+        vocab,
+        std::move(vec_rules),
+        std::move(stacks),
+        /* .partial_utf8 = */     {},
+        /* .lazy =*/              false,
+        /* .awaiting_trigger = */ false,
+        /* .trigger_buffer = */   "",
+        /* .trigger_tokens   = */ {},
+        /* .trigger_words    = */ {},
+    };
 }
 
-struct llama_grammar * llama_grammar_init_impl(const struct llama_vocab * vocab, const char * grammar_str, const char * grammar_root) {
+struct llama_grammar * llama_grammar_init_impl(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root,
+                              bool lazy,
+                     const char ** trigger_words,
+                            size_t num_trigger_words,
+               const llama_token * trigger_tokens,
+                            size_t num_trigger_tokens) {
     llama_grammar_parser parser;
 
     // if there is a grammar, parse it
@@ -1035,10 +1053,31 @@ struct llama_grammar * llama_grammar_init_impl(const struct llama_vocab * vocab,
         }
     } while (true);
 
+    std::vector<llama_token>    vec_trigger_tokens;
+    std::vector<std::string> vec_trigger_words;
+    for (size_t i = 0; i < num_trigger_tokens; i++) {
+        GGML_ASSERT(trigger_tokens != nullptr);
+        vec_trigger_tokens.push_back(trigger_tokens[i]);
+    }
+    for (size_t i = 0; i < num_trigger_words; i++) {
+        GGML_ASSERT(trigger_words != nullptr);
+        vec_trigger_words.push_back(trigger_words[i]);
+    }
+
     // Important: vec_rules has to be moved here, not copied, because stacks contains
     // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
     // then the pointers would be invalidated when the local vec_rules goes out of scope.
-    return new llama_grammar { vocab, std::move(vec_rules), std::move(stacks), {}, };
+    return new llama_grammar {
+        vocab,
+        std::move(vec_rules),
+        std::move(stacks),
+        /* .partial_utf8 = */     {},
+        /* .lazy = */             lazy,
+        /* .awaiting_trigger = */ lazy,
+        /* .trigger_buffer = */   "",
+        std::move(vec_trigger_tokens),
+        std::move(vec_trigger_words),
+    };
 }
 
 void llama_grammar_free_impl(struct llama_grammar * grammar) {
@@ -1055,6 +1094,11 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra
         grammar.rules,
         grammar.stacks,
         grammar.partial_utf8,
+        grammar.lazy,
+        grammar.awaiting_trigger,
+        grammar.trigger_buffer,
+        grammar.trigger_tokens,
+        grammar.trigger_words,
     };
 
     // redirect elements in stacks to point to new rules
@@ -1076,6 +1120,10 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra
 void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_data_array * cur_p) {
     GGML_ASSERT(grammar.vocab != nullptr);
 
+    if (grammar.awaiting_trigger) {
+        return;
+    }
+
     bool allow_eog = false;
     for (const auto & stack : grammar.stacks) {
         if (stack.empty()) {
@@ -1115,6 +1163,34 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_
 void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token) {
     GGML_ASSERT(grammar.vocab != nullptr);
 
+    const auto & piece = grammar.vocab->token_to_piece(token);
+
+    if (grammar.awaiting_trigger) {
+        if (std::find(grammar.trigger_tokens.begin(), grammar.trigger_tokens.end(), token) != grammar.trigger_tokens.end()) {
+            grammar.awaiting_trigger = false;
+            grammar.trigger_buffer.clear();
+            llama_grammar_accept_str(grammar, piece);
+            LLAMA_LOG_DEBUG("Grammar triggered on token %u (`%s`)", token, piece.c_str());
+            return;
+        } else {
+            // TODO: consider a smarter incremental substring search algorithm (store last position to search from).
+            grammar.trigger_buffer += piece;
+            for (const auto & word : grammar.trigger_words) {
+                auto pos = grammar.trigger_buffer.find(word);
+                if (pos != std::string::npos) {
+                    grammar.awaiting_trigger = false;
+                    auto constrained_str = grammar.trigger_buffer.substr(pos);
+                    grammar.trigger_buffer.clear();
+                    llama_grammar_accept_str(grammar, constrained_str);
+                    LLAMA_LOG_DEBUG("Grammar triggered on word `%s`", word.c_str());
+                    return;
+                }
+            }
+            LLAMA_LOG_DEBUG("Grammar still awaiting trigger after token %d (`%s`) (buffer: `%s`)\n", token, piece.c_str(), grammar.trigger_buffer.c_str());
+            return;
+        }
+    }
+
     if (grammar.vocab->is_eog(token)) {
         for (const auto & stack : grammar.stacks) {
             if (stack.empty()) {
@@ -1124,8 +1200,10 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
         GGML_ABORT("fatal error");
     }
 
-    const std::string & piece = grammar.vocab->token_to_piece(token);
+    llama_grammar_accept_str(grammar, piece);
+}
 
+void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string & piece) {
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar.partial_utf8);
     const auto & code_points = decoded.first;
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index f8b40c651..252d54d4c 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -114,6 +114,15 @@ struct llama_grammar {
 
     // buffer for partially generated UTF-8 sequence from accepted tokens
     llama_partial_utf8 partial_utf8;
+
+    // lazy grammars wait for trigger words or tokens before constraining the sampling.
+    // we still ahve trigger_tokens for non-lazy grammars to force printing of special trigger tokens.
+    // (useful e.g. for tool_choice=required)
+    bool                     lazy             = false;
+    bool                     awaiting_trigger = false; // Initialized to true for lazy grammars only
+    std::string              trigger_buffer;           // Output buffered by lazy grammar. Will be cleared once trigger is found.
+    std::vector<llama_token> trigger_tokens;           // Tokens that trigger a lazy grammar, or tokens to force printing of (even if special).
+    std::vector<std::string> trigger_words;
 };
 
 //
@@ -127,7 +136,15 @@ struct llama_grammar * llama_grammar_init_impl(
         size_t n_rules,
         size_t start_rule_index);
 
-struct llama_grammar * llama_grammar_init_impl(const struct llama_vocab * vocab, const char * grammar_str, const char * grammar_root);
+struct llama_grammar * llama_grammar_init_impl(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root,
+                              bool lazy,
+                     const char ** trigger_words,
+                            size_t num_trigger_words,
+               const llama_token * trigger_tokens,
+                            size_t num_trigger_tokens);
 
 void llama_grammar_free_impl(struct llama_grammar * grammar);
 
@@ -141,3 +158,7 @@ void llama_grammar_apply_impl(
 void llama_grammar_accept_impl(
               struct llama_grammar & grammar,
                        llama_token   token);
+
+void llama_grammar_accept_str(
+              struct llama_grammar & grammar,
+                 const std::string & piece);
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index b3a12386e..26974f539 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1433,13 +1433,30 @@ static void llama_sampler_grammar_apply(struct llama_sampler * smpl, llama_token
     }
 }
 
+// Fwd declare to break reset --> init_impl --> llama_sampler_grammar_i --> reset cycle.
+static struct llama_sampler * llama_sampler_init_grammar_impl(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root,
+                              bool lazy,
+                     const char ** trigger_words,
+                            size_t num_trigger_words,
+               const llama_token * trigger_tokens,
+                            size_t num_trigger_tokens);
+
 static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_grammar *) smpl->ctx;
     if (!ctx->grammar) {
         return;
     }
 
-    auto * grammar_new = llama_grammar_init_impl(ctx->grammar->vocab, ctx->grammar_str.c_str(), ctx->grammar_root.c_str());
+    std::vector<const char *>  trigger_words;
+    for (auto & word : ctx->grammar->trigger_words) {
+        trigger_words.push_back(word.c_str());
+    }
+    auto * grammar_new = llama_grammar_init_impl(ctx->grammar->vocab, ctx->grammar_str.c_str(), ctx->grammar_root.c_str(),
+                                                 ctx->grammar->lazy, trigger_words.data(), trigger_words.size(),
+                                                 ctx->grammar->trigger_tokens.data(), ctx->grammar->trigger_tokens.size());
 
     llama_grammar_free_impl(ctx->grammar);
     ctx->grammar = grammar_new;
@@ -1448,7 +1465,7 @@ static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {
 static struct llama_sampler * llama_sampler_grammar_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_grammar *) smpl->ctx;
 
-    auto * result = llama_sampler_init_grammar(ctx->vocab, nullptr, nullptr);
+    auto * result = llama_sampler_init_grammar_impl(ctx->vocab, nullptr, nullptr, false, nullptr, 0, nullptr, 0);
 
     // copy the state
     {
@@ -1484,7 +1501,15 @@ static struct llama_sampler_i llama_sampler_grammar_i = {
     /* .free   = */ llama_sampler_grammar_free,
 };
 
-struct llama_sampler * llama_sampler_init_grammar(const struct llama_vocab * vocab, const char * grammar_str, const char * grammar_root) {
+static struct llama_sampler * llama_sampler_init_grammar_impl(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root,
+                              bool lazy,
+                     const char ** trigger_words,
+                            size_t num_trigger_words,
+               const llama_token * trigger_tokens,
+                            size_t num_trigger_tokens) {
     auto * ctx = new llama_sampler_grammar;
 
     if (grammar_str != nullptr && grammar_str[0] != '\0') {
@@ -1492,7 +1517,7 @@ struct llama_sampler * llama_sampler_init_grammar(const struct llama_vocab * voc
             /* .vocab        = */ vocab,
             /* .grammar_str  = */ grammar_str,
             /* .grammar_root = */ grammar_root,
-            /* .grammar      = */ llama_grammar_init_impl(vocab, grammar_str, grammar_root),
+            /* .grammar      = */ llama_grammar_init_impl(vocab, grammar_str, grammar_root, lazy, trigger_words, num_trigger_words, trigger_tokens, num_trigger_tokens),
         };
     } else {
         *ctx = {
@@ -1509,6 +1534,24 @@ struct llama_sampler * llama_sampler_init_grammar(const struct llama_vocab * voc
     };
 }
 
+struct llama_sampler * llama_sampler_init_grammar(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root) {
+    return llama_sampler_init_grammar_impl(vocab, grammar_str, grammar_root, /* lazy= */ false, nullptr, 0, nullptr, 0);
+}
+
+struct llama_sampler * llama_sampler_init_grammar_lazy(
+        const struct llama_vocab * vocab,
+                      const char * grammar_str,
+                      const char * grammar_root,
+                     const char ** trigger_words,
+                            size_t num_trigger_words,
+               const llama_token * trigger_tokens,
+                            size_t num_trigger_tokens) {
+    return llama_sampler_init_grammar_impl(vocab, grammar_str, grammar_root, /* lazy= */ true, trigger_words, num_trigger_words, trigger_tokens, num_trigger_tokens);
+}
+
 // penalties
 
 struct llama_sampler_penalties {
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 3fa43c295..40f83ff0d 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -93,6 +93,7 @@ if (NOT WIN32)
     llama_target_and_test(test-grammar-parser.cpp)
     llama_target_and_test(test-grammar-integration.cpp)
     llama_target_and_test(test-llama-grammar.cpp)
+    llama_target_and_test(test-chat.cpp)
     # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
     if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
         llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 190643136..4563f9dcb 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -328,7 +328,7 @@ int main(void) {
     // test llama_chat_format_single for system message
     printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
     std::vector<common_chat_msg> chat2;
-    common_chat_msg sys_msg{"system", "You are a helpful assistant"};
+    common_chat_msg sys_msg{"system", "You are a helpful assistant", {}};
 
     auto fmt_sys = [&](std::string tmpl_str) {
         minja::chat_template tmpl(tmpl_str, "", "");
@@ -352,10 +352,10 @@ int main(void) {
 
     // test llama_chat_format_single for user message
     printf("\n\n=== llama_chat_format_single (user message) ===\n\n");
-    chat2.push_back({"system", "You are a helpful assistant"});
-    chat2.push_back({"user", "Hello"});
-    chat2.push_back({"assistant", "I am assistant"});
-    common_chat_msg new_msg{"user", "How are you"};
+    chat2.push_back({"system", "You are a helpful assistant", {}});
+    chat2.push_back({"user", "Hello", {}});
+    chat2.push_back({"assistant", "I am assistant", {}});
+    common_chat_msg new_msg{"user", "How are you", {}};
 
     auto fmt_single = [&](std::string tmpl_str) {
         minja::chat_template tmpl(tmpl_str, "", "");
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
new file mode 100644
index 000000000..ccc65d87a
--- /dev/null
+++ b/tests/test-chat.cpp
@@ -0,0 +1,521 @@
+//  Tests chat handling, including grammar generation and parsing for tool calling, for various templates.
+//
+//  Also acts as a CLI to generate a Markdown summary of the formats of Jinja templates,
+//  e.g. given Minja (http://github.com/google/minja) checked out in parent dir:
+//
+//    cmake -B build && cmake --build build --parallel && ./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null
+//
+#include <fstream>
+#include <iostream>
+#include <json.hpp>
+#include <string>
+
+#include "chat-template.hpp"
+#include "chat.hpp"
+#include "llama-grammar.h"
+#include "unicode.h"
+
+using json = nlohmann::ordered_json;
+
+static common_chat_msg msg_from_json(const json & message) {
+    common_chat_msg ret{
+        "assistant",
+        "",
+        {},
+    };
+    if (message.contains("content") && !message.at("content").is_null()) {
+        ret.content = message.at("content").get<std::string>();
+    }
+    auto has_tool_calls = message.contains("tool_calls");
+    if (has_tool_calls) {
+        for (const auto & tc : message.at("tool_calls")) {
+            const auto & arguments = tc.at("function").at("arguments");
+            ret.tool_calls.push_back({
+                tc.at("function").at("name").get<std::string>(),
+                arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
+                tc.contains("id") ? tc.at("id").get<std::string>() : "",
+            });
+        }
+    }
+    return ret;
+}
+
+template <class T> static void assert_equals(const T & expected, const T & actual) {
+    if (expected != actual) {
+        std::cerr << "Expected: " << expected << std::endl;
+        std::cerr << "Actual: " << actual << std::endl;
+        std::cerr << std::flush;
+        throw std::runtime_error("Test failed");
+    }
+}
+
+static std::string read_file(const std::string & path) {
+    std::cerr << "# Reading: " << path << std::endl << std::flush;
+    std::ifstream fs(path, std::ios_base::binary);
+    if (!fs.is_open()) {
+        fs = std::ifstream("../" + path, std::ios_base::binary);
+        if (!fs.is_open()) {
+            throw std::runtime_error("Failed to open file: " + path);
+        }
+    }
+    fs.seekg(0, std::ios_base::end);
+    auto size = fs.tellg();
+    fs.seekg(0);
+    std::string out;
+    out.resize(static_cast<size_t>(size));
+    fs.read(&out[0], static_cast<std::streamsize>(size));
+    return out;
+}
+
+static std::unique_ptr<llama_grammar> build_grammar(const std::string & grammar_str) {
+    return std::unique_ptr<llama_grammar>(
+        llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0));
+}
+
+// TODO: extract to common helper (copied from test-grammar-integration.cpp)
+static bool match_string(const std::string & input, llama_grammar * grammar) {
+    const auto cpts = unicode_cpts_from_utf8(input);
+
+    auto & stacks_cur = llama_grammar_get_stacks(grammar);
+
+    for (const auto & cpt : cpts) {
+        llama_grammar_accept(grammar, cpt);
+
+        if (stacks_cur.empty()) {
+            // no stacks means that the grammar failed to match at this point
+            return false;
+        }
+    }
+
+    for (const auto & stack : stacks_cur) {
+        if (stack.empty()) {
+            // An empty stack means that the grammar has been completed
+            return true;
+        }
+    }
+
+    return false;
+}
+
+// Dumps `{"a": 1}` as `"{\"a\": 1}"`, unlike nlohmann::json::dump which would dump it as `"{\"a\":1}"`.
+static std::string dump(const json & j) {
+    return minja::Value(j).dump(-1, /* to_json= */ true);
+}
+
+static void assert_msg_equals(const common_chat_msg & expected, const common_chat_msg & actual) {
+    assert_equals(expected.role, actual.role);
+    assert_equals(expected.content, actual.content);
+    assert_equals(expected.tool_calls.size(), actual.tool_calls.size());
+    for (size_t i = 0; i < expected.tool_calls.size(); i++) {
+        const auto & expected_tool_call = expected.tool_calls[i];
+        const auto & actual_tool_call   = actual.tool_calls[i];
+        assert_equals(expected_tool_call.name, actual_tool_call.name);
+        assert_equals(dump(json::parse(expected_tool_call.arguments)), dump(json::parse(actual_tool_call.arguments)));
+        assert_equals(expected_tool_call.id, actual_tool_call.id);
+    }
+}
+
+const auto special_function_tool = json::parse(R"({
+  "type": "function",
+  "function": {
+    "name": "special_function",
+    "description": "I'm special",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "arg1": {
+          "type": "integer",
+          "description": "The arg."
+        }
+      },
+      "required": ["arg1"]
+    }
+  }
+})");
+const auto python_tool           = json::parse(R"({
+  "type": "function",
+  "function": {
+    "name": "python",
+    "description": "an ipython interpreter",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "code": {
+          "type": "string",
+          "description": "Python code to execute."
+        }
+      },
+      "required": ["code"]
+    }
+  }
+})");
+const auto code_interpreter_tool = json::parse(R"({
+  "type": "function",
+  "function": {
+    "name": "code_interpreter",
+    "description": "an ipython interpreter",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "code": {
+          "type": "string",
+          "description": "Python code to execute."
+        }
+      },
+      "required": ["code"]
+    }
+  }
+})");
+const json tools                 = { special_function_tool, python_tool };
+const json llama_3_1_tools       = { special_function_tool, code_interpreter_tool };
+
+struct delta_data {
+    std::string        delta;
+    std::string        grammar;
+    common_chat_format format;
+};
+
+static delta_data init_delta(const common_chat_template & tmpl, const std::vector<std::string> & end_tokens,
+                             const json & user_message, const json & delta_message, const json & tools,
+                             const json & tool_choice) {
+    common_chat_inputs inputs;
+    inputs.parallel_tool_calls = true;
+    inputs.messages            = json::array();
+    inputs.messages.push_back(user_message);
+    inputs.tools       = tools;
+    inputs.tool_choice = tool_choice;
+    auto params_prefix = common_chat_params_init(tmpl, inputs);
+
+    inputs.messages.push_back(delta_message);
+    inputs.add_generation_prompt = false;
+    auto params_full             = common_chat_params_init(tmpl, inputs);
+
+    std::string prefix = params_prefix.prompt;
+    std::string full   = params_full.prompt;
+
+    // Check full starts with prefix
+    if (full.find(prefix) != 0) {
+        fprintf(stderr, "Full:\n%s\n\nPrefix:\n%s\n\n", full.c_str(), prefix.c_str());
+        throw std::runtime_error("Full message does not start with prefix");
+    }
+
+    if (full == prefix) {
+        throw std::runtime_error("Full message is the same as the prefix");
+    }
+
+    auto delta = full.substr(prefix.size());
+
+    // Strip end tokens
+    for (const auto & end_token : end_tokens) {
+        // rfind to find the last occurrence
+        auto pos = delta.rfind(end_token);
+        if (pos != std::string::npos) {
+            delta = delta.substr(0, pos);
+            break;
+        }
+    }
+    return { delta, params_full.grammar, params_full.format };
+}
+
+/*
+  Applies the template to 1 user message w/ add_generation_prompt=true, then w/ the test message w/ add_generation_prompt=false,
+  gets the diff, removes any end tokens and parses the result w/ the grammar, checking that
+  the parsed message is the same as the test_message
+*/
+static void test_template(const common_chat_template & tmpl, const std::vector<std::string> & end_tokens,
+                          const json & test_message, const json & tools = {}, const std::string & expected_delta = "",
+                          bool skip_grammar_test = false, bool skip_parser_test = false) {
+    common_chat_msg expected_msg = msg_from_json(test_message);
+
+    auto user_message = json{
+        { "role",    "user"          },
+        { "content", "Hello, world!" }
+    };
+
+    for (const auto & tool_choice : json({ "auto", "required" })) {
+        auto data = init_delta(tmpl, end_tokens, user_message, test_message, tools, tool_choice);
+        if (!expected_delta.empty()) {
+            assert_equals(expected_delta, data.delta);
+        }
+
+        if (!skip_parser_test) {
+            const auto msg = common_chat_parse(data.delta, data.format);
+            assert_msg_equals(expected_msg, msg);
+        }
+
+        if (!expected_msg.tool_calls.empty()) {
+            GGML_ASSERT(!data.grammar.empty());
+        }
+        if (!data.grammar.empty()) {
+            auto grammar = build_grammar(data.grammar);
+            if (!grammar) {
+                throw std::runtime_error("Failed to build grammar");
+            }
+            // TODO: exercice lazy grammars + triggers here, instead of skipping the test
+            if (!skip_grammar_test) {
+                if (!match_string(data.delta, grammar.get())) {
+                    throw std::runtime_error("Failed to match delta against grammar:\n\n" + data.delta +
+                                             "\n\nGrammar: " + data.grammar);
+                }
+            }
+        }
+    }
+}
+
+static void test_template_output_parsers() {
+    auto text_message = json{
+        { "role",    "assistant"     },
+        { "content", "Hello, world!" },
+    };
+    auto tool_call_message = json{
+        { "role",       "assistant"                },
+        { "content",    {}                         },
+        { "tool_calls", json{ {
+                            { "type", "function" },
+                            { "function", { { "name", "special_function" }, { "arguments", "{\"arg1\": 1}" } } },
+                        } } }
+    };
+    auto tool_call_message_with_id                   = json::parse(tool_call_message.dump());
+    tool_call_message_with_id["tool_calls"][0]["id"] = "123456789";
+
+    auto python_tool_call_message = json{
+        { "role",       "assistant"                },
+        { "content",    {}                         },
+        { "tool_calls", json{ {
+                            { "type", "function" },
+                            { "function",
+                              {
+                                  { "name", "python" },
+                                  { "arguments",
+                                    {
+                                        { "code", "print('hey')" },
+                                    } },
+                              } },
+                        } } }
+    };
+    auto code_interpreter_tool_call_message = json{
+        { "role",       "assistant"                },
+        { "content",    {}                         },
+        { "tool_calls", json{ {
+                            { "type", "function" },
+                            { "function",
+                              {
+                                  { "name", "code_interpreter" },
+                                  { "arguments",
+                                    {
+                                        { "code", "print('hey')" },
+                                    } },
+                              } },
+                        } } }
+    };
+
+    common_chat_inputs inputs_no_tools;
+    inputs_no_tools.messages = {
+        { { "role", "user" }, { "content", "Hey" } }
+    };
+
+    common_chat_inputs inputs_tools = inputs_no_tools;
+    inputs_tools.tools              = json::array();
+    inputs_tools.tools.push_back(special_function_tool);
+
+    common_chat_inputs inputs_tools_builtin = inputs_no_tools;
+    inputs_tools_builtin.tools              = json::array();
+    inputs_tools_builtin.tools.push_back(python_tool);
+
+    {
+        const common_chat_template tmpl(read_file("models/templates/google-gemma-2-2b-it.jinja"), "<s>", "</s>");
+        std::vector<std::string>   end_tokens{ "<end_of_turn>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_params_init(tmpl, inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_GENERIC, common_chat_params_init(tmpl, inputs_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_GENERIC,
+                      common_chat_params_init(
+                          common_chat_template(read_file("models/templates/microsoft-Phi-3.5-mini-instruct.jinja"),
+                                               "<s>", "</s>"),
+                          inputs_tools)
+                          .format);
+
+        // Generic tool calls doesn't generate / parse content-only messages symmetrically.
+
+        assert_msg_equals(msg_from_json(text_message),
+                          common_chat_parse("{\n"
+                                            "  \"response\": \"Hello, world!\"\n"
+                                            "}",
+                                            common_chat_params_init(tmpl, inputs_tools).format));
+        test_template(tmpl, end_tokens, tool_call_message_with_id, tools,
+                      "{\n"
+                      "  \"tool_calls\": [\n"
+                      "    {\n"
+                      "      \"name\": \"special_function\",\n"
+                      "      \"arguments\": {\n"
+                      "        \"arg1\": 1\n"
+                      "      },\n"
+                      "      \"id\": \"123456789\"\n"
+                      "    }\n"
+                      "  ]\n"
+                      "}");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "</s>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_MISTRAL_NEMO, common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(
+            tmpl, end_tokens, tool_call_message_with_id, tools,
+            "[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]",
+            /* skip_grammar_test= */ true);
+    }
+    {
+        const common_chat_template tmpl(
+            read_file("models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja"), "<s>", "</s>");
+        std::vector<std::string> end_tokens{ "<|im_end|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_params_init(tmpl, inputs_tools).format);
+        assert_equals(
+            COMMON_CHAT_FORMAT_HERMES_2_PRO,
+            common_chat_params_init(
+                common_chat_template(read_file("models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja"),
+                                     "<s>", "</s>"),
+                inputs_tools)
+                .format);
+        assert_equals(
+            COMMON_CHAT_FORMAT_HERMES_2_PRO,
+            common_chat_params_init(
+                common_chat_template(read_file("models/templates/Qwen-Qwen2.5-7B-Instruct.jinja"), "<s>", "</s>"),
+                inputs_tools)
+                .format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "<tool_call>\n"
+                      "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
+                      "</tool_call>");
+        test_template(tmpl, end_tokens, python_tool_call_message, tools,
+                      "<tool_call>\n"
+                      "{\"name\": \"python\", \"arguments\": {\"code\": \"print('hey')\"}}\n"
+                      "</tool_call>");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "<|eom_id|>", "<|eot_id|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_params_init(tmpl, inputs_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
+                      common_chat_params_init(tmpl, inputs_tools_builtin).format);
+        assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
+                      common_chat_params_init(
+                          common_chat_template(read_file("models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja"),
+                                               "<s>", "</s>"),
+                          inputs_tools_builtin)
+                          .format);
+
+        // test_template(tmpl, end_tokens, text_message, tools, R"(?)", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, code_interpreter_tool_call_message, llama_3_1_tools,
+                      "<|python_tag|>code_interpreter.call(code=\"print('hey')\")");
+        test_template(tmpl, end_tokens, python_tool_call_message, tools,
+                      "<|python_tag|>python.call(code=\"print('hey')\")");
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "<|eom_id|>", "<|eot_id|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/meetkai-functionary-medium-v3.1.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "<|eom_id|>", "<|eot_id|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
+                      common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "<function=special_function>{\"arg1\": 1}</function>");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/meetkai-functionary-medium-v3.2.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "<|eom_id|>", "<|eot_id|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, common_chat_params_init(tmpl, inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, {},
+                      "all\n"
+                      "Hello, world!",
+                      /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "special_function\n"
+                      "{\"arg1\": 1}");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/fireworks-ai-llama-3-firefunction-v2.jinja"), "<s>",
+                                        "</s>");
+        std::vector<std::string>   end_tokens{ "<|eot_id|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_FIREFUNCTION_V2, common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      " functools[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]");
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja"),
+                                        "<s>", "</s>");
+        std::vector<std::string>   end_tokens{ "<｜end▁of▁sentence｜>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, tool_call_message, tools,
+                      "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n"
+                      "```json\n"
+                      "{\"arg1\": 1}\n"
+                      "```<｜tool▁call▁end｜>");
+    }
+}
+
+int main(int argc, char ** argv) {
+#ifndef _WIN32
+    if (argc > 1) {
+        common_chat_inputs inputs;
+        inputs.messages = {
+            { { "role", "user" }, { "content", "Hey" } }
+        };
+        inputs.tools = json::array({ special_function_tool });
+
+        std::cout << "| Template | Format |\n";
+        std::cout << "|----------|--------|\n";
+
+        for (int i = 1; i < argc; i++) {
+            std::string path = argv[i];
+            if (path.rfind(".jinja") != path.size() - 6) {
+                std::cerr << "Skipping non-jinja file: " << path << std::endl;
+                continue;
+            }
+            common_chat_template tmpl(read_file(path), "", "");
+            auto                 parts = string_split(path, "/");
+            auto                 name  = parts[parts.size() - 1];
+            std::cout << "| " << name << " | " << common_chat_format_name(common_chat_params_init(tmpl, inputs).format)
+                      << " |\n";
+        }
+    } else
+#endif
+    {
+        test_template_output_parsers();
+        std::cout << "\n[chat] All tests passed!" << std::endl;
+    }
+    return 0;
+}
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index e1bdbb925..288e08f51 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -13,7 +13,7 @@
 using json = nlohmann::ordered_json;
 
 static llama_grammar * build_grammar(const std::string & grammar_str) {
-    return llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root");
+    return llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0);
 }
 
 static bool test_build_grammar_fails(const std::string & grammar_str) {

From 553f1e46e9e864514bbd6bf4009146db66be0541 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Thu, 30 Jan 2025 22:01:06 +0000
Subject: [PATCH 295/376] `ci`: ccache for all github worfklows (#11516)

---
 .github/workflows/build.yml | 136 ++++++++++++++++++++++++++++++++++--
 1 file changed, 130 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7eaf9c460..c02dd6a81 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -43,6 +43,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-arm64
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -108,6 +114,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-x64
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -172,6 +184,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-cpu-cmake
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         run: |
@@ -249,6 +267,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         run: |
@@ -296,6 +320,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-latest-cmake-rpc
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         run: |
@@ -325,6 +355,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-vulkan
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         run: |
@@ -364,6 +400,12 @@ jobs:
           sudo apt-get update
           sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-hip
+          evict-old-files: 1d
+
       - name: Build with native CMake HIP support
         id: cmake_build
         run: |
@@ -396,6 +438,12 @@ jobs:
           apt-get update
           apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-musa
+          evict-old-files: 1d
+
       - name: Build with native CMake MUSA support
         id: cmake_build
         run: |
@@ -435,6 +483,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-sycl
+          evict-old-files: 1d
+
       - name: Build
         id: cmake_build
         run: |
@@ -479,6 +533,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-sycl-fp16
+          evict-old-files: 1d
+
       - name: Build
         id: cmake_build
         run: |
@@ -500,6 +560,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-ios
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -531,6 +597,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-tvos
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -566,6 +638,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-swift
+          evict-old-files: 1d
+
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -607,6 +685,12 @@ jobs:
       - name: Clone
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-msys2
+          evict-old-files: 1d
+
       - name: Setup ${{ matrix.sys }}
         uses: msys2/setup-msys2@v2
         with:
@@ -675,6 +759,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-${{ matrix.build }}
+          evict-old-files: 1d
+
       - name: Clone Kompute submodule
         id: clone_kompute
         if: ${{ matrix.build == 'kompute-x64' }}
@@ -813,6 +903,8 @@ jobs:
         - name: Clone
           id: checkout
           uses: actions/checkout@v4
+          with:
+            fetch-depth: 0
 
         - name: Install dependencies
           env:
@@ -821,6 +913,12 @@ jobs:
               apt update
               apt install -y cmake build-essential ninja-build libgomp1 git
 
+        - name: ccache
+          uses: hendrikmuhs/ccache-action@v1.2.16
+          with:
+            key: ubuntu-latest-cmake-cuda
+            evict-old-files: 1d
+
         - name: Build with CMake
           run: |
             cmake -S . -B build -G Ninja \
@@ -847,6 +945,12 @@ jobs:
         with:
             fetch-depth: 0
 
+      - name: Install ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
+          evict-old-files: 1d
+
       - name: Install Cuda Toolkit 11.7
         if: ${{ matrix.cuda == '11.7' }}
         run: |
@@ -903,11 +1007,6 @@ jobs:
           echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
           echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
 
-      - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2
-        with:
-          key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
-
       - name: Install Ninja
         id: install_ninja
         run: |
@@ -987,6 +1086,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-sycl
+          evict-old-files: 1d
+
       - name: Install
         run:  |
           scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
@@ -1066,9 +1171,10 @@ jobs:
           & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
 
       - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2
+        uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: ${{ github.job }}
+          evict-old-files: 1d
 
       - name: Build
         id: cmake_build
@@ -1098,6 +1204,12 @@ jobs:
         with:
             fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-hip-release
+          evict-old-files: 1d
+
       - name: Install
         id: depends
         run: |
@@ -1195,6 +1307,12 @@ jobs:
       - name: Clone
         uses: actions/checkout@v4
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: android-build
+          evict-old-files: 1d
+
       - name: Set up JDK
         uses: actions/setup-java@v3
         with:
@@ -1232,6 +1350,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: release
+          evict-old-files: 1d
+
       - name: Determine tag name
         id: tag
         shell: bash

From a2df2787b32e0846205f7151dfad88ceab592beb Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 31 Jan 2025 06:04:53 +0100
Subject: [PATCH 296/376] server : update help metrics processing/deferred
 (#11512)

This commit updates the help text for the metrics `requests_processing`
and `requests_deferred` to be more grammatically correct.

Currently the returned metrics look like this:
```console
\# HELP llamacpp:requests_processing Number of request processing.
\# TYPE llamacpp:requests_processing gauge
llamacpp:requests_processing 0
\# HELP llamacpp:requests_deferred Number of request deferred.
\# TYPE llamacpp:requests_deferred gauge
llamacpp:requests_deferred 0
```

With this commit, the metrics will look like this:
```console
\# HELP llamacpp:requests_processing Number of requests processing.
\# TYPE llamacpp:requests_processing gauge
llamacpp:requests_processing 0
\# HELP llamacpp:requests_deferred Number of requests deferred.
\# TYPE llamacpp:requests_deferred gauge
llamacpp:requests_deferred 0
```
This is also consistent with the description of the metrics in the
server examples [README.md](https://github.com/ggerganov/llama.cpp/tree/master/examples/server#get-metrics-prometheus-compatible-metrics-exporter).
---
 examples/server/server.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index d1ea343dd..1ebcb5085 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3633,11 +3633,11 @@ int main(int argc, char ** argv) {
                     {"value",  (uint64_t) res_metrics->kv_cache_tokens_count}
             },{
                     {"name",  "requests_processing"},
-                    {"help",  "Number of request processing."},
+                    {"help",  "Number of requests processing."},
                     {"value",  (uint64_t) res_metrics->n_processing_slots}
             },{
                     {"name",  "requests_deferred"},
-                    {"help",  "Number of request deferred."},
+                    {"help",  "Number of requests deferred."},
                     {"value",  (uint64_t) res_metrics->n_tasks_deferred}
             }}}
         };

From 1bd3047a939e561adfb3c7dd2e17c4cc7a4e4e6f Mon Sep 17 00:00:00 2001
From: Steve Grubb <ausearch.1@gmail.com>
Date: Fri, 31 Jan 2025 00:58:55 -0500
Subject: [PATCH 297/376] common: Add missing va_end (#11529)

The va_copy man page states that va_end must be called to revert
whatever the copy did. For some implementaions, not calling va_end
has no consequences. For others it could leak memory.
---
 common/log.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/common/log.cpp b/common/log.cpp
index 04c7c0ed1..0b8994ae1 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -206,6 +206,7 @@ public:
                 vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
             }
 #endif
+            va_end(args_copy);
         }
 
         entry.level = level;

From 4a2b196d03d52da31236390e9f5694a88d43d11d Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Fri, 31 Jan 2025 08:12:40 +0000
Subject: [PATCH 298/376] server : fix --jinja when there's no tools or schema
 (typo was forcing JSON) (#11531)

---
 examples/server/tests/unit/test_chat_completion.py | 7 +++----
 examples/server/utils.hpp                          | 2 +-
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index 0be04bab5..f5d8b0572 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -14,11 +14,10 @@ def create_server():
     "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
     [
         (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
+        (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
-        # TODO: fix testing of non-tool jinja mode
-        # (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
-        # (None, "Book", "What is the best book", 8, "I want to play with", 23, 8, "length", True, "This is not a chat template, it is"),
-        # ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
     ]
 )
 def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 3d2c04666..70bd6a42c 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -642,7 +642,7 @@ static json oaicompat_completion_params_parse(
         inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
         inputs.stream = stream;
         // TODO: support mixing schema w/ tools beyond generic format.
-        inputs.json_schema = json_value(llama_params, "json_schema", json::object());
+        inputs.json_schema = json_value(llama_params, "json_schema", json());
         auto chat_params = common_chat_params_init(tmpl, inputs);
 
         llama_params["chat_format"] = static_cast<int>(chat_params.format);

From 5783575c9d99c4d9370495800663aa5397ceb0be Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Fri, 31 Jan 2025 08:24:29 +0000
Subject: [PATCH 299/376] Fix chatml fallback for unsupported builtin templates
 (when --jinja not enabled) (#11533)

---
 examples/server/server.cpp | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 1ebcb5085..e7daceef1 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1858,7 +1858,12 @@ struct server_context {
             llama_init_dft.context.reset();
         }
 
-        chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
+        if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
+            LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
+            chat_templates = common_chat_templates_from_model(model, "chatml");
+        } else {
+            chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
+        }
         GGML_ASSERT(chat_templates.template_default.get() != nullptr);
 
         return true;
@@ -4435,14 +4440,6 @@ int main(int argc, char ** argv) {
 
     LOG_INF("%s: model loaded\n", __func__);
 
-    // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
-    if (params.chat_template.empty()) {
-        if (!ctx_server.validate_builtin_chat_template(params.use_jinja)) {
-            LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
-            params.chat_template = "chatml";
-        }
-    }
-
     // print sample chat example to make it clear which template is used
     LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
         ctx_server.chat_templates.template_default->source().c_str(),

From b1bcd309fc8ac929cbd4a6207b3a19886bda031f Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Fri, 31 Jan 2025 13:48:31 +0000
Subject: [PATCH 300/376] fix stop regression (#11543)

---
 examples/server/utils.hpp | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 70bd6a42c..94e189457 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -484,13 +484,14 @@ static bool ends_with(const std::string & str, const std::string & suffix) {
 
 static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
     if (!text.empty() && !stop.empty()) {
-        auto it = std::find(stop.rbegin(), stop.rend(), text.back());
-        while (it != stop.rend()) {
-            size_t length = std::distance(it, stop.rend());
-            if (text.length() >= length && 0 == text.compare(text.length() - length, length, stop)) {
-                return text.length() - length;
+        const char text_last_char = text.back();
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
+            if (stop[char_index] == text_last_char) {
+                const std::string current_partial = stop.substr(0, char_index + 1);
+                if (ends_with(text, current_partial)) {
+                    return text.size() - char_index - 1;
+                }
             }
-            it = std::find(std::next(it), stop.rend(), text.back());
         }
     }
 

From a83f528688324a21484a97af1d1be5e1bc8d4c8e Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Fri, 31 Jan 2025 14:15:25 +0000
Subject: [PATCH 301/376] `tool-call`: fix llama 3.x and functionary 3.2, play
 nice w/ pydantic_ai package, update readme (#11539)

* An empty tool_call_id is better than none!

* sync: minja (tool call name optional https://github.com/google/minja/pull/36)

* Force-disable parallel_tool_calls if template doesn't support it

* More debug logs

* Llama 3.x tools: accept / trigger on more varied spaced outputs

* Fix empty content for functionary v3.2 tool call

* Add proper tool call docs to server README

* readme: function calling *is* supported now

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/chat-template.hpp   |   4 +-
 common/chat.cpp            |  21 +++++--
 examples/server/README.md  | 110 ++++++++++++++++++++++++++++++++++---
 examples/server/server.cpp |   5 +-
 examples/server/utils.hpp  |   4 ++
 5 files changed, 129 insertions(+), 15 deletions(-)

diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index 75ba5d938..58e119a3b 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -283,10 +283,12 @@ class chat_template {
                     message["role"] = "user";
                     auto obj = json {
                         {"tool_response", {
-                            {"tool", message.at("name")},
                             {"content", message.at("content")},
                         }},
                     };
+                    if (message.contains("name")) {
+                        obj["tool_response"]["name"] = message.at("name");
+                    }
                     if (message.contains("tool_call_id")) {
                         obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
                     }
diff --git a/common/chat.cpp b/common/chat.cpp
index d9a654892..58db12af9 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -384,14 +384,19 @@ static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const com
             tool_rules.push_back(
                 builder.add_rule(
                     name + "-call",
-                    "\"{\" ( \"\\\"type\\\": \\\"function\\\", \" | space ) "
+                    "\"{\" space "
+                    "( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? "
                     "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
                         builder.add_schema(name + "-args", parameters) +
                     " \"}\""));
             data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
         });
         data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
+        data.grammar_triggers.push_back({"{\n  \"name\":", /* .at_start = */ true});
+        data.grammar_triggers.push_back({"{\n    \"name\":", /* .at_start = */ true});
         data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
+        data.grammar_triggers.push_back({"{\n  \"type\": \"function\"", /* .at_start = */ true});
+        data.grammar_triggers.push_back({"{\n    \"type\": \"function\"", /* .at_start = */ true});
         if (!builtin_tools.empty()) {
             data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
         }
@@ -586,9 +591,17 @@ static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & in
         }
     }
     // TODO: tighten & simplify.
-    auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
-    res.content = content;
-    return res;
+    try {
+        auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
+        res.content = content + res.content;
+        return res;
+    } catch (const std::exception & e) {
+        LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what());
+        common_chat_msg res;
+        res.role = "assistant";
+        res.content = input;
+        return res;
+    }
 }
 
 static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
diff --git a/examples/server/README.md b/examples/server/README.md
index ce1ae8858..276b43013 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -126,7 +126,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') |
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
-| `--jinja` | Enable experimental Jinja templating engine (needed for tool use) |
+| `--jinja` | Enable experimental Jinja templating engine (required for tool use) |
 
 **Example-specific params**
 
@@ -1069,7 +1069,7 @@ Given a ChatML-formatted json description in `messages`, it returns the predicte
 
 *Options:*
 
-See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
+See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also supported.
 
 The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
 
@@ -1117,17 +1117,111 @@ curl http://localhost:8080/v1/chat/completions \
 }'
 ```
 
-... and even tool usage (needs `--jinja` flag):
+*Tool call support*
+
+[Function calling](https://platform.openai.com/docs/guides/function-calling) is supported for all models (see https://github.com/ggerganov/llama.cpp/pull/9639):
+
+- Requires `--jinja` flag
+- Native tool call formats supported:
+  - Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2
+  - Functionary v3.1 / v3.2
+  - Hermes 2/3, Qwen 2.5
+  - Mistral Nemo
+  - Firefunction v2
+  - DeepSeek R1 (WIP / seems reluctant to call any tools?)
+
+  <details>
+  <summary>Show some common templates and which format handler they use</summary>
+
+  | Template | Format |
+  |----------|--------|
+  | CohereForAI-c4ai-command-r-plus-default.jinja | generic tool calls |
+  | CohereForAI-c4ai-command-r-plus-rag.jinja | generic tool calls |
+  | CohereForAI-c4ai-command-r-plus-tool_use.jinja | generic tool calls |
+  | MiniMaxAI-MiniMax-Text-01.jinja | generic tool calls |
+  | NexaAIDev-Octopus-v2.jinja | generic tool calls |
+  | NousResearch-Hermes-2-Pro-Llama-3-8B-default.jinja | generic tool calls |
+  | NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | hermes 2 pro tool calls |
+  | NousResearch-Hermes-2-Pro-Mistral-7B-default.jinja | generic tool calls |
+  | NousResearch-Hermes-2-Pro-Mistral-7B-tool_use.jinja | hermes 2 pro tool calls |
+  | NousResearch-Hermes-3-Llama-3.1-70B-default.jinja | generic tool calls |
+  | NousResearch-Hermes-3-Llama-3.1-70B-tool_use.jinja | hermes 2 pro tool calls |
+  | OrionStarAI-Orion-14B-Chat.jinja | generic tool calls |
+  | Qwen-QwQ-32B-Preview.jinja | hermes 2 pro tool calls |
+  | Qwen-Qwen2-7B-Instruct.jinja | generic tool calls |
+  | Qwen-Qwen2-VL-7B-Instruct.jinja | generic tool calls |
+  | Qwen-Qwen2.5-7B-Instruct.jinja | hermes 2 pro tool calls |
+  | Qwen-Qwen2.5-Math-7B-Instruct.jinja | hermes 2 pro tool calls |
+  | TheBloke-FusionNet_34Bx2_MoE-AWQ.jinja | generic tool calls |
+  | abacusai-Fewshot-Metamath-OrcaVicuna-Mistral.jinja | generic tool calls |
+  | bofenghuang-vigogne-2-70b-chat.jinja | generic tool calls |
+  | databricks-dbrx-instruct.jinja | generic tool calls |
+  | deepseek-ai-DeepSeek-Coder-V2-Instruct.jinja | generic tool calls |
+  | deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja | deepseek r1 tool calls |
+  | deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja | deepseek r1 tool calls |
+  | deepseek-ai-DeepSeek-R1-Distill-Qwen-7B.jinja | deepseek r1 tool calls |
+  | deepseek-ai-DeepSeek-V2.5.jinja | deepseek r1 tool calls |
+  | deepseek-ai-deepseek-coder-33b-instruct.jinja | generic tool calls |
+  | google-gemma-2-2b-it.jinja | generic tool calls |
+  | google-gemma-7b-it.jinja | generic tool calls |
+  | indischepartij-MiniCPM-3B-OpenHermes-2.5-v2.jinja | generic tool calls |
+  | mattshumer-Reflection-Llama-3.1-70B.jinja | generic tool calls |
+  | meetkai-functionary-medium-v3.2.jinja | functionary v3.2 tool calls |
+  | meta-llama-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
+  | meta-llama-Llama-3.2-3B-Instruct.jinja | llama 3.x tool calls |
+  | meta-llama-Llama-3.3-70B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
+  | meta-llama-Meta-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
+  | microsoft-Phi-3-medium-4k-instruct.jinja | generic tool calls |
+  | microsoft-Phi-3-mini-4k-instruct.jinja | generic tool calls |
+  | microsoft-Phi-3-small-8k-instruct.jinja | generic tool calls |
+  | microsoft-Phi-3.5-mini-instruct.jinja | generic tool calls |
+  | microsoft-Phi-3.5-vision-instruct.jinja | generic tool calls |
+  | mistralai-Mistral-7B-Instruct-v0.2.jinja | generic tool calls |
+  | mistralai-Mistral-Large-Instruct-2407.jinja | mistral nemo tool calls |
+  | mistralai-Mistral-Large-Instruct-2411.jinja | generic tool calls |
+  | mistralai-Mistral-Nemo-Instruct-2407.jinja | mistral nemo tool calls |
+  | mistralai-Mixtral-8x7B-Instruct-v0.1.jinja | generic tool calls |
+  | mlabonne-AlphaMonarch-7B.jinja | generic tool calls |
+  | nvidia-Llama-3.1-Nemotron-70B-Instruct-HF.jinja | llama 3.x tool calls (w/ builtin tools) |
+  | openchat-openchat-3.5-0106.jinja | generic tool calls |
+  | teknium-OpenHermes-2.5-Mistral-7B.jinja | generic tool calls |
+
+  This table can be generated with:
+
+  ```bash
+  ./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null
+
+  </details>
+
+- Generic tool call is supported when the template isn't recognized by native format handlers (you'll see `Chat format: Generic` in the logs).
+  - Use `--chat-template-file` to override the template when appropriate (see examples below)
+  - Generic support may consume more tokens and be less efficient than a model's native format.
+
+- Run with:
 
   ```shell
-  llama-server --jinja -hfr lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF -hff Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf -fa
+  # Native support:
+  llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
+  llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
+  llama-server --jinja -fa -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q6_K
+  llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
+  llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \
+    --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B )
 
-  # https://huggingface.co/meetkai/functionary-medium-v3.2
-  llama-server --jinja -hfr bartowski/functionary-medium-v3.2-GGUF -hff functionary-medium-v3.2-IQ4_XS.gguf -fa
+  # Native support requires the right template for these GGUFs:
+  llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \
+    --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use )
+  llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \
+    --chat-template-file <( python scripts/get_chat_template.py fireworks-ai/firellama-3-firefunction-v2 )
 
-  # https://huggingface.co/meetkai/functionary-medium-v3.1
-  llama-server --jinja -hfr meetkai/functionary-medium-v3.1-GGUF -hff functionary-medium-llama-3.1.Q4_0.gguf -fa
+  # Generic format support
+  llama-server --jinja -fa -hf bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M
+  llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q4_K_M
+  ```
 
+- Test in CLI:
+
+  ```bash
   curl http://localhost:8080/v1/chat/completions -d '{
     "model": "gpt-3.5-turbo",
     "tools": [
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index e7daceef1..3451e96a2 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -345,7 +345,7 @@ struct server_task {
             auto it = data.find("chat_format");
             if (it != data.end()) {
                 params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
-                LOG_DBG("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
+                LOG_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
             } else {
                 params.oaicompat_chat_format = defaults.oaicompat_chat_format;
             }
@@ -697,6 +697,7 @@ struct server_task_result_cmpl_final : server_task_result {
         std::string finish_reason = "length";
         common_chat_msg message;
         if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+            LOG_DBG("Parsing chat message: %s\n", content.c_str());
             message = common_chat_parse(content, oaicompat_chat_format);
             finish_reason = message.tool_calls.empty() ? "stop" : "tool_calls";
         } else {
@@ -713,7 +714,7 @@ struct server_task_result_cmpl_final : server_task_result {
                         {"name", tc.name},
                         {"arguments", tc.arguments},
                     }},
-                    {"id", tc.id.empty() ? json() : json(tc.id)},
+                    {"id", tc.id},
                 });
             }
         }
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 94e189457..bfe623c4c 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -641,6 +641,10 @@ static json oaicompat_completion_params_parse(
         inputs.tools = tools;
         inputs.tool_choice = tool_choice;
         inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
+        if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) {
+            LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n");
+            inputs.parallel_tool_calls = false;
+        }
         inputs.stream = stream;
         // TODO: support mixing schema w/ tools beyond generic format.
         inputs.json_schema = json_value(llama_params, "json_schema", json());

From aa6fb1321333fae8853d0cdc26bcb5d438e650a1 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Fri, 31 Jan 2025 17:12:40 +0000
Subject: [PATCH 302/376] `ci`: use sccache on windows instead of ccache
 (#11545)

* Use sccache on ci for windows

* Detect sccache in cmake
---
 .github/workflows/build.yml |  6 ++++++
 ggml/src/CMakeLists.txt     | 12 +++++++++---
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index c02dd6a81..022b9bd03 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -689,6 +689,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: windows-msys2
+          variant: sccache
           evict-old-files: 1d
 
       - name: Setup ${{ matrix.sys }}
@@ -763,6 +764,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-${{ matrix.build }}
+          variant: sccache
           evict-old-files: 1d
 
       - name: Clone Kompute submodule
@@ -949,6 +951,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
+          variant: sccache
           evict-old-files: 1d
 
       - name: Install Cuda Toolkit 11.7
@@ -1090,6 +1093,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-sycl
+          variant: sccache
           evict-old-files: 1d
 
       - name: Install
@@ -1174,6 +1178,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: ${{ github.job }}
+          variant: sccache
           evict-old-files: 1d
 
       - name: Build
@@ -1208,6 +1213,7 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-hip-release
+          variant: sccache
           evict-old-files: 1d
 
       - name: Install
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 566709135..0002ac18a 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -93,12 +93,18 @@ endif()
 
 if (GGML_CCACHE)
     find_program(GGML_CCACHE_FOUND ccache)
+    find_program(GGML_SCCACHE_FOUND sccache)
 
-    if (GGML_CCACHE_FOUND)
+    if (GGML_CCACHE_FOUND OR GGML_SCCACHE_FOUND)
+        if(GGML_CCACHE_FOUND)
+            set(GGML_CCACHE_VARIANT ccache)
+        else()
+            set(GGML_CCACHE_VARIANT sccache)
+        endif()
         # TODO: should not be set globally
-        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
+        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${GGML_CCACHE_VARIANT}")
         set(ENV{CCACHE_SLOPPINESS} time_macros)
-        message(STATUS "ccache found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
+        message(STATUS "${GGML_CCACHE_VARIANT} found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
     else()
         message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF")
     endif ()

From 5bbc7362cb93265f4c853fd89800a6255cc26985 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sat, 1 Feb 2025 00:01:20 +0000
Subject: [PATCH 303/376] ci: simplify cmake build commands (#11548)

---
 .github/workflows/build.yml | 86 +++++++++++++------------------------
 1 file changed, 30 insertions(+), 56 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 022b9bd03..03eabbbe5 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -59,16 +59,14 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_CURL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DGGML_RPC=ON
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
         id: cmake_test
@@ -199,13 +197,11 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_CURL=ON \
             -DGGML_RPC=ON
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -283,26 +279,22 @@ jobs:
         id: cmake_build
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Build (no OpenMP)
         id: cmake_build_no_openmp
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -335,11 +327,9 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DGGML_RPC=ON
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -372,11 +362,9 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DGGML_VULKAN=ON
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -493,13 +481,11 @@ jobs:
         id: cmake_build
         run: |
           source /opt/intel/oneapi/setvars.sh
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
   ubuntu-22-cmake-sycl-fp16:
     runs-on: ubuntu-22.04
@@ -543,14 +529,12 @@ jobs:
         id: cmake_build
         run: |
           source /opt/intel/oneapi/setvars.sh
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx \
             -DGGML_SYCL_F16=ON
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
   macOS-latest-cmake-ios:
     runs-on: macos-latest
@@ -576,9 +560,7 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
-          cmake -G Xcode .. \
+          cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
@@ -587,7 +569,7 @@ jobs:
             -DCMAKE_SYSTEM_NAME=iOS \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
             -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
 
   macOS-latest-cmake-tvos:
     runs-on: macos-latest
@@ -613,9 +595,7 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
-          cmake -G Xcode .. \
+          cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
@@ -624,7 +604,7 @@ jobs:
             -DCMAKE_SYSTEM_NAME=tvOS \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
             -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
 
   macOS-latest-swift:
     runs-on: macos-latest
@@ -654,17 +634,15 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
-          cmake -G Xcode .. \
+          cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
             -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
-          sudo cmake --install . --config Release
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+          sudo cmake --install build --config Release
 
       - name: xcodebuild for swift package
         id: xcodebuild
@@ -806,21 +784,19 @@ jobs:
         run: |
           git clone https://github.com/KhronosGroup/OpenCL-Headers
           cd OpenCL-Headers
-          mkdir build && cd build
-          cmake .. `
+          cmake -B build `
             -DBUILD_TESTING=OFF `
             -DOPENCL_HEADERS_BUILD_TESTING=OFF `
             -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
             -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
-          cmake --build . --target install
+          cmake --build build --target install
           git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
           cd OpenCL-ICD-Loader
-          mkdir build-arm64-release && cd build-arm64-release
-          cmake .. `
+          cmake -B build-arm64-release `
             -A arm64 `
             -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
             -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
-          cmake --build . --target install --config release
+          cmake --build build-arm64-release --target install --config release
 
       - name: Build
         id: cmake_build
@@ -1284,9 +1260,7 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
-          cmake -G Xcode .. \
+          cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
@@ -1295,8 +1269,8 @@ jobs:
             -DCMAKE_SYSTEM_NAME=iOS \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
             -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-          sudo cmake --install . --config Release
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+          sudo cmake --install build --config Release
 
       - name: xcodebuild for swift package
         id: xcodebuild

From ecef206ccb186a1cde8dd2523b1da3e12f593f9e Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Sat, 1 Feb 2025 11:30:54 +0100
Subject: [PATCH 304/376] Implement s3:// protocol (#11511)

For those that want to pull from s3

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 9cecae48c..cf61f4add 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -65,6 +65,13 @@ static int printe(const char * fmt, ...) {
     return ret;
 }
 
+static std::string strftime_fmt(const char * fmt, const std::tm & tm) {
+    std::ostringstream oss;
+    oss << std::put_time(&tm, fmt);
+
+    return oss.str();
+}
+
 class Opt {
   public:
     int init(int argc, const char ** argv) {
@@ -698,6 +705,39 @@ class LlamaData {
         return download(url, bn, true);
     }
 
+    int s3_dl(const std::string & model, const std::string & bn) {
+        const size_t slash_pos = model.find('/');
+        if (slash_pos == std::string::npos) {
+            return 1;
+        }
+
+        const std::string bucket     = model.substr(0, slash_pos);
+        const std::string key        = model.substr(slash_pos + 1);
+        const char * access_key = std::getenv("AWS_ACCESS_KEY_ID");
+        const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY");
+        if (!access_key || !secret_key) {
+            printe("AWS credentials not found in environment\n");
+            return 1;
+        }
+
+        // Generate AWS Signature Version 4 headers
+        // (Implementation requires HMAC-SHA256 and date handling)
+        // Get current timestamp
+        const time_t                   now     = time(nullptr);
+        const tm                       tm      = *gmtime(&now);
+        const std::string              date     = strftime_fmt("%Y%m%d", tm);
+        const std::string              datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm);
+        const std::vector<std::string> headers  = {
+            "Authorization: AWS4-HMAC-SHA256 Credential=" + std::string(access_key) + "/" + date +
+                "/us-east-1/s3/aws4_request",
+            "x-amz-content-sha256: UNSIGNED-PAYLOAD", "x-amz-date: " + datetime
+        };
+
+        const std::string url = "https://" + bucket + ".s3.amazonaws.com/" + key;
+
+        return download(url, bn, true, headers);
+    }
+
     std::string basename(const std::string & path) {
         const size_t pos = path.find_last_of("/\\");
         if (pos == std::string::npos) {
@@ -738,6 +778,9 @@ class LlamaData {
             rm_until_substring(model_, "github:");
             rm_until_substring(model_, "://");
             ret = github_dl(model_, bn);
+        } else if (string_starts_with(model_, "s3://")) {
+            rm_until_substring(model_, "://");
+            ret = s3_dl(model_, bn);
         } else {  // ollama:// or nothing
             rm_until_substring(model_, "ollama.com/library/");
             rm_until_substring(model_, "://");

From cfd74c86dbaa95ed30aa6b30e14d8801eb975d63 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sat, 1 Feb 2025 12:24:51 +0000
Subject: [PATCH 305/376] `sync`: minja
 (https://github.com/google/minja/commit/418a2364b56dc9be4ed9a1a2b0fb16fb53a7a22e)
 (#11574)

---
 common/minja.hpp | 49 +++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 46 insertions(+), 3 deletions(-)

diff --git a/common/minja.hpp b/common/minja.hpp
index f0e80fd7c..bcb5a0824 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -693,7 +693,7 @@ enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline };
 
 class TemplateToken {
 public:
-    enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter };
+    enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter, Break, Continue };
 
     static std::string typeToString(Type t) {
         switch (t) {
@@ -714,6 +714,8 @@ public:
             case Type::EndFilter: return "endfilter";
             case Type::Generation: return "generation";
             case Type::EndGeneration: return "endgeneration";
+            case Type::Break: return "break";
+            case Type::Continue: return "continue";
         }
         return "Unknown";
     }
@@ -815,6 +817,22 @@ struct CommentTemplateToken : public TemplateToken {
     CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {}
 };
 
+enum class LoopControlType { Break, Continue };
+
+class LoopControlException : public std::runtime_error {
+public:
+    LoopControlType control_type;
+    LoopControlException(const std::string & message, LoopControlType control_type) : std::runtime_error(message), control_type(control_type) {}
+    LoopControlException(LoopControlType control_type)
+      : std::runtime_error((std::ostringstream() << (control_type == LoopControlType::Continue ? "continue" : "break") << " outside of a loop").str()),
+        control_type(control_type) {}
+};
+
+struct LoopControlTemplateToken : public TemplateToken {
+    LoopControlType control_type;
+    LoopControlTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, location, pre, post), control_type(control_type) {}
+};
+
 class TemplateNode {
     Location location_;
 protected:
@@ -825,6 +843,12 @@ public:
     void render(std::ostringstream & out, const std::shared_ptr<Context> & context) const {
         try {
             do_render(out, context);
+        } catch (const LoopControlException & e) {
+            // TODO: make stack creation lazy. Only needed if it was thrown outside of a loop.
+            std::ostringstream err;
+            err << e.what();
+            if (location_.source) err << error_location_suffix(*location_.source, location_.pos);
+            throw LoopControlException(err.str(), e.control_type);
         } catch (const std::exception & e) {
             std::ostringstream err;
             err << e.what();
@@ -897,6 +921,15 @@ public:
     }
 };
 
+class LoopControlNode : public TemplateNode {
+    LoopControlType control_type_;
+  public:
+    LoopControlNode(const Location & location, LoopControlType control_type) : TemplateNode(location), control_type_(control_type) {}
+    void do_render(std::ostringstream &, const std::shared_ptr<Context> &) const override {
+      throw LoopControlException(control_type_);
+    }
+};
+
 class ForNode : public TemplateNode {
     std::vector<std::string> var_names;
     std::shared_ptr<Expression> iterable;
@@ -961,7 +994,12 @@ public:
                   loop.set("last", i == (n - 1));
                   loop.set("previtem", i > 0 ? filtered_items.at(i - 1) : Value());
                   loop.set("nextitem", i < n - 1 ? filtered_items.at(i + 1) : Value());
-                  body->render(out, loop_context);
+                  try {
+                      body->render(out, loop_context);
+                  } catch (const LoopControlException & e) {
+                      if (e.control_type == LoopControlType::Break) break;
+                      if (e.control_type == LoopControlType::Continue) continue;
+                  }
               }
           }
       };
@@ -2159,7 +2197,7 @@ private:
       static std::regex comment_tok(R"(\{#([-~]?)(.*?)([-~]?)#\})");
       static std::regex expr_open_regex(R"(\{\{([-~])?)");
       static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
-      static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter)\b)");
+      static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)");
       static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
       static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
       static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
@@ -2291,6 +2329,9 @@ private:
             } else if (keyword == "endfilter") {
               auto post_space = parseBlockClose();
               tokens.push_back(std::make_unique<EndFilterTemplateToken>(location, pre_space, post_space));
+            } else if (keyword == "break" || keyword == "continue") {
+              auto post_space = parseBlockClose();
+              tokens.push_back(std::make_unique<LoopControlTemplateToken>(location, pre_space, post_space, keyword == "break" ? LoopControlType::Break : LoopControlType::Continue));
             } else {
               throw std::runtime_error("Unexpected block: " + keyword);
             }
@@ -2414,6 +2455,8 @@ private:
               children.emplace_back(std::make_shared<FilterNode>(token->location, std::move(filter_token->filter), std::move(body)));
           } else if (dynamic_cast<CommentTemplateToken*>(token.get())) {
               // Ignore comments
+          } else if (auto ctrl_token = dynamic_cast<LoopControlTemplateToken*>(token.get())) {
+              children.emplace_back(std::make_shared<LoopControlNode>(token->location, ctrl_token->control_type));
           } else if (dynamic_cast<EndForTemplateToken*>(token.get())
                   || dynamic_cast<EndSetTemplateToken*>(token.get())
                   || dynamic_cast<EndMacroTemplateToken*>(token.get())

From 53debe6f3c9cca87e9520a83ee8c14d88977afa4 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sat, 1 Feb 2025 18:22:38 +0000
Subject: [PATCH 306/376] ci: use sccache on windows HIP jobs (#11553)

---
 .github/workflows/build.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 03eabbbe5..7392f2bfe 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1154,7 +1154,6 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: ${{ github.job }}
-          variant: sccache
           evict-old-files: 1d
 
       - name: Build
@@ -1189,7 +1188,6 @@ jobs:
         uses: hendrikmuhs/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-hip-release
-          variant: sccache
           evict-old-files: 1d
 
       - name: Install

From 0cec062a638700495673f5494d200b74340538be Mon Sep 17 00:00:00 2001
From: piDack <104877312+piDack@users.noreply.github.com>
Date: Sun, 2 Feb 2025 15:48:46 +0800
Subject: [PATCH 307/376] llama : add support for GLM-Edge and GLM-Edge-V
 series models (#10573)

* add glm edge chat model

* use config partial_rotary_factor as rope ratio

* support for glm edge model

* vision model support

* remove debug info

* fix format

* llava.cpp trailing whitespace

* remove unused AutoTokenizer

* Update src/llama.cpp for not contain <|end|> or </s>

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>

* add edge template

* fix chat template

* fix confict

* fix confict

* fix ci err

* fix format err

* fix template err

* 9b hf chat support

* format

* format clip.cpp

* fix format

* Apply suggestions from code review

* Apply suggestions from code review

* Update examples/llava/clip.cpp

* fix format

* minor : style

---------

Co-authored-by: liyuhang <yuhang.li@zhipuai.cn>
Co-authored-by: piDack <pcdack@hotmail.co>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: liyuhang <yuhang.li@aminer.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 README.md                                     |   3 +-
 convert_hf_to_gguf.py                         |  58 +---
 examples/llava/README-glmedge.md              |  43 +++
 examples/llava/clip.cpp                       | 110 ++++++-
 examples/llava/clip.h                         |   2 +
 .../glmedge-convert-image-encoder-to-gguf.py  | 280 ++++++++++++++++++
 examples/llava/glmedge-surgery.py             |  33 +++
 examples/llava/llava.cpp                      |  17 ++
 gguf-py/gguf/constants.py                     |   3 +
 src/llama-arch.cpp                            |   3 +
 src/llama-chat.cpp                            |  11 +-
 src/llama-chat.h                              |   1 +
 src/llama-model.cpp                           |  28 +-
 src/llama.cpp                                 |  35 ++-
 tests/test-chat-template.cpp                  |   8 +
 15 files changed, 568 insertions(+), 67 deletions(-)
 create mode 100644 examples/llava/README-glmedge.md
 create mode 100644 examples/llava/glmedge-convert-image-encoder-to-gguf.py
 create mode 100644 examples/llava/glmedge-surgery.py

diff --git a/README.md b/README.md
index d40309875..7f306d199 100644
--- a/README.md
+++ b/README.md
@@ -96,7 +96,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
 - [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
 - [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
-- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
+- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
 - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
 - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
 - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
@@ -117,6 +117,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
+- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
 - [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
 
 </details>
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 63b54a9cf..018a2a588 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -648,7 +648,7 @@ class Model:
         if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
             # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
             res = "jina-v2-code"
-        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
+        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
             # ref: https://huggingface.co/THUDM/glm-4-9b-chat
             res = "chatglm-bpe"
         if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
@@ -4513,7 +4513,7 @@ class JaisModel(Model):
         self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
 
 
-@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
+@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(Model):
     model_arch = gguf.MODEL_ARCH.CHATGLM
 
@@ -4619,47 +4619,15 @@ class ChatGLMModel(Model):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["padded_vocab_size"]
+        vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
         assert max(tokenizer.get_vocab().values()) < vocab_size
 
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
-        for token, rank in mergeable_ranks.items():
-            vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) >= 2 and len(merged) <= 7
-            merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
-
-        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
-        added_vocab = tokenizer.get_added_vocab()
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                if tokenizer.added_tokens_decoder[i].special:
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.USER_DEFINED)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
+        tokens, toktypes, tokpre = self.get_vocab_base()
         self.gguf_writer.add_tokenizer_model("gpt2")
         self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.merges = merges
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
         # only add special tokens when they were not already loaded from config.json
         special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
         special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
@@ -4670,16 +4638,20 @@ class ChatGLMModel(Model):
     def set_gguf_parameters(self):
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        n_head_kv = self.hparams.get("multi_query_group_num", n_head)
+        n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)
-        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed))
-        self.gguf_writer.add_block_count(self.hparams["num_layers"])
+        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
+        self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
         self.gguf_writer.add_head_count(n_head)
         self.gguf_writer.add_head_count_kv(n_head_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_rope_dimension_count(64)
+        if "attention_dim" in self.hparams:
+            rope_dim = self.hparams["attention_dim"]
+        else:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_add_bos_token(False)
         rope_freq = 10000
         if "rope_ratio" in self.hparams:
@@ -4689,7 +4661,7 @@ class ChatGLMModel(Model):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
-        if name.endswith(".rotary_pos_emb.inv_freq"):
+        if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
             return []
 
         name = name.removeprefix("transformer.")
diff --git a/examples/llava/README-glmedge.md b/examples/llava/README-glmedge.md
new file mode 100644
index 000000000..603d01474
--- /dev/null
+++ b/examples/llava/README-glmedge.md
@@ -0,0 +1,43 @@
+# GLMV-EDGE
+
+Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
+
+## Usage
+Build with cmake or run `make llama-llava-cli` to build it.
+
+After building, run: `./llama-llava-cli` to see the usage. For example:
+
+```sh
+./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
+```
+
+**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
+**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
+
+## GGUF conversion
+
+1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
+
+```sh
+git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
+```
+
+2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
+
+```sh
+python ./examples/llava/glmedge-surgery.py -m ../model_path
+```
+
+4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
+
+```sh
+python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
+```
+
+5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
+
+```sh
+python convert_hf_to_gguf.py ../model_path
+```
+
+Now both the LLM part and the image encoder are in the `model_path` directory.
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 24073c5a9..7367d44cb 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -102,6 +102,7 @@ static std::string format(const char * fmt, ...) {
 #define KEY_HAS_VIS_ENC         "clip.has_vision_encoder"
 #define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
 #define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
+#define KEY_HAS_GLM_PROJ        "clip.has_glm_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
 #define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #define KEY_USE_GELU            "clip.use_gelu"
@@ -160,6 +161,15 @@ static std::string format(const char * fmt, ...) {
 #define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
 #define TN_MINICPMV_LN "resampler.ln_%s.%s"
 
+#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
+#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
+#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
+#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
+#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
+#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
+#define TN_GLM_BOI_W "adapter.boi"
+#define TN_GLM_EOI_W "adapter.eoi"
+
 
 enum projector_type {
     PROJECTOR_TYPE_MLP,
@@ -167,6 +177,7 @@ enum projector_type {
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_RESAMPLER,
+    PROJECTOR_TYPE_GLM_EDGE,
     PROJECTOR_TYPE_MERGER,
     PROJECTOR_TYPE_UNKNOWN,
 };
@@ -176,6 +187,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LDP, "ldp" },
     { PROJECTOR_TYPE_LDPV2, "ldpv2"},
     { PROJECTOR_TYPE_RESAMPLER, "resampler"},
+    { PROJECTOR_TYPE_GLM_EDGE, "adapter"},
     { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
 };
 
@@ -500,6 +512,12 @@ struct clip_vision_model {
     struct ggml_tensor * mm_4_w = NULL;
     struct ggml_tensor * mm_4_b = NULL;
 
+    //GLMV-Edge projection
+    struct ggml_tensor * mm_model_adapter_conv_w;
+    struct ggml_tensor * mm_model_adapter_conv_b;
+    struct ggml_tensor * boi_w;
+    struct ggml_tensor * eoi_w;
+
     // MobileVLM projection
     struct ggml_tensor * mm_model_mlp_1_w;
     struct ggml_tensor * mm_model_mlp_1_b;
@@ -560,6 +578,7 @@ struct clip_ctx {
     bool has_vision_encoder  = false;
     bool has_llava_projector = false;
     bool has_minicpmv_projector = false;
+    bool has_glm_projector = false;
     bool has_qwen2vl_merger = false;
     int minicpmv_version = 2;
 
@@ -638,7 +657,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
 
     const int batch_size = imgs->size;
 
-    if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
+    if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
         GGML_ASSERT(batch_size == 1);
     }
 
@@ -734,8 +753,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // loop over layers
-    if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
-        // TODO: figure out why we doing thing in this way ???
+    if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
         n_layer += 1;
     }
     for (int il = 0; il < n_layer - 1; il++) {
@@ -1095,7 +1113,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             GGML_ASSERT(false);
         }
     }
-    else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+    // glm projector
+    else if (ctx->has_glm_projector) {
+        if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
+            size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
+            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
+            embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
+            embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
+            embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
+            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
+            embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
+            //GLU
+            {
+                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+                embeddings = ggml_norm(ctx0, embeddings, eps);
+                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
+                embeddings = ggml_gelu_inplace(ctx0, embeddings);
+                struct ggml_tensor * x = embeddings;
+                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
+                x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
+                embeddings = ggml_silu_inplace(ctx0, embeddings);
+                embeddings = ggml_mul(ctx0, embeddings,x);
+                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
+            }
+        } else {
+            GGML_ABORT("fatel error");
+        }
+    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
         embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
 
         embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@@ -1284,6 +1328,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
         }
 
+        idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ);
+        if (idx != -1) {
+            new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx);
+        }
+
         idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
         if (idx != -1) {
             new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
@@ -1308,6 +1357,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
             LOG_INF("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
             LOG_INF("%s: minicpmv_projector:  %d\n", __func__, new_clip->has_minicpmv_projector);
+            LOG_INF("%s: glm_projector:  %d\n", __func__, new_clip->has_glm_projector);
             LOG_INF("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
             LOG_INF("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
@@ -1575,6 +1625,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
             vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
         }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
+            vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight"));
+            vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias"));
+            vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight"));
+            vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight"));
+            vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias"));
+            vision_model.mm_model_mlp_1_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
+            vision_model.mm_model_mlp_2_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight"));
+            vision_model.mm_model_mlp_3_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
+            vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W);
+            vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W);
+        }
         else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
             vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
             vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
@@ -2115,6 +2177,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         return true;
     }
 
+    if (ctx->has_glm_projector) {
+        res_imgs->size = 1;
+        res_imgs->data = new clip_image_f32[res_imgs->size];
+        clip_image_u8 resized_image;
+        int32_t sz=ctx->vision_model.hparams.image_size;
+        bicubic_resize(*img, resized_image,sz,sz);
+        clip_image_f32 * res = clip_image_f32_init();
+        //clip_image_save_to_bmp(resized_image, "resized.bmp");
+        normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
+        res_imgs->data[0] = *res;
+        clip_image_f32_free(res);
+        return true;
+    }
+
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         LOG_ERR("This gguf file seems to have no vision encoder\n");
@@ -2300,7 +2376,8 @@ void clip_free(clip_ctx * ctx) {
 }
 
 size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
-    return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
+    int extra_tokens = ctx->has_glm_projector ? 2 : 0;
+    return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
 size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
@@ -2342,7 +2419,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
 
     int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
 
-    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
         n_patches /= 4;
     } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
         if (ctx->minicpmv_version == 2) {
@@ -2475,6 +2552,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     if (ctx->has_minicpmv_projector) {
         GGML_ASSERT(batch_size == 1);
     }
+    if (ctx->has_glm_projector) {
+        GGML_ASSERT(batch_size == 1);
+        ggml_tensor * boi = ctx->vision_model.boi_w;
+        ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
+        vec = (float*)(vec+ggml_nelements(boi)); //offset for boi
+    }
 
     // build the inference graph
     ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
@@ -2627,7 +2710,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
             free(positions_data);
 
-            {
+            if (!ctx->has_glm_projector) {
                 struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
                 int* patches_data = (int*)malloc(ggml_nbytes(patches));
                 for (int i = 0; i < num_patches; i++) {
@@ -2651,6 +2734,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     // copy the embeddings to the location passed by the user
     ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
 
+    if (ctx->has_glm_projector) {
+        //eoi
+        ggml_tensor * eoi = ctx->vision_model.eoi_w;
+        int offset = ggml_nelements(embeddings);
+        ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi));
+    }
+
     return true;
 }
 
@@ -2812,6 +2902,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return 3584;
         }
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){
+        return ctx->vision_model.mm_model_mlp_3_w->ne[1];
+    }
     if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
         return ctx->vision_model.mm_1_b->ne[0];
     }
@@ -2827,6 +2920,9 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
     return 0;
 }
 
+bool clip_is_glm(const struct clip_ctx * ctx) {
+    return ctx->has_glm_projector;
+}
 bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
     return ctx->has_qwen2vl_merger;
 }
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index 1603edd26..841b4f6f9 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -93,6 +93,8 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
 
 CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
 
+CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/examples/llava/glmedge-convert-image-encoder-to-gguf.py b/examples/llava/glmedge-convert-image-encoder-to-gguf.py
new file mode 100644
index 000000000..848ef1cf3
--- /dev/null
+++ b/examples/llava/glmedge-convert-image-encoder-to-gguf.py
@@ -0,0 +1,280 @@
+import argparse
+import os
+import json
+import re
+
+import torch
+import numpy as np
+from gguf import *
+
+TEXT = "clip.text"
+VISION = "clip.vision"
+from transformers import SiglipVisionModel, SiglipVisionConfig
+
+def k(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
+    if name in (
+        "logit_scale",
+        "text_model.embeddings.position_ids",
+        "vision_model.embeddings.position_ids",
+    ):
+        return True
+
+    if name in (
+        "vision_model.head.probe",
+        "vision_model.head.attention.in_proj_weight",
+        "vision_model.head.attention.in_proj_bias",
+        "vision_model.head.attention.out_proj.weight",
+        "vision_model.head.attention.out_proj.bias",
+        "vision_model.head.layernorm.weight",
+        "vision_model.head.layernorm.bias",
+        "vision_model.head.mlp.fc1.weight",
+        "vision_model.head.mlp.fc1.bias",
+        "vision_model.head.mlp.fc2.weight",
+        "vision_model.head.mlp.fc2.bias"
+    ):
+        return True
+
+    if name.startswith("v") and not has_vision:
+        return True
+
+    if name.startswith("t") and not has_text:
+        return True
+
+    return False
+
+
+def get_tensor_name(name: str) -> str:
+    if "projection" in name:
+        return name
+    if "mm_projector" in name:
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
+
+    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
+
+
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
+ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
+ap.add_argument("--text-only", action="store_true", required=False,
+                help="Save a text-only model. It can't be used to encode images")
+ap.add_argument("--vision-only", action="store_true", required=False,
+                help="Save a vision-only model. It can't be used to encode texts")
+ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
+                help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
+ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
+                help="The clip model is from openclip (for ViT-SO400M type))")
+ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
+ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
+# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
+# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
+default_image_mean = [0.5, 0.5, 0.5]
+default_image_std = [0.5, 0.5, 0.5]
+ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
+ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
+
+# with proper
+args = ap.parse_args()
+
+
+if args.text_only and args.vision_only:
+    print("--text-only and --image-only arguments cannot be specified at the same time.")
+    exit(1)
+
+if args.use_f32:
+    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
+
+# output in the same directory as the model if output_dir is None
+dir_model = args.model_dir
+
+if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
+    vocab = None
+    tokens = None
+else:
+    with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
+        vocab = json.load(f)
+        tokens = [key for key in vocab]
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+    config = json.load(f)
+    if args.clip_model_is_vision:
+        v_hparams = config
+        t_hparams = None
+    else:
+        v_hparams = config["vision_config"]
+        t_hparams = None
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if args.use_f32:
+    ftype = 0
+
+vision_config = SiglipVisionConfig(**v_hparams)
+model = SiglipVisionModel(vision_config)
+model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
+
+fname_middle = None
+has_text_encoder = False
+has_vision_encoder = True
+has_glm_projector = True
+if args.text_only:
+    fname_middle = "text-"
+    has_vision_encoder = False
+elif args.llava_projector is not None:
+    fname_middle = "mmproj-"
+    has_text_encoder = False
+    has_glm_projector = True
+elif args.vision_only:
+    fname_middle = "vision-"
+    has_text_encoder = False
+else:
+    fname_middle = ""
+
+output_dir = args.output_dir if args.output_dir is not None else dir_model
+os.makedirs(output_dir, exist_ok=True)
+output_prefix = os.path.basename(output_dir).replace("ggml_", "")
+fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
+fout = GGUFWriter(path=fname_out, arch="clip")
+
+fout.add_bool("clip.has_text_encoder", has_text_encoder)
+fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
+fout.add_bool("clip.has_glm_projector", has_glm_projector)
+fout.add_file_type(ftype)
+model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
+fout.add_name(model_name)
+if has_glm_projector:
+    fout.add_description("image encoder for glm4v")
+    fout.add_string("clip.projector_type", "adapter")
+else:
+    fout.add_description("two-tower CLIP model")
+
+if has_text_encoder:
+    assert t_hparams is not None
+    assert tokens is not None
+    # text_model hparams
+    fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
+    fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
+    fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
+    fout.add_token_list(tokens)
+
+if has_vision_encoder:
+    # vision_model hparams
+    fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
+    fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
+    fout.add_uint32("clip.vision.projection_dim", 0)
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
+    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
+
+    image_mean = args.image_mean if args.image_mean is not None else default_image_mean
+    image_std = args.image_std if args.image_std is not None else default_image_std
+    fout.add_array("clip.vision.image_mean", image_mean)
+    fout.add_array("clip.vision.image_std", image_std)
+
+fout.add_bool("clip.use_gelu", True)
+
+
+if has_glm_projector:
+    # model.vision_model.encoder.layers.pop(-1)  # pyright: ignore[reportAttributeAccessIssue]
+    projector = torch.load(args.llava_projector)
+    for name, data in projector.items():
+        name = get_tensor_name(name)
+        # pw and dw conv ndim==4
+        if data.ndim == 2 or data.ndim == 4:
+            data = data.squeeze().numpy().astype(np.float16)
+        else:
+            data = data.squeeze().numpy().astype(np.float32)
+        if name.startswith("vision."):
+            name=name.replace("vision.","")
+        fout.add_tensor(name, data)
+        print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
+        # print(f"Projector {name} tensors added\n")
+
+state_dict = model.state_dict()  # pyright: ignore[reportAttributeAccessIssue]
+for name, data in state_dict.items():
+    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
+        # we don't need this
+        print(f"skipping parameter: {name}")
+        continue
+
+    name = get_tensor_name(name)
+    data = data.squeeze().numpy()
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if n_dims == 4:
+        print(f"tensor {name} is always saved in f16")
+        data = data.astype(np.float16)
+        ftype_cur = 1
+    elif ftype == 1:
+        if name[-7:] == ".weight" and n_dims == 2:
+            # print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            # print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            # print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
+    # print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
+    fout.add_tensor(name, data)
+
+
+fout.write_header_to_file()
+fout.write_kv_data_to_file()
+fout.write_tensors_to_file()
+fout.close()
+
+print("Done. Output file: " + fname_out)
diff --git a/examples/llava/glmedge-surgery.py b/examples/llava/glmedge-surgery.py
new file mode 100644
index 000000000..16bb915d0
--- /dev/null
+++ b/examples/llava/glmedge-surgery.py
@@ -0,0 +1,33 @@
+import argparse
+import os
+import torch
+from transformers import AutoModel
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", help="Path to GLM model")
+args = ap.parse_args()
+
+# find the model part that includes the the multimodal projector weights
+model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
+checkpoint = model.state_dict()
+
+# get a list of mm tensor names
+mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
+
+# store these tensors in a new dictionary and torch.save them
+projector = {name: checkpoint[name].float() for name in mm_tensors}
+torch.save(projector, f"{args.model}/glm.projector")
+
+clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
+if len(clip_tensors) > 0:
+    clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
+    torch.save(clip, f"{args.model}/glm.clip")
+
+    # added tokens should be removed to be able to convert Mistral models
+    if os.path.exists(f"{args.model}/added_tokens.json"):
+        with open(f"{args.model}/added_tokens.json", "w") as f:
+            f.write("{}\n")
+
+print("Done!")
+print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
+print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 2cac7933d..300714045 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -311,6 +311,20 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         img_res_v.size = 0;
         img_res_v.data = nullptr;
     }
+    else if (clip_is_glm(ctx_clip)){
+        struct clip_image_size * load_image_size = clip_image_size_init();
+        load_image_size->width = img_res_v.data[0].nx;
+        load_image_size->height = img_res_v.data[0].ny;
+        clip_add_load_image_size(ctx_clip, load_image_size);
+
+        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
+        int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
+        *n_img_pos = (pos * pos + 2);
+        if (!encoded){
+            LOG_ERR("Unable to encode image \n");
+            return false;
+        }
+    }
     else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
         *n_img_pos = clip_n_patches(ctx_clip);
@@ -395,6 +409,9 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
     if (clip_is_minicpmv(ctx_clip)) {
         num_max_patches = 10;
     }
+    if (clip_is_glm(ctx_clip)) {
+        num_max_patches = 1;
+    }
     float * image_embd;
     if (clip_is_qwen2vl(ctx_clip)) {
         // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 8fe84df21..ecac5b4bb 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -1357,6 +1357,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index a7260f495..97a1e7e5e 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -1024,6 +1024,9 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_OUTPUT,          "output" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
             { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
             { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 5c19bab24..028a64794 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -51,6 +51,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
     { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGML_3         },
     { "chatglm4",          LLM_CHAT_TEMPLATE_CHATGML_4         },
+    { "glmedge",           LLM_CHAT_TEMPLATE_GLMEDGE           },
     { "minicpm",           LLM_CHAT_TEMPLATE_MINICPM           },
     { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
     { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
@@ -115,7 +116,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
-        return LLM_CHAT_TEMPLATE_FALCON_3;
+        return tmpl_contains("</s>") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -440,6 +441,14 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|assistant|>";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) {
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>" << "\n" << message->content;
+        }
+        if (add_ass) {
+            ss << "<|assistant|>";
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
         // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
         for (auto message : chat) {
diff --git a/src/llama-chat.h b/src/llama-chat.h
index 3a4d07ce3..2f6a0e3e2 100644
--- a/src/llama-chat.h
+++ b/src/llama-chat.h
@@ -31,6 +31,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_LLAMA_3,
     LLM_CHAT_TEMPLATE_CHATGML_3,
     LLM_CHAT_TEMPLATE_CHATGML_4,
+    LLM_CHAT_TEMPLATE_GLMEDGE,
     LLM_CHAT_TEMPLATE_MINICPM,
     LLM_CHAT_TEMPLATE_EXAONE_3,
     LLM_CHAT_TEMPLATE_RWKV_WORLD,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 18bd0b071..0487c978b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1093,8 +1093,20 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
-                    case 28: type = LLM_TYPE_6B; break;
-                    case 40: type = LLM_TYPE_9B; break;
+                    case 28: {
+                        if (hparams.n_head(0) == 16) {
+                            type = LLM_TYPE_1_5B;
+                        } else {
+                            type = LLM_TYPE_6B;
+                        }
+                    } break;
+                    case 40: {
+                        if (hparams.n_head(0) == 24) {
+                            type = LLM_TYPE_4B;
+                        } else {
+                            type = LLM_TYPE_9B;
+                        }
+                    } break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
@@ -3068,9 +3080,17 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         auto & layer = layers[i];
 
                         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);
+                        if (layer.wqkv == nullptr) {
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        }
 
                         layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
 
diff --git a/src/llama.cpp b/src/llama.cpp
index 192b20a27..5760017e0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -7215,17 +7215,30 @@ struct llm_build_context {
                 struct ggml_tensor * Qcur = nullptr;
                 struct ggml_tensor * Kcur = nullptr;
                 struct ggml_tensor * Vcur = nullptr;
-
-                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
-
+                if (model.type == LLM_TYPE_1_5B || model.type == LLM_TYPE_4B || model.type == LLM_TYPE_9B) {
+                    Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                    if (model.layers[il].bq) {
+                        Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    }
+                    Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                    if (model.layers[il].bk) {
+                        Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    }
+                    Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                    if (model.layers[il].bv) {
+                        Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    }
+                } else {
+                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+                    cb(cur, "wqkv", il);
+                    if (model.layers[il].bqkv) {
+                        cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                        cb(cur, "bqkv", il);
+                    }
+                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                }
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 4563f9dcb..e0314ae1d 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -175,6 +175,14 @@ int main(void) {
             /* .bos_token= */ "",
             /* .eos_token= */ "",
         },
+        {
+            /* .name= */ "GLMEdge",
+            /* .template_str= */ "{% for item in messages %}{% if item['role'] == 'system' %}<|system|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>\n{{ item['content'] }}{% endif %}{% endfor %}<|assistant|>",
+            /* .expected_output= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
+            /* .expected_output_jinja= */ "<|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
+            /* .bos_token= */ "",
+            /* .eos_token= */ "",
+        },
         {
             /* .name= */ "MiniCPM-3B-OpenHermes-2.5-v2-GGUF",
             /* .template_str= */ u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",

From ff227703d6d6e1888bdc7af6138514092ffcdb96 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C5=82=20Moskal?= <michal@moskal.me>
Date: Sat, 1 Feb 2025 23:55:32 -0800
Subject: [PATCH 308/376] sampling : support for llguidance grammars (#10224)

* initial porting of previous LLG patch

* update for new APIs

* build: integrate llguidance as an external project

* use '%llguidance' as marker to enable llg lark syntax

* add some docs

* clarify docs

* code style fixes

* remove llguidance.h from .gitignore

* fix tests when llg is enabled

* pass vocab not model to llama_sampler_init_llg()

* copy test-grammar-integration.cpp to test-llguidance.cpp

* clang fmt

* fix ref-count bug

* build and run test

* gbnf -> lark syntax

* conditionally include llguidance test based on LLAMA_LLGUIDANCE flag

* rename llguidance test file to test-grammar-llguidance.cpp

* add gh action for llg test

* align tests with LLG grammar syntax and JSON Schema spec

* llama_tokenizer() in fact requires valid utf8

* update llg

* format file

* add $LLGUIDANCE_LOG_LEVEL support

* fix whitespace

* fix warning

* include <cmath> for INFINITY

* add final newline

* fail llama_sampler_init_llg() at runtime

* Link gbnf_to_lark.py script; fix links; refer to llg docs for lexemes

* simplify #includes

* improve doc string for LLAMA_LLGUIDANCE

* typo in merge

* bump llguidance to 0.6.12
---
 .github/workflows/build.yml           |   30 +
 CMakeLists.txt                        |    1 +
 common/CMakeLists.txt                 |   28 +
 common/json-schema-to-grammar.cpp     |    9 +-
 common/json-schema-to-grammar.h       |    3 +-
 common/llguidance.cpp                 |  270 ++++++
 common/sampling.cpp                   |   22 +-
 common/sampling.h                     |    3 +
 docs/llguidance.md                    |   51 ++
 tests/CMakeLists.txt                  |    3 +
 tests/test-grammar-integration.cpp    |    2 +-
 tests/test-grammar-llguidance.cpp     | 1140 +++++++++++++++++++++++++
 tests/test-json-schema-to-grammar.cpp |    2 +-
 13 files changed, 1555 insertions(+), 9 deletions(-)
 create mode 100644 common/llguidance.cpp
 create mode 100644 docs/llguidance.md
 create mode 100644 tests/test-grammar-llguidance.cpp

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7392f2bfe..8f9c82f87 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -302,6 +302,36 @@ jobs:
           cd build
           ctest -L main --verbose --timeout 900
 
+  ubuntu-latest-llguidance:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_LLGUIDANCE=ON
+          cmake --build . --config Release -j $(nproc)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
   ubuntu-latest-cmake-rpc:
     runs-on: ubuntu-latest
 
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4c62d1788..74b48d24d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -80,6 +80,7 @@ option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 
 # 3rd party libs
 option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
+option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 72f0915c1..e61015d2a 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -65,6 +65,7 @@ add_library(${TARGET} STATIC
     console.h
     json-schema-to-grammar.cpp
     json.hpp
+    llguidance.cpp
     log.cpp
     log.h
     minja.hpp
@@ -91,6 +92,33 @@ if (LLAMA_CURL)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
 endif ()
 
+if (LLAMA_LLGUIDANCE)
+    include(ExternalProject)
+    set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
+    set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
+    ExternalProject_Add(llguidance_ext
+        GIT_REPOSITORY https://github.com/guidance-ai/llguidance
+        # v0.6.12:
+        GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
+        PREFIX ${CMAKE_BINARY_DIR}/llguidance
+        SOURCE_DIR ${LLGUIDANCE_SRC}
+        BUILD_IN_SOURCE TRUE
+        CONFIGURE_COMMAND ""
+        BUILD_COMMAND cargo build --release
+        INSTALL_COMMAND ""
+        BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h
+        UPDATE_COMMAND ""
+    )
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
+
+    add_library(llguidance STATIC IMPORTED)
+    set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a)
+    add_dependencies(llguidance llguidance_ext)
+
+    target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance)
+endif ()
+
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
 target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 1f47e313e..3ebcc3d9f 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -991,7 +991,14 @@ public:
     }
 };
 
-std::string json_schema_to_grammar(const json & schema) {
+std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
+#ifdef LLAMA_USE_LLGUIDANCE
+    if (!force_gbnf) {
+        return "%llguidance {}\nstart: %json " + schema.dump();
+    }
+#else
+    (void)force_gbnf;
+#endif // LLAMA_USE_LLGUIDANCE
     return build_grammar([&](const common_grammar_builder & callbacks) {
         auto copy = schema;
         callbacks.resolve_refs(copy);
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
index ba4112cb9..62a3b0a44 100644
--- a/common/json-schema-to-grammar.h
+++ b/common/json-schema-to-grammar.h
@@ -5,7 +5,8 @@
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
 
-std::string json_schema_to_grammar(const nlohmann::ordered_json & schema);
+std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
+                                   bool force_gbnf = false);
 
 struct common_grammar_builder {
     std::function<std::string(const std::string &, const std::string &)> add_rule;
diff --git a/common/llguidance.cpp b/common/llguidance.cpp
new file mode 100644
index 000000000..7aa8ddd80
--- /dev/null
+++ b/common/llguidance.cpp
@@ -0,0 +1,270 @@
+#include "sampling.h"
+#include "log.h"
+
+#ifdef LLAMA_USE_LLGUIDANCE
+
+#    include "llguidance.h"
+#    include <cmath>
+
+struct llama_sampler_llg {
+    const llama_vocab * vocab;
+    std::string         grammar_kind;
+    std::string         grammar_data;
+    LlgTokenizer *      tokenizer;
+    LlgConstraint *     grammar;
+    LlgMaskResult       llg_res;
+    bool                has_llg_res;
+};
+
+static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
+                                             const char * grammar_data) {
+    LlgConstraintInit cinit;
+    llg_constraint_init_set_defaults(&cinit, tokenizer);
+    const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
+    if (log_level && *log_level) {
+        cinit.log_stderr_level = atoi(log_level);
+    }
+    auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
+    if (llg_get_error(c)) {
+        LOG_ERR("llg error: %s\n", llg_get_error(c));
+        llg_free_constraint(c);
+        return nullptr;
+    }
+    return c;
+}
+
+static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
+    return "llguidance";
+}
+
+static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
+    auto * ctx = (llama_sampler_llg *) smpl->ctx;
+    if (ctx->grammar) {
+        LlgCommitResult res;
+        llg_commit_token(ctx->grammar, token, &res);
+        ctx->has_llg_res = false;
+    }
+}
+
+static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
+    auto * ctx = (llama_sampler_llg *) smpl->ctx;
+    if (ctx->grammar) {
+        if (!ctx->has_llg_res) {
+            if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
+                ctx->has_llg_res = true;
+            } else {
+                LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
+                llg_free_constraint(ctx->grammar);
+                ctx->grammar = nullptr;
+            }
+        }
+        if (ctx->has_llg_res) {
+            if (ctx->llg_res.is_stop) {
+                for (size_t i = 0; i < cur_p->size; ++i) {
+                    if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
+                        cur_p->data[i].logit = -INFINITY;
+                    }
+                }
+            } else {
+                const uint32_t * mask = ctx->llg_res.sample_mask;
+                for (size_t i = 0; i < cur_p->size; ++i) {
+                    auto token = cur_p->data[i].id;
+                    if ((mask[token / 32] & (1 << (token % 32))) == 0) {
+                        cur_p->data[i].logit = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void llama_sampler_llg_reset(llama_sampler * smpl) {
+    auto * ctx = (llama_sampler_llg *) smpl->ctx;
+    if (!ctx->grammar) {
+        return;
+    }
+
+    auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
+    llg_free_constraint(ctx->grammar);
+    ctx->grammar     = grammar_new;
+    ctx->has_llg_res = false;
+}
+
+static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
+    const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
+
+    auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
+
+    // copy the state
+    {
+        auto * result_ctx = (llama_sampler_llg *) result->ctx;
+
+        if (ctx->grammar) {
+            result_ctx->grammar_kind = ctx->grammar_kind;
+            result_ctx->grammar_data = ctx->grammar_data;
+            result_ctx->grammar      = llg_clone_constraint(ctx->grammar);
+            result_ctx->tokenizer    = llg_clone_tokenizer(ctx->tokenizer);
+        }
+    }
+
+    return result;
+}
+
+static void llama_sampler_llg_free(llama_sampler * smpl) {
+    const auto * ctx = (llama_sampler_llg *) smpl->ctx;
+
+    if (ctx->grammar) {
+        llg_free_constraint(ctx->grammar);
+        llg_free_tokenizer(ctx->tokenizer);
+    }
+
+    delete ctx;
+}
+
+static llama_sampler_i llama_sampler_llg_i = {
+    /* .name   = */ llama_sampler_llg_name,
+    /* .accept = */ llama_sampler_llg_accept_impl,
+    /* .apply  = */ llama_sampler_llg_apply,
+    /* .reset  = */ llama_sampler_llg_reset,
+    /* .clone  = */ llama_sampler_llg_clone,
+    /* .free   = */ llama_sampler_llg_free,
+};
+
+static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
+                                            uint32_t * output_tokens, size_t output_tokens_len) {
+    const llama_vocab * vocab = (const llama_vocab *) user_data;
+    int                 r     = 0;
+    try {
+        r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
+                           true);
+    } catch (const std::exception & e) {
+        GGML_ABORT("llama_tokenize failed: %s\n", e.what());
+    }
+    if (r < 0) {
+        return -r;
+    }
+    return r;
+}
+
+static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
+    // TODO store the tokenizer in the vocab somehow
+    static const llama_vocab * vocab_cache;
+    static LlgTokenizer *      tokenizer_cache;
+
+    if (vocab_cache == vocab) {
+        return llg_clone_tokenizer(tokenizer_cache);
+    }
+
+    auto tok_eos = llama_vocab_eot(vocab);
+    if (tok_eos == LLAMA_TOKEN_NULL) {
+        tok_eos = llama_vocab_eos(vocab);
+    }
+
+    size_t vocab_size = llama_vocab_n_tokens(vocab);
+
+    auto token_lens       = new uint32_t[vocab_size];
+    // we typically have ~7 bytes per token; let's go on the safe side here
+    auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
+    auto token_bytes      = new uint8_t[token_bytes_size];
+
+    size_t offset = 0;
+    for (size_t i = 0; i < vocab_size; i++) {
+        size_t max_token = 1024;
+        if (token_bytes_size - offset < max_token) {
+            GGML_ABORT("token_bytes buffer too small\n");
+        }
+
+        llama_token token = i;
+        auto        dp    = (char *) token_bytes + offset;
+        auto        size  = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
+        if (size < 0) {
+            GGML_ABORT("llama_detokenize failed\n");
+        }
+        if (size == 0) {
+            size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
+            if (size < 0) {
+                GGML_ABORT("llama_detokenize failed\n");
+            }
+            if (size != 0) {
+                *dp = '\xff';  // special token prefix marker
+                size += 1;
+            }
+        }
+
+        token_lens[i] = size;
+        offset += size;
+    }
+
+    LlgTokenizerInit tinit = {
+        /* .vocab_size                         = */ (uint32_t) vocab_size,
+        /* .tok_eos                            = */ (uint32_t) tok_eos,
+        /* .token_lens                         = */ token_lens,
+        /* .token_bytes                        = */ token_bytes,
+        /* .tokenizer_json                     = */ nullptr,
+        /* .tokenize_assumes_string            = */ true,
+        /* .tokenize_fn                        = */ llama_sampler_llg_tokenize_fn,
+        /* .use_approximate_greedy_tokenize_fn = */ false,
+        /* .tokenize_user_data                 = */ vocab,
+    };
+
+    char           error_buffer[1024];
+    LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
+
+    delete[] token_bytes;
+    delete[] token_lens;
+
+    if (tokenizer == nullptr) {
+        LOG_ERR("llg tokenizer error: %s\n", error_buffer);
+        return tokenizer;
+    }
+
+    if (tokenizer_cache) {
+        llg_free_tokenizer(tokenizer_cache);
+    }
+    vocab_cache     = vocab;
+    tokenizer_cache = tokenizer;
+
+    return llg_clone_tokenizer(tokenizer_cache);
+}
+
+llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
+                                       const char * grammar_data) {
+    auto * ctx = new llama_sampler_llg;
+
+    if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
+        auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
+        *ctx           = {
+            /* .vocab        = */ vocab,
+            /* .grammar_kind = */ grammar_kind,
+            /* .grammar_data = */ grammar_data,
+            /* .tokenizer    = */ tokenizer,
+            /* .grammar      = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
+            /* .llg_res      = */ {},
+            /* .has_llg_res  = */ false,
+        };
+    } else {
+        *ctx = {
+            /* .vocab        = */ vocab,
+            /* .grammar_kind = */ {},
+            /* .grammar_data = */ {},
+            /* .tokenizer    = */ nullptr,
+            /* .grammar      = */ nullptr,
+            /* .llg_res      = */ {},
+            /* .has_llg_res  = */ false,
+        };
+    }
+
+    return new llama_sampler{
+        /* .iface = */ &llama_sampler_llg_i,
+        /* .ctx   = */ ctx,
+    };
+}
+
+#else
+
+llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
+    LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
+    return nullptr;
+}
+
+#endif  // LLAMA_USE_LLGUIDANCE
diff --git a/common/sampling.cpp b/common/sampling.cpp
index bc7e49fdb..e4b21ca10 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -156,13 +156,25 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     for (const auto & str : params.grammar_trigger_words) {
         trigger_words.push_back(str.word.c_str());
     }
+
+    struct llama_sampler * grmr;
+    if (params.grammar.compare(0, 11, "%llguidance") == 0) {
+#ifdef LLAMA_USE_LLGUIDANCE
+        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
+#else
+        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
+#endif // LLAMA_USE_LLGUIDANCE
+    } else {
+        grmr = params.grammar_lazy
+             ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
+                                               trigger_words.data(), trigger_words.size(),
+                                               params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
+             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
+    }
+
     auto * result = new common_sampler {
         /* .params = */ params,
-        /* .grmr   = */ params.grammar_lazy
-            ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
-                                              trigger_words.data(), trigger_words.size(),
-                                              params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
-            :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
+        /* .grmr   = */ grmr,
         /* .chain  = */ llama_sampler_chain_init(lparams),
         /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur    = */ {},
diff --git a/common/sampling.h b/common/sampling.h
index 348911b18..2064421db 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -102,3 +102,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
 
 std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
 std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
+
+llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
+                const char * grammar_kind, const char * grammar_data);
diff --git a/docs/llguidance.md b/docs/llguidance.md
new file mode 100644
index 000000000..792d20704
--- /dev/null
+++ b/docs/llguidance.md
@@ -0,0 +1,51 @@
+# LLGuidance Support in llama.cpp
+
+[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently.
+
+LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process.
+
+## Building
+
+To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option:
+
+```sh
+cmake -B build -DLLAMA_LLGUIDANCE=ON
+make -C build -j
+```
+
+This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install).
+
+## Interface
+
+There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance.
+
+For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format.
+
+## Performance
+
+Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md).
+
+## JSON Schema
+
+LLGuidance adheres closely to the JSON Schema specification. For example:
+
+- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed.
+- any whitespace is allowed.
+- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first).
+
+Unsupported schemas result in an error message—no keywords are silently ignored.
+
+## Why Not Reuse GBNF Format?
+
+GBNF lacks the concept of a lexer.
+
+Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes.
+LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest.
+
+However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase.
+The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically.
+See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details.
+
+## Error Handling
+
+Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future.
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 40f83ff0d..7a158d602 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -86,6 +86,9 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
 
+if (LLAMA_LLGUIDANCE)
+    llama_target_and_test(test-grammar-llguidance.cpp ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
+endif ()
 
 if (NOT WIN32)
     # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 288e08f51..890608648 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -129,7 +129,7 @@ static void test_grammar(const std::string & test_desc, const std::string & gram
     test(test_desc + ". Grammar: " + grammar_str, grammar_str, passing_strings, failing_strings);
 }
 static void test_schema(const std::string & test_desc, const std::string & schema_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
-    test(test_desc + ". Schema: " + schema_str, json_schema_to_grammar(json::parse(schema_str)), passing_strings, failing_strings);
+    test(test_desc + ". Schema: " + schema_str, json_schema_to_grammar(json::parse(schema_str), true), passing_strings, failing_strings);
 }
 
 static void test_simple_grammar() {
diff --git a/tests/test-grammar-llguidance.cpp b/tests/test-grammar-llguidance.cpp
new file mode 100644
index 000000000..8b696006b
--- /dev/null
+++ b/tests/test-grammar-llguidance.cpp
@@ -0,0 +1,1140 @@
+#ifdef NDEBUG
+#    undef NDEBUG
+#endif
+
+#include "unicode.h"
+#include "sampling.h"
+
+#include <cassert>
+#include <string>
+#include <vector>
+
+static const llama_vocab * vocab;
+
+static bool match_string(const std::string & input, llama_sampler * grammar) {
+    llama_sampler_reset(grammar);
+    auto tokens = common_tokenize(vocab, input, false, false);
+
+    auto n_vocab = llama_vocab_n_tokens(vocab);
+
+    std::vector<llama_token_data> cur;
+    cur.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token) n_vocab; token_id++) {
+        cur.emplace_back(llama_token_data{ token_id, 0.0f, 0.0f });
+    }
+    auto tok_arr = llama_token_data_array{ cur.data(), cur.size(), -1, false };
+
+    for (const auto token : tokens) {
+        for (llama_token token_id = 0; token_id < (llama_token) n_vocab; token_id++) {
+            cur[token_id].logit = 0.0f;
+        }
+        llama_sampler_apply(grammar, &tok_arr);
+        if (cur[token].logit < 0.0f) {
+            return false;
+        }
+        llama_sampler_accept(grammar, token);
+    }
+
+    // do we allow EOS at the end? if so the grammar is accepting
+
+    auto tok_eos = llama_vocab_eot(vocab);
+    if (tok_eos == LLAMA_TOKEN_NULL) {
+        tok_eos = llama_vocab_eos(vocab);
+    }
+
+    cur[tok_eos].logit = 0.0f;
+    llama_sampler_apply(grammar, &tok_arr);
+
+    return cur[tok_eos].logit >= 0.0f;
+}
+
+static void test(const std::string & test_desc, const std::string & grammar_str,
+                 const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
+    fprintf(stderr, "⚫ Testing %s\n%s\n", test_desc.c_str(), grammar_str.c_str());
+    fflush(stderr);
+
+    auto * grammar = llama_sampler_init_llg(vocab, "lark", grammar_str.c_str());
+
+    fprintf(stderr, "  🔵 Valid strings:\n");
+
+    // Passing strings
+    for (const auto & test_string : passing_strings) {
+        fprintf(stderr, "    \"%s\" ", test_string.c_str());
+        fflush(stderr);
+
+        bool matched = match_string(test_string, grammar);
+
+        if (!matched) {
+            fprintf(stderr, "❌ (failed to match)\n");
+
+            // DEBUG: Write strings to files so that we can analyze more easily with gbnf-validator program to see exactly where things failed.
+            // DEBUG: Write the grammar_str to test-grammar-integration.grammar.gbnf
+            FILE * grammar_file = fopen("test-grammar-integration.grammar.gbnf", "w");
+            if (grammar_file) {
+                fprintf(grammar_file, "%s", grammar_str.c_str());
+                fclose(grammar_file);
+            }
+
+            // DEBUG: Write the test string to test-grammar-integration.string.txt
+            FILE * string_file = fopen("test-grammar-integration.string.txt", "w");
+            if (string_file) {
+                fprintf(string_file, "%s", test_string.c_str());
+                fclose(string_file);
+            }
+
+            fprintf(stderr,
+                    "\n NOTE: Debug grammar file generated. To analyze this failure in detail, run the following "
+                    "command:     ./llama-gbnf-validator test-grammar-integration.grammar.gbnf "
+                    "test-grammar-integration.string.txt\n\n");
+        } else {
+            fprintf(stdout, "✅︎\n");
+        }
+
+        assert(matched);
+    }
+
+    fprintf(stderr, "  🟠 Invalid strings:\n");
+
+    // Failing strings
+    for (const auto & test_string : failing_strings) {
+        fprintf(stderr, "    \"%s\" ", test_string.c_str());
+        fflush(stderr);
+
+        bool matched = match_string(test_string, grammar);
+
+        if (matched) {
+            fprintf(stderr, "❌ (incorrectly matched)\n");
+        } else {
+            fprintf(stdout, "✅︎\n");
+        }
+        assert(!matched);
+    }
+
+    llama_sampler_free(grammar);
+}
+
+static void test_grammar(const std::string & test_desc, const std::string & grammar_str,
+                         const std::vector<std::string> & passing_strings,
+                         const std::vector<std::string> & failing_strings) {
+    test(test_desc + ". Grammar: " + grammar_str, grammar_str, passing_strings, failing_strings);
+}
+
+static void test_schema(const std::string & test_desc, const std::string & schema_str,
+                        const std::vector<std::string> & passing_strings,
+                        const std::vector<std::string> & failing_strings) {
+    test(test_desc + ". Schema: " + schema_str, "%llguidance {}\nstart: %json " + schema_str, passing_strings,
+         failing_strings);
+}
+
+static void test_simple_grammar() {
+    test_schema("min 0",
+                R"""({
+            "type": "integer",
+            "minimum": 0
+        })""",
+                // Passing strings
+                {
+                    "0",
+                    "10",
+                    "12",
+                    "10000",
+                },
+                // Failing strings
+                {
+                    "-1",
+                    "-10",
+                    "-10000",
+                    "-100000000000000000000000000000000",
+                    // "100000000000000000000000000000000",
+                    "00",
+                    "01",
+                    "-0",
+                });
+    test_schema("min 2",
+                // Schema
+                R"""({
+            "type": "integer",
+            "minimum": 2
+        })""",
+                // Passing strings
+                {
+                    "2",
+                    "3",
+                    "4",
+                    "10",
+                    "20",
+                    "1234567890000000",
+                },
+                // Failing strings
+                {
+                    "0", "1", "-1", "-100", "0", "1", "01", "02",
+                    // "12345678900000000",
+                });
+    test_schema("min 456",
+                R"""({
+            "type": "integer",
+            "minimum": 456
+        })""",
+                // Passing strings
+                {
+                    "456",
+                    "4560",
+                    "457",
+                    "460",
+                    "500",
+                },
+                // Failing strings
+                {
+                    "455",
+                    "356",
+                    "50",
+                    "050",
+                    "-1",
+                    "-456",
+                });
+    test_schema("min -123",
+                R"""({
+            "type": "integer",
+            "minimum": -123
+        })""",
+                // Passing strings
+                {
+                    "-123",
+                    "-122",
+                    "-11",
+                    "-1",
+                    "0",
+                    "1",
+                    "123",
+                    "1234",
+                    "2345",
+                },
+                // Failing strings
+                {
+                    "-1234",
+                    "-124",
+                });
+
+    test_schema("max 9999",
+                // Schema
+                R"""({
+            "type": "integer",
+            "maximum": 9999
+        })""",
+                // Passing strings
+                {
+                    "-99999",
+                    "0",
+                    "9999",
+                },
+                // Failing strings
+                {
+                    "10000",
+                    "99991",
+                });
+    test_schema("max -9999",
+                // Schema
+                R"""({
+            "type": "integer",
+            "maximum": -9999
+        })""",
+                // Passing strings
+                {
+                    "-10000",
+                    "-9999",
+                },
+                // Failing strings
+                {
+                    "-9998",
+                    "0",
+                    "9999",
+                });
+    test_schema("min 5 max 30",
+                // Schema
+                R"""({
+            "type": "integer",
+            "minimum": 5,
+            "maximum": 30
+        })""",
+                // Passing strings
+                {
+                    "5",
+                    "10",
+                    "30",
+                },
+                // Failing strings
+                {
+                    "05",
+                    "4",
+                    "-1",
+                    "31",
+                    "123",
+                    "0123",
+                });
+    test_schema("min -1 max 1",
+                R"""({
+            "type": "integer",
+            "minimum": -1,
+            "maximum": 1
+        })""",
+                // Passing strings
+                {
+                    "-1",
+                    "0",
+                    "1",
+                },
+                // Failing strings
+                {
+                    "-11",
+                    "-10",
+                    "-2",
+                    "2",
+                    "10",
+                    "11",
+                });
+    test_schema("min -123 max 42",
+                R"""({
+            "type": "integer",
+            "minimum": -123,
+            "maximum": 42
+        })""",
+                // Passing strings
+                {
+                    "-123",
+                    "-122",
+                    "-13",
+                    "-11",
+                    "-2",
+                    "-1",
+                    "0",
+                    "1",
+                    "5",
+                    "10",
+                    "39",
+                    "40",
+                    "42",
+                },
+                // Failing strings
+                {
+                    "-0123",
+                    "-124",
+                    "-1123",
+                    "-200",
+                    "43",
+                    "123",
+                    "0123",
+                });
+    test_schema("exclusive min / max",
+                // Schema
+                R"""({
+            "type": "integer",
+            "exclusiveMinimum": 0,
+            "exclusiveMaximum": 10000
+        })""",
+                // Passing strings
+                {
+                    "1",
+                    "9999",
+                },
+                // Failing strings
+                {
+                    "0",
+                    "01",
+                    "10000",
+                    "99999",
+                });
+
+    // Test case for a simple grammar
+    test_grammar("simple grammar",
+                 R"""(
+            start: expr
+            expr: term ("+" term)*
+            term: number
+            number: /[0-9]+/ )""",
+                 // Passing strings
+                 {
+                     "42",
+                     "1+2+3+4+5",
+                     "123+456",
+                 },
+                 // Failing strings
+                 {
+                     "+",
+                     "/ 3",
+                     "1+2+3+4+5+",
+                     "12a45",
+                 });
+}
+
+static void test_complex_grammar() {
+    // Test case for a more complex grammar, with both failure strings and success strings
+    test_grammar("medium complexity grammar",
+                 // Grammar
+                 R"""(
+            start: expression
+            expression: term ws (("+"|"-") ws term)*
+            term: factor ws (("*"|"/") ws factor)*
+            factor: number | variable | "(" expression ")" | function-call
+            number: /[0-9]+/
+            variable: /[a-zA-Z_][a-zA-Z0-9_]*/
+            function-call: variable ws "(" (expression ("," ws expression)*)? ")"
+            ws: /[ \t\n\r]?/ )""",
+                 // Passing strings
+                 { "42",
+                   "1*2*3*4*5",
+                   "x",
+                   "x+10",
+                   "x1+y2",
+                   "(a+b)*(c-d)",
+                   "func()",
+                   "func(x,y+2)",
+                   "a*(b+c)-d/e",
+                   "f(g(x),h(y,z))",
+                   "x + 10",
+                   "x1 + y2",
+                   "(a + b) * (c - d)",
+                   "func()",
+                   "func(x, y + 2)",
+                   "a * (b + c) - d / e",
+                   "f(g(x), h(y, z))",
+                   "123+456",
+                   "123*456*789-123/456+789*123",
+                   "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456" },
+                 // Failing strings
+                 {
+                     "+",
+                     "/ 3x",
+                     "x + + y",
+                     "a * / b",
+                     "func(,)",
+                     "func(x y)",
+                     "(a + b",
+                     "x + y)",
+                     "a + b * (c - d",
+                     "42 +",
+                     "x +",
+                     "x + 10 +",
+                     "(a + b) * (c - d",
+                     "func(",
+                     "func(x, y + 2",
+                     "a * (b + c) - d /",
+                     "f(g(x), h(y, z)",
+                     "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/",
+                 });
+}
+
+static void test_special_chars() {
+    // A collection of tests to exercise special characters such as "."
+    test_grammar("special characters",
+                 // Grammar
+                 R"""(
+            start: /.../ "abc" /.../
+            )""",
+                 // Passing strings
+                 { "abcabcabc", "aaaabcccc",
+                   // NOTE: Also ensures that multi-byte characters still count as a single character
+                   "🔵🟠✅abc❌🟠🔵" },
+                 // Failing strings
+                 { "aaabcccc", "aaaaabcccc", "aaaabccc", "aaaabccccc", "🔵🟠✅❌abc❌✅🟠🔵", "🔵🟠abc🟠🔵" });
+}
+
+static void test_quantifiers() {
+    // A collection of tests to exercise * + and ? quantifiers
+
+    test_grammar(
+        "* quantifier",
+        // Grammar
+        R"""(start: "a"*)""",
+        // Passing strings
+        { "", "a", "aaaaa", "aaaaaaaaaaaaaaaaaa", "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" },
+        // Failing strings
+        { "b", "ab", "aab", "ba", "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" });
+    test_grammar(
+        "+ quantifier",
+        // Grammar
+        R"""(start: "a"+)""",
+        // Passing strings
+        { "a", "aaaaa", "aaaaaaaaaaaaaaaaaa", "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" },
+        // Failing strings
+        { "", "b", "ab", "aab", "ba", "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" });
+    test_grammar("? quantifier",
+                 // Grammar
+                 R"""(start: "a"?)""",
+                 // Passing strings
+                 { "", "a" },
+                 // Failing strings
+                 {
+                     "b",
+                     "ab",
+                     "aa",
+                     "ba",
+                 });
+    test_grammar("mixed quantifiers",
+                 // Grammar
+                 R"""(
+            start: cons+ vowel* cons? (vowel cons)*
+            vowel: /[aeiouy]/
+            cons: /[bcdfghjklmnpqrstvwxyz]/
+            )""",
+                 // Passing strings
+                 {
+                     "yes",
+                     "no",
+                     "noyes",
+                     "crwth",
+                     "four",
+                     "bryyyy",
+                 },
+                 // Failing strings
+                 {
+                     "yess",
+                     "yesno",
+                     "forty",
+                     "catyyy",
+                 });
+    test_grammar("simple exact repetition",
+                 // Grammar
+                 R"""(
+            start: /[ab]{4}/
+        )""",
+                 // Passing strings
+                 {
+                     "aaaa",
+                     "bbbb",
+                     "abab",
+                 },
+                 // Failing strings
+                 {
+                     "a",
+                     "b",
+                     "aaaaa",
+                 });
+    test_grammar("simple min repetition",
+                 // Grammar
+                 R"""(
+            start: /[ab]{4,}/
+        )""",
+                 // Passing strings
+                 {
+                     "aaaa",
+                     "aaaaab",
+                     "bbbb",
+                     "ababab",
+                 },
+                 // Failing strings
+                 {
+                     "",
+                     "aba",
+                 });
+    test_grammar("simple max repetition",
+                 // Grammar
+                 R"""(
+            start: /[ab]{0,4}/
+        )""",
+                 // Passing strings
+                 {
+                     "",
+                     "a",
+                     "aa",
+                     "aaa",
+                     "aaab",
+                 },
+                 // Failing strings
+                 {
+                     "aaaaa",
+                 });
+    // test_grammar("min / max repetition",
+    //              // Grammar
+    //              R"""(
+    //         start: ("0x" /[A-F0-9]{2}/ " "?){3,5}
+    //     )""",
+    //              // Passing strings
+    //              {
+    //                  "0xFF 0x12 0xAB",
+    //                  "0xFF 0x12 0xAB 0x00 0x00",
+    //              },
+    //              // Failing strings
+    //              {
+    //                  "",
+    //                  "0xFF",
+    //                  "0xFF 0x12",
+    //                  "0xFF 0x12 0xAB 0x00 0x00 0x00",
+    //              });
+}
+
+static void test_json_schema() {
+    // Note that this is similar to the regular grammar tests,
+    //  but we convert each json schema to a grammar before parsing.
+    // Otherwise, this test structure is the same.
+
+    test_schema("empty schema (object)",
+                // Schema
+                R"""(
+            {"type":"object"}
+        )""",
+                // Passing strings
+                {
+                    R"""({})""",
+                    R"""({"foo": "bar"})""",
+                },
+                // Failing strings
+                {
+                    "",
+                    "[]",
+                    "null",
+                    R"""("")""",
+                    "true",
+                });
+
+    test_schema(
+        "exotic formats (list)",
+        // Schema
+        R"""({
+            "items": [
+                { "format": "date" },
+                { "format": "uuid" },
+                { "format": "time" },
+                { "format": "date-time" }
+            ]
+        })""",
+        // Passing strings
+        {
+            // "{}", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            // "[]", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            R"""(["2012-04-23", "12345678-1234-1234-1234-1234567890ab", "18:25:43.511Z", "2012-04-23T18:25:43.511Z"])""",
+            //R"""(["2012-04-23","12345678-1234-1234-1234-1234567890ab"])""", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            //R"""({"foo": "bar"})""", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+        },
+        // Failing strings
+        {
+            R"""(["foo", "bar"])""",
+            R"""(["12345678-1234-1234-1234-1234567890ab"])""",
+        });
+
+    test_schema("string",
+                // Schema
+                R"""({
+            "type": "string"
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                    R"""("bar")""",
+                    R"""("")""",
+                },
+                // Failing strings
+                {
+                    R"""({})""",
+                    R"""("foo": "bar")""",
+                });
+
+    test_schema("string w/ min length 1",
+                // Schema
+                R"""({
+            "type": "string",
+            "minLength": 1
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                    R"""("bar")""",
+                },
+                // Failing strings
+                {
+                    R"""("")""",
+                    R"""({})""",
+                    R"""("foo": "bar")""",
+                });
+
+    test_schema("string w/ min length 3",
+                // Schema
+                R"""({
+                "type": "string",
+                "minLength": 3
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                    R"""("bar")""",
+                    R"""("foobar")""",
+                },
+                // Failing strings
+                {
+                    R"""("")""",
+                    R"""("f")""",
+                    R"""("fo")""",
+                });
+
+    test_schema("string w/ max length",
+                // Schema
+                R"""({
+            "type": "string",
+            "maxLength": 3
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                    R"""("bar")""",
+                    R"""("")""",
+                    R"""("f")""",
+                    R"""("fo")""",
+                },
+                // Failing strings
+                {
+                    R"""("foobar")""",
+                });
+
+    test_schema("string w/ min & max length",
+                // Schema
+                R"""({
+            "type": "string",
+            "minLength": 1,
+            "maxLength": 4
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                    R"""("bar")""",
+                    R"""("f")""",
+                    R"""("barf")""",
+                },
+                // Failing strings
+                {
+                    R"""("")""",
+                    R"""("barfo")""",
+                    R"""("foobar")""",
+                });
+
+    test_schema("boolean",
+                // Schema
+                R"""({
+            "type": "boolean"
+        })""",
+                // Passing strings
+                {
+                    "true",
+                    "false",
+                },
+                // Failing strings
+                {
+                    R"""("")""",
+                    R"""("true")""",
+                    R"""(True)""",
+                    R"""(FALSE)""",
+                });
+
+    test_schema("integer",
+                // Schema
+                R"""({
+            "type": "integer"
+        })""",
+                // Passing strings
+                {
+                    R"""(0)""",
+                    R"""(12345)""",
+                    R"""(1234567890123456)""",
+                },
+                // Failing strings
+                {
+                    R"""()""",
+                    R"""(01)""",
+                    R"""(007)""",
+                    R"""(12345678901234567  )""",
+                });
+
+    test_schema("string const",
+                // Schema
+                R"""({
+            "const": "foo"
+        })""",
+                // Passing strings
+                {
+                    R"""("foo")""",
+                },
+                // Failing strings
+                {
+                    R"""(foo)""",
+                    R"""("bar")""",
+                });
+
+    test_schema("non-string const",
+                // Schema
+                R"""({
+            "const": true
+        })""",
+                // Passing strings
+                {
+                    R"""(true)""",
+                },
+                // Failing strings
+                {
+                    R"""()""",
+                    R"""(foo)""",
+                    R"""("true")""",
+                });
+
+    test_schema("non-string const",
+                // Schema
+                R"""({
+            "enum": ["red", "amber", "green", null, 42, ["foo"]]
+        })""",
+                // Passing strings
+                {
+                    R"""("red")""",
+                    R"""(null)""",
+                    R"""(42)""",
+                    R"""(["foo"])""",
+                },
+                // Failing strings
+                {
+                    R"""()""",
+                    R"""(420)""",
+                    R"""(true)""",
+                    R"""(foo)""",
+                });
+
+    test_schema("simple pattern",
+                // Schema
+                R"""({
+            "pattern": "^[a-zA-Z0-9_-]*$"
+        })""",
+                // Passing strings
+                {
+                    R"""("")""",
+                    R"""("He_llo-12")""",
+                },
+                // Failing strings
+                {
+                    R"""("!")""",
+                    R"""("Hello World")""",
+                });
+
+    test_schema("pattern with escapes",
+                // Schema
+                R"""({
+            "pattern": "^a\\^\\$\\.\\[\\]\\(\\)\\|\\{\\}\\*\\+\\?b$"
+        })""",
+                // Passing strings
+                {
+                    R"""("a^$.[]()|{}*+?b")""",
+                },
+                // Failing strings
+                {
+                    R"""("ab")""",
+                });
+
+    test_schema("",
+                // Schema
+                R"""(
+            {
+                "type": ["array", "null"],
+                "items": { "type": "string" }
+            }
+        )""",
+                // Passing strings
+                {
+                    "null",
+                    "[]",
+                    "[\"123\"]",
+                    "[\"foo\", \"bar\"]",
+                },
+                // Failing strings
+                {
+                    "",
+                    "[123]",
+                    "\"foo\"",
+                    "[\"foo\", 42]",
+                });
+
+    test_schema("min+max items",
+                // Schema
+                R"""({
+            "items": {
+                "type": ["number", "integer"]
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+                // Passing strings
+                {
+                    R"""([1, 2, 3])""",
+                    R"""([1, 2, 3, 4])""",
+                    R"""([1, 2, 3, 4, 5])""",
+                    // this is in fact correct; keyword do not apply if the type is wrong
+                    R"""(1)""",
+                },
+                // Failing strings
+                {
+                    R"""([1, 2])""",
+                    R"""([1, 2, 3, 4, 5, 6])""",
+                });
+
+    // Properties (from: https://json-schema.org/understanding-json-schema/reference/object#properties)
+    test_schema("object properties",
+                // Schema
+                R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            },
+            "additionalProperties": false
+        })""",
+                // Passing strings
+                {
+                    R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+                    // "By default, leaving out properties is valid"
+                    R"""({ "street_name": "Pennsylvania" })""",
+                    R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+                    // "By extension, even an empty object is valid"
+                    R"""({})""",
+                    R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+                },
+                // Failing strings
+                {
+                    // Change datatype from number to string
+                    R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+                    // Reorder properties
+                    R"""({ "street_name": "Pennsylvania", "number": 1600 })""",
+                    // Reorder properties
+                    R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+                    // Additional properties set to false
+                    R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
+
+                });
+
+    test_schema("additional properties can't override other properties",
+                R"""({
+            "properties": {
+                "a": {"type": "integer"},
+                "b": {"type": "integer"}
+            },
+            "additionalProperties": true
+        })""",
+                // Passing strings
+                {
+                    R"""({"a": 42})""",
+                    R"""({"c": ""})""",
+                    R"""({"a": 42, "c": ""})""",
+                    R"""({"a_": ""})""",
+                },
+                // Failing strings
+                {
+                    R"""()""",
+                    R"""({"a": ""})""",
+                    R"""({"a": "", "b": ""})""",
+                });
+
+    // Properties (from: https://json-schema.org/understanding-json-schema/reference/object#properties)
+    test_schema("object properties, additionalProperties: true",
+                // Schema
+                R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            },
+            "additionalProperties": true
+        })""",
+                // Passing strings
+                {
+                    // "By extension, even an empty object is valid"
+                    R"""({})""",
+                    R"""({"number":1600,"street_name":"Pennsylvania","street_type":"Avenue"})""",
+                    // "By default, leaving out properties is valid"
+                    R"""({ "street_name": "Pennsylvania" })""",
+                    R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+                    // "By default, providing additional properties is valid"
+                    R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
+                    R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+                },
+                // Failing strings
+                {
+                    // Change datatype from number to string
+                    R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+                    // Reorder properties
+                    R"""({ "street_name": "Pennsylvania", "number": 1600, "street_type":"Avenue"})""",
+                });
+
+    // Additional properties: false
+    test_schema(
+        "required + optional props each in original order",
+        // Schema
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            },
+            "additionalProperties": false
+        })""",
+        // Passing strings
+        {
+            R"""({ "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_type":"Avenue"})""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // Spaces are permitted around enum values
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+        },
+        // Failing strings
+        {
+            // Reorder properties
+            R"""({ "street_type": "Avenue", "number": 1600 })""",
+            // Add "direction"
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue", "direction": "NW" })""",
+        });
+
+    test_schema("required + optional props each in original order",
+                // Schema
+                R"""({
+            "properties": {
+                "b": {"type": "string"},
+                "a": {"type": "string"},
+                "d": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "required": ["a", "b"],
+            "additionalProperties": false
+        })""",
+                // Passing strings
+                {
+                    R"""({"b": "foo", "a": "bar"})""",
+                    R"""({"b":"foo","a":"bar","d":"qux"})""",
+                    R"""({"b":"foo", "a":"bar", "d":"qux", "c":"baz"})""",
+                },
+                // Failing strings
+                {
+                    R"""({"a": "foo", "b": "bar"})""",
+                    R"""({"b": "bar"})""",
+                    R"""({"a": "foo", "c": "baz"})""",
+                    R"""({"a":"foo", "b":"bar", "c":"baz", "d":"qux"})""",
+                });
+
+    // NOTE: Example from https://json-schema.org/learn/getting-started-step-by-step#define-required-properties
+    test_schema(
+        "required props",
+        // Schema
+        R"""({
+            "$schema": "https://json-schema.org/draft/2020-12/schema",
+            "$id": "https://example.com/product.schema.json",
+            "title": "Product",
+            "description": "A product from Acme's catalog",
+            "type": "object",
+            "properties": {
+                "productId": {
+                "description": "The unique identifier for a product",
+                "type": "integer"
+                },
+                "productName": {
+                "description": "Name of the product",
+                "type": "string"
+                },
+                "price": {
+                "description": "The price of the product",
+                "type": "number",
+                "exclusiveMinimum": 0
+                },
+                "tags": {
+                "description": "Tags for the product",
+                "type": "array",
+                "items": {
+                    "type": "string"
+                },
+                "minItems": 1,
+                "DISABLED_uniqueItems": true
+                },
+                "dimensions": {
+                "type": "object",
+                "properties": {
+                    "length": {
+                    "type": "number"
+                    },
+                    "width": {
+                    "type": "number"
+                    },
+                    "height": {
+                    "type": "number"
+                    }
+                },
+                "required": [ "length", "width", "height" ]
+                }
+            },
+            "required": [ "productId", "productName", "price" ]
+        })""",
+        // Passing strings
+        {
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50})""",
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green"]})""",
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green"], "dimensions": {"length": 785, "width": 250.5, "height": -0.359}})""",
+        },
+        // Failing strings
+        {
+            R"""({})""",  // Missing all required properties
+            R"""({"productName": "A green door", "price": 12.50, "productId": 1})""",  // Out of order properties
+            // `exclusiveMinimum` is OK for llg
+            R"""({"productId": 1, "productName": "A green door", "price": -12.50})""",
+            R"""({"productId": 1, "productName": "A green door"})""",  // Missing required property (price)
+            R"""({"productName": "A green door", "price": 12.50})""",  // Missing required property (productId)
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": []})""",  // tags is empty, but minItems is 1
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "dimensions": {"length": 785, "width": 250.5, "height": -0.359}, "tags": ["home", "green"]})""",  // Tags and dimensions are out of order
+            // TODO: The following line should fail, but currently it passes. `uniqueItems` is not supported, as it would likely be too difficult to implement.
+            // R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green", "home"]})""",
+        });
+}
+
+int main(int argc, const char ** argv) {
+    fprintf(stdout, "Running llguidance integration tests...\n");
+
+    if (argc != 2) {
+        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
+        return 1;
+    }
+
+    const char * vocab_file = argv[1];
+
+    fprintf(stderr, "reading vocab from: '%s'\n", vocab_file);
+
+    llama_model *   model;
+    llama_context * ctx;
+
+    llama_backend_init();
+
+    // load the vocab
+    {
+        auto mparams = llama_model_default_params();
+
+        mparams.vocab_only = true;
+
+        model = llama_model_load_from_file(vocab_file, mparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, vocab_file);
+            return 1;
+        }
+
+        // needed?
+        auto cparams = llama_context_default_params();
+
+        ctx = llama_init_from_model(model, cparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, vocab_file);
+            llama_model_free(model);
+            return 1;
+        }
+    }
+
+    vocab = llama_model_get_vocab(model);
+
+    test_simple_grammar();
+    test_complex_grammar();
+    test_special_chars();
+    test_quantifiers();
+    test_json_schema();
+    fprintf(stdout, "All tests passed.\n");
+    return 0;
+}
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index 9d2db91f5..f38994c92 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -1246,7 +1246,7 @@ int main() {
 
     test_all("C++", [](const TestCase & tc) {
         try {
-            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema)));
+            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema), true));
             tc.verify_status(SUCCESS);
         } catch (const std::runtime_error & ex) {
             fprintf(stderr, "Error: %s\n", ex.what());

From 69804487e0b10f2c5c06316f0ac0eb6ada68433f Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sun, 2 Feb 2025 09:10:15 +0000
Subject: [PATCH 309/376] Fix exotic ci env that lacks ostringstream::str
 (#11581)

---
 common/minja.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/minja.hpp b/common/minja.hpp
index bcb5a0824..e77eb69d5 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -824,7 +824,7 @@ public:
     LoopControlType control_type;
     LoopControlException(const std::string & message, LoopControlType control_type) : std::runtime_error(message), control_type(control_type) {}
     LoopControlException(LoopControlType control_type)
-      : std::runtime_error((std::ostringstream() << (control_type == LoopControlType::Continue ? "continue" : "break") << " outside of a loop").str()),
+      : std::runtime_error((control_type == LoopControlType::Continue ? "continue" : "break") + std::string(" outside of a loop")),
         control_type(control_type) {}
 };
 

From bfcce4d693617ec843d0b2510f6ee16e6bc6720d Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sun, 2 Feb 2025 09:25:38 +0000
Subject: [PATCH 310/376] `tool-call`: support Command R7B (+ return tool_plan
 "thoughts" in API) (#11585)

* `tool-call`: support Command R7B (w/ tool_plan return)

* `tool-call`: cleaner preservation of tokens + warn when likely bad chat template override

* `tool-call`: test cleanup / handle lazy grammar triggers
---
 common/chat.cpp                               |  86 +++++++++-
 common/chat.hpp                               |   2 +
 common/common.h                               |   3 +
 examples/server/README.md                     |  22 ++-
 examples/server/server.cpp                    |  52 ++++--
 examples/server/utils.hpp                     |   1 +
 ...AI-c4ai-command-r7b-12-2024-tool_use.jinja | 156 ++++++++++++++++++
 tests/test-chat.cpp                           | 154 +++++++++++++----
 8 files changed, 420 insertions(+), 56 deletions(-)
 create mode 100644 models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index 58db12af9..f87583d85 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -16,6 +16,7 @@ std::string common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
+        case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -317,6 +318,79 @@ static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input)
     return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
 }
 
+static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
+    common_chat_params data;
+    data.grammar_lazy = inputs.tool_choice != "required";
+    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+        auto schemas = json::array();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool["function"];
+            schemas.push_back({
+                {"type", "object"},
+                {"properties", {
+                    {"tool_call_id", {
+                        {"type", "string"},
+                        // Command-R's template expects an integer string.
+                        {"pattern", "^[0-9]{1,10}$"},
+                    }},
+                    {"tool_name", {
+                        {"type", "string"},
+                        {"const", function["name"]},
+                    }},
+                    {"parameters", function["parameters"]},
+                }},
+                {"required", json::array({"tool_call_id", "tool_name", "parameters"})},
+            });
+        });
+        auto schema = json {
+            {"type", "array"},
+            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
+            {"minItems", 1},
+        };
+        if (!inputs.parallel_tool_calls) {
+            schema["maxItems"] = 1;
+        }
+        builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\"");
+    }, grammar_options);
+    data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false});
+    data.preserved_tokens = {
+        "<|START_RESPONSE|>",
+        "<|END_RESPONSE|>",
+        "<|START_THINKING|>",
+        "<|END_THINKING|>",
+        "<|END_ACTION|>",
+    };
+    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
+    return data;
+}
+static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
+    static std::regex response_regex("<\\|START_RESPONSE\\|>(.*?)<\\|END_RESPONSE\\|>");
+    static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
+    std::smatch match;
+
+    common_chat_msg result;
+    result.role = "assistant";
+    if (std::regex_match(input, match, response_regex)) {
+        result.content = match[1].str();
+    } else if (std::regex_match(input, match, thought_action_regex)) {
+        result.tool_plan = match[1].str();
+        auto actions_str = match[2].str();
+        auto actions = json::parse(actions_str);
+        for (const auto & action : actions) {
+            result.tool_calls.push_back({
+                /* .name = */      action["tool_name"],
+                /* .arguments = */ action["parameters"].dump(),
+                /* .id = */        action["tool_call_id"],
+            });
+        }
+    } else {
+        LOG_ERR("Failed to parse command_r output");
+        result.content = input;
+    }
+    return result;
+}
+
 static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
     if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
         throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
@@ -462,6 +536,10 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
                 "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
         });
         data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
+        data.preserved_tokens = {
+            "<｜tool▁sep｜>",
+            "<｜tool▁call▁end｜>",
+        };
         builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
     }, grammar_options);
     data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
@@ -704,8 +782,7 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
         auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
         builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
         data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
-        // Not really a trigger but need to print this special token to get a successful parse.
-        data.grammar_triggers.push_back({"</tool_call>", /* .at_start = */ false});
+        data.preserved_tokens = { "</tool_call>" };
     }, grammar_options);
 
     data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
@@ -822,6 +899,9 @@ common_chat_params common_chat_params_init(const common_chat_template & tmpl, co
     if (src.find("[TOOL_CALLS]") != std::string::npos) {
         return common_chat_params_init_mistral_nemo(tmpl, inputs);
     }
+    if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) {
+        return common_chat_params_init_command_r7b(tmpl, inputs);
+    }
     return common_chat_params_init_generic(tmpl, inputs);
 }
 
@@ -855,6 +935,8 @@ common_chat_msg common_chat_parse(const std::string & input, common_chat_format
             return common_chat_parse_hermes_2_pro(input);
         case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
             return common_chat_parse_firefunction_v2(input);
+        case COMMON_CHAT_FORMAT_COMMAND_R7B:
+            return common_chat_parse_command_r7b(input);
         default:
             throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
     }
diff --git a/common/chat.hpp b/common/chat.hpp
index ca165aa13..33e64a430 100644
--- a/common/chat.hpp
+++ b/common/chat.hpp
@@ -32,6 +32,7 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
+    COMMON_CHAT_FORMAT_COMMAND_R7B,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -42,6 +43,7 @@ struct common_chat_params {
     std::string                         grammar;
     bool                                grammar_lazy = false;
     std::vector<common_grammar_trigger> grammar_triggers;
+    std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
 };
 
diff --git a/common/common.h b/common/common.h
index 6c1809277..b208d0c7e 100644
--- a/common/common.h
+++ b/common/common.h
@@ -4,6 +4,7 @@
 
 #include "llama-cpp.h"
 
+#include <set>
 #include <string>
 #include <vector>
 #include <sstream>
@@ -163,6 +164,7 @@ struct common_params_sampling {
     bool                                grammar_lazy = false;
     std::vector<common_grammar_trigger> grammar_trigger_words;  // optional trigger words to trigger lazy grammar
     std::vector<llama_token>            grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
+    std::set<llama_token>               preserved_tokens;
 
     std::vector<llama_logit_bias> logit_bias; // logit biases to apply
 
@@ -621,6 +623,7 @@ struct common_chat_msg {
     std::string role;
     std::string content;
     std::vector<common_tool_call> tool_calls;
+    std::string tool_plan = "";
 };
 
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
diff --git a/examples/server/README.md b/examples/server/README.md
index 276b43013..e9d0374ad 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -1128,6 +1128,7 @@ curl http://localhost:8080/v1/chat/completions \
   - Hermes 2/3, Qwen 2.5
   - Mistral Nemo
   - Firefunction v2
+  - Command R7B
   - DeepSeek R1 (WIP / seems reluctant to call any tools?)
 
   <details>
@@ -1202,21 +1203,28 @@ curl http://localhost:8080/v1/chat/completions \
   ```shell
   # Native support:
   llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
-  llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
-  llama-server --jinja -fa -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q6_K
+  llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L
   llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
-  llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \
-    --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B )
+  llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
 
   # Native support requires the right template for these GGUFs:
+
+  llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \
+    --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use )
+
   llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \
     --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use )
+
   llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \
-    --chat-template-file <( python scripts/get_chat_template.py fireworks-ai/firellama-3-firefunction-v2 )
+    --chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use )
+
+  llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \
+    --chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use )
 
   # Generic format support
-  llama-server --jinja -fa -hf bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M
-  llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q4_K_M
+  llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0
+  llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0
+  llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K
   ```
 
 - Test in CLI:
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 3451e96a2..e0acc4705 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -131,6 +131,11 @@ struct slot_params {
             lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
         }
 
+        std::vector<std::string> grammar_trigger_words;
+        for (const auto & trigger : sampling.grammar_trigger_words) {
+            grammar_trigger_words.push_back(trigger.word);
+        }
+
         return json {
             {"n_predict",                 n_predict},     // Server configured n_predict
             {"seed",                      sampling.seed},
@@ -165,8 +170,9 @@ struct slot_params {
             {"n_probs",                   sampling.n_probs},
             {"min_keep",                  sampling.min_keep},
             {"grammar",                   sampling.grammar},
-            // {"grammar_trigger_words",     sampling.grammar_trigger_words},
+            {"grammar_trigger_words",     grammar_trigger_words},
             {"grammar_trigger_tokens",    sampling.grammar_trigger_tokens},
+            {"preserved_tokens",          sampling.preserved_tokens},
             {"samplers",                  samplers},
             {"speculative.n_max",         speculative.n_max},
             {"speculative.n_min",         speculative.n_min},
@@ -363,12 +369,26 @@ struct server_task {
                     if (ids.size() == 1) {
                         LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
                         params.sampling.grammar_trigger_tokens.push_back(ids[0]);
+                        params.sampling.preserved_tokens.insert(ids[0]);
                         continue;
                     }
                     LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
                     params.sampling.grammar_trigger_words.push_back(trigger);
                 }
             }
+            const auto preserved_tokens = data.find("preserved_tokens");
+            if (preserved_tokens != data.end()) {
+                for (const auto & t : *preserved_tokens) {
+                    auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
+                    if (ids.size() == 1) {
+                        LOG_DBG("Preserved token: %d\n", ids[0]);
+                        params.sampling.preserved_tokens.insert(ids[0]);
+                    } else {
+                        // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
+                        LOG_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
+                    }
+                }
+            }
             if (params.sampling.grammar_lazy) {
                 GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0);
             }
@@ -695,19 +715,19 @@ struct server_task_result_cmpl_final : server_task_result {
 
     json to_json_oaicompat_chat() {
         std::string finish_reason = "length";
-        common_chat_msg message;
+        common_chat_msg msg;
         if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
             LOG_DBG("Parsing chat message: %s\n", content.c_str());
-            message = common_chat_parse(content, oaicompat_chat_format);
-            finish_reason = message.tool_calls.empty() ? "stop" : "tool_calls";
+            msg = common_chat_parse(content, oaicompat_chat_format);
+            finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
         } else {
-            message.content = content;
+            msg.content = content;
         }
 
         json tool_calls;
-        if (!message.tool_calls.empty()) {
+        if (!msg.tool_calls.empty()) {
             tool_calls = json::array();
-            for (const auto & tc : message.tool_calls) {
+            for (const auto & tc : msg.tool_calls) {
                 tool_calls.push_back({
                     {"type", "function"},
                     {"function", {
@@ -719,14 +739,19 @@ struct server_task_result_cmpl_final : server_task_result {
             }
         }
 
+        json message {
+            {"content", msg.content},
+            {"tool_calls", tool_calls},
+            {"role", "assistant"},
+        };
+        if (!msg.tool_plan.empty()) {
+            message["tool_plan"] = msg.tool_plan;
+        }
+
         json choice {
             {"finish_reason", finish_reason},
             {"index", 0},
-            {"message", json {
-                {"content", message.content},
-                {"tool_calls", tool_calls},
-                {"role", "assistant"},
-            }},
+            {"message", message},
         };
 
         if (!stream && probs_output.size() > 0) {
@@ -2833,8 +2858,7 @@ struct server_context {
         server_slot * slot_batched = nullptr;
 
         auto accept_special_token = [&](server_slot & slot, llama_token token) {
-            const auto & trigger_tokens = slot.params.sampling.grammar_trigger_tokens;
-            return params_base.special || std::find(trigger_tokens.begin(), trigger_tokens.end(), token) != trigger_tokens.end();
+            return params_base.special || slot.params.sampling.preserved_tokens.find(token) != slot.params.sampling.preserved_tokens.end();
         };
 
         // frist, add sampled tokens from any ongoing sequences
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index bfe623c4c..fefdce55b 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -662,6 +662,7 @@ static json oaicompat_completion_params_parse(
             });
         }
         llama_params["grammar_triggers"] = grammar_triggers;
+        llama_params["preserved_tokens"] = chat_params.preserved_tokens;
         for (const auto & stop : chat_params.additional_stops) {
             llama_params["stop"].push_back(stop);
         }
diff --git a/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja b/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja
new file mode 100644
index 000000000..078e9f545
--- /dev/null
+++ b/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja
@@ -0,0 +1,156 @@
+{{ bos_token }}{%- macro document_turn(documents) -%}
+{# format documents into chat turn #}
+<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[
+    {"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}}
+]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
+    {
+        "tool_call_id": "0",
+        "results": {
+{% for doc in documents %}
+            "{{ loop.index0 }}": {{doc|tojson}}{% if not loop.last %},
+            {% endif %}
+{% endfor %}
+
+        },
+        "is_error": null
+    }
+]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}
+{%- macro tool_call_id_to_int(messages, tool_call_id) %}
+{%- set counter = namespace(value=0) %}
+{%- set tool_call_id_seen = namespace(value=false) %}
+{%- for msg in messages %}
+    {%- if msg.tool_calls %}
+        {%- for tool_call in msg.tool_calls %}
+            {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}
+                {{ counter.value }}
+                {%- set tool_call_id_seen.value = true %}
+            {%- endif %}
+            {%- set counter.value = counter.value + 1 %}
+        {%- endfor %}
+    {%- endif %}
+{%- endfor %}
+{%- endmacro %}
+{%- macro format_tool_message(messages, tool_msg) -%}
+{# format tool message #}
+    {
+        "tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}",
+        "results": {
+            "0": {{ tool_msg.content|tojson }}
+        },
+        "is_error": null
+    }
+{%- endmacro -%}
+{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}
+{%- set tool_idx = namespace(value=0) %}
+{%- set tool_ids_seen = namespace(value=[]) %}
+{%- set sent_documents = namespace(value=false) %}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble
+You are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.
+
+Your information cutoff date is June 2024.
+
+You have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.
+{% if tools or documents %}
+
+You have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.
+
+## Tool Use
+Think about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.
+
+0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.
+    You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.
+    NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.
+
+Then carry out your plan by repeatedly executing the following steps.
+1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing "tool_name" and "parameters" fields.
+    When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.
+2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.
+    Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its "tool_call_id".
+3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.
+    You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.
+    NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.
+
+You can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.
+
+4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.
+{% if enable_citations %}
+
+## Grounding
+Importantly, note that "Reflection" and "Response" above can be grounded.
+Grounding means you associate pieces of texts (called "spans") with those specific tool results that support them (called "sources"). And you use a pair of tags "<co>" and "</co>" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as "{tool_call_id}:[{list of result indices}]", before they are joined together by ",". E.g., "<co>span</co: 0:[1,2],1:[0]>" means that "span" is supported by result 1 and 2 from "tool_call_id=0" as well as result 0 from "tool_call_id=1".
+{% endif %}
+
+## Available Tools
+Here is the list of tools that you have available to you.
+You can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.
+Each tool is represented as a JSON object with fields like "name", "description", "parameters" (per JSON Schema), and optionally, "responses" (per JSON Schema).
+
+```json
+[
+{% if documents %}
+    {"name": "direct-injected-document", "description": "This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!", "parameters": {"type": "object", "properties": {}, "required": []}, "responses": {"200": {"description": "Successfully returned a list of chunked text snippets from the directly uploaded documents.", "content": {"application/json": {"schema": {"type": "array", "items": {"type": "object", "required": ["url", "snippet"], "properties": {"url": {"type": "string", "description": "The url of the uploaded document."}, "snippet": {"type": "string", "description": "The text snippet for the returned document chunk."}}}}}}}}}{%- if tools %},{% endif %}
+
+{% endif %}
+{% for tool in tools %}
+    {"name": "{{ tool['function']['name'] }}", "description": "{{tool['function']['description']}}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null}{%- if not loop.last %},{% endif %}
+
+{% endfor %}
+]
+```
+
+{% endif %}
+# Default Preamble
+The following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.
+- Your name is Command.
+- You are a large language model built by Cohere.
+- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.
+- If the input is ambiguous, ask clarifying follow-up questions.
+- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).
+- Use LaTeX to generate mathematical notation for complex equations.
+- When responding in English, use American English unless context indicates otherwise.
+- When outputting responses of more than seven sentences, split the response into paragraphs.
+- Prefer the active voice.
+- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.
+- Use gender-neutral pronouns for unspecified persons.
+- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.
+- Use the third person when asked to write a summary.
+- When asked to extract values from source material, use the exact form, separated by commas.
+- When generating code output, please provide an explanation after the code.
+- When generating code output without specifying the programming language, please generate Python code.
+- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.
+{%- if developer_preamble %}
+
+
+# Developer Preamble
+The following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.
+{{ developer_preamble }}
+{%- endif -%}
+<|END_OF_TURN_TOKEN|>
+{%- for message in messages %}
+    {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>
+    {%- elif message.role|lower == 'user' %}
+<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}
+    {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}
+<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[
+    {% for tc in message.tool_calls %}
+    {"tool_call_id": "{{ tool_idx.value }}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}
+
+    {% set tool_idx.value = tool_idx.value + 1 %}
+    {% endfor %}
+]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}
+    {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
+{{ format_tool_message(messages, message) }}
+    {%- for msg in messages[loop.index0 + 1:] %}
+        {%- if msg.role|lower == 'tool' %},
+{{ format_tool_message(messages, msg) }}
+            {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}
+        {%- else %}
+            {%- break %}
+        {%- endif %}
+    {%- endfor %}
+
+]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>
+    {%- endif %}
+{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
\ No newline at end of file
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index ccc65d87a..9956c1f1f 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -22,9 +22,13 @@ static common_chat_msg msg_from_json(const json & message) {
         "assistant",
         "",
         {},
+        /* .tool_plan = */ "",
     };
     if (message.contains("content") && !message.at("content").is_null()) {
-        ret.content = message.at("content").get<std::string>();
+        ret.content = message.at("content");
+    }
+    if (message.contains("tool_plan")) {
+        ret.tool_plan = message.at("tool_plan");
     }
     auto has_tool_calls = message.contains("tool_calls");
     if (has_tool_calls) {
@@ -171,8 +175,7 @@ const json llama_3_1_tools       = { special_function_tool, code_interpreter_too
 
 struct delta_data {
     std::string        delta;
-    std::string        grammar;
-    common_chat_format format;
+    common_chat_params params;
 };
 
 static delta_data init_delta(const common_chat_template & tmpl, const std::vector<std::string> & end_tokens,
@@ -214,7 +217,7 @@ static delta_data init_delta(const common_chat_template & tmpl, const std::vecto
             break;
         }
     }
-    return { delta, params_full.grammar, params_full.format };
+    return { delta, params_full };
 }
 
 /*
@@ -224,7 +227,7 @@ static delta_data init_delta(const common_chat_template & tmpl, const std::vecto
 */
 static void test_template(const common_chat_template & tmpl, const std::vector<std::string> & end_tokens,
                           const json & test_message, const json & tools = {}, const std::string & expected_delta = "",
-                          bool skip_grammar_test = false, bool skip_parser_test = false) {
+                          bool expect_grammar_triggered = true) {
     common_chat_msg expected_msg = msg_from_json(test_message);
 
     auto user_message = json{
@@ -238,45 +241,110 @@ static void test_template(const common_chat_template & tmpl, const std::vector<s
             assert_equals(expected_delta, data.delta);
         }
 
-        if (!skip_parser_test) {
-            const auto msg = common_chat_parse(data.delta, data.format);
+        if (expect_grammar_triggered) {
+            const auto msg = common_chat_parse(data.delta, data.params.format);
             assert_msg_equals(expected_msg, msg);
         }
 
         if (!expected_msg.tool_calls.empty()) {
-            GGML_ASSERT(!data.grammar.empty());
+            GGML_ASSERT(!data.params.grammar.empty());
         }
-        if (!data.grammar.empty()) {
-            auto grammar = build_grammar(data.grammar);
+        if (!data.params.grammar.empty()) {
+            auto grammar = build_grammar(data.params.grammar);
             if (!grammar) {
                 throw std::runtime_error("Failed to build grammar");
             }
-            // TODO: exercice lazy grammars + triggers here, instead of skipping the test
-            if (!skip_grammar_test) {
-                if (!match_string(data.delta, grammar.get())) {
-                    throw std::runtime_error("Failed to match delta against grammar:\n\n" + data.delta +
-                                             "\n\nGrammar: " + data.grammar);
+            auto earliest_trigger_pos = std::string::npos;
+            auto constrained = data.delta;
+            for (const auto & trigger : data.params.grammar_triggers) {
+                auto pos = constrained.find(trigger.word);
+                if (pos == std::string::npos) {
+                    continue;
                 }
+                if (pos > 0 && trigger.at_start) {
+                    fprintf(stderr, "Trigger %s not at start of message, skipping:\n\n%s\n\n", trigger.word.c_str(), constrained.c_str());
+                    continue;
+                }
+                if (earliest_trigger_pos == std::string::npos || pos < earliest_trigger_pos) {
+                    earliest_trigger_pos = pos;
+                }
+            }
+            auto grammar_triggered = false;
+            if (earliest_trigger_pos != std::string::npos) {
+                constrained = constrained.substr(earliest_trigger_pos);
+                grammar_triggered = true;
+            }
+            if (data.params.grammar_lazy) {
+                assert_equals(expect_grammar_triggered, grammar_triggered);
+            }
+
+            if (grammar_triggered && !match_string(constrained, grammar.get())) {
+                throw std::runtime_error("Failed to match delta against grammar:\n\n" + data.delta +
+                                            "\n\nGrammar: " + data.params.grammar);
             }
         }
     }
 }
 
 static void test_template_output_parsers() {
-    auto text_message = json{
+    json text_message {
         { "role",    "assistant"     },
         { "content", "Hello, world!" },
     };
-    auto tool_call_message = json{
+    json tool_calls = json::array({{
+        { "type", "function" },
+        { "function", { { "name", "special_function" }, { "arguments", "{\"arg1\": 1}" } } },
+    }});
+
+    json tool_call_message {
+        { "role",       "assistant"},
+        { "content",    {}},
+        { "tool_calls", {
+            {
+                { "type", "function" },
+                { "function", {
+                    { "name", "special_function" },
+                    { "arguments", "{\"arg1\": 1}" },
+                }},
+            },
+        }},
+    };
+    json tool_call_message_with_id {
+        { "role",       "assistant"},
+        { "content",    {}},
+        { "tool_calls", {
+            {
+                { "type", "function" },
+                { "function", {
+                    { "name", "special_function" },
+                    { "arguments", "{\"arg1\": 1}" },
+                }},
+                {"id", "123456789"},
+            },
+        }},
         { "role",       "assistant"                },
         { "content",    {}                         },
-        { "tool_calls", json{ {
-                            { "type", "function" },
-                            { "function", { { "name", "special_function" }, { "arguments", "{\"arg1\": 1}" } } },
-                        } } }
+        { "tool_calls", tool_calls                  }
+    };
+    json tool_call_plan_message_with_idx {
+        { "role",       "assistant"},
+        { "content",    {}},
+        { "tool_plan",  "I'm not so sure"},
+        { "tool_calls", {
+            {
+                { "type", "function" },
+                { "function", {
+                    { "name", "special_function" },
+                    { "arguments", "{\"arg1\": 1}" },
+                }},
+                // Index of the tool call in the tool_calls array
+                {"id", "0"},
+            },
+        }},
+        { "role",       "assistant"                },
+        { "content",    {}                         },
+        { "tool_calls", tool_calls                  }
     };
-    auto tool_call_message_with_id                   = json::parse(tool_call_message.dump());
-    tool_call_message_with_id["tool_calls"][0]["id"] = "123456789";
 
     auto python_tool_call_message = json{
         { "role",       "assistant"                },
@@ -322,6 +390,27 @@ static void test_template_output_parsers() {
     inputs_tools_builtin.tools              = json::array();
     inputs_tools_builtin.tools.push_back(python_tool);
 
+    {
+        // Not supported yet
+        const common_chat_template tmpl(read_file("models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja"), "<s>", "</s>");
+        assert_equals(COMMON_CHAT_FORMAT_GENERIC, common_chat_params_init(tmpl, inputs_tools).format);
+    }
+    {
+        const common_chat_template tmpl(read_file("models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja"), "<s>", "</s>");
+        std::vector<std::string>   end_tokens{ "<|END_OF_TURN_TOKEN|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_params_init(tmpl, inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_COMMAND_R7B,    common_chat_params_init(tmpl, inputs_tools).format);
+
+        test_template(tmpl, end_tokens, tool_call_plan_message_with_idx, tools,
+                      "<|START_THINKING|>I'm not so sure<|END_THINKING|>"
+                      "<|START_ACTION|>[\n"
+                      "    {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n"
+                      "]<|END_ACTION|>");
+        test_template(tmpl, end_tokens, text_message, tools,
+                      "<|START_RESPONSE|>Hello, world!<|END_RESPONSE|>",
+                      /* expect_grammar_triggered= */ false);
+    }
     {
         const common_chat_template tmpl(read_file("models/templates/google-gemma-2-2b-it.jinja"), "<s>", "</s>");
         std::vector<std::string>   end_tokens{ "<end_of_turn>" };
@@ -362,11 +451,10 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_MISTRAL_NEMO, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(
             tmpl, end_tokens, tool_call_message_with_id, tools,
-            "[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]",
-            /* skip_grammar_test= */ true);
+            "[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]");
     }
     {
         const common_chat_template tmpl(
@@ -388,7 +476,7 @@ static void test_template_output_parsers() {
                 inputs_tools)
                 .format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<tool_call>\n"
                       "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
@@ -413,7 +501,7 @@ static void test_template_output_parsers() {
                           inputs_tools_builtin)
                           .format);
 
-        // test_template(tmpl, end_tokens, text_message, tools, R"(?)", /* skip_grammar_test= */ true);
+        // test_template(tmpl, end_tokens, text_message, tools, R"(?)", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, code_interpreter_tool_call_message, llama_3_1_tools,
                       "<|python_tag|>code_interpreter.call(code=\"print('hey')\")");
         test_template(tmpl, end_tokens, python_tool_call_message, tools,
@@ -428,7 +516,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}");
     }
@@ -440,7 +528,7 @@ static void test_template_output_parsers() {
         assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
                       common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<function=special_function>{\"arg1\": 1}</function>");
     }
@@ -455,7 +543,7 @@ static void test_template_output_parsers() {
         test_template(tmpl, end_tokens, text_message, {},
                       "all\n"
                       "Hello, world!",
-                      /* skip_grammar_test= */ true);
+                      /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "special_function\n"
                       "{\"arg1\": 1}");
@@ -467,7 +555,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_FIREFUNCTION_V2, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       " functools[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]");
     }
@@ -478,7 +566,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* skip_grammar_test= */ true);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n"
                       "```json\n"

From 84ec8a58f7b6aad6887bbfbd1321f3ff417341a5 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Sun, 2 Feb 2025 16:14:48 +0100
Subject: [PATCH 311/376] Name colors (#11573)

It's more descriptive, use #define's so we can use compile-time
concatenations.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 common/log.cpp       | 10 ----------
 common/log.h         | 10 ++++++++++
 examples/run/run.cpp | 15 ++++++++-------
 3 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/common/log.cpp b/common/log.cpp
index 0b8994ae1..4bfbecf15 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -14,16 +14,6 @@ void common_log_set_verbosity_thold(int verbosity) {
     common_log_verbosity_thold = verbosity;
 }
 
-#define LOG_COL_DEFAULT "\033[0m"
-#define LOG_COL_BOLD    "\033[1m"
-#define LOG_COL_RED     "\033[31m"
-#define LOG_COL_GREEN   "\033[32m"
-#define LOG_COL_YELLOW  "\033[33m"
-#define LOG_COL_BLUE    "\033[34m"
-#define LOG_COL_MAGENTA "\033[35m"
-#define LOG_COL_CYAN    "\033[36m"
-#define LOG_COL_WHITE   "\033[37m"
-
 static int64_t t_us() {
     return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
diff --git a/common/log.h b/common/log.h
index 66605cc69..85dd4393b 100644
--- a/common/log.h
+++ b/common/log.h
@@ -2,6 +2,16 @@
 
 #include "ggml.h" // for ggml_log_level
 
+#define LOG_COL_DEFAULT "\033[0m"
+#define LOG_COL_BOLD    "\033[1m"
+#define LOG_COL_RED     "\033[31m"
+#define LOG_COL_GREEN   "\033[32m"
+#define LOG_COL_YELLOW  "\033[33m"
+#define LOG_COL_BLUE    "\033[34m"
+#define LOG_COL_MAGENTA "\033[35m"
+#define LOG_COL_CYAN    "\033[36m"
+#define LOG_COL_WHITE   "\033[37m"
+
 #ifndef __GNUC__
 #    define LOG_ATTRIBUTE_FORMAT(...)
 #elif defined(__MINGW32__)
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index cf61f4add..ca9273155 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -24,15 +24,16 @@
 #include <string>
 #include <vector>
 
+#include "chat-template.hpp"
 #include "common.h"
 #include "json.hpp"
 #include "linenoise.cpp/linenoise.h"
 #include "llama-cpp.h"
-#include "chat-template.hpp"
+#include "log.h"
 
 #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
 [[noreturn]] static void sigint_handler(int) {
-    printf("\n\033[0m");
+    printf("\n" LOG_COL_DEFAULT);
     exit(0);  // not ideal, but it's the only way to guarantee exit in all cases
 }
 #endif
@@ -890,7 +891,7 @@ static int check_context_size(const llama_context_ptr & ctx, const llama_batch &
     const int n_ctx      = llama_n_ctx(ctx.get());
     const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
     if (n_ctx_used + batch.n_tokens > n_ctx) {
-        printf("\033[0m\n");
+        printf(LOG_COL_DEFAULT "\n");
         printe("context size exceeded\n");
         return 1;
     }
@@ -953,7 +954,7 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
         batch = llama_batch_get_one(&new_token_id, 1);
     }
 
-    printf("\033[0m");
+    printf(LOG_COL_DEFAULT);
     return 0;
 }
 
@@ -962,7 +963,7 @@ static int read_user_input(std::string & user_input) {
 #ifdef WIN32
     printf(
         "\r%*s"
-        "\r\033[0m%s",
+        "\r" LOG_COL_DEFAULT "%s",
         get_terminal_width(), " ", prompt_prefix);
 
     std::getline(std::cin, user_input);
@@ -999,7 +1000,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
                              const bool stdout_a_terminal) {
     // Set response color
     if (stdout_a_terminal) {
-        printf("\033[33m");
+        printf(LOG_COL_YELLOW);
     }
 
     if (generate(llama_data, prompt, response)) {
@@ -1008,7 +1009,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
     }
 
     // End response with color reset and newline
-    printf("\n%s", stdout_a_terminal ? "\033[0m" : "");
+    printf("\n%s", stdout_a_terminal ? LOG_COL_DEFAULT : "");
     return 0;
 }
 

From 864a0b67a6c8f648c43ce8271f9cb2e12dd5df6e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sun, 2 Feb 2025 19:31:09 +0100
Subject: [PATCH 312/376] CUDA: use mma PTX instructions for FlashAttention
 (#11583)

* CUDA: use mma PTX instructions for FlashAttention

* __shfl_sync workaround for movmatrix

* add __shfl_sync to HIP

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 Makefile                                      |   2 +-
 ggml/include/ggml.h                           |   2 +-
 ggml/src/ggml-cuda/CMakeLists.txt             |   2 +-
 ggml/src/ggml-cuda/common.cuh                 |   6 +-
 ggml/src/ggml-cuda/fattn-common.cuh           | 181 ++++-
 ggml/src/ggml-cuda/fattn-mma-f16.cuh          | 637 +++++++++++++++++
 ggml/src/ggml-cuda/fattn-tile-f16.cu          |  24 +-
 ggml/src/ggml-cuda/fattn-tile-f32.cu          |  19 +-
 ggml/src/ggml-cuda/fattn-vec-f16.cuh          |   9 +-
 ggml/src/ggml-cuda/fattn-vec-f32.cuh          |   8 +-
 ggml/src/ggml-cuda/fattn-wmma-f16.cu          | 648 ++++++++++++++++++
 ggml/src/ggml-cuda/fattn-wmma-f16.cuh         | 542 +--------------
 ggml/src/ggml-cuda/fattn.cu                   | 174 ++---
 ggml/src/ggml-cuda/mma.cuh                    | 335 +++++++--
 ggml/src/ggml-cuda/mmq.cu                     |   2 +-
 ggml/src/ggml-cuda/mmq.cuh                    | 349 +++++-----
 .../fattn-mma-f16-instance-cpb16.cu           |  10 +
 .../fattn-mma-f16-instance-cpb32.cu           |  10 +
 .../fattn-mma-f16-instance-cpb64.cu           |  10 +
 .../fattn-mma-f16-instance-cpb8.cu            |  10 +
 .../fattn-wmma-f16-instance-kqfloat-cpb16.cu  |  10 -
 .../fattn-wmma-f16-instance-kqfloat-cpb32.cu  |   9 -
 .../fattn-wmma-f16-instance-kqhalf-cpb16.cu   |  10 -
 .../fattn-wmma-f16-instance-kqhalf-cpb32.cu   |  10 -
 .../fattn-wmma-f16-instance-kqhalf-cpb8.cu    |   8 -
 .../template-instances/generate_cu_files.py   |  24 +-
 ggml/src/ggml-cuda/vendors/hip.h              |   1 +
 ggml/src/ggml-hip/CMakeLists.txt              |   2 +-
 ggml/src/ggml-musa/CMakeLists.txt             |   2 +-
 29 files changed, 2058 insertions(+), 998 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/fattn-mma-f16.cuh
 create mode 100644 ggml/src/ggml-cuda/fattn-wmma-f16.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb16.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb32.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb64.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb8.cu
 delete mode 100644 ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu
 delete mode 100644 ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu
 delete mode 100644 ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu
 delete mode 100644 ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu
 delete mode 100644 ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu

diff --git a/Makefile b/Makefile
index ef152d246..dc3de3cb1 100644
--- a/Makefile
+++ b/Makefile
@@ -596,7 +596,7 @@ ifdef GGML_RPC
 	OBJ_GGML_EXT += ggml/src/ggml-rpc.o
 endif # GGML_RPC
 
-OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
+OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
 OBJ_CUDA_TMPL     += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
 
 ifdef GGML_CUDA_FA_ALL_QUANTS
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 1198dc1fd..5bd8d9c8b 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1775,7 +1775,7 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   k);
 
-#define GGML_KQ_MASK_PAD 32
+#define GGML_KQ_MASK_PAD 64
 
     // q:    [n_embd, n_batch,     n_head,    1]
     // k:    [n_embd, n_kv,        n_head_kv, 1]
diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index 14761650f..119fd39b8 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -28,7 +28,7 @@ if (CUDAToolkit_FOUND)
     list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h")
 
     file(GLOB   GGML_SOURCES_CUDA "*.cu")
-    file(GLOB   SRCS "template-instances/fattn-wmma*.cu")
+    file(GLOB   SRCS "template-instances/fattn-mma*.cu")
     list(APPEND GGML_SOURCES_CUDA ${SRCS})
     file(GLOB   SRCS "template-instances/mmq*.cu")
     list(APPEND GGML_SOURCES_CUDA ${SRCS})
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 8d8d3932e..88be8fc8a 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -148,7 +148,7 @@ typedef float2 dfloat2;
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
 
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define INT8_MMA_AVAILABLE
+#define NEW_MMA_AVAILABLE
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 
 #if !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= GGML_CUDA_CC_QY1)
@@ -159,11 +159,13 @@ static constexpr bool fast_fp16_available(const int cc) {
     return cc >= GGML_CUDA_CC_PASCAL && cc != 610;
 }
 
+// Any FP16 tensor cores are available.
 static constexpr bool fp16_mma_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_VOLTA;
 }
 
-static constexpr bool int8_mma_available(const int cc) {
+// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
+static constexpr bool new_mma_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_TURING;
 }
 
diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index ee9752da6..cfd7c0f44 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -516,6 +516,104 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
         nullptr;
 }
 
+template<int D, int ncols, int KQ_stride> // D == head size
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_stream_k_fixup(
+        float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne11) {
+    const float * dst_fixup_data = ((const float *) dst_fixup) + gridDim.x*(2*2*ncols);
+
+    const int iter_k = ne11 / KQ_stride;
+    const int iter_j = (ne01 + (ncols - 1)) / ncols;
+
+    const int bidx0 = blockIdx.x;
+
+    const int kbc0      = (bidx0 + 0)*iter_k*iter_j*ne02 / gridDim.x;
+    const int kbc0_stop = (bidx0 + 1)*iter_k*iter_j*ne02 / gridDim.x;
+
+    const bool did_not_have_any_data   = kbc0 == kbc0_stop;
+    const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
+    const bool did_not_write_last      = kbc0/iter_k == kbc0_stop/iter_k && kbc0_stop % iter_k != 0;
+    if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) {
+        return;
+    }
+
+    const int channel = kbc0 / (iter_k*iter_j);
+    const int jt      = (kbc0 - channel*iter_k*iter_j) / iter_k;
+
+    dst += jt*ncols*ne02*D + channel*D;
+
+    // Load the partial result that needs a fixup:
+    float dst_val[ncols] = {0.0f};
+    float max_val[ncols] = {0.0f};
+    float rowsum[ncols]  = {0.0f};
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (jt*ncols + j >= ne01) {
+            break;
+        }
+        dst_val[j] = dst[j*ne02*D + threadIdx.x];
+
+        const float2 tmp = dst_fixup[bidx0*ncols + j];
+        max_val[j] = tmp.x;
+        rowsum[j]  = tmp.y;
+    }
+
+    // Iterate over previous blocks and compute the combined results.
+    // All CUDA blocks that get here must have a previous block that needs a fixup.
+    int bidx = bidx0 - 1;
+    int kbc_stop = kbc0;
+    while(true) {
+        const int kbc = bidx*iter_k*iter_j*ne02 / gridDim.x;
+        if (kbc == kbc_stop) { // Did not have any data.
+            bidx--;
+            kbc_stop = kbc;
+            continue;
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            if (jt*ncols + j >= ne01) {
+                break;
+            }
+            const float dst_add = dst_fixup_data[bidx*ncols*D + j*D + threadIdx.x];
+
+            const float2 tmp = dst_fixup[(gridDim.x + bidx)*ncols + j];
+
+            // Scale the current and new value accumulators depending on the max. values.
+            const float max_val_new = fmaxf(max_val[j], tmp.x);
+
+            const float diff_val = max_val[j] - max_val_new;
+            const float diff_add = tmp.x      - max_val_new;
+
+            const float scale_val = diff_val >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_val) : 0.0f;
+            const float scale_add = diff_add >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_add) : 0.0f;
+
+            dst_val[j] = scale_val*dst_val[j] + scale_add*dst_add;
+            rowsum[j]  = scale_val*rowsum[j]  + scale_add*tmp.y;
+
+            max_val[j] = max_val_new;
+        }
+
+        // If this block started in a previous tile we are done and don't need to combine additional partial results.
+        if (kbc % iter_k == 0 || kbc/iter_k < kbc0/iter_k) {
+            break;
+        }
+        bidx--;
+        kbc_stop = kbc;
+    }
+
+    // Write back final result:
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (jt*ncols + j >= ne01) {
+            return;
+        }
+        dst[j*ne02*D + threadIdx.x] = dst_val[j] / rowsum[j];
+    }
+}
+
 template<int D, int parallel_blocks> // D == head size
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
@@ -581,10 +679,11 @@ static void on_no_fattn_vec_case(const int D) {
     }
 }
 
-template <int D, int parallel_blocks>
+// parallel_blocks == 0 is stream-k decomposition
+template <int D, int cols_per_block, int parallel_blocks, int KQ_stride>
 void launch_fattn(
     ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
-    const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V
+    const int nwarps, const size_t nbytes_shared, const bool need_f16_K, const bool need_f16_V
 ) {
     const ggml_tensor * Q = dst->src[0];
     const ggml_tensor * K = dst->src[1];
@@ -603,20 +702,23 @@ void launch_fattn(
 
     GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
 
+    GGML_ASSERT(Q->ne[3] == 1);
+
     ggml_cuda_pool & pool = ctx.pool();
     cudaStream_t main_stream = ctx.stream();
+    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
 
     ggml_cuda_pool_alloc<half>   K_f16(pool);
     ggml_cuda_pool_alloc<half>   V_f16(pool);
     ggml_cuda_pool_alloc<float>  dst_tmp(pool);
     ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
 
-    char * K_data = (char *) K->data;
+    const char * K_data = (const char *) K->data;
     size_t nb11 = K->nb[1];
     size_t nb12 = K->nb[2];
     size_t nb13 = K->nb[3];
 
-    char * V_data = (char *) V->data;
+    const char * V_data = (const char *) V->data;
     size_t nb21 = V->nb[1];
     size_t nb22 = V->nb[2];
     size_t nb23 = V->nb[3];
@@ -649,39 +751,60 @@ void launch_fattn(
         nb23 = nb23*bs*sizeof(half)/ts;
     }
 
-    if (parallel_blocks > 1) {
-        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
-        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
-    }
+    const int ntiles_x = ((Q->ne[1] + cols_per_block - 1) / cols_per_block);
+    const int ntiles_total = ntiles_x*Q->ne[2]*Q->ne[3];
 
     const dim3 block_dim(WARP_SIZE, nwarps, 1);
-    const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
-    const int  shmem = 0;
+    dim3 blocks_num;
+    if (parallel_blocks == 0) {
+        // For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup.
+        const int tiles_nwaves  = (ntiles_total - nsm - 1) / nsm;
+        const bool tiles_inefficient = 3*nsm < 2*tiles_nwaves*ntiles_total;
+        const bool short_context = K->ne[1] < 4096;
+
+        const int nblocks_stream_k = 2*nsm;
+
+        blocks_num.x = short_context && !tiles_inefficient ? ntiles_total : nblocks_stream_k;
+        blocks_num.y = 1;
+        blocks_num.z = 1;
+
+        dst_tmp_meta.alloc(blocks_num.x*cols_per_block * (2*2 + D) * sizeof(float));
+    } else {
+        blocks_num.x = parallel_blocks*ntiles_x;
+        blocks_num.y = Q->ne[2];
+        blocks_num.z = Q->ne[3];
+
+        if (parallel_blocks > 1) {
+            dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+            dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+        }
+    }
+
 
     float scale         = 1.0f;
     float max_bias      = 0.0f;
     float logit_softcap = 0.0f;
 
-    memcpy(&scale,         (float *) KQV->op_params + 0, sizeof(float));
-    memcpy(&max_bias,      (float *) KQV->op_params + 1, sizeof(float));
-    memcpy(&logit_softcap, (float *) KQV->op_params + 2, sizeof(float));
+    memcpy(&scale,         (const float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias,      (const float *) KQV->op_params + 1, sizeof(float));
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
 
     if (logit_softcap != 0.0f) {
         scale /= logit_softcap;
     }
 
     const uint32_t n_head      = Q->ne[2];
-    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+    const uint32_t n_head_log2 = 1u << uint32_t(floorf(log2f(float(n_head))));
 
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
+    fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
         (const char *) Q->data,
         K_data,
         V_data,
         mask ? ((const char *) mask->data) : nullptr,
-        (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+        (parallel_blocks) > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
         scale, max_bias, m0, m1, n_head_log2, logit_softcap,
         Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
         K->ne[0], K->ne[1], K->ne[2], K->ne[3],
@@ -693,16 +816,22 @@ void launch_fattn(
     );
     CUDA_CHECK(cudaGetLastError());
 
-    if ((parallel_blocks) == 1) {
-        return;
+    if constexpr (parallel_blocks == 0) {
+        if (blocks_num.x % ntiles_total != 0) { // Fixup is only needed if the SMs work on fractional tiles.
+            const dim3 block_dim_combine(D, 1, 1);
+            const dim3 blocks_num_combine = blocks_num;
+
+            flash_attn_stream_k_fixup<D, cols_per_block, KQ_stride>
+                <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
+                ((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], K->ne[1]);
+        }
+    } else if constexpr (parallel_blocks > 1) {
+        const dim3 block_dim_combine(D, 1, 1);
+        const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+
+        flash_attn_combine_results<D, parallel_blocks>
+            <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
+            (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
     }
-
-    const dim3 block_dim_combine(D, 1, 1);
-    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
-    const int  shmem_combine = 0;
-
-    flash_attn_combine_results<D, parallel_blocks>
-        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
-        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
     CUDA_CHECK(cudaGetLastError());
 }
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
new file mode 100644
index 000000000..05bc91a3b
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -0,0 +1,637 @@
+#include "common.cuh"
+#include "mma.cuh"
+#include "fattn-common.cuh"
+
+template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap, bool needs_fixup, bool is_fixup>
+static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
+        const float2 * const __restrict__ Q_f2,
+        const half2  * const __restrict__ K_h2,
+        const half2  * const __restrict__ V_h2,
+        const half   * const __restrict__ maskh,
+        float2       * const __restrict__ dstk,
+        float2       * const __restrict__ dstk_fixup,
+        const float scale,
+        const float slope,
+        const float logit_softcap,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3,
+        const int jt,
+        const int kb0_start,
+        const int kb0_stop) {
+#ifdef NEW_MMA_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    typedef mma_A_I16K8<half2> mma_A;
+    typedef mma_B_J8K8<half2>  mma_B;
+    typedef mma_C_I16J8<float> mma_C_KQ;
+    typedef mma_C_I16J8<half2> mma_C_VKQ;
+
+    static_assert(nwarps*mma_B::J % ncols == 0, "bad nwarps");
+    constexpr int np = nwarps*mma_B::J / ncols; // Number of parallel CUDA warps per Q column.
+
+    static_assert(D         % nwarps == 0, "bad D");
+    static_assert(KQ_stride % nwarps == 0, "bad KQ_stride");
+
+    constexpr int D2_padded = D/2 + 4; // Size of D in half2, padded to avoid shared memory bank conflicts.
+    extern __shared__ half2 tile_KV[]; // Temporary shared buffer for loading K/V data with KQ_stride*D logical elements.
+
+    const int stride_Q    = nb01 / sizeof(float2);
+    const int stride_KV   = nb11 / sizeof(half2);
+    const int stride_mask = nb31 / sizeof(half);
+
+    mma_B Q_B[D/(2*mma_B::K)];
+    mma_C_VKQ VKQ_C[D/mma_C_VKQ::I];
+
+    float2    KQ_rowsum = {0.0f, 0.0f};
+    float2       KQ_max = {-FLT_MAX/2.0f, -FLT_MAX/2.0f};
+    float2 KQ_max_scale = {0.0f, 0.0f};
+
+    // Temporarily load Q data into tile_KV, will be loaded into registers afterwards.
+    // The loading is done with decreasing granularity for D for better memory bandwidth.
+    const half2 scale_h2 = make_half2(scale, scale);
+#pragma unroll
+    for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+        const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+        const int k0_stop  =                             D/2 - (D/2) % (1*stride_k);
+        const int stride_j = WARP_SIZE / stride_k;
+
+        if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
+            break;
+        }
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps*stride_j) {
+            const int j = j0 + threadIdx.y*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+
+            if (jt*ncols + j < ne01) {
+#pragma unroll
+                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+                    const float2 tmp = Q_f2[(jt*ncols + j)*stride_Q + k];
+                    tile_KV[j*D2_padded + k] = scale_h2 * make_half2(tmp.x, tmp.y);
+                }
+            } else {
+#pragma unroll
+                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+                    tile_KV[j*D2_padded + k] = make_half2(0.0f, 0.0f);
+                }
+            }
+        }
+    }
+
+    __syncthreads();
+
+    {
+        const int j0 = (threadIdx.y / np) * mma_B::J;
+
+#pragma unroll
+        for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
+            Q_B[k0/mma_B::K].load_ldmatrix(tile_KV + j0*D2_padded + k0, D2_padded);
+        }
+    }
+
+    __syncthreads();
+
+    // Iterate over ne11 == previous tokens:
+    for (int kb0 = kb0_start; kb0 < kb0_stop; ++kb0) {
+        const int k_VKQ_0 = kb0*KQ_stride;
+        mma_C_KQ KQ_C[KQ_stride/(np*mma_C_KQ::I)];
+
+        // Load K data into tile with decreasing granularity for D for better memory bandwidth:
+        static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
+#pragma unroll
+        for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+            const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+            const int k0_stop  =                             D/2 - (D/2) % (1*stride_k);
+            const int stride_i = WARP_SIZE / stride_k;
+
+#pragma unroll
+            for (int i_KQ_0 = 0; i_KQ_0 < KQ_stride; i_KQ_0 += nwarps*stride_i) {
+                const int i_KQ = i_KQ_0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+
+#pragma unroll
+                for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += stride_k) {
+                    const int k_KQ = k_KQ_0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+                    tile_KV[i_KQ*D2_padded + k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV + k_KQ];
+                }
+            }
+        }
+
+        __syncthreads();
+
+        // Calculate tile of KQ:
+#pragma unroll
+        for (int i_KQ_00 = 0; i_KQ_00 < KQ_stride; i_KQ_00 += np*mma_A::I) {
+            const int i_KQ_0 = i_KQ_00 + (threadIdx.y % np)*mma_A::I;
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += mma_A::K) {
+                mma_A K_A;
+                K_A.load_ldmatrix(tile_KV + i_KQ_0*D2_padded + k_KQ_0, D2_padded);
+                KQ_C[i_KQ_00/(np*mma_A::I)].mma(K_A, Q_B[k_KQ_0/mma_A::K]);
+            }
+        }
+
+        __syncthreads();
+
+        if (use_logit_softcap) {
+            static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+            for (int i = 0; i < KQ_stride/(np*mma_C_KQ::I); ++i) {
+#pragma unroll
+                for (int l = 0; l < mma_C_KQ::ne; ++l) {
+                    KQ_C[i].x[l] = logit_softcap*tanhf(KQ_C[i].x[l]);
+                }
+            }
+        }
+
+        if (maskh) {
+            static_assert(KQ_stride % (np       *mma_C_KQ::I) == 0, "bad loop size");
+            static_assert(ncols     % (nwarps/np*mma_C_KQ::J) == 0, "bad loop size");
+#pragma unroll
+            for (int i00 = 0; i00 < KQ_stride; i00 += np*mma_C_KQ::I) {
+                const int i0 = i00 + (threadIdx.y % np)*mma_C_KQ::I;
+#pragma unroll
+                for (int l = 0; l < mma_C_KQ::ne; ++l) {
+                    const int i = i0 + mma_C_KQ::get_i(l);
+                    const int j = (threadIdx.y / np)*mma_C_KQ::J + mma_C_KQ::get_j(l);
+
+                    KQ_C[i00/(np*mma_C_KQ::I)].x[l] += slope*__half2float(maskh[j*stride_mask + k_VKQ_0 + i]);
+                }
+            }
+        }
+
+        // Calculate softmax for each KQ column using the current max. value.
+        // The divisor is stored in KQ_rowsum and will be applied at the end.
+        float2 KQ_max_new = KQ_max;
+        static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+        for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
+#pragma unroll
+            for (int l0 = 0; l0 < mma_C_KQ::ne; l0 += 2) {
+                KQ_max_new.x = fmaxf(KQ_max_new.x, KQ_C[k].x[l0 + 0]);
+                KQ_max_new.y = fmaxf(KQ_max_new.y, KQ_C[k].x[l0 + 1]);
+            }
+        }
+
+        // Values per KQ column are spread across 8 threads, does not need full warp reduce:
+#pragma unroll
+        for (int offset = 16; offset > 2; offset >>= 1) {
+            KQ_max_new.x = fmaxf(KQ_max_new.x, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.x, offset, WARP_SIZE));
+            KQ_max_new.y = fmaxf(KQ_max_new.y, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.y, offset, WARP_SIZE));
+        }
+
+        {
+            const float2 diff = make_float2(KQ_max.x - KQ_max_new.x, KQ_max.y - KQ_max_new.y);
+            KQ_max_scale = make_float2(expf(diff.x), expf(diff.y));
+            if (diff.x <= SOFTMAX_FTZ_THRESHOLD) {
+                KQ_max_scale.x = 0.0f;
+            }
+            if (diff.y <= SOFTMAX_FTZ_THRESHOLD) {
+                KQ_max_scale.y = 0.0f;
+            }
+            KQ_max = KQ_max_new;
+        }
+
+        float2 KQ_rowsum_add = make_float2(0.0f, 0.0f);
+        static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+        for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
+#pragma unroll
+            for (int l = 0; l < mma_C_KQ::ne; ++l) {
+                const float KQ_max_l = l % 2 == 0 ? KQ_max.x : KQ_max.y;
+                const float diff = KQ_C[k].x[l] - KQ_max_l;
+                KQ_C[k].x[l] = expf(diff);
+                if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                    KQ_C[k].x[l] = 0.0f;
+                }
+
+                if (l % 2 == 0) {
+                    KQ_rowsum_add.x += KQ_C[k].x[l];
+                } else {
+                    KQ_rowsum_add.y += KQ_C[k].x[l];
+                }
+            }
+        }
+
+        // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+        KQ_rowsum.x = KQ_max_scale.x*KQ_rowsum.x + KQ_rowsum_add.x;
+        KQ_rowsum.y = KQ_max_scale.y*KQ_rowsum.y + KQ_rowsum_add.y;
+
+        const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale.x, KQ_max_scale.y);
+#pragma unroll
+        for (int i = 0; i < D/mma_C_VKQ::I; ++i) {
+#pragma unroll
+            for (int l = 0; l < mma_C_VKQ::ne; ++l) {
+                VKQ_C[i].x[l] *= KQ_max_scale_h2;
+            }
+        }
+
+        // Convert KQ C tiles into B tiles for VKQ calculation:
+        mma_B B[KQ_stride/(np*2*mma_B::K)];
+        static_assert(KQ_stride % (np*2*mma_B::K) == 0, "bad loop size");
+#pragma unroll
+        for (int k = 0; k < KQ_stride/(np*2*mma_B::K); ++k) {
+            B[k] = KQ_C[k].to_mma_B();
+        }
+
+        // Load V data into tile with decreasing granularity for D for better memory bandwidth:
+        static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
+#pragma unroll
+        for (int stride_i : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+            const int i0_start = stride_i == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_i);
+            const int i0_stop  =                             D/2 - (D/2) % (1*stride_i);
+            const int stride_k = WARP_SIZE / stride_i;
+
+#pragma unroll
+            for (int k_V_0 = 0; k_V_0 < KQ_stride; k_V_0 += nwarps*stride_k) {
+                const int k_V = k_V_0 + threadIdx.y*stride_k + (stride_i == WARP_SIZE ? 0 : threadIdx.x / stride_i);
+
+#pragma unroll
+                for (int i_V_0 = i0_start; i_V_0 < i0_stop; i_V_0 += stride_i) {
+                    const int i_V = i_V_0 + (stride_i == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_i);
+
+                    tile_KV[k_V*D2_padded + i_V] = V_h2[(k_VKQ_0 + k_V)*stride_KV + i_V];
+                }
+            }
+        }
+
+        __syncthreads();
+
+        // Calculate VKQ tile:
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += mma_C_VKQ::I) {
+            static_assert((KQ_stride/2) % (np*mma_A::K) == 0, "bad loop size");
+#pragma unroll
+            for (int k00 = 0; k00 < KQ_stride/2; k00 += np*mma_A::K) {
+                const int k0 = k00 + (threadIdx.y % np)*mma_A::K;
+
+                mma_A A;
+                A.load_ldmatrix_trans(tile_KV + 2*k0*D2_padded + i_VKQ_0/2, D2_padded);
+                VKQ_C[i_VKQ_0/mma_C_VKQ::I].mma(A, B[k00/(np*mma_A::K)]);
+            }
+        }
+
+        __syncthreads();
+    }
+
+    // Finally, sum up partial KQ rowsums.
+    // The partial sums are spread across 8 threads each, does not need full reduce.
+#pragma unroll
+    for (int offset = 16; offset > 2; offset >>= 1) {
+        KQ_rowsum.x += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.x, offset, WARP_SIZE);
+        KQ_rowsum.y += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.y, offset, WARP_SIZE);
+    }
+
+    // Write VKQ accumulators to shared memory in column-major format.
+    // It's faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
+    // Also for np > 1 the combination is done via these values in shared memory.
+    const int j_cwd = threadIdx.y*mma_B::J + mma_B::get_j(-1); // j combine write data
+#pragma unroll
+    for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
+        const mma_B B = VKQ_C[k0/mma_B::K].to_mma_B(); // Conversion of C to B matrix puts it in column-major format.
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int k = k0 + mma_B::get_k(l);
+
+            tile_KV[j_cwd*D2_padded + k] = B.x[l];
+        }
+    }
+
+    const int j_cwmo = (threadIdx.x % (2*mma_C_VKQ::J)) / mma_C_VKQ::J; // j combine write meta offset
+    const int j_cwm = threadIdx.y*(2*mma_C_VKQ::J) + 2*mma_C_VKQ::get_j(-1) + j_cwmo; // j combine write meta
+    const float2 KQ_cmr = make_float2(((const float *) &KQ_max)[j_cwmo], ((const float *) &KQ_rowsum)[j_cwmo]); // KQ combine max rowsum
+
+    if (((!needs_fixup && !is_fixup) || np > 1) && threadIdx.x < 2*mma_C_VKQ::J) {
+        // Use the 16 bytes of padding in each row to store the meta data: KQ max, KQ rowsum, KQ max scale.
+        ((float2 *) tile_KV)[j_cwm*(D2_padded/2) + D/4] = KQ_cmr;
+    }
+
+    __syncthreads();
+
+    static_assert(np == 1 || np == 2 || np == 4, "bad np");
+    if (np == 1) {
+        // No combination is needed, the meta data can be directly written from registers to VRAM.
+        if (needs_fixup && threadIdx.x < mma_B::J) {
+            float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
+            dstk_fixup_meta[j_cwm] = KQ_cmr;
+        }
+        if (is_fixup && threadIdx.x < mma_B::J) {
+            float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
+            dstk_fixup_meta[j_cwm] = KQ_cmr;
+        }
+    } else if (threadIdx.y % np == 0) {
+        // Combine the meta data for parallel warps via shared memory.
+        // Warps with threadIdx.y % np != 0 must NOT return early.
+        // All threads must return simultaneously to avoid race conditions with work on the next tile.
+
+        float * meta_j = (float *) tile_KV + (threadIdx.y*mma_B::J + threadIdx.x)*D2_padded + D/2;
+
+        float KQ_cm = -FLT_MAX/2; // KQ combine max per parallel warp.
+        if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+            KQ_cm = meta_j[0];
+        }
+
+        float KQ_cmn = KQ_cm; // KQ combine max new, max between all parallel warps.
+#pragma unroll
+        for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
+            KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, WARP_SIZE));
+        }
+
+        const float KQ_cms = expf(KQ_cm - KQ_cmn); // KQ combine max scale per warp.
+        float KQ_crs = 0.0f; // KQ combine rowsum, scaled sum of all parallel warps.
+        if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+            KQ_crs = KQ_cms*meta_j[1];
+        }
+#pragma unroll
+        for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
+            KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, WARP_SIZE);
+        }
+
+        // Write back combined meta data:
+        if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+            meta_j[0] = KQ_cmn; // Combined max. KQ values.
+            meta_j[1] = KQ_crs; // Combined KQ rowsums.
+            meta_j[2] = KQ_cms; // KQ max scales per parallel warp.
+        }
+        if (needs_fixup && threadIdx.x < mma_B::J) {
+            float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
+            dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
+        }
+        if (is_fixup && threadIdx.x < mma_B::J) {
+            float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
+            dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
+        }
+    }
+
+    if (np > 1) {
+        __syncthreads();
+    }
+
+    if (np == 1 || threadIdx.y % np == 0) {
+        // The first 2*2*gridDim.x*ncols floats in dstk_fixup are for storing max. values and row sums.
+        // The values after that are for the partial results of the individual blocks.
+        float2 * dstk_fixup_data = dstk_fixup + gridDim.x*(2*ncols) + blockIdx.x*(ncols*(D/2));
+
+#pragma unroll
+        for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+            const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+            const int k0_stop  =                             D/2 - (D/2) % (1*stride_k);
+            const int stride_j = WARP_SIZE / stride_k;
+
+            if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
+                break;
+            }
+
+#pragma unroll
+            for (int j0_dst = 0; j0_dst < ncols; j0_dst += (nwarps/np)*stride_j) {
+                const int j_dst = j0_dst + (threadIdx.y/np)*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+                const int j_tile_KV = (j_dst/mma_B::J)*(np*mma_B::J) + j_dst % mma_B::J;
+
+                if (!is_fixup && jt*ncols + j_dst >= ne01) {
+                    continue;
+                }
+                const float * meta_j = (const float *) tile_KV + j_tile_KV*D2_padded + D/2;
+#pragma unroll
+                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+                    float2 dstk_val = make_float2(0.0f, 0.0f);
+#pragma unroll
+                    for (int ip = 0; ip < np; ++ip) {
+                        const float KQ_crs = np == 1 ? 1.0f : meta_j[ip*mma_B::J*D2_padded + 2];
+                        const float2 dstk_val_add = __half22float2(tile_KV[(j_tile_KV + ip*mma_B::J)*D2_padded + k]);
+                        dstk_val.x += dstk_val_add.x*KQ_crs;
+                        dstk_val.y += dstk_val_add.y*KQ_crs;
+                    }
+
+                    if (!needs_fixup && !is_fixup) {
+                        const float KQ_rowsum_j = meta_j[1];
+                        dstk_val.x /= KQ_rowsum_j;
+                        dstk_val.y /= KQ_rowsum_j;
+                    }
+
+                    if (is_fixup) {
+                        dstk_fixup_data[j_dst*(D/2) + k] = dstk_val;
+                    } else {
+                        dstk[(jt*ncols + j_dst)*ne02*(D/2) + k] = dstk_val;
+                    }
+                }
+            }
+        }
+    }
+
+    if (np > 1) {
+        __syncthreads();
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+}
+
+template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap>
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(nwarps*WARP_SIZE, 2)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const float logit_softcap,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    static_assert(FATTN_KQ_STRIDE % KQ_stride == 0, "bad KQ_stride");
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+
+    const int iter_k = ne11 / KQ_stride;
+    const int iter_j = (ne01 + (ncols - 1)) / ncols;
+
+    // kbc == k block continuous, current index in continuous ijk space.
+    int       kbc      = (blockIdx.x + 0)*iter_k*iter_j*ne02 / gridDim.x;
+    const int kbc_stop = (blockIdx.x + 1)*iter_k*iter_j*ne02 / gridDim.x;
+
+    // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined.
+    // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup).
+    // In the most general case >2 seams can fall into the same tile.
+
+    // kb0 == k start index when in the output tile.
+    int kb0_start = kbc % iter_k;
+    int kb0_stop  = min(iter_k, kb0_start + kbc_stop - kbc);
+    while (kbc < kbc_stop && kb0_stop == iter_k) {
+        const int channel = kbc / (iter_k*iter_j);
+        const int jt      = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
+
+        const float2 * Q_f2  = (const float2 *) (Q + nb02* channel);
+        const half2  * K_h2  = (const half2  *) (K + nb12*(channel / gqa_ratio));
+        const half2  * V_h2  = (const half2  *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
+        const half   * maskh = mask ? (const half  *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
+        float2       * dstk  = ((float2 *) dst) + channel*(D/2);
+
+        const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
+
+        constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
+        if (kb0_start == 0) {
+            constexpr bool needs_fixup = false; // CUDA block is working on an entire tile.
+            flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+                (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+                ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+                jt, kb0_start, kb0_stop);
+        } else {
+            constexpr bool needs_fixup = true; // CUDA block is working on the beginning of a tile.
+            flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+                (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+                ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+                jt, kb0_start, kb0_stop);
+        }
+
+        kbc += iter_k;
+        kbc -= kbc % iter_k;
+
+        kb0_start = 0;
+        kb0_stop  = min(iter_k, kbc_stop - kbc);
+    }
+
+    if (kbc >= kbc_stop) {
+        return;
+    }
+
+    const int channel = kbc / (iter_k*iter_j);
+    const int jt      = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
+
+    const float2 * Q_f2  = (const float2 *) (Q + nb02* channel);
+    const half2  * K_h2  = (const half2  *) (K + nb12*(channel / gqa_ratio));
+    const half2  * V_h2  = (const half2  *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
+    const half   * maskh = mask ? (const half  *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
+    float2       * dstk  = ((float2 *) dst) + channel*(D/2);
+
+    const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
+
+    constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
+    constexpr bool needs_fixup = false;
+    flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+        (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+        ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+        jt, kb0_start, kb0_stop);
+}
+
+template <int D, int cols_per_block>
+void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    typedef mma_A_I16K8<half2> mma_A;
+    typedef mma_B_J8K8<half2>  mma_B;
+
+    static_assert(D              % mma_B::K == 0, "bad D");
+    static_assert(cols_per_block % mma_B::J == 0, "bad cols_per_block");
+
+    const ggml_tensor * KQV = dst;
+
+    constexpr int    KQ_stride     = D <= 128 ? 64 : 32;
+    constexpr int    nwarps        = (KQ_stride == 32 && cols_per_block <= 16) ?
+                                     cols_per_block/mma_B::J * KQ_stride/mma_A::I : (cols_per_block <= 8 ? 4 : 8);
+    constexpr size_t nbytes_shared = std::max(KQ_stride, nwarps*mma_B::J) * (D + 8) * sizeof(half);
+
+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
+    fattn_kernel_t fattn_kernel;
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
+    } else {
+        constexpr bool use_logit_softcap = true;
+        fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
+    }
+    launch_fattn<D, cols_per_block, 0, KQ_stride>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
+}
+
+#define DECL_FATTN_MMA_F16_CASE(D, cols_per_block)                          \
+    template void ggml_cuda_flash_attn_ext_mma_f16_case                     \
+    <D, cols_per_block>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
+
+extern DECL_FATTN_MMA_F16_CASE( 64,  8);
+extern DECL_FATTN_MMA_F16_CASE( 80,  8);
+extern DECL_FATTN_MMA_F16_CASE( 96,  8);
+extern DECL_FATTN_MMA_F16_CASE(112,  8);
+extern DECL_FATTN_MMA_F16_CASE(128,  8);
+extern DECL_FATTN_MMA_F16_CASE(256,  8);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 16);
+extern DECL_FATTN_MMA_F16_CASE( 80, 16);
+extern DECL_FATTN_MMA_F16_CASE( 96, 16);
+extern DECL_FATTN_MMA_F16_CASE(112, 16);
+extern DECL_FATTN_MMA_F16_CASE(128, 16);
+extern DECL_FATTN_MMA_F16_CASE(256, 16);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 32);
+extern DECL_FATTN_MMA_F16_CASE( 80, 32);
+extern DECL_FATTN_MMA_F16_CASE( 96, 32);
+extern DECL_FATTN_MMA_F16_CASE(112, 32);
+extern DECL_FATTN_MMA_F16_CASE(128, 32);
+extern DECL_FATTN_MMA_F16_CASE(256, 32);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 64);
+extern DECL_FATTN_MMA_F16_CASE( 80, 64);
+extern DECL_FATTN_MMA_F16_CASE( 96, 64);
+extern DECL_FATTN_MMA_F16_CASE(112, 64);
+extern DECL_FATTN_MMA_F16_CASE(128, 64);
+extern DECL_FATTN_MMA_F16_CASE(256, 64);
diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
index 4d314dacb..d4edbad07 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -45,7 +45,17 @@ static __global__ void flash_attn_tile_ext_f16(
         const int ne2,
         const int ne3) {
 #ifdef FP16_AVAILABLE
+
+#ifndef FLASH_ATTN_AVAILABLE
+    NO_DEVICE_CODE;
+    return;
+#endif // FLASH_ATTN_AVAILABLE
+
     // Skip unused kernel variants for faster compilation:
+#ifdef FP16_MMA_AVAILABLE
+    NO_DEVICE_CODE;
+    return;
+#endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
         NO_DEVICE_CODE;
         return;
@@ -288,16 +298,18 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
     const ggml_tensor * Q = dst->src[0];
     switch (Q->ne[0]) {
         case  64: {
-            constexpr int      D = 64;
-            constexpr int nwarps = 8;
+            constexpr int    D             = 64;
+            constexpr int    nwarps        = 8;
+            constexpr size_t nbytes_shared = 0;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+            launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
         } break;
         case 128: {
-            constexpr int      D = 128;
-            constexpr int nwarps = 8;
+            constexpr int    D             = 128;
+            constexpr int    nwarps        = 8;
+            constexpr size_t nbytes_shared = 0;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+            launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
         } break;
         default: {
             GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu
index bb3360447..0d274f332 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -48,7 +48,12 @@ static __global__ void flash_attn_tile_ext_f32(
     NO_DEVICE_CODE;
     return;
 #endif // FLASH_ATTN_AVAILABLE
+
     // Skip unused kernel variants for faster compilation:
+#ifdef FP16_MMA_AVAILABLE
+    NO_DEVICE_CODE;
+    return;
+#endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
         NO_DEVICE_CODE;
         return;
@@ -287,16 +292,18 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
     const ggml_tensor * Q = dst->src[0];
     switch (Q->ne[0]) {
         case  64: {
-            constexpr int      D = 64;
-            constexpr int nwarps = 8;
+            constexpr int    D             = 64;
+            constexpr int    nwarps        = 8;
+            constexpr size_t nbytes_shared = 0;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+            launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
         } break;
         case 128: {
-            constexpr int      D = 128;
-            constexpr int nwarps = 8;
+            constexpr int    D             = 128;
+            constexpr int    nwarps        = 8;
+            constexpr size_t nbytes_shared = 0;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+            launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
         } break;
         default: {
             GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
index 34a2992c7..d9ac44246 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -42,6 +42,12 @@ static __global__ void flash_attn_vec_ext_f16(
         const int ne2,
         const int ne3) {
 #ifdef FP16_AVAILABLE
+
+#ifndef FLASH_ATTN_AVAILABLE
+    NO_DEVICE_CODE;
+    return;
+#endif // FLASH_ATTN_AVAILABLE
+
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(D == 128 || D == 256)) {
         NO_DEVICE_CODE;
@@ -303,7 +309,8 @@ void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx,
     fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
     constexpr bool need_f16_K = D != 128;
     constexpr bool need_f16_V = D != 128 && D != 64;
-    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
+    constexpr size_t nbytes_shared = 0;
+    launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
 }
 
 template <int D, ggml_type type_K, ggml_type type_V>
diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
index a28fc8b7f..6ef8f9dcc 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -41,6 +41,11 @@ static __global__ void flash_attn_vec_ext_f32(
         const int ne1,
         const int ne2,
         const int ne3) {
+#ifndef FLASH_ATTN_AVAILABLE
+    NO_DEVICE_CODE;
+    return;
+#endif // FLASH_ATTN_AVAILABLE
+
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(D == 128 || D == 256)) {
         NO_DEVICE_CODE;
@@ -284,7 +289,8 @@ void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx,
     fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
     constexpr bool need_f16_K = D != 128;
     constexpr bool need_f16_V = D != 128 && D != 64;
-    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
+    constexpr size_t nbytes_shared = 0;
+    launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
 }
 
 template <int D, ggml_type type_K, ggml_type type_V>
diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
new file mode 100644
index 000000000..1054ff95d
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -0,0 +1,648 @@
+// Old and deprecated WMMA FlashAttention implementation.
+// It is still needed for Volta since the memory layout of NVIDIA tensor cores changed with Turing.
+// Long-term the WMMA code should be replaced with a dedicated Volta implementation.
+
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-wmma-f16.cuh"
+
+#ifdef FP16_MMA_AVAILABLE
+#include <mma.h>
+#endif // FP16_MMA_AVAILABLE
+
+// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
+template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(nwarps*WARP_SIZE, 1)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const float logit_softcap,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
+    const int ip  =        blockIdx.x % parallel_blocks;  // Index in group of blocks running for the same column in parallel.
+
+    static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
+    static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
+    constexpr int frag_m = ncols == 8 ? 32 : 16;
+    constexpr int frag_n = ncols == 8 ?  8 : 16;
+    static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half>                          frag_c_VKQ;
+
+    constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
+    constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
+    static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
+
+    // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
+    constexpr int D_padded = D + 8;
+    constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
+    constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float * Q_f   = (const float *) (Q + nb02* blockIdx.y              + nb01*ic0);
+    const half  * K_h   = (const half  *) (K + nb12*(blockIdx.y / gqa_ratio));
+    const half  * V_h   = (const half  *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half  * maskh = (const half  *)  mask + (nb31/sizeof(half))* ic0;
+    const half2 * mask2 = (const half2 *)  mask + (nb31/sizeof(half))*(ic0/2);
+
+    const int stride_Q  = nb01 / sizeof(float);
+    const int stride_KV = nb11 / sizeof(half);
+
+    const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
+    const half  slopeh = __float2half(slopef);
+    const half2 slope2 = make_half2(slopef, slopef);
+
+    const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
+
+    frag_b Q_b[D/16][ncols/frag_n];
+
+    // A single buffer for temporarily holding tiles of KQ and VKQ parts:
+    constexpr int mem_KQ = ncols*kqs_padded*kqar;
+    constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
+    __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
+    float * KQ_f = (float *) KQ;
+    half2 * KQ2 = (half2 *) KQ;
+
+    float    KQ_rowsum_f[ncols/nwarps] = {0.0f};
+    float       KQ_max_f[ncols/nwarps];
+    float KQ_max_scale_f[ncols/nwarps] = {0.0f};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_f[j] = -FLT_MAX/2.0f;
+    }
+
+    half2    KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+    half2       KQ_max_h2[ncols/nwarps];
+    half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
+    }
+
+    __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
+    half2 * VKQ2 = (half2 *) VKQ;
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                break;
+            }
+            VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
+        }
+    }
+
+    // Convert Q to half and apply scale, temporarily store in KQ:
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
+        }
+    }
+
+    __syncthreads();
+
+    // Load Q into tensor core fragments/registers since it will be used frequently:
+#pragma unroll
+    for (int i0 = 0; i0 < D; i0 += 16) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+            nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+        }
+    }
+
+    __syncthreads();
+
+    // Iterate over ne11 == previous tokens:
+    for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
+        // Calculate tile of KQ:
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
+            frag_c_KQ KQ_c[ncols/frag_n];
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
+            }
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
+                frag_a_K K_a;
+                nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
+                }
+            }
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+        // Calculate softmax for each KQ column using the current max. value.
+        // The divisor is stored in KQ_rowsum and will be applied at the end.
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (std::is_same<KQ_acc_t, float>::value) {
+                float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
+
+                    if (use_logit_softcap) {
+                        KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
+                    }
+                }
+
+                float KQ_max_new = KQ_max_f[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
+                    KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = warp_reduce_max(KQ_max_new);
+
+                const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_f[j0/nwarps] = expf(diff);
+                if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                    KQ_max_scale_f[j0/nwarps] = 0.0f;
+                }
+                KQ_max_f[j0/nwarps] = KQ_max_new;
+
+                float KQ_rowsum_add = 0.0f;
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
+                    KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
+                    if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                        KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
+                    }
+                    KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
+                    KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
+            } else {
+                half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
+
+                    if (use_logit_softcap) {
+                        // There is no dedicated tangens hyperbolicus function for half2.
+                        KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
+                        KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
+                                               /(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
+
+                        KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
+                    }
+                }
+
+                half2 KQ_max_new = KQ_max_h2[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
+                    KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
+                const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
+                const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
+                KQ_max_h2[j0/nwarps] = KQ_max_new;
+
+                half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
+                    KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
+                    const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                    *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
+                    KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
+                    KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
+            }
+        }
+
+        __syncthreads();
+
+        frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+                nvcuda::wmma::load_matrix_sync(
+                    KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
+                    KQ + j0*(kqar*kqs_padded) + k,
+                    kqar*kqs_padded);
+            }
+        }
+
+        frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
+            }
+
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+
+                frag_a_V v_a;
+                nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
+                }
+            }
+        }
+
+        __syncthreads();
+
+        const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync(
+                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+                    VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
+                    D_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            half2 VKQ_scale;
+            if (std::is_same<KQ_acc_t, float>::value) {
+                VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
+            } else {
+                VKQ_scale = KQ_max_scale_h2[j0/nwarps];
+            }
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+                if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                    break;
+                }
+
+                half2 VKQ_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int l = 0; l < VKQ_ratio; ++l) {
+                    VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
+                }
+                VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j_VKQ = j0 + threadIdx.y;
+        if (ic0 + j_VKQ >= ne01) {
+            return;
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+
+        float KQ_rowsum_j;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
+        } else {
+            KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            float dst_val = VKQ[j_VKQ*D_padded + i];
+            if (parallel_blocks == 1) {
+                dst_val /= KQ_rowsum_j;
+            }
+            dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
+        }
+
+        if (parallel_blocks == 1 || threadIdx.x != 0) {
+            continue;
+        }
+
+        float2 dst_meta_val;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            dst_meta_val.x = KQ_max_f[j0/nwarps];
+        } else {
+            dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
+        }
+        dst_meta_val.y = KQ_rowsum_j;
+        dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+}
+
+constexpr int get_max_power_of_2(int x) {
+    return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
+}
+
+static_assert(get_max_power_of_2(1) == 1, "Test failed.");
+static_assert(get_max_power_of_2(2) == 2, "Test failed.");
+static_assert(get_max_power_of_2(4) == 4, "Test failed.");
+static_assert(get_max_power_of_2(6) == 2, "Test failed.");
+
+// Number of VKQ rows calculated in parallel:
+constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
+    return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
+}
+
+static_assert(get_VKQ_stride(128, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride(128, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
+static_assert(get_VKQ_stride( 64, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride( 64, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 64, 4, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 80, 1, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 2, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");
+
+template <int D, int cols_per_block, typename KQ_acc_t>
+void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+
+    constexpr int nwarps = 4;
+
+    constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
+    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
+    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
+
+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
+    if (4*blocks_num_pb1 < 2*nsm) {
+        constexpr int parallel_blocks = 4;
+        fattn_kernel_t fattn_kernel;
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        } else {
+            constexpr bool use_logit_softcap = true;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        }
+        launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+        return;
+    }
+    if (2*blocks_num_pb1 < 2*nsm) {
+        constexpr int parallel_blocks = 2;
+        fattn_kernel_t fattn_kernel;
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        } else {
+            constexpr bool use_logit_softcap = true;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        }
+        launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+        return;
+    }
+    constexpr int parallel_blocks = 1;
+    fattn_kernel_t fattn_kernel;
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        fattn_kernel = flash_attn_ext_f16<
+            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+    } else {
+        constexpr bool use_logit_softcap = true;
+        fattn_kernel = flash_attn_ext_f16<
+            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+    }
+    launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+}
+
+void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+
+    const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
+
+    if (prec != GGML_PREC_DEFAULT) {
+        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
+            constexpr int cols_per_block = 16;
+            switch (Q->ne[0]) {
+                case 64:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+                    break;
+                case 80:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+                    break;
+                case 96:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+                    break;
+                case 112:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+                    break;
+                case 128:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                    break;
+                case 256:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
+                    break;
+                default:
+                    GGML_ABORT("fatal error");
+                    break;
+            }
+        } else {
+            constexpr int cols_per_block = 32;
+            switch (Q->ne[0]) {
+                case 64:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+                    break;
+                case 80:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+                    break;
+                case 96:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+                    break;
+                case 112:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+                    break;
+                case 128:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                    break;
+                // case 256:
+                //     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                //     break;
+                default:
+                    GGML_ABORT("fatal error");
+                    break;
+            }
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
+        constexpr int cols_per_block = 8;
+        switch (Q->ne[0]) {
+            case 64:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+                break;
+            case 96:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+                break;
+            case 128:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+                break;
+            case 256:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+                break;
+            default:
+                GGML_ABORT("fatal error");
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 32) {
+        constexpr int cols_per_block = 16;
+        switch (Q->ne[0]) {
+            case 64:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+                break;
+            case 80:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+                break;
+            case 96:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+                break;
+            case 112:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+                break;
+            case 128:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+                break;
+            case 256:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+                break;
+            default:
+                GGML_ABORT("fatal error");
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 32;
+    switch (Q->ne[0]) {
+        case 64:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+            break;
+        case 80:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+            break;
+        case 96:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+            break;
+        case 112:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+            break;
+        case 128:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+            break;
+        case 256:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
index 860d0e6dc..beeea95eb 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
@@ -1,543 +1,3 @@
 #include "common.cuh"
-#include "fattn-common.cuh"
 
-#ifdef FP16_MMA_AVAILABLE
-#include <mma.h>
-#endif // FP16_MMA_AVAILABLE
-
-// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
-template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
-__launch_bounds__(nwarps*WARP_SIZE, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void flash_attn_ext_f16(
-        const char * __restrict__ Q,
-        const char * __restrict__ K,
-        const char * __restrict__ V,
-        const char * __restrict__ mask,
-        float      * __restrict__ dst,
-        float2     * __restrict__ dst_meta,
-        const float scale,
-        const float max_bias,
-        const float m0,
-        const float m1,
-        const uint32_t n_head_log2,
-        const float logit_softcap,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int nb31,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int nb21,
-        const int nb22,
-        const int nb23,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3) {
-#ifdef FP16_MMA_AVAILABLE
-    // Skip unused kernel variants for faster compilation:
-    if (use_logit_softcap && !(D == 128 || D == 256)) {
-        NO_DEVICE_CODE;
-        return;
-    }
-
-    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
-
-    const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
-    const int ip  =        blockIdx.x % parallel_blocks;  // Index in group of blocks running for the same column in parallel.
-
-    static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
-    static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
-    constexpr int frag_m = ncols == 8 ? 32 : 16;
-    constexpr int frag_n = ncols == 8 ?  8 : 16;
-    static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
-    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
-    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
-    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
-    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
-    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half>                          frag_c_VKQ;
-
-    constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
-    constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
-    static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
-
-    // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
-    constexpr int D_padded = D + 8;
-    constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
-    constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
-
-    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float * Q_f   = (const float *) (Q + nb02* blockIdx.y              + nb01*ic0);
-    const half  * K_h   = (const half  *) (K + nb12*(blockIdx.y / gqa_ratio));
-    const half  * V_h   = (const half  *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
-    const half  * maskh = (const half  *)  mask + (nb31/sizeof(half))* ic0;
-    const half2 * mask2 = (const half2 *)  mask + (nb31/sizeof(half))*(ic0/2);
-
-    const int stride_Q  = nb01 / sizeof(float);
-    const int stride_KV = nb11 / sizeof(half);
-
-    const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
-    const half  slopeh = __float2half(slopef);
-    const half2 slope2 = make_half2(slopef, slopef);
-
-    const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
-
-    frag_b Q_b[D/16][ncols/frag_n];
-
-    // A single buffer for temporarily holding tiles of KQ and VKQ parts:
-    constexpr int mem_KQ = ncols*kqs_padded*kqar;
-    constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
-    __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
-    float * KQ_f = (float *) KQ;
-    half2 * KQ2 = (half2 *) KQ;
-
-    float    KQ_rowsum_f[ncols/nwarps] = {0.0f};
-    float       KQ_max_f[ncols/nwarps];
-    float KQ_max_scale_f[ncols/nwarps] = {0.0f};
-
-#pragma unroll
-    for (int j = 0; j < ncols/nwarps; ++j) {
-        KQ_max_f[j] = -FLT_MAX/2.0f;
-    }
-
-    half2    KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
-    half2       KQ_max_h2[ncols/nwarps];
-    half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
-
-#pragma unroll
-    for (int j = 0; j < ncols/nwarps; ++j) {
-        KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
-    }
-
-    __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
-    half2 * VKQ2 = (half2 *) VKQ;
-#pragma unroll
-    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
-#pragma unroll
-        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-            if (i0 + WARP_SIZE > D/2 && i >= D/2) {
-                break;
-            }
-            VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
-        }
-    }
-
-    // Convert Q to half and apply scale, temporarily store in KQ:
-#pragma unroll
-    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
-#pragma unroll
-        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-            if (i0 + WARP_SIZE > D && i >= D) {
-                break;
-            }
-            KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
-        }
-    }
-
-    __syncthreads();
-
-    // Load Q into tensor core fragments/registers since it will be used frequently:
-#pragma unroll
-    for (int i0 = 0; i0 < D; i0 += 16) {
-#pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-            nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
-        }
-    }
-
-    __syncthreads();
-
-    // Iterate over ne11 == previous tokens:
-    for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
-        // Calculate tile of KQ:
-#pragma unroll
-        for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
-            frag_c_KQ KQ_c[ncols/frag_n];
-#pragma unroll
-            for (int j = 0; j < ncols/frag_n; ++j) {
-                nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
-            }
-#pragma unroll
-            for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
-                frag_a_K K_a;
-                nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
-#pragma unroll
-                for (int j = 0; j < ncols/frag_n; ++j) {
-                    nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
-                }
-            }
-#pragma unroll
-            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-                nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
-            }
-        }
-
-        __syncthreads();
-
-        // Calculate softmax for each KQ column using the current max. value.
-        // The divisor is stored in KQ_rowsum and will be applied at the end.
-#pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-            const int j = j0 + threadIdx.y;
-
-            if (std::is_same<KQ_acc_t, float>::value) {
-                float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
-
-                    if (use_logit_softcap) {
-                        KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
-                    }
-                }
-
-                float KQ_max_new = KQ_max_f[j0/nwarps];
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
-                    KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
-                }
-                KQ_max_new = warp_reduce_max(KQ_max_new);
-
-                const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
-                KQ_max_scale_f[j0/nwarps] = expf(diff);
-                if (diff <= SOFTMAX_FTZ_THRESHOLD) {
-                    KQ_max_scale_f[j0/nwarps] = 0.0f;
-                }
-                KQ_max_f[j0/nwarps] = KQ_max_new;
-
-                float KQ_rowsum_add = 0.0f;
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
-                    KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
-                    if (diff <= SOFTMAX_FTZ_THRESHOLD) {
-                        KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
-                    }
-                    KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
-                    KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
-                }
-                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
-
-                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
-                KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
-            } else {
-                half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
-
-                    if (use_logit_softcap) {
-                        // There is no dedicated tangens hyperbolicus function for half2.
-                        KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
-                        KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
-                                               /(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
-
-                        KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
-                    }
-                }
-
-                half2 KQ_max_new = KQ_max_h2[j0/nwarps];
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
-                    KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
-                }
-                KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
-                const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
-                KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
-                const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
-                *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
-                KQ_max_h2[j0/nwarps] = KQ_max_new;
-
-                half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
-#pragma unroll
-                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
-                    const int k = k0 + threadIdx.x;
-
-                    const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
-                    KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
-                    const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
-                    *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
-                    KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
-                    KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
-                }
-                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
-
-                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
-                KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
-            }
-        }
-
-        __syncthreads();
-
-        frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
-#pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-#pragma unroll
-            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
-                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
-                nvcuda::wmma::load_matrix_sync(
-                    KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
-                    KQ + j0*(kqar*kqs_padded) + k,
-                    kqar*kqs_padded);
-            }
-        }
-
-        frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
-#pragma unroll
-        for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
-#pragma unroll
-            for (int j = 0; j < ncols/frag_n; ++j) {
-                nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
-            }
-
-#pragma unroll
-            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
-                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
-
-                frag_a_V v_a;
-                nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
-#pragma unroll
-                for (int j = 0; j < ncols/frag_n; ++j) {
-                    nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
-                }
-            }
-        }
-
-        __syncthreads();
-
-        const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
-#pragma unroll
-        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
-#pragma unroll
-            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-                nvcuda::wmma::store_matrix_sync(
-                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
-                    VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
-                    D_padded, nvcuda::wmma::mem_col_major);
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-            const int j = j0 + threadIdx.y;
-
-            half2 VKQ_scale;
-            if (std::is_same<KQ_acc_t, float>::value) {
-                VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
-            } else {
-                VKQ_scale = KQ_max_scale_h2[j0/nwarps];
-            }
-
-#pragma unroll
-            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
-                const int i = i0 + threadIdx.x;
-                if (i0 + WARP_SIZE > D/2 && i >= D/2) {
-                    break;
-                }
-
-                half2 VKQ_add = make_half2(0.0f, 0.0f);
-#pragma unroll
-                for (int l = 0; l < VKQ_ratio; ++l) {
-                    VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
-                }
-                VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
-            }
-        }
-
-        __syncthreads();
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-        const int j_VKQ = j0 + threadIdx.y;
-        if (ic0 + j_VKQ >= ne01) {
-            return;
-        }
-        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
-
-        float KQ_rowsum_j;
-        if (std::is_same<KQ_acc_t, float>::value) {
-            KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
-        } else {
-            KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
-        }
-
-#pragma unroll
-        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-            if (i0 + WARP_SIZE > D && i >= D) {
-                break;
-            }
-            float dst_val = VKQ[j_VKQ*D_padded + i];
-            if (parallel_blocks == 1) {
-                dst_val /= KQ_rowsum_j;
-            }
-            dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
-        }
-
-        if (parallel_blocks == 1 || threadIdx.x != 0) {
-            continue;
-        }
-
-        float2 dst_meta_val;
-        if (std::is_same<KQ_acc_t, float>::value) {
-            dst_meta_val.x = KQ_max_f[j0/nwarps];
-        } else {
-            dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
-        }
-        dst_meta_val.y = KQ_rowsum_j;
-        dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
-    }
-#else
-   NO_DEVICE_CODE;
-#endif // FP16_MMA_AVAILABLE
-}
-
-constexpr int get_max_power_of_2(int x) {
-    return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
-}
-
-static_assert(get_max_power_of_2(1) == 1, "Test failed.");
-static_assert(get_max_power_of_2(2) == 2, "Test failed.");
-static_assert(get_max_power_of_2(4) == 4, "Test failed.");
-static_assert(get_max_power_of_2(6) == 2, "Test failed.");
-
-// Number of VKQ rows calculated in parallel:
-constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
-    return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
-}
-
-static_assert(get_VKQ_stride(128, 1, 32) ==  32, "Test failed.");
-static_assert(get_VKQ_stride(128, 2, 32) ==  64, "Test failed.");
-static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
-static_assert(get_VKQ_stride( 64, 1, 32) ==  32, "Test failed.");
-static_assert(get_VKQ_stride( 64, 2, 32) ==  64, "Test failed.");
-static_assert(get_VKQ_stride( 64, 4, 32) ==  64, "Test failed.");
-static_assert(get_VKQ_stride( 80, 1, 16) ==  16, "Test failed.");
-static_assert(get_VKQ_stride( 80, 2, 16) ==  16, "Test failed.");
-static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");
-
-template <int D, int cols_per_block, typename KQ_acc_t>
-void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV = dst;
-    const ggml_tensor * Q   = dst->src[0];
-
-    constexpr int nwarps = 4;
-
-    constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
-    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
-    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
-
-    float logit_softcap;
-    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
-
-    if (4*blocks_num_pb1 < 2*nsm) {
-        constexpr int parallel_blocks = 4;
-        fattn_kernel_t fattn_kernel;
-        if (logit_softcap == 0.0f) {
-            constexpr bool use_logit_softcap = false;
-            fattn_kernel = flash_attn_ext_f16<
-                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-        } else {
-            constexpr bool use_logit_softcap = true;
-            fattn_kernel = flash_attn_ext_f16<
-                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-        }
-        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
-        return;
-    }
-    if (2*blocks_num_pb1 < 2*nsm) {
-        constexpr int parallel_blocks = 2;
-        fattn_kernel_t fattn_kernel;
-        if (logit_softcap == 0.0f) {
-            constexpr bool use_logit_softcap = false;
-            fattn_kernel = flash_attn_ext_f16<
-                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-        } else {
-            constexpr bool use_logit_softcap = true;
-            fattn_kernel = flash_attn_ext_f16<
-                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-        }
-        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
-        return;
-    }
-    constexpr int parallel_blocks = 1;
-    fattn_kernel_t fattn_kernel;
-    if (logit_softcap == 0.0f) {
-        constexpr bool use_logit_softcap = false;
-        fattn_kernel = flash_attn_ext_f16<
-            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-    } else {
-        constexpr bool use_logit_softcap = true;
-        fattn_kernel = flash_attn_ext_f16<
-            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
-    }
-    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
-}
-
-#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t)                         \
-    template void ggml_cuda_flash_attn_ext_wmma_f16_case                              \
-    <D, cols_per_block, KQ_acc_t>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(112, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(128, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE(112, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE(128, 32, float);
-// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64,  8, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96,  8, half);
-extern DECL_FATTN_WMMA_F16_CASE(128,  8, half);
-extern DECL_FATTN_WMMA_F16_CASE(256,  8, half);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(112, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(128, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(112, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(128, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
+void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index 0b26b0f8e..b1e66d470 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -1,5 +1,6 @@
 #include "common.cuh"
 #include "fattn-common.cuh"
+#include "fattn-mma-f16.cuh"
 #include "fattn-tile-f16.cuh"
 #include "fattn-tile-f32.cuh"
 #include "fattn-vec-f16.cuh"
@@ -7,144 +8,56 @@
 #include "fattn-wmma-f16.cuh"
 #include "fattn.cuh"
 
-#include <cstdint>
+template <int cols_per_block>
+static void ggml_cuda_flash_attn_ext_mma_f16_switch_hs(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
 
-static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV = dst;
-    const ggml_tensor * Q   = dst->src[0];
-
-    const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
-
-    if (prec != GGML_PREC_DEFAULT) {
-        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
-            constexpr int cols_per_block = 16;
-            switch (Q->ne[0]) {
-                case 64:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
-                    break;
-                case 80:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
-                    break;
-                case 96:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
-                    break;
-                case 112:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
-                    break;
-                case 128:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
-                    break;
-                case 256:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
-                    break;
-                default:
-                    GGML_ABORT("fatal error");
-                    break;
-            }
-        } else {
-            constexpr int cols_per_block = 32;
-            switch (Q->ne[0]) {
-                case 64:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
-                    break;
-                case 80:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
-                    break;
-                case 96:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
-                    break;
-                case 112:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
-                    break;
-                case 128:
-                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
-                    break;
-                // case 256:
-                //     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
-                //     break;
-                default:
-                    GGML_ABORT("fatal error");
-                    break;
-            }
-        }
-        return;
-    }
-
-    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
-        constexpr int cols_per_block = 8;
-        switch (Q->ne[0]) {
-            case 64:
-                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
-                break;
-            case 96:
-                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
-                break;
-            case 128:
-                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
-                break;
-            case 256:
-                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
-                break;
-            default:
-                GGML_ABORT("fatal error");
-                break;
-        }
-        return;
-    }
-
-    if (Q->ne[1] <= 32) {
-        constexpr int cols_per_block = 16;
-        switch (Q->ne[0]) {
-            case 64:
-                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
-                break;
-            case 80:
-                ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
-                break;
-            case 96:
-                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
-                break;
-            case 112:
-                ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
-                break;
-            case 128:
-                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
-                break;
-            case 256:
-                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
-                break;
-            default:
-                GGML_ABORT("fatal error");
-                break;
-        }
-        return;
-    }
-
-    constexpr int cols_per_block = 32;
     switch (Q->ne[0]) {
         case 64:
-            ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case< 64, cols_per_block>(ctx, dst);
             break;
         case 80:
-            ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case< 80, cols_per_block>(ctx, dst);
             break;
         case 96:
-            ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case< 96, cols_per_block>(ctx, dst);
             break;
         case 112:
-            ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case<112, cols_per_block>(ctx, dst);
             break;
         case 128:
-            ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case<128, cols_per_block>(ctx, dst);
             break;
         case 256:
-            ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+            ggml_cuda_flash_attn_ext_mma_f16_case<256, cols_per_block>(ctx, dst);
             break;
         default:
             GGML_ABORT("fatal error");
             break;
     }
 }
+
+static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * Q = dst->src[0];
+
+    if (Q->ne[1] <= 8) {
+        ggml_cuda_flash_attn_ext_mma_f16_switch_hs<8>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 16) {
+        ggml_cuda_flash_attn_ext_mma_f16_switch_hs<16>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 32) {
+        ggml_cuda_flash_attn_ext_mma_f16_switch_hs<32>(ctx, dst);
+        return;
+    }
+
+    ggml_cuda_flash_attn_ext_mma_f16_switch_hs<64>(ctx, dst);
+}
+
 #define FATTN_VEC_F16_CASE(D, type_K, type_V)                               \
     if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) {    \
         ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
@@ -322,11 +235,19 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
         return;
     }
 
-    if (!fp16_mma_available(cc)) {
-        if (Q->ne[1] <= 8) {
-            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+    if (!new_mma_available(cc)) {
+        if (prec == GGML_PREC_DEFAULT) {
+            if (Q->ne[1] <= 8) {
+                ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+            } else {
+                ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+            }
         } else {
-            ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+            if (Q->ne[1] <= 8) {
+                ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+            } else {
+                ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
+            }
         }
         return;
     }
@@ -341,5 +262,10 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
         }
     }
 
-    ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+    // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
+    if (cc == GGML_CUDA_CC_VOLTA) {
+        ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+    }
+
+    ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
 }
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index 7d11540af..9788a1389 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -1,11 +1,67 @@
+// This file contains primitives that expose the tensor core PTX instructions for CUDA code.
+// The primitives can be used in a similar way as the nvcuda::wmma interface but with a well-defined memory layout.
+// The documentation for the PTX instructions can be found under:
+//   https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-multiply-accumulate-operation-using-mma-instruction
+//
+// Like with nvcuda::wmma there are three types of matrix tiles: A, B, and C with A @ B = C.
+// A is a row-major matrix with shape I x K.
+// B is a column-major matrix with shape K x J.
+// C is a column-major matrix with shape I x J.
+// Note that along their lowest dimension I, J, and K are measured in physical 32 bit elements instead of logical elements.
+// The functions get_i, get_j, and get_k can be used to get the physical 32 bit index of the lth element of a thread within a tile.
+// All matrix tiles have ne physical 32 bit elements per warp.
+//
+// As described in the documentation, all pointers for load_ldmatrix must be to shared memory and aligned to 16 bytes.
+
 #include "common.cuh"
 
-struct mma_int_A_I16K4 {
+
+#if CUDART_VERSION >= 11800
+
+static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
+    int ret = 0;
+
+#ifdef NEW_MMA_AVAILABLE
+    asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
+        : "+r"(ret) : "r"(x));
+#else
+    NO_DEVICE_CODE;
+#endif // defined(NEW_MMA_AVAILABLE)
+    return ret;
+}
+
+#else
+
+static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
+    // Imagine transposing row-major matrix to column-major matrix.
+    const int src_i_low  = 2 * (threadIdx.x % 4);
+    const int src_i_high = src_i_low + 1;
+    const int src_j      = threadIdx.x / 4;
+
+    const int src_laneid_low  = src_i_low  * 4 + src_j / 2;
+    const int src_laneid_high = src_i_high * 4 + src_j / 2;
+
+    const int shift_low  = ((src_j + 0) % 2) * 16;
+    const int shift_high = ((src_j + 1) % 2) * 16;
+
+    const int ret_low  = (__shfl_sync(0xFFFFFFFF, x, src_laneid_low,  WARP_SIZE) >> shift_low)  & 0x0000FFFF;
+    const int ret_high = (__shfl_sync(0xFFFFFFFF, x, src_laneid_high, WARP_SIZE) << shift_high) & 0xFFFF0000;
+
+    return ret_low | ret_high;
+}
+
+#endif // CUDART_VERSION >= 11800
+
+
+template <typename T>
+struct mma_A_I16K4 {
+    static_assert(sizeof(T) == 4, "bad type size");
+
     static constexpr int I  = 16;
     static constexpr int K  = 4;
     static constexpr int ne = 2;
 
-    int x[ne] = {0};
+    T x[ne];
 
     static __device__ __forceinline__ int get_i(const int l) {
         const int ret = (l%2) * (I/2) + threadIdx.x / K;
@@ -21,27 +77,35 @@ struct mma_int_A_I16K4 {
         return ret;
     }
 
-    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE)
-        const int * xs = xs0 + (threadIdx.x%I)*stride;
-        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
-            : "+r"(x[0]), "+r"(x[1])
-            : "l"(xs));
-#else
+    __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
 #pragma unroll
         for (int l = 0; l < ne; ++l) {
             x[l] = xs0[get_i(l)*stride + get_k(l)];
         }
-#endif // defined(INT8_MMA_AVAILABLE)
+    }
+
+    __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+        int * xi = (int *) x;
+        const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride;
+        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+            : "+r"(xi[0]), "+r"(xi[1])
+            : "l"(xs));
+#else
+        load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
     }
 };
 
-struct mma_int_A_I16K8 {
+template <typename T>
+struct mma_A_I16K8 {
+    static_assert(sizeof(T) == 4, "bad type size");
+
     static constexpr int I  = 16;
     static constexpr int K  = 8;
     static constexpr int ne = 4;
 
-    int x[ne] = {0};
+    T x[ne];
 
     static __device__ __forceinline__ int get_i(const int l) {
         const int ret = (l%2) * (I/2) + threadIdx.x / (K/2);
@@ -57,31 +121,62 @@ struct mma_int_A_I16K8 {
         return ret;
     }
 
-    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE)
-        const int * xs = xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
-        asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
-            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
-            : "l"(xs));
-#else
+    __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
 #pragma unroll
         for (int l = 0; l < ne; ++l) {
             x[l] = xs0[get_i(l)*stride + get_k(l)];
         }
-#endif // defined(INT8_MMA_AVAILABLE)
     }
 
-    __device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
-        ((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
+    __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+        int * xi = (int * ) x;
+        const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
+        asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
+            : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+            : "l"(xs));
+#else
+        GGML_UNUSED(xs0);
+        GGML_UNUSED(stride);
+        NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ void load_ldmatrix_trans(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+        int * xi = (int * ) x;
+        const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
+        asm("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
+            : "+r"(xi[0]), "+r"(xi[2]), "+r"(xi[1]), "+r"(xi[3])
+            : "l"(xs));
+#else
+        GGML_UNUSED(xs0);
+        GGML_UNUSED(stride);
+        NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ void transpose() {
+        int * xi  = (int *) x;
+        xi[0] = ggml_cuda_movmatrix(xi[0]);
+
+        const int tmp = ggml_cuda_movmatrix(xi[1]);
+        xi[1] = ggml_cuda_movmatrix(xi[2]);
+        xi[2] = tmp;
+
+        xi[3] = ggml_cuda_movmatrix(xi[3]);
     }
 };
 
-struct mma_int_B_J8K4 {
+template <typename T>
+struct mma_B_J8K4 {
+    static_assert(sizeof(T) == 4, "bad type size");
+
     static constexpr int J  = 8;
     static constexpr int K  = 4;
     static constexpr int ne = 1;
 
-    int x[ne] = {0};
+    T x[ne];
 
     static __device__ __forceinline__ int get_j(const int /* l */) {
         const int ret = threadIdx.x / K;
@@ -97,27 +192,34 @@ struct mma_int_B_J8K4 {
         return ret;
     }
 
-    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
-        const int * xs = xs0 + (threadIdx.x%J)*stride;
-        asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
-            : "+r"(x[0])
-            : "l"(xs));
-#else
+    __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
 #pragma unroll
         for (int l = 0; l < ne; ++l) {
             x[l] = xs0[get_j(l)*stride + get_k(l)];
         }
-#endif // defined(INT8_MMA_AVAILABLE)
+    }
+
+    __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+        int * xi = (int *) x;
+        const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride;
+        asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
+            : "+r"(xi[0]) : "l"(xs));
+#else
+        load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
     }
 };
 
-struct mma_int_B_J8K8 {
+template <typename T>
+struct mma_B_J8K8 {
+    static_assert(sizeof(T) == 4, "bad type size");
+
     static constexpr int J  = 8;
     static constexpr int K  = 8;
     static constexpr int ne = 2;
 
-    int x[ne] = {0};
+    T x[ne];
 
     static __device__ __forceinline__ int get_j(const int /* l */) {
         const int ret = threadIdx.x / (K/2);
@@ -133,22 +235,31 @@ struct mma_int_B_J8K8 {
         return ret;
     }
 
-    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
-        const int * xs = xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
-        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
-            : "+r"(x[0]), "+r"(x[1])
-            : "l"(xs));
-#else
+    __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
 #pragma unroll
         for (int l = 0; l < ne; ++l) {
             x[l] = xs0[get_j(l)*stride + get_k(l)];
         }
-#endif // defined(INT8_MMA_AVAILABLE)
+    }
+
+    __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+        int * xi = (int *) x;
+        const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
+        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+            : "+r"(xi[0]), "+r"(xi[1])
+            : "l"(xs));
+#else
+        load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
     }
 };
 
-struct mma_int_C_I16J8 {
+template <typename T>
+struct mma_C_I16J8 {};
+
+template <>
+struct mma_C_I16J8<int> {
     static constexpr int I  = 16;
     static constexpr int J  = 8;
     static constexpr int ne = 4;
@@ -169,8 +280,8 @@ struct mma_int_C_I16J8 {
         return ret;
     }
 
-    __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
-#ifdef INT8_MMA_AVAILABLE
+    __device__ __forceinline__ void mma(const mma_A_I16K4<int> & mma_A, const mma_B_J8K4<int> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
             : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
@@ -188,11 +299,11 @@ struct mma_int_C_I16J8 {
         GGML_UNUSED(mma_A);
         GGML_UNUSED(mma_B);
         NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
-    __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
-#ifdef INT8_MMA_AVAILABLE
+    __device__ __forceinline__ void mma(const mma_A_I16K8<int> & mma_A, const mma_B_J8K8<int> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
             : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
@@ -216,6 +327,132 @@ struct mma_int_C_I16J8 {
         GGML_UNUSED(mma_A);
         GGML_UNUSED(mma_B);
         NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
+    }
+};
+
+template <>
+struct mma_C_I16J8<half2> {
+    static constexpr int I  = 16;
+    static constexpr int J  = 4;
+    static constexpr int ne = 2;
+
+    half2 x[ne] = {{0.0f, 0.0f}, {0.0f, 0.0f}};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = l * (I/2) + threadIdx.x / J;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x % J;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    __device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
+        int * Axi = (int *) mma_A.x;
+        int * Bxi = (int *) mma_B.x;
+        int * xi  = (int *) x;
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+        asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
+            : "+r"(xi[0]), "+r"(xi[1])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+        // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
+        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
+            : "+r"(xi[0]), "+r"(xi[1])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
+        asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
+            : "+r"(xi[0]), "+r"(xi[1])
+            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
+        mma_B_J8K8<half2> mma_B;
+
+        int * xi   = (int *) x;
+        int * Bxi  = (int *) mma_B.x;
+        Bxi[0] = ggml_cuda_movmatrix(xi[0]);
+        Bxi[1] = ggml_cuda_movmatrix(xi[1]);
+
+        return mma_B;
+    }
+};
+
+template <>
+struct mma_C_I16J8<float> {
+    static constexpr int I  = 16;
+    static constexpr int J  = 8;
+    static constexpr int ne = 4;
+
+    float x[ne] = {0.0f, 0.0f, 0.0f, 0.0f};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l/2) * (I/2) + threadIdx.x / (J/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_j(const int l) {
+        const int ret = 2 * (threadIdx.x % (J/2)) + l%2;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    __device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
+        int * Axi = (int *) mma_A.x;
+        int * Bxi = (int *) mma_B.x;
+        int * xi  = (int *) x;
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+        asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+            : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+        // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
+        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+            : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
+        asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+            : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
+        mma_B_J8K8<half2> mma_B;
+        mma_B.x[0] = make_half2(x[0], x[1]);
+        mma_B.x[1] = make_half2(x[2], x[3]);
+
+        int * Bxi  = (int *) mma_B.x;
+        Bxi[0] = ggml_cuda_movmatrix(Bxi[0]);
+        Bxi[1] = ggml_cuda_movmatrix(Bxi[1]);
+
+        return mma_B;
+    }
+
+    __device__ __forceinline__ void load_generic(const float * __restrict__ xs0, const int & stride) {
+#pragma unroll
+        for (int l = 0; l < ne; ++l) {
+            x[l] = xs0[get_j(l)*stride + get_i(l)];
+        }
     }
 };
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 270251df4..83cb78cbd 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -132,7 +132,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return false;
     }
 
-    if (int8_mma_available(cc)) {
+    if (new_mma_available(cc)) {
         return true;
     }
 
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 3cd508a1d..c05c84778 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -87,7 +87,7 @@ struct tile_x_sizes {
 };
 
 static constexpr int get_mmq_x_max_host(const int cc) {
-    return int8_mma_available(cc) ? 128 :
+    return new_mma_available(cc) ? 128 :
 #ifdef GGML_CUDA_FORCE_MMQ
         cc >= GGML_CUDA_CC_VOLTA && cc < GGML_CUDA_CC_OFFSET_AMD ? 128                     : 64;
 #else
@@ -96,9 +96,9 @@ static constexpr int get_mmq_x_max_host(const int cc) {
 }
 
 static constexpr __device__ int get_mmq_x_max_device() {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     return 128;
-#else // INT8_MMA_AVAILABLE
+#else // NEW_MMA_AVAILABLE
 
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     return 128;
@@ -116,7 +116,7 @@ static constexpr __device__ int get_mmq_x_max_device() {
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
 
 #endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 static constexpr int get_mmq_y_host(const int cc) {
@@ -209,10 +209,10 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
 #define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
 
 static int mmq_get_granularity_host(const int mmq_x, const int cc) {
-    return int8_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
+    return new_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
 }
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
     return mmq_x >= 48 ? 16 : 8;
 }
@@ -220,21 +220,21 @@ static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
 static constexpr __device__ int mmq_get_granularity_device(const int /* mmq_x */) {
     return 8;
 }
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
 // ------------------------------------------------------------
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI4_0;
     const int kqsx = threadIdx.x % QI4_0;
@@ -250,12 +250,12 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
         const int qs0 = get_int_b2(bxi->qs, kqsx);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0]     = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808);
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808);
 #else
         x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
@@ -271,11 +271,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
 #else
         x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -322,14 +322,14 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI4_1;
     const int kqsx = threadIdx.x % QI4_1;
@@ -345,12 +345,12 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
         const int qs0 = get_int_b4(bxi->qs, kqsx);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0]     = (qs0 >> 0) & 0x0F0F0F0F;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
         x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
@@ -366,11 +366,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
 #else
         x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -417,14 +417,14 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI5_0;
     const int kqsx = threadIdx.x % QI5_0;
@@ -456,13 +456,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
         qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
@@ -478,25 +478,25 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
 #else
         x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI5_1;
     const int kqsx = threadIdx.x % QI5_1;
@@ -526,13 +526,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
         qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
@@ -548,25 +548,25 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
 #else
         x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_tile + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI8_0;
     const int kqsx = threadIdx.x % QI8_0;
@@ -581,13 +581,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
         x_qs[i*(2*WARP_SIZE + 1)     + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = 2*WARP_SIZE / QI8_0;
@@ -603,11 +603,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0             + kbxd] = bxi->d;
 #else
         x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -645,9 +645,9 @@ template <int mmq_x, int mmq_y, int nwarps, mmq_q8_1_ds_layout ds_layout>
 static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_A_I16K8<int> mma_A;
+    typedef mma_B_J8K8<int>  mma_B;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
@@ -672,7 +672,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
             const int k0 = k00 + k01;
 
-            A[n][k01/QI8_0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
+            A[n][k01/QI8_0].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
         }
 
 #pragma unroll
@@ -695,7 +695,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
             mma_B  B;
             float dB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+            B.load_generic(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); // faster than load_ldmatrix
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
@@ -711,7 +711,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C;
-                C.mma_K8(A[n][k01/QI8_0], B);
+                C.mma(A[n][k01/QI8_0], B);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
@@ -756,9 +756,9 @@ template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_A_I16K8<int> mma_A;
+    typedef mma_B_J8K8<int>  mma_B;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
@@ -782,7 +782,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
             const int k0 = k00 + k01;
 
-            A[n][k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
+            A[n][k01/QI8_1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
         }
 
 #pragma unroll
@@ -805,7 +805,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
             mma_B    B;
             float2 dsB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+            B.load_generic(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); // faster than load_ldmatrix
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
@@ -817,7 +817,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C;
-                C.mma_K8(A[n][k01/QI8_1], B);
+                C.mma(A[n][k01/QI8_1], B);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
@@ -864,12 +864,12 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a(
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 
-    typedef mma_int_A_I16K4 mma_A;
-    typedef mma_int_A_I16K8 mma_A_K8;
-    typedef mma_int_B_J8K4  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_A_I16K4<int> mma_A;
+    typedef mma_A_I16K8<int> mma_A_K8;
+    typedef mma_B_J8K4<int>  mma_B;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
@@ -893,7 +893,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
             const int k0 = k00 + k01;
 
-            ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
+            ((mma_A_K8 *) A[n])[k01/8].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
         }
 
 #pragma unroll
@@ -916,8 +916,9 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
             mma_B B[2];
             float dB[mma_C::ne/2];
 
-            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
-            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+            // Here load_generic is faster than load_ldmatrix.
+            B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
@@ -929,8 +930,8 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C[2];
-                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
-                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+                C[0].mma(A[n][k01/4 + 0], B[0]);
+                C[1].mma(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
@@ -942,20 +943,20 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
 #else
     GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
     NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % QI2_K;
 
@@ -977,11 +978,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
             const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int sc_m = bxi->scales[kqsx];
@@ -992,11 +993,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4));
 #endif // FAST_FP16_AVAILABLE
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik;
 #else
         x_dm[i*(WARP_SIZE + 1)       + kqsx] = x_dm_ik;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -1051,12 +1052,12 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a(
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 
-    typedef mma_int_A_I16K4 mma_A;
-    typedef mma_int_A_I16K8 mma_A_K8;
-    typedef mma_int_B_J8K4  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_A_I16K4<int> mma_A;
+    typedef mma_A_I16K8<int> mma_A_K8;
+    typedef mma_B_J8K4<int>  mma_B;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
@@ -1081,7 +1082,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
             const int k0 = k00 + k01;
 
-            ((mma_A_K8 *) A[n])[k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
+            ((mma_A_K8 *) A[n])[k01/QI8_1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
         }
     }
 
@@ -1118,24 +1119,25 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
             mma_B B[2];
 
-            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
-            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+            // Here load_generic is faster than load_ldmatrix.
+            B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
 
             mma_C Cm[2];
             if (k01 >= WARP_SIZE * 3/4) {
                 mma_A A1;
                 A1.x[0] = 0x01010101;
                 A1.x[1] = 0x01010101;
-                Cm[0].mma_K4(A1, B[0]);
-                Cm[1].mma_K4(A1, B[1]);
+                Cm[0].mma(A1, B[0]);
+                Cm[1].mma(A1, B[1]);
             }
 
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C Cd[2];
 
-                Cd[0].mma_K4(A[n][k01/4 + 0], B[0]);
-                Cd[1].mma_K4(A[n][k01/4 + 1], B[1]);
+                Cd[0].mma(A[n][k01/4 + 0], B[0]);
+                Cd[1].mma(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
@@ -1172,13 +1174,13 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
 #else
     GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
     NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
@@ -1186,7 +1188,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_df + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % QI3_K;
 
@@ -1212,11 +1214,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
             const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
     }
 
@@ -1242,7 +1244,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int8_t * sc8 = (const int8_t *) &sc;
         const float d = bxi->d;
 
@@ -1252,10 +1254,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         }
 #else
         x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = sc;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
-#ifndef INT8_MMA_AVAILABLE
+#ifndef NEW_MMA_AVAILABLE
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
         int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
@@ -1268,7 +1270,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         x_df[i] = bxi->d;
     }
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1317,7 +1319,7 @@ static __device__ __forceinline__ int unpack_scales_q45_K(const int * scales, co
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
@@ -1325,7 +1327,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_dm + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
@@ -1338,15 +1340,15 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
         const int qs0 = get_int_b4(bxi->qs, threadIdx.x);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
         x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
@@ -1407,7 +1409,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
     }
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1446,7 +1448,7 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + WARP_SIZE*2);
 #else
@@ -1454,7 +1456,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_dm + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
@@ -1478,16 +1480,16 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0;
         const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + QI5_K/4;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kq0] = ql0 | qh0;
         x_qs[i*(2*WARP_SIZE + 1)     + kq1] = ql1 | qh1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
@@ -1548,7 +1550,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
     }
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1587,7 +1589,7 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
     int   * x_sc = (int   *) (x_df + WARP_SIZE/QI6_K);
@@ -1596,7 +1598,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_df + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
@@ -1619,13 +1621,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int kq0 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + 0;
         const int kq1 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + QI6_K/2;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
         x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
         x_qs[i*(2*WARP_SIZE + 1)     + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI6_K;  // == 1 if QK_K == 256
@@ -1641,11 +1643,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q6_K       + kbxd] = bxi->d;
 #else
         x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
 #pragma unroll
@@ -1658,11 +1660,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
 #else
         x_sc[i*(WARP_SIZE/8) + i/8   + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -1702,11 +1704,11 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 
-    typedef mma_int_A_I16K4 mma_A;
-    typedef mma_int_B_J8K4  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_A_I16K4<int> mma_A;
+    typedef mma_B_J8K4<int>  mma_B;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
@@ -1732,8 +1734,8 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
         for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
             const int k0 = k00 + k01;
 
-            A[n][k01/4 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0),        MMQ_MMA_TILE_X_K_Q6_K);
-            A[n][k01/4 + 1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
+            A[n][k01/4 + 0].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0),        MMQ_MMA_TILE_X_K_Q6_K);
+            A[n][k01/4 + 1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
         }
 
 #pragma unroll
@@ -1771,8 +1773,9 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
             mma_B B[2];
             float dB[mma_C::ne/2];
 
-            B[0].load(y_qs + j0*MMQ_TILE_Y_K + 0        + k01, MMQ_TILE_Y_K);
-            B[1].load(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
+            // Here load_generic is faster than load_ldmatrix.
+            B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + 0        + k01, MMQ_TILE_Y_K);
+            B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
@@ -1784,8 +1787,8 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C[2];
-                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
-                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+                C[0].mma(A[n][k01/4 + 0], B[0]);
+                C[1].mma(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
@@ -1805,20 +1808,20 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
 #else
     GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
     NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_nl(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = threadIdx.x / QI4_NL;
     const int kqsx = threadIdx.x % QI4_NL;
@@ -1836,13 +1839,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int aux_q4 = get_int_b2(bxi->qs, kqsx);
         const int2 v = get_int_from_table_16(aux_q4);
         const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI4_NL;
@@ -1858,25 +1861,25 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = __half2float(bxi->d);
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + kbxd] = __half2float(bxi->d);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xxs(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % (QI2_XXS/2);
 
@@ -1905,36 +1908,36 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
             const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid0;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int ls = aux32 >> 28;
         const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/4;
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xs(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % (QI2_XS/2);
 
@@ -1959,38 +1962,38 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
             const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int ls = bxi->scales[kqsx];
         const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
 #else
         x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_s(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % (QI2_S/2);
 
@@ -2022,38 +2025,38 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
             const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int ls = bxi->scales[kqsx];
         const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
 #else
         x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_xxs(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % (QI3_XXS/2);
 
@@ -2080,36 +2083,36 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
             const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int ls = aux32 >> 28;
         const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/2;
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/2;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_s(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % (QI3_S/2);
 
@@ -2143,36 +2146,36 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
             const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid_l;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F);
         const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = ls*d;
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = ls*d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq1_s(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_ds = (half2 *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_ds = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kqsx = threadIdx.x % QI1_S;
 
@@ -2198,37 +2201,37 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             const int grid0 = (grid >> 0) & 0x0F0F0F0F;
             const int grid1 = (grid >> 4) & 0x0F0F0F0F;
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1;
 #else
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid0;
             x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
         }
 
         const float  d1q   = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1);
         const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_ds[i*MMQ_MMA_TILE_X_K_Q8_1 + kqsx] = make_half2(d1q, d1q*delta);
 #else
         x_ds[i*(WARP_SIZE/4) + i/4   + kqsx] = make_half2(d1q, d1q*delta);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_xs(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     const int kbx  = 0;           // threadIdx.x / QI4_XS
     const int kqsx = threadIdx.x; // threadIdx.x % QI4_XS
@@ -2246,13 +2249,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int aux_q4 = get_int_b4(bxi->qs, kqsx);
         const int2 v = get_int_from_table_16(aux_q4);
         const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
 #pragma unroll
@@ -2270,11 +2273,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F)
             | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x % 8] = d * (ls - 32);
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + threadIdx.x % 8] = d * (ls - 32);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 }
 
@@ -2307,16 +2310,16 @@ template<int mmq_x, int mmq_y, int nwarps, bool need_check>
 static __device__ __forceinline__ void mmq_write_back_mma(
     const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
 
-    typedef mma_int_C_I16J8 mma_C;
+    typedef mma_C_I16J8<int> mma_C;
 
     constexpr int granularity = mmq_get_granularity_device(mmq_x);
     constexpr int rows_per_warp = 2 * granularity;
     constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_C::I);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y");
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
@@ -2505,13 +2508,13 @@ static __device__ void mul_mat_q_process_tile(
     int * tile_y = (int *) data_mul_mat_q;
     int * tile_x = tile_y + GGML_PAD(mmq_x*(WARP_SIZE + WARP_SIZE/QI8_1), nwarps*WARP_SIZE);
 
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_mma;
     constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
 #else
     constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_dp4a;
     constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 
     constexpr int blocks_per_iter = MMQ_ITER_K / qk;
 
@@ -2643,7 +2646,7 @@ static __global__ void mul_mat_q(
     const int jt =  kbc /    (blocks_per_ne00*nty);
     const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
 
-    constexpr bool fixup = true; // Last index writes it data to fixup buffer to avoid data races with other blocks.
+    constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
         (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
             it, jt, kb0_start, kb0_stop);
@@ -2749,7 +2752,7 @@ template<ggml_type type>
 static int mmq_get_shmem(const int mmq_x, const int mmq_y, const int cc) {
     const tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(type, mmq_y);
     const int mmq_tile_x_k = mmq_get_mma_tile_x_k(type);
-    const int shmem_x = int8_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
+    const int shmem_x = new_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
     const int shmem_y = mmq_x*sizeof(block_q8_1_mmq);
     return shmem_x + GGML_PAD(shmem_y, MMQ_NWARPS*WARP_SIZE*sizeof(int));
 }
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb16.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb16.cu
new file mode 100644
index 000000000..f09bdeff7
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb16.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 16);
+DECL_FATTN_MMA_F16_CASE(80, 16);
+DECL_FATTN_MMA_F16_CASE(96, 16);
+DECL_FATTN_MMA_F16_CASE(112, 16);
+DECL_FATTN_MMA_F16_CASE(128, 16);
+DECL_FATTN_MMA_F16_CASE(256, 16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb32.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb32.cu
new file mode 100644
index 000000000..221108873
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb32.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 32);
+DECL_FATTN_MMA_F16_CASE(80, 32);
+DECL_FATTN_MMA_F16_CASE(96, 32);
+DECL_FATTN_MMA_F16_CASE(112, 32);
+DECL_FATTN_MMA_F16_CASE(128, 32);
+DECL_FATTN_MMA_F16_CASE(256, 32);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb64.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb64.cu
new file mode 100644
index 000000000..d24b08575
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb64.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 64);
+DECL_FATTN_MMA_F16_CASE(80, 64);
+DECL_FATTN_MMA_F16_CASE(96, 64);
+DECL_FATTN_MMA_F16_CASE(112, 64);
+DECL_FATTN_MMA_F16_CASE(128, 64);
+DECL_FATTN_MMA_F16_CASE(256, 64);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb8.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb8.cu
new file mode 100644
index 000000000..bdf86c0ea
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-cpb8.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 8);
+DECL_FATTN_MMA_F16_CASE(80, 8);
+DECL_FATTN_MMA_F16_CASE(96, 8);
+DECL_FATTN_MMA_F16_CASE(112, 8);
+DECL_FATTN_MMA_F16_CASE(128, 8);
+DECL_FATTN_MMA_F16_CASE(256, 8);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu
deleted file mode 100644
index 2d94e65c2..000000000
--- a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu
+++ /dev/null
@@ -1,10 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 16, float);
-DECL_FATTN_WMMA_F16_CASE(80, 16, float);
-DECL_FATTN_WMMA_F16_CASE(96, 16, float);
-DECL_FATTN_WMMA_F16_CASE(112, 16, float);
-DECL_FATTN_WMMA_F16_CASE(128, 16, float);
-DECL_FATTN_WMMA_F16_CASE(256, 16, float);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu
deleted file mode 100644
index c3d9df3c4..000000000
--- a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu
+++ /dev/null
@@ -1,9 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 32, float);
-DECL_FATTN_WMMA_F16_CASE(80, 32, float);
-DECL_FATTN_WMMA_F16_CASE(96, 32, float);
-DECL_FATTN_WMMA_F16_CASE(112, 32, float);
-DECL_FATTN_WMMA_F16_CASE(128, 32, float);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu
deleted file mode 100644
index bb680e401..000000000
--- a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu
+++ /dev/null
@@ -1,10 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 16, half);
-DECL_FATTN_WMMA_F16_CASE(80, 16, half);
-DECL_FATTN_WMMA_F16_CASE(96, 16, half);
-DECL_FATTN_WMMA_F16_CASE(112, 16, half);
-DECL_FATTN_WMMA_F16_CASE(128, 16, half);
-DECL_FATTN_WMMA_F16_CASE(256, 16, half);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu
deleted file mode 100644
index 073f71b1f..000000000
--- a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu
+++ /dev/null
@@ -1,10 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 32, half);
-DECL_FATTN_WMMA_F16_CASE(80, 32, half);
-DECL_FATTN_WMMA_F16_CASE(96, 32, half);
-DECL_FATTN_WMMA_F16_CASE(112, 32, half);
-DECL_FATTN_WMMA_F16_CASE(128, 32, half);
-DECL_FATTN_WMMA_F16_CASE(256, 32, half);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu
deleted file mode 100644
index d30710c5f..000000000
--- a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu
+++ /dev/null
@@ -1,8 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 8, half);
-DECL_FATTN_WMMA_F16_CASE(96, 8, half);
-DECL_FATTN_WMMA_F16_CASE(128, 8, half);
-DECL_FATTN_WMMA_F16_CASE(256, 8, half);
diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
index d7874e6ea..a2628f16e 100755
--- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
+++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
@@ -12,13 +12,13 @@ SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.p
 DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
 """
 
-SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+SOURCE_FATTN_MMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
 
-#include "../fattn-wmma-f16.cuh"
+#include "../fattn-mma-f16.cuh"
 
 """
 
-SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
+SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size}, {cols_per_block});\n"
 
 TYPES_MMQ = [
     "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
@@ -57,20 +57,12 @@ for vkq_size in [16, 32]:
                 with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f:
                     f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
 
-for kq_acc_t in ["half", "float"]:
-    for cols_per_block in [8, 16, 32]:
-        if kq_acc_t == "float" and cols_per_block == 8:
-            continue
+for cols_per_block in [8, 16, 32, 64]:
+    with open(f"fattn-mma-f16-instance-cpb{cols_per_block}.cu", "w") as f:
+        f.write(SOURCE_FATTN_MMA_START)
 
-        with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
-            f.write(SOURCE_FATTN_WMMA_START)
-
-            for head_size in [64, 80, 96, 112, 128, 256]:
-                if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
-                    continue
-                if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
-                    continue
-                f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))
+        for head_size in [64, 80, 96, 112, 128, 256]:
+            f.write(SOURCE_FATTN_MMA_CASE.format(cols_per_block=cols_per_block, head_size=head_size))
 
 for type in TYPES_MMQ:
     with open(f"mmq-instance-{get_short_name(type)}.cu", "w") as f:
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 8594093f0..129478ed7 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -25,6 +25,7 @@
 #define CU_MEM_LOCATION_TYPE_DEVICE hipMemLocationTypeDevice
 #define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
 #define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
+#define __shfl_sync(mask, var, laneMask, width) __shfl(var, laneMask, width)
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
 #define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
 #define cublasCreate hipblasCreate
diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index 7a877bdc1..eb03e10fa 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -50,7 +50,7 @@ file(GLOB   GGML_HEADERS_ROCM "../ggml-cuda/*.cuh")
 list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h")
 
 file(GLOB   GGML_SOURCES_ROCM "../ggml-cuda/*.cu")
-file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
+file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
 list(APPEND GGML_SOURCES_ROCM ${SRCS})
 file(GLOB   SRCS "../ggml-cuda/template-instances/mmq*.cu")
 list(APPEND GGML_SOURCES_ROCM ${SRCS})
diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt
index 415b2b2e0..2f555416e 100644
--- a/ggml/src/ggml-musa/CMakeLists.txt
+++ b/ggml/src/ggml-musa/CMakeLists.txt
@@ -29,7 +29,7 @@ if (MUSAToolkit_FOUND)
     list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h")
 
     file(GLOB   GGML_SOURCES_MUSA "../ggml-cuda/*.cu")
-    file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
+    file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
     list(APPEND GGML_SOURCES_MUSA ${SRCS})
     file(GLOB   SRCS "../ggml-cuda/template-instances/mmq*.cu")
     list(APPEND GGML_SOURCES_MUSA ${SRCS})

From 90f9b88afb6447d3929843a2aa98c0f11074762d Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sun, 2 Feb 2025 19:58:34 +0000
Subject: [PATCH 313/376] nit: more informative crash when grammar sampler
 fails (#11593)

---
 src/llama-grammar.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index 6be5cbe0e..9b518d1ac 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -1213,5 +1213,7 @@ void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string
     }
 
     grammar.partial_utf8 = decoded.second;
-    GGML_ASSERT(!grammar.stacks.empty());
+    if (grammar.stacks.empty()) {
+        throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece);
+    }
 }

From 4d0598e1445a64c99cf2faac72f8d5d023f1e6a1 Mon Sep 17 00:00:00 2001
From: uvos <philipp@uvos.xyz>
Date: Sun, 2 Feb 2025 22:08:05 +0100
Subject: [PATCH 314/376] HIP: add GGML_CUDA_CC_IS_* for amd familys as
 increasing cc archtectures for amd gpus are not supersets of eatch other
 (#11601)

This fixes a bug where RDNA1 gpus other than gfx1010 where not handled correctly
---
 ggml/src/ggml-cuda/common.cuh   | 7 +++++++
 ggml/src/ggml-cuda/ggml-cuda.cu | 4 ++--
 ggml/src/ggml-cuda/mmq.cu       | 2 +-
 ggml/src/ggml-cuda/mmq.cuh      | 2 +-
 4 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 88be8fc8a..232163c1c 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -61,6 +61,13 @@
 #define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
 #define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
 
+#define GGML_CUDA_CC_IS_RDNA(cc)  (cc >= GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
+#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
+#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3)
+#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA)
+#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA && cc < GGML_CUDA_CC_RDNA1)
+
 #define GGML_CUDA_CC_QY1        210
 #define GGML_CUDA_CC_QY2        220
 
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 383131c77..bda10aec1 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1205,7 +1205,7 @@ static void ggml_cuda_op_mul_mat_cublas(
 
         CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
 
-        if (compute_capability == GGML_CUDA_CC_CDNA) {
+        if (GGML_CUDA_CC_IS_CDNA(compute_capability)) {
             const float alpha = 1.0f;
             const float beta = 0.0f;
             CUBLAS_CHECK(
@@ -1750,7 +1750,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
         beta  = &beta_f32;
     }
 
-    if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
+    if (GGML_CUDA_CC_IS_CDNA(ggml_cuda_info().devices[ctx.device].cc)) {
         cu_compute_type = CUBLAS_COMPUTE_32F;
         alpha = &alpha_f32;
         beta  = &beta_f32;
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 83cb78cbd..45212f66c 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -148,5 +148,5 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return cc < GGML_CUDA_CC_VOLTA || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
     }
 
-    return (cc < GGML_CUDA_CC_RDNA3 && cc != GGML_CUDA_CC_CDNA && cc != GGML_CUDA_CC_VEGA20) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
+    return (!GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc) && !GGML_CUDA_CC_IS_GCN(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
 }
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index c05c84778..7a2c4d85b 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -120,7 +120,7 @@ static constexpr __device__ int get_mmq_x_max_device() {
 }
 
 static constexpr int get_mmq_y_host(const int cc) {
-    return cc >= GGML_CUDA_CC_OFFSET_AMD ? (cc == GGML_CUDA_CC_RDNA1 ? 64 : 128) : (cc >= GGML_CUDA_CC_VOLTA ? 128 : 64);
+    return cc >= GGML_CUDA_CC_OFFSET_AMD ? (GGML_CUDA_CC_IS_RDNA1(cc)  ? 64 : 128) : (cc >= GGML_CUDA_CC_VOLTA ? 128 : 64);
 }
 
 static constexpr __device__ int get_mmq_y_device() {

From 396856b40029dd6747d2fbdb179e828683418045 Mon Sep 17 00:00:00 2001
From: uvos <philipp@uvos.xyz>
Date: Sun, 2 Feb 2025 22:40:09 +0100
Subject: [PATCH 315/376] CUDA/HIP: add support for selectable warp size to mmv
 (#11519)

CUDA/HIP: add support for selectable warp size to mmv
---
 ggml/src/ggml-cuda/common.cuh    |  8 +++++++
 ggml/src/ggml-cuda/mmv.cu        | 38 ++++++++++++++++++++------------
 ggml/src/ggml-cuda/vendors/hip.h |  2 ++
 3 files changed, 34 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 232163c1c..174916bc9 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -176,6 +176,14 @@ static constexpr bool new_mma_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_TURING;
 }
 
+static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+    return __AMDGCN_WAVEFRONT_SIZE;
+#else
+    return 32;
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+}
+
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu
index ac45f2d17..5a9ddd958 100644
--- a/ggml/src/ggml-cuda/mmv.cu
+++ b/ggml/src/ggml-cuda/mmv.cu
@@ -5,9 +5,10 @@ template <typename T, typename type_acc, int block_size>
 static __global__ void mul_mat_vec(
         const T * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
         const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) {
-    const int64_t row     = blockIdx.x;
-    const int64_t channel = blockIdx.z;
-    const int     tid     = threadIdx.x;
+    const int64_t row       = blockIdx.x;
+    const int64_t channel   = blockIdx.z;
+    const int     tid       = threadIdx.x;
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
     x   += (channel/channel_ratio)*stride_channel_x + row*stride_row;
     y   +=  channel               *stride_channel_y;
@@ -18,8 +19,8 @@ static __global__ void mul_mat_vec(
     extern __shared__ char data_mmv[];
     float * buf_iw = (float *) data_mmv;
 
-    if (block_size > WARP_SIZE) {
-        if (tid < WARP_SIZE) {
+    if (block_size > warp_size) {
+        if (tid < warp_size) {
             buf_iw[tid] = 0.0f;
         }
         __syncthreads();
@@ -67,16 +68,16 @@ static __global__ void mul_mat_vec(
         static_assert(std::is_same<T, void>::value, "unsupported type");
     }
 
-    sumf = warp_reduce_sum(sumf);
+    sumf = warp_reduce_sum<warp_size>(sumf);
 
-    if (block_size > WARP_SIZE) {
-        buf_iw[tid/WARP_SIZE] = sumf;
+    if (block_size > warp_size) {
+        buf_iw[tid/warp_size] = sumf;
         __syncthreads();
-        if (tid >= WARP_SIZE) {
+        if (tid >= warp_size) {
             return;
         }
         sumf = buf_iw[tid];
-        sumf = warp_reduce_sum(sumf);
+        sumf = warp_reduce_sum<warp_size>(sumf);
     }
 
     if (tid != 0) {
@@ -96,10 +97,19 @@ static void launch_mul_mat_vec_cuda(
     GGML_ASSERT(stride_row % 2 == 0);
     GGML_ASSERT(nchannels_y % nchannels_x == 0);
     const int64_t channel_ratio = nchannels_y / nchannels_x;
+    int device;
+    int warp_size;
 
-    int64_t block_size_best = WARP_SIZE;
-    int64_t niter_best      = (ncols + 2*WARP_SIZE - 1) / (2*WARP_SIZE);
-    for (int64_t block_size = 2*WARP_SIZE; block_size <= 256; block_size += WARP_SIZE) {
+    CUDA_CHECK(cudaGetDevice(&device));
+    warp_size = ggml_cuda_info().devices[device].warp_size;
+
+    int64_t block_size_best = warp_size;
+    int64_t niter_best      = (ncols + 2*warp_size - 1) / (2*warp_size);
+    int64_t max_block_size  = 256;
+    if(ggml_cuda_info().devices[device].cc > GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_info().devices[device].cc < GGML_CUDA_CC_RDNA1) {
+        max_block_size = 128;
+    }
+    for (int64_t block_size = 2*warp_size; block_size <= max_block_size; block_size += warp_size) {
         const int64_t niter = (ncols + 2*block_size - 1) / (2*block_size);
         if (niter < niter_best) {
             niter_best      = niter;
@@ -107,7 +117,7 @@ static void launch_mul_mat_vec_cuda(
         }
     }
 
-    const int smem = WARP_SIZE*sizeof(float);
+    const int smem = warp_size*sizeof(float);
     const dim3 block_nums(nrows, 1, nchannels_y);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 129478ed7..81964611c 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -1,5 +1,6 @@
 #pragma once
 
+#define HIP_ENABLE_WARP_SYNC_BUILTINS 1
 #include <hip/hip_runtime.h>
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
@@ -8,6 +9,7 @@
 // for rocblas_initialize()
 #include "rocblas/rocblas.h"
 #endif // __HIP_PLATFORM_AMD__
+
 #define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
 #define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F

From 6eecde3cc8fda44da7794042e3668de4af3c32c6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sun, 2 Feb 2025 23:48:29 +0100
Subject: [PATCH 316/376] HIP: fix flash_attn_stream_k_fixup warning (#11604)

---
 ggml/src/ggml-cuda/fattn-common.cuh | 10 ++++++++++
 ggml/src/ggml-cuda/softmax.cu       |  4 ++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index cfd7c0f44..d40ee2da4 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -516,6 +516,12 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
         nullptr;
 }
 
+// The HIP compiler for some reason complains that it can't unroll a loop because of the jt*ncols + j >= ne01 conditional.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpass-failed"
+#endif // __clang__
+
 template<int D, int ncols, int KQ_stride> // D == head size
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
@@ -614,6 +620,10 @@ static __global__ void flash_attn_stream_k_fixup(
     }
 }
 
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif // __clang__
+
 template<int D, int parallel_blocks> // D == head size
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu
index da377200e..aac6e0999 100644
--- a/ggml/src/ggml-cuda/softmax.cu
+++ b/ggml/src/ggml-cuda/softmax.cu
@@ -18,7 +18,7 @@ __device__ float __forceinline__ t2f32<half>(half val) {
 #ifdef __clang__
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wpass-failed"
-#endif
+#endif // __clang__
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
         const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y,
@@ -126,7 +126,7 @@ static __global__ void soft_max_f32(
 }
 #ifdef __clang__
 #pragma clang diagnostic pop
-#endif
+#endif // __clang__
 
 static __global__ void soft_max_back_f32(
         const float * grad, const float * dstf, float * dst, const int ncols, const float scale) {

From d92cb67e37abc23b1c6f7b0ef27a9889da8537e3 Mon Sep 17 00:00:00 2001
From: mashdragon <122402293+mashdragon@users.noreply.github.com>
Date: Mon, 3 Feb 2025 09:42:55 +0000
Subject: [PATCH 317/376] server : (webui) Fix Shift+Enter handling (#11609)

* Fix Shift+Enter handling

`exact` on the Enter handler means the message is not sent when Shift+Enter is pressed anyway

* build index.html.gz

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 examples/server/public/index.html.gz | Bin 1207150 -> 1207129 bytes
 examples/server/webui/index.html     |   1 -
 2 files changed, 1 deletion(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 582ccc0d3f8d07ce79dd9e978772f9c5ea85c426..3a2529aa2fb84485aa480d3d476e5ef27eab8e27 100644
GIT binary patch
delta 8942
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diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index d3893ea4e..882570c81 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -154,7 +154,6 @@
             placeholder="Type a message (Shift+Enter to add a new line)"
             v-model="inputMsg"
             @keydown.enter.exact.prevent="sendMessage"
-            @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
             :disabled="isGenerating"
             id="msg-input"
             dir="auto"

From 21c84b5d2dc04050714567501bf78762bfa17846 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 3 Feb 2025 13:25:56 +0100
Subject: [PATCH 318/376] CUDA: fix Volta FlashAttention logic (#11615)

---
 ggml/src/ggml-cuda/fattn-wmma-f16.cu | 2 +-
 ggml/src/ggml-cuda/fattn.cu          | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index 1054ff95d..45702ad65 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -561,7 +561,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_ten
                     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
                     break;
                 // case 256:
-                //     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                //     ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
                 //     break;
                 default:
                     GGML_ABORT("fatal error");
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index b1e66d470..b0cf152f5 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -235,7 +235,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
         return;
     }
 
-    if (!new_mma_available(cc)) {
+    if (!fp16_mma_available(cc)) {
         if (prec == GGML_PREC_DEFAULT) {
             if (Q->ne[1] <= 8) {
                 ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
@@ -265,6 +265,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
     if (cc == GGML_CUDA_CC_VOLTA) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+        return;
     }
 
     ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);

From 8ec05832fa8409c49b3bbd13f957c6ae8486e618 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 3 Feb 2025 14:57:08 +0200
Subject: [PATCH 319/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index ddb9d817e..34f1cbf69 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-32f0b85987396945afea2291d5f4c5862434292b
+498e0ecd2c4f9379439fd413805af10e8e9ff349

From 5598f475be3e31430fbe17ebb85654ec90dc201e Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 3 Feb 2025 16:45:38 +0100
Subject: [PATCH 320/376] server : remove CPPHTTPLIB_NO_EXCEPTIONS define
 (#11622)

This commit removes the CPPHTTPLIB_NO_EXCEPTIONS define from the server
code.

The motivation for this is that when using a debug build the server
would crash when an exception was throws and terminate the server
process, as it was unhandled. When CPPHTTPLIB_NO_EXCEPTIONS is set
cpp_httplib will not call the exception handler, which would normally
return a 500 error to the client. This caused tests to fail when using
a debug build.

Fixes: https://github.com/ggerganov/llama.cpp/issues/11613
---
 examples/server/utils.hpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index fefdce55b..5f97df5fd 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -5,10 +5,6 @@
 #include "llama.h"
 #include "common/base64.hpp"
 
-#ifndef NDEBUG
-// crash the server in debug mode, otherwise send an http 500 error
-#define CPPHTTPLIB_NO_EXCEPTIONS 1
-#endif
 // increase max payload length to allow use of larger context size
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
 #include "httplib.h"

From 1d1e6a90bcf485ad2dee309c31cf19bd802465e5 Mon Sep 17 00:00:00 2001
From: Woof Dog <197125663+woof-dog@users.noreply.github.com>
Date: Mon, 3 Feb 2025 22:16:27 +0000
Subject: [PATCH 321/376] server : (webui) allow typing and submitting during
 llm response (#11626)

---
 examples/server/public/index.html.gz | Bin 1207129 -> 1207175 bytes
 examples/server/webui/index.html     |   1 -
 examples/server/webui/src/main.js    |   8 ++++++++
 3 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 3a2529aa2fb84485aa480d3d476e5ef27eab8e27..df3cb1bef96b805742b458d0abf6c9b16826fae8 100644
GIT binary patch
delta 868279
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diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index 882570c81..1ad85e739 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -154,7 +154,6 @@
             placeholder="Type a message (Shift+Enter to add a new line)"
             v-model="inputMsg"
             @keydown.enter.exact.prevent="sendMessage"
-            :disabled="isGenerating"
             id="msg-input"
             dir="auto"
           ></textarea>
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index 90f4ca368..bc8a6d80f 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -469,6 +469,14 @@ const mainApp = createApp({
     },
     async sendMessage() {
       if (!this.inputMsg) return;
+
+      if (this.isGenerating) {
+        this.stopGeneration();
+        while (this.isGenerating) {
+          await new Promise((resolve) => setTimeout(resolve, 10));
+        }
+      }
+
       const currConvId = this.viewingConvId;
 
       StorageUtils.appendMsg(currConvId, {

From b3451785aca16fbf4214eeba7e4612676cdf8ccb Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Tue, 4 Feb 2025 00:10:52 +0100
Subject: [PATCH 322/376] server : (webui) revert hacky solution from #11626
 (#11634)

---
 examples/server/public/index.html.gz | Bin 1207175 -> 1207128 bytes
 examples/server/webui/src/main.js    |  11 +++--------
 2 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index df3cb1bef96b805742b458d0abf6c9b16826fae8..e876776e82eeba329b1e808cd46684e32aca94f5 100644
GIT binary patch
delta 881624
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zMp^HW24y_#81|cEO5G`(9?GSHy>-`vgIu3tntbBZ3^|LQdH2*kRFBY?FDM(OJz+ha
z$10(h`iy^71@Vgv%9E6BHxMv$f0IN(3}Aj%?{nY>Bd)eu4WP)U5Tb|M%Xk58D-onn
zyC7{Z)ut*|Y^R=DNS(~jw%hUpYgV+)pvo16hSzT|Q)p1@KcZLfh-)L=;&D5j;N31I
z&Z^PpilfB?X5}n+v}3S|4$2!(4yLGz<X=iZg7|;%eB4r<8#94`N=wHt9u;X}yge{N
zTo{#SAr8FIV?7;?a@5!{M5>G=_K9Qrpa1PY{jdM=pV5`;Wl!2q*mCvmI9;}@O51_K
zUiGSt|AcdLhe5hL+NVnY`@jF!|A4lq@w%$@r%CNZ@dF=Y?j`Jh`H$!#iXU3VEUmoi
z3p;-&bn}PpZWup^(>9^&-tH(ScfvAIej^Y3-p(lC#zYLuV3pejgjKO6iCaB`tx>J0
zAD6Zig@`E<hD8xOn~f^F$DLR9w05XcZ5^S%H>|9~K0f9w|Mg$dc2f<mmk^O@S0C0B
z%DYde#_zhwx2`HXRUp>EVHk<pBX`8}!ODM*gj2d*YXo^Kyr2n18(4)-HY9P2sU43U
ziU~U}kwwT^c|I&l=W#<%QA+8wj4ett;@lRi;B29dE%*Zxj|@v!)rmeH<&@cRNk$HD
z!;q?NTU{*$c&?sK-RwTQb0};B)-?u~ULJrq=7-#wI6uI%g#O03o=X2h11~!~SjR9f
y$>?;O<KQX!7j}wJ=Iekmqp>}*URe|qeDq|*uh)Y6w&3coC;tc3!&J#`1|tAFA(^cJ

diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
index bc8a6d80f..50197a355 100644
--- a/examples/server/webui/src/main.js
+++ b/examples/server/webui/src/main.js
@@ -468,14 +468,9 @@ const mainApp = createApp({
       URL.revokeObjectURL(url);
     },
     async sendMessage() {
-      if (!this.inputMsg) return;
-
-      if (this.isGenerating) {
-        this.stopGeneration();
-        while (this.isGenerating) {
-          await new Promise((resolve) => setTimeout(resolve, 10));
-        }
-      }
+      // prevent sending empty message
+      // also allow typing the message while generating, but does not allow sending it (to match UX/UI behavior of other chat apps)
+      if (!this.inputMsg || this.isGenerating) return;
 
       const currConvId = this.viewingConvId;
 

From cde383323959544abe10a4d79e1d3e1ee479933c Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Mon, 3 Feb 2025 23:49:27 +0000
Subject: [PATCH 323/376] `tool-call`: allow `--chat-template chatml` w/
 `--jinja`, default to chatml upon parsing issue, avoid double bos (#11616)

* tool-call: allow `--jinja --chat-template chatml`

* fix double bos issue (drop bos/eos tokens from jinja template)

* add missing try catch around jinja parsing to default to chatml

* Simplify default chatml logic
---
 common/common.cpp                             | 58 +++++++----
 .../server/tests/unit/test_chat_completion.py |  7 +-
 examples/server/tests/unit/test_tool_call.py  | 98 +++++++++++++++----
 3 files changed, 124 insertions(+), 39 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 6c81d18f9..edba6fb4b 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1869,11 +1869,19 @@ std::string common_chat_format_example(const common_chat_template & tmpl, bool u
     return common_chat_apply_template(tmpl, msgs, true, use_jinja);
 }
 
+#define CHATML_TEMPLATE_SRC \
+    "{%- for message in messages -%}\n" \
+    "  {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \
+    "{%- endfor -%}\n" \
+    "{%- if add_generation_prompt -%}\n" \
+    "  {{- '<|im_start|>assistant\n' -}}\n" \
+    "{%- endif -%}"
+
 common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override)
 {
-    auto vocab = llama_model_get_vocab(model);
-    std::string default_template_src = chat_template_override;
-    std::string template_tool_use_src = chat_template_override;
+    std::string default_template_src;
+    std::string template_tool_use_src;
+
     bool has_explicit_template = !chat_template_override.empty();
     if (chat_template_override.empty()) {
         auto str = llama_model_chat_template(model, /* name */ nullptr);
@@ -1886,21 +1894,21 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
             template_tool_use_src = str;
             has_explicit_template = true;
         }
+    } else {
+        default_template_src = chat_template_override;
     }
     if (default_template_src.empty() || default_template_src == "chatml") {
         if (!template_tool_use_src.empty()) {
             default_template_src = template_tool_use_src;
         } else {
-            default_template_src = R"(
-                {%- for message in messages -%}
-                    {{- "<|im_start|>" + message.role + "\n" + message.content + "<|im_end|>\n" -}}
-                {%- endfor -%}
-                {%- if add_generation_prompt -%}
-                    {{- "<|im_start|>assistant\n" -}}
-                {%- endif -%}
-            )";
+            default_template_src = CHATML_TEMPLATE_SRC;
         }
     }
+    std::string token_bos;
+    std::string token_eos;
+    // TODO: update logic that adds BOS and EOS tokens to the tokenized prompt, in favour of the template.
+#if 0
+    auto vocab = llama_model_get_vocab(model);
     const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
         if (token == LLAMA_TOKEN_NULL) {
             if (default_template_src.find(jinja_variable_name) != std::string::npos
@@ -1912,15 +1920,25 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
             return common_token_to_piece(vocab, token, true);
         }
     };
-    auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
-    auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
-    return {
-        has_explicit_template,
-        std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
-        template_tool_use_src.empty()
-            ? nullptr
-            : std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos)
-    };
+    token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
+    token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
+#endif
+    try {
+        return {
+            has_explicit_template,
+            std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
+            template_tool_use_src.empty()
+                ? nullptr
+                : std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos),
+        };
+    } catch (const std::exception & e) {
+        LOG_ERR("%s: failed to parse chat template: %s\n", __func__, e.what());
+        return {
+            has_explicit_template,
+            std::make_unique<minja::chat_template>(CHATML_TEMPLATE_SRC, token_bos, token_eos),
+            nullptr,
+        };
+    }
 }
 
 //
diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py
index f5d8b0572..f23d5cff4 100644
--- a/examples/server/tests/unit/test_chat_completion.py
+++ b/examples/server/tests/unit/test_chat_completion.py
@@ -13,9 +13,12 @@ def create_server():
 @pytest.mark.parametrize(
     "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
     [
+        (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None),
+        (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None),
         (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
-        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
-        (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True,  None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'),
+        (None, "Book", "What is the best book", 8, "^ blue",                    23, 8, "length", True, "This is not a chat template, it is"),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
     ]
diff --git a/examples/server/tests/unit/test_tool_call.py b/examples/server/tests/unit/test_tool_call.py
index e6ed9c9be..62a48a0d9 100644
--- a/examples/server/tests/unit/test_tool_call.py
+++ b/examples/server/tests/unit/test_tool_call.py
@@ -67,8 +67,8 @@ WEATHER_TOOL = {
 
 
 def do_test_completion_with_required_tool_tiny(template_name: str, tool: dict, argument_key: str | None):
-    n_predict = 512
     global server
+    n_predict = 512
     # server = ServerPreset.stories15m_moe()
     server.jinja = True
     server.n_predict = n_predict
@@ -139,29 +139,49 @@ def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict,
 @pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [
     (TEST_TOOL,    "success",  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
     (PYTHON_TOOL,  "code",     "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
+    # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
     (TEST_TOOL,    "success",  "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
     (PYTHON_TOOL,  "code",     "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+
     (TEST_TOOL,    "success",  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
     (PYTHON_TOOL,  "code",     "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
     (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
     (PYTHON_TOOL,  "code",     "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
     (PYTHON_TOOL,  "code",     "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
     (PYTHON_TOOL,  "code",     "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
-    (TEST_TOOL,    "success",  "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
-    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
     (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
     (TEST_TOOL,    "success",  "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
     (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      "chatml"),
     # TODO: fix these
     # (TEST_TOOL,    "success",  "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
     # (PYTHON_TOOL,  "code",     "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
 ])
-def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: Tuple[str, str | None] | None):
+def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
     n_predict = 512
     server.n_slots = 1
     server.jinja = True
@@ -169,10 +189,12 @@ def test_completion_with_required_tool_real_model(tool: dict, argument_key: str
     server.n_predict = n_predict
     server.model_hf_repo = hf_repo
     server.model_hf_file = None
-    if template_override:
+    if isinstance(template_override, tuple):
         (template_hf_repo, template_variant) = template_override
         server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
         assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
     server.start(timeout_seconds=TIMEOUT_SERVER_START)
     res = server.make_request("POST", "/chat/completions", data={
         "max_tokens": n_predict,
@@ -252,18 +274,36 @@ def test_completion_without_tool_call_slow(template_name: str, n_predict: int, t
 @pytest.mark.slow
 @pytest.mark.parametrize("hf_repo,template_override", [
     ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
-    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
     ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
     ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
-    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
-    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    "chatml"),
+
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      "chatml"),
+
     ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
     ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       "chatml"),
+
     ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
+    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+
     # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
     # ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
 ])
-def test_weather_tool_call(hf_repo: str, template_override: Tuple[str, str | None] | None):
+def test_weather_tool_call(hf_repo: str, template_override: str | Tuple[str, str | None] | None):
     global server
     server.n_slots = 1
     server.jinja = True
@@ -271,10 +311,12 @@ def test_weather_tool_call(hf_repo: str, template_override: Tuple[str, str | Non
     server.n_predict = 512
     server.model_hf_repo = hf_repo
     server.model_hf_file = None
-    if template_override:
+    if isinstance(template_override, tuple):
         (template_hf_repo, template_variant) = template_override
         server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
         assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
     server.start(timeout_seconds=TIMEOUT_SERVER_START)
     res = server.make_request("POST", "/chat/completions", data={
         "max_tokens": 256,
@@ -298,19 +340,39 @@ def test_weather_tool_call(hf_repo: str, template_override: Tuple[str, str | Non
 
 @pytest.mark.slow
 @pytest.mark.parametrize("expected_arguments_override,hf_repo,template_override", [
-    (None,                 "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
     (None,                 "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (None,                 "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
     (None,                 "bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai-functionary-medium-v3.2", None)),
-    ('{"code":"print("}',  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
-    (None,                 "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    (None,                 "bartowski/functionary-small-v3.2-GGUF:Q8_0",       "chatml"),
+
+    (None,                 "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    ('{"code":"print("}',  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
+    ('{"code":"print("}',  "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    (None,                 "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
     ('{"code":"print("}',  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    ('{"code":"print("}',  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
     (None,                 "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
-    (None,                 "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",      ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
-    (None,                 "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")),
+    (None,                 "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
+    (None,                 "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (None,                 "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    "chatml"),
+
+    (None,                 "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")),
+    (None,                 "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      "chatml"),
+
     (None,                 "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    (None,                 "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
+    # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
+    (None,                 "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+
     # (None,                 "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
 ])
-def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo: str, template_override: Tuple[str, str | None] | None):
+def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
     global server
     server.n_slots = 1
     server.jinja = True
@@ -318,10 +380,12 @@ def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo:
     server.n_predict = 128
     server.model_hf_repo = hf_repo
     server.model_hf_file = None
-    if template_override:
+    if isinstance(template_override, tuple):
         (template_hf_repo, template_variant) = template_override
         server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
         assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
     server.start(timeout_seconds=TIMEOUT_SERVER_START)
     res = server.make_request("POST", "/chat/completions", data={
         "max_tokens": 256,

From b34aedd558dcf67f7e96b0260d6b554877bd3499 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 4 Feb 2025 09:30:42 +0200
Subject: [PATCH 324/376] ci : do not stale-close roadmap issues

---
 .github/workflows/close-issue.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml
index f63860d14..276a217d4 100644
--- a/.github/workflows/close-issue.yml
+++ b/.github/workflows/close-issue.yml
@@ -17,7 +17,7 @@ jobs:
     steps:
       - uses: actions/stale@v5
         with:
-          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
+          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
           days-before-issue-stale: 30
           days-before-issue-close: 14
           stale-issue-label: "stale"

From 8f8290ada96194138bb3d4dc6958323bc728ef83 Mon Sep 17 00:00:00 2001
From: Christian Kastner <ckk@kvr.at>
Date: Tue, 4 Feb 2025 00:17:15 +0100
Subject: [PATCH 325/376] cmake: Add ability to pass in GGML_BUILD_NUMBER
 (ggml/1096)

This makes git as a dependency optional, and is useful in the case where
ggml is built not from git, but from a tarball, or a distribution source
package.

This conditional also affects GGML_BUILD_COMMIT. Nothing seems to be
using it, though, so there doesn't seem much value factor it out, or
even require it.
---
 ggml/CMakeLists.txt | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 7c069e420..75b5ea3b4 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -274,22 +274,25 @@ endif()
 
 # Generate version info based on git commit.
 
-find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
-execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
-    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-    OUTPUT_VARIABLE GGML_BUILD_NUMBER
-    OUTPUT_STRIP_TRAILING_WHITESPACE
-)
+if(NOT DEFINED GGML_BUILD_NUMBER)
+    find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
+    execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE GGML_BUILD_NUMBER
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
 
-if(GGML_BUILD_NUMBER EQUAL 1)
-    message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
+    if(GGML_BUILD_NUMBER EQUAL 1)
+        message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
+    endif()
+
+    execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE GGML_BUILD_COMMIT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
 endif()
 
-execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
-    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-    OUTPUT_VARIABLE GGML_BUILD_COMMIT
-    OUTPUT_STRIP_TRAILING_WHITESPACE
-)
 
 # Capture variables prefixed with GGML_.
 

From 7c9e0ca52039530b6ddf16015cf1263491e92d75 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 4 Feb 2025 12:59:21 +0200
Subject: [PATCH 326/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 34f1cbf69..26a105f64 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-498e0ecd2c4f9379439fd413805af10e8e9ff349
+694244a6e40dc255f6bb4376fb17431c06633e6c

From 387a1598ca094a4755303ec964c3b09b4c5c300e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 4 Feb 2025 13:04:10 +0200
Subject: [PATCH 327/376] authors : update

---
 AUTHORS | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 82 insertions(+), 1 deletion(-)

diff --git a/AUTHORS b/AUTHORS
index 2eb60806a..6796b2941 100644
--- a/AUTHORS
+++ b/AUTHORS
@@ -1,4 +1,4 @@
-# date: Thu Nov 28 20:46:15 EET 2024
+# date: Tue Feb  4 13:04:05 EET 2025
 # this file is auto-generated by scripts/gen-authors.sh
 
 0cc4m <picard12@live.de>
@@ -20,6 +20,8 @@ Adithya Balaji <adithya.b94@gmail.com>
 AdithyanI <adithyan.i4internet@gmail.com>
 Adrian <smith.adriane@gmail.com>
 Adrian Hesketh <a-h@users.noreply.github.com>
+Adrien Gallouët <adrien@gallouet.fr>
+Adrien Gallouët <angt@huggingface.co>
 Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com>
 Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
 AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
@@ -55,6 +57,7 @@ Ananta Bastola <anantarajbastola@gmail.com>
 Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
 András Salamon <ott2@users.noreply.github.com>
 Andreas (Andi) Kunar <andreask@msn.com>
+Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
 Andrei <abetlen@gmail.com>
 Andrew Canis <andrew.canis@gmail.com>
 Andrew Downing <andrew2085@gmail.com>
@@ -91,13 +94,17 @@ Ben Siraphob <bensiraphob@gmail.com>
 Ben Williams <ben@719ben.com>
 Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
 Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
+Benson Wong <mostlygeek@gmail.com>
 Bernat Vadell <hounter.caza@gmail.com>
+Bernhard M. Wiedemann <githubbmwprimary@lsmod.de>
 Bert Wagner <github@bertwagner.com>
+Billel Mokeddem <billel.mokeddem.ml@gmail.com>
 Bingan <70050083+binganao@users.noreply.github.com>
 Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com>
 Bodo Graumann <mail@bodograumann.de>
 Bono Lv <lvscar@users.noreply.github.com>
 Borislav Stanimirov <b.stanimirov@abv.bg>
+Borislav Stanimirov <b@ibob.bg>
 Branden Butler <bwtbutler@hotmail.com>
 Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
 Brian <mofosyne@gmail.com>
@@ -117,6 +124,7 @@ Casey Primozic <casey@cprimozic.net>
 Casey Primozic <me@ameo.link>
 CausalLM <148736309+CausalLM@users.noreply.github.com>
 Cebtenzzre <cebtenzzre@gmail.com>
+CentricStorm <CentricStorm@users.noreply.github.com>
 Chad Brewbaker <crb002@gmail.com>
 Changyeon Kim <cyzero.kim@samsung.com>
 Chao Jiang <jc19chaoj@zoho.com>
@@ -131,12 +139,15 @@ Chris Kuehl <ckuehl@ckuehl.me>
 Christian Demsar <christian@github.email.demsar.us>
 Christian Demsar <crasm@git.vczf.us>
 Christian Falch <875252+chrfalch@users.noreply.github.com>
+Christian Kastner <ckk@kvr.at>
 Christian Kögler <ck3d@gmx.de>
 Christian Köhnenkamp <cvk5@me.com>
 Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
+Christopher Nielsen <62156882+mascguy@users.noreply.github.com>
 Clark Saben <76020733+csaben@users.noreply.github.com>
 Clint Herron <hanclinto@gmail.com>
 Conrad Kramer <conrad@conradkramer.com>
+Corentin REGAL <corentin.regal@gmail.com>
 CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
 Csaba Kecskemeti <csaba.kecskemeti@gmail.com>
 Cuong Trinh Manh <nguoithichkhampha@gmail.com>
@@ -176,6 +187,7 @@ Dibakar Gope <dibakar.gope@arm.com>
 Didzis Gosko <didzis@users.noreply.github.com>
 Diego Devesa <slarengh@gmail.com>
 Diogo Teles Sant'Anna <diogoteles@google.com>
+Djip007 <3705339+Djip007@users.noreply.github.com>
 Djip007 <djip.perois@free.fr>
 Don Mahurin <dmahurin@users.noreply.github.com>
 DooWoong Lee (David) <manics99@naver.com>
@@ -193,6 +205,7 @@ Edward Taylor <edeetee@gmail.com>
 Elaine <elaine.zosa@gmail.com>
 Elbios <141279586+Elbios@users.noreply.github.com>
 Elton Kola <eltonkola@gmail.com>
+Emreerdog <34742675+Emreerdog@users.noreply.github.com>
 Engininja2 <139037756+Engininja2@users.noreply.github.com>
 Equim <sayaka@ekyu.moe>
 Eric Curtin <ecurtin@redhat.com>
@@ -233,6 +246,7 @@ Fred Douglas <43351173+fredlas@users.noreply.github.com>
 Frederik Vogel <Schaltfehler@users.noreply.github.com>
 Gabe Goodhart <gabe.l.hart@gmail.com>
 Gabe Goodhart <ghart@us.ibm.com>
+Gaetan Bisson <gaetan@fenua.org>
 GainLee <perfecter.gen@gmail.com>
 Galunid <karolek1231456@gmail.com>
 Gary Linscott <glinscott@gmail.com>
@@ -249,6 +263,7 @@ Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
 Guillaume Wenzek <gwenzek@users.noreply.github.com>
 Guoliang Hua <32868157+nbcsm@users.noreply.github.com>
 Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
+Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com>
 Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
 Haggai Nuchi <h.nuchi@gmail.com>
 Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
@@ -259,11 +274,13 @@ Haoxiang Fei <tonyfettes@tonyfettes.com>
 Harald Fernengel <harald.fernengel@here.com>
 Hatsune Miku <129688334+at8u@users.noreply.github.com>
 HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com>
+Haus1 <haus.xda@gmail.com>
 Henk Poley <HenkPoley@gmail.com>
 Henri Vasserman <henv@hot.ee>
 Henrik Forstén <henrik.forsten@gmail.com>
 Herman Semenov <GermanAizek@yandex.ru>
 Hesen Peng <hesen.peng@gmail.com>
+HimariO <dsfhe49854@gmail.com>
 Hoang Nguyen <hugo53@users.noreply.github.com>
 Hong Bo PENG <penghb@cn.ibm.com>
 Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
@@ -280,6 +297,7 @@ Icecream95 <the.real.icecream95@gmail.com>
 Ido S <ido.pluto@gmail.com>
 IgnacioFDM <ignaciofdm@gmail.com>
 Igor Okulist <okigan@gmail.com>
+Ihar Hrachyshka <ihrachys@redhat.com>
 Ikko Eltociear Ashimine <eltociear@gmail.com>
 Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
 Ionoclast Laboratories <brigham@ionoclast.com>
@@ -289,12 +307,14 @@ Ivan <nekotekina@gmail.com>
 Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
 Ivan Komarov <Ivan.Komarov@dfyz.info>
 Ivan Stepanov <ivanstepanovftw@gmail.com>
+JFLFY2255 <JFLFY2255@163.com>
 JH23X <165871467+JH23X@users.noreply.github.com>
 Jack Mousseau <jack@software.inc>
 Jack Mousseau <jmousseau@users.noreply.github.com>
 JackJollimore <130917767+JackJollimore@users.noreply.github.com>
 Jaeden Amero <jaeden@patater.com>
 Jaemin Son <woalsdnd@gmail.com>
+Jafar Uruç <jafar.uruc@gmail.com>
 Jag Chadha <jagtesh@gmail.com>
 Jakub N <jakubniemczyk97@gmail.com>
 James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
@@ -315,6 +335,7 @@ Jeffrey Morgan <jmorganca@gmail.com>
 Jeffrey Quesnelle <emozilla@nousresearch.com>
 Jeroen Mostert <jeroen.mostert@cm.com>
 Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
+Jett Janiak <jettjaniak@gmail.com>
 Jeximo <jeximo@gmail.com>
 Jhen-Jie Hong <iainst0409@gmail.com>
 Jiahao Li <liplus17@163.com>
@@ -343,6 +364,7 @@ Josh Ramer <josh.ramer@icloud.com>
 Joyce <joycebrum@google.com>
 Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
 Judd <foldl@users.noreply.github.com>
+Juk Armstrong <69222624+jukofyork@users.noreply.github.com>
 Julius Arkenberg <arki05@users.noreply.github.com>
 Jun Hee Yoo <contact.jhyoo@gmail.com>
 Jun Jie <71215065+junnjiee16@users.noreply.github.com>
@@ -357,6 +379,7 @@ Justine Tunney <jtunney@mozilla.com>
 Juuso Alasuutari <juuso.alasuutari@gmail.com>
 KASR <karim.asrih@gmail.com>
 Kamil Tomšík <info@tomsik.cz>
+Karol Kontny <82021046+kkontny@users.noreply.github.com>
 Karsten Weiss <knweiss@gmail.com>
 Karthick <j.karthic2004@gmail.com>
 Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
@@ -376,6 +399,7 @@ Kolen Cheung <ickc@users.noreply.github.com>
 Konstantin Herud <konstantin.herud@denkbares.com>
 Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
 Kunshang Ji <kunshang.ji@intel.com>
+Kyle Bruene <KyleBruene@users.noreply.github.com>
 Kyle Liang <liangmanlai@gmail.com>
 Kyle Mistele <kyle@mistele.com>
 Kylin <56434533+KyL0N@users.noreply.github.com>
@@ -394,6 +418,7 @@ Liu Jia <jia3.liu@intel.com>
 LoganDark <github@logandark.mozmail.com>
 Loïc Carrère <loic.carrere@gmail.com>
 LostRuins <39025047+LostRuins@users.noreply.github.com>
+LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
 Luciano <lucianostrika44@gmail.com>
 Luo Tian <lt@basecity.com>
 Lyle Dean <dean@lyle.dev>
@@ -423,6 +448,7 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
 Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
 Matheus C. França <matheus-catarino@hotmail.com>
 Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
+Mathieu Baudier <mbaudier@argeo.org>
 Mathieu Geli <mathieu.geli@gmail.com>
 Mathieu Nayrolles <MathieuNls@users.noreply.github.com>
 Mathijs Henquet <mathijs.henquet@gmail.com>
@@ -444,6 +470,7 @@ Meng, Hengyu <hengyu.meng@intel.com>
 Mengqing Cao <cmq0113@163.com>
 Merrick Christensen <merrick.christensen@gmail.com>
 Michael Coppola <m18coppola@gmail.com>
+Michael Engel <mengel@redhat.com>
 Michael Francis <edude03@gmail.com>
 Michael Hueschen <m@mhueschen.dev>
 Michael Kesper <mkesper@schokokeks.org>
@@ -452,7 +479,9 @@ Michael Podvitskiy <podvitskiymichael@gmail.com>
 Michael Potter <NanoTekGuy@Gmail.com>
 Michael de Gans <michael.john.degans@gmail.com>
 Michaël de Vries <vriesdemichael@gmail.com>
+Michał Moskal <michal@moskal.me>
 Michał Tuszyński <srgtuszy@gmail.com>
+Michelle Tan <41475767+MichelleTanPY@users.noreply.github.com>
 Mihai <mihai.chirculescu@yahoo.com>
 Mike <ytianhui2004@gmail.com>
 Mikko Juola <mikjuo@gmail.com>
@@ -477,6 +506,7 @@ Neo Zhang <14088817+arthw@users.noreply.github.com>
 Neo Zhang <zhang.jianyu@outlook.com>
 Neo Zhang Jianyu <jianyu.zhang@intel.com>
 Neuman Vong <neuman.vong@gmail.com>
+NeverLucky <92274250+nvrxq@users.noreply.github.com>
 Nexes the Old <124105151+Nexesenex@users.noreply.github.com>
 Nexesenex <124105151+Nexesenex@users.noreply.github.com>
 Niall Coates <1349685+Niall-@users.noreply.github.com>
@@ -484,11 +514,15 @@ Nicholai Tukanov <nicholaitukanov@gmail.com>
 Nico Bosshard <nico@bosshome.ch>
 Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
 Nicolás Pérez <nicolas_perez@brown.edu>
+Nicolò Scipione <nicolo.scipione@codeplay.com>
 Nigel Bosch <pnigelb@gmail.com>
+Nikita Sarychev <42014488+sARY77@users.noreply.github.com>
 Niklas Korz <niklas@niklaskorz.de>
 NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
+Nikolaos Pothitos <pothitos@di.uoa.gr>
 Nikolas <127742645+nneubacher@users.noreply.github.com>
 Nindaleth <Nindaleth@users.noreply.github.com>
+Nuno <rare-magma@posteo.eu>
 OSecret <135510162+OLSecret@users.noreply.github.com>
 Oleksandr Nikitin <oleksandr@tvori.info>
 Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
@@ -504,6 +538,7 @@ Pavel Zloi <github.com@drteam.rocks>
 Pavol Rusnak <pavol@rusnak.io>
 Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com>
 Pedro Cuenca <pedro@huggingface.co>
+Peter <peter277@users.noreply.github.com>
 Peter Sugihara <peter@campsh.com>
 Phil H <5756783+phiharri@users.noreply.github.com>
 Philip Taron <philip.taron@gmail.com>
@@ -529,9 +564,12 @@ Rand Xie <randxiexyy29@gmail.com>
 Randall Fitzgerald <randall@dasaku.net>
 Random Fly <renfei8@live.cn>
 Reinforce-II <fate@eastal.com>
+Rémy Oudompheng <oudomphe@phare.normalesup.org>
 Ren Xuancheng <jklj077@users.noreply.github.com>
 Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com>
+Reza Kakhki <rezakakhki.de@gmail.com>
 RhinoDevel <RhinoDevel@users.noreply.github.com>
+Riccardo Orlando <Riccorl@users.noreply.github.com>
 Riceball LEE <snowyu.lee@gmail.com>
 Rich Dougherty <rich@rd.nz>
 Richard Kiss <him@richardkiss.com>
@@ -544,6 +582,8 @@ Riley Stewart <ristew@users.noreply.github.com>
 Rinne <AsakusaRinne@gmail.com>
 Rinne <liu_yaohui1998@126.com>
 Robert Brisita <986796+rbrisita@users.noreply.github.com>
+Robert Collins <roberto.tomas.cuentas@gmail.com>
+Robert Ormandi <52251610+ormandi@users.noreply.github.com>
 Robert Sung-wook Shin <edp1096@users.noreply.github.com>
 Robey Holderith <robey@flaminglunchbox.net>
 Robyn <robyngraf@users.noreply.github.com>
@@ -559,7 +599,9 @@ Roni <sulpher@gmx.net>
 Ronny Brendel <ronnybrendel@gmail.com>
 Ronsor <ronsor@ronsor.pw>
 Rowan Hart <rowanbhart@gmail.com>
+Ruan <47767371+ruanych@users.noreply.github.com>
 Ruchira Hasaranga <ruchira66@gmail.com>
+Rudi Servo <rudiservo@gmail.com>
 Ruixin Huang <18860020911@163.com>
 Rune <43761327+Rune-AI@users.noreply.github.com>
 RunningLeon <maningsheng@sensetime.com>
@@ -623,12 +665,14 @@ Steven Roussey <sroussey@gmail.com>
 Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
 StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com>
 Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
+Sukriti Sharma <Ssukriti@users.noreply.github.com>
 SuperUserNameMan <yoann@terminajones.com>
 Sutou Kouhei <kou@cozmixng.org>
 Tai Duc Nguyen <taiducnguyen.drexel@gmail.com>
 Taikono-Himazin <kazu@po.harenet.ne.jp>
 Tameem <113388789+AhmadTameem@users.noreply.github.com>
 Tamotsu Takahashi <ttakah+github@gmail.com>
+Tei Home <taiteitonghome@proton.me>
 Thái Hoàng Tâm <75922889+RoyalHeart@users.noreply.github.com>
 Thatcher Chamberlin <j.thatcher.c@gmail.com>
 Theia Vogel <theia@vgel.me>
@@ -640,6 +684,7 @@ Tim Miller <drasticactions@users.noreply.github.com>
 Tim Wang <overocean@gmail.com>
 Timmy Knight <r2d2fish@gmail.com>
 Timothy Cronin <40186632+4imothy@users.noreply.github.com>
+Ting Lou <louting@189.cn>
 Ting Lou <ting.lou@gmail.com>
 Ting Sun <suntcrick@gmail.com>
 Tobias Lütke <tobi@shopify.com>
@@ -661,6 +706,7 @@ Uzo Nweke <uzoechi@gmail.com>
 Vaibhav Srivastav <vaibhavs10@gmail.com>
 Val Kharitonov <mail@kharvd.com>
 Valentin Konovalov <valle.ketsujin@gmail.com>
+Valentin Mamedov <45292985+Inf1delis@users.noreply.github.com>
 Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com>
 Vali Malinoiu <0x4139@gmail.com>
 Victor Nogueira <felladrin@gmail.com>
@@ -673,13 +719,17 @@ Vladimir Malyutin <first-leon@yandex.ru>
 Vladimir Zorin <vladimir@deviant.guru>
 VoidIsVoid <343750470@qq.com>
 Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com>
+Wang Qin <37098874+wangqin0@users.noreply.github.com>
+Wang Ran (汪然) <wangr@smail.nju.edu.cn>
 WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com>
 Weird Constructor <weirdconstructor@gmail.com>
 Welby Seely <welbyseely@gmail.com>
 Wentai Zhang <rchardx@gmail.com>
 WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com>
 William Tambellini <william.tambellini@gmail.com>
+William Tambellini <wtambellini@sdl.com>
 Willy Tarreau <w@1wt.eu>
+Woof Dog <197125663+woof-dog@users.noreply.github.com>
 Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com>
 Wu Jian Ping <wujjpp@hotmail.com>
 Wu Jian Ping <wujp@greatld.com>
@@ -692,6 +742,7 @@ Xie Yanbo <xieyanbo@gmail.com>
 Xingchen Song(宋星辰) <xingchensong1996@163.com>
 Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com>
 Xuan Son Nguyen <thichthat@gmail.com>
+Xuan-Son Nguyen <thichthat@gmail.com>
 Yaiko <elyaiko@hotmail.com>
 Yann Follet <131855179+YannFollet@users.noreply.github.com>
 Yaroslav <yaroslav.yashin@me.com>
@@ -702,7 +753,9 @@ Yoshi Suhara <y.suhara@gmail.com>
 Yoshi Suhara <ysuhara@nvidia.com>
 Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
 Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
+Yüg <eugeniosegalaweb@gmail.com>
 Yui <dev@sleepyyui.com>
+Yun Dou <dixyes@gmail.com>
 Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
 Yusuf Kağan Hanoğlu <hanoglu@yahoo.com>
 Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com>
@@ -714,18 +767,23 @@ Zhang Peiyuan <a1286225768@gmail.com>
 Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com>
 Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com>
 Zhiyuan Li <lizhiyuan@uniartisan.com>
+Zhiyuan Li <uniartisan2017@gmail.com>
 ZhouYuChen <zhouyuchen@naver.com>
 Ziad Ben Hadj-Alouane <zied.benhadjalouane@gmail.com>
 Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com>
 Zsapi <martin1.zsapka@gmail.com>
 a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com>
+a3sh <38979186+A3shTnT@users.noreply.github.com>
 adel boussaken <netdur@gmail.com>
 afrideva <95653597+afrideva@users.noreply.github.com>
+ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
 agray3 <agray3@users.noreply.github.com>
 akawrykow <142945436+akawrykow@users.noreply.github.com>
+alek3y <44779186+alek3y@users.noreply.github.com>
 alexpinel <93524949+alexpinel@users.noreply.github.com>
 alonfaraj <alonfaraj@gmail.com>
 alwqx <kenan3015@gmail.com>
+amd-dwang <dong.wang@amd.com>
 amd-lalithnc <lalithnc@amd.com>
 amritahs-ibm <amritahs@linux.vnet.ibm.com>
 andrijdavid <david@geek.mg>
@@ -737,6 +795,7 @@ arch-btw <57669023+arch-btw@users.noreply.github.com>
 arcrank <arcrank@gmail.com>
 ardfork <134447697+ardfork@users.noreply.github.com>
 arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
+aryantandon01 <80969509+aryantandon01@users.noreply.github.com>
 at8u <129688334+at8u@users.noreply.github.com>
 automaticcat <daogiatuank54@gmail.com>
 awatuna <23447591+awatuna@users.noreply.github.com>
@@ -751,12 +810,14 @@ bryanSwk <93190252+bryanSwk@users.noreply.github.com>
 bsilvereagle <bsilvereagle@users.noreply.github.com>
 bssrdf <merlintiger@hotmail.com>
 byte-6174 <88070277+byte-6174@users.noreply.github.com>
+cduk <19917266+cduk@users.noreply.github.com>
 cebtenzzre <cebtenzzre@gmail.com>
 chaihahaha <chai836275709@gmail.com>
 chiranko <96988916+chiranko@users.noreply.github.com>
 clibdev <52199778+clibdev@users.noreply.github.com>
 clyang <clyang@clyang.net>
 cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
+codezjx <code.zjx@gmail.com>
 coezbek <c.oezbek@gmail.com>
 comex <comexk@gmail.com>
 compilade <113953597+compilade@users.noreply.github.com>
@@ -780,14 +841,17 @@ drbh <david.richard.holtz@gmail.com>
 ds5t5 <145942675+ds5t5@users.noreply.github.com>
 dylan <canardleteer@users.noreply.github.com>
 eastriver <lee@eastriver.dev>
+ebraminio <ebrahim@gnu.org>
 ebraminio <ebraminio@gmail.com>
 eiery <19350831+eiery@users.noreply.github.com>
 eric8607242 <e0928021388@gmail.com>
 fairydreaming <166155368+fairydreaming@users.noreply.github.com>
 fengerhu1 <2748250768@qq.com>
+fj-y-saito <85871716+fj-y-saito@users.noreply.github.com>
 fraxy-v <65565042+fraxy-v@users.noreply.github.com>
 github-actions[bot] <github-actions[bot]@users.noreply.github.com>
 gliptic <gliptic@users.noreply.github.com>
+gn64 <yukikaze.jp@gmail.com>
 goerch <jhr.walter@t-online.de>
 grahameth <96447521+grahameth@users.noreply.github.com>
 gtygo <gtydoit@gmail.com>
@@ -812,10 +876,12 @@ icppWorld <124377669+icppWorld@users.noreply.github.com>
 igarnier <igarnier@protonmail.com>
 intelmatt <61025942+intelmatt@users.noreply.github.com>
 iohub <rickyang.pro@gmail.com>
+issixx <46835150+issixx@users.noreply.github.com>
 jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
 jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
 jameswu2014 <545426914@qq.com>
 jdomke <28772296+jdomke@users.noreply.github.com>
+jiahao su <damow890@gmail.com>
 jiez <373447296@qq.com>
 jneem <joeneeman@gmail.com>
 joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
@@ -828,6 +894,7 @@ junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
 jwj7140 <32943891+jwj7140@users.noreply.github.com>
 k.h.lai <adrian.k.h.lai@outlook.com>
 kaizau <kaizau@users.noreply.github.com>
+kallewoof <kalle.alm@gmail.com>
 kalomaze <66376113+kalomaze@users.noreply.github.com>
 kang <tpdns9032100@gmail.com>
 katsu560 <118887472+katsu560@users.noreply.github.com>
@@ -835,6 +902,7 @@ kchro3 <62481661+kchro3@users.noreply.github.com>
 khimaros <me@khimaros.com>
 kiltyj <kiltyj@gmail.com>
 klosax <131523366+klosax@users.noreply.github.com>
+krystiancha <krystian@krystianch.com>
 kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
 kunnis <kunnis@users.noreply.github.com>
 kuronekosaiko <EvanChanJ@163.com>
@@ -847,6 +915,8 @@ ldwang <ftgreat@163.com>
 le.chang <cljs118@126.com>
 leejet <leejet714@gmail.com>
 leo-pony <nengjunma@outlook.com>
+lexasub <lexakopp2212@gmail.com>
+lhez <quic_lih@quicinc.com>
 limitedAtonement <limitedAtonement@users.noreply.github.com>
 liuwei-git <14815172+liuwei-git@users.noreply.github.com>
 lon <114724657+longregen@users.noreply.github.com>
@@ -855,10 +925,13 @@ ltoniazzi <61414566+ltoniazzi@users.noreply.github.com>
 luoyu-intel <yu.luo@intel.com>
 m3ndax <adrian.goessl@outlook.com>
 maddes8cht <55592906+maddes8cht@users.noreply.github.com>
+mahorozte <41834471+mahorozte@users.noreply.github.com>
 makomk <makosoft@googlemail.com>
 manikbhandari <mbbhandarimanik2@gmail.com>
 maor-ps <154728172+maor-ps@users.noreply.github.com>
+mashdragon <122402293+mashdragon@users.noreply.github.com>
 matiaslin <45382001+matiaslin@users.noreply.github.com>
+matt23654 <matthew.webber@protonmail.com>
 matteo <matteogeniaccio@yahoo.it>
 mdrokz <mohammadmunshi@gmail.com>
 mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
@@ -868,6 +941,7 @@ mmyjona <jonathan.gonse@gmail.com>
 momonga <115213907+mmnga@users.noreply.github.com>
 momonga <146910567+mmngays@users.noreply.github.com>
 moritzbrantner <31051084+moritzbrantner@users.noreply.github.com>
+musoles <135031143+musoles@users.noreply.github.com>
 mzcu <milos.cubrilo@gmail.com>
 nanahi <130121847+na-na-hi@users.noreply.github.com>
 ngc92 <7938269+ngc92@users.noreply.github.com>
@@ -885,6 +959,7 @@ oobabooga <112222186+oobabooga@users.noreply.github.com>
 opparco <parco.opaai@gmail.com>
 ostix360 <55257054+ostix360@users.noreply.github.com>
 pculliton <phillipculliton@gmail.com>
+peidaqi <peidaqi@gmail.com>
 pengxin99 <pengxin.yuan@intel.com>
 perserk <perserk@gmail.com>
 piDack <104877312+piDack@users.noreply.github.com>
@@ -892,10 +967,12 @@ pmysl <piotr.myslinski@outlook.com>
 postmasters <namnguyen@google.com>
 pudepiedj <pudepiedj@gmail.com>
 qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
+qingy1337 <qxli2@students.everettcc.edu>
 qouoq <qouoq@fastmail.com>
 qunash <anzoria@gmail.com>
 rabidcopy <rabidcopy@yahoo.com>
 rankaiyx <rankaiyx@rankaiyx.com>
+redbeard <bharrington@alticon.net>
 rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com>
 rhuddleston <ryan.huddleston@percona.com>
 rimoliga <53384203+rimoliga@users.noreply.github.com>
@@ -912,6 +989,7 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
 slaren <2141330+slaren@users.noreply.github.com>
 slaren <slarengh@gmail.com>
 snadampal <87143774+snadampal@users.noreply.github.com>
+someone13574 <81528246+someone13574@users.noreply.github.com>
 standby24x7 <standby24x7@gmail.com>
 staviq <staviq@gmail.com>
 stduhpf <stephduh@live.fr>
@@ -931,6 +1009,7 @@ uint256_t <konndennsa@gmail.com>
 uint256_t <maekawatoshiki1017@gmail.com>
 unbounded <haakon@likedan.net>
 uvos <devnull@uvos.xyz>
+uvos <philipp@uvos.xyz>
 valiray <133289098+valiray@users.noreply.github.com>
 vb <vaibhavs10@gmail.com>
 vik <vikhyatk@gmail.com>
@@ -951,6 +1030,7 @@ xaedes <xaedes@googlemail.com>
 xctan <axunlei@gmail.com>
 xloem <0xloem@gmail.com>
 yangli2 <yangli2@gmail.com>
+ymcki <84055651+ymcki@users.noreply.github.com>
 yuiseki <yuiseki@gmail.com>
 yuri@FreeBSD <yurivict@users.noreply.github.com>
 zakkor <edward.partenie@gmail.com>
@@ -963,4 +1043,5 @@ zrm <trustiosity.zrm@gmail.com>
 杨朱 · Kiki <baofa.fan@daocloud.io>
 源文雨 <41315874+fumiama@users.noreply.github.com>
 蕭澧邦 <45505768+shou692199@users.noreply.github.com>
+谢乃闻 <sienaiwun@users.noreply.github.com>
 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

From 534c46b53c23613628d72e93c63ea01ea4d1e2ac Mon Sep 17 00:00:00 2001
From: Jhen-Jie Hong <iainst0409@gmail.com>
Date: Tue, 4 Feb 2025 19:07:18 +0800
Subject: [PATCH 328/376] metal : use residency set for other platforms
 (#11648)

---
 ggml/src/ggml-metal/ggml-metal.m | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 76f8e4291..9605914ff 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -20,7 +20,10 @@
 #define GGML_METAL_MAX_COMMAND_BUFFERS 8
 
 // create residency sets only on macOS >= 15.0
-#if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000
+#if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000 || \
+    TARGET_OS_IOS && __IPHONE_OS_VERSION_MAX_ALLOWED >= 180000 || \
+    TARGET_OS_TV && __TV_OS_VERSION_MAX_ALLOWED >= 180000 || \
+    TARGET_OS_VISION && __VISION_OS_VERSION_MAX_ALLOWED >= 200000
 #define GGML_METAL_HAS_RESIDENCY_SETS 1
 #endif
 
@@ -1071,7 +1074,7 @@ static bool ggml_backend_metal_buffer_rset_init(
     }
 
 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
-    if (@available(macOS 15.0, *)) {
+    if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
         MTLResidencySetDescriptor * desc = [[MTLResidencySetDescriptor alloc] init];
         desc.label = @"ggml_backend_metal";
         desc.initialCapacity = ctx->n_buffers;
@@ -1106,7 +1109,7 @@ static bool ggml_backend_metal_buffer_rset_init(
 // rset free
 static void ggml_backend_metal_buffer_rset_free(struct ggml_backend_metal_buffer_context * ctx) {
 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
-    if (@available(macOS 15.0, *)) {
+    if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
         if (ctx->rset) {
             [ctx->rset endResidency];
             [ctx->rset removeAllAllocations];

From f117d84b48104992ba16961b35a96fa93efbb355 Mon Sep 17 00:00:00 2001
From: Jhen-Jie Hong <iainst0409@gmail.com>
Date: Tue, 4 Feb 2025 19:15:24 +0800
Subject: [PATCH 329/376] swift : fix llama-vocab api usage (#11645)

* swiftui : fix vocab api usage

* batched.swift : fix vocab api usage
---
 examples/batched.swift/Sources/main.swift         | 15 ++++++++++-----
 .../llama.swiftui/llama.cpp.swift/LibLlama.swift  | 12 +++++++-----
 2 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index 371917b2e..55c31166c 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -31,6 +31,11 @@ defer {
     llama_model_free(model)
 }
 
+guard let vocab = llama_model_get_vocab(model) else {
+    print("Failed to get vocab")
+    exit(1)
+}
+
 var tokens = tokenize(text: prompt, add_bos: true)
 
 let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel)
@@ -41,7 +46,7 @@ context_params.n_batch = UInt32(max(n_len, n_parallel))
 context_params.n_threads = 8
 context_params.n_threads_batch = 8
 
-let context = llama_new_context_with_model(model, context_params)
+let context = llama_init_from_model(model, context_params)
 guard context != nil else {
     print("Failed to initialize context")
     exit(1)
@@ -141,7 +146,7 @@ while n_cur <= n_len {
         let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
 
         // is it an end of stream? -> mark the stream as finished
-        if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
+        if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
@@ -207,7 +212,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
     let utf8Count = text.utf8.count
     let n_tokens = utf8Count + (add_bos ? 1 : 0)
     let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
-    let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
+    let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
     var swiftTokens: [llama_token] = []
     for i in 0 ..< tokenCount {
         swiftTokens.append(tokens[Int(i)])
@@ -218,12 +223,12 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false)
+    let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
         let check = llama_token_to_piece(
-            model,
+            vocab,
             token,
             &result,
             Int32(result.count),
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 477c3e6f2..ee7141a66 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -24,6 +24,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama
 actor LlamaContext {
     private var model: OpaquePointer
     private var context: OpaquePointer
+    private var vocab: OpaquePointer
     private var sampling: UnsafeMutablePointer<llama_sampler>
     private var batch: llama_batch
     private var tokens_list: [llama_token]
@@ -47,6 +48,7 @@ actor LlamaContext {
         self.sampling = llama_sampler_chain_init(sparams)
         llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4))
         llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234))
+        vocab = llama_model_get_vocab(model)
     }
 
     deinit {
@@ -79,7 +81,7 @@ actor LlamaContext {
         ctx_params.n_threads       = Int32(n_threads)
         ctx_params.n_threads_batch = Int32(n_threads)
 
-        let context = llama_new_context_with_model(model, ctx_params)
+        let context = llama_init_from_model(model, ctx_params)
         guard let context else {
             print("Could not load context!")
             throw LlamaError.couldNotInitializeContext
@@ -151,7 +153,7 @@ actor LlamaContext {
 
         new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1)
 
-        if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
+        if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
             print("\n")
             is_done = true
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
@@ -297,7 +299,7 @@ actor LlamaContext {
         let utf8Count = text.utf8.count
         let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
         let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
-        let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
+        let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
 
         var swiftTokens: [llama_token] = []
         for i in 0..<tokenCount {
@@ -316,7 +318,7 @@ actor LlamaContext {
         defer {
             result.deallocate()
         }
-        let nTokens = llama_token_to_piece(model, token, result, 8, 0, false)
+        let nTokens = llama_token_to_piece(vocab, token, result, 8, 0, false)
 
         if nTokens < 0 {
             let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -324,7 +326,7 @@ actor LlamaContext {
             defer {
                 newResult.deallocate()
             }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false)
+            let nNewTokens = llama_token_to_piece(vocab, token, newResult, -nTokens, 0, false)
             let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
             return Array(bufferPointer)
         } else {

From 106045e7bb8db481bb2ebbc60e3b53cb27ada117 Mon Sep 17 00:00:00 2001
From: Shelby Jenkins <47464908+ShelbyJenkins@users.noreply.github.com>
Date: Tue, 4 Feb 2025 05:20:55 -0600
Subject: [PATCH 330/376] readme : add llm_client Rust crate to readme bindings
 (#11628)

[This crate](https://github.com/ShelbyJenkins/llm_client) has been in a usable state for quite awhile, so I figured now is fair to add it.

It installs from crates.io, and automatically downloads the llama.cpp repo and builds it for the target platform - with the goal being the easiest user experience possible.

It also integrates model presets and choosing the largest quant given the target's available VRAM. So a user just has to specify one of the presets (I manually add the most popular models), and it will download from hugging face.

So, it's like a Rust Ollama, but it's not really for chatting. It makes heavy use of llama.cpp's grammar system to do structured output for decision making and control flow tasks.
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 7f306d199..d68330d2a 100644
--- a/README.md
+++ b/README.md
@@ -136,6 +136,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
 - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
+- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
 - C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)

From db288b60cb49eab69703f995379b8d75c18bf5b3 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Tue, 4 Feb 2025 15:48:53 +0000
Subject: [PATCH 331/376] `tool-call`: command r7b fix for normal responses
 (#11608)

* fix command r7b normal response regex + add to server test

* test multiline non-tool-call responses in test-chat
---
 common/chat.cpp                              |  2 +-
 examples/server/tests/unit/test_tool_call.py |  8 ++++---
 tests/test-chat.cpp                          | 24 +++++++++++---------
 3 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index f87583d85..4a113c0ca 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -365,7 +365,7 @@ static common_chat_params common_chat_params_init_command_r7b(const common_chat_
     return data;
 }
 static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
-    static std::regex response_regex("<\\|START_RESPONSE\\|>(.*?)<\\|END_RESPONSE\\|>");
+    static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>");
     static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
     std::smatch match;
 
diff --git a/examples/server/tests/unit/test_tool_call.py b/examples/server/tests/unit/test_tool_call.py
index 62a48a0d9..4a551404f 100644
--- a/examples/server/tests/unit/test_tool_call.py
+++ b/examples/server/tests/unit/test_tool_call.py
@@ -273,6 +273,7 @@ def test_completion_without_tool_call_slow(template_name: str, n_predict: int, t
 
 @pytest.mark.slow
 @pytest.mark.parametrize("hf_repo,template_override", [
+    ("bartowski/c4ai-command-r7b-12-2024-GGUF:Q4_K_M",   ("CohereForAI/c4ai-command-r7b-12-2024", "tool_use")),
     ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
     ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
 
@@ -303,12 +304,13 @@ def test_completion_without_tool_call_slow(template_name: str, n_predict: int, t
     # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
     # ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
 ])
-def test_weather_tool_call(hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+def test_weather(hf_repo: str, template_override: Tuple[str, str | None] | None):
     global server
+    n_predict = 512
     server.n_slots = 1
     server.jinja = True
     server.n_ctx = 8192
-    server.n_predict = 512
+    server.n_predict = n_predict
     server.model_hf_repo = hf_repo
     server.model_hf_file = None
     if isinstance(template_override, tuple):
@@ -319,7 +321,7 @@ def test_weather_tool_call(hf_repo: str, template_override: str | Tuple[str, str
         server.chat_template = template_override
     server.start(timeout_seconds=TIMEOUT_SERVER_START)
     res = server.make_request("POST", "/chat/completions", data={
-        "max_tokens": 256,
+        "max_tokens": n_predict,
         "messages": [
             {"role": "user", "content": "What is the weather in Istanbul?"},
         ],
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 9956c1f1f..50bd40738 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -289,7 +289,7 @@ static void test_template(const common_chat_template & tmpl, const std::vector<s
 static void test_template_output_parsers() {
     json text_message {
         { "role",    "assistant"     },
-        { "content", "Hello, world!" },
+        { "content", "Hello, world!\nWhat's up?" },
     };
     json tool_calls = json::array({{
         { "type", "function" },
@@ -379,7 +379,7 @@ static void test_template_output_parsers() {
 
     common_chat_inputs inputs_no_tools;
     inputs_no_tools.messages = {
-        { { "role", "user" }, { "content", "Hey" } }
+        { { "role", "user" }, { "content", "Hey\nThere" } }
     };
 
     common_chat_inputs inputs_tools = inputs_no_tools;
@@ -408,7 +408,8 @@ static void test_template_output_parsers() {
                       "    {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n"
                       "]<|END_ACTION|>");
         test_template(tmpl, end_tokens, text_message, tools,
-                      "<|START_RESPONSE|>Hello, world!<|END_RESPONSE|>",
+                      "<|START_RESPONSE|>Hello, world!\n"
+                      "What's up?<|END_RESPONSE|>",
                       /* expect_grammar_triggered= */ false);
     }
     {
@@ -428,7 +429,7 @@ static void test_template_output_parsers() {
 
         assert_msg_equals(msg_from_json(text_message),
                           common_chat_parse("{\n"
-                                            "  \"response\": \"Hello, world!\"\n"
+                                            "  \"response\": \"Hello, world!\\nWhat's up?\"\n"
                                             "}",
                                             common_chat_params_init(tmpl, inputs_tools).format));
         test_template(tmpl, end_tokens, tool_call_message_with_id, tools,
@@ -451,7 +452,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_MISTRAL_NEMO, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(
             tmpl, end_tokens, tool_call_message_with_id, tools,
             "[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]");
@@ -476,7 +477,7 @@ static void test_template_output_parsers() {
                 inputs_tools)
                 .format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<tool_call>\n"
                       "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
@@ -516,7 +517,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}");
     }
@@ -528,7 +529,7 @@ static void test_template_output_parsers() {
         assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
                       common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<function=special_function>{\"arg1\": 1}</function>");
     }
@@ -542,7 +543,8 @@ static void test_template_output_parsers() {
 
         test_template(tmpl, end_tokens, text_message, {},
                       "all\n"
-                      "Hello, world!",
+                      "Hello, world!\n"
+                      "What's up?",
                       /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "special_function\n"
@@ -555,7 +557,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_FIREFUNCTION_V2, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       " functools[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]");
     }
@@ -566,7 +568,7 @@ static void test_template_output_parsers() {
 
         assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, common_chat_params_init(tmpl, inputs_tools).format);
 
-        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!", /* expect_grammar_triggered= */ false);
+        test_template(tmpl, end_tokens, text_message, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
         test_template(tmpl, end_tokens, tool_call_message, tools,
                       "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n"
                       "```json\n"

From 1bef571f6a23c36a26dabacba631763f9a893b83 Mon Sep 17 00:00:00 2001
From: Radoslav Gerganov <rgerganov@gmail.com>
Date: Tue, 4 Feb 2025 18:16:20 +0200
Subject: [PATCH 332/376] arg : list RPC devices first when using
 --list-devices (#11655)

List devices in the same order as they appear when evaluating the model
and splitting tensors across devices, i.e. RPC devices come first in the
list.

ref #11435
---
 common/arg.cpp | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index f5e9b294f..76b898881 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1465,15 +1465,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--list-devices"},
         "print list of available devices and exit",
         [](common_params &) {
-            printf("Available devices:\n");
+            std::vector<ggml_backend_dev_t> rpc_devices;
+            std::vector<ggml_backend_dev_t> all_devices;
             for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
                 auto * dev = ggml_backend_dev_get(i);
                 if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
-                    size_t free, total;
-                    ggml_backend_dev_memory(dev, &free, &total);
-                    printf("  %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+                    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+                    if (ggml_backend_reg_name(reg) == std::string("RPC")) {
+                        rpc_devices.push_back(dev);
+                    } else {
+                        all_devices.push_back(dev);
+                    }
                 }
             }
+            // insert RPC devices in front
+            all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
+            printf("Available devices:\n");
+            for (size_t i = 0; i < all_devices.size(); ++i) {
+                auto * dev = all_devices[i];
+                size_t free, total;
+                ggml_backend_dev_memory(dev, &free, &total);
+                printf("  %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+            }
             exit(0);
         }
     ));

From 3962fc1a7956dd0afacfbce10fd1a3ffd3ad857e Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Tue, 4 Feb 2025 18:25:42 +0100
Subject: [PATCH 333/376] server : add try..catch to places not covered by
 set_exception_handler (#11620)

* server : add try..catch to places not covered by set_exception_handler

* log_server_request: rm try catch, add reminder
---
 examples/server/server.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index e0acc4705..9cdf2058f 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -3353,6 +3353,8 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
         return;
     }
 
+    // reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch
+
     LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
 
     LOG_DBG("request:  %s\n", req.body.c_str());
@@ -3439,9 +3441,13 @@ int main(int argc, char ** argv) {
             message = "Unknown Exception";
         }
 
-        json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
-        LOG_WRN("got exception: %s\n", formatted_error.dump().c_str());
-        res_error(res, formatted_error);
+        try {
+            json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
+            LOG_WRN("got exception: %s\n", formatted_error.dump().c_str());
+            res_error(res, formatted_error);
+        } catch (const std::exception & e) {
+            LOG_ERR("got another exception: %s | while hanlding exception: %s\n", e.what(), message.c_str());
+        }
     });
 
     svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {

From 3ec9fd4b77b6aca03a3c2bf678eae3f9517d6904 Mon Sep 17 00:00:00 2001
From: fxzjshm <11426482+fxzjshm@users.noreply.github.com>
Date: Wed, 5 Feb 2025 02:18:38 +0800
Subject: [PATCH 334/376] HIP: force max threads per block to be 1024 (#11621)

Some old/vendor forked version of llvm still use 256. Explicitly set it to 1024 to align with upstream llvm.

Signed-off-by: fxzjshm <fxzjshm@163.com>
---
 ggml/src/ggml-hip/CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index eb03e10fa..f4a468363 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -46,6 +46,9 @@ endif()
 
 message(STATUS "HIP and hipBLAS found")
 
+# Workaround old compilers
+set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} --gpu-max-threads-per-block=1024")
+
 file(GLOB   GGML_HEADERS_ROCM "../ggml-cuda/*.cuh")
 list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h")
 

From fd08255d0dea6625596c0367ee0a11d195f36762 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 4 Feb 2025 22:21:42 +0100
Subject: [PATCH 335/376] CUDA: non-contiguous (RMS) norm support (#11659)

* CUDA: non-contiguous (RMS) norm support

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-cuda/ggml-cuda.cu      |  4 ++
 ggml/src/ggml-cuda/norm.cu           | 89 ++++++++++++++++++----------
 ggml/src/ggml-metal/ggml-metal.m     |  5 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp |  2 +
 src/llama.cpp                        |  6 +-
 tests/test-backend-ops.cpp           | 38 ++++++++----
 6 files changed, 97 insertions(+), 47 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index bda10aec1..70a598099 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -38,6 +38,7 @@
 #include "ggml-cuda/upscale.cuh"
 #include "ggml-cuda/wkv6.cuh"
 #include "ggml-cuda/gla.cuh"
+#include "ggml.h"
 
 #include <algorithm>
 #include <array>
@@ -3139,6 +3140,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             break;
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
+            return true;
         case GGML_OP_RMS_NORM_BACK:
             return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
             break;
@@ -3181,7 +3183,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
+            return true;
         case GGML_OP_GROUP_NORM:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
         case GGML_OP_ARANGE:
diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
index d991ec972..f127616ed 100644
--- a/ggml/src/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -1,12 +1,20 @@
 #include "norm.cuh"
+#include <cstdint>
 
 template <int block_size>
-static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
+static __global__ void norm_f32(
+        const float * x, float * dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
+        const int64_t stride_sample, const float eps) {
+    const int nrows     = gridDim.x;
+    const int nchannels = gridDim.y;
 
-    x   += int64_t(row)*ncols;
-    dst += int64_t(row)*ncols;
+    const int row       = blockIdx.x;
+    const int channel   = blockIdx.y;
+    const int sample    = blockIdx.z;
+    const int tid       = threadIdx.x;
+
+    x   += sample*stride_sample + channel*stride_channel + row*stride_row;
+    dst += ((sample*nchannels + channel)*nrows + row)*ncols;
 
     float2 mean_var = make_float2(0.0f, 0.0f);
 
@@ -97,12 +105,19 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
 }
 
 template <int block_size>
-static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
+static __global__ void rms_norm_f32(
+        const float * x, float * dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
+        const int64_t stride_sample, const float eps) {
+    const int nrows     = gridDim.x;
+    const int nchannels = gridDim.y;
 
-    x   += int64_t(row)*ncols;
-    dst += int64_t(row)*ncols;
+    const int row       = blockIdx.x;
+    const int channel   = blockIdx.y;
+    const int sample    = blockIdx.z;
+    const int tid       = threadIdx.x;
+
+    x   += sample*stride_sample + channel*stride_channel + row*stride_row;
+    dst += ((sample*nchannels + channel)*nrows + row)*ncols;
 
     float tmp = 0.0f; // partial sum for thread in warp
 
@@ -186,13 +201,16 @@ static __global__ void rms_norm_back_f32(
     }
 }
 
-static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+static void norm_f32_cuda(
+        const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
+        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, cudaStream_t stream) {
+    const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
-        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+        norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+        norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 
@@ -207,13 +225,16 @@ static void group_norm_f32_cuda(
     }
 }
 
-static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+static void rms_norm_f32_cuda(
+        const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
+        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, cudaStream_t stream) {
+    const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
-        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+        rms_norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+        rms_norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 
@@ -229,23 +250,26 @@ static void rms_norm_back_f32_cuda(const float * grad, const float * xf, float *
 
 void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
-    const float * src0_d = (const float *)src0->data;
-    float * dst_d = (float *)dst->data;
+    const float * src0_d = (const float *) src0->data;
+    float * dst_d = (float *) dst->data;
     cudaStream_t stream = ctx.stream();
 
-    GGML_ASSERT(ggml_is_contiguous(src0));
-
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
     GGML_ASSERT(eps >= 0.0f);
 
-    norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+    const size_t ts0 = ggml_type_size(src0->type);
+    GGML_ASSERT(nb00 == ts0);
+    const int64_t s01 = nb01 / ts0;
+    const int64_t s02 = nb02 / ts0;
+    const int64_t s03 = nb03 / ts0;
+
+    norm_f32_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream);
 }
 
 void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -254,8 +278,6 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
-    GGML_ASSERT(ggml_is_contiguous(src0));
-
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -271,23 +293,26 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
 
 void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
-    const float * src0_d = (const float *)src0->data;
-    float * dst_d = (float *)dst->data;
+    const float * src0_d = (const float *) src0->data;
+    float * dst_d = (float *) dst->data;
     cudaStream_t stream = ctx.stream();
 
-    GGML_ASSERT(ggml_is_contiguous(src0));
-
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
     GGML_ASSERT(eps >= 0.0f);
 
-    rms_norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+    const size_t ts0 = ggml_type_size(src0->type);
+    GGML_ASSERT(nb00 == ts0);
+    const int64_t s01 = nb01 / ts0;
+    const int64_t s02 = nb02 / ts0;
+    const int64_t s03 = nb03 / ts0;
+
+    rms_norm_f32_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream);
 }
 
 void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 9605914ff..0a264be37 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1206,10 +1206,11 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_GROUP_NORM:
             return has_simdgroup_reduction;
         case GGML_OP_RMS_NORM:
-            return has_simdgroup_reduction && (op->ne[0] % 4 == 0);
+            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
         case GGML_OP_ARGMAX:
-        case GGML_OP_NORM:
             return true;
+        case GGML_OP_NORM:
+            return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
         case GGML_OP_ROPE:
             {
                 const int mode = ((const int32_t *) op->op_params)[2];
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 9ca3959ab..48ac489a6 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -8182,9 +8182,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
+            return true;
         case GGML_OP_NORM:
         case GGML_OP_GROUP_NORM:
         case GGML_OP_RMS_NORM:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_ADD:
         case GGML_OP_ACC:
         case GGML_OP_MUL:
diff --git a/src/llama.cpp b/src/llama.cpp
index 5760017e0..aae3c69b5 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -4610,7 +4610,8 @@ struct llm_build_context {
                         ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
                 cb(k_pe, "k_pe", il);
 
-                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+                // TODO: the CUDA backend used to not support non-cont. (RMS) norm, investigate removing ggml_cont
+                kv_compressed = ggml_cont(ctx0, kv_compressed);
                 kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
                         model.layers[il].attn_kv_a_norm, NULL,
                         LLM_NORM_RMS, cb, il);
@@ -6464,7 +6465,8 @@ struct llm_build_context {
                         ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
                 cb(k_pe, "k_pe", il);
 
-                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+                // TODO: the CUDA backend used to not support non-cont. (RMS) norm, investigate removing ggml_cont
+                kv_compressed = ggml_cont(ctx0, kv_compressed);
                 kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
                         model.layers[il].attn_kv_a_norm, NULL,
                         LLM_NORM_RMS, cb, il);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 4c5c4dd9c..1bfd41254 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1674,21 +1674,28 @@ struct test_silu_back : public test_case {
 struct test_norm : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
-    float eps;
+    const bool v; // whether a is a non-contiguous view
+    const float eps;
 
     std::string vars() override {
-        return VARS_TO_STR3(type, ne, eps);
+        return VARS_TO_STR4(type, ne, v, eps);
     }
 
     test_norm(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 5, 4, 3},
+            bool v = false,
             float eps = 1e-6f)
-        : type(type), ne(ne), eps(eps) {}
+        : type(type), ne(ne), v(v), eps(eps) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
         ggml_set_name(a, "a");
 
+        if (v) {
+            a = ggml_view_4d(ctx, a, a->ne[0]/2, a->ne[1]/2, a->ne[2]/2, a->ne[3]/2, a->nb[1], a->nb[2], a->nb[3], 0);
+            ggml_set_name(a, "view of a");
+        }
+
         ggml_tensor * out = ggml_norm(ctx, a, eps);
         ggml_set_name(out, "out");
 
@@ -1700,22 +1707,29 @@ struct test_norm : public test_case {
 struct test_rms_norm : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
-    float eps;
+    const bool v; // whether a is a non-contiguous view
+    const float eps;
 
     std::string vars() override {
-        return VARS_TO_STR3(type, ne, eps);
+        return VARS_TO_STR4(type, ne, v, eps);
     }
 
     test_rms_norm(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 5, 4, 3},
+            bool v = false,
             float eps = 1e-6f)
-        : type(type), ne(ne), eps(eps) {}
+        : type(type), ne(ne), v(v), eps(eps) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
         ggml_set_param(ctx, a);
         ggml_set_name(a, "a");
 
+        if (v) {
+            a = ggml_view_4d(ctx, a, a->ne[0]/2, a->ne[1]/2, a->ne[2]/2, a->ne[3]/2, a->nb[1], a->nb[2], a->nb[3], 0);
+            ggml_set_name(a, "view of a");
+        }
+
         ggml_tensor * out = ggml_rms_norm(ctx, a, eps);
         ggml_set_name(out, "out");
 
@@ -1741,7 +1755,7 @@ struct test_rms_norm : public test_case {
 struct test_rms_norm_back : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
-    float eps;
+    const float eps;
 
     std::string vars() override {
         return VARS_TO_STR3(type, ne, eps);
@@ -2919,7 +2933,7 @@ struct test_group_norm : public test_case {
     const float eps;
 
     std::string vars() override {
-        return VARS_TO_STR3(type, ne, num_groups);
+        return VARS_TO_STR4(type, ne, num_groups, eps);
     }
 
     test_group_norm(ggml_type type = GGML_TYPE_F32,
@@ -3964,9 +3978,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_scale());
     test_cases.emplace_back(new test_silu_back());
 
-    for (float eps : {0.0f, 1e-7f, 1e-4f, 1e-1f}) {
-        test_cases.emplace_back(new test_norm         (GGML_TYPE_F32, {64, 5, 4, 3}, eps));
-        test_cases.emplace_back(new test_rms_norm     (GGML_TYPE_F32, {64, 5, 4, 3}, eps));
+    for (float eps : {0.0f, 1e-6f, 1e-4f, 1e-1f}) {
+        for (bool v : {false, true}) {
+            test_cases.emplace_back(new test_norm    (GGML_TYPE_F32, {64, 5, 4, 3}, v, eps));
+            test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 5, 4, 3}, v, eps));
+        }
         test_cases.emplace_back(new test_rms_norm_back(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
     }
 

From 9f4cc8f8d310b13ab3fc93a9be81b1d1376a7fa6 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Wed, 5 Feb 2025 01:00:12 +0000
Subject: [PATCH 336/376] `sync`: minja (#11641)

* `sync`: minja

https://github.com/google/minja/commit/182de30cdaee78ba86179122f8047b3bdbab7f7f

https://github.com/google/minja/pull/46

https://github.com/google/minja/pull/45
---
 common/chat-template.hpp | 211 +++++++++++++++++++++++++++++++++------
 common/chat.cpp          |  45 +++++++--
 common/common.cpp        |   9 +-
 common/minja.hpp         |   8 +-
 examples/run/run.cpp     |  10 +-
 tests/test-chat.cpp      |   8 +-
 6 files changed, 233 insertions(+), 58 deletions(-)

diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index 58e119a3b..0e88fb361 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -33,6 +33,29 @@ struct chat_template_caps {
     bool requires_typed_content = false;
 };
 
+struct chat_template_inputs {
+    nlohmann::ordered_json messages;
+    nlohmann::ordered_json tools;
+    bool add_generation_prompt = true;
+    nlohmann::ordered_json extra_context;
+    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
+};
+
+struct chat_template_options {
+    bool apply_polyfills = true;
+    bool use_bos_token = true;
+    bool use_eos_token = true;
+    bool define_strftime_now = true;
+
+    bool polyfill_tools = true;
+    bool polyfill_tool_call_examples = true;
+    bool polyfill_tool_calls = true;
+    bool polyfill_tool_responses = true;
+    bool polyfill_system_role = true;
+    bool polyfill_object_arguments = true;
+    bool polyfill_typed_content = true;
+};
+
 class chat_template {
 
   private:
@@ -41,6 +64,7 @@ class chat_template {
     std::string bos_token_;
     std::string eos_token_;
     std::shared_ptr<minja::TemplateNode> template_root_;
+    std::string tool_call_example_;
 
     std::string try_raw_render(
         const nlohmann::ordered_json & messages,
@@ -49,7 +73,18 @@ class chat_template {
         const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
     {
         try {
-            auto prompt = apply(messages, tools, add_generation_prompt, extra_context, /* adjust_inputs= */ false);
+            chat_template_inputs inputs;
+            inputs.messages = messages;
+            inputs.tools = tools;
+            inputs.add_generation_prompt = add_generation_prompt;
+            inputs.extra_context = extra_context;
+            // Use fixed date for tests
+            inputs.now = std::chrono::system_clock::from_time_t(0);
+
+            chat_template_options opts;
+            opts.apply_polyfills = false;
+
+            auto prompt = apply(inputs, opts);
             // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
             return prompt;
         } catch (const std::exception & e) {
@@ -176,6 +211,58 @@ class chat_template {
             caps_.supports_tool_responses = contains(out, "Some response!");
             caps_.supports_tool_call_id = contains(out, "call_911_");
         }
+
+        try {
+            if (!caps_.supports_tools) {
+                const json user_msg {
+                    {"role", "user"},
+                    {"content", "Hey"},
+                };
+                const json args {
+                    {"arg1", "some_value"},
+                };
+                const json tool_call_msg {
+                    {"role", "assistant"},
+                    {"content", nullptr},
+                    {"tool_calls", json::array({
+                        {
+                            // TODO: detect if requires numerical id or fixed length == 6 like Nemo
+                            {"id", "call_1___"},
+                            {"type", "function"},
+                            {"function", {
+                                {"name", "tool_name"},
+                                {"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))},
+                            }},
+                        },
+                    })},
+                };
+                std::string prefix, full;
+                {
+                    chat_template_inputs inputs;
+                    inputs.messages = json::array({user_msg});
+                    inputs.add_generation_prompt = true;
+                    prefix = apply(inputs);
+                }
+                {
+                    chat_template_inputs inputs;
+                    inputs.messages = json::array({user_msg, tool_call_msg});
+                    inputs.add_generation_prompt = false;
+                    full = apply(inputs);
+                }
+
+                if (full.find(prefix) != 0) {
+                    if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
+                        prefix = prefix.substr(0, prefix.size() - eos_token_.size());
+                    }
+                }
+                if (full.find(prefix) != 0) {
+                    fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
+                }
+                tool_call_example_ = full.substr(prefix.size());
+            }
+        } catch (const std::exception & e) {
+            fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
+        }
     }
 
     const std::string & source() const { return source_; }
@@ -183,28 +270,72 @@ class chat_template {
     const std::string & eos_token() const { return eos_token_; }
     const chat_template_caps & original_caps() const { return caps_; }
 
+    // Deprecated, please use the form with chat_template_inputs and chat_template_options
     std::string apply(
         const nlohmann::ordered_json & messages,
         const nlohmann::ordered_json & tools,
         bool add_generation_prompt,
         const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
-        bool adjust_inputs = true) const
+        bool apply_polyfills = true)
+    {
+        fprintf(stderr, "[%s] Deprecated!\n", __func__);
+        chat_template_inputs inputs;
+        inputs.messages = messages;
+        inputs.tools = tools;
+        inputs.add_generation_prompt = add_generation_prompt;
+        inputs.extra_context = extra_context;
+        inputs.now = std::chrono::system_clock::now();
+
+        chat_template_options opts;
+        opts.apply_polyfills = apply_polyfills;
+
+        return apply(inputs, opts);
+    }
+
+    std::string apply(
+        const chat_template_inputs & inputs,
+        const chat_template_options & opts = chat_template_options()) const
     {
         json actual_messages;
 
-        auto needs_adjustments = adjust_inputs && (false
-            || !caps_.supports_system_role
-            || !caps_.supports_tools
-            || !caps_.supports_tool_responses
-            || !caps_.supports_tool_calls
-            || caps_.requires_object_arguments
-            || caps_.requires_typed_content
+        auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+        auto has_tool_calls = false;
+        auto has_tool_responses = false;
+        auto has_string_content = false;
+        for (const auto & message : inputs.messages) {
+            if (message.contains("tool_calls") && !message["tool_calls"].is_null()) {
+                has_tool_calls = true;
+            }
+            if (message.contains("role") && message["role"] == "tool") {
+                has_tool_responses = true;
+            }
+            if (message.contains("content") && message["content"].is_string()) {
+                has_string_content = true;
+            }
+        }
+
+        auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
+        auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
+        auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
+        auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
+        auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
+        auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
+        auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
+
+        auto needs_polyfills = opts.apply_polyfills && (false
+            || polyfill_system_role
+            || polyfill_tools
+            || polyfill_tool_calls
+            || polyfill_tool_responses
+            || polyfill_object_arguments
+            || polyfill_typed_content
         );
-        if (needs_adjustments) {
+
+        if (needs_polyfills) {
             actual_messages = json::array();
 
             auto add_message = [&](const json & msg) {
-                if (caps_.requires_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
+                if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
                     actual_messages.push_back({
                         {"role", msg.at("role")},
                         {"content", {{
@@ -227,9 +358,17 @@ class chat_template {
                     pending_system.clear();
                 }
             };
-            auto needs_tools_in_system = !tools.is_null() && tools.size() > 0 && !caps_.supports_tools;
 
-            for (const auto & message_ : needs_tools_in_system ? add_system(messages, "Available tools: " + tools.dump(2)) : messages) {
+            json adjusted_messages;
+            if (polyfill_tools) {
+                adjusted_messages = add_system(inputs.messages,
+                    "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
+                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
+            } else {
+                adjusted_messages = inputs.messages;
+            }
+
+            for (const auto & message_ : adjusted_messages) {
                 auto message = message_;
                 if (!message.contains("role") || !message.contains("content")) {
                     throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
@@ -237,7 +376,7 @@ class chat_template {
                 std::string role = message.at("role");
 
                 if (message.contains("tool_calls")) {
-                    if (caps_.requires_object_arguments || !caps_.supports_tool_calls) {
+                    if (polyfill_object_arguments || polyfill_tool_calls) {
                         for (auto & tool_call : message.at("tool_calls")) {
                             if (tool_call["type"] == "function") {
                                 auto & function = tool_call.at("function");
@@ -252,7 +391,7 @@ class chat_template {
                             }
                         }
                     }
-                    if (!caps_.supports_tool_calls) {
+                    if (polyfill_tool_calls) {
                         auto content = message.at("content");
                         auto tool_calls = json::array();
                         for (const auto & tool_call : message.at("tool_calls")) {
@@ -279,7 +418,7 @@ class chat_template {
                         message.erase("tool_calls");
                     }
                 }
-                if (!caps_.supports_tool_responses && role == "tool") {
+                if (polyfill_tool_responses && role == "tool") {
                     message["role"] = "user";
                     auto obj = json {
                         {"tool_response", {
@@ -296,7 +435,7 @@ class chat_template {
                     message.erase("name");
                 }
 
-                if (!message["content"].is_null() && !caps_.supports_system_role) {
+                if (!message["content"].is_null() && polyfill_system_role) {
                     std::string content = message.at("content");
                     if (role == "system") {
                         if (!pending_system.empty()) pending_system += "\n";
@@ -315,28 +454,36 @@ class chat_template {
                 }
                 add_message(message);
             }
-            if (!caps_.supports_system_role) {
-                flush_sys();
-            }
+            flush_sys();
         } else {
-            actual_messages = messages;
+            actual_messages = inputs.messages;
         }
 
         auto context = minja::Context::make(json({
             {"messages", actual_messages},
-            {"add_generation_prompt", add_generation_prompt},
-            {"bos_token", bos_token_},
-            {"eos_token", eos_token_},
+            {"add_generation_prompt", inputs.add_generation_prompt},
         }));
+        context->set("bos_token", opts.use_bos_token ? bos_token_ : "");
+        context->set("eos_token", opts.use_eos_token ? eos_token_ : "");
+        if (opts.define_strftime_now) {
+            auto now = inputs.now;
+            context->set("strftime_now", Value::callable([now](const std::shared_ptr<minja::Context> &, minja::ArgumentsValue & args) {
+                args.expectArgs("strftime_now", {1, 1}, {0, 0});
+                auto format = args.args[0].get<std::string>();
 
-        if (!tools.is_null()) {
-            auto tools_val = minja::Value(tools);
-            context->set("tools", tools_val);
+                auto time = std::chrono::system_clock::to_time_t(now);
+                auto local_time = *std::localtime(&time);
+                std::ostringstream ss;
+                ss << std::put_time(&local_time, format.c_str());
+                return ss.str();
+            }));
         }
-        if (!extra_context.is_null()) {
-            for (auto & kv : extra_context.items()) {
-                minja::Value val(kv.value());
-                context->set(kv.key(), val);
+        if (!inputs.tools.is_null()) {
+            context->set("tools", minja::Value(inputs.tools));
+        }
+        if (!inputs.extra_context.is_null()) {
+            for (auto & kv : inputs.extra_context.items()) {
+                context->set(kv.key(), minja::Value(kv.value()));
             }
         }
 
@@ -353,7 +500,7 @@ class chat_template {
             std::string existing_system = messages_with_system.at(0).at("content");
             messages_with_system[0] = json {
                 {"role", "system"},
-                {"content", existing_system + "\n" + system_prompt},
+                {"content", existing_system + "\n\n" + system_prompt},
             };
         } else {
             messages_with_system.insert(messages_with_system.begin(), json {
diff --git a/common/chat.cpp b/common/chat.cpp
index 4a113c0ca..ef1c6fb3d 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -163,6 +163,28 @@ static void foreach_function(const json & tools, const std::function<void(const
     }
 }
 
+static std::string apply(
+    const common_chat_template & tmpl,
+    const nlohmann::ordered_json & messages,
+    const nlohmann::ordered_json & tools,
+    bool add_generation_prompt,
+    const nlohmann::ordered_json & extra_context = nlohmann::ordered_json())
+{
+    minja::chat_template_inputs tmpl_inputs;
+    tmpl_inputs.messages = messages;
+    tmpl_inputs.tools = tools;
+    tmpl_inputs.add_generation_prompt = add_generation_prompt;
+    tmpl_inputs.extra_context = extra_context;
+    // TODO: add flag to control date/time, if only for testing purposes.
+    // tmpl_inputs.now = std::chrono::system_clock::now();
+
+    minja::chat_template_options tmpl_opts;
+    tmpl_opts.use_bos_token = false;
+    tmpl_opts.use_eos_token = false;
+
+    return tmpl.apply(tmpl_inputs, tmpl_opts);
+}
+
 static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
     common_chat_params data;
 
@@ -244,7 +266,7 @@ static common_chat_params common_chat_params_init_generic(const common_chat_temp
         inputs.messages,
         "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
 
-    data.prompt = tmpl.apply(tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_GENERIC;
     return data;
 }
@@ -310,7 +332,7 @@ static common_chat_params common_chat_params_init_mistral_nemo(const common_chat
         builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
     }, grammar_options);
     data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
     return data;
 }
@@ -360,7 +382,7 @@ static common_chat_params common_chat_params_init_command_r7b(const common_chat_
         "<|END_THINKING|>",
         "<|END_ACTION|>",
     };
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
     return data;
 }
@@ -477,7 +499,7 @@ static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const com
         builder.add_rule("root", string_join(tool_rules, " | "));
     }, grammar_options);
     data.additional_stops.push_back("<|eom_id|>");
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
         {"tools_in_user_message", false},
         {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
     });
@@ -542,7 +564,8 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
         };
         builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
     }, grammar_options);
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = prompt;
     data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
     return data;
 }
@@ -556,10 +579,10 @@ static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input)
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
     fprintf(stderr, "%s\n", __func__);
     common_chat_params data;
-    data.prompt = tmpl.apply(inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
+    data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
         {"datetime", "Jan 29 2025 13:00:00 GMT"},
         {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
-    }, /* adjust_inputs= */ false);
+    });
     if (!inputs.tools.is_null() && !inputs.tools.empty()) {
         data.grammar_lazy = inputs.tool_choice != "required";
         data.grammar = build_grammar([&](const common_grammar_builder & builder) {
@@ -603,7 +626,7 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
     // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
     // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
     common_chat_params data;
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
     if (!inputs.tools.is_null() && !inputs.tools.empty()) {
         data.grammar_lazy = inputs.tool_choice != "required";
@@ -730,7 +753,7 @@ static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(con
         data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
     }, grammar_options);
 
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     // TODO: if (has_raw_python)
     data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
     return data;
@@ -785,7 +808,7 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
         data.preserved_tokens = { "</tool_call>" };
     }, grammar_options);
 
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
     return data;
 }
@@ -846,7 +869,7 @@ static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input)
 
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
     common_chat_params data;
-    data.prompt = tmpl.apply(inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
     data.grammar_lazy = false;
     if (!inputs.json_schema.is_null()) {
diff --git a/common/common.cpp b/common/common.cpp
index edba6fb4b..8661e164a 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1904,10 +1904,6 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
             default_template_src = CHATML_TEMPLATE_SRC;
         }
     }
-    std::string token_bos;
-    std::string token_eos;
-    // TODO: update logic that adds BOS and EOS tokens to the tokenized prompt, in favour of the template.
-#if 0
     auto vocab = llama_model_get_vocab(model);
     const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
         if (token == LLAMA_TOKEN_NULL) {
@@ -1920,9 +1916,8 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
             return common_token_to_piece(vocab, token, true);
         }
     };
-    token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
-    token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
-#endif
+    auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
+    auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
     try {
         return {
             has_explicit_template,
diff --git a/common/minja.hpp b/common/minja.hpp
index e77eb69d5..c304b5c66 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -2194,7 +2194,7 @@ private:
     }
 
     TemplateTokenVector tokenize() {
-      static std::regex comment_tok(R"(\{#([-~]?)(.*?)([-~]?)#\})");
+      static std::regex comment_tok(R"(\{#([-~]?)([\s\S\r\n]*?)([-~]?)#\})");
       static std::regex expr_open_regex(R"(\{\{([-~])?)");
       static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
       static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)");
@@ -2615,6 +2615,7 @@ inline std::shared_ptr<Context> Context::builtins() {
   }));
   globals.set("join", simple_function("join", { "items", "d" }, [](const std::shared_ptr<Context> &, Value & args) {
     auto do_join = [](Value & items, const std::string & sep) {
+      if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
       std::ostringstream oss;
       auto first = true;
       for (size_t i = 0, n = items.size(); i < n; ++i) {
@@ -2695,6 +2696,10 @@ inline std::shared_ptr<Context> Context::builtins() {
     return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
       args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
       auto & items = args.args[0];
+      if (items.is_null())
+        return Value::array();
+      if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
+
       auto filter_fn = context->get(args.args[1]);
       if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
 
@@ -2772,6 +2777,7 @@ inline std::shared_ptr<Context> Context::builtins() {
       auto & items = args.args[0];
       if (items.is_null())
         return Value::array();
+      if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
       auto attr_name = args.args[1].get<std::string>();
 
       bool has_test = false;
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index ca9273155..39353ba30 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -848,7 +848,15 @@ static int apply_chat_template(const common_chat_template & tmpl, LlamaData & ll
             });
         }
         try {
-            auto result = tmpl.apply(messages, /* tools= */ json(), append);
+            minja::chat_template_inputs tmpl_inputs;
+            tmpl_inputs.messages = messages;
+            tmpl_inputs.add_generation_prompt = append;
+
+            minja::chat_template_options tmpl_opts;
+            tmpl_opts.use_bos_token = false;
+            tmpl_opts.use_eos_token = false;
+
+            auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
             llama_data.fmtted.resize(result.size() + 1);
             memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
             return result.size();
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 50bd40738..b78da2cdb 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -18,12 +18,8 @@
 using json = nlohmann::ordered_json;
 
 static common_chat_msg msg_from_json(const json & message) {
-    common_chat_msg ret{
-        "assistant",
-        "",
-        {},
-        /* .tool_plan = */ "",
-    };
+    common_chat_msg ret;
+    ret.role = "assistant";
     if (message.contains("content") && !message.at("content").is_null()) {
         ret.content = message.at("content");
     }

From 1ec208083cb896dd8772a2f962151fa3d4a74e36 Mon Sep 17 00:00:00 2001
From: SAMI <samuel.koesnadi@stud.uni-due.de>
Date: Wed, 5 Feb 2025 14:45:40 +0700
Subject: [PATCH 337/376] llava: add quantization for the visual projector
 LLAVA, Qwen2VL (#11644)

* Added quantization for visual projector
* Added README
* Fixed the clip quantize implementation in the file

* Fixed the gcc warning regarding minor linting

* Removed trailing whitespace
---
 examples/llava/CMakeLists.txt        |  7 ++++
 examples/llava/README-quantize.md    | 44 +++++++++++++++++++++
 examples/llava/clip-quantize-cli.cpp | 59 ++++++++++++++++++++++++++++
 examples/llava/clip.cpp              |  8 ++--
 4 files changed, 113 insertions(+), 5 deletions(-)
 create mode 100644 examples/llava/README-quantize.md
 create mode 100644 examples/llava/clip-quantize-cli.cpp

diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt
index 3ce0d60c8..319effd19 100644
--- a/examples/llava/CMakeLists.txt
+++ b/examples/llava/CMakeLists.txt
@@ -50,3 +50,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+set(TARGET llama-llava-clip-quantize-cli)
+add_executable(${TARGET} clip-quantize-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/llava/README-quantize.md b/examples/llava/README-quantize.md
new file mode 100644
index 000000000..b931513ab
--- /dev/null
+++ b/examples/llava/README-quantize.md
@@ -0,0 +1,44 @@
+# Quantizing CLIP Visual Projector
+
+This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
+
+## Usage
+
+To quantize a CLIP visual projector model, use the following command:
+
+```sh
+./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
+```
+
+After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
+
+### Arguments
+
+- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
+- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
+- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
+
+### Quantization Types
+
+The following quantization types are supported, based on the `enum ggml_type` definition:
+
+- `2` - `q4_0`: 4-bit quantization with a single scale value.
+- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
+- `6` - `q5_0`: 5-bit quantization with a single scale value.
+- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
+- `8` - `q8_0`: 8-bit quantization with a single scale value.
+
+### Example
+
+To quantize a model using the `q4_0` quantization type, you would run:
+
+```sh
+./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
+```
+
+This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
+
+## Notes
+
+- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
+- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.
diff --git a/examples/llava/clip-quantize-cli.cpp b/examples/llava/clip-quantize-cli.cpp
new file mode 100644
index 000000000..566506954
--- /dev/null
+++ b/examples/llava/clip-quantize-cli.cpp
@@ -0,0 +1,59 @@
+#include "arg.h"
+#include "base64.hpp"
+#include "log.h"
+#include "common.h"
+#include "sampling.h"
+#include "clip.h"
+#include "llava.h"
+#include "llama.h"
+#include "ggml.h"
+
+static void print_usage(int argc, char ** argv) {
+    (void) argc;
+
+    fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
+    fprintf(stderr, "  type = 2 - q4_0\n");
+    fprintf(stderr, "  type = 3 - q4_1\n");
+    fprintf(stderr, "  type = 6 - q5_0\n");
+    fprintf(stderr, "  type = 7 - q5_1\n");
+    fprintf(stderr, "  type = 8 - q8_0\n");
+}
+
+int main(int argc, char ** argv) {
+    if (argc != 4) {
+        print_usage(argc, argv);
+        return 1;
+    }
+
+    const std::string fname_inp = argv[1];
+    const std::string fname_out = argv[2];
+
+    const int itype = atoi(argv[3]);
+
+    const int64_t t_main_start_us = ggml_time_us();
+
+    int64_t t_quantize_us = 0;
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
+            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
+            return 1;
+        }
+
+        t_quantize_us = ggml_time_us() - t_start_us;
+    }
+
+    // report timing
+    {
+        const int64_t t_main_end_us = ggml_time_us();
+
+        printf("\n");
+        printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
+        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
+    }
+
+    return 0;
+}
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 7367d44cb..271cf2a2a 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -2745,10 +2745,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 }
 
 bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
-    ggml_type type = GGML_TYPE_Q4_1;
-
     assert(itype < GGML_TYPE_COUNT);
-    type = static_cast<ggml_type>(itype);
+    ggml_type type = static_cast<ggml_type>(itype);
 
     auto * ctx_clip = clip_model_load(fname_inp, 2);
 
@@ -2801,8 +2799,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             }
         }
 
-        // quantize only 2D tensors
-        quantize &= (ggml_n_dims(cur) == 2);
+        // quantize only 2D tensors and bigger than block size
+        quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type);
 
         if (quantize) {
             new_type = type;

From fa62da9b2dc03539a30f1306f59c4c6ffbe4f50a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 5 Feb 2025 08:58:31 +0100
Subject: [PATCH 338/376] CUDA: support for mat. mul. with ne03 != ne13
 (#11656)

---
 ggml/src/ggml-cuda/ggml-cuda.cu |  27 +++-----
 ggml/src/ggml-cuda/mmv.cu       | 114 ++++++++++++++++++++------------
 2 files changed, 81 insertions(+), 60 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 70a598099..4dbaefdba 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1366,8 +1366,6 @@ static void ggml_cuda_op_mul_mat(
     const int64_t ne13 = src1->ne[3];
     const int64_t nrows1 = ggml_nrows(src1);
 
-    GGML_ASSERT(ne03 == ne13);
-
     const int64_t ne0 = dst->ne[0];
     const int64_t ne1 = dst->ne[1];
 
@@ -1381,9 +1379,11 @@ static void ggml_cuda_op_mul_mat(
 
     GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
-    GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
+    GGML_ASSERT(ne12 % ne02 == 0);
+    GGML_ASSERT(ne13 % ne03 == 0);
 
     const int64_t i02_divisor = ne12 / ne02;
+    const int64_t i03_divisor = ne13 / ne03;
 
     const size_t src0_ts = ggml_type_size(src0->type);
     const size_t src0_bs = ggml_blck_size(src0->type);
@@ -1399,6 +1399,7 @@ static void ggml_cuda_op_mul_mat(
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
+    GGML_ASSERT(!(split && ne03 < ne13));
 
     ggml_tensor_extra_gpu * src0_extra = split ? (ggml_tensor_extra_gpu *) src0->extra : nullptr;
 
@@ -1562,7 +1563,8 @@ static void ggml_cuda_op_mul_mat(
                 }
 
                 // for split tensors the data begins at i0 == i0_offset_low
-                char  *  src0_dd_i =  dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
+                const size_t nbytes_src0_matrix = ne01*ne00*src0_ts / src0_bs;
+                char  *  src0_dd_i =  dev[id].src0_dd + ((i03/i03_divisor)*ne02 + (i02/i02_divisor)) * nbytes_src0_matrix;
                 float * src1_ddf_i = dev[id].src1_ddf + (i0*ne11 + src1_col_0) * ne10;
                 char  * src1_ddq_i = dev[id].src1_ddq +  src1_ddq_i_offset;
                 float *   dst_dd_i =   dev[id].dst_dd + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
@@ -1606,8 +1608,9 @@ static void ggml_cuda_op_mul_mat(
                     CUDA_CHECK(cudaGetLastError());
                 }
 
-                if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
-                    CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream));
+                if (src1_col_0 == 0 && !src0_is_contiguous && i03 % i03_divisor == 0 && i02 % i02_divisor == 0) {
+                    CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
+                        src0_dd_i, src0, i03/i03_divisor, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream));
                 }
 
                 // do the computation
@@ -1882,7 +1885,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (!split && use_mul_mat_vec && dst->ne[3] == 1 && (src0->ne[1] < MMV_MAX_ROWS || any_gpus_without_fp16_mma)) {
+    if (!split && use_mul_mat_vec && (src0->ne[1] < MMV_MAX_ROWS || any_gpus_without_fp16_mma)) {
         // the custom F16 vector kernel can be used over batched cuBLAS GEMM
         // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
         ggml_cuda_mul_mat_vec(ctx, src0, src1, dst);
@@ -2216,12 +2219,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
             ggml_cuda_op_rms_norm_back(ctx, dst);
             break;
         case GGML_OP_MUL_MAT:
-            if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
-                GGML_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
-                return false;
-            } else {
-                ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
-            }
+            ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
             break;
         case GGML_OP_MUL_MAT_ID:
             ggml_cuda_mul_mat_id(ctx, dst);
@@ -2998,9 +2996,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) {
                     return false;
                 }
-                if (op->op == GGML_OP_MUL_MAT && a->ne[3] != b->ne[3]) {
-                    return false;
-                }
 #ifdef GGML_USE_MUSA
                 if (b->type == GGML_TYPE_F16 && b->ne[2]*b->ne[3] > 1 &&
                     !ggml_is_transposed(a) && !ggml_is_transposed(b)) {
diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu
index 5a9ddd958..f89ed03b5 100644
--- a/ggml/src/ggml-cuda/mmv.cu
+++ b/ggml/src/ggml-cuda/mmv.cu
@@ -1,18 +1,21 @@
+#include "ggml.h"
 #include "common.cuh"
 #include "mmv.cuh"
 
 template <typename T, typename type_acc, int block_size>
 static __global__ void mul_mat_vec(
         const T * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
-        const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) {
+        const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
+        const int64_t sample_ratio, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst) {
     const int64_t row       = blockIdx.x;
-    const int64_t channel   = blockIdx.z;
+    const int64_t channel   = blockIdx.y;
+    const int64_t sample    = blockIdx.z;
     const int     tid       = threadIdx.x;
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-    x   += (channel/channel_ratio)*stride_channel_x + row*stride_row;
-    y   +=  channel               *stride_channel_y;
-    dst +=  channel               *stride_channel_dst;
+    x   +=  (sample/sample_ratio)*stride_sample_x   + (channel/channel_ratio)*stride_channel_x + row*stride_row;
+    y   +=   sample              *stride_sample_y   +  channel               *stride_channel_y;
+    dst +=   sample              *stride_sample_dst +  channel               *stride_channel_dst;
 
     const float2 * y2 = (const float2 *) y;
 
@@ -91,12 +94,15 @@ template <typename T, typename type_acc>
 static void launch_mul_mat_vec_cuda(
         const T * x, const float * y, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
+        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
+        const int64_t nsamples_y, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         cudaStream_t stream) {
     GGML_ASSERT(ncols      % 2 == 0);
     GGML_ASSERT(stride_row % 2 == 0);
     GGML_ASSERT(nchannels_y % nchannels_x == 0);
+    GGML_ASSERT(nsamples_y  % nsamples_x  == 0);
     const int64_t channel_ratio = nchannels_y / nchannels_x;
+    const int64_t sample_ratio  = nsamples_y  / nsamples_x;
     int device;
     int warp_size;
 
@@ -118,40 +124,48 @@ static void launch_mul_mat_vec_cuda(
     }
 
     const int smem = warp_size*sizeof(float);
-    const dim3 block_nums(nrows, 1, nchannels_y);
+    const dim3 block_nums(nrows, nchannels_y, nsamples_y);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
             mul_mat_vec<T, type_acc,  32><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   64: {
             mul_mat_vec<T, type_acc,  64><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   96: {
             mul_mat_vec<T, type_acc,  96><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  128: {
             mul_mat_vec<T, type_acc, 128><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  160: {
             mul_mat_vec<T, type_acc, 160><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  192: {
             mul_mat_vec<T, type_acc, 192><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  224: {
             mul_mat_vec<T, type_acc, 224><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  256: {
             mul_mat_vec<T, type_acc, 256><<<block_nums, block_dims, smem, stream>>>
-                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
+                (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         default: {
             GGML_ABORT("fatal error");
@@ -163,16 +177,19 @@ template<typename T>
 static void mul_mat_vec_cuda(
         const T * x, const float * y, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
+        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
+        const int64_t nsamples_y, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         enum ggml_prec prec, cudaStream_t stream) {
     switch (prec) {
         case GGML_PREC_DEFAULT: {
-            launch_mul_mat_vec_cuda<T, half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
-                stride_channel_x, stride_channel_y, stride_channel_dst, stream);
+            launch_mul_mat_vec_cuda<T, half>
+                (x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
         } break;
         case GGML_PREC_F32: {
-            launch_mul_mat_vec_cuda<T, float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
-                stride_channel_x, stride_channel_y, stride_channel_dst, stream);
+            launch_mul_mat_vec_cuda<T, float>
+                (x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
         } break;
     }
 }
@@ -181,10 +198,19 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type  == GGML_TYPE_F32);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
-    GGML_ASSERT(src1->ne[1] == 1);
+    const size_t ts_src0 = ggml_type_size(src0->type);
+    const size_t ts_src1 = ggml_type_size(src1->type);
+    const size_t ts_dst  = ggml_type_size(dst->type);
+
+    GGML_ASSERT(ne11 == 1);
+    GGML_ASSERT(ne12 == ne2);
+    GGML_ASSERT(ne13 == ne3);
+
+    GGML_ASSERT(nb00 == ts_src0);
+    GGML_ASSERT(nb10 == ts_src1);
+    GGML_ASSERT(nb0  == ts_dst);
 
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
@@ -192,29 +218,22 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     const float * src1_d = (const float *) src1->data;
     float       *  dst_d = (float       *)  dst->data;
 
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne12 = src1->ne[2];
-    GGML_ASSERT(dst->ne[2] == ne12);
-
-    GGML_ASSERT(src0->ne[3] == 1);
-    GGML_ASSERT(src1->ne[3] == 1);
-    GGML_ASSERT( dst->ne[3] == 1);
-
-    const int64_t stride_row         = src0->nb[1] / ggml_type_size(src0->type);
-    const int64_t channel_stride_x   = src0->nb[2] / ggml_type_size(src0->type);
-    const int64_t channel_stride_y   = src1->nb[2] / ggml_type_size(src1->type);
-    const int64_t channel_stride_dst =  dst->nb[2] / ggml_type_size( dst->type);
+    const int64_t s01 = src0->nb[1] / ts_src0;
+    const int64_t s02 = src0->nb[2] / ts_src0;
+    const int64_t s12 = src1->nb[2] / ts_src1;
+    const int64_t s2  =  dst->nb[2] / ts_dst;
+    const int64_t s03 = src0->nb[3] / ts_src0;
+    const int64_t s13 = src1->nb[3] / ts_src1;
+    const int64_t s3  =  dst->nb[3] / ts_dst;
 
     switch (src0->type) {
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0->data;
-            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
-                channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
+            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, s01, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, prec, ctx.stream());
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
-            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
-                channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
+            mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, s01, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, prec, ctx.stream());
         } break;
         default:
             GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
@@ -243,20 +262,27 @@ void ggml_cuda_op_mul_mat_vec(
     const int64_t stride_row         = ne00;
     const int64_t nchannels_x        = 1;
     const int64_t nchannels_y        = 1;
-    const int64_t channel_stride_x   = 0;
-    const int64_t channel_stride_y   = 0;
-    const int64_t channel_stride_dst = 0;
+    const int64_t stride_channel_x   = 0;
+    const int64_t stride_channel_y   = 0;
+    const int64_t stride_channel_dst = 0;
+    const int64_t nsamples_x         = 1;
+    const int64_t nsamples_y         = 1;
+    const int64_t stride_sample_x    = 0;
+    const int64_t stride_sample_y    = 0;
+    const int64_t stride_sample_dst  = 0;
 
     switch (src0->type) {
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0_dd_i;
             mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
-                nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
+                nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
             mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
-                nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
+                nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         default:
             GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));

From d774ab3acc4fee41fbed6dbfc192b57d5f79f34b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 5 Feb 2025 10:57:42 +0200
Subject: [PATCH 339/376] metal : adjust support conditions for norm operators
 (#11671)

cont #11659

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal.m | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 0a264be37..c63dbad20 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1204,13 +1204,13 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_SUM_ROWS:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_GROUP_NORM:
-            return has_simdgroup_reduction;
+            return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
         case GGML_OP_RMS_NORM:
             return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
         case GGML_OP_ARGMAX:
             return true;
         case GGML_OP_NORM:
-            return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
+            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
         case GGML_OP_ROPE:
             {
                 const int mode = ((const int32_t *) op->op_params)[2];

From c3db0480bb820e120249014b380e1f4badf2a1c1 Mon Sep 17 00:00:00 2001
From: Matvey Soloviev <blackhole89@gmail.com>
Date: Thu, 6 Feb 2025 01:55:25 +0100
Subject: [PATCH 340/376] readme : add link to Autopen under UIs (#11684)

Autopen (https://github.com/blackhole89/autopen) is a graphical text editor that uses llama.cpp to tokenize the buffer on the fly, score the buffer, visualise token logits and allow you to switch back and forth between different possible completions at any point. It hopefully meets the criteria for inclusion, as the dependency on llama.cpp is stated prominently.
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index d68330d2a..11f3d0286 100644
--- a/README.md
+++ b/README.md
@@ -189,6 +189,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [ramalama](https://github.com/containers/ramalama) (MIT)
 - [semperai/amica](https://github.com/semperai/amica) (MIT)
 - [withcatai/catai](https://github.com/withcatai/catai) (MIT)
+- [Autopen](https://github.com/blackhole89/autopen) (GPL)
 
 </details>
 

From 902368a06b915b860236cfc97ff885b2aceae256 Mon Sep 17 00:00:00 2001
From: Charles Duffy <charles@dyfis.net>
Date: Wed, 5 Feb 2025 19:52:31 -0600
Subject: [PATCH 341/376] metal : avoid breaking build when metal API predates
 TARGET_OS_VISION (#11690)

Avoids breakage in nix flake build introduced by b0569130c5e9c671152c913d82803b7c2f014ff9
---
 ggml/src/ggml-metal/ggml-metal.m | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index c63dbad20..944d90af3 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -19,6 +19,10 @@
 // max number of MTLCommandBuffer used to submit a graph for processing
 #define GGML_METAL_MAX_COMMAND_BUFFERS 8
 
+#ifndef TARGET_OS_VISION
+#define TARGET_OS_VISION 0
+#endif
+
 // create residency sets only on macOS >= 15.0
 #if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000 || \
     TARGET_OS_IOS && __IPHONE_OS_VERSION_MAX_ALLOWED >= 180000 || \

From 1b598b30581bad59e5af86c94362f9a30f261fac Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 6 Feb 2025 00:02:18 -0600
Subject: [PATCH 342/376] vulkan: use smaller combined allocations to avoid
 fragmentation (#11551)

---
 ggml/src/ggml-alloc.c                | 14 +-------------
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 19 +++++++++++++++++--
 2 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index 9a3bf9f29..7244a9cbb 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -989,19 +989,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
             this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
         }
 
-        if (this_size > max_size) {
-            GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
-                    __func__, t->name,
-                    ggml_backend_buft_name(buft),
-                    this_size, max_size);
-            for (size_t i = 0; i < n_buffers; i++) {
-                ggml_backend_buffer_free(buffers[i]);
-            }
-            free(buffers);
-            return NULL;
-        }
-
-        if ((cur_buf_size + this_size) > max_size) {
+        if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
             // allocate tensors in the current buffer
             if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
                 return NULL;
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 48ac489a6..2e1bcf691 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -156,6 +156,7 @@ struct vk_device_struct {
     vk::PhysicalDeviceProperties properties;
     std::string name;
     uint64_t max_memory_allocation_size;
+    uint64_t suballocation_block_size;
     bool fp16;
     bool pipeline_robustness;
     vk::Device device;
@@ -2269,6 +2270,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->physical_device.getProperties2(&props2);
         device->properties = props2.properties;
+        device->vendor_id = device->properties.vendorID;
 
         const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
 
@@ -2280,7 +2282,20 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
         }
 
-        device->vendor_id = device->properties.vendorID;
+        const char* GGML_VK_SUBALLOCATION_BLOCK_SIZE = getenv("GGML_VK_SUBALLOCATION_BLOCK_SIZE");
+
+        if (GGML_VK_SUBALLOCATION_BLOCK_SIZE != nullptr) {
+            device->suballocation_block_size = std::stoul(GGML_VK_SUBALLOCATION_BLOCK_SIZE);
+#if defined(_WIN32)
+        } else if (device->vendor_id == VK_VENDOR_ID_NVIDIA) {
+            // Limit batching of allocations to 1GB by default to avoid fragmentation issues
+            device->suballocation_block_size = 1024*1024*1024;
+#endif
+        } else {
+            device->suballocation_block_size = device->max_memory_allocation_size;
+        }
+        device->suballocation_block_size = std::min(device->suballocation_block_size, device->max_memory_allocation_size);
+
         device->subgroup_size = subgroup_props.subgroupSize;
         device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
         if (sm_builtins) {
@@ -7561,7 +7576,7 @@ static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type
 
 static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
     ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    return ctx->device->max_memory_allocation_size;
+    return ctx->device->suballocation_block_size;
 }
 
 static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {

From 8a7e3bf17aa5a8412854787746c92a28623a8925 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?R=C3=A9my=20O?= <remyoudompheng@gmail.com>
Date: Thu, 6 Feb 2025 07:09:59 +0100
Subject: [PATCH 343/376] vulkan: initial support for IQ4_XS quantization
 (#11501)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 25 ++++++++++++
 .../vulkan-shaders/copy_from_quant.comp       |  2 +-
 .../vulkan-shaders/copy_to_quant.comp         |  2 +-
 .../vulkan-shaders/dequant_funcs.comp         | 38 ++++++++++++++++++-
 .../vulkan-shaders/dequant_funcs_cm2.comp     | 23 +++++++++++
 .../vulkan-shaders/dequant_iq4_xs.comp        | 34 +++++++++++++++++
 .../vulkan-shaders/flash_attn_cm2.comp        |  2 +-
 .../vulkan-shaders/get_rows_quant.comp        |  2 +-
 .../vulkan-shaders/mul_mat_vec.comp           |  2 +-
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    | 21 +++++++++-
 .../vulkan-shaders/mul_mm_cm2.comp            |  2 +-
 .../src/ggml-vulkan/vulkan-shaders/types.comp | 28 +++++++++++---
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  1 +
 13 files changed, 169 insertions(+), 13 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2e1bcf691..1c99ebe2e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1622,6 +1622,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         //CREATE_FA(GGML_TYPE_IQ2_S, iq2_s)
         //CREATE_FA(GGML_TYPE_IQ3_XXS, iq3_xxs)
         //CREATE_FA(GGML_TYPE_IQ3_S, iq3_s)
+        //CREATE_FA(GGML_TYPE_IQ4_XS, iq4_xs)
         CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl)
 #undef CREATE_FA
 
@@ -1655,6 +1656,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f16,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f16,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f16,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f16,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
 
         CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
@@ -1673,6 +1675,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
         CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
@@ -1726,6 +1729,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         } else {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
@@ -1744,6 +1748,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
             CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
@@ -1770,6 +1775,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             } else {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
@@ -1788,6 +1794,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
                 CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             }
         }
@@ -1837,6 +1844,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
@@ -1861,6 +1869,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM2
@@ -1902,6 +1911,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
         // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
@@ -1926,6 +1936,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
             CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM
@@ -1962,6 +1973,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f32_f32_len,   mul_mat_vec_iq2_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f32_f32_len, mul_mat_vec_iq3_xxs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f32_f32_len,   mul_mat_vec_iq3_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f32_f32_len,  mul_mat_vec_iq4_xs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f32_f32_len,  mul_mat_vec_iq4_nl_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
 
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
@@ -1981,6 +1993,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f16_f32_len,   mul_mat_vec_iq2_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f16_f32_len, mul_mat_vec_iq3_xxs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f16_f32_len,   mul_mat_vec_iq3_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f16_f32_len,  mul_mat_vec_iq4_xs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f16_f32_len,  mul_mat_vec_iq4_nl_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true);
     }
 
@@ -2001,6 +2014,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   mul_mat_vec_id_iq2_s_f32_len,   mul_mat_vec_id_iq2_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", mul_mat_vec_id_iq3_xxs_f32_len, mul_mat_vec_id_iq3_xxs_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  mul_mat_vec_id_iq4_xs_f32_len,  mul_mat_vec_id_iq4_xs_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true);
 
     // dequant shaders
@@ -2020,6 +2034,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_S],   "dequant_iq2_s",   dequant_iq2_s_len,   dequant_iq2_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_XXS], "dequant_iq3_xxs", dequant_iq3_xxs_len, dequant_iq3_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S],   "dequant_iq3_s",   dequant_iq3_s_len,   dequant_iq3_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_XS],  "dequant_iq4_xs",  dequant_iq4_xs_len,  dequant_iq4_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
 
     // get_rows
@@ -2035,6 +2050,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_S],   "get_rows_iq2_s",   get_rows_iq2_s_len,   get_rows_iq2_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs", get_rows_iq3_xxs_len, get_rows_iq3_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S],   "get_rows_iq3_s",   get_rows_iq3_s_len,   get_rows_iq3_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -2049,6 +2065,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_S],   "get_rows_iq2_s_f32",   get_rows_iq2_s_f32_len,   get_rows_iq2_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs_f32", get_rows_iq3_xxs_f32_len, get_rows_iq3_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S],   "get_rows_iq3_s_f32",   get_rows_iq3_s_f32_len,   get_rows_iq3_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs_f32",  get_rows_iq4_xs_f32_len,  get_rows_iq4_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
@@ -2995,6 +3012,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3048,6 +3066,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3084,6 +3103,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3132,6 +3152,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -3163,6 +3184,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_IQ3_XXS:
         case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             break;
         default:
@@ -8037,6 +8059,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ2_S:
                     case GGML_TYPE_IQ3_XXS:
                     case GGML_TYPE_IQ3_S:
+                    case GGML_TYPE_IQ4_XS:
                     case GGML_TYPE_IQ4_NL:
                         break;
                     default:
@@ -8110,6 +8133,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 //case GGML_TYPE_IQ2_S:
                 //case GGML_TYPE_IQ3_XXS:
                 //case GGML_TYPE_IQ3_S:
+                //case GGML_TYPE_IQ4_XS:
                 case GGML_TYPE_IQ4_NL:
                     break;
                 default:
@@ -8132,6 +8156,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ2_S:
                     case GGML_TYPE_IQ3_XXS:
                     case GGML_TYPE_IQ3_S:
+                    case GGML_TYPE_IQ4_XS:
                     case GGML_TYPE_IQ4_NL:
                         return true;
                     default:
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
index aeae5400d..9c9fe9626 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp
@@ -12,7 +12,7 @@ layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
 #endif
 
 void main() {
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
     if (gl_LocalInvocationIndex.x != 0) {
         return;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
index d4b068e61..660811086 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
@@ -217,7 +217,7 @@ void quantize(uint dst_idx, uint src_idx)
 #endif
 
 void main() {
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
     if (gl_LocalInvocationIndex.x != 0) {
         return;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
index ee6877531..ecfdbfaa8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
@@ -304,6 +304,42 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
 }
 #endif
 
+#if defined(DATA_A_IQ4_XS)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint iq = 16 * ib32 + (iqs % 16);
+
+    const uint sl = (data_a[a_offset + ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
+    const uint sh = (data_a[a_offset + ib].scales_h >> (2 * ib32)) & 3;
+    const uint qshift = (iqs & 16) >> 2;
+    u8vec2 qs = u8vec2(data_a[a_offset + ib].qs[iq], data_a[a_offset + ib].qs[iq + 1]);
+    qs = (qs >> qshift) & uint8_t(0xF);
+
+    const float dl = float(int(sl | (sh << 4)) - 32);
+    return dl * vec2(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y]);
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    const uint ib32 = iqs / 32;
+    const uint iq = 16 * ib32 + (iqs % 16);
+
+    const uint sl = (data_a[a_offset + ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
+    const uint sh = (data_a[a_offset + ib].scales_h >> (2 * ib32)) & 3;
+    const uint qshift = (iqs & 16) >> 2;
+    u8vec4 qs = u8vec4(
+        data_a[a_offset + ib].qs[iq + 0],
+        data_a[a_offset + ib].qs[iq + 1],
+        data_a[a_offset + ib].qs[iq + 2],
+        data_a[a_offset + ib].qs[iq + 3]
+    );
+    qs = (qs >> qshift) & uint8_t(0xF);
+
+    const float dl = float(int(sl | (sh << 4)) - 32);
+    return dl * vec4(
+        kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y],
+        kvalues_iq4nl[qs.z], kvalues_iq4nl[qs.w]);
+}
+#endif
+
 #if defined(DATA_A_IQ4_NL)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
@@ -321,7 +357,7 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif
 
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
 vec2 get_dm(uint ib, uint a_offset) {
     return vec2(float(data_a[a_offset + ib].d), 0);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index 974efd3f9..78c3bddf2 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -454,6 +454,27 @@ float16_t dequantFuncIQ3_S(const in decodeBufIQ3_S bl, const in uint blockCoords
 }
 #endif
 
+#if defined(DATA_A_IQ4_XS)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_XS {
+   block_iq4_xs block;
+};
+
+float16_t dequantFuncIQ4_XS(const in decodeBufIQ4_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    const float16_t d = bl.block.d;
+    const uint idx = coordInBlock[1];
+
+    const uint ib32 = (idx & 0xE0) >> 5; // 0..7
+
+    const uint sl = (bl.block.scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
+    const uint sh = ((bl.block.scales_h) >> (2 * ib32)) & 3;
+    const uint qshift = (idx & 16) >> 2;
+    const uint q = (bl.block.qs[16 * ib32 + (idx % 16)] >> qshift) & 0xF;
+
+    float16_t ret = d * float16_t(int(sl | (sh << 4)) - 32) * float16_t(kvalues_iq4nl[q]);
+    return ret;
+}
+#endif
 
 #if defined(DATA_A_IQ4_NL)
 layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL {
@@ -504,6 +525,8 @@ float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoor
 #define dequantFuncA dequantFuncIQ3_XXS
 #elif defined(DATA_A_IQ3_S)
 #define dequantFuncA dequantFuncIQ3_S
+#elif defined(DATA_A_IQ4_XS)
+#define dequantFuncA dequantFuncIQ4_XS
 #elif defined(DATA_A_IQ4_NL)
 #define dequantFuncA dequantFuncIQ4_NL
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp
new file mode 100644
index 000000000..f930852a4
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq4_xs data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    // Each thread handles 1 subblock (1 scale and 32 quantized values)
+    const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    if (ib >= p.nel / 256) {
+        return;
+    }
+
+    const uint ib32 = gl_LocalInvocationID.x % 8;
+
+    const float d = float(data_a[ib].d);
+    // Scales are 6 bits
+    const uint scale = ((data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF)
+                     | (((data_a[ib].scales_h >> (2 * ib32)) & 3) << 4);
+    const float dl = d * (int(scale) - 32);
+
+    const uint b_idx = 256 * ib + 32 * ib32;
+    const uint q_idx = 16 * ib32;
+    [[unroll]] for (uint l = 0; l < 16; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(dl * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]);
+        data_b[b_idx + l + 16] = D_TYPE(dl * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >>  4]);
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index 043a53023..ba88ce79a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -104,7 +104,7 @@ ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE ele
 #endif
 
 void main() {
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
 #endif
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
index 09dc43d8d..c16a2a9f6 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
@@ -12,7 +12,7 @@ void main() {
     const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
     const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
 
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
 #endif
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
index 48156e7ba..d7e99727d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
@@ -133,7 +133,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 void main() {
     const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
 
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
 #endif
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index d0559aac8..33b2234e7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -95,7 +95,7 @@ shared ACC_TYPE coopmat_stage[TM * TN * NUM_WARPS];
 #endif
 
 void main() {
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
 #endif
 
@@ -547,6 +547,25 @@ void main() {
             const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> (16 * (idx % 2));
             const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
 
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ4_XS)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint ib32 = (idx % 128) / 16;         // 0..7
+            const uint iq = 16 * ib32 + 2 * (idx % 8);
+
+            const uint sl = (data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
+            const uint sh = ((data_a[ib].scales_h) >> (2 * ib32)) & 3;
+            const uint qshift = (idx & 8) >> 1;
+            u8vec2 qs = u8vec2(data_a[ib].qs[iq], data_a[ib].qs[iq + 1]);
+            qs = (qs >> qshift) & uint8_t(0xF);
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = d * float(int(sl | (sh << 4)) - 32) * vec2(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y]);
+
             buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
             buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
 #elif defined(DATA_A_IQ4_NL)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 27c5d68b3..7e29bbfec 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -106,7 +106,7 @@ D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem
 #endif
 
 void main() {
-#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_NL)
+#if defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
     init_iq_shmem(gl_WorkGroupSize);
 #endif
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
index 9e56a3530..db643a54c 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
@@ -1026,6 +1026,23 @@ void init_iq_shmem(uvec3 wgsize)
 #define A_TYPE_PACKED16 block_iq3_s_packed16
 #endif
 
+#define QUANT_K_IQ4_XS 256
+#define QUANT_R_IQ4_XS 1
+
+struct block_iq4_xs
+{
+    float16_t d;
+    uint16_t scales_h;
+    uint8_t scales_l[QUANT_K_IQ4_XS/64];
+    uint8_t qs[QUANT_K_IQ4_XS/2];
+};
+
+#if defined(DATA_A_IQ4_XS)
+#define QUANT_K QUANT_K_IQ4_XS
+#define QUANT_R QUANT_R_IQ4_XS
+#define A_TYPE block_iq4_xs
+#endif
+
 #define QUANT_K_IQ4_NL 32
 #define QUANT_R_IQ4_NL 2
 
@@ -1042,7 +1059,13 @@ struct block_iq4_nl_packed16
 };
 
 #if defined(DATA_A_IQ4_NL)
+#define QUANT_K QUANT_K_IQ4_NL
+#define QUANT_R QUANT_R_IQ4_NL
+#define A_TYPE block_iq4_nl
+#define A_TYPE_PACKED16 block_iq4_nl_packed16
+#endif
 
+#if defined(DATA_A_IQ4_NL) || defined(DATA_A_IQ4_XS)
 const int8_t kvalues_iq4nl_const[16] = {
     int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
     int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113)
@@ -1058,11 +1081,6 @@ void init_iq_shmem(uvec3 wgsize)
     }
     barrier();
 }
-
-#define QUANT_K QUANT_K_IQ4_NL
-#define QUANT_R QUANT_R_IQ4_NL
-#define A_TYPE block_iq4_nl
-#define A_TYPE_PACKED16 block_iq4_nl_packed16
 #endif
 
 #endif // !defined(GGML_TYPES_COMP)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 93ddbfadc..77e7e1148 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -60,6 +60,7 @@ const std::vector<std::string> type_names = {
     "iq2_s",
     "iq3_xxs",
     "iq3_s",
+    "iq4_xs",
     "iq4_nl"
 };
 

From 2c6c8df56d8a3edd657b9a295e95d469a37f0044 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 6 Feb 2025 00:15:30 -0600
Subject: [PATCH 344/376] vulkan: optimize coopmat2 iq2/iq3 callbacks (#11521)

* vulkan: optimize coopmat2 iq2/iq3 callbacks

* build: trigger CI on GLSL compute shader changes
---
 .github/workflows/build.yml                   |  4 +-
 .../vulkan-shaders/dequant_funcs_cm2.comp     | 79 +++++++++----------
 2 files changed, 40 insertions(+), 43 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 8f9c82f87..6841ba589 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -10,10 +10,10 @@ on:
   push:
     branches:
       - master
-    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
index 78c3bddf2..0eba37420 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp
@@ -323,15 +323,16 @@ float16_t dequantFuncIQ2_XXS(const in decodeBufIQ2_XXS bl, const in uint blockCo
     const uint8_t qs = bl.block.qs[iqs];
     const uint signscale = pack32(u16vec2(bl16.block.qs[4*ib32+2], bl16.block.qs[4*ib32+3]));
 
-    const float16_t dscale = bl.block.d * 0.25hf * (0.5hf + float16_t(signscale >> 28));
+    const float dscale = float(bl.block.d) * 0.25 * (0.5 + float(signscale >> 28));
     uint sign = bitfieldExtract(signscale, 7 * int(ib8), 7);
     sign |= bitCount(sign) << 7;
 
-    const uint8_t g = unpack8(iq2xxs_grid[qs][(idx & 4) >> 2])[idx & 3];
+    uint g2 = iq2xxs_grid[qs][(idx & 4) >> 2];
+    g2 >>= (idx & 2) * 8;
+    const vec2 g = vec2(unpack8(g2));
 
-    float16_t ret = dscale * float16_t(g) * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
-
-    return ret;
+    vec2 ret = dscale * g * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
+    return float16_t(ret[idx & 1]);
 }
 #endif
 
@@ -350,14 +351,16 @@ float16_t dequantFuncIQ2_XS(const in decodeBufIQ2_XS bl, const in uint blockCoor
     const uint iqs = (idx & 0xF8) >> 3;    // 0..63
 
     const uint16_t qs = bl.block.qs[iqs];
-    const float16_t dscale = bl.block.d * 0.25hf * (0.5hf + float16_t((bl.block.scales[is] >> sshift) & 0xF));
+    const float dscale = float(bl.block.d) * 0.25 * (0.5 + float((bl.block.scales[is] >> sshift) & 0xF));
 
     uint sign = uint(qs >> 9);
     sign |= bitCount(sign) << 7;
-    const uint8_t g = unpack8(iq2xs_grid[qs & 0x1FF][(idx & 4) >> 2])[idx & 3];
+    uint g2 = iq2xs_grid[qs & 0x1FF][(idx & 4) >> 2];
+    g2 >>= (idx & 2) * 8;
+    const vec2 g = vec2(unpack8(g2));
 
-    float16_t ret = dscale * float16_t(g) * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
-    return ret;
+    vec2 ret = dscale * g * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf);
+    return float16_t(ret[idx & 1]);
 }
 #endif
 
@@ -369,24 +372,23 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2
 float16_t dequantFuncIQ2_S(const in decodeBufIQ2_S bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
     uint idx = coordInBlock[1];
-    uint lsb = idx & 1;
-    idx /= 2;
 
-    const uint ib8 = (idx % 128) / 4; // 0..31
-    const uint ib32 = ib8 / 4;        // 0..7
+    const uint ib32 = (idx & 0xE0) >> 5;        // 0..7
+    const uint ib8 = (idx & 0xF8) >> 3;         // 0..31
+    const uint qhshift = 2 * (ib8 % 4);
 
-    const uint scale = (bl.block.scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+    const uint scale = (bl.block.scales[ib32] >> ((idx & 0x10) >> 2)) & 0xf;
     const uint qs = bl.block.qs[ib8];
     const uint qh = bl.block.qh[ib32];
-    const uint qhshift = 2 * (ib8 % 4);
-    const uint sign = bl.block.qs[QUANT_K / 8 + ib8] >> (2 * (idx % 4));
+    const uint sign = bl.block.qs[QUANT_K / 8 + ib8] >> (idx & 0x6);
 
     const float d = float(bl.block.d);
     const float db = d * 0.25 * (0.5 + scale);
-    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
-    const uint16_t grid = unpack16(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 2) >> 1])[idx & 1];
-    const vec2 v = db * vec2(sign01) * vec2(unpack8(grid));
-    return float16_t(v[lsb]);
+    const ivec2 sign01 = 1 - (2 & ivec2(sign << 1, sign));
+    uint g2 = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 4) >> 2];
+    g2 >>= (idx & 2) * 8;
+    const vec2 v = db * vec2(sign01) * vec2(unpack8(g2));
+    return float16_t(v[idx & 1]);
 }
 #endif
 
@@ -401,28 +403,25 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3
 
 float16_t dequantFuncIQ3_XXS(const in decodeBufIQ3_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
+    decodeBufIQ3_XXS_packed16 bl16 = decodeBufIQ3_XXS_packed16(bl);
     uint idx = coordInBlock[1];
-    uint lsb = idx & 1;
-    idx /= 2;
 
-    const uint iqs = (idx % 128) / 2;           // 0..63
-    const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values
+    const uint iqs = (idx & 0xFC) >> 2;             // 0..63
+    const uint is = QUANT_K / 4 + ((idx & 0xE0) >> 3);// 8 values
 
     const float d = float(bl.block.d);
     const uint qs = bl.block.qs[iqs];
-    const uint signs = pack32(u8vec4(
-        bl.block.qs[is+0],
-        bl.block.qs[is+1],
-        bl.block.qs[is+2],
-        bl.block.qs[is+3]
+    const uint signs = pack32(u16vec2(
+        bl16.block.qs[is/2+0],
+        bl16.block.qs[is/2+1]
     ));
     const float db = d * 0.5 * (0.5 + (signs >> 28));
     const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7);
-    const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (2 * (idx % 4));
-    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(int8_t(sign << 1), int8_t(sign))));
-    const uint grid = iq3xxs_grid[qs] >> (16 * (idx & 1));
+    const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (idx & 0x6);
+    const ivec2 sign01 = ivec2(1 - (2 & ivec2(sign << 1, sign)));
+    const uint grid = iq3xxs_grid[qs] >> (16 * ((idx & 2) >> 1));
     const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
-    return float16_t(v[lsb]);
+    return float16_t(v[idx & 1]);
 }
 #endif
 
@@ -434,23 +433,21 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3
 float16_t dequantFuncIQ3_S(const in decodeBufIQ3_S bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
     uint idx = coordInBlock[1];
-    uint lsb = idx & 1;
-    idx /= 2;
 
-    const uint iqs = (idx % 128) / 2;           // 0..63
-    const uint iqh = iqs / 8;
+    const uint iqs = (idx & 0xFC) >> 2;           // 0..63
+    const uint iqh = (idx & 0xE0) >> 5;
 
     const float d = float(bl.block.d);
     const uint qs = bl.block.qs[iqs];
     const uint qh = bl.block.qh[iqh];
-    const int8_t sign = int8_t(bl.block.signs[iqs / 2] >> (2 * (idx % 4)));
+    const int8_t sign = int8_t(bl.block.signs[iqs / 2] >> (idx & 0x6));
     const uint scale = bl.block.scales[iqs / 16];
-    const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign)));
+    const ivec2 sign01 = ivec2(1 - (2 & ivec2(sign << 1, sign)));
     const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf));
-    const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> (16 * (idx % 2));
+    const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> ((idx & 2) << 3);
     const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy);
 
-    return float16_t(v[lsb]);
+    return float16_t(v[idx & 1]);
 }
 #endif
 

From 8d4d2be143a3919c3d194b9bf7a221e50abed893 Mon Sep 17 00:00:00 2001
From: junchao-zhao <68935141+junchao-loongson@users.noreply.github.com>
Date: Thu, 6 Feb 2025 17:20:00 +0800
Subject: [PATCH 345/376] ggml : fix LoongArch compile error with 128-bit SIMD
 (#11701)

---
 ggml/src/ggml-cpu/ggml-cpu-quants.c | 169 +++++++++++++++-------------
 1 file changed, 91 insertions(+), 78 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c
index 88303ff0e..72ec58cee 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-quants.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c
@@ -297,6 +297,90 @@ static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
 static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
 #endif
 
+#if defined(__loongarch_sx)
+
+static __m128i lsx_packs_w(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_w(a, 15);
+    tmp1 = __lsx_vsat_w(b, 15);
+    return __lsx_vpickev_h(tmp1, tmp);
+}
+
+static __m128i lsx_packs_h(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_h(a, 7);
+    tmp1 = __lsx_vsat_h(b, 7);
+    return __lsx_vpickev_b(tmp1, tmp);
+}
+
+static __m128i lsx_packus_h(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_hu(a, 7);
+    tmp1 = __lsx_vsat_hu(b, 7);
+    return __lsx_vpickev_b(tmp1, tmp);
+}
+
+static __m128i lsx_maddubs_h(__m128i a, __m128i b) {
+    __m128i tmp1, tmp2;
+    tmp1 = __lsx_vmulwev_h_b(a, b);
+    tmp2 = __lsx_vmulwod_h_b(a, b);
+    return __lsx_vsadd_h(tmp1, tmp2);
+}
+
+static __m128i lsx_madd_h(__m128i a, __m128i b) {
+    __m128i tmp1, tmp2;
+    tmp1 = __lsx_vmulwev_w_h(a, b);
+    tmp2 = __lsx_vmulwod_w_h(a, b);
+    return __lsx_vadd_w(tmp1, tmp2);
+}
+
+static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) {
+    v4i32 __ret = {d, c, b, a};
+    return (__m128i)__ret;
+}
+
+static __m128i lsx_shuffle_b(__m128i a, __m128i b) {
+    __m128i mask_f, zero, tmp0, tmp2, mask;
+    int f = 0x8f;
+    mask_f = __lsx_vreplgr2vr_b(f);
+    zero = __lsx_vldi(0);
+    tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits
+    tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or  with 0x10 prepare for positive
+    mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask
+    tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones
+    return __lsx_vshuf_b(a, zero, tmp2);
+}
+
+static __m128i lsx_hadd_h(__m128i a, __m128i b) {
+    __m128i tmp1 = __lsx_vpickev_h(b, a);
+    __m128i tmp2 = __lsx_vpickod_h(b, a);
+    return __lsx_vadd_h(tmp1, tmp2);
+}
+
+static __m128i lsx_hadd_w(__m128i a, __m128i b) {
+    __m128i tmp1 = __lsx_vpickev_w(b, a);
+    __m128i tmp2 = __lsx_vpickod_w(b, a);
+    return __lsx_vadd_w(tmp1, tmp2);
+}
+
+static __m128 lsx_hadd_s(__m128 a, __m128 b) {
+    __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a);
+    __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a);
+
+    return __lsx_vfadd_s(tmp1, tmp2);
+}
+
+static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
+    __m128 res_0 =lsx_hadd_s(a, b);
+    __m128 res_1 =lsx_hadd_s(c, d);
+    __m128 res =lsx_hadd_s(res_0, res_1);
+    res =lsx_hadd_s(res, res);
+    res =lsx_hadd_s(res, res);
+
+    return ((v4f32)res)[0];
+}
+#endif
+
 #if defined(__loongarch_asx)
 
 #ifdef __clang__
@@ -395,11 +479,6 @@ static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1
     return (__m256i)__ret;
 }
 
-static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) {
-    v4i32 __ret = {d, c, b, a};
-    return (__m128i)__ret;
-}
-
 static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) {
     v4i64 __ret = {d, c, b, a};
     return (__m256i)__ret;
@@ -409,18 +488,6 @@ static __m256i lasx_insertf128( __m128i x, __m128i y) {
     return lasx_set_q(x, y);
 }
 
-static __m128i lsx_shuffle_b(__m128i a, __m128i b) {
-    __m128i mask_f, zero, tmp0, tmp2, mask;
-    int f = 0x8f;
-    mask_f = __lsx_vreplgr2vr_b(f);
-    zero = __lsx_vldi(0);
-    tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits
-    tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or  with 0x10 prepare for positive
-    mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask
-    tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones
-    return __lsx_vshuf_b(a, zero, tmp2);
-}
-
 static __m256i lasx_shuffle_b(__m256i a, __m256i b) {
     __m256i mask_f, zero, tmp0, tmp2, mask;
     int f = 0x8f;
@@ -482,25 +549,6 @@ static __m128 lasx_extractf128( __m256 a, int pos) {
     return ret;
 }
 
-static __m128i lsx_hadd_h(__m128i a, __m128i b) {
-    __m128i tmp1 = __lsx_vpickev_h(b, a);
-    __m128i tmp2 = __lsx_vpickod_h(b, a);
-    return __lsx_vadd_h(tmp1, tmp2);
-}
-
-static __m128i lsx_hadd_w(__m128i a, __m128i b) {
-    __m128i tmp1 = __lsx_vpickev_w(b, a);
-    __m128i tmp2 = __lsx_vpickod_w(b, a);
-    return __lsx_vadd_w(tmp1, tmp2);
-}
-
-static __m128 lsx_hadd_s(__m128 a, __m128 b) {
-    __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a);
-    __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a);
-
-    return __lsx_vfadd_s(tmp1, tmp2);
-}
-
 static __m256i lasx_maddubs_h(__m256i a, __m256i b) {
     __m256i tmp1, tmp2;
     tmp1 = __lasx_xvmulwev_h_b(a, b);
@@ -529,42 +577,6 @@ static __m256i lasx_packs_h(__m256i a, __m256i b) {
     return __lasx_xvpickev_b(tmp1, tmp);
 }
 
-static __m128i lsx_packs_w(__m128i a, __m128i b) {
-    __m128i tmp, tmp1;
-    tmp = __lsx_vsat_w(a, 15);
-    tmp1 = __lsx_vsat_w(b, 15);
-    return __lsx_vpickev_h(tmp1, tmp);
-}
-
-static __m128i lsx_packs_h(__m128i a, __m128i b) {
-    __m128i tmp, tmp1;
-    tmp = __lsx_vsat_h(a, 7);
-    tmp1 = __lsx_vsat_h(b, 7);
-    return __lsx_vpickev_b(tmp1, tmp);
-}
-
-static __m128i lsx_packus_h(__m128i a, __m128i b) {
-    __m128i tmp, tmp1;
-    tmp = __lsx_vsat_hu(a, 7);
-    tmp1 = __lsx_vsat_hu(b, 7);
-    return __lsx_vpickev_b(tmp1, tmp);
-}
-
-
-static __m128i lsx_maddubs_h(__m128i a, __m128i b) {
-    __m128i tmp1, tmp2;
-    tmp1 = __lsx_vmulwev_h_b(a, b);
-    tmp2 = __lsx_vmulwod_h_b(a, b);
-    return __lsx_vsadd_h(tmp1, tmp2);
-}
-
-static __m128i lsx_madd_h(__m128i a, __m128i b) {
-    __m128i tmp1, tmp2;
-    tmp1 = __lsx_vmulwev_w_h(a, b);
-    tmp2 = __lsx_vmulwod_w_h(a, b);
-    return __lsx_vadd_w(tmp1, tmp2);
-}
-
 // multiply int8_t, add results pairwise twice
 static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
     // Get absolute values of x vectors
@@ -2232,21 +2244,22 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     }
 
     sumf = hsum_float_8(acc);
+
 #elif defined(__loongarch_sx)
     // set constants
     const __m128i low_mask = __lsx_vreplgr2vr_b(0xF);
     const __m128i off = __lsx_vreplgr2vr_b(8);
 
     // Initialize accumulator with zeros
-    __m128 acc_0 = __lsx_vldi(0);
-    __m128 acc_1 = __lsx_vldi(0);
-    __m128 acc_2 = __lsx_vldi(0);
-    __m128 acc_3 = __lsx_vldi(0);
+    __m128 acc_0 = (__m128)__lsx_vldi(0);
+    __m128 acc_1 = (__m128)__lsx_vldi(0);
+    __m128 acc_2 = (__m128)__lsx_vldi(0);
+    __m128 acc_3 = (__m128)__lsx_vldi(0);
 
     for (; ib + 1 < nb; ib += 2) {
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
+        const __m128 d_0_1 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
         const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0);
 
@@ -2264,7 +2277,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
+        const __m128 d_2_3 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
 
         const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0);
 

From c0d4843225eed38903ea71ef302a02fa0b27f048 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <adrien@gallouet.fr>
Date: Thu, 6 Feb 2025 12:08:13 +0100
Subject: [PATCH 346/376] build : fix llama.pc (#11658)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <adrien@gallouet.fr>
---
 CMakeLists.txt    |  2 +-
 cmake/llama.pc.in | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 74b48d24d..7b2a1845e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -233,4 +233,4 @@ configure_file(cmake/llama.pc.in
         @ONLY)
 
 install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        DESTINATION lib/pkgconfig)
+        DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in
index 0b2b6bcfa..6fb58b5f6 100644
--- a/cmake/llama.pc.in
+++ b/cmake/llama.pc.in
@@ -1,10 +1,10 @@
 prefix=@CMAKE_INSTALL_PREFIX@
-exec_prefix=${prefix}
-libdir=${exec_prefix}/lib
-includedir=${prefix}/include
+exec_prefix=@CMAKE_INSTALL_PREFIX@
+libdir=@CMAKE_INSTALL_FULL_LIBDIR@
+includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
 
 Name: llama
 Description: Port of Facebook's LLaMA model in C/C++
-Version: @PROJECT_VERSION@
-Libs: -L${libdir} -lggml  -lggml-base -lllama
+Version: @LLAMA_INSTALL_VERSION@
+Libs: -L${libdir} -lggml -lggml-base -lllama
 Cflags: -I${includedir}

From 9dd7a0390feffcc1f4b17eb7692a6e43030d85af Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 6 Feb 2025 13:41:37 +0200
Subject: [PATCH 347/376] llama : add log about loading model tensors (#11699)

---
 src/llama-model.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 0487c978b..e30db66e9 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1275,6 +1275,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
     const bool use_mmap_buffer = true;
 
+    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, use_mmap_buffer ? "true" : "false");
+
     // build a list of buffer types for the CPU and GPU devices
     pimpl->cpu_buft_list = make_cpu_buft_list(devices);
     for (auto * dev : devices) {

From 194b2e69f8da3a22395c74fd9acd6d5835437b96 Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Thu, 6 Feb 2025 17:12:35 +0530
Subject: [PATCH 348/376] SYCL: Adjust support condition for norm operators
 (#11674)

SYCL does not support non contiguous tensors for norm operations
---
 ggml/src/ggml-sycl/ggml-sycl.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 2984ed82e..aab34a752 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4537,14 +4537,17 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_NORM:
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
         case GGML_OP_LOG:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
+            return true;
+        case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
+        case GGML_OP_GROUP_NORM:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
@@ -4576,7 +4579,6 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
-        case GGML_OP_GROUP_NORM:
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
         case GGML_OP_LEAKY_RELU:

From 9ab42dc722ad19a12af80f38a06474d498f96da3 Mon Sep 17 00:00:00 2001
From: Tei Home <taiteitonghome@proton.me>
Date: Thu, 6 Feb 2025 20:16:15 +0800
Subject: [PATCH 349/376] docs: update fedora cuda guide for 12.8 release
 (#11393)

* docs: update fedora cuda guide for 12.8 release

* docs: build cuda update
---
 docs/build.md       |  61 ++++++++++++++---
 docs/cuda-fedora.md | 159 ++++++++++++++++----------------------------
 2 files changed, 109 insertions(+), 111 deletions(-)

diff --git a/docs/build.md b/docs/build.md
index dd6495028..afb7a0402 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -125,21 +125,66 @@ For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
 
 ## CUDA
 
-This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
+This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed.
 
-If you are using Fedora (using Fedora Workstation, or an 'Atomic' variant such as Silverblue), or would like to set up CUDA in a toolbox, please consider our [Fedora CUDA guide](./cuda-fedora.md). Unfortunately, the process is not as simple as one might expect.
+#### Download directly from NVIDIA
+You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
 
-- Using `CMake`:
 
-  ```bash
-  cmake -B build -DGGML_CUDA=ON
-  cmake --build build --config Release
-  ```
+#### Compile and run inside a Fedora Toolbox Container
+We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
 
-The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
+**Recommended for:**
+
+- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
+- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde).
+- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
+
+
+### Compilation
+```bash
+cmake -B build -DGGML_CUDA=ON
+cmake --build build --config Release
+```
+
+### Override Compute Capability Specifications
+
+If `nvcc` cannot detect your gpu, you may get compile-warnings such as:
+ ```text
+nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
+```
+
+To override the `native` GPU detection:
+
+#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
+
+```text
+GeForce RTX 4090      8.9
+GeForce RTX 3080 Ti   8.6
+GeForce RTX 3070      8.6
+```
+
+#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list.
+
+```bash
+cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
+```
+
+### Runtime CUDA environmental variables
+
+You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.
+
+```bash
+# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device.
+CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
+```
+
+### Unified Memory
 
 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
 
+### Performance Tuning
+
 The following compilation options are also available to tweak performance:
 
 | Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
diff --git a/docs/cuda-fedora.md b/docs/cuda-fedora.md
index b993386c8..9c88b7694 100644
--- a/docs/cuda-fedora.md
+++ b/docs/cuda-fedora.md
@@ -1,17 +1,16 @@
 # Setting Up CUDA on Fedora
 
 In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
+
 - [Fedora Workstation](https://fedoraproject.org/workstation/)
 - [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
 - [Fedora Spins](https://fedoraproject.org/spins)
-- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.`, `Arch Linux`, and `Ubuntu`.
-
+- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`.
 
 ## Table of Contents
 
 - [Prerequisites](#prerequisites)
-- [Monitoring NVIDIA CUDA Repositories](#monitoring-nvidia-cuda-repositories)
-- [Using the Fedora 39 CUDA Repository](#using-the-fedora-39-cuda-repository)
+- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository)
 - [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
 - [Installing Essential Development Tools](#installing-essential-development-tools)
 - [Adding the CUDA Repository](#adding-the-cuda-repository)
@@ -29,44 +28,33 @@ In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox
 ## Prerequisites
 
 - **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
-- **NVIDIA Drivers and Graphics Card installed on Host System (optional)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
+- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
 - **Internet connectivity** to download packages.
 
-### Monitoring NVIDIA CUDA Repositories
+### Using the Fedora 41 CUDA Repository
 
-Before proceeding, it is advisable to check if NVIDIA has updated their CUDA repositories for your Fedora version. NVIDIA's repositories can be found at:
+The latest release is 41.
 
-- [Fedora 40 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora40/x86_64/)
 - [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
 
-As of the latest update, these repositories do not contain the `cuda` meta-package or are missing essential components.
-
-### Using the Fedora 39 CUDA Repository
-
-Since the newer repositories are incomplete, we'll use the Fedora 39 repository:
-
-- [Fedora 39 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/)
-
-**Note:** Fedora 39 is no longer maintained, so we recommend using a toolbox environment to prevent system conflicts.
+**Note:** We recommend using a toolbox environment to prevent system conflicts.
 
 ## Creating a Fedora Toolbox Environment
 
-This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using a Fedora 39 toolbox allows us to install the necessary packages without affecting the host system.
+This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system.
 
 **Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
 
-We do not recommend installing on the host system, as Fedora 39 is out-of-maintenance, and instead you should upgrade to a maintained version of Fedora for your host.
-
-1. **Create a Fedora 39 Toolbox:**
+1. **Create a Fedora 41 Toolbox:**
 
    ```bash
-   toolbox create --image registry.fedoraproject.org/fedora-toolbox:39 --container fedora-toolbox-39-cuda
+   toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda
    ```
 
 2. **Enter the Toolbox:**
 
    ```bash
-   toolbox enter --container fedora-toolbox-39-cuda
+   toolbox enter --container fedora-toolbox-41-cuda
    ```
 
    Inside the toolbox, you have root privileges and can install packages without affecting the host system.
@@ -85,7 +73,7 @@ We do not recommend installing on the host system, as Fedora 39 is out-of-mainte
    sudo dnf install vim-default-editor --allowerasing
    ```
 
-   The `--allowerasing` flag resolves any package conflicts.
+   The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package.
 
 3. **Install Development Tools and Libraries:**
 
@@ -100,7 +88,7 @@ We do not recommend installing on the host system, as Fedora 39 is out-of-mainte
 Add the NVIDIA CUDA repository to your DNF configuration:
 
 ```bash
-sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo
+sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo
 ```
 
 After adding the repository, synchronize the package manager again:
@@ -109,106 +97,62 @@ After adding the repository, synchronize the package manager again:
 sudo dnf distro-sync
 ```
 
-## Installing `nvidia-driver-libs`
+## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
 
-Attempt to install `nvidia-driver-libs`:
+We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go).
 
 ```bash
-sudo dnf install nvidia-driver-libs
+ls -la /usr/lib64/libcuda.so.1
 ```
 
 **Explanation:**
 
-- `nvidia-driver-libs` contains necessary NVIDIA driver libraries required by CUDA.
-- This step might fail due to conflicts with existing NVIDIA drivers on the host system.
+- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA,
+  on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries.
 
-## Manually Resolving Package Conflicts
+### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found).
+
+```bash
+sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
+```
+
+### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found).
 
 If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
 
-### 1. Download the `nvidia-driver-libs` RPM
+#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies)
 
 ```bash
-sudo dnf download --arch x86_64 nvidia-driver-libs
+sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs
 ```
 
-You should see a file similar to:
-
-```
-nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
-```
-
-### 2. Attempt to Install the RPM
+#### 2. Update the RPM database to assume the installation of these packages.
 
 ```bash
-sudo dnf install nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
-```
-
-**Expected Error:**
-
-Installation may fail with errors pointing to conflicts with `egl-gbm` and `egl-wayland`.
-
-**Note: It is important to carefully read the error messages to identify the exact paths that need to be excluded.**
-
-### 3. Download Dependencies
-
-```bash
-sudo dnf download --arch x86_64 egl-gbm egl-wayland
-```
-
-### 4. Install `egl-gbm` with Excluded Paths
-
-Exclude conflicting files during installation:
-
-```bash
-sudo rpm --install --verbose --hash \
-  --excludepath=/usr/lib64/libnvidia-egl-gbm.so.1.1.2 \
-  --excludepath=/usr/share/egl/egl_external_platform.d/15_nvidia_gbm.json \
-  egl-gbm-1.1.2^20240919gitb24587d-3.fc39.x86_64.rpm
-```
-
-**Explanation:**
-
-- The `--excludepath` option skips installing files that conflict with existing files.
-- Adjust the paths based on the error messages you receive.
-
-### 5. Install `egl-wayland` with Excluded Paths
-
-```bash
-sudo rpm --install --verbose --hash \
-  --excludepath=/usr/share/egl/egl_external_platform.d/10_nvidia_wayland.json \
-  egl-wayland-1.1.17^20241118giteeb29e1-5.fc39.x86_64.rpm
-```
-
-### 6. Install `nvidia-driver-libs` with Excluded Paths
-
-```bash
-sudo rpm --install --verbose --hash \
-  --excludepath=/usr/share/glvnd/egl_vendor.d/10_nvidia.json \
-  --excludepath=/usr/share/nvidia/nvoptix.bin \
-  nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
+sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
 ```
 
 **Note:**
 
-- Replace the paths with the ones causing conflicts in your installation if they differ.
-- The `--verbose` and `--hash` options provide detailed output during installation.
+- The `--justdb` option only updates the RPM database, without touching the filesystem.
 
-## Finalizing the Installation of `nvidia-driver-libs`
+#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
 
 After manually installing the dependencies, run:
 
 ```bash
-sudo dnf install nvidia-driver-libs
+sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
 ```
 
 You should receive a message indicating the package is already installed:
 
 ```
-Package nvidia-driver-libs-3:560.35.05-1.fc39.x86_64 is already installed.
-Dependencies resolved.
+Updating and loading repositories:
+Repositories loaded.
+Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed.
+Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed.
+
 Nothing to do.
-Complete!
 ```
 
 ## Installing the CUDA Meta-Package
@@ -233,7 +177,7 @@ To use CUDA, add its binary directory to your system's `PATH`.
 
    **Explanation:**
 
-   - We add to  `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
+   - We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
    - The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
 
 2. **Make the Script Executable:**
@@ -262,26 +206,33 @@ You should see output similar to:
 
 ```
 nvcc: NVIDIA (R) Cuda compiler driver
-Copyright (c) 2005-2024 NVIDIA Corporation
-Built on Tue_Oct_29_23:50:19_PDT_2024
-Cuda compilation tools, release 12.6, V12.6.85
-Build cuda_12.6.r12.6/compiler.35059454_0
+Copyright (c) 2005-2025 NVIDIA Corporation
+Built on Wed_Jan_15_19:20:09_PST_2025
+Cuda compilation tools, release 12.8, V12.8.61
+Build cuda_12.8.r12.8/compiler.35404655_0
 ```
 
 This output confirms that the CUDA compiler is accessible and indicates the installed version.
 
 ## Conclusion
 
-You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 39 CUDA repository. By manually resolving package conflicts and configuring the environment, you can develop CUDA applications without affecting your host system.
+You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system.
 
 ## Troubleshooting
 
 - **Installation Failures:**
-  - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
-  - Use the `--excludepath` option with `rpm` to exclude conflicting files during manual installations.
 
-- **Driver Conflicts:**
-  - Since the host system may already have NVIDIA drivers installed, conflicts can arise. Using the toolbox environment helps isolate these issues.
+  - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
+  - You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations.
+
+- **Rebooting the Container:**
+
+  - Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue:
+
+  ```bash
+  # on the host system
+  podman container restart --all
+  ```
 
 - **Environment Variables Not Set:**
   - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
@@ -291,10 +242,12 @@ You have successfully set up CUDA on Fedora within a toolbox environment using t
 ## Additional Notes
 
 - **Updating CUDA in the Future:**
+
   - Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
   - When an updated repository becomes available, adjust your `dnf` configuration accordingly.
 
 - **Building `llama.cpp`:**
+
   - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
   - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.
 

From 2fb3c32a1634488a5265d1304ab37628eeb5480d Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Thu, 6 Feb 2025 17:32:29 +0100
Subject: [PATCH 350/376] server : (webui) migrate project to ReactJS with
 typescript (#11688)

* init version

* fix auto scroll

* bring back copy btn

* bring back thought process

* add lint and format check on CI

* remove lang from html tag

* allow multiple generations at the same time

* lint and format combined

* fix unused var

* improve MarkdownDisplay

* fix more latex

* fix code block cannot be selected while generating
---
 .github/workflows/server.yml                  |   25 +-
 examples/server/public/index.html.gz          |  Bin 1207128 -> 1219985 bytes
 examples/server/webui/.gitignore              |   24 +
 examples/server/webui/.prettierignore         |   10 +
 examples/server/webui/eslint.config.js        |   26 +
 examples/server/webui/index.html              |  353 +-
 examples/server/webui/package-lock.json       | 6655 ++++++++++++-----
 examples/server/webui/package.json            |   48 +-
 .../webui/public/demo-conversation.json       |    2 +-
 examples/server/webui/src/App.tsx             |   36 +
 examples/server/webui/src/Config.ts           |   89 +
 .../webui/src/components/ChatMessage.tsx      |  229 +
 .../webui/src/components/ChatScreen.tsx       |  123 +
 .../server/webui/src/components/Header.tsx    |  182 +
 .../webui/src/components/MarkdownDisplay.tsx  |  254 +
 .../webui/src/components/SettingDialog.tsx    |  306 +
 .../server/webui/src/components/Sidebar.tsx   |   95 +
 examples/server/webui/src/highlight-config.js |   60 -
 .../webui/src/{styles.scss => index.scss}     |   25 +-
 examples/server/webui/src/katex-gpt.js        |   66 -
 examples/server/webui/src/main.js             |  704 --
 examples/server/webui/src/main.tsx            |   10 +
 .../server/webui/src/utils/app.context.tsx    |  304 +
 examples/server/webui/src/utils/misc.ts       |   87 +
 examples/server/webui/src/utils/storage.ts    |  138 +
 examples/server/webui/src/utils/types.ts      |   25 +
 examples/server/webui/src/vite-env.d.ts       |    1 +
 examples/server/webui/tsconfig.app.json       |   26 +
 examples/server/webui/tsconfig.json           |    7 +
 examples/server/webui/tsconfig.node.json      |   24 +
 .../webui/{vite.config.js => vite.config.ts}  |   49 +-
 31 files changed, 7100 insertions(+), 2883 deletions(-)
 create mode 100644 examples/server/webui/.gitignore
 create mode 100644 examples/server/webui/.prettierignore
 create mode 100644 examples/server/webui/eslint.config.js
 create mode 100644 examples/server/webui/src/App.tsx
 create mode 100644 examples/server/webui/src/Config.ts
 create mode 100644 examples/server/webui/src/components/ChatMessage.tsx
 create mode 100644 examples/server/webui/src/components/ChatScreen.tsx
 create mode 100644 examples/server/webui/src/components/Header.tsx
 create mode 100644 examples/server/webui/src/components/MarkdownDisplay.tsx
 create mode 100644 examples/server/webui/src/components/SettingDialog.tsx
 create mode 100644 examples/server/webui/src/components/Sidebar.tsx
 delete mode 100644 examples/server/webui/src/highlight-config.js
 rename examples/server/webui/src/{styles.scss => index.scss} (83%)
 delete mode 100644 examples/server/webui/src/katex-gpt.js
 delete mode 100644 examples/server/webui/src/main.js
 create mode 100644 examples/server/webui/src/main.tsx
 create mode 100644 examples/server/webui/src/utils/app.context.tsx
 create mode 100644 examples/server/webui/src/utils/misc.ts
 create mode 100644 examples/server/webui/src/utils/storage.ts
 create mode 100644 examples/server/webui/src/utils/types.ts
 create mode 100644 examples/server/webui/src/vite-env.d.ts
 create mode 100644 examples/server/webui/tsconfig.app.json
 create mode 100644 examples/server/webui/tsconfig.json
 create mode 100644 examples/server/webui/tsconfig.node.json
 rename examples/server/webui/{vite.config.js => vite.config.ts} (58%)

diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 0cbc3d640..3a29107d0 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -81,13 +81,36 @@ jobs:
         with:
           node-version: '22.11.0'
 
+      - name: WebUI - Install dependencies
+        id: webui_lint
+        run: |
+          cd examples/server/webui
+          npm ci
+
+      - name: WebUI - Check code format
+        id: webui_format
+        run: |
+          git config --global --add safe.directory $(realpath .)
+          cd examples/server/webui
+          git status
+
+          npm run format
+          git status
+          modified_files="$(git status -s)"
+          echo "Modified files: ${modified_files}"
+          if [ -n "${modified_files}" ]; then
+            echo "Files do not follow coding style. To fix: npm run format"
+            echo "${modified_files}"
+            exit 1
+          fi
+
       - name: Verify bundled index.html
         id: verify_server_index_html
         run: |
           git config --global --add safe.directory $(realpath .)
           cd examples/server/webui
           git status
-          npm ci
+
           npm run build
           git status
           modified_files="$(git status -s)"
diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index e876776e82eeba329b1e808cd46684e32aca94f5..646988ad8e494e1bcccfd2fba6f549796aebd983 100644
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diff --git a/examples/server/webui/.gitignore b/examples/server/webui/.gitignore
new file mode 100644
index 000000000..a547bf36d
--- /dev/null
+++ b/examples/server/webui/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
diff --git a/examples/server/webui/.prettierignore b/examples/server/webui/.prettierignore
new file mode 100644
index 000000000..c0cb165b3
--- /dev/null
+++ b/examples/server/webui/.prettierignore
@@ -0,0 +1,10 @@
+**/.vscode
+**/.github
+**/.git
+**/.svn
+**/.hg
+**/node_modules
+**/dist
+**/build
+
+*.config.js
diff --git a/examples/server/webui/eslint.config.js b/examples/server/webui/eslint.config.js
new file mode 100644
index 000000000..7c0d39b89
--- /dev/null
+++ b/examples/server/webui/eslint.config.js
@@ -0,0 +1,26 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+
+export default tseslint.config(
+  { ignores: ['dist'] },
+  {
+    extends: [js.configs.recommended, ...tseslint.configs.recommended],
+    files: ['**/*.{ts,tsx}'],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+    plugins: {
+      'react-hooks': reactHooks,
+      'react-refresh': reactRefresh,
+    },
+    rules: {
+      ...reactHooks.configs.recommended.rules,
+      'react-refresh/only-export-components': 'off',
+      '@typescript-eslint/no-unused-vars': 'off',
+    },
+  },
+)
diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html
index 1ad85e739..471f46b3a 100644
--- a/examples/server/webui/index.html
+++ b/examples/server/webui/index.html
@@ -1,341 +1,16 @@
-<!DOCTYPE html>
+<!doctype html>
 <html>
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
-  <meta name="color-scheme" content="light dark">
-  <title>🦙 llama.cpp - chat</title>
-</head>
-
-<body>
-  <div id="app" class="opacity-0"> <!-- opacity-0 will be removed on app mounted -->
-    <div class="flex flex-row drawer lg:drawer-open">
-      <input id="toggle-drawer" type="checkbox" class="drawer-toggle" checked />
-
-      <!-- sidebar -->
-      <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
-        <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
-        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
-          <div class="flex flex-row items-center justify-between mb-4 mt-4">
-            <h2 class="font-bold ml-4">Conversations</h2>
-
-            <!-- close sidebar button -->
-            <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
-              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-arrow-bar-left" viewBox="0 0 16 16">
-                <path fill-rule="evenodd" d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"/>
-              </svg>
-            </label>
-          </div>
-
-          <!-- list of conversations -->
-          <div :class="{
-            'btn btn-ghost justify-start': true,
-            'btn-active': messages.length === 0,
-          }" @click="newConversation">
-            + New conversation
-          </div>
-          <div v-for="conv in conversations" :class="{
-            'btn btn-ghost justify-start font-normal': true,
-            'btn-active': conv.id === viewingConvId,
-          }" @click="setViewingConv(conv.id)" dir="auto">
-            <span class="truncate">{{ conv.messages[0].content }}</span>
-          </div>
-          <div class="text-center text-xs opacity-40 mt-auto mx-4">
-            Conversations are saved to browser's localStorage
-          </div>
-        </div>
-      </div>
-
-      <!-- main view -->
-      <div class="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
-        <!-- header -->
-        <div class="flex flex-row items-center mt-6 mb-6">
-          <!-- open sidebar button -->
-          <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
-            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-list" viewBox="0 0 16 16">
-              <path fill-rule="evenodd" d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"/>
-            </svg>
-          </label>
-
-          <div class="grow text-2xl font-bold ml-2">llama.cpp</div>
-
-          <!-- action buttons (top right) -->
-          <div class="flex items-center">
-            <div v-if="messages.length > 0" class="dropdown dropdown-end">
-              <!-- "..." button -->
-              <button tabindex="0" role="button" class="btn m-1" :disabled="isGenerating">
-                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-three-dots-vertical" viewBox="0 0 16 16">
-                  <path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0"/>
-                </svg>
-              </button>
-              <!-- "delete" dropdown menu -->
-              <ul tabindex="0" class="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
-                <li @click="downloadConv(viewingConvId)"><a>Download</a></li>
-                <li class="text-error" @click="deleteConv(viewingConvId)"><a>Delete</a></li>
-              </ul>
-            </div>
-            <div class="tooltip tooltip-bottom" data-tip="Settings">
-              <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
-                <!-- settings button -->
-                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
-                  <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
-                  <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z"/>
-                </svg>
-              </button>
-            </div>
-
-            <!-- theme controller is copied from https://daisyui.com/components/theme-controller/ -->
-            <div class="tooltip tooltip-bottom" data-tip="Themes">
-              <div class="dropdown dropdown-end dropdown-bottom">
-                <div tabindex="0" role="button" class="btn m-1">
-                  <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-palette2" viewBox="0 0 16 16">
-                    <path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z"/>
-                  </svg>
-                </div>
-                <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
-                  <li>
-                    <button
-                      class="btn btn-sm btn-block btn-ghost justify-start"
-                      :class="{ 'btn-active': selectedTheme === 'auto' }"
-                      @click="setSelectedTheme('auto')">
-                      auto
-                    </button>
-                  </li>
-                  <li v-for="theme in themes">
-                    <input
-                      type="radio"
-                      name="theme-dropdown"
-                      class="theme-controller btn btn-sm btn-block btn-ghost justify-start"
-                      :aria-label="theme"
-                      :value="theme"
-                      :checked="selectedTheme === theme"
-                      @click="setSelectedTheme(theme)" />
-                  </li>
-                </ul>
-              </div>
-            </div>
-          </div>
-        </div>
-
-        <!-- chat messages -->
-        <div id="messages-list" class="flex flex-col grow overflow-y-auto">
-          <div class="mt-auto flex justify-center">
-            <!-- placeholder to shift the message to the bottom -->
-            {{ messages.length === 0 ? 'Send a message to start' : '' }}
-          </div>
-          <div v-for="msg in messages" class="group">
-            <message-bubble
-              :config="config"
-              :msg="msg"
-              :key="msg.id"
-              :is-generating="isGenerating"
-              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
-              :regenerate-msg="regenerateMsg"></message-bubble>
-          </div>
-
-          <!-- pending (ongoing) assistant message -->
-          <div id="pending-msg" class="group">
-            <message-bubble
-              v-if="pendingMsg"
-              :config="config"
-              :msg="pendingMsg"
-              :key="pendingMsg.id"
-              :is-generating="isGenerating"
-              :show-thought-in-progress="config.showThoughtInProgress"
-              :edit-user-msg-and-regenerate="() => {}"
-              :regenerate-msg="() => {}"></message-bubble>
-          </div>
-        </div>
-
-        <!-- chat input -->
-        <div class="flex flex-row items-center mt-8 mb-6">
-          <textarea
-            class="textarea textarea-bordered w-full"
-            placeholder="Type a message (Shift+Enter to add a new line)"
-            v-model="inputMsg"
-            @keydown.enter.exact.prevent="sendMessage"
-            id="msg-input"
-            dir="auto"
-          ></textarea>
-          <button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
-          <button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
-        </div>
-      </div>
-
-    </div>
-
-
-    <!-- modal for editing config -->
-    <dialog class="modal" :class="{'modal-open': showConfigDialog}">
-      <div class="modal-box">
-        <h3 class="text-lg font-bold mb-6">Settings</h3>
-        <div class="h-[calc(90vh-12rem)] overflow-y-auto">
-          <p class="opacity-40 mb-6">Settings below are saved in browser's localStorage</p>
-          <settings-modal-short-input :config-key="'apiKey'" :config-default="configDefault" :config-info="configInfo" v-model="config.apiKey"></settings-modal-short-input>
-          <label class="form-control mb-2">
-            <div class="label">System Message</div>
-            <textarea class="textarea textarea-bordered h-24" :placeholder="'Default: ' + configDefault.systemMessage" v-model="config.systemMessage"></textarea>
-          </label>
-          <template v-for="configKey in ['temperature', 'top_k', 'top_p', 'min_p', 'max_tokens']">
-            <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-          </template>
-          <!-- TODO: add more sampling-related configs, please regroup them into different "collapse" sections -->
-          <!-- Section: Other sampler settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Other sampler settings</summary>
-            <div class="collapse-content">
-              <!-- Samplers queue -->
-              <settings-modal-short-input label="Samplers queue" :config-key="'samplers'" :config-default="configDefault" :config-info="configInfo" v-model="config.samplers"></settings-modal-short-input>
-              <!-- Samplers -->
-              <template v-for="configKey in ['dynatemp_range', 'dynatemp_exponent', 'typical_p', 'xtc_probability', 'xtc_threshold']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-              </template>
-            </div>
-          </details>
-          <!-- Section: Penalties settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Penalties settings</summary>
-            <div class="collapse-content">
-              <template v-for="configKey in ['repeat_last_n', 'repeat_penalty', 'presence_penalty', 'frequency_penalty', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_penalty_last_n']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
-              </template>
-            </div>
-          </details>
-          <!-- Section: Reasoning models -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Reasoning models</summary>
-            <div class="collapse-content">
-              <div class="flex flex-row items-center mb-2">
-                <input type="checkbox" class="checkbox" v-model="config.showThoughtInProgress" />
-                <span class="ml-4">Expand though process by default for generating message</span>
-              </div>
-              <div class="flex flex-row items-center mb-2">
-                <input type="checkbox" class="checkbox" v-model="config.excludeThoughtOnReq" />
-                <span class="ml-4">Exclude thought process when sending request to API (Recommended for DeepSeek-R1)</span>
-              </div>
-            </div>
-          </details>
-          <!-- Section: Advanced config -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Advanced config</summary>
-            <div class="collapse-content">
-              <div class="flex flex-row items-center mb-2" v-if="isDev">
-                <!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
-                <button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
-              </div>
-              <div class="flex flex-row items-center mb-2">
-                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
-                <span class="ml-4">Show tokens per second</span>
-              </div>
-              <label class="form-control mb-2">
-                <!-- Custom parameters input -->
-                <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
-                <textarea class="textarea textarea-bordered h-24" placeholder="Example: { &quot;mirostat&quot;: 1, &quot;min_p&quot;: 0.1 }" v-model="config.custom"></textarea>
-              </label>
-            </div>
-          </details>
-        </div>
-
-        <!-- action buttons -->
-        <div class="modal-action">
-          <button class="btn" @click="resetConfigDialog">Reset to default</button>
-          <button class="btn" @click="closeAndDiscardConfigDialog">Close</button>
-          <button class="btn btn-primary" @click="closeAndSaveConfigDialog">Save</button>
-        </div>
-      </div>
-    </dialog>
-
-  </div>
-
-
-  <!-- Template to be used as message bubble -->
-  <template id="message-bubble">
-    <div :class="{
-      'chat': true,
-      'chat-start': msg.role !== 'user',
-      'chat-end': msg.role === 'user',
-    }">
-      <div :class="{
-        'chat-bubble markdown': true,
-        'chat-bubble-base-300': msg.role !== 'user',
-      }">
-        <!-- textarea for editing message -->
-        <template v-if="editingContent !== null">
-          <textarea
-            dir="auto"
-            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
-            v-model="editingContent"></textarea>
-          <br/>
-          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
-          <button class="btn mt-2" @click="editMsg()">Submit</button>
-        </template>
-        <template v-else>
-          <!-- show loading dots for pending message -->
-          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
-          <!-- render message as markdown -->
-          <div v-else dir="auto">
-            <details v-if="msg.role === 'assistant' && splitMsgContent.cot" class="collapse bg-base-200 collapse-arrow mb-4" :open="splitMsgContent.isThinking && showThoughtInProgress">
-              <summary class="collapse-title">
-                <span v-if="splitMsgContent.isThinking">
-                  <span v-if="isGenerating" class="loading loading-spinner loading-md mr-2" style="vertical-align: middle;"></span>
-                  <b>Thinking</b>
-                </span>
-                <b v-else>Thought Process</b>
-              </summary>
-              <vue-markdown :source="splitMsgContent.cot" dir="auto" class="collapse-content"></vue-markdown>
-            </details>
-            <vue-markdown :source="splitMsgContent.content"></vue-markdown>
-          </div>
-          <!-- render timings if enabled -->
-          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
-            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
-            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
-              <b>Prompt</b><br/>
-              - Tokens: {{ timings.prompt_n }}<br/>
-              - Time: {{ timings.prompt_ms }} ms<br/>
-              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
-              <b>Generation</b><br/>
-              - Tokens: {{ timings.predicted_n }}<br/>
-              - Time: {{ timings.predicted_ms }} ms<br/>
-              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
-            </div>
-          </div>
-        </template>
-      </div>
-    </div>
-    <!-- actions for each message -->
-    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
-      <!-- user message -->
-      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
-        ✍️ Edit
-      </button>
-      <!-- assistant message -->
-      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-        🔄 Regenerate
-      </button>
-      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
-        📋 Copy
-      </button>
-    </div>
-  </template>
-
-
-  <!-- Template to be used by settings modal -->
-  <template id="settings-modal-short-input">
-    <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
-      <!-- Show help message on hovering on the input label -->
-      <div class="dropdown dropdown-hover">
-        <div tabindex="0" role="button" class="font-bold">{{ label || configKey }}</div>
-        <div class="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
-          {{ configInfo[configKey] || '(no help message available)' }}
-        </div>
-      </div>
-      <!-- Here we forward v-model from parent to child component, see: https://stackoverflow.com/questions/47311936/v-model-and-child-components -->
-      <input type="text" class="grow" :placeholder="'Default: ' + (configDefault[configKey] || 'none')" :value="modelValue" @input="$emit('update:modelValue', $event.target.value)" />
-    </label>
-  </template>
-
-  <script type="module" src="/src/main.js"></script>
-</body>
-
+  <head>
+    <meta charset="UTF-8" />
+    <meta
+      name="viewport"
+      content="width=device-width, initial-scale=1, maximum-scale=1"
+    />
+    <meta name="color-scheme" content="light dark" />
+    <title>🦙 llama.cpp - chat</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
 </html>
diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json
index bbebccbf2..e69fd2aa5 100644
--- a/examples/server/webui/package-lock.json
+++ b/examples/server/webui/package-lock.json
@@ -14,16 +14,36 @@
         "daisyui": "^4.12.14",
         "highlight.js": "^11.10.0",
         "katex": "^0.16.15",
-        "markdown-it": "^14.1.0",
         "postcss": "^8.4.49",
+        "react": "^18.3.1",
+        "react-dom": "^18.3.1",
+        "react-markdown": "^9.0.3",
+        "react-router": "^7.1.5",
+        "rehype-highlight": "^7.0.2",
+        "rehype-katex": "^7.0.1",
+        "remark-breaks": "^4.0.0",
+        "remark-gfm": "^4.0.0",
+        "remark-math": "^6.0.0",
         "tailwindcss": "^3.4.15",
         "textlinestream": "^1.1.1",
-        "vite-plugin-singlefile": "^2.0.3",
-        "vue": "^3.5.13"
+        "vite-plugin-singlefile": "^2.0.3"
       },
       "devDependencies": {
-        "sass-embedded": "^1.83.0",
-        "vite": "^5.4.10"
+        "@eslint/js": "^9.17.0",
+        "@types/markdown-it": "^14.1.2",
+        "@types/node": "^22.13.1",
+        "@types/react": "^18.3.18",
+        "@types/react-dom": "^18.3.5",
+        "@vitejs/plugin-react": "^4.3.4",
+        "eslint": "^9.17.0",
+        "eslint-plugin-react-hooks": "^5.0.0",
+        "eslint-plugin-react-refresh": "^0.4.16",
+        "globals": "^15.14.0",
+        "prettier": "^3.4.2",
+        "sass-embedded": "^1.83.4",
+        "typescript": "~5.6.2",
+        "typescript-eslint": "^8.18.2",
+        "vite": "^6.0.5"
       }
     },
     "node_modules/@alloc/quick-lru": {
@@ -38,6 +58,302 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/@ampproject/remapping": {
+      "version": "2.3.0",
+      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
+      "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@jridgewell/gen-mapping": "^0.3.5",
+        "@jridgewell/trace-mapping": "^0.3.24"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.26.2",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz",
+      "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-validator-identifier": "^7.25.9",
+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/compat-data": {
+      "version": "7.26.5",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.26.5.tgz",
+      "integrity": "sha512-XvcZi1KWf88RVbF9wn8MN6tYFloU5qX8KjuF3E1PVBmJ9eypXfs4GRiJwLuTZL0iSnJUKn1BFPa5BPZZJyFzPg==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/core": {
+      "version": "7.26.7",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.26.7.tgz",
+      "integrity": "sha512-SRijHmF0PSPgLIBYlWnG0hyeJLwXE2CgpsXaMOrtt2yp9/86ALw6oUlj9KYuZ0JN07T4eBMVIW4li/9S1j2BGA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@ampproject/remapping": "^2.2.0",
+        "@babel/code-frame": "^7.26.2",
+        "@babel/generator": "^7.26.5",
+        "@babel/helper-compilation-targets": "^7.26.5",
+        "@babel/helper-module-transforms": "^7.26.0",
+        "@babel/helpers": "^7.26.7",
+        "@babel/parser": "^7.26.7",
+        "@babel/template": "^7.25.9",
+        "@babel/traverse": "^7.26.7",
+        "@babel/types": "^7.26.7",
+        "convert-source-map": "^2.0.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.2",
+        "json5": "^2.2.3",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/babel"
+      }
+    },
+    "node_modules/@babel/generator": {
+      "version": "7.26.5",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.26.5.tgz",
+      "integrity": "sha512-2caSP6fN9I7HOe6nqhtft7V4g7/V/gfDsC3Ag4W7kEzzvRGKqiv0pu0HogPiZ3KaVSoNDhUws6IJjDjpfmYIXw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/parser": "^7.26.5",
+        "@babel/types": "^7.26.5",
+        "@jridgewell/gen-mapping": "^0.3.5",
+        "@jridgewell/trace-mapping": "^0.3.25",
+        "jsesc": "^3.0.2"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-compilation-targets": {
+      "version": "7.26.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.26.5.tgz",
+      "integrity": "sha512-IXuyn5EkouFJscIDuFF5EsiSolseme1s0CZB+QxVugqJLYmKdxI1VfIBOst0SUu4rnk2Z7kqTwmoO1lp3HIfnA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/compat-data": "^7.26.5",
+        "@babel/helper-validator-option": "^7.25.9",
+        "browserslist": "^4.24.0",
+        "lru-cache": "^5.1.1",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-module-imports": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.25.9.tgz",
+      "integrity": "sha512-tnUA4RsrmflIM6W6RFTLFSXITtl0wKjgpnLgXyowocVPrbYrLUXSBXDgTs8BlbmIzIdlBySRQjINYs2BAkiLtw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/traverse": "^7.25.9",
+        "@babel/types": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-module-transforms": {
+      "version": "7.26.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.26.0.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-module-imports": "^7.25.9",
+        "@babel/helper-validator-identifier": "^7.25.9",
+        "@babel/traverse": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0"
+      }
+    },
+    "node_modules/@babel/helper-plugin-utils": {
+      "version": "7.26.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.26.5.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-string-parser": {
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+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.25.9.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
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+    "node_modules/@babel/helper-validator-identifier": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.25.9.tgz",
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+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-option": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.25.9.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helpers": {
+      "version": "7.26.7",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.26.7.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/template": "^7.25.9",
+        "@babel/types": "^7.26.7"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/parser": {
+      "version": "7.26.7",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.26.7.tgz",
+      "integrity": "sha512-kEvgGGgEjRUutvdVvZhbn/BxVt+5VSpwXz1j3WYXQbXDo8KzFOPNG2GQbdAiNq8g6wn1yKk7C/qrke03a84V+w==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/types": "^7.26.7"
+      },
+      "bin": {
+        "parser": "bin/babel-parser.js"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@babel/plugin-transform-react-jsx-self": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-self/-/plugin-transform-react-jsx-self-7.25.9.tgz",
+      "integrity": "sha512-y8quW6p0WHkEhmErnfe58r7x0A70uKphQm8Sp8cV7tjNQwK56sNVK0M73LK3WuYmsuyrftut4xAkjjgU0twaMg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-plugin-utils": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0-0"
+      }
+    },
+    "node_modules/@babel/plugin-transform-react-jsx-source": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-source/-/plugin-transform-react-jsx-source-7.25.9.tgz",
+      "integrity": "sha512-+iqjT8xmXhhYv4/uiYd8FNQsraMFZIfxVSqxxVSZP0WbbSAWvBXAul0m/zu+7Vv4O/3WtApy9pmaTMiumEZgfg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-plugin-utils": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0-0"
+      }
+    },
+    "node_modules/@babel/template": {
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.25.9.tgz",
+      "integrity": "sha512-9DGttpmPvIxBb/2uwpVo3dqJ+O6RooAFOS+lB+xDqoE2PVCE8nfoHMdZLpfCQRLwvohzXISPZcgxt80xLfsuwg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/code-frame": "^7.25.9",
+        "@babel/parser": "^7.25.9",
+        "@babel/types": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/traverse": {
+      "version": "7.26.7",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.26.7.tgz",
+      "integrity": "sha512-1x1sgeyRLC3r5fQOM0/xtQKsYjyxmFjaOrLJNtZ81inNjyJHGIolTULPiSc/2qe1/qfpFLisLQYFnnZl7QoedA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/code-frame": "^7.26.2",
+        "@babel/generator": "^7.26.5",
+        "@babel/parser": "^7.26.7",
+        "@babel/template": "^7.25.9",
+        "@babel/types": "^7.26.7",
+        "debug": "^4.3.1",
+        "globals": "^11.1.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/traverse/node_modules/globals": {
+      "version": "11.12.0",
+      "resolved": "https://registry.npmjs.org/globals/-/globals-11.12.0.tgz",
+      "integrity": "sha512-WOBp/EEGUiIsJSp7wcv/y6MO+lV9UoncWqxuFfm8eBwzWNgyfBd6Gz+IeKQ9jCmyhoH99g15M3T+QaVHFjizVA==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/types": {
+      "version": "7.26.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.26.7.tgz",
+      "integrity": "sha512-t8kDRGrKXyp6+tjUh7hw2RLyclsW4TRoRvRHtSyAX9Bb5ldlFh+90YAYY6awRXrlB4G5G2izNeGySpATlFzmOg==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.25.9",
+        "@babel/helper-validator-identifier": "^7.25.9"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
     "node_modules/@bufbuild/protobuf": {
       "version": "2.2.3",
       "resolved": "https://registry.npmjs.org/@bufbuild/protobuf/-/protobuf-2.2.3.tgz",
@@ -46,9 +362,9 @@
       "license": "(Apache-2.0 AND BSD-3-Clause)"
     },
     "node_modules/@esbuild/aix-ppc64": {
-      "version": "0.21.5",
-      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz",
-      "integrity": "sha512-1SDgH6ZSPTlggy1yI6+Dbkiz8xzpHJEVAlF/AM1tHPLsf5STom9rwtjE4hKAF20FfXXNTFqEYXyJNWh1GiZedQ==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.2.tgz",
+      "integrity": "sha512-thpVCb/rhxE/BnMLQ7GReQLLN8q9qbHmI55F4489/ByVg2aQaQ6kbcLb6FHkocZzQhxc4gx0sCk0tJkKBFzDhA==",
       "cpu": [
         "ppc64"
       ],
@@ -58,13 +374,13 @@
         "aix"
       ],
       "engines": {
-        "node": ">=12"
+        "node": ">=18"
       }
     },
     "node_modules/@esbuild/android-arm": {
-      "version": "0.21.5",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.21.5.tgz",
-      "integrity": "sha512-vCPvzSjpPHEi1siZdlvAlsPxXl7WbOVUBBAowWug4rJHb68Ox8KualB+1ocNvT5fjv6wpkX6o/iEpbDrf68zcg==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.2.tgz",
+      "integrity": "sha512-tmwl4hJkCfNHwFB3nBa8z1Uy3ypZpxqxfTQOcHX+xRByyYgunVbZ9MzUUfb0RxaHIMnbHagwAxuTL+tnNM+1/Q==",
       "cpu": [
         "arm"
       ],
@@ -74,13 +390,13 @@
         "android"
       ],
       "engines": {
-        "node": ">=12"
+        "node": ">=18"
       }
     },
     "node_modules/@esbuild/android-arm64": {
-      "version": "0.21.5",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.21.5.tgz",
-      "integrity": "sha512-c0uX9VAUBQ7dTDCjq+wdyGLowMdtR/GoC2U5IYk/7D1H1JYC0qseD7+11iMP2mRLN9RcCMRcjC4YMclCzGwS/A==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.2.tgz",
+      "integrity": "sha512-cNLgeqCqV8WxfcTIOeL4OAtSmL8JjcN6m09XIgro1Wi7cF4t/THaWEa7eL5CMoMBdjoHOTh/vwTO/o2TRXIyzg==",
       "cpu": [
         "arm64"
       ],
@@ -90,13 +406,13 @@
         "android"
       ],
       "engines": {
-        "node": ">=12"
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@@ -106,13 +422,29 @@
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+      "optional": true,
+      "os": [
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@@ -122,13 +454,13 @@
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@@ -138,13 +470,13 @@
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-        "node": ">=12"
+        "node": ">=18"
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@@ -154,13 +486,13 @@
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@@ -170,13 +502,13 @@
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-        "node": ">=12"
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@@ -186,13 +518,13 @@
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@@ -202,13 +534,13 @@
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@@ -218,13 +550,13 @@
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@@ -234,13 +566,13 @@
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@@ -250,13 +582,13 @@
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@@ -266,13 +598,13 @@
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@@ -282,13 +614,13 @@
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@@ -298,13 +630,29 @@
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+        "arm64"
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+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "netbsd"
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@@ -314,13 +662,29 @@
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+        "arm64"
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+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "openbsd"
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@@ -330,13 +694,13 @@
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@@ -346,13 +710,13 @@
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@@ -362,13 +726,13 @@
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@@ -378,13 +742,13 @@
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@@ -393,14 +757,331 @@
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+        "yaml": {
+          "optional": true
         }
       }
     },
     "node_modules/vite-plugin-singlefile": {
-      "version": "2.0.3",
-      "resolved": "https://registry.npmjs.org/vite-plugin-singlefile/-/vite-plugin-singlefile-2.0.3.tgz",
-      "integrity": "sha512-OEBEwdX8nCGPSdtaB1D7rryYnT+YfPTS8ojL1TDyeUF+bWDCTfRriQqw6T0vl9EbKI/KMg7szN3awst6cLrKkA==",
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/vite-plugin-singlefile/-/vite-plugin-singlefile-2.1.0.tgz",
+      "integrity": "sha512-7tJo+UgZABlKpY/nubth/wxJ4+pUGREPnEwNOknxwl2MM0zTvF14KTU4Ln1lc140gjLLV5mjDrvuoquU7OZqCg==",
       "license": "MIT",
       "dependencies": {
         "micromatch": "^4.0.8"
@@ -3280,29 +6425,173 @@
         "node": ">18.0.0"
       },
       "peerDependencies": {
-        "rollup": "^4.24.3",
-        "vite": "^5.4.10"
+        "rollup": "^4.28.1",
+        "vite": "^5.4.11 || ^6.0.0"
       }
     },
-    "node_modules/vue": {
-      "version": "3.5.13",
-      "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.13.tgz",
-      "integrity": "sha512-wmeiSMxkZCSc+PM2w2VRsOYAZC8GdipNFRTsLSfodVqI9mbejKeXEGr8SckuLnrQPGe3oJN5c3K0vpoU9q/wCQ==",
+    "node_modules/web-namespaces": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/web-namespaces/-/web-namespaces-2.0.1.tgz",
+      "integrity": "sha512-bKr1DkiNa2krS7qxNtdrtHAmzuYGFQLiQ13TsorsdT6ULTkPLKuu5+GsFpDlg6JFjUTwX2DyhMPG2be8uPrqsQ==",
+      "license": "MIT",
+      "funding": {
+        "type": "github",
+        "url": "https://github.com/sponsors/wooorm"
+      }
+    },
+    "node_modules/which": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
+      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+      "license": "ISC",
+      "dependencies": {
+        "isexe": "^2.0.0"
+      },
+      "bin": {
+        "node-which": "bin/node-which"
+      },
+      "engines": {
+        "node": ">= 8"
+      }
+    },
+    "node_modules/word-wrap": {
+      "version": "1.2.5",
+      "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
+      "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/wrap-ansi": {
+      "version": "8.1.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz",
+      "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==",
       "license": "MIT",
       "dependencies": {
-        "@vue/compiler-dom": "3.5.13",
-        "@vue/compiler-sfc": "3.5.13",
-        "@vue/runtime-dom": "3.5.13",
-        "@vue/server-renderer": "3.5.13",
-        "@vue/shared": "3.5.13"
+        "ansi-styles": "^6.1.0",
+        "string-width": "^5.0.1",
+        "strip-ansi": "^7.0.1"
       },
-      "peerDependencies": {
-        "typescript": "*"
+      "engines": {
+        "node": ">=12"
       },
-      "peerDependenciesMeta": {
-        "typescript": {
-          "optional": true
-        }
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi-cjs": {
+      "name": "wrap-ansi",
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
+      "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "license": "MIT",
+      "dependencies": {
+        "ansi-styles": "^4.0.0",
+        "string-width": "^4.1.0",
+        "strip-ansi": "^6.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
+      "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==",
+      "license": "MIT"
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/string-width": {
+      "version": "4.2.3",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
+      "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+      "license": "MIT",
+      "dependencies": {
+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/ansi-styles": {
+      "version": "6.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz",
+      "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/yallist": {
+      "version": "3.1.1",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+      "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==",
+      "dev": true,
+      "license": "ISC"
+    },
+    "node_modules/yaml": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.7.0.tgz",
+      "integrity": "sha512-+hSoy/QHluxmC9kCIJyL/uyFmLmc+e5CFR5Wa+bpIhIj85LVb9ZH2nVnqrHoSvKogwODv0ClqZkmiSSaIH5LTA==",
+      "license": "ISC",
+      "bin": {
+        "yaml": "bin.mjs"
+      },
+      "engines": {
+        "node": ">= 14"
+      }
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/zwitch": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
+      "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==",
+      "license": "MIT",
+      "funding": {
+        "type": "github",
+        "url": "https://github.com/sponsors/wooorm"
       }
     }
   }
diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json
index 2836cce00..f3c7dde43 100644
--- a/examples/server/webui/package.json
+++ b/examples/server/webui/package.json
@@ -5,13 +5,10 @@
   "type": "module",
   "scripts": {
     "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview",
-    "analyze": "ANALYZE=1 npx vite-bundle-visualizer"
-  },
-  "devDependencies": {
-    "sass-embedded": "^1.83.0",
-    "vite": "^5.4.10"
+    "build": "tsc -b && vite build",
+    "format": "eslint . && prettier --write .",
+    "lint": "eslint .",
+    "preview": "vite preview"
   },
   "dependencies": {
     "@sec-ant/readable-stream": "^0.6.0",
@@ -20,11 +17,42 @@
     "daisyui": "^4.12.14",
     "highlight.js": "^11.10.0",
     "katex": "^0.16.15",
-    "markdown-it": "^14.1.0",
     "postcss": "^8.4.49",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-markdown": "^9.0.3",
+    "react-router": "^7.1.5",
+    "rehype-highlight": "^7.0.2",
+    "rehype-katex": "^7.0.1",
+    "remark-breaks": "^4.0.0",
+    "remark-gfm": "^4.0.0",
+    "remark-math": "^6.0.0",
     "tailwindcss": "^3.4.15",
     "textlinestream": "^1.1.1",
-    "vite-plugin-singlefile": "^2.0.3",
-    "vue": "^3.5.13"
+    "vite-plugin-singlefile": "^2.0.3"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.17.0",
+    "@types/markdown-it": "^14.1.2",
+    "@types/node": "^22.13.1",
+    "@types/react": "^18.3.18",
+    "@types/react-dom": "^18.3.5",
+    "@vitejs/plugin-react": "^4.3.4",
+    "eslint": "^9.17.0",
+    "eslint-plugin-react-hooks": "^5.0.0",
+    "eslint-plugin-react-refresh": "^0.4.16",
+    "globals": "^15.14.0",
+    "prettier": "^3.4.2",
+    "sass-embedded": "^1.83.4",
+    "typescript": "~5.6.2",
+    "typescript-eslint": "^8.18.2",
+    "vite": "^6.0.5"
+  },
+  "prettier": {
+    "trailingComma": "es5",
+    "tabWidth": 2,
+    "semi": true,
+    "singleQuote": true,
+    "bracketSameLine": false
   }
 }
diff --git a/examples/server/webui/public/demo-conversation.json b/examples/server/webui/public/demo-conversation.json
index 75ab599dd..338b4aea5 100644
--- a/examples/server/webui/public/demo-conversation.json
+++ b/examples/server/webui/public/demo-conversation.json
@@ -11,7 +11,7 @@
     {
       "id": 1734087548327,
       "role": "assistant",
-      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
+      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\n$2x + y = z$\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
       "timings": {
         "prompt_n": 1,
         "prompt_ms": 28.923,
diff --git a/examples/server/webui/src/App.tsx b/examples/server/webui/src/App.tsx
new file mode 100644
index 000000000..d151ba291
--- /dev/null
+++ b/examples/server/webui/src/App.tsx
@@ -0,0 +1,36 @@
+import { HashRouter, Outlet, Route, Routes } from 'react-router';
+import Header from './components/Header';
+import Sidebar from './components/Sidebar';
+import { AppContextProvider } from './utils/app.context';
+import ChatScreen from './components/ChatScreen';
+
+function App() {
+  return (
+    <HashRouter>
+      <div className="flex flex-row drawer lg:drawer-open">
+        <AppContextProvider>
+          <Routes>
+            <Route element={<AppLayout />}>
+              <Route path="/chat/:convId" element={<ChatScreen />} />
+              <Route path="*" element={<ChatScreen />} />
+            </Route>
+          </Routes>
+        </AppContextProvider>
+      </div>
+    </HashRouter>
+  );
+}
+
+function AppLayout() {
+  return (
+    <>
+      <Sidebar />
+      <div className="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
+        <Header />
+        <Outlet />
+      </div>
+    </>
+  );
+}
+
+export default App;
diff --git a/examples/server/webui/src/Config.ts b/examples/server/webui/src/Config.ts
new file mode 100644
index 000000000..1860ffcc9
--- /dev/null
+++ b/examples/server/webui/src/Config.ts
@@ -0,0 +1,89 @@
+import daisyuiThemes from 'daisyui/src/theming/themes';
+import { isNumeric } from './utils/misc';
+
+export const isDev = import.meta.env.MODE === 'development';
+
+// constants
+export const BASE_URL = new URL('.', document.baseURI).href
+  .toString()
+  .replace(/\/$/, '');
+
+export const CONFIG_DEFAULT = {
+  // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
+  apiKey: '',
+  systemMessage: 'You are a helpful assistant.',
+  showTokensPerSecond: false,
+  showThoughtInProgress: false,
+  excludeThoughtOnReq: true,
+  // make sure these default values are in sync with `common.h`
+  samplers: 'edkypmxt',
+  temperature: 0.8,
+  dynatemp_range: 0.0,
+  dynatemp_exponent: 1.0,
+  top_k: 40,
+  top_p: 0.95,
+  min_p: 0.05,
+  xtc_probability: 0.0,
+  xtc_threshold: 0.1,
+  typical_p: 1.0,
+  repeat_last_n: 64,
+  repeat_penalty: 1.0,
+  presence_penalty: 0.0,
+  frequency_penalty: 0.0,
+  dry_multiplier: 0.0,
+  dry_base: 1.75,
+  dry_allowed_length: 2,
+  dry_penalty_last_n: -1,
+  max_tokens: -1,
+  custom: '', // custom json-stringified object
+};
+export const CONFIG_INFO: Record<string, string> = {
+  apiKey: 'Set the API Key if you are using --api-key option for the server.',
+  systemMessage: 'The starting message that defines how model should behave.',
+  samplers:
+    'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
+  temperature:
+    'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
+  dynatemp_range:
+    'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
+  dynatemp_exponent:
+    'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
+  top_k: 'Keeps only k top tokens.',
+  top_p:
+    'Limits tokens to those that together have a cumulative probability of at least p',
+  min_p:
+    'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
+  xtc_probability:
+    'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
+  xtc_threshold:
+    'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
+  typical_p:
+    'Sorts and limits tokens based on the difference between log-probability and entropy.',
+  repeat_last_n: 'Last n tokens to consider for penalizing repetition',
+  repeat_penalty:
+    'Controls the repetition of token sequences in the generated text',
+  presence_penalty:
+    'Limits tokens based on whether they appear in the output or not.',
+  frequency_penalty:
+    'Limits tokens based on how often they appear in the output.',
+  dry_multiplier:
+    'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
+  dry_base:
+    'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
+  dry_allowed_length:
+    'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
+  dry_penalty_last_n:
+    'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
+  max_tokens: 'The maximum number of token per output.',
+  custom: '', // custom json-stringified object
+};
+// config keys having numeric value (i.e. temperature, top_k, top_p, etc)
+export const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT)
+  .filter((e) => isNumeric(e[1]))
+  .map((e) => e[0]);
+// list of themes supported by daisyui
+export const THEMES = ['light', 'dark']
+  // make sure light & dark are always at the beginning
+  .concat(
+    Object.keys(daisyuiThemes).filter((t) => t !== 'light' && t !== 'dark')
+  );
diff --git a/examples/server/webui/src/components/ChatMessage.tsx b/examples/server/webui/src/components/ChatMessage.tsx
new file mode 100644
index 000000000..01d2fb80c
--- /dev/null
+++ b/examples/server/webui/src/components/ChatMessage.tsx
@@ -0,0 +1,229 @@
+import { useMemo, useState } from 'react';
+import { useAppContext } from '../utils/app.context';
+import { Message, PendingMessage } from '../utils/types';
+import { classNames } from '../utils/misc';
+import MarkdownDisplay, { CopyButton } from './MarkdownDisplay';
+
+interface SplitMessage {
+  content: PendingMessage['content'];
+  thought?: string;
+  isThinking?: boolean;
+}
+
+export default function ChatMessage({
+  msg,
+  id,
+  scrollToBottom,
+  isPending,
+}: {
+  msg: Message | PendingMessage;
+  id?: string;
+  scrollToBottom: (requiresNearBottom: boolean) => void;
+  isPending?: boolean;
+}) {
+  const { viewingConversation, replaceMessageAndGenerate, config } =
+    useAppContext();
+  const [editingContent, setEditingContent] = useState<string | null>(null);
+  const timings = useMemo(
+    () =>
+      msg.timings
+        ? {
+            ...msg.timings,
+            prompt_per_second:
+              (msg.timings.prompt_n / msg.timings.prompt_ms) * 1000,
+            predicted_per_second:
+              (msg.timings.predicted_n / msg.timings.predicted_ms) * 1000,
+          }
+        : null,
+    [msg.timings]
+  );
+
+  // for reasoning model, we split the message into content and thought
+  // TODO: implement this as remark/rehype plugin in the future
+  const { content, thought, isThinking }: SplitMessage = useMemo(() => {
+    if (msg.content === null || msg.role !== 'assistant') {
+      return { content: msg.content };
+    }
+    let actualContent = '';
+    let thought = '';
+    let isThinking = false;
+    let thinkSplit = msg.content.split('<think>', 2);
+    actualContent += thinkSplit[0];
+    while (thinkSplit[1] !== undefined) {
+      // <think> tag found
+      thinkSplit = thinkSplit[1].split('</think>', 2);
+      thought += thinkSplit[0];
+      isThinking = true;
+      if (thinkSplit[1] !== undefined) {
+        // </think> closing tag found
+        isThinking = false;
+        thinkSplit = thinkSplit[1].split('<think>', 2);
+        actualContent += thinkSplit[0];
+      }
+    }
+    return { content: actualContent, thought, isThinking };
+  }, [msg]);
+
+  if (!viewingConversation) return null;
+
+  const regenerate = async () => {
+    replaceMessageAndGenerate(viewingConversation.id, msg.id, undefined, () =>
+      scrollToBottom(true)
+    );
+  };
+
+  return (
+    <div className="group" id={id}>
+      <div
+        className={classNames({
+          chat: true,
+          'chat-start': msg.role !== 'user',
+          'chat-end': msg.role === 'user',
+        })}
+      >
+        <div
+          className={classNames({
+            'chat-bubble markdown': true,
+            'chat-bubble-base-300': msg.role !== 'user',
+          })}
+        >
+          {/* textarea for editing message */}
+          {editingContent !== null && (
+            <>
+              <textarea
+                dir="auto"
+                className="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+                value={editingContent}
+                onChange={(e) => setEditingContent(e.target.value)}
+              ></textarea>
+              <br />
+              <button
+                className="btn btn-ghost mt-2 mr-2"
+                onClick={() => setEditingContent(null)}
+              >
+                Cancel
+              </button>
+              <button
+                className="btn mt-2"
+                onClick={() =>
+                  replaceMessageAndGenerate(
+                    viewingConversation.id,
+                    msg.id,
+                    editingContent
+                  )
+                }
+              >
+                Submit
+              </button>
+            </>
+          )}
+          {/* not editing content, render message */}
+          {editingContent === null && (
+            <>
+              {content === null ? (
+                <>
+                  {/* show loading dots for pending message */}
+                  <span className="loading loading-dots loading-md"></span>
+                </>
+              ) : (
+                <>
+                  {/* render message as markdown */}
+                  <div dir="auto">
+                    {thought && (
+                      <details
+                        className="collapse bg-base-200 collapse-arrow mb-4"
+                        open={isThinking && config.showThoughtInProgress}
+                      >
+                        <summary className="collapse-title">
+                          {isPending && isThinking ? (
+                            <span>
+                              <span
+                                v-if="isGenerating"
+                                className="loading loading-spinner loading-md mr-2"
+                                style={{ verticalAlign: 'middle' }}
+                              ></span>
+                              <b>Thinking</b>
+                            </span>
+                          ) : (
+                            <b>Thought Process</b>
+                          )}
+                        </summary>
+                        <div className="collapse-content">
+                          <MarkdownDisplay content={thought} />
+                        </div>
+                      </details>
+                    )}
+                    <MarkdownDisplay content={content} />
+                  </div>
+                </>
+              )}
+              {/* render timings if enabled */}
+              {timings && config.showTokensPerSecond && (
+                <div className="dropdown dropdown-hover dropdown-top mt-2">
+                  <div
+                    tabIndex={0}
+                    role="button"
+                    className="cursor-pointer font-semibold text-sm opacity-60"
+                  >
+                    Speed: {timings.predicted_per_second.toFixed(1)} t/s
+                  </div>
+                  <div className="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
+                    <b>Prompt</b>
+                    <br />- Tokens: {timings.prompt_n}
+                    <br />- Time: {timings.prompt_ms} ms
+                    <br />- Speed: {timings.prompt_per_second.toFixed(1)} t/s
+                    <br />
+                    <b>Generation</b>
+                    <br />- Tokens: {timings.predicted_n}
+                    <br />- Time: {timings.predicted_ms} ms
+                    <br />- Speed: {timings.predicted_per_second.toFixed(1)} t/s
+                    <br />
+                  </div>
+                </div>
+              )}
+            </>
+          )}
+        </div>
+      </div>
+
+      {/* actions for each message */}
+      {msg.content !== null && (
+        <div
+          className={classNames({
+            'mx-4 mt-2 mb-2': true,
+            'text-right': msg.role === 'user',
+          })}
+        >
+          {/* user message */}
+          {msg.role === 'user' && (
+            <button
+              className="badge btn-mini show-on-hover"
+              onClick={() => setEditingContent(msg.content)}
+              disabled={msg.content === null}
+            >
+              ✍️ Edit
+            </button>
+          )}
+          {/* assistant message */}
+          {msg.role === 'assistant' && (
+            <>
+              {!isPending && (
+                <button
+                  className="badge btn-mini show-on-hover mr-2"
+                  onClick={regenerate}
+                  disabled={msg.content === null}
+                >
+                  🔄 Regenerate
+                </button>
+              )}
+              <CopyButton
+                className="badge btn-mini show-on-hover mr-2"
+                content={msg.content}
+              />
+            </>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/examples/server/webui/src/components/ChatScreen.tsx b/examples/server/webui/src/components/ChatScreen.tsx
new file mode 100644
index 000000000..d679f4ebb
--- /dev/null
+++ b/examples/server/webui/src/components/ChatScreen.tsx
@@ -0,0 +1,123 @@
+import { useEffect, useRef, useState } from 'react';
+import { useAppContext } from '../utils/app.context';
+import StorageUtils from '../utils/storage';
+import { useNavigate } from 'react-router';
+import ChatMessage from './ChatMessage';
+import { PendingMessage } from '../utils/types';
+
+export default function ChatScreen() {
+  const {
+    viewingConversation,
+    sendMessage,
+    isGenerating,
+    stopGenerating,
+    pendingMessages,
+  } = useAppContext();
+  const [inputMsg, setInputMsg] = useState('');
+  const containerRef = useRef<HTMLDivElement>(null);
+  const navigate = useNavigate();
+
+  const currConvId = viewingConversation?.id ?? '';
+  const pendingMsg: PendingMessage | undefined = pendingMessages[currConvId];
+
+  const scrollToBottom = (requiresNearBottom: boolean) => {
+    if (!containerRef.current) return;
+    const msgListElem = containerRef.current;
+    const spaceToBottom =
+      msgListElem.scrollHeight -
+      msgListElem.scrollTop -
+      msgListElem.clientHeight;
+    if (!requiresNearBottom || spaceToBottom < 50) {
+      setTimeout(
+        () => msgListElem.scrollTo({ top: msgListElem.scrollHeight }),
+        1
+      );
+    }
+  };
+
+  // scroll to bottom when conversation changes
+  useEffect(() => {
+    scrollToBottom(false);
+  }, [viewingConversation?.id]);
+
+  const sendNewMessage = async () => {
+    if (inputMsg.trim().length === 0 || isGenerating(currConvId)) return;
+    const convId = viewingConversation?.id ?? StorageUtils.getNewConvId();
+    const lastInpMsg = inputMsg;
+    setInputMsg('');
+    if (!viewingConversation) {
+      // if user is creating a new conversation, redirect to the new conversation
+      navigate(`/chat/${convId}`);
+    }
+    scrollToBottom(false);
+    // auto scroll as message is being generated
+    const onChunk = () => scrollToBottom(true);
+    if (!(await sendMessage(convId, inputMsg, onChunk))) {
+      // restore the input message if failed
+      setInputMsg(lastInpMsg);
+    }
+  };
+
+  return (
+    <>
+      {/* chat messages */}
+      <div
+        id="messages-list"
+        className="flex flex-col grow overflow-y-auto"
+        ref={containerRef}
+      >
+        <div className="mt-auto flex justify-center">
+          {/* placeholder to shift the message to the bottom */}
+          {viewingConversation ? '' : 'Send a message to start'}
+        </div>
+        {viewingConversation?.messages.map((msg) => (
+          <ChatMessage key={msg.id} msg={msg} scrollToBottom={scrollToBottom} />
+        ))}
+
+        {pendingMsg && (
+          <ChatMessage
+            msg={pendingMsg}
+            scrollToBottom={scrollToBottom}
+            isPending
+            id="pending-msg"
+          />
+        )}
+      </div>
+
+      {/* chat input */}
+      <div className="flex flex-row items-center mt-8 mb-6">
+        <textarea
+          className="textarea textarea-bordered w-full"
+          placeholder="Type a message (Shift+Enter to add a new line)"
+          value={inputMsg}
+          onChange={(e) => setInputMsg(e.target.value)}
+          onKeyDown={(e) => {
+            if (e.key === 'Enter' && e.shiftKey) return;
+            if (e.key === 'Enter' && !e.shiftKey) {
+              e.preventDefault();
+              sendNewMessage();
+            }
+          }}
+          id="msg-input"
+          dir="auto"
+        ></textarea>
+        {isGenerating(currConvId) ? (
+          <button
+            className="btn btn-neutral ml-2"
+            onClick={() => stopGenerating(currConvId)}
+          >
+            Stop
+          </button>
+        ) : (
+          <button
+            className="btn btn-primary ml-2"
+            onClick={sendNewMessage}
+            disabled={inputMsg.trim().length === 0}
+          >
+            Send
+          </button>
+        )}
+      </div>
+    </>
+  );
+}
diff --git a/examples/server/webui/src/components/Header.tsx b/examples/server/webui/src/components/Header.tsx
new file mode 100644
index 000000000..015264abc
--- /dev/null
+++ b/examples/server/webui/src/components/Header.tsx
@@ -0,0 +1,182 @@
+import { useEffect, useState } from 'react';
+import StorageUtils from '../utils/storage';
+import { useAppContext } from '../utils/app.context';
+import { classNames } from '../utils/misc';
+import daisyuiThemes from 'daisyui/src/theming/themes';
+import { THEMES } from '../Config';
+import { useNavigate } from 'react-router';
+import SettingDialog from './SettingDialog';
+
+export default function Header() {
+  const navigate = useNavigate();
+  const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme());
+  const [showSettingDialog, setShowSettingDialog] = useState(false);
+
+  const setTheme = (theme: string) => {
+    StorageUtils.setTheme(theme);
+    setSelectedTheme(theme);
+  };
+
+  useEffect(() => {
+    document.body.setAttribute('data-theme', selectedTheme);
+    document.body.setAttribute(
+      'data-color-scheme',
+      // @ts-expect-error daisyuiThemes complains about index type, but it should work
+      daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto'
+    );
+  }, [selectedTheme]);
+
+  const { isGenerating, viewingConversation } = useAppContext();
+  const isCurrConvGenerating = isGenerating(viewingConversation?.id ?? '');
+
+  const removeConversation = () => {
+    if (isCurrConvGenerating || !viewingConversation) return;
+    const convId = viewingConversation.id;
+    if (window.confirm('Are you sure to delete this conversation?')) {
+      StorageUtils.remove(convId);
+      navigate('/');
+    }
+  };
+
+  const downloadConversation = () => {
+    if (isCurrConvGenerating || !viewingConversation) return;
+    const convId = viewingConversation.id;
+    const conversationJson = JSON.stringify(viewingConversation, null, 2);
+    const blob = new Blob([conversationJson], { type: 'application/json' });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = `conversation_${convId}.json`;
+    document.body.appendChild(a);
+    a.click();
+    document.body.removeChild(a);
+    URL.revokeObjectURL(url);
+  };
+
+  return (
+    <div className="flex flex-row items-center mt-6 mb-6">
+      {/* open sidebar button */}
+      <label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          width="16"
+          height="16"
+          fill="currentColor"
+          className="bi bi-list"
+          viewBox="0 0 16 16"
+        >
+          <path
+            fillRule="evenodd"
+            d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"
+          />
+        </svg>
+      </label>
+
+      <div className="grow text-2xl font-bold ml-2">llama.cpp</div>
+
+      {/* action buttons (top right) */}
+      <div className="flex items-center">
+        <div v-if="messages.length > 0" className="dropdown dropdown-end">
+          {/* "..." button */}
+          <button
+            tabIndex={0}
+            role="button"
+            className="btn m-1"
+            disabled={isCurrConvGenerating}
+          >
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              width="16"
+              height="16"
+              fill="currentColor"
+              className="bi bi-three-dots-vertical"
+              viewBox="0 0 16 16"
+            >
+              <path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0" />
+            </svg>
+          </button>
+          {/* dropdown menu */}
+          <ul
+            tabIndex={0}
+            className="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow"
+          >
+            <li onClick={downloadConversation}>
+              <a>Download</a>
+            </li>
+            <li className="text-error" onClick={removeConversation}>
+              <a>Delete</a>
+            </li>
+          </ul>
+        </div>
+        <div className="tooltip tooltip-bottom" data-tip="Settings">
+          <button className="btn" onClick={() => setShowSettingDialog(true)}>
+            {/* settings button */}
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              width="16"
+              height="16"
+              fill="currentColor"
+              className="bi bi-gear"
+              viewBox="0 0 16 16"
+            >
+              <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0" />
+              <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z" />
+            </svg>
+          </button>
+        </div>
+
+        {/* theme controller is copied from https://daisyui.com/components/theme-controller/ */}
+        <div className="tooltip tooltip-bottom" data-tip="Themes">
+          <div className="dropdown dropdown-end dropdown-bottom">
+            <div tabIndex={0} role="button" className="btn m-1">
+              <svg
+                xmlns="http://www.w3.org/2000/svg"
+                width="16"
+                height="16"
+                fill="currentColor"
+                className="bi bi-palette2"
+                viewBox="0 0 16 16"
+              >
+                <path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z" />
+              </svg>
+            </div>
+            <ul
+              tabIndex={0}
+              className="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto"
+            >
+              <li>
+                <button
+                  className={classNames({
+                    'btn btn-sm btn-block btn-ghost justify-start': true,
+                    'btn-active': selectedTheme === 'auto',
+                  })}
+                  onClick={() => setTheme('auto')}
+                >
+                  auto
+                </button>
+              </li>
+              {THEMES.map((theme) => (
+                <li key={theme}>
+                  <input
+                    type="radio"
+                    name="theme-dropdown"
+                    className="theme-controller btn btn-sm btn-block btn-ghost justify-start"
+                    aria-label={theme}
+                    value={theme}
+                    checked={selectedTheme === theme}
+                    onChange={(e) => e.target.checked && setTheme(theme)}
+                  />
+                </li>
+              ))}
+            </ul>
+          </div>
+        </div>
+      </div>
+
+      <SettingDialog
+        show={showSettingDialog}
+        onClose={() => setShowSettingDialog(false)}
+      />
+    </div>
+  );
+}
diff --git a/examples/server/webui/src/components/MarkdownDisplay.tsx b/examples/server/webui/src/components/MarkdownDisplay.tsx
new file mode 100644
index 000000000..8ab8de655
--- /dev/null
+++ b/examples/server/webui/src/components/MarkdownDisplay.tsx
@@ -0,0 +1,254 @@
+import React, { useMemo, useState } from 'react';
+import Markdown, { ExtraProps } from 'react-markdown';
+import remarkGfm from 'remark-gfm';
+import rehypeHightlight from 'rehype-highlight';
+import rehypeKatex from 'rehype-katex';
+import remarkMath from 'remark-math';
+import remarkBreaks from 'remark-breaks';
+import 'katex/dist/katex.min.css';
+import { classNames, copyStr } from '../utils/misc';
+import { ElementContent, Root } from 'hast';
+import { visit } from 'unist-util-visit';
+
+export default function MarkdownDisplay({ content }: { content: string }) {
+  const preprocessedContent = useMemo(
+    () => preprocessLaTeX(content),
+    [content]
+  );
+  return (
+    <Markdown
+      remarkPlugins={[remarkGfm, remarkMath, remarkBreaks]}
+      rehypePlugins={[rehypeHightlight, rehypeKatex, rehypeCustomCopyButton]}
+      components={{
+        button: (props) => (
+          <CopyCodeButton {...props} origContent={preprocessedContent} />
+        ),
+      }}
+    >
+      {preprocessedContent}
+    </Markdown>
+  );
+}
+
+const CopyCodeButton: React.ElementType<
+  React.ClassAttributes<HTMLButtonElement> &
+    React.HTMLAttributes<HTMLButtonElement> &
+    ExtraProps & { origContent: string }
+> = ({ node, origContent }) => {
+  const startOffset = node?.position?.start.offset ?? 0;
+  const endOffset = node?.position?.end.offset ?? 0;
+
+  const copiedContent = useMemo(
+    () =>
+      origContent
+        .substring(startOffset, endOffset)
+        .replace(/^```[^\n]+\n/g, '')
+        .replace(/```$/g, ''),
+    [origContent, startOffset, endOffset]
+  );
+
+  return (
+    <div
+      className={classNames({
+        'text-right sticky top-4 mb-2 mr-2 h-0': true,
+        'display-none': !node?.position,
+      })}
+    >
+      <CopyButton className="badge btn-mini" content={copiedContent} />
+    </div>
+  );
+};
+
+export const CopyButton = ({
+  content,
+  className,
+}: {
+  content: string;
+  className?: string;
+}) => {
+  const [copied, setCopied] = useState(false);
+  return (
+    <button
+      className={className}
+      onClick={() => {
+        copyStr(content);
+        setCopied(true);
+      }}
+      onMouseLeave={() => setCopied(false)}
+    >
+      {copied ? 'Copied!' : '📋 Copy'}
+    </button>
+  );
+};
+
+/**
+ * This injects the "button" element before each "pre" element.
+ * The actual button will be replaced with a react component in the MarkdownDisplay.
+ * We don't replace "pre" node directly because it will cause the node to re-render, which causes this bug: https://github.com/ggerganov/llama.cpp/issues/9608
+ */
+function rehypeCustomCopyButton() {
+  return function (tree: Root) {
+    visit(tree, 'element', function (node) {
+      if (node.tagName === 'pre' && !node.properties.visited) {
+        const preNode = { ...node };
+        // replace current node
+        preNode.properties.visited = 'true';
+        node.tagName = 'div';
+        node.properties = {
+          className: 'relative my-4',
+        };
+        // add node for button
+        const btnNode: ElementContent = {
+          type: 'element',
+          tagName: 'button',
+          properties: {},
+          children: [],
+          position: node.position,
+        };
+        node.children = [btnNode, preNode];
+      }
+    });
+  };
+}
+
+/**
+ * The part below is copied and adapted from:
+ * https://github.com/danny-avila/LibreChat/blob/main/client/src/utils/latex.ts
+ * (MIT License)
+ */
+
+// Regex to check if the processed content contains any potential LaTeX patterns
+const containsLatexRegex =
+  /\\\(.*?\\\)|\\\[.*?\\\]|\$.*?\$|\\begin\{equation\}.*?\\end\{equation\}/;
+
+// Regex for inline and block LaTeX expressions
+const inlineLatex = new RegExp(/\\\((.+?)\\\)/, 'g');
+const blockLatex = new RegExp(/\\\[(.*?[^\\])\\\]/, 'gs');
+
+// Function to restore code blocks
+const restoreCodeBlocks = (content: string, codeBlocks: string[]) => {
+  return content.replace(
+    /<<CODE_BLOCK_(\d+)>>/g,
+    (_, index) => codeBlocks[index]
+  );
+};
+
+// Regex to identify code blocks and inline code
+const codeBlockRegex = /(```[\s\S]*?```|`.*?`)/g;
+
+export const processLaTeX = (_content: string) => {
+  let content = _content;
+  // Temporarily replace code blocks and inline code with placeholders
+  const codeBlocks: string[] = [];
+  let index = 0;
+  content = content.replace(codeBlockRegex, (match) => {
+    codeBlocks[index] = match;
+    return `<<CODE_BLOCK_${index++}>>`;
+  });
+
+  // Escape dollar signs followed by a digit or space and digit
+  let processedContent = content.replace(/(\$)(?=\s?\d)/g, '\\$');
+
+  // If no LaTeX patterns are found, restore code blocks and return the processed content
+  if (!containsLatexRegex.test(processedContent)) {
+    return restoreCodeBlocks(processedContent, codeBlocks);
+  }
+
+  // Convert LaTeX expressions to a markdown compatible format
+  processedContent = processedContent
+    .replace(inlineLatex, (_: string, equation: string) => `$${equation}$`) // Convert inline LaTeX
+    .replace(blockLatex, (_: string, equation: string) => `$$${equation}$$`); // Convert block LaTeX
+
+  // Restore code blocks
+  return restoreCodeBlocks(processedContent, codeBlocks);
+};
+
+/**
+ * Preprocesses LaTeX content by replacing delimiters and escaping certain characters.
+ *
+ * @param content The input string containing LaTeX expressions.
+ * @returns The processed string with replaced delimiters and escaped characters.
+ */
+export function preprocessLaTeX(content: string): string {
+  // Step 1: Protect code blocks
+  const codeBlocks: string[] = [];
+  content = content.replace(/(```[\s\S]*?```|`[^`\n]+`)/g, (_, code) => {
+    codeBlocks.push(code);
+    return `<<CODE_BLOCK_${codeBlocks.length - 1}>>`;
+  });
+
+  // Step 2: Protect existing LaTeX expressions
+  const latexExpressions: string[] = [];
+
+  // Protect block math ($$...$$), \[...\], and \(...\) as before.
+  content = content.replace(
+    /(\$\$[\s\S]*?\$\$|\\\[[\s\S]*?\\\]|\\\(.*?\\\))/g,
+    (match) => {
+      latexExpressions.push(match);
+      return `<<LATEX_${latexExpressions.length - 1}>>`;
+    }
+  );
+
+  // Protect inline math ($...$) only if it does NOT match a currency pattern.
+  // We assume a currency pattern is one where the inner content is purely numeric (with optional decimals).
+  content = content.replace(/\$([^$]+)\$/g, (match, inner) => {
+    if (/^\s*\d+(?:\.\d+)?\s*$/.test(inner)) {
+      // This looks like a currency value (e.g. "$123" or "$12.34"),
+      // so don't protect it.
+      return match;
+    } else {
+      // Otherwise, treat it as a LaTeX expression.
+      latexExpressions.push(match);
+      return `<<LATEX_${latexExpressions.length - 1}>>`;
+    }
+  });
+
+  // Step 3: Escape dollar signs that are likely currency indicators.
+  // (Now that inline math is protected, this will only escape dollars not already protected)
+  content = content.replace(/\$(?=\d)/g, '\\$');
+
+  // Step 4: Restore LaTeX expressions
+  content = content.replace(
+    /<<LATEX_(\d+)>>/g,
+    (_, index) => latexExpressions[parseInt(index)]
+  );
+
+  // Step 5: Restore code blocks
+  content = content.replace(
+    /<<CODE_BLOCK_(\d+)>>/g,
+    (_, index) => codeBlocks[parseInt(index)]
+  );
+
+  // Step 6: Apply additional escaping functions
+  content = escapeBrackets(content);
+  content = escapeMhchem(content);
+
+  return content;
+}
+
+export function escapeBrackets(text: string): string {
+  const pattern =
+    /(```[\S\s]*?```|`.*?`)|\\\[([\S\s]*?[^\\])\\]|\\\((.*?)\\\)/g;
+  return text.replace(
+    pattern,
+    (
+      match: string,
+      codeBlock: string | undefined,
+      squareBracket: string | undefined,
+      roundBracket: string | undefined
+    ): string => {
+      if (codeBlock != null) {
+        return codeBlock;
+      } else if (squareBracket != null) {
+        return `$$${squareBracket}$$`;
+      } else if (roundBracket != null) {
+        return `$${roundBracket}$`;
+      }
+      return match;
+    }
+  );
+}
+
+export function escapeMhchem(text: string) {
+  return text.replaceAll('$\\ce{', '$\\\\ce{').replaceAll('$\\pu{', '$\\\\pu{');
+}
diff --git a/examples/server/webui/src/components/SettingDialog.tsx b/examples/server/webui/src/components/SettingDialog.tsx
new file mode 100644
index 000000000..ae8117fd2
--- /dev/null
+++ b/examples/server/webui/src/components/SettingDialog.tsx
@@ -0,0 +1,306 @@
+import { useState } from 'react';
+import { useAppContext } from '../utils/app.context';
+import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config';
+import { isDev } from '../Config';
+import StorageUtils from '../utils/storage';
+
+type SettKey = keyof typeof CONFIG_DEFAULT;
+
+const COMMON_SAMPLER_KEYS: SettKey[] = [
+  'temperature',
+  'top_k',
+  'top_p',
+  'min_p',
+  'max_tokens',
+];
+const OTHER_SAMPLER_KEYS: SettKey[] = [
+  'dynatemp_range',
+  'dynatemp_exponent',
+  'typical_p',
+  'xtc_probability',
+  'xtc_threshold',
+];
+const PENALTY_KEYS: SettKey[] = [
+  'repeat_last_n',
+  'repeat_penalty',
+  'presence_penalty',
+  'frequency_penalty',
+  'dry_multiplier',
+  'dry_base',
+  'dry_allowed_length',
+  'dry_penalty_last_n',
+];
+
+export default function SettingDialog({
+  show,
+  onClose,
+}: {
+  show: boolean;
+  onClose: () => void;
+}) {
+  const { config, saveConfig } = useAppContext();
+
+  // clone the config object to prevent direct mutation
+  const [localConfig, setLocalConfig] = useState<typeof CONFIG_DEFAULT>(
+    JSON.parse(JSON.stringify(config))
+  );
+
+  const resetConfig = () => {
+    if (window.confirm('Are you sure to reset all settings?')) {
+      setLocalConfig(CONFIG_DEFAULT);
+    }
+  };
+
+  const handleSave = () => {
+    saveConfig(localConfig);
+    onClose();
+  };
+
+  const debugImportDemoConv = async () => {
+    const res = await fetch('/demo-conversation.json');
+    const demoConv = await res.json();
+    StorageUtils.remove(demoConv.id);
+    for (const msg of demoConv.messages) {
+      StorageUtils.appendMsg(demoConv.id, msg);
+    }
+    onClose();
+  };
+
+  return (
+    <dialog className={`modal ${show ? 'modal-open' : ''}`}>
+      <div className="modal-box">
+        <h3 className="text-lg font-bold mb-6">Settings</h3>
+        <div className="h-[calc(90vh-12rem)] overflow-y-auto">
+          <p className="opacity-40 mb-6">
+            Settings below are saved in browser's localStorage
+          </p>
+
+          <SettingsModalShortInput
+            configKey="apiKey"
+            configDefault={CONFIG_DEFAULT}
+            value={localConfig.apiKey}
+            onChange={(value) =>
+              setLocalConfig({ ...localConfig, apiKey: value })
+            }
+          />
+
+          <label className="form-control mb-2">
+            <div className="label">
+              System Message (will be disabled if left empty)
+            </div>
+            <textarea
+              className="textarea textarea-bordered h-24"
+              placeholder={`Default: ${CONFIG_DEFAULT.systemMessage}`}
+              value={localConfig.systemMessage}
+              onChange={(e) =>
+                setLocalConfig({
+                  ...localConfig,
+                  systemMessage: e.target.value,
+                })
+              }
+            />
+          </label>
+
+          {COMMON_SAMPLER_KEYS.map((key) => (
+            <SettingsModalShortInput
+              key={key}
+              configKey={key}
+              configDefault={CONFIG_DEFAULT}
+              value={localConfig[key]}
+              onChange={(value) =>
+                setLocalConfig({ ...localConfig, [key]: value })
+              }
+            />
+          ))}
+
+          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary className="collapse-title font-bold">
+              Other sampler settings
+            </summary>
+            <div className="collapse-content">
+              <SettingsModalShortInput
+                label="Samplers queue"
+                configKey="samplers"
+                configDefault={CONFIG_DEFAULT}
+                value={localConfig.samplers}
+                onChange={(value) =>
+                  setLocalConfig({ ...localConfig, samplers: value })
+                }
+              />
+              {OTHER_SAMPLER_KEYS.map((key) => (
+                <SettingsModalShortInput
+                  key={key}
+                  configKey={key}
+                  configDefault={CONFIG_DEFAULT}
+                  value={localConfig[key]}
+                  onChange={(value) =>
+                    setLocalConfig({ ...localConfig, [key]: value })
+                  }
+                />
+              ))}
+            </div>
+          </details>
+
+          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary className="collapse-title font-bold">
+              Penalties settings
+            </summary>
+            <div className="collapse-content">
+              {PENALTY_KEYS.map((key) => (
+                <SettingsModalShortInput
+                  key={key}
+                  configKey={key}
+                  configDefault={CONFIG_DEFAULT}
+                  value={localConfig[key]}
+                  onChange={(value) =>
+                    setLocalConfig({ ...localConfig, [key]: value })
+                  }
+                />
+              ))}
+            </div>
+          </details>
+
+          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary className="collapse-title font-bold">
+              Reasoning models
+            </summary>
+            <div className="collapse-content">
+              <div className="flex flex-row items-center mb-2">
+                <input
+                  type="checkbox"
+                  className="checkbox"
+                  checked={localConfig.showThoughtInProgress}
+                  onChange={(e) =>
+                    setLocalConfig({
+                      ...localConfig,
+                      showThoughtInProgress: e.target.checked,
+                    })
+                  }
+                />
+                <span className="ml-4">
+                  Expand though process by default for generating message
+                </span>
+              </div>
+              <div className="flex flex-row items-center mb-2">
+                <input
+                  type="checkbox"
+                  className="checkbox"
+                  checked={localConfig.excludeThoughtOnReq}
+                  onChange={(e) =>
+                    setLocalConfig({
+                      ...localConfig,
+                      excludeThoughtOnReq: e.target.checked,
+                    })
+                  }
+                />
+                <span className="ml-4">
+                  Exclude thought process when sending request to API
+                  (Recommended for DeepSeek-R1)
+                </span>
+              </div>
+            </div>
+          </details>
+
+          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary className="collapse-title font-bold">
+              Advanced config
+            </summary>
+            <div className="collapse-content">
+              {/* this button only shows in dev mode, used to import a demo conversation to test message rendering */}
+              {isDev && (
+                <div className="flex flex-row items-center mb-2">
+                  <button className="btn" onClick={debugImportDemoConv}>
+                    (debug) Import demo conversation
+                  </button>
+                </div>
+              )}
+              <div className="flex flex-row items-center mb-2">
+                <input
+                  type="checkbox"
+                  className="checkbox"
+                  checked={localConfig.showTokensPerSecond}
+                  onChange={(e) =>
+                    setLocalConfig({
+                      ...localConfig,
+                      showTokensPerSecond: e.target.checked,
+                    })
+                  }
+                />
+                <span className="ml-4">Show tokens per second</span>
+              </div>
+              <label className="form-control mb-2">
+                <div className="label inline">
+                  Custom JSON config (For more info, refer to{' '}
+                  <a
+                    className="underline"
+                    href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md"
+                    target="_blank"
+                    rel="noopener noreferrer"
+                  >
+                    server documentation
+                  </a>
+                  )
+                </div>
+                <textarea
+                  className="textarea textarea-bordered h-24"
+                  placeholder='Example: { "mirostat": 1, "min_p": 0.1 }'
+                  value={localConfig.custom}
+                  onChange={(e) =>
+                    setLocalConfig({ ...localConfig, custom: e.target.value })
+                  }
+                />
+              </label>
+            </div>
+          </details>
+        </div>
+
+        <div className="modal-action">
+          <button className="btn" onClick={resetConfig}>
+            Reset to default
+          </button>
+          <button className="btn" onClick={onClose}>
+            Close
+          </button>
+          <button className="btn btn-primary" onClick={handleSave}>
+            Save
+          </button>
+        </div>
+      </div>
+    </dialog>
+  );
+}
+
+function SettingsModalShortInput({
+  configKey,
+  configDefault,
+  value,
+  onChange,
+  label,
+}: {
+  configKey: SettKey;
+  configDefault: typeof CONFIG_DEFAULT;
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  value: any;
+  onChange: (value: string) => void;
+  label?: string;
+}) {
+  return (
+    <label className="input input-bordered join-item grow flex items-center gap-2 mb-2">
+      <div className="dropdown dropdown-hover">
+        <div tabIndex={0} role="button" className="font-bold">
+          {label || configKey}
+        </div>
+        <div className="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
+          {CONFIG_INFO[configKey] ?? '(no help message available)'}
+        </div>
+      </div>
+      <input
+        type="text"
+        className="grow"
+        placeholder={`Default: ${configDefault[configKey] || 'none'}`}
+        value={value}
+        onChange={(e) => onChange(e.target.value)}
+      />
+    </label>
+  );
+}
diff --git a/examples/server/webui/src/components/Sidebar.tsx b/examples/server/webui/src/components/Sidebar.tsx
new file mode 100644
index 000000000..2fc934e46
--- /dev/null
+++ b/examples/server/webui/src/components/Sidebar.tsx
@@ -0,0 +1,95 @@
+import { useEffect, useMemo, useState } from 'react';
+import { classNames } from '../utils/misc';
+import { Conversation } from '../utils/types';
+import StorageUtils from '../utils/storage';
+import { useNavigate, useParams } from 'react-router';
+
+export default function Sidebar() {
+  const params = useParams();
+  const navigate = useNavigate();
+  const currConv = useMemo(
+    () => StorageUtils.getOneConversation(params.convId ?? ''),
+    [params.convId]
+  );
+
+  const [conversations, setConversations] = useState<Conversation[]>([]);
+
+  useEffect(() => {
+    const handleConversationChange = () => {
+      setConversations(StorageUtils.getAllConversations());
+    };
+    StorageUtils.onConversationChanged(handleConversationChange);
+    handleConversationChange();
+    return () => {
+      StorageUtils.offConversationChanged(handleConversationChange);
+    };
+  }, []);
+
+  return (
+    <>
+      <input
+        id="toggle-drawer"
+        type="checkbox"
+        className="drawer-toggle"
+        defaultChecked
+      />
+
+      <div className="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
+        <label
+          htmlFor="toggle-drawer"
+          aria-label="close sidebar"
+          className="drawer-overlay"
+        ></label>
+        <div className="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
+          <div className="flex flex-row items-center justify-between mb-4 mt-4">
+            <h2 className="font-bold ml-4">Conversations</h2>
+
+            {/* close sidebar button */}
+            <label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
+              <svg
+                xmlns="http://www.w3.org/2000/svg"
+                width="16"
+                height="16"
+                fill="currentColor"
+                className="bi bi-arrow-bar-left"
+                viewBox="0 0 16 16"
+              >
+                <path
+                  fillRule="evenodd"
+                  d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"
+                />
+              </svg>
+            </label>
+          </div>
+
+          {/* list of conversations */}
+          <div
+            className={classNames({
+              'btn btn-ghost justify-start': true,
+              'btn-active': !currConv,
+            })}
+            onClick={() => navigate('/')}
+          >
+            + New conversation
+          </div>
+          {conversations.map((conv) => (
+            <div
+              key={conv.id}
+              className={classNames({
+                'btn btn-ghost justify-start font-normal': true,
+                'btn-active': conv.id === currConv?.id,
+              })}
+              onClick={() => navigate(`/chat/${conv.id}`)}
+              dir="auto"
+            >
+              <span className="truncate">{conv.messages[0].content}</span>
+            </div>
+          ))}
+          <div className="text-center text-xs opacity-40 mt-auto mx-4">
+            Conversations are saved to browser's localStorage
+          </div>
+        </div>
+      </div>
+    </>
+  );
+}
diff --git a/examples/server/webui/src/highlight-config.js b/examples/server/webui/src/highlight-config.js
deleted file mode 100644
index 96c7028f9..000000000
--- a/examples/server/webui/src/highlight-config.js
+++ /dev/null
@@ -1,60 +0,0 @@
-import hljs from 'highlight.js/lib/core';
-
-// only import commonly used languages to reduce bundle size
-
-import python from 'highlight.js/lib/languages/python';
-import javascript from 'highlight.js/lib/languages/javascript';
-import json from 'highlight.js/lib/languages/json';
-import bash from 'highlight.js/lib/languages/bash';
-import yaml from 'highlight.js/lib/languages/yaml';
-import markdown from 'highlight.js/lib/languages/markdown';
-import scss from 'highlight.js/lib/languages/scss';
-import xml from 'highlight.js/lib/languages/xml';
-import ruby from 'highlight.js/lib/languages/ruby';
-import go from 'highlight.js/lib/languages/go';
-import java from 'highlight.js/lib/languages/java';
-import rust from 'highlight.js/lib/languages/rust';
-import scala from 'highlight.js/lib/languages/scala';
-import cpp from 'highlight.js/lib/languages/cpp';
-import csharp from 'highlight.js/lib/languages/csharp';
-import swift from 'highlight.js/lib/languages/swift';
-import dart from 'highlight.js/lib/languages/dart';
-import elixir from 'highlight.js/lib/languages/elixir';
-import kotlin from 'highlight.js/lib/languages/kotlin';
-import lua from 'highlight.js/lib/languages/lua';
-import php from 'highlight.js/lib/languages/php';
-import latex from 'highlight.js/lib/languages/latex';
-
-hljs.registerLanguage('python', python);
-hljs.registerLanguage('javascript', javascript);
-hljs.registerLanguage('json', json);
-hljs.registerLanguage('yaml', yaml);
-hljs.registerLanguage('markdown', markdown);
-hljs.registerLanguage('xml', xml);
-hljs.registerLanguage('ruby', ruby);
-hljs.registerLanguage('go', go);
-hljs.registerLanguage('java', java);
-hljs.registerLanguage('rust', rust);
-hljs.registerLanguage('scala', scala);
-hljs.registerLanguage('csharp', csharp);
-hljs.registerLanguage('swift', swift);
-hljs.registerLanguage('dart', dart);
-hljs.registerLanguage('elixir', elixir);
-hljs.registerLanguage('kotlin', kotlin);
-hljs.registerLanguage('lua', lua);
-hljs.registerLanguage('php', php);
-hljs.registerLanguage('latex', latex);
-
-// reuse some languages to further reduce bundle size
-
-hljs.registerLanguage('shell', bash);
-hljs.registerLanguage('bash', bash);
-hljs.registerLanguage('sh', bash);
-
-hljs.registerLanguage('css', scss);
-hljs.registerLanguage('scss', scss);
-
-hljs.registerLanguage('c', cpp);
-hljs.registerLanguage('cpp', cpp);
-
-export default hljs;
diff --git a/examples/server/webui/src/styles.scss b/examples/server/webui/src/index.scss
similarity index 83%
rename from examples/server/webui/src/styles.scss
rename to examples/server/webui/src/index.scss
index 34fe2aaf0..9253b6a02 100644
--- a/examples/server/webui/src/styles.scss
+++ b/examples/server/webui/src/index.scss
@@ -1,15 +1,28 @@
-@use "sass:meta";
+@use 'sass:meta';
 
 @tailwind base;
 @tailwind components;
 @tailwind utilities;
 
 .markdown {
-  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
+  h1,
+  h2,
+  h3,
+  h4,
+  h5,
+  h6,
+  ul,
+  ol,
+  li {
+    all: revert;
+  }
   pre {
     @apply whitespace-pre-wrap rounded-lg p-2;
     border: 1px solid currentColor;
   }
+  p {
+    @apply mb-2;
+  }
   /* TODO: fix markdown table */
 }
 
@@ -19,7 +32,9 @@
 .btn-mini {
   @apply cursor-pointer hover:shadow-md;
 }
-.chat-screen { max-width: 900px; }
+.chat-screen {
+  max-width: 900px;
+}
 
 .chat-bubble-base-300 {
   --tw-bg-opacity: 1;
@@ -46,3 +61,7 @@
   background: transparent !important;
   padding: 0.5em !important;
 }
+
+.katex-display {
+  margin: 0 0 !important;
+}
diff --git a/examples/server/webui/src/katex-gpt.js b/examples/server/webui/src/katex-gpt.js
deleted file mode 100644
index 7c7c5e22c..000000000
--- a/examples/server/webui/src/katex-gpt.js
+++ /dev/null
@@ -1,66 +0,0 @@
-import katex from 'katex';
-
-// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
-// MIT license
-
-const defaultOptions = {
-  delimiters: [
-    { left: '\\[', right: '\\]', display: true },
-    { left: '\\(', right: '\\)', display: false },
-  ],
-};
-
-export function renderLatexHTML(content, display = false) {
-  return katex.renderToString(content, {
-    throwOnError: false,
-    output: 'mathml',
-    displayMode: display,
-  });
-}
-
-function escapedBracketRule(options) {
-  return (state, silent) => {
-    const max = state.posMax;
-    const start = state.pos;
-
-    for (const { left, right, display } of options.delimiters) {
-
-      // Check if it starts with the left delimiter
-      if (!state.src.slice(start).startsWith(left)) continue;
-
-      // Skip the length of the left delimiter
-      let pos = start + left.length;
-
-      // Find the matching right delimiter
-      while (pos < max) {
-        if (state.src.slice(pos).startsWith(right)) {
-          break;
-        }
-        pos++;
-      }
-
-      // No matching right delimiter found, skip to the next match
-      if (pos >= max) continue;
-
-      // If not in silent mode, convert LaTeX formula to MathML
-      if (!silent) {
-        const content = state.src.slice(start + left.length, pos);
-        try {
-          const renderedContent = renderLatexHTML(content, display);
-          const token = state.push('html_inline', '', 0);
-          token.content = renderedContent;
-        } catch (e) {
-          console.error(e);
-        }
-      }
-
-      // Update position, skip the length of the right delimiter
-      state.pos = pos + right.length;
-      return true;
-    }
-  }
-}
-
-export default function (md, options = defaultOptions) {
-  md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
-}
diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js
deleted file mode 100644
index 50197a355..000000000
--- a/examples/server/webui/src/main.js
+++ /dev/null
@@ -1,704 +0,0 @@
-import './styles.scss';
-import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
-import MarkdownIt from 'markdown-it';
-import TextLineStream from 'textlinestream';
-
-// math formula rendering
-import 'katex/dist/katex.min.css';
-import markdownItKatexGpt from './katex-gpt';
-import markdownItKatexNormal from '@vscode/markdown-it-katex';
-
-// code highlighting
-import hljs from './highlight-config';
-import daisyuiThemes from 'daisyui/src/theming/themes';
-
-// ponyfill for missing ReadableStream asyncIterator on Safari
-import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
-
-const isDev = import.meta.env.MODE === 'development';
-
-// types
-/** @typedef {{ id: number, role: 'user' | 'assistant', content: string, timings: any }} Message */
-/** @typedef {{ role: 'user' | 'assistant', content: string }} APIMessage */
-/** @typedef {{ id: string, lastModified: number, messages: Array<Message> }} Conversation */
-
-// utility functions
-const isString = (x) => !!x.toLowerCase;
-const isBoolean = (x) => x === true || x === false;
-const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
-const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
-const copyStr = (textToCopy) => {
-  // Navigator clipboard api needs a secure context (https)
-  if (navigator.clipboard && window.isSecureContext) {
-    navigator.clipboard.writeText(textToCopy);
-  } else {
-    // Use the 'out of viewport hidden text area' trick
-    const textArea = document.createElement('textarea');
-    textArea.value = textToCopy;
-    // Move textarea out of the viewport so it's not visible
-    textArea.style.position = 'absolute';
-    textArea.style.left = '-999999px';
-    document.body.prepend(textArea);
-    textArea.select();
-    document.execCommand('copy');
-  }
-};
-
-// constants
-const BASE_URL = isDev
-  ? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
-  : (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
-console.log({ BASE_URL });
-
-const CONFIG_DEFAULT = {
-  // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
-  apiKey: '',
-  systemMessage: 'You are a helpful assistant.',
-  showTokensPerSecond: false,
-  showThoughtInProgress: false,
-  excludeThoughtOnReq: true,
-  // make sure these default values are in sync with `common.h`
-  samplers: 'edkypmxt',
-  temperature: 0.8,
-  dynatemp_range: 0.0,
-  dynatemp_exponent: 1.0,
-  top_k: 40,
-  top_p: 0.95,
-  min_p: 0.05,
-  xtc_probability: 0.0,
-  xtc_threshold: 0.1,
-  typical_p: 1.0,
-  repeat_last_n: 64,
-  repeat_penalty: 1.0,
-  presence_penalty: 0.0,
-  frequency_penalty: 0.0,
-  dry_multiplier: 0.0,
-  dry_base: 1.75,
-  dry_allowed_length: 2,
-  dry_penalty_last_n: -1,
-  max_tokens: -1,
-  custom: '', // custom json-stringified object
-};
-const CONFIG_INFO = {
-  apiKey: 'Set the API Key if you are using --api-key option for the server.',
-  systemMessage: 'The starting message that defines how model should behave.',
-  samplers: 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
-  temperature: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
-  dynatemp_range: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
-  dynatemp_exponent: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
-  top_k: 'Keeps only k top tokens.',
-  top_p: 'Limits tokens to those that together have a cumulative probability of at least p',
-  min_p: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
-  xtc_probability: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
-  xtc_threshold: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
-  typical_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
-  repeat_last_n: 'Last n tokens to consider for penalizing repetition',
-  repeat_penalty: 'Controls the repetition of token sequences in the generated text',
-  presence_penalty: 'Limits tokens based on whether they appear in the output or not.',
-  frequency_penalty: 'Limits tokens based on how often they appear in the output.',
-  dry_multiplier: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
-  dry_base: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
-  dry_allowed_length: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
-  dry_penalty_last_n: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
-  max_tokens: 'The maximum number of token per output.',
-  custom: '', // custom json-stringified object
-};
-// config keys having numeric value (i.e. temperature, top_k, top_p, etc)
-const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
-// list of themes supported by daisyui
-const THEMES = ['light', 'dark']
-  // make sure light & dark are always at the beginning
-  .concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));
-
-// markdown support
-const VueMarkdown = defineComponent(
-  (props) => {
-    const md = shallowRef(new MarkdownIt({
-      breaks: true,
-      highlight: function (str, lang) { // Add highlight.js
-        if (lang && hljs.getLanguage(lang)) {
-          try {
-            return '<pre dir="auto"><code class="hljs">' +
-                   hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
-                   '</code></pre>';
-          } catch (__) {}
-        }
-        return '<pre dir="auto"><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
-      }
-    }));
-    // support latex with double dollar sign and square brackets
-    md.value.use(markdownItKatexGpt, {
-      delimiters: [
-        { left: '\\[', right: '\\]', display: true },
-        { left: '\\(', right: '\\)', display: false },
-        { left: '$$', right: '$$', display: false },
-        // do not add single dollar sign here, other wise it will confused with dollar used for money symbol
-      ],
-      throwOnError: false,
-    });
-    // support latex with single dollar sign
-    md.value.use(markdownItKatexNormal, { throwOnError: false });
-    // add copy button to code blocks
-    const origFenchRenderer = md.value.renderer.rules.fence;
-    md.value.renderer.rules.fence = (tokens, idx, ...args) => {
-      const content = tokens[idx].content;
-      const origRendered = origFenchRenderer(tokens, idx, ...args);
-      return `<div class="relative my-4">
-        <div class="text-right sticky top-4 mb-2 mr-2 h-0">
-          <button class="badge btn-mini" onclick="copyStr(${escapeAttr(JSON.stringify(content))})">📋 Copy</button>
-        </div>
-        ${origRendered}
-      </div>`;
-    };
-    window.copyStr = copyStr;
-    const content = computed(() => md.value.render(props.source));
-    return () => h('div', { innerHTML: content.value });
-  },
-  { props: ['source'] }
-);
-
-// input field to be used by settings modal
-const SettingsModalShortInput = defineComponent({
-  template: document.getElementById('settings-modal-short-input').innerHTML,
-  props: {
-    label: { type: String, required: false },
-    configKey: String,
-    configDefault: Object,
-    configInfo: Object,
-    modelValue: [Object, String, Number],
-  },
-});
-
-// message bubble component
-const MessageBubble = defineComponent({
-  components: {
-    VueMarkdown
-  },
-  template: document.getElementById('message-bubble').innerHTML,
-  props: {
-    config: Object,
-    msg: Object,
-    isGenerating: Boolean,
-    showThoughtInProgress: Boolean,
-    editUserMsgAndRegenerate: Function,
-    regenerateMsg: Function,
-  },
-  data() {
-    return {
-      editingContent: null,
-    };
-  },
-  computed: {
-    timings() {
-      if (!this.msg.timings) return null;
-      return {
-        ...this.msg.timings,
-        prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
-        predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
-      };
-    },
-    splitMsgContent() {
-      const content = this.msg.content;
-      if (this.msg.role !== 'assistant') {
-        return { content };
-      }
-      let actualContent = '';
-      let cot = '';
-      let isThinking = false;
-      let thinkSplit = content.split('<think>', 2);
-      actualContent += thinkSplit[0];
-      while (thinkSplit[1] !== undefined) {
-        // <think> tag found
-        thinkSplit = thinkSplit[1].split('</think>', 2);
-        cot += thinkSplit[0];
-        isThinking = true;
-        if (thinkSplit[1] !== undefined) {
-          // </think> closing tag found
-          isThinking = false;
-          thinkSplit = thinkSplit[1].split('<think>', 2);
-          actualContent += thinkSplit[0];
-        }
-      }
-      return { content: actualContent, cot, isThinking };
-    },
-  },
-  methods: {
-    copyMsg() {
-      copyStr(this.msg.content);
-    },
-    editMsg() {
-      this.editUserMsgAndRegenerate({
-        ...this.msg,
-        content: this.editingContent,
-      });
-      this.editingContent = null;
-    },
-  },
-});
-
-// coversations is stored in localStorage
-// format: { [convId]: { id: string, lastModified: number, messages: [...] } }
-// convId is a string prefixed with 'conv-'
-const StorageUtils = {
-  /**
-   * manage conversations
-   * @returns {Array<Conversation>}
-   */
-  getAllConversations() {
-    const res = [];
-    for (const key in localStorage) {
-      if (key.startsWith('conv-')) {
-        res.push(JSON.parse(localStorage.getItem(key)));
-      }
-    }
-    res.sort((a, b) => b.lastModified - a.lastModified);
-    return res;
-  },
-  /**
-   * can return null if convId does not exist
-   * @param {string} convId
-   * @returns {Conversation | null}
-   */
-  getOneConversation(convId) {
-    return JSON.parse(localStorage.getItem(convId) || 'null');
-  },
-  /**
-   * if convId does not exist, create one
-   * @param {string} convId
-   * @param {Message} msg
-   */
-  appendMsg(convId, msg) {
-    if (msg.content === null) return;
-    const conv = StorageUtils.getOneConversation(convId) || {
-      id: convId,
-      lastModified: Date.now(),
-      messages: [],
-    };
-    conv.messages.push(msg);
-    conv.lastModified = Date.now();
-    localStorage.setItem(convId, JSON.stringify(conv));
-  },
-  /**
-   * Get new conversation id
-   * @returns {string}
-   */
-  getNewConvId() {
-    return `conv-${Date.now()}`;
-  },
-  /**
-   * remove conversation by id
-   * @param {string} convId
-   */
-  remove(convId) {
-    localStorage.removeItem(convId);
-  },
-  /**
-   * remove all conversations
-   * @param {string} convId
-   */
-  filterAndKeepMsgs(convId, predicate) {
-    const conv = StorageUtils.getOneConversation(convId);
-    if (!conv) return;
-    conv.messages = conv.messages.filter(predicate);
-    conv.lastModified = Date.now();
-    localStorage.setItem(convId, JSON.stringify(conv));
-  },
-  /**
-   * remove last message from conversation
-   * @param {string} convId
-   * @returns {Message | undefined}
-   */
-  popMsg(convId) {
-    const conv = StorageUtils.getOneConversation(convId);
-    if (!conv) return;
-    const msg = conv.messages.pop();
-    conv.lastModified = Date.now();
-    if (conv.messages.length === 0) {
-      StorageUtils.remove(convId);
-    } else {
-      localStorage.setItem(convId, JSON.stringify(conv));
-    }
-    return msg;
-  },
-
-  // manage config
-  getConfig() {
-    const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
-    // to prevent breaking changes in the future, we always provide default value for missing keys
-    return {
-      ...CONFIG_DEFAULT,
-      ...savedVal,
-    };
-  },
-  setConfig(config) {
-    localStorage.setItem('config', JSON.stringify(config));
-  },
-  getTheme() {
-    return localStorage.getItem('theme') || 'auto';
-  },
-  setTheme(theme) {
-    if (theme === 'auto') {
-      localStorage.removeItem('theme');
-    } else {
-      localStorage.setItem('theme', theme);
-    }
-  },
-};
-
-// scroll to bottom of chat messages
-// if requiresNearBottom is true, only auto-scroll if user is near bottom
-const chatScrollToBottom = (requiresNearBottom) => {
-  const msgListElem = document.getElementById('messages-list');
-  const spaceToBottom = msgListElem.scrollHeight - msgListElem.scrollTop - msgListElem.clientHeight;
-  if (!requiresNearBottom || (spaceToBottom < 100)) {
-    setTimeout(() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }), 1);
-  }
-};
-
-// wrapper for SSE
-async function* sendSSEPostRequest(url, fetchOptions) {
-  const res = await fetch(url, fetchOptions);
-  const lines = res.body
-    .pipeThrough(new TextDecoderStream())
-    .pipeThrough(new TextLineStream());
-  for await (const line of asyncIterator(lines)) {
-    if (isDev) console.log({line});
-    if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
-      const data = JSON.parse(line.slice(5));
-      yield data;
-    } else if (line.startsWith('error:')) {
-      const data = JSON.parse(line.slice(6));
-      throw new Error(data.message || 'Unknown error');
-    }
-  }
-};
-
-const mainApp = createApp({
-  components: {
-    VueMarkdown,
-    SettingsModalShortInput,
-    MessageBubble,
-  },
-  data() {
-    return {
-      conversations: StorageUtils.getAllConversations(),
-      /** @type {Array<Message>} */
-      messages: [],
-      viewingConvId: StorageUtils.getNewConvId(),
-      inputMsg: '',
-      isGenerating: false,
-      /** @type {Array<Message> | null} */
-      pendingMsg: null, // the on-going message from assistant
-      stopGeneration: () => {},
-      selectedTheme: StorageUtils.getTheme(),
-      config: StorageUtils.getConfig(),
-      showConfigDialog: false,
-      // const
-      themes: THEMES,
-      /** @type {CONFIG_DEFAULT} */
-      configDefault: {...CONFIG_DEFAULT},
-      configInfo: {...CONFIG_INFO},
-      isDev,
-    }
-  },
-  computed: {},
-  mounted() {
-    document.getElementById('app').classList.remove('opacity-0'); // show app
-    // scroll to the bottom when the pending message height is updated
-    const pendingMsgElem = document.getElementById('pending-msg');
-    const resizeObserver = new ResizeObserver(() => {
-      if (this.isGenerating) chatScrollToBottom(true);
-    });
-    resizeObserver.observe(pendingMsgElem);
-    this.setSelectedTheme(this.selectedTheme);
-  },
-  watch: {
-    viewingConvId: function(val, oldVal) {
-      if (val != oldVal) {
-        this.fetchMessages();
-        chatScrollToBottom();
-        this.hideSidebar();
-      }
-    }
-  },
-  methods: {
-    hideSidebar() {
-      document.getElementById('toggle-drawer').checked = false;
-    },
-    setSelectedTheme(theme) {
-      this.selectedTheme = theme;
-      document.body.setAttribute('data-theme', theme);
-      document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
-      StorageUtils.setTheme(theme);
-    },
-    newConversation() {
-      if (this.isGenerating) return;
-      this.viewingConvId = StorageUtils.getNewConvId();
-    },
-    setViewingConv(convId) {
-      if (this.isGenerating) return;
-      this.viewingConvId = convId;
-    },
-    deleteConv(convId) {
-      if (this.isGenerating) return;
-      if (window.confirm('Are you sure to delete this conversation?')) {
-        StorageUtils.remove(convId);
-        if (this.viewingConvId === convId) {
-          this.viewingConvId = StorageUtils.getNewConvId();
-        }
-        this.fetchConversation();
-        this.fetchMessages();
-      }
-    },
-    downloadConv(convId) {
-      const conversation = StorageUtils.getOneConversation(convId);
-      if (!conversation) {
-        alert('Conversation not found.');
-        return;
-      }
-      const conversationJson = JSON.stringify(conversation, null, 2);
-      const blob = new Blob([conversationJson], { type: 'application/json' });
-      const url = URL.createObjectURL(blob);
-      const a = document.createElement('a');
-      a.href = url;
-      a.download = `conversation_${convId}.json`;
-      document.body.appendChild(a);
-      a.click();
-      document.body.removeChild(a);
-      URL.revokeObjectURL(url);
-    },
-    async sendMessage() {
-      // prevent sending empty message
-      // also allow typing the message while generating, but does not allow sending it (to match UX/UI behavior of other chat apps)
-      if (!this.inputMsg || this.isGenerating) return;
-
-      const currConvId = this.viewingConvId;
-
-      StorageUtils.appendMsg(currConvId, {
-        id: Date.now(),
-        role: 'user',
-        content: this.inputMsg,
-      });
-      this.fetchConversation();
-      this.fetchMessages();
-      this.inputMsg = '';
-      this.generateMessage(currConvId);
-      chatScrollToBottom();
-    },
-    async generateMessage(currConvId) {
-      if (this.isGenerating) return;
-      this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
-      this.isGenerating = true;
-
-      try {
-        /** @type {CONFIG_DEFAULT} */
-        const config = this.config;
-        const abortController = new AbortController();
-        this.stopGeneration = () => abortController.abort();
-        /** @type {Array<APIMessage>} */
-        let messages = [
-          { role: 'system', content: config.systemMessage },
-          ...normalizeMsgsForAPI(this.messages),
-        ];
-        if (config.excludeThoughtOnReq) {
-          messages = filterThoughtFromMsgs(messages);
-        }
-        if (isDev) console.log({messages});
-        const params = {
-          messages,
-          stream: true,
-          cache_prompt: true,
-          samplers: config.samplers,
-          temperature: config.temperature,
-          dynatemp_range: config.dynatemp_range,
-          dynatemp_exponent: config.dynatemp_exponent,
-          top_k: config.top_k,
-          top_p: config.top_p,
-          min_p: config.min_p,
-          typical_p: config.typical_p,
-          xtc_probability: config.xtc_probability,
-          xtc_threshold: config.xtc_threshold,
-          repeat_last_n: config.repeat_last_n,
-          repeat_penalty: config.repeat_penalty,
-          presence_penalty: config.presence_penalty,
-          frequency_penalty: config.frequency_penalty,
-          dry_multiplier: config.dry_multiplier,
-          dry_base: config.dry_base,
-          dry_allowed_length: config.dry_allowed_length,
-          dry_penalty_last_n: config.dry_penalty_last_n,
-          max_tokens: config.max_tokens,
-          timings_per_token: !!config.showTokensPerSecond,
-          ...(config.custom.length ? JSON.parse(config.custom) : {}),
-        };
-        const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
-          method: 'POST',
-          headers: {
-            'Content-Type': 'application/json',
-            ...(config.apiKey ? {'Authorization': `Bearer ${config.apiKey}`} : {})
-          },
-          body: JSON.stringify(params),
-          signal: abortController.signal,
-        });
-        for await (const chunk of chunks) {
-          const stop = chunk.stop;
-          const addedContent = chunk.choices[0].delta.content;
-          const lastContent = this.pendingMsg.content || '';
-          if (addedContent) {
-            this.pendingMsg = {
-              id: this.pendingMsg.id,
-              role: 'assistant',
-              content: lastContent + addedContent,
-            };
-          }
-          const timings = chunk.timings;
-          if (timings && config.showTokensPerSecond) {
-            // only extract what's really needed, to save some space
-            this.pendingMsg.timings = {
-              prompt_n: timings.prompt_n,
-              prompt_ms: timings.prompt_ms,
-              predicted_n: timings.predicted_n,
-              predicted_ms: timings.predicted_ms,
-            };
-          }
-        }
-
-        StorageUtils.appendMsg(currConvId, this.pendingMsg);
-        this.fetchConversation();
-        this.fetchMessages();
-        setTimeout(() => document.getElementById('msg-input').focus(), 1);
-      } catch (error) {
-        if (error.name === 'AbortError') {
-          // user stopped the generation via stopGeneration() function
-          StorageUtils.appendMsg(currConvId, this.pendingMsg);
-          this.fetchConversation();
-          this.fetchMessages();
-        } else {
-          console.error(error);
-          alert(error);
-          // pop last user message
-          const lastUserMsg = StorageUtils.popMsg(currConvId);
-          this.inputMsg = lastUserMsg ? lastUserMsg.content : '';
-        }
-      }
-
-      this.pendingMsg = null;
-      this.isGenerating = false;
-      this.stopGeneration = () => {};
-      this.fetchMessages();
-      chatScrollToBottom();
-    },
-
-    // message actions
-    regenerateMsg(msg) {
-      if (this.isGenerating) return;
-      // TODO: somehow keep old history (like how ChatGPT has different "tree"). This can be done by adding "sub-conversations" with "subconv-" prefix, and new message will have a list of subconvIds
-      const currConvId = this.viewingConvId;
-      StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
-      this.fetchConversation();
-      this.fetchMessages();
-      this.generateMessage(currConvId);
-    },
-    editUserMsgAndRegenerate(msg) {
-      if (this.isGenerating) return;
-      const currConvId = this.viewingConvId;
-      const newContent = msg.content;
-      StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
-      StorageUtils.appendMsg(currConvId, {
-        id: Date.now(),
-        role: 'user',
-        content: newContent,
-      });
-      this.fetchConversation();
-      this.fetchMessages();
-      this.generateMessage(currConvId);
-    },
-
-    // settings dialog methods
-    closeAndSaveConfigDialog() {
-      try {
-        if (this.config.custom.length) JSON.parse(this.config.custom);
-      } catch (error) {
-        alert('Invalid JSON for custom config. Please either fix it or leave it empty.');
-        return;
-      }
-      for (const key of CONFIG_NUMERIC_KEYS) {
-        if (isNaN(this.config[key]) || this.config[key].toString().trim().length === 0) {
-          alert(`Invalid number for ${key} (expected an integer or a float)`);
-          return;
-        }
-        this.config[key] = parseFloat(this.config[key]);
-      }
-      this.showConfigDialog = false;
-      StorageUtils.setConfig(this.config);
-    },
-    closeAndDiscardConfigDialog() {
-      this.showConfigDialog = false;
-      this.config = StorageUtils.getConfig();
-    },
-    resetConfigDialog() {
-      if (window.confirm('Are you sure to reset all settings?')) {
-        this.config = {...CONFIG_DEFAULT};
-      }
-    },
-
-    // sync state functions
-    fetchConversation() {
-      this.conversations = StorageUtils.getAllConversations();
-    },
-    fetchMessages() {
-      this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
-    },
-
-    // debug functions
-    async debugImportDemoConv() {
-      const res = await fetch('/demo-conversation.json');
-      const demoConv = await res.json();
-      StorageUtils.remove(demoConv.id);
-      for (const msg of demoConv.messages) {
-        StorageUtils.appendMsg(demoConv.id, msg);
-      }
-      this.fetchConversation();
-    }
-  },
-});
-mainApp.config.errorHandler = alert;
-try {
-  mainApp.mount('#app');
-} catch (err) {
-  console.error(err);
-  document.getElementById('app').innerHTML = `<div style="margin:2em auto">
-    Failed to start app. Please try clearing localStorage and try again.<br/>
-    <br/>
-    <button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
-  </div>`;
-}
-
-/**
- * filter out redundant fields upon sending to API
- * @param {Array<APIMessage>} messages
- * @returns {Array<APIMessage>}
- */
-function normalizeMsgsForAPI(messages) {
-  return messages.map((msg) => {
-    return {
-      role: msg.role,
-      content: msg.content,
-    };
-  });
-}
-
-/**
- * recommended for DeepsSeek-R1, filter out content between <think> and </think> tags
- * @param {Array<APIMessage>} messages
- * @returns {Array<APIMessage>}
- */
-function filterThoughtFromMsgs(messages) {
-  return messages.map((msg) => {
-    return {
-      role: msg.role,
-      content: msg.role === 'assistant'
-        ? msg.content.split('</think>').at(-1).trim()
-        : msg.content,
-    };
-  });
-}
diff --git a/examples/server/webui/src/main.tsx b/examples/server/webui/src/main.tsx
new file mode 100644
index 000000000..631e96a79
--- /dev/null
+++ b/examples/server/webui/src/main.tsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react';
+import { createRoot } from 'react-dom/client';
+import './index.scss';
+import App from './App.tsx';
+
+createRoot(document.getElementById('root')!).render(
+  <StrictMode>
+    <App />
+  </StrictMode>
+);
diff --git a/examples/server/webui/src/utils/app.context.tsx b/examples/server/webui/src/utils/app.context.tsx
new file mode 100644
index 000000000..d50f825c3
--- /dev/null
+++ b/examples/server/webui/src/utils/app.context.tsx
@@ -0,0 +1,304 @@
+import React, { createContext, useContext, useEffect, useState } from 'react';
+import { APIMessage, Conversation, Message, PendingMessage } from './types';
+import StorageUtils from './storage';
+import {
+  filterThoughtFromMsgs,
+  normalizeMsgsForAPI,
+  getSSEStreamAsync,
+} from './misc';
+import { BASE_URL, CONFIG_DEFAULT, isDev } from '../Config';
+import { matchPath, useLocation } from 'react-router';
+
+interface AppContextValue {
+  viewingConversation: Conversation | null;
+  pendingMessages: Record<Conversation['id'], PendingMessage>;
+  isGenerating: (convId: string) => boolean;
+  sendMessage: (
+    convId: string,
+    content: string,
+    onChunk?: CallbackGeneratedChunk
+  ) => Promise<boolean>;
+  stopGenerating: (convId: string) => void;
+  replaceMessageAndGenerate: (
+    convId: string,
+    origMsgId: Message['id'],
+    content?: string,
+    onChunk?: CallbackGeneratedChunk
+  ) => Promise<void>;
+
+  config: typeof CONFIG_DEFAULT;
+  saveConfig: (config: typeof CONFIG_DEFAULT) => void;
+}
+
+// for now, this callback is only used for scrolling to the bottom of the chat
+type CallbackGeneratedChunk = () => void;
+
+// eslint-disable-next-line @typescript-eslint/no-explicit-any
+const AppContext = createContext<AppContextValue>({} as any);
+
+export const AppContextProvider = ({
+  children,
+}: {
+  children: React.ReactElement;
+}) => {
+  const { pathname } = useLocation();
+  const params = matchPath('/chat/:convId', pathname);
+  const convId = params?.params?.convId;
+
+  const [viewingConversation, setViewingConversation] =
+    useState<Conversation | null>(null);
+  const [pendingMessages, setPendingMessages] = useState<
+    Record<Conversation['id'], PendingMessage>
+  >({});
+  const [aborts, setAborts] = useState<
+    Record<Conversation['id'], AbortController>
+  >({});
+  const [config, setConfig] = useState(StorageUtils.getConfig());
+
+  useEffect(() => {
+    const handleConversationChange = (changedConvId: string) => {
+      if (changedConvId !== convId) return;
+      setViewingConversation(StorageUtils.getOneConversation(convId));
+    };
+    StorageUtils.onConversationChanged(handleConversationChange);
+    setViewingConversation(StorageUtils.getOneConversation(convId ?? ''));
+    return () => {
+      StorageUtils.offConversationChanged(handleConversationChange);
+    };
+  }, [convId]);
+
+  const setPending = (convId: string, pendingMsg: PendingMessage | null) => {
+    // if pendingMsg is null, remove the key from the object
+    if (!pendingMsg) {
+      setPendingMessages((prev) => {
+        const newState = { ...prev };
+        delete newState[convId];
+        return newState;
+      });
+    } else {
+      setPendingMessages((prev) => ({ ...prev, [convId]: pendingMsg }));
+    }
+  };
+
+  const setAbort = (convId: string, controller: AbortController | null) => {
+    if (!controller) {
+      setAborts((prev) => {
+        const newState = { ...prev };
+        delete newState[convId];
+        return newState;
+      });
+    } else {
+      setAborts((prev) => ({ ...prev, [convId]: controller }));
+    }
+  };
+
+  ////////////////////////////////////////////////////////////////////////
+  // public functions
+
+  const isGenerating = (convId: string) => !!pendingMessages[convId];
+
+  const generateMessage = async (
+    convId: string,
+    onChunk?: CallbackGeneratedChunk
+  ) => {
+    if (isGenerating(convId)) return;
+
+    const config = StorageUtils.getConfig();
+    const currConversation = StorageUtils.getOneConversation(convId);
+    if (!currConversation) {
+      throw new Error('Current conversation is not found');
+    }
+
+    const abortController = new AbortController();
+    setAbort(convId, abortController);
+
+    let pendingMsg: PendingMessage = {
+      id: Date.now() + 1,
+      role: 'assistant',
+      content: null,
+    };
+    setPending(convId, pendingMsg);
+
+    try {
+      // prepare messages for API
+      let messages: APIMessage[] = [
+        ...(config.systemMessage.length === 0
+          ? []
+          : [{ role: 'system', content: config.systemMessage } as APIMessage]),
+        ...normalizeMsgsForAPI(currConversation?.messages ?? []),
+      ];
+      if (config.excludeThoughtOnReq) {
+        messages = filterThoughtFromMsgs(messages);
+      }
+      if (isDev) console.log({ messages });
+
+      // prepare params
+      const params = {
+        messages,
+        stream: true,
+        cache_prompt: true,
+        samplers: config.samplers,
+        temperature: config.temperature,
+        dynatemp_range: config.dynatemp_range,
+        dynatemp_exponent: config.dynatemp_exponent,
+        top_k: config.top_k,
+        top_p: config.top_p,
+        min_p: config.min_p,
+        typical_p: config.typical_p,
+        xtc_probability: config.xtc_probability,
+        xtc_threshold: config.xtc_threshold,
+        repeat_last_n: config.repeat_last_n,
+        repeat_penalty: config.repeat_penalty,
+        presence_penalty: config.presence_penalty,
+        frequency_penalty: config.frequency_penalty,
+        dry_multiplier: config.dry_multiplier,
+        dry_base: config.dry_base,
+        dry_allowed_length: config.dry_allowed_length,
+        dry_penalty_last_n: config.dry_penalty_last_n,
+        max_tokens: config.max_tokens,
+        timings_per_token: !!config.showTokensPerSecond,
+        ...(config.custom.length ? JSON.parse(config.custom) : {}),
+      };
+
+      // send request
+      const fetchResponse = await fetch(`${BASE_URL}/v1/chat/completions`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          ...(config.apiKey
+            ? { Authorization: `Bearer ${config.apiKey}` }
+            : {}),
+        },
+        body: JSON.stringify(params),
+        signal: abortController.signal,
+      });
+      if (fetchResponse.status !== 200) {
+        const body = await fetchResponse.json();
+        throw new Error(body?.error?.message || 'Unknown error');
+      }
+      const chunks = getSSEStreamAsync(fetchResponse);
+      for await (const chunk of chunks) {
+        // const stop = chunk.stop;
+        if (chunk.error) {
+          throw new Error(chunk.error?.message || 'Unknown error');
+        }
+        const addedContent = chunk.choices[0].delta.content;
+        const lastContent = pendingMsg.content || '';
+        if (addedContent) {
+          pendingMsg = {
+            id: pendingMsg.id,
+            role: 'assistant',
+            content: lastContent + addedContent,
+          };
+        }
+        const timings = chunk.timings;
+        if (timings && config.showTokensPerSecond) {
+          // only extract what's really needed, to save some space
+          pendingMsg.timings = {
+            prompt_n: timings.prompt_n,
+            prompt_ms: timings.prompt_ms,
+            predicted_n: timings.predicted_n,
+            predicted_ms: timings.predicted_ms,
+          };
+        }
+        setPending(convId, pendingMsg);
+        onChunk?.();
+      }
+    } catch (err) {
+      setPending(convId, null);
+      if ((err as Error).name === 'AbortError') {
+        // user stopped the generation via stopGeneration() function
+        // we can safely ignore this error
+      } else {
+        console.error(err);
+        // eslint-disable-next-line @typescript-eslint/no-explicit-any
+        alert((err as any)?.message ?? 'Unknown error');
+        throw err; // rethrow
+      }
+    }
+
+    if (pendingMsg.content) {
+      StorageUtils.appendMsg(currConversation.id, {
+        id: pendingMsg.id,
+        content: pendingMsg.content,
+        role: pendingMsg.role,
+        timings: pendingMsg.timings,
+      });
+    }
+    setPending(convId, null);
+    onChunk?.(); // trigger scroll to bottom
+  };
+
+  const sendMessage = async (
+    convId: string,
+    content: string,
+    onChunk?: CallbackGeneratedChunk
+  ): Promise<boolean> => {
+    if (isGenerating(convId) || content.trim().length === 0) return false;
+
+    StorageUtils.appendMsg(convId, {
+      id: Date.now(),
+      role: 'user',
+      content,
+    });
+
+    try {
+      await generateMessage(convId, onChunk);
+      return true;
+    } catch (_) {
+      // rollback
+      StorageUtils.popMsg(convId);
+    }
+    return false;
+  };
+
+  const stopGenerating = (convId: string) => {
+    setPending(convId, null);
+    aborts[convId]?.abort();
+  };
+
+  // if content is undefined, we remove last assistant message
+  const replaceMessageAndGenerate = async (
+    convId: string,
+    origMsgId: Message['id'],
+    content?: string,
+    onChunk?: CallbackGeneratedChunk
+  ) => {
+    if (isGenerating(convId)) return;
+
+    StorageUtils.filterAndKeepMsgs(convId, (msg) => msg.id < origMsgId);
+    if (content) {
+      StorageUtils.appendMsg(convId, {
+        id: Date.now(),
+        role: 'user',
+        content,
+      });
+    }
+
+    await generateMessage(convId, onChunk);
+  };
+
+  const saveConfig = (config: typeof CONFIG_DEFAULT) => {
+    StorageUtils.setConfig(config);
+    setConfig(config);
+  };
+
+  return (
+    <AppContext.Provider
+      value={{
+        isGenerating,
+        viewingConversation,
+        pendingMessages,
+        sendMessage,
+        stopGenerating,
+        replaceMessageAndGenerate,
+        config,
+        saveConfig,
+      }}
+    >
+      {children}
+    </AppContext.Provider>
+  );
+};
+
+export const useAppContext = () => useContext(AppContext);
diff --git a/examples/server/webui/src/utils/misc.ts b/examples/server/webui/src/utils/misc.ts
new file mode 100644
index 000000000..0c887ee83
--- /dev/null
+++ b/examples/server/webui/src/utils/misc.ts
@@ -0,0 +1,87 @@
+// @ts-expect-error this package does not have typing
+import TextLineStream from 'textlinestream';
+import { APIMessage, Message } from './types';
+
+// ponyfill for missing ReadableStream asyncIterator on Safari
+import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
+import { isDev } from '../Config';
+
+// eslint-disable-next-line @typescript-eslint/no-explicit-any
+export const isString = (x: any) => !!x.toLowerCase;
+// eslint-disable-next-line @typescript-eslint/no-explicit-any
+export const isBoolean = (x: any) => x === true || x === false;
+// eslint-disable-next-line @typescript-eslint/no-explicit-any
+export const isNumeric = (n: any) => !isString(n) && !isNaN(n) && !isBoolean(n);
+export const escapeAttr = (str: string) =>
+  str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
+
+// wrapper for SSE
+export async function* getSSEStreamAsync(fetchResponse: Response) {
+  if (!fetchResponse.body) throw new Error('Response body is empty');
+  const lines: ReadableStream<string> = fetchResponse.body
+    .pipeThrough(new TextDecoderStream())
+    .pipeThrough(new TextLineStream());
+  // @ts-expect-error asyncIterator complains about type, but it should work
+  for await (const line of asyncIterator(lines)) {
+    if (isDev) console.log({ line });
+    if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
+      const data = JSON.parse(line.slice(5));
+      yield data;
+    } else if (line.startsWith('error:')) {
+      const data = JSON.parse(line.slice(6));
+      throw new Error(data.message || 'Unknown error');
+    }
+  }
+}
+
+// copy text to clipboard
+export const copyStr = (textToCopy: string) => {
+  // Navigator clipboard api needs a secure context (https)
+  if (navigator.clipboard && window.isSecureContext) {
+    navigator.clipboard.writeText(textToCopy);
+  } else {
+    // Use the 'out of viewport hidden text area' trick
+    const textArea = document.createElement('textarea');
+    textArea.value = textToCopy;
+    // Move textarea out of the viewport so it's not visible
+    textArea.style.position = 'absolute';
+    textArea.style.left = '-999999px';
+    document.body.prepend(textArea);
+    textArea.select();
+    document.execCommand('copy');
+  }
+};
+
+/**
+ * filter out redundant fields upon sending to API
+ */
+export function normalizeMsgsForAPI(messages: Message[]) {
+  return messages.map((msg) => {
+    return {
+      role: msg.role,
+      content: msg.content,
+    };
+  }) as APIMessage[];
+}
+
+/**
+ * recommended for DeepsSeek-R1, filter out content between <think> and </think> tags
+ */
+export function filterThoughtFromMsgs(messages: APIMessage[]) {
+  return messages.map((msg) => {
+    return {
+      role: msg.role,
+      content:
+        msg.role === 'assistant'
+          ? msg.content.split('</think>').at(-1)!.trim()
+          : msg.content,
+    } as APIMessage;
+  });
+}
+
+export function classNames(classes: Record<string, boolean>): string {
+  return Object.entries(classes)
+    .filter(([_, value]) => value)
+    .map(([key, _]) => key)
+    .join(' ');
+}
diff --git a/examples/server/webui/src/utils/storage.ts b/examples/server/webui/src/utils/storage.ts
new file mode 100644
index 000000000..8c03fa781
--- /dev/null
+++ b/examples/server/webui/src/utils/storage.ts
@@ -0,0 +1,138 @@
+// coversations is stored in localStorage
+// format: { [convId]: { id: string, lastModified: number, messages: [...] } }
+
+import { CONFIG_DEFAULT } from '../Config';
+import { Conversation, Message } from './types';
+
+const event = new EventTarget();
+
+type CallbackConversationChanged = (convId: string) => void;
+let onConversationChangedHandlers: [
+  CallbackConversationChanged,
+  EventListener,
+][] = [];
+const dispatchConversationChange = (convId: string) => {
+  event.dispatchEvent(
+    new CustomEvent('conversationChange', { detail: { convId } })
+  );
+};
+
+// convId is a string prefixed with 'conv-'
+const StorageUtils = {
+  /**
+   * manage conversations
+   */
+  getAllConversations(): Conversation[] {
+    const res = [];
+    for (const key in localStorage) {
+      if (key.startsWith('conv-')) {
+        res.push(JSON.parse(localStorage.getItem(key) ?? '{}'));
+      }
+    }
+    res.sort((a, b) => b.lastModified - a.lastModified);
+    return res;
+  },
+  /**
+   * can return null if convId does not exist
+   */
+  getOneConversation(convId: string): Conversation | null {
+    return JSON.parse(localStorage.getItem(convId) || 'null');
+  },
+  /**
+   * if convId does not exist, create one
+   */
+  appendMsg(convId: string, msg: Message): void {
+    if (msg.content === null) return;
+    const conv = StorageUtils.getOneConversation(convId) || {
+      id: convId,
+      lastModified: Date.now(),
+      messages: [],
+    };
+    conv.messages.push(msg);
+    conv.lastModified = Date.now();
+    localStorage.setItem(convId, JSON.stringify(conv));
+    dispatchConversationChange(convId);
+  },
+  /**
+   * Get new conversation id
+   */
+  getNewConvId(): string {
+    return `conv-${Date.now()}`;
+  },
+  /**
+   * remove conversation by id
+   */
+  remove(convId: string): void {
+    localStorage.removeItem(convId);
+    dispatchConversationChange(convId);
+  },
+  /**
+   * remove all conversations
+   */
+  filterAndKeepMsgs(
+    convId: string,
+    predicate: (msg: Message) => boolean
+  ): void {
+    const conv = StorageUtils.getOneConversation(convId);
+    if (!conv) return;
+    conv.messages = conv.messages.filter(predicate);
+    conv.lastModified = Date.now();
+    localStorage.setItem(convId, JSON.stringify(conv));
+    dispatchConversationChange(convId);
+  },
+  /**
+   * remove last message from conversation
+   */
+  popMsg(convId: string): Message | undefined {
+    const conv = StorageUtils.getOneConversation(convId);
+    if (!conv) return;
+    const msg = conv.messages.pop();
+    conv.lastModified = Date.now();
+    if (conv.messages.length === 0) {
+      StorageUtils.remove(convId);
+    } else {
+      localStorage.setItem(convId, JSON.stringify(conv));
+    }
+    dispatchConversationChange(convId);
+    return msg;
+  },
+
+  // event listeners
+  onConversationChanged(callback: CallbackConversationChanged) {
+    const fn = (e: Event) => callback((e as CustomEvent).detail.convId);
+    onConversationChangedHandlers.push([callback, fn]);
+    event.addEventListener('conversationChange', fn);
+  },
+  offConversationChanged(callback: CallbackConversationChanged) {
+    const fn = onConversationChangedHandlers.find(([cb, _]) => cb === callback);
+    if (fn) {
+      event.removeEventListener('conversationChange', fn[1]);
+    }
+    onConversationChangedHandlers = [];
+  },
+
+  // manage config
+  getConfig(): typeof CONFIG_DEFAULT {
+    const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
+    // to prevent breaking changes in the future, we always provide default value for missing keys
+    return {
+      ...CONFIG_DEFAULT,
+      ...savedVal,
+    };
+  },
+  setConfig(config: typeof CONFIG_DEFAULT) {
+    localStorage.setItem('config', JSON.stringify(config));
+  },
+  getTheme(): string {
+    return localStorage.getItem('theme') || 'auto';
+  },
+  setTheme(theme: string) {
+    if (theme === 'auto') {
+      localStorage.removeItem('theme');
+    } else {
+      localStorage.setItem('theme', theme);
+    }
+  },
+};
+
+export default StorageUtils;
diff --git a/examples/server/webui/src/utils/types.ts b/examples/server/webui/src/utils/types.ts
new file mode 100644
index 000000000..d04c6e3dc
--- /dev/null
+++ b/examples/server/webui/src/utils/types.ts
@@ -0,0 +1,25 @@
+export interface TimingReport {
+  prompt_n: number;
+  prompt_ms: number;
+  predicted_n: number;
+  predicted_ms: number;
+}
+
+export interface Message {
+  id: number;
+  role: 'user' | 'assistant' | 'system';
+  content: string;
+  timings?: TimingReport;
+}
+
+export type APIMessage = Pick<Message, 'role' | 'content'>;
+
+export interface Conversation {
+  id: string; // format: `conv-{timestamp}`
+  lastModified: number; // timestamp from Date.now()
+  messages: Message[];
+}
+
+export type PendingMessage = Omit<Message, 'content'> & {
+  content: string | null;
+};
diff --git a/examples/server/webui/src/vite-env.d.ts b/examples/server/webui/src/vite-env.d.ts
new file mode 100644
index 000000000..11f02fe2a
--- /dev/null
+++ b/examples/server/webui/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/examples/server/webui/tsconfig.app.json b/examples/server/webui/tsconfig.app.json
new file mode 100644
index 000000000..cb24c26f3
--- /dev/null
+++ b/examples/server/webui/tsconfig.app.json
@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    "target": "ES2021",
+    "useDefineForClassFields": true,
+    "lib": ["ES2021", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["src"]
+}
diff --git a/examples/server/webui/tsconfig.json b/examples/server/webui/tsconfig.json
new file mode 100644
index 000000000..1ffef600d
--- /dev/null
+++ b/examples/server/webui/tsconfig.json
@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}
diff --git a/examples/server/webui/tsconfig.node.json b/examples/server/webui/tsconfig.node.json
new file mode 100644
index 000000000..db0becc8b
--- /dev/null
+++ b/examples/server/webui/tsconfig.node.json
@@ -0,0 +1,24 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "ES2022",
+    "lib": ["ES2023"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["vite.config.ts"]
+}
diff --git a/examples/server/webui/vite.config.js b/examples/server/webui/vite.config.ts
similarity index 58%
rename from examples/server/webui/vite.config.js
rename to examples/server/webui/vite.config.ts
index 6619a630d..184fdc6b9 100644
--- a/examples/server/webui/vite.config.js
+++ b/examples/server/webui/vite.config.ts
@@ -1,8 +1,11 @@
-
+import { defineConfig, PluginOption } from 'vite';
+import react from '@vitejs/plugin-react';
 import { viteSingleFile } from 'vite-plugin-singlefile';
-import path from 'path';
-import fs from 'fs';
-import zlib from 'zlib';
+import path from 'node:path';
+import fs from 'node:fs';
+import zlib from 'node:zlib';
+
+/* eslint-disable */
 
 const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary
 
@@ -15,20 +18,26 @@ const GUIDE_FOR_FRONTEND = `
 -->
 `.trim();
 
+const FRONTEND_PLUGINS = [react()];
+
 const BUILD_PLUGINS = [
+  ...FRONTEND_PLUGINS,
   viteSingleFile(),
   (function llamaCppPlugin() {
-    let config;
+    let config: any;
     return {
       name: 'llamacpp:build',
       apply: 'build',
-      async configResolved(_config) {
+      async configResolved(_config: any) {
         config = _config;
       },
       writeBundle() {
         const outputIndexHtml = path.join(config.build.outDir, 'index.html');
-        const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
-        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });
+        const content =
+          GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
+        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), {
+          level: 9,
+        });
 
         // because gzip header contains machine-specific info, we must remove these data from the header
         // timestamp
@@ -42,18 +51,26 @@ const BUILD_PLUGINS = [
         if (compressed.byteLength > MAX_BUNDLE_SIZE) {
           throw new Error(
             `Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
-            `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
+              `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`
           );
         }
 
-        const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
+        const targetOutputFile = path.join(
+          config.build.outDir,
+          '../../public/index.html.gz'
+        );
         fs.writeFileSync(targetOutputFile, compressed);
-      }
-    }
+      },
+    } satisfies PluginOption;
   })(),
 ];
 
-/** @type {import('vite').UserConfig} */
-export default {
-  plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
-};
+export default defineConfig({
+  // @ts-ignore
+  plugins: process.env.ANALYZE ? FRONTEND_PLUGINS : BUILD_PLUGINS,
+  server: {
+    proxy: {
+      '/v1': 'http://localhost:8080',
+    },
+  },
+});

From 1d20e53c40c3cc848ba2b95f5bf7c075eeec8b19 Mon Sep 17 00:00:00 2001
From: Patrick Peng <retr0@retr0.blog>
Date: Thu, 6 Feb 2025 09:29:13 -0500
Subject: [PATCH 351/376] rpc: fix known RCE in rpc-server (ggml/1103)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Add bounds checking in `rpc_server::copy_tensor` to prevent out-of-bounds writes
+ Check if  `(uint8_t *)dst->data + ggml_nbytes(src)` remains within the destination buffer’s allocated region.
---
 ggml/src/ggml-rpc/ggml-rpc.cpp | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 3d0c46578..97873acc7 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -1045,7 +1045,28 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
         ggml_free(ctx);
         return false;
     }
-    GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer);
+
+    uint64_t src_size   = (uint64_t) ggml_nbytes(src);
+    uint64_t dst_data   = (uint64_t) dst->data;
+    uint64_t dst_base   = (uint64_t) ggml_backend_buffer_get_base(dst->buffer);
+    uint64_t dst_buf_sz = (uint64_t) ggml_backend_buffer_get_size(dst->buffer);
+
+    if (dst_data + src_size > dst_base + dst_buf_sz) {
+        GGML_PRINT_DEBUG("[%s] out-of-bounds write in rpc_server::copy_tensor:\n"
+                         "    write range : [0x%" PRIx64 ", 0x%" PRIx64 "]\n"
+                         "    buffer base: [0x%" PRIx64 ", 0x%" PRIx64 "]\n",
+                         __func__,
+                         dst_data,
+                         dst_data + src_size,
+                         dst_base,
+                         dst_base + dst_buf_sz);
+        ggml_free(ctx);
+        return false;
+    }
+
+    GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n",
+                     __func__, (void*) src->buffer, (void*) dst->buffer);
+
     response.result = ggml_backend_buffer_copy_tensor(src, dst);
     ggml_free(ctx);
     return true;

From 8a59053f63fffc24e730cd3ea067760abfe4a919 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 6 Feb 2025 21:23:03 +0200
Subject: [PATCH 352/376] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 26a105f64..255109ab7 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-694244a6e40dc255f6bb4376fb17431c06633e6c
+08b538031f7f944e84f472483ef5d26bf5190ead

From 855cd0734aca26c86cc23d94aefd34f934464ac9 Mon Sep 17 00:00:00 2001
From: tv1wnd <55383215+tv1wnd@users.noreply.github.com>
Date: Thu, 6 Feb 2025 22:48:51 +0100
Subject: [PATCH 353/376] llama : fix old glm4 models (#11670)

---
 src/llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index aae3c69b5..3d5a928a8 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -7217,7 +7217,7 @@ struct llm_build_context {
                 struct ggml_tensor * Qcur = nullptr;
                 struct ggml_tensor * Kcur = nullptr;
                 struct ggml_tensor * Vcur = nullptr;
-                if (model.type == LLM_TYPE_1_5B || model.type == LLM_TYPE_4B || model.type == LLM_TYPE_9B) {
+                if (model.layers[il].wqkv == nullptr) {
                     Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                     if (model.layers[il].bq) {
                         Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);

From 225bbbfa39930cda38a2e5d1f3e5b38226732009 Mon Sep 17 00:00:00 2001
From: Jinyang He <hejinyang@loongson.cn>
Date: Fri, 7 Feb 2025 15:38:31 +0800
Subject: [PATCH 354/376] ggml : optimize and build warning fix for LoongArch
 (#11709)

* ggml : optimize convert f32<->f16 for loongarch_asx

* ggml : optimize loongarch_asx extend i16,i8,u8 to i32,i16

* ggml : Fix warnings when run cpu CI locally on LoongArch
---
 ggml/src/ggml-cpu/ggml-cpu-impl.h   | 18 +++++---------
 ggml/src/ggml-cpu/ggml-cpu-quants.c | 37 ++++++-----------------------
 ggml/src/ggml-cpu/ggml-cpu.c        | 24 +++++++------------
 3 files changed, 22 insertions(+), 57 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h
index d71076ad1..9ddd972a5 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-impl.h
+++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h
@@ -360,21 +360,15 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
 #endif
 
 #if defined(__loongarch_asx)
-
-typedef union {
-    int32_t i;
-    float f;
-} ft_union;
-
 /* float type data load instructions */
-static __m128 __lsx_vreplfr2vr_s(float val) {
-    ft_union fi_tmpval = {.f = val};
-    return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i);
+static __m128 __lsx_vreplfr2vr_s(const float val) {
+    v4f32 res = {val, val, val, val};
+    return (__m128)res;
 }
 
-static __m256 __lasx_xvreplfr2vr_s(float val) {
-    ft_union fi_tmpval = {.f = val};
-    return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i);
+static __m256 __lasx_xvreplfr2vr_s(const float val) {
+    v8f32 res = {val, val, val, val, val, val, val, val};
+    return (__m256)res;
 }
 #endif
 
diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c
index 72ec58cee..27ec14935 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-quants.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c
@@ -501,30 +501,15 @@ static __m256i lasx_shuffle_b(__m256i a, __m256i b) {
 }
 
 static __m256i lasx_extu8_16(__m128i a) {
-    __m128i zero = __lsx_vldi(0);
-    __m128i vlo = __lsx_vilvl_b(zero, a);
-    __m128i vhi = __lsx_vilvh_b(zero, a);
-    return lasx_set_q(vhi, vlo);
+    return __lasx_vext2xv_hu_bu(____m256i(a));
 }
 
 static __m256i lasx_ext8_16(__m128i a) {
-     __m128i sign = __lsx_vslti_b(a, 0);
-     __m128i vlo = __lsx_vilvl_b(sign, a);
-     __m128i vhi = __lsx_vilvh_b(sign, a);
-     return lasx_set_q(vhi, vlo);
+    return __lasx_vext2xv_h_b(____m256i(a));
 }
 
 static __m256i lasx_ext16_32(__m128i a) {
-    __m256i tmp1;
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6);
-    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7);
-    return tmp1;
+    return __lasx_vext2xv_w_h(____m256i(a));
 }
 
 static __m128i lasx_extracti128( __m256i a, int pos) {
@@ -592,12 +577,10 @@ static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
 // horizontally add 8 floats
 static inline float hsum_float_8(const __m256 x) {
     __m128 res = lasx_extractf128(x, 1);
-    ft_union tmp;
     res = __lsx_vfadd_s(res, lasx_extractf128(x, 0));
     res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res));
     res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0));
-    tmp.i = __lsx_vpickve2gr_w(res, 0);
-    return tmp.f;
+    return ((v4f32)res)[0];
 }
 
 // horizontally add 8 int32_t
@@ -939,7 +922,6 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
 
 #elif defined(__loongarch_asx)
     for (int i = 0; i < nb; i++) {
-        ft_union fi;
         __m256 v0 = (__m256)__lasx_xvld( x , 0);
         __m256 v1 = (__m256)__lasx_xvld( x , 32);
         __m256 v2 = (__m256)__lasx_xvld( x , 64);
@@ -957,8 +939,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
         max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
         __m128 tmp = max4;
         max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 ));
-        fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
-        const float max_scalar = fi.f;
+        const float max_scalar = ((v4f32)max4)[0];
 
         // Quantize these floats
         const float d = max_scalar / 127.f;
@@ -1263,7 +1244,6 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
 
 #elif defined(__loongarch_asx)
     for (int i = 0; i < nb; i++) {
-        ft_union ft;
         __m256 v0 = (__m256)__lasx_xvld( x , 0 );
         __m256 v1 = (__m256)__lasx_xvld( x , 32 );
         __m256 v2 = (__m256)__lasx_xvld( x , 64 );
@@ -1281,8 +1261,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
         max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
         __m128 tmp = max4;
         max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 ));
-        ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
-        const float max_scalar = ft.f;
+        const float max_scalar = ((v4f32)max4)[0];
 
         // Quantize these floats
         const float d = max_scalar / 127.f;
@@ -6154,9 +6133,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1);
 
 
-    ft_union fi;
-    fi.i = __lsx_vpickve2gr_w(acc_m, 0);
-    *s = hsum_float_8(acc) + fi.f ;
+    *s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
 #else
 
     const uint8_t * scales = (const uint8_t*)&utmp[0];
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index e809f05d2..59efaeb71 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1078,29 +1078,23 @@ do {                                                              \
 #define GGML_F16_STEP 32
 #define GGML_F16_EPR  8
 
-// F16 arithmetic is not supported by AVX, so we use F32 instead
+// F16 arithmetic is not supported by LASX, so we use F32 instead
 
 #define GGML_F32Cx8          __m256
 #define GGML_F32Cx8_ZERO    (__m256)__lasx_xvldi(0)
 #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x))
 
 static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) {
-    float tmp[8];
-
-    for (int i = 0; i < 8; i++) {
-        tmp[i] = GGML_FP16_TO_FP32(x[i]);
-    }
-
-    return (__m256)__lasx_xvld(tmp, 0);
+    __m256i a;
+    memcpy(&a, x, sizeof(ggml_fp16_t) * 8);
+    a = __lasx_xvpermi_d(a, 0 | (1 << 4));
+    return __lasx_xvfcvtl_s_h(a);
 }
+
 static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
-    float arr[8];
-
-    __lasx_xvst(y, arr, 0);
-
-    for (int i = 0; i < 8; i++) {
-        x[i] = GGML_FP32_TO_FP16(arr[i]);
-    }
+    __m256i a = __lasx_xvfcvt_h_s(y, y);
+    a = __lasx_xvpermi_d(a, 0 | (2 << 2));
+    memcpy(x, &a, sizeof(ggml_fp16_t) * 8);
 }
 #define GGML_F32Cx8_LOAD(x)     __lasx_f32cx8_load(x)
 #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y)

From b7552cfcbc7defccd8bdefd0a7b9c47d145ed3d7 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 7 Feb 2025 09:15:22 +0100
Subject: [PATCH 355/376] common : add default embeddings presets (#11677)

* common : add default embeddings presets

This commit adds default embeddings presets for the following models:
- bge-small-en-v1.5
- e5-small-v2
- gte-small

These can be used with llama-embedding and llama-server.

For example, with llama-embedding:
```console
./build/bin/llama-embedding --embd-gte-small-default -p "Hello, how are you?"
```

And with llama-server:
```console
./build/bin/llama-server --embd-gte-small-default
```
And the embeddings endpoint can then be called with a POST request:
```console
curl --request POST \
    --url http://localhost:8080/embeddings \
    --header "Content-Type: application/json" \
    --data '{"input": "Hello, how are you?"}'
```

I'm not sure if these are the most common embedding models but hopefully
this can be a good starting point for discussion and further
improvements.

Refs: https://github.com/ggerganov/llama.cpp/issues/10932
---
 common/arg.cpp | 42 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/common/arg.cpp b/common/arg.cpp
index 76b898881..152f671ab 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2324,5 +2324,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_TTS}));
 
+    add_opt(common_arg(
+        {"--embd-bge-small-en-default"},
+        string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
+            params.hf_file = "bge-small-en-v1.5-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--embd-e5-small-en-default"},
+        string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
+            params.hf_file = "e5-small-v2-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--embd-gte-small-default"},
+        string_format("use default gte-small model (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
+            params.hf_file = "gte-small-q8_0.gguf";
+            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+            params.embd_normalize = 2;
+            params.n_ctx = 512;
+            params.verbose_prompt = true;
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+
     return ctx_arg;
 }

From ec3bc8270bc67b58955748d40a3e558a05b2d8f2 Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan.biswas@gmail.com>
Date: Fri, 7 Feb 2025 14:57:53 +0530
Subject: [PATCH 356/376] SYCL: remove XMX info from print devices (#11712)

---
 ggml/src/ggml-sycl/ggml-sycl.cpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index aab34a752..3d24d2165 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -103,11 +103,10 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
     name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
 
     auto global_mem_size = prop.get_global_mem_size()/1000000;
-    std::string xmx = gpu_has_xmx(device) ? "yes" : "no";
-    GGML_LOG_INFO("|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|%14s|\n", id, device_type.c_str(),
+    GGML_LOG_INFO("|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
             name.c_str(), version.c_str(), prop.get_max_compute_units(),
             prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
-            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str(), xmx.c_str());
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
 }
 
 void ggml_backend_sycl_print_sycl_devices() {
@@ -118,16 +117,16 @@ void ggml_backend_sycl_print_sycl_devices() {
 
     GGML_LOG_INFO(
         "|  |                   |                                       |      "
-        " |Max    |        |Max  |Global |                     |         XMX  |\n");
+        " |Max    |        |Max  |Global |                     |\n");
     GGML_LOG_INFO(
         "|  |                   |                                       |      "
-        " |compute|Max work|sub  |mem    |                     |          or  |\n");
+        " |compute|Max work|sub  |mem    |                     |\n");
     GGML_LOG_INFO(
         "|ID|        Device Type|                                   "
-        "Name|Version|units  |group   |group|size   |       Driver version| Tensor Cores |\n");
+        "Name|Version|units  |group   |group|size   |       Driver version|\n");
     GGML_LOG_INFO(
         "|--|-------------------|---------------------------------------|------"
-        "-|-------|--------|-----|-------|---------------------|--------------|\n");
+        "-|-------|--------|-----|-------|---------------------|\n");
 
     for (int id = 0; id < device_count; ++id) {
       sycl::device device = dpct::dev_mgr::instance().get_device(id);

From 7ee953a64a40c09438b2064539becdbc577cefd0 Mon Sep 17 00:00:00 2001
From: Christian Fillion <cfillion@users.noreply.github.com>
Date: Fri, 7 Feb 2025 04:33:27 -0500
Subject: [PATCH 357/376] llama : add llama_sampler_init for safe usage of
 llama_sampler_free (#11727)

The C API in llama.h claims users can implement `llama_sampler_i` to
create custom `llama_sampler`. The sampler chain takes ownership and
calls `llama_sampler_free` on them. However, `llama_sampler_free` is
hard-coded to use `delete`. This is undefined behavior if the object
wasn't also allocated via `new` from libllama's C++ runtime. Callers
in C and C-compatible languages do not use C++'s `new` operator. C++
callers may not be sharing the same heap as libllama.
---
 common/llguidance.cpp  |   6 +-
 include/llama.h        |   5 +-
 src/llama-sampling.cpp | 121 ++++++++++++++++++++++-------------------
 3 files changed, 70 insertions(+), 62 deletions(-)

diff --git a/common/llguidance.cpp b/common/llguidance.cpp
index 7aa8ddd80..2feeb93c8 100644
--- a/common/llguidance.cpp
+++ b/common/llguidance.cpp
@@ -254,10 +254,10 @@ llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * g
         };
     }
 
-    return new llama_sampler{
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_llg_i,
-        /* .ctx   = */ ctx,
-    };
+        /* .ctx   = */ ctx
+    );
 }
 
 #else
diff --git a/include/llama.h b/include/llama.h
index 61907ed40..3784f7d39 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1114,11 +1114,12 @@ extern "C" {
     };
 
     struct llama_sampler {
-        struct llama_sampler_i  * iface;
-        llama_sampler_context_t   ctx;
+        const struct llama_sampler_i * iface;
+        llama_sampler_context_t        ctx;
     };
 
     // mirror of llama_sampler_i:
+    LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
     LLAMA_API const char *           llama_sampler_name  (const struct llama_sampler * smpl);
     LLAMA_API void                   llama_sampler_accept(      struct llama_sampler * smpl, llama_token token);
     LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p);
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 26974f539..990b61297 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -316,6 +316,13 @@ static uint32_t get_rng_seed(uint32_t seed) {
 
 // llama_sampler API
 
+struct llama_sampler * llama_sampler_init(const struct llama_sampler_i * iface, llama_sampler_context_t ctx) {
+    return new llama_sampler {
+        /* .iface = */ iface,
+        /* .ctx   = */ ctx,
+    };
+}
+
 const char * llama_sampler_name(const struct llama_sampler * smpl) {
     if (!smpl->iface) {
         return "(null)";
@@ -347,10 +354,10 @@ struct llama_sampler * llama_sampler_clone(const struct llama_sampler * smpl) {
     }
 
     if (smpl->ctx == nullptr) {
-        return new llama_sampler {
+        return llama_sampler_init(
             /* .iface = */ smpl->iface,
-            /* .ctx   = */ nullptr,
-        };
+            /* .ctx   = */ nullptr
+        );
     }
 
     GGML_ABORT("the sampler does not support cloning");
@@ -472,15 +479,15 @@ static struct llama_sampler_i llama_sampler_chain_i = {
 };
 
 struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_chain_i,
         /* .ctx   = */ new llama_sampler_chain {
             /* .params      = */ params,
             /* .samplers    = */ {},
             /* .t_sample_us = */ 0,
             /* .n_sample    = */ 0,
-        },
-    };
+        }
+    );
 }
 
 void llama_sampler_chain_add(struct llama_sampler * chain, struct llama_sampler * smpl) {
@@ -546,10 +553,10 @@ static struct llama_sampler_i llama_sampler_greedy_i = {
 };
 
 struct llama_sampler * llama_sampler_init_greedy() {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_greedy_i,
-        /* .ctx   = */ nullptr,
-    };
+        /* .ctx   = */ nullptr
+    );
 }
 
 // dist
@@ -608,14 +615,14 @@ static struct llama_sampler_i llama_sampler_dist_i = {
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
     auto seed_cur = get_rng_seed(seed);
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_dist_i,
         /* .ctx   = */ new llama_sampler_dist {
             /* .seed     = */ seed,
             /* .seed_cur = */ seed_cur,
             /* .rng      = */ std::mt19937(seed_cur),
-        },
-    };
+        }
+    );
 }
 
 // softmax
@@ -638,10 +645,10 @@ static struct llama_sampler_i llama_sampler_softmax_i = {
 };
 
 struct llama_sampler * llama_sampler_init_softmax() {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_softmax_i,
-        /* .ctx   = */ nullptr,
-    };
+        /* .ctx   = */ nullptr
+    );
 }
 
 // top-k
@@ -678,12 +685,12 @@ static struct llama_sampler_i llama_sampler_top_k_i = {
 };
 
 struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_top_k_i,
         /* .ctx   = */ new llama_sampler_top_k {
             /* .k = */ k,
-        },
-    };
+        }
+    );
 }
 
 // top-p
@@ -744,13 +751,13 @@ static struct llama_sampler_i llama_sampler_top_p_i = {
 };
 
 struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_top_p_i,
         /* .ctx   = */ new llama_sampler_top_p {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
-        },
-    };
+        }
+    );
 }
 
 // min-p
@@ -840,13 +847,13 @@ static struct llama_sampler_i llama_sampler_min_p_i = {
 };
 
 struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_min_p_i,
         /* .ctx   = */ new llama_sampler_min_p {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
-        },
-    };
+        }
+    );
 }
 
 // typical
@@ -939,13 +946,13 @@ static struct llama_sampler_i llama_sampler_typical_i = {
 };
 
 struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_typical_i,
         /* .ctx   = */ new llama_sampler_typical {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
-        },
-    };
+        }
+    );
 }
 
 // temp
@@ -983,12 +990,12 @@ static struct llama_sampler_i llama_sampler_temp_i = {
 };
 
 struct llama_sampler * llama_sampler_init_temp(float temp) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_temp_i,
         /* .ctx   = */ new llama_sampler_temp {
             /*.temp = */ temp,
-        },
-    };
+        }
+    );
 }
 
 // temp-ext
@@ -1093,14 +1100,14 @@ static struct llama_sampler_i llama_sampler_temp_ext_i = {
 };
 
 struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_temp_ext_i,
         /* .ctx   = */ new llama_sampler_temp_ext {
             /* .temp     = */ temp,
             /* .delta    = */ delta,
             /* .exponent = */ exponent,
-        },
-    };
+        }
+    );
 }
 
 // xtc
@@ -1185,7 +1192,7 @@ static struct llama_sampler_i llama_sampler_xtc_i = {
 
 struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
     auto seed_cur = get_rng_seed(seed);
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_xtc_i,
         /* .ctx   = */ new llama_sampler_xtc {
             /* .probability   = */ p,
@@ -1194,8 +1201,8 @@ struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep,
             /* .seed          = */ seed,
             /* .seed_cur      = */ seed_cur,
             /* .rng           = */ std::mt19937(seed_cur),
-        },
-    };
+        }
+    );
 }
 
 // mirostat
@@ -1292,7 +1299,7 @@ static struct llama_sampler_i llama_sampler_mirostat_i = {
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
     auto seed_cur = get_rng_seed(seed);
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_mirostat_i,
         /* .ctx   = */ new llama_sampler_mirostat {
             /* .n_vocab  = */ n_vocab,
@@ -1303,8 +1310,8 @@ struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t see
             /* .m        = */ m,
             /* .mu       = */ 2.0f*tau,
             /* .rng      = */ std::mt19937(seed_cur),
-        },
-    };
+        }
+    );
 }
 
 // mirostat v2
@@ -1391,7 +1398,7 @@ static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
     auto seed_cur = get_rng_seed(seed);
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_mirostat_v2_i,
         /* .ctx   = */ new llama_sampler_mirostat_v2 {
             /* .seed     = */ seed,
@@ -1400,8 +1407,8 @@ struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau,
             /* .eta      = */ eta,
             /* .mu       = */ 2.0f*tau,
             /* .rng      = */ std::mt19937(seed_cur),
-        },
-    };
+        }
+    );
 }
 
 // grammar
@@ -1528,10 +1535,10 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
         };
     }
 
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_grammar_i,
-        /* .ctx   = */ ctx,
-    };
+        /* .ctx   = */ ctx
+    );
 }
 
 struct llama_sampler * llama_sampler_init_grammar(
@@ -1678,7 +1685,7 @@ struct llama_sampler * llama_sampler_init_penalties(
         float penalty_present) {
     penalty_last_n = std::max(penalty_last_n, 0);
 
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_penalties_i,
         /* .ctx   = */ new llama_sampler_penalties {
             /* .penalty_last_n  = */ penalty_last_n,
@@ -1687,8 +1694,8 @@ struct llama_sampler * llama_sampler_init_penalties(
             /* .penalty_present = */ penalty_present,
             /* .prev            = */ ring_buffer<llama_token>(penalty_last_n),
             /* .token_count     = */ {},
-        },
-    };
+        }
+    );
 }
 
 // DRY
@@ -2041,7 +2048,7 @@ struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab,
         }
     }
 
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_dry_i,
         /* .ctx   = */ new llama_sampler_dry {
             /* .total_context_size     = */ context_size,
@@ -2053,8 +2060,8 @@ struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab,
             /* .dry_repeat_count       = */ dry_enabled ? std::vector<int>(effective_dry_penalty_last_n, 0) : std::vector<int>{},
             /* .dry_max_token_repeat   = */ {},
             /* .last_tokens            = */ dry_enabled ? ring_buffer<llama_token>(effective_dry_penalty_last_n) : ring_buffer<llama_token>(0),
-        },
-    };
+        }
+    );
 }
 
 // wrapper for test-sampling.cpp
@@ -2155,14 +2162,14 @@ struct llama_sampler * llama_sampler_init_logit_bias(
                          int32_t   n_vocab,
                          int32_t   n_logit_bias,
           const llama_logit_bias * logit_bias) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_logit_bias_i,
         /* .ctx   = */ new llama_sampler_logit_bias {
             /* .n_vocab    = */ n_vocab,
             /* .logit_bias = */ std::vector<llama_logit_bias>(logit_bias, logit_bias + n_logit_bias),
             /* .to_search  = */ {},
-        },
-    };
+        }
+    );
 }
 
 // infill
@@ -2377,14 +2384,14 @@ static struct llama_sampler_i llama_sampler_infill_i = {
 };
 
 struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
-    return new llama_sampler {
+    return llama_sampler_init(
         /* .iface = */ &llama_sampler_infill_i,
         /* .ctx   = */ new llama_sampler_infill {
             /* .vocab = */ vocab,
             /* .buf0  = */ std::vector<char>(512),
             /* .buf1  = */ std::vector<char>(512),
-        },
-    };
+        }
+    );
 }
 
 // utils

From c026ba3c23765a648ca27c7a15ecf179f8e27f26 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Fri, 7 Feb 2025 04:26:03 -0600
Subject: [PATCH 358/376] vulkan: print shared memory size (#11719)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 1c99ebe2e..4c962fde9 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2780,8 +2780,9 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     std::string matrix_cores = coopmat2_support ? "NV_coopmat2" : coopmat_support ? "KHR_coopmat" : "none";
 
     std::string device_name = props2.properties.deviceName.data();
-    GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %d | warp size: %zu | matrix cores: %s\n",
-              idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16, subgroup_size, matrix_cores.c_str());
+    GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %d | warp size: %zu | shared memory: %d | matrix cores: %s\n",
+              idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16, subgroup_size,
+              props2.properties.limits.maxComputeSharedMemorySize, matrix_cores.c_str());
 
     if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
         GGML_LOG_DEBUG("ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want.\n");

From 333820d7491cd31c707a340ff23b984a84e40154 Mon Sep 17 00:00:00 2001
From: magicse <magicse@users.noreply.github.com>
Date: Fri, 7 Feb 2025 15:48:47 +0200
Subject: [PATCH 359/376] llama : fix progress dots (#11730)

* Update llama.cpp

For display progress dots in terminal.
Without this it didn't display dots progress during loading model from file.

* Update llama.cpp

removed trailing spaces
---
 src/llama.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 3d5a928a8..c3da3c43b 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -9428,7 +9428,6 @@ static struct llama_model * llama_model_load_from_file_impl(
         struct llama_model_params params) {
     ggml_time_init();
 
-    llama_model * model = new llama_model(params);
 
     unsigned cur_percentage = 0;
     if (params.progress_callback == NULL) {
@@ -9447,6 +9446,8 @@ static struct llama_model * llama_model_load_from_file_impl(
         };
     }
 
+    llama_model * model = new llama_model(params);
+
     // create list of devices to use with this model
     if (params.devices) {
         for (ggml_backend_dev_t * dev = params.devices; *dev; ++dev) {

From 2d219b389e8c8c40bce547b08c8aa7add60fde1f Mon Sep 17 00:00:00 2001
From: Christian Fillion <cfillion@users.noreply.github.com>
Date: Fri, 7 Feb 2025 08:55:47 -0500
Subject: [PATCH 360/376] vocab : ignore invalid UTF-8 input in the BPE
 tokenizer (#11729)

Silently insert U+FFFD(s) (Unicode replacement character) instead until the
next valid codepoint can be found.

This fixes `llama_tokenize` throwing an exception across the C API boundary
or libllama's module boundary (the caller's runtime might be incompatible!)

Returing a proper error code might be desirable, however the signature
of `llama_tokenize` doesn't allow it as all return values already have
existing meaning.
---
 src/unicode.cpp | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/unicode.cpp b/src/unicode.cpp
index 89180da41..a32ae6d08 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -618,7 +618,14 @@ std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
     result.reserve(utf8.size());
     size_t offset = 0;
     while (offset < utf8.size()) {
-        result.push_back(unicode_cpt_from_utf8(utf8, offset));
+        try {
+            result.push_back(unicode_cpt_from_utf8(utf8, offset));
+        }
+        catch (const std::invalid_argument & /*ex*/) {
+            // Silently ignore invalid UTF-8 input to avoid leaking the exception beyond llama_tokenize
+            ++offset;
+            result.emplace_back(0xFFFD); // replacement character
+        }
     }
     return result;
 }

From ed926d8833df3c29797edbc98dafd9b575aa0729 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 7 Feb 2025 16:05:34 +0200
Subject: [PATCH 361/376] llama : fix defrag logic (#11707)

* llama : fix defrag logic

ggml-ci

* cont : better logic

ggml-ci

* cont : clamp fragmentation to 0.0

ggml-ci
---
 src/llama.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index c3da3c43b..3b6a21d81 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8801,12 +8801,14 @@ static int llama_decode_impl(
     //llama_synchronize(&lctx);
 
     // decide if we need to defrag the kv cache
-    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
-        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
+    if (cparams.causal_attn && cparams.defrag_thold > 0.0f) {
+        // - do not defrag small contexts (i.e. < 2048 tokens)
+        // - count the padding towards the number of used tokens
+        const float fragmentation = kv_self.n >= 2048 ? std::max(0.0f, 1.0f - float(kv_self.used + llama_kv_cache_get_padding(cparams))/float(kv_self.n)) : 0.0f;
 
         // queue defragmentation for next llama_kv_cache_update
         if (fragmentation > cparams.defrag_thold) {
-            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
+            LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation);
 
             llama_kv_cache_defrag(kv_self);
         }

From d2fe216fb2fb7ca8627618c9ea3a2e7886325780 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Fri, 7 Feb 2025 14:42:46 +0000
Subject: [PATCH 362/376] Make logging more verbose (#11714)

Debugged an issue with a user who was on a read-only filesystem.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 examples/run/run.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index 39353ba30..eab60cad1 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -346,7 +346,7 @@ class HttpClient {
         if (!output_file.empty()) {
             output_file_partial = output_file + ".partial";
             if (!out.open(output_file_partial, "ab")) {
-                printe("Failed to open file\n");
+                printe("Failed to open file for writing\n");
 
                 return 1;
             }

From 0cf867160ca9d492e06c0bc688b337cffd1eb187 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Sat, 8 Feb 2025 10:42:34 +0100
Subject: [PATCH 363/376] server : (webui) fix numeric settings being saved as
 string (#11739)

* server : (webui) fix numeric settings being saved as string

* add some more comments
---
 examples/server/public/index.html.gz          | Bin 1219985 -> 1220088 bytes
 .../webui/src/components/MarkdownDisplay.tsx  |   1 +
 .../webui/src/components/SettingDialog.tsx    |  89 ++++++++++--------
 3 files changed, 53 insertions(+), 37 deletions(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 646988ad8e494e1bcccfd2fba6f549796aebd983..84bde767fba7f5ad4a7029b3cded5ebe61e4fdbf 100644
GIT binary patch
delta 1120283
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diff --git a/examples/server/webui/src/components/MarkdownDisplay.tsx b/examples/server/webui/src/components/MarkdownDisplay.tsx
index 8ab8de655..814920a74 100644
--- a/examples/server/webui/src/components/MarkdownDisplay.tsx
+++ b/examples/server/webui/src/components/MarkdownDisplay.tsx
@@ -23,6 +23,7 @@ export default function MarkdownDisplay({ content }: { content: string }) {
         button: (props) => (
           <CopyCodeButton {...props} origContent={preprocessedContent} />
         ),
+        // note: do not use "pre", "p" or other basic html elements here, it will cause the node to re-render when the message is being generated (this should be a bug with react-markdown, not sure how to fix it)
       }}
     >
       {preprocessedContent}
diff --git a/examples/server/webui/src/components/SettingDialog.tsx b/examples/server/webui/src/components/SettingDialog.tsx
index ae8117fd2..5565ab7bb 100644
--- a/examples/server/webui/src/components/SettingDialog.tsx
+++ b/examples/server/webui/src/components/SettingDialog.tsx
@@ -3,6 +3,7 @@ import { useAppContext } from '../utils/app.context';
 import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config';
 import { isDev } from '../Config';
 import StorageUtils from '../utils/storage';
+import { isBoolean, isNumeric, isString } from '../utils/misc';
 
 type SettKey = keyof typeof CONFIG_DEFAULT;
 
@@ -52,7 +53,42 @@ export default function SettingDialog({
   };
 
   const handleSave = () => {
-    saveConfig(localConfig);
+    // copy the local config to prevent direct mutation
+    const newConfig: typeof CONFIG_DEFAULT = JSON.parse(
+      JSON.stringify(localConfig)
+    );
+    // validate the config
+    for (const key in newConfig) {
+      const value = newConfig[key as SettKey];
+      const mustBeBoolean = isBoolean(CONFIG_DEFAULT[key as SettKey]);
+      const mustBeString = isString(CONFIG_DEFAULT[key as SettKey]);
+      const mustBeNumeric = isNumeric(CONFIG_DEFAULT[key as SettKey]);
+      if (mustBeString) {
+        if (!isString(value)) {
+          alert(`Value for ${key} must be string`);
+          return;
+        }
+      } else if (mustBeNumeric) {
+        const trimedValue = value.toString().trim();
+        const numVal = Number(trimedValue);
+        if (isNaN(numVal) || !isNumeric(numVal) || trimedValue.length === 0) {
+          alert(`Value for ${key} must be numeric`);
+          return;
+        }
+        // force conversion to number
+        // @ts-expect-error this is safe
+        newConfig[key] = numVal;
+      } else if (mustBeBoolean) {
+        if (!isBoolean(value)) {
+          alert(`Value for ${key} must be boolean`);
+          return;
+        }
+      } else {
+        console.error(`Unknown default type for key ${key}`);
+      }
+    }
+    if (isDev) console.log('Saving config', newConfig);
+    saveConfig(newConfig);
     onClose();
   };
 
@@ -66,6 +102,11 @@ export default function SettingDialog({
     onClose();
   };
 
+  const onChange = (key: SettKey) => (value: string | boolean) => {
+    // note: we do not perform validation here, because we may get incomplete value as user is still typing it
+    setLocalConfig({ ...localConfig, [key]: value });
+  };
+
   return (
     <dialog className={`modal ${show ? 'modal-open' : ''}`}>
       <div className="modal-box">
@@ -79,9 +120,7 @@ export default function SettingDialog({
             configKey="apiKey"
             configDefault={CONFIG_DEFAULT}
             value={localConfig.apiKey}
-            onChange={(value) =>
-              setLocalConfig({ ...localConfig, apiKey: value })
-            }
+            onChange={onChange('apiKey')}
           />
 
           <label className="form-control mb-2">
@@ -92,12 +131,7 @@ export default function SettingDialog({
               className="textarea textarea-bordered h-24"
               placeholder={`Default: ${CONFIG_DEFAULT.systemMessage}`}
               value={localConfig.systemMessage}
-              onChange={(e) =>
-                setLocalConfig({
-                  ...localConfig,
-                  systemMessage: e.target.value,
-                })
-              }
+              onChange={(e) => onChange('systemMessage')(e.target.value)}
             />
           </label>
 
@@ -107,9 +141,7 @@ export default function SettingDialog({
               configKey={key}
               configDefault={CONFIG_DEFAULT}
               value={localConfig[key]}
-              onChange={(value) =>
-                setLocalConfig({ ...localConfig, [key]: value })
-              }
+              onChange={onChange(key)}
             />
           ))}
 
@@ -123,9 +155,7 @@ export default function SettingDialog({
                 configKey="samplers"
                 configDefault={CONFIG_DEFAULT}
                 value={localConfig.samplers}
-                onChange={(value) =>
-                  setLocalConfig({ ...localConfig, samplers: value })
-                }
+                onChange={onChange('samplers')}
               />
               {OTHER_SAMPLER_KEYS.map((key) => (
                 <SettingsModalShortInput
@@ -133,9 +163,7 @@ export default function SettingDialog({
                   configKey={key}
                   configDefault={CONFIG_DEFAULT}
                   value={localConfig[key]}
-                  onChange={(value) =>
-                    setLocalConfig({ ...localConfig, [key]: value })
-                  }
+                  onChange={onChange(key)}
                 />
               ))}
             </div>
@@ -152,9 +180,7 @@ export default function SettingDialog({
                   configKey={key}
                   configDefault={CONFIG_DEFAULT}
                   value={localConfig[key]}
-                  onChange={(value) =>
-                    setLocalConfig({ ...localConfig, [key]: value })
-                  }
+                  onChange={onChange(key)}
                 />
               ))}
             </div>
@@ -171,10 +197,7 @@ export default function SettingDialog({
                   className="checkbox"
                   checked={localConfig.showThoughtInProgress}
                   onChange={(e) =>
-                    setLocalConfig({
-                      ...localConfig,
-                      showThoughtInProgress: e.target.checked,
-                    })
+                    onChange('showThoughtInProgress')(e.target.checked)
                   }
                 />
                 <span className="ml-4">
@@ -187,10 +210,7 @@ export default function SettingDialog({
                   className="checkbox"
                   checked={localConfig.excludeThoughtOnReq}
                   onChange={(e) =>
-                    setLocalConfig({
-                      ...localConfig,
-                      excludeThoughtOnReq: e.target.checked,
-                    })
+                    onChange('excludeThoughtOnReq')(e.target.checked)
                   }
                 />
                 <span className="ml-4">
@@ -220,10 +240,7 @@ export default function SettingDialog({
                   className="checkbox"
                   checked={localConfig.showTokensPerSecond}
                   onChange={(e) =>
-                    setLocalConfig({
-                      ...localConfig,
-                      showTokensPerSecond: e.target.checked,
-                    })
+                    onChange('showTokensPerSecond')(e.target.checked)
                   }
                 />
                 <span className="ml-4">Show tokens per second</span>
@@ -245,9 +262,7 @@ export default function SettingDialog({
                   className="textarea textarea-bordered h-24"
                   placeholder='Example: { "mirostat": 1, "min_p": 0.1 }'
                   value={localConfig.custom}
-                  onChange={(e) =>
-                    setLocalConfig({ ...localConfig, custom: e.target.value })
-                  }
+                  onChange={(e) => onChange('custom')(e.target.value)}
                 />
               </label>
             </div>

From 3ab410f55f26038de45d6cc569aaa5cb29acb918 Mon Sep 17 00:00:00 2001
From: Nikolaos Pothitos <pothitos@di.uoa.gr>
Date: Sat, 8 Feb 2025 11:43:04 +0200
Subject: [PATCH 364/376] readme : update front-end framework (#11753)

After the migration to React with #11688
---
 examples/server/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index e9d0374ad..d0b262f0e 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -220,7 +220,7 @@ services:
 The project includes a web-based user interface that enables interaction with the model through the `/chat/completions` endpoint.
 
 The web UI is developed using:
-- `vue` framework for frontend development
+- `react` framework for frontend development
 - `tailwindcss` and `daisyui` for styling
 - `vite` for build tooling
 

From d80be897acdca45f8f7ea2e52c930b1d0e738a14 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 8 Feb 2025 10:46:07 +0100
Subject: [PATCH 365/376] CUDA: fix min. version for movmatrix (#11751)

---
 ggml/src/ggml-cuda/mma.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index 9788a1389..bbc0a35ae 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -16,7 +16,7 @@
 #include "common.cuh"
 
 
-#if CUDART_VERSION >= 11800
+#if CUDART_VERSION >= 11080
 
 static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
     int ret = 0;
@@ -50,7 +50,7 @@ static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
     return ret_low | ret_high;
 }
 
-#endif // CUDART_VERSION >= 11800
+#endif // CUDART_VERSION >= 11080
 
 
 template <typename T>

From 4d3465c5aeca8be29cac77f1535c35f4fb274eca Mon Sep 17 00:00:00 2001
From: Karol Kontny <82021046+kkontny@users.noreply.github.com>
Date: Sat, 8 Feb 2025 15:30:53 +0100
Subject: [PATCH 366/376] ggml: Fix data race in ggml threadpool (#11736)

After the barrier in last iteration is executed, still the loop termination
condition will be executed. However main thread can destroy the cgraph object
and its nodes already, then another thread will access it, but the thing is already gone.
Also trouble can happen when n_nodes == 0 or abort is called, but I'm not sure if the
prior situation is possible.

Last syncronization should be done after the loop to ensure the cgraph/cplan won't be
accessed after the main thread exits from the function.
---
 ggml/src/ggml-cpu/ggml-cpu.c | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 59efaeb71..fdb430a43 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -13856,9 +13856,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             tp->ec    = GGML_STATUS_ABORTED;
         }
 
-        ggml_barrier(state->threadpool);
+        if (node_n + 1 < cgraph->n_nodes) {
+            ggml_barrier(state->threadpool);
+        }
     }
 
+    ggml_barrier(state->threadpool);
+
     return 0;
 }
 

From bdcf8b6a56f4d49d3420ae5b21cdf9a116040551 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 8 Feb 2025 16:49:38 +0200
Subject: [PATCH 367/376] cont : fix mmap flag print (#11699)

---
 src/llama-model.cpp | 2 +-
 src/llama.cpp       | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e30db66e9..0f4b62c43 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1275,7 +1275,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
     const bool use_mmap_buffer = true;
 
-    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, use_mmap_buffer ? "true" : "false");
+    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, ml.use_mmap ? "true" : "false");
 
     // build a list of buffer types for the CPU and GPU devices
     pimpl->cpu_buft_list = make_cpu_buft_list(devices);
diff --git a/src/llama.cpp b/src/llama.cpp
index 3b6a21d81..607f27861 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -9430,7 +9430,6 @@ static struct llama_model * llama_model_load_from_file_impl(
         struct llama_model_params params) {
     ggml_time_init();
 
-
     unsigned cur_percentage = 0;
     if (params.progress_callback == NULL) {
         params.progress_callback_user_data = &cur_percentage;

From aaa55053076f3d5d7da4d9a877cb77e112dabed4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 8 Feb 2025 18:08:43 +0200
Subject: [PATCH 368/376] server : minor log updates (#11760)

ggml-ci
---
 examples/server/server.cpp | 32 +++++++++++++++++---------------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 9cdf2058f..0aff9b86b 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -334,24 +334,24 @@ struct server_task {
         if (data.contains("json_schema") && !data.contains("grammar")) {
             try {
                 auto schema                  = json_value(data, "json_schema", json::object());
-                LOG_DBG("JSON schema: %s\n", schema.dump(2).c_str());
+                SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
                 params.sampling.grammar      = json_schema_to_grammar(schema);
-                LOG_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
+                SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
             } catch (const std::exception & e) {
                 throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
             }
         } else {
             params.sampling.grammar      = json_value(data, "grammar", defaults.sampling.grammar);
-            LOG_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
+            SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
             params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
-            LOG_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
+            SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
         }
 
         {
             auto it = data.find("chat_format");
             if (it != data.end()) {
                 params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
-                LOG_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
+                SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
             } else {
                 params.oaicompat_chat_format = defaults.oaicompat_chat_format;
             }
@@ -367,12 +367,12 @@ struct server_task {
 
                     auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
                     if (ids.size() == 1) {
-                        LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
+                        SRV_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
                         params.sampling.grammar_trigger_tokens.push_back(ids[0]);
                         params.sampling.preserved_tokens.insert(ids[0]);
                         continue;
                     }
-                    LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
+                    SRV_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
                     params.sampling.grammar_trigger_words.push_back(trigger);
                 }
             }
@@ -381,11 +381,11 @@ struct server_task {
                 for (const auto & t : *preserved_tokens) {
                     auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
                     if (ids.size() == 1) {
-                        LOG_DBG("Preserved token: %d\n", ids[0]);
+                        SRV_DBG("Preserved token: %d\n", ids[0]);
                         params.sampling.preserved_tokens.insert(ids[0]);
                     } else {
                         // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
-                        LOG_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
+                        SRV_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
                     }
                 }
             }
@@ -717,7 +717,7 @@ struct server_task_result_cmpl_final : server_task_result {
         std::string finish_reason = "length";
         common_chat_msg msg;
         if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
-            LOG_DBG("Parsing chat message: %s\n", content.c_str());
+            SRV_DBG("Parsing chat message: %s\n", content.c_str());
             msg = common_chat_parse(content, oaicompat_chat_format);
             finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
         } else {
@@ -1885,7 +1885,7 @@ struct server_context {
         }
 
         if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
-            LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
+            SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
             chat_templates = common_chat_templates_from_model(model, "chatml");
         } else {
             chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
@@ -3355,10 +3355,10 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
 
     // reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch
 
-    LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
+    SRV_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
 
-    LOG_DBG("request:  %s\n", req.body.c_str());
-    LOG_DBG("response: %s\n", res.body.c_str());
+    SRV_DBG("request:  %s\n", req.body.c_str());
+    SRV_DBG("response: %s\n", res.body.c_str());
 }
 
 std::function<void(int)> shutdown_handler;
@@ -3860,7 +3860,9 @@ int main(int argc, char ** argv) {
 
         try {
             const auto & prompt = data.at("prompt");
-            LOG_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
+            // TODO: this log can become very long, put it behind a flag or think about a more compact format
+            //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
+
             std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
             tasks.reserve(tokenized_prompts.size());
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {

From e6e658319952f7ad269dc11275b9edddc721fc6d Mon Sep 17 00:00:00 2001
From: Woof Dog <197125663+woof-dog@users.noreply.github.com>
Date: Sat, 8 Feb 2025 19:09:55 +0000
Subject: [PATCH 369/376] server : (webui) increase edit textarea size (#11763)

---
 examples/server/public/index.html.gz          | Bin 1220088 -> 1220092 bytes
 .../webui/src/components/ChatMessage.tsx      |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 84bde767fba7f5ad4a7029b3cded5ebe61e4fdbf..662a30f00ed8b44560424889686a078bfe0d1b49 100644
GIT binary patch
delta 545216
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diff --git a/examples/server/webui/src/components/ChatMessage.tsx b/examples/server/webui/src/components/ChatMessage.tsx
index 01d2fb80c..2666f81c0 100644
--- a/examples/server/webui/src/components/ChatMessage.tsx
+++ b/examples/server/webui/src/components/ChatMessage.tsx
@@ -92,7 +92,7 @@ export default function ChatMessage({
             <>
               <textarea
                 dir="auto"
-                className="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+                className="textarea textarea-bordered bg-base-100 text-base-content max-w-2xl w-[calc(90vw-8em)] h-24"
                 value={editingContent}
                 onChange={(e) => setEditingContent(e.target.value)}
               ></textarea>

From 55ac8c7791ff44aeb82bd7fe3ca2041844fc77f1 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <thichthat@gmail.com>
Date: Sat, 8 Feb 2025 21:54:50 +0100
Subject: [PATCH 370/376] server : (webui) revamp Settings dialog, add Pyodide
 interpreter (#11759)

* redo Settings modal UI

* add python code interpreter

* fix auto scroll

* build

* fix overflow for long output lines

* bring back sticky copy button

* adapt layout on mobile view

* fix multiple lines output and color scheme

* handle python exception

* better state management

* add webworker

* add headers

* format code

* speed up by loading pyodide on page load

* (small tweak) add small animation to make it feels like claude
---
 examples/server/public/index.html.gz          | Bin 1220092 -> 1225022 bytes
 examples/server/server.cpp                    |   3 +
 examples/server/webui/package-lock.json       |  10 +
 examples/server/webui/package.json            |   1 +
 examples/server/webui/src/App.tsx             |  15 +-
 examples/server/webui/src/Config.ts           |   3 +
 .../src/components/CanvasPyInterpreter.tsx    | 195 ++++++
 .../webui/src/components/ChatMessage.tsx      |  10 +-
 .../webui/src/components/ChatScreen.tsx       | 149 +++--
 .../server/webui/src/components/Header.tsx    |  12 +-
 .../webui/src/components/MarkdownDisplay.tsx  |  73 ++-
 .../webui/src/components/SettingDialog.tsx    | 587 ++++++++++++------
 examples/server/webui/src/index.scss          |   6 +
 .../server/webui/src/utils/app.context.tsx    |  25 +-
 examples/server/webui/src/utils/common.tsx    |  38 ++
 examples/server/webui/src/utils/misc.ts       |   3 +
 examples/server/webui/src/utils/types.ts      |  11 +
 examples/server/webui/vite.config.ts          |   4 +
 18 files changed, 873 insertions(+), 272 deletions(-)
 create mode 100644 examples/server/webui/src/components/CanvasPyInterpreter.tsx
 create mode 100644 examples/server/webui/src/utils/common.tsx

diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz
index 662a30f00ed8b44560424889686a078bfe0d1b49..141e8092057ac90b38ce3cc7efe7951849691a8d 100644
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diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 0aff9b86b..0718806c8 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -4378,6 +4378,9 @@ int main(int argc, char ** argv) {
                     res.set_content("Error: gzip is not supported by this browser", "text/plain");
                 } else {
                     res.set_header("Content-Encoding", "gzip");
+                    // COEP and COOP headers, required by pyodide (python interpreter)
+                    res.set_header("Cross-Origin-Embedder-Policy", "require-corp");
+                    res.set_header("Cross-Origin-Opener-Policy", "same-origin");
                     res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
                 }
                 return false;
diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json
index e69fd2aa5..c6c5de3c0 100644
--- a/examples/server/webui/package-lock.json
+++ b/examples/server/webui/package-lock.json
@@ -8,6 +8,7 @@
       "name": "webui",
       "version": "0.0.0",
       "dependencies": {
+        "@heroicons/react": "^2.2.0",
         "@sec-ant/readable-stream": "^0.6.0",
         "@vscode/markdown-it-katex": "^1.1.1",
         "autoprefixer": "^10.4.20",
@@ -902,6 +903,15 @@
         "node": "^18.18.0 || ^20.9.0 || >=21.1.0"
       }
     },
+    "node_modules/@heroicons/react": {
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/@heroicons/react/-/react-2.2.0.tgz",
+      "integrity": "sha512-LMcepvRaS9LYHJGsF0zzmgKCUim/X3N/DQKc4jepAXJ7l8QxJ1PmxJzqplF2Z3FE4PqBAIGyJAQ/w4B5dsqbtQ==",
+      "license": "MIT",
+      "peerDependencies": {
+        "react": ">= 16 || ^19.0.0-rc"
+      }
+    },
     "node_modules/@humanfs/core": {
       "version": "0.19.1",
       "resolved": "https://registry.npmjs.org/@humanfs/core/-/core-0.19.1.tgz",
diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json
index f3c7dde43..3be2b14de 100644
--- a/examples/server/webui/package.json
+++ b/examples/server/webui/package.json
@@ -11,6 +11,7 @@
     "preview": "vite preview"
   },
   "dependencies": {
+    "@heroicons/react": "^2.2.0",
     "@sec-ant/readable-stream": "^0.6.0",
     "@vscode/markdown-it-katex": "^1.1.1",
     "autoprefixer": "^10.4.20",
diff --git a/examples/server/webui/src/App.tsx b/examples/server/webui/src/App.tsx
index d151ba291..2ce734682 100644
--- a/examples/server/webui/src/App.tsx
+++ b/examples/server/webui/src/App.tsx
@@ -1,8 +1,9 @@
 import { HashRouter, Outlet, Route, Routes } from 'react-router';
 import Header from './components/Header';
 import Sidebar from './components/Sidebar';
-import { AppContextProvider } from './utils/app.context';
+import { AppContextProvider, useAppContext } from './utils/app.context';
 import ChatScreen from './components/ChatScreen';
+import SettingDialog from './components/SettingDialog';
 
 function App() {
   return (
@@ -22,13 +23,23 @@ function App() {
 }
 
 function AppLayout() {
+  const { showSettings, setShowSettings } = useAppContext();
   return (
     <>
       <Sidebar />
-      <div className="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
+      <div
+        className="drawer-content grow flex flex-col h-screen w-screen mx-auto px-4 overflow-auto"
+        id="main-scroll"
+      >
         <Header />
         <Outlet />
       </div>
+      {
+        <SettingDialog
+          show={showSettings}
+          onClose={() => setShowSettings(false)}
+        />
+      }
     </>
   );
 }
diff --git a/examples/server/webui/src/Config.ts b/examples/server/webui/src/Config.ts
index 1860ffcc9..779ed9bf7 100644
--- a/examples/server/webui/src/Config.ts
+++ b/examples/server/webui/src/Config.ts
@@ -10,6 +10,7 @@ export const BASE_URL = new URL('.', document.baseURI).href
 
 export const CONFIG_DEFAULT = {
   // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
+  // Do not use nested objects, keep it single level. Prefix the key if you need to group them.
   apiKey: '',
   systemMessage: 'You are a helpful assistant.',
   showTokensPerSecond: false,
@@ -36,6 +37,8 @@ export const CONFIG_DEFAULT = {
   dry_penalty_last_n: -1,
   max_tokens: -1,
   custom: '', // custom json-stringified object
+  // experimental features
+  pyIntepreterEnabled: false,
 };
 export const CONFIG_INFO: Record<string, string> = {
   apiKey: 'Set the API Key if you are using --api-key option for the server.',
diff --git a/examples/server/webui/src/components/CanvasPyInterpreter.tsx b/examples/server/webui/src/components/CanvasPyInterpreter.tsx
new file mode 100644
index 000000000..c2707fe20
--- /dev/null
+++ b/examples/server/webui/src/components/CanvasPyInterpreter.tsx
@@ -0,0 +1,195 @@
+import { useEffect, useState } from 'react';
+import { useAppContext } from '../utils/app.context';
+import { OpenInNewTab, XCloseButton } from '../utils/common';
+import { CanvasType } from '../utils/types';
+import { PlayIcon, StopIcon } from '@heroicons/react/24/outline';
+import StorageUtils from '../utils/storage';
+
+const canInterrupt = typeof SharedArrayBuffer === 'function';
+
+// adapted from https://pyodide.org/en/stable/usage/webworker.html
+const WORKER_CODE = `
+importScripts("https://cdn.jsdelivr.net/pyodide/v0.27.2/full/pyodide.js");
+
+let stdOutAndErr = [];
+
+let pyodideReadyPromise = loadPyodide({
+  stdout: (data) => stdOutAndErr.push(data),
+  stderr: (data) => stdOutAndErr.push(data),
+});
+
+let alreadySetBuff = false;
+
+self.onmessage = async (event) => {
+  stdOutAndErr = [];
+
+  // make sure loading is done
+  const pyodide = await pyodideReadyPromise;
+  const { id, python, context, interruptBuffer } = event.data;
+
+  if (interruptBuffer && !alreadySetBuff) {
+    pyodide.setInterruptBuffer(interruptBuffer);
+    alreadySetBuff = true;
+  }
+
+  // Now load any packages we need, run the code, and send the result back.
+  await pyodide.loadPackagesFromImports(python);
+
+  // make a Python dictionary with the data from content
+  const dict = pyodide.globals.get("dict");
+  const globals = dict(Object.entries(context));
+  try {
+    self.postMessage({ id, running: true });
+    // Execute the python code in this context
+    const result = pyodide.runPython(python, { globals });
+    self.postMessage({ result, id, stdOutAndErr });
+  } catch (error) {
+    self.postMessage({ error: error.message, id });
+  }
+  interruptBuffer[0] = 0;
+};
+`;
+
+let worker: Worker;
+const interruptBuffer = canInterrupt
+  ? new Uint8Array(new SharedArrayBuffer(1))
+  : null;
+
+const startWorker = () => {
+  if (!worker) {
+    worker = new Worker(
+      URL.createObjectURL(new Blob([WORKER_CODE], { type: 'text/javascript' }))
+    );
+  }
+};
+
+if (StorageUtils.getConfig().pyIntepreterEnabled) {
+  startWorker();
+}
+
+const runCodeInWorker = (
+  pyCode: string,
+  callbackRunning: () => void
+): {
+  donePromise: Promise<string>;
+  interrupt: () => void;
+} => {
+  startWorker();
+  const id = Math.random() * 1e8;
+  const context = {};
+  if (interruptBuffer) {
+    interruptBuffer[0] = 0;
+  }
+
+  const donePromise = new Promise<string>((resolve) => {
+    worker.onmessage = (event) => {
+      const { error, stdOutAndErr, running } = event.data;
+      if (id !== event.data.id) return;
+      if (running) {
+        callbackRunning();
+        return;
+      } else if (error) {
+        resolve(error.toString());
+      } else {
+        resolve(stdOutAndErr.join('\n'));
+      }
+    };
+    worker.postMessage({ id, python: pyCode, context, interruptBuffer });
+  });
+
+  const interrupt = () => {
+    console.log('Interrupting...');
+    console.trace();
+    if (interruptBuffer) {
+      interruptBuffer[0] = 2;
+    }
+  };
+
+  return { donePromise, interrupt };
+};
+
+export default function CanvasPyInterpreter() {
+  const { canvasData, setCanvasData } = useAppContext();
+
+  const [code, setCode] = useState(canvasData?.content ?? ''); // copy to avoid direct mutation
+  const [running, setRunning] = useState(false);
+  const [output, setOutput] = useState('');
+  const [interruptFn, setInterruptFn] = useState<() => void>();
+  const [showStopBtn, setShowStopBtn] = useState(false);
+
+  const runCode = async (pycode: string) => {
+    interruptFn?.();
+    setRunning(true);
+    setOutput('Loading Pyodide...');
+    const { donePromise, interrupt } = runCodeInWorker(pycode, () => {
+      setOutput('Running...');
+      setShowStopBtn(canInterrupt);
+    });
+    setInterruptFn(() => interrupt);
+    const out = await donePromise;
+    setOutput(out);
+    setRunning(false);
+    setShowStopBtn(false);
+  };
+
+  // run code on mount
+  useEffect(() => {
+    setCode(canvasData?.content ?? '');
+    runCode(canvasData?.content ?? '');
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [canvasData?.content]);
+
+  if (canvasData?.type !== CanvasType.PY_INTERPRETER) {
+    return null;
+  }
+
+  return (
+    <div className="card bg-base-200 w-full h-full shadow-xl">
+      <div className="card-body">
+        <div className="flex justify-between items-center mb-4">
+          <span className="text-lg font-bold">Python Interpreter</span>
+          <XCloseButton
+            className="bg-base-100"
+            onClick={() => setCanvasData(null)}
+          />
+        </div>
+        <div className="grid grid-rows-3 gap-4 h-full">
+          <textarea
+            className="textarea textarea-bordered w-full h-full font-mono"
+            value={code}
+            onChange={(e) => setCode(e.target.value)}
+          ></textarea>
+          <div className="font-mono flex flex-col row-span-2">
+            <div className="flex items-center mb-2">
+              <button
+                className="btn btn-sm bg-base-100"
+                onClick={() => runCode(code)}
+                disabled={running}
+              >
+                <PlayIcon className="h-6 w-6" /> Run
+              </button>
+              {showStopBtn && (
+                <button
+                  className="btn btn-sm bg-base-100 ml-2"
+                  onClick={() => interruptFn?.()}
+                >
+                  <StopIcon className="h-6 w-6" /> Stop
+                </button>
+              )}
+              <span className="grow text-right text-xs">
+                <OpenInNewTab href="https://github.com/ggerganov/llama.cpp/issues/11762">
+                  Report a bug
+                </OpenInNewTab>
+              </span>
+            </div>
+            <textarea
+              className="textarea textarea-bordered h-full dark-color"
+              value={output}
+              readOnly
+            ></textarea>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/examples/server/webui/src/components/ChatMessage.tsx b/examples/server/webui/src/components/ChatMessage.tsx
index 2666f81c0..ec72196ba 100644
--- a/examples/server/webui/src/components/ChatMessage.tsx
+++ b/examples/server/webui/src/components/ChatMessage.tsx
@@ -149,11 +149,17 @@ export default function ChatMessage({
                           )}
                         </summary>
                         <div className="collapse-content">
-                          <MarkdownDisplay content={thought} />
+                          <MarkdownDisplay
+                            content={thought}
+                            isGenerating={isPending}
+                          />
                         </div>
                       </details>
                     )}
-                    <MarkdownDisplay content={content} />
+                    <MarkdownDisplay
+                      content={content}
+                      isGenerating={isPending}
+                    />
                   </div>
                 </>
               )}
diff --git a/examples/server/webui/src/components/ChatScreen.tsx b/examples/server/webui/src/components/ChatScreen.tsx
index d679f4ebb..dbc683ed1 100644
--- a/examples/server/webui/src/components/ChatScreen.tsx
+++ b/examples/server/webui/src/components/ChatScreen.tsx
@@ -1,9 +1,11 @@
-import { useEffect, useRef, useState } from 'react';
+import { useEffect, useState } from 'react';
 import { useAppContext } from '../utils/app.context';
 import StorageUtils from '../utils/storage';
 import { useNavigate } from 'react-router';
 import ChatMessage from './ChatMessage';
-import { PendingMessage } from '../utils/types';
+import { CanvasType, PendingMessage } from '../utils/types';
+import { classNames } from '../utils/misc';
+import CanvasPyInterpreter from './CanvasPyInterpreter';
 
 export default function ChatScreen() {
   const {
@@ -12,24 +14,24 @@ export default function ChatScreen() {
     isGenerating,
     stopGenerating,
     pendingMessages,
+    canvasData,
   } = useAppContext();
   const [inputMsg, setInputMsg] = useState('');
-  const containerRef = useRef<HTMLDivElement>(null);
   const navigate = useNavigate();
 
   const currConvId = viewingConversation?.id ?? '';
   const pendingMsg: PendingMessage | undefined = pendingMessages[currConvId];
 
   const scrollToBottom = (requiresNearBottom: boolean) => {
-    if (!containerRef.current) return;
-    const msgListElem = containerRef.current;
+    const mainScrollElem = document.getElementById('main-scroll');
+    if (!mainScrollElem) return;
     const spaceToBottom =
-      msgListElem.scrollHeight -
-      msgListElem.scrollTop -
-      msgListElem.clientHeight;
+      mainScrollElem.scrollHeight -
+      mainScrollElem.scrollTop -
+      mainScrollElem.clientHeight;
     if (!requiresNearBottom || spaceToBottom < 50) {
       setTimeout(
-        () => msgListElem.scrollTo({ top: msgListElem.scrollHeight }),
+        () => mainScrollElem.scrollTo({ top: mainScrollElem.scrollHeight }),
         1
       );
     }
@@ -58,66 +60,87 @@ export default function ChatScreen() {
     }
   };
 
+  const hasCanvas = !!canvasData;
+
   return (
-    <>
-      {/* chat messages */}
+    <div
+      className={classNames({
+        'grid lg:gap-8 grow transition-[300ms]': true,
+        'grid-cols-[1fr_0fr] lg:grid-cols-[1fr_1fr]': hasCanvas, // adapted for mobile
+        'grid-cols-[1fr_0fr]': !hasCanvas,
+      })}
+    >
       <div
-        id="messages-list"
-        className="flex flex-col grow overflow-y-auto"
-        ref={containerRef}
+        className={classNames({
+          'flex flex-col w-full max-w-[900px] mx-auto': true,
+          'hidden lg:flex': hasCanvas, // adapted for mobile
+          flex: !hasCanvas,
+        })}
       >
-        <div className="mt-auto flex justify-center">
-          {/* placeholder to shift the message to the bottom */}
-          {viewingConversation ? '' : 'Send a message to start'}
+        {/* chat messages */}
+        <div id="messages-list" className="grow">
+          <div className="mt-auto flex justify-center">
+            {/* placeholder to shift the message to the bottom */}
+            {viewingConversation ? '' : 'Send a message to start'}
+          </div>
+          {viewingConversation?.messages.map((msg) => (
+            <ChatMessage
+              key={msg.id}
+              msg={msg}
+              scrollToBottom={scrollToBottom}
+            />
+          ))}
+
+          {pendingMsg && (
+            <ChatMessage
+              msg={pendingMsg}
+              scrollToBottom={scrollToBottom}
+              isPending
+              id="pending-msg"
+            />
+          )}
         </div>
-        {viewingConversation?.messages.map((msg) => (
-          <ChatMessage key={msg.id} msg={msg} scrollToBottom={scrollToBottom} />
-        ))}
 
-        {pendingMsg && (
-          <ChatMessage
-            msg={pendingMsg}
-            scrollToBottom={scrollToBottom}
-            isPending
-            id="pending-msg"
-          />
+        {/* chat input */}
+        <div className="flex flex-row items-center pt-8 pb-6 sticky bottom-0 bg-base-100">
+          <textarea
+            className="textarea textarea-bordered w-full"
+            placeholder="Type a message (Shift+Enter to add a new line)"
+            value={inputMsg}
+            onChange={(e) => setInputMsg(e.target.value)}
+            onKeyDown={(e) => {
+              if (e.key === 'Enter' && e.shiftKey) return;
+              if (e.key === 'Enter' && !e.shiftKey) {
+                e.preventDefault();
+                sendNewMessage();
+              }
+            }}
+            id="msg-input"
+            dir="auto"
+          ></textarea>
+          {isGenerating(currConvId) ? (
+            <button
+              className="btn btn-neutral ml-2"
+              onClick={() => stopGenerating(currConvId)}
+            >
+              Stop
+            </button>
+          ) : (
+            <button
+              className="btn btn-primary ml-2"
+              onClick={sendNewMessage}
+              disabled={inputMsg.trim().length === 0}
+            >
+              Send
+            </button>
+          )}
+        </div>
+      </div>
+      <div className="w-full sticky top-[7em] h-[calc(100vh-9em)]">
+        {canvasData?.type === CanvasType.PY_INTERPRETER && (
+          <CanvasPyInterpreter />
         )}
       </div>
-
-      {/* chat input */}
-      <div className="flex flex-row items-center mt-8 mb-6">
-        <textarea
-          className="textarea textarea-bordered w-full"
-          placeholder="Type a message (Shift+Enter to add a new line)"
-          value={inputMsg}
-          onChange={(e) => setInputMsg(e.target.value)}
-          onKeyDown={(e) => {
-            if (e.key === 'Enter' && e.shiftKey) return;
-            if (e.key === 'Enter' && !e.shiftKey) {
-              e.preventDefault();
-              sendNewMessage();
-            }
-          }}
-          id="msg-input"
-          dir="auto"
-        ></textarea>
-        {isGenerating(currConvId) ? (
-          <button
-            className="btn btn-neutral ml-2"
-            onClick={() => stopGenerating(currConvId)}
-          >
-            Stop
-          </button>
-        ) : (
-          <button
-            className="btn btn-primary ml-2"
-            onClick={sendNewMessage}
-            disabled={inputMsg.trim().length === 0}
-          >
-            Send
-          </button>
-        )}
-      </div>
-    </>
+    </div>
   );
 }
diff --git a/examples/server/webui/src/components/Header.tsx b/examples/server/webui/src/components/Header.tsx
index 015264abc..505350313 100644
--- a/examples/server/webui/src/components/Header.tsx
+++ b/examples/server/webui/src/components/Header.tsx
@@ -5,12 +5,11 @@ import { classNames } from '../utils/misc';
 import daisyuiThemes from 'daisyui/src/theming/themes';
 import { THEMES } from '../Config';
 import { useNavigate } from 'react-router';
-import SettingDialog from './SettingDialog';
 
 export default function Header() {
   const navigate = useNavigate();
   const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme());
-  const [showSettingDialog, setShowSettingDialog] = useState(false);
+  const { setShowSettings } = useAppContext();
 
   const setTheme = (theme: string) => {
     StorageUtils.setTheme(theme);
@@ -54,7 +53,7 @@ export default function Header() {
   };
 
   return (
-    <div className="flex flex-row items-center mt-6 mb-6">
+    <div className="flex flex-row items-center pt-6 pb-6 sticky top-0 z-10 bg-base-100">
       {/* open sidebar button */}
       <label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
         <svg
@@ -109,7 +108,7 @@ export default function Header() {
           </ul>
         </div>
         <div className="tooltip tooltip-bottom" data-tip="Settings">
-          <button className="btn" onClick={() => setShowSettingDialog(true)}>
+          <button className="btn" onClick={() => setShowSettings(true)}>
             {/* settings button */}
             <svg
               xmlns="http://www.w3.org/2000/svg"
@@ -172,11 +171,6 @@ export default function Header() {
           </div>
         </div>
       </div>
-
-      <SettingDialog
-        show={showSettingDialog}
-        onClose={() => setShowSettingDialog(false)}
-      />
     </div>
   );
 }
diff --git a/examples/server/webui/src/components/MarkdownDisplay.tsx b/examples/server/webui/src/components/MarkdownDisplay.tsx
index 814920a74..5b7a72591 100644
--- a/examples/server/webui/src/components/MarkdownDisplay.tsx
+++ b/examples/server/webui/src/components/MarkdownDisplay.tsx
@@ -9,8 +9,16 @@ import 'katex/dist/katex.min.css';
 import { classNames, copyStr } from '../utils/misc';
 import { ElementContent, Root } from 'hast';
 import { visit } from 'unist-util-visit';
+import { useAppContext } from '../utils/app.context';
+import { CanvasType } from '../utils/types';
 
-export default function MarkdownDisplay({ content }: { content: string }) {
+export default function MarkdownDisplay({
+  content,
+  isGenerating,
+}: {
+  content: string;
+  isGenerating?: boolean;
+}) {
   const preprocessedContent = useMemo(
     () => preprocessLaTeX(content),
     [content]
@@ -21,7 +29,11 @@ export default function MarkdownDisplay({ content }: { content: string }) {
       rehypePlugins={[rehypeHightlight, rehypeKatex, rehypeCustomCopyButton]}
       components={{
         button: (props) => (
-          <CopyCodeButton {...props} origContent={preprocessedContent} />
+          <CodeBlockButtons
+            {...props}
+            isGenerating={isGenerating}
+            origContent={preprocessedContent}
+          />
         ),
         // note: do not use "pre", "p" or other basic html elements here, it will cause the node to re-render when the message is being generated (this should be a bug with react-markdown, not sure how to fix it)
       }}
@@ -31,11 +43,12 @@ export default function MarkdownDisplay({ content }: { content: string }) {
   );
 }
 
-const CopyCodeButton: React.ElementType<
+const CodeBlockButtons: React.ElementType<
   React.ClassAttributes<HTMLButtonElement> &
     React.HTMLAttributes<HTMLButtonElement> &
-    ExtraProps & { origContent: string }
-> = ({ node, origContent }) => {
+    ExtraProps & { origContent: string; isGenerating?: boolean }
+> = ({ node, origContent, isGenerating }) => {
+  const { config } = useAppContext();
   const startOffset = node?.position?.start.offset ?? 0;
   const endOffset = node?.position?.end.offset ?? 0;
 
@@ -48,14 +61,33 @@ const CopyCodeButton: React.ElementType<
     [origContent, startOffset, endOffset]
   );
 
+  const codeLanguage = useMemo(
+    () =>
+      origContent
+        .substring(startOffset, startOffset + 10)
+        .match(/^```([^\n]+)\n/)?.[1] ?? '',
+    [origContent, startOffset]
+  );
+
+  const canRunCode =
+    !isGenerating &&
+    config.pyIntepreterEnabled &&
+    codeLanguage.startsWith('py');
+
   return (
     <div
       className={classNames({
-        'text-right sticky top-4 mb-2 mr-2 h-0': true,
+        'text-right sticky top-[7em] mb-2 mr-2 h-0': true,
         'display-none': !node?.position,
       })}
     >
       <CopyButton className="badge btn-mini" content={copiedContent} />
+      {canRunCode && (
+        <RunPyCodeButton
+          className="badge btn-mini ml-2"
+          content={copiedContent}
+        />
+      )}
     </div>
   );
 };
@@ -82,6 +114,31 @@ export const CopyButton = ({
   );
 };
 
+export const RunPyCodeButton = ({
+  content,
+  className,
+}: {
+  content: string;
+  className?: string;
+}) => {
+  const { setCanvasData } = useAppContext();
+  return (
+    <>
+      <button
+        className={className}
+        onClick={() =>
+          setCanvasData({
+            type: CanvasType.PY_INTERPRETER,
+            content,
+          })
+        }
+      >
+        ▶️ Run
+      </button>
+    </>
+  );
+};
+
 /**
  * This injects the "button" element before each "pre" element.
  * The actual button will be replaced with a react component in the MarkdownDisplay.
@@ -95,9 +152,7 @@ function rehypeCustomCopyButton() {
         // replace current node
         preNode.properties.visited = 'true';
         node.tagName = 'div';
-        node.properties = {
-          className: 'relative my-4',
-        };
+        node.properties = {};
         // add node for button
         const btnNode: ElementContent = {
           type: 'element',
diff --git a/examples/server/webui/src/components/SettingDialog.tsx b/examples/server/webui/src/components/SettingDialog.tsx
index 5565ab7bb..592b93fa3 100644
--- a/examples/server/webui/src/components/SettingDialog.tsx
+++ b/examples/server/webui/src/components/SettingDialog.tsx
@@ -3,18 +3,27 @@ import { useAppContext } from '../utils/app.context';
 import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config';
 import { isDev } from '../Config';
 import StorageUtils from '../utils/storage';
-import { isBoolean, isNumeric, isString } from '../utils/misc';
+import { classNames, isBoolean, isNumeric, isString } from '../utils/misc';
+import {
+  BeakerIcon,
+  ChatBubbleOvalLeftEllipsisIcon,
+  Cog6ToothIcon,
+  FunnelIcon,
+  HandRaisedIcon,
+  SquaresPlusIcon,
+} from '@heroicons/react/24/outline';
+import { OpenInNewTab } from '../utils/common';
 
 type SettKey = keyof typeof CONFIG_DEFAULT;
 
-const COMMON_SAMPLER_KEYS: SettKey[] = [
+const BASIC_KEYS: SettKey[] = [
   'temperature',
   'top_k',
   'top_p',
   'min_p',
   'max_tokens',
 ];
-const OTHER_SAMPLER_KEYS: SettKey[] = [
+const SAMPLER_KEYS: SettKey[] = [
   'dynatemp_range',
   'dynatemp_exponent',
   'typical_p',
@@ -32,6 +41,223 @@ const PENALTY_KEYS: SettKey[] = [
   'dry_penalty_last_n',
 ];
 
+enum SettingInputType {
+  SHORT_INPUT,
+  LONG_INPUT,
+  CHECKBOX,
+  CUSTOM,
+}
+
+interface SettingFieldInput {
+  type: Exclude<SettingInputType, SettingInputType.CUSTOM>;
+  label: string | React.ReactElement;
+  help?: string | React.ReactElement;
+  key: SettKey;
+}
+
+interface SettingFieldCustom {
+  type: SettingInputType.CUSTOM;
+  key: SettKey;
+  component:
+    | string
+    | React.FC<{
+        value: string | boolean | number;
+        onChange: (value: string) => void;
+      }>;
+}
+
+interface SettingSection {
+  title: React.ReactElement;
+  fields: (SettingFieldInput | SettingFieldCustom)[];
+}
+
+const ICON_CLASSNAME = 'w-4 h-4 mr-1 inline';
+
+const SETTING_SECTIONS: SettingSection[] = [
+  {
+    title: (
+      <>
+        <Cog6ToothIcon className={ICON_CLASSNAME} />
+        General
+      </>
+    ),
+    fields: [
+      {
+        type: SettingInputType.SHORT_INPUT,
+        label: 'API Key',
+        key: 'apiKey',
+      },
+      {
+        type: SettingInputType.LONG_INPUT,
+        label: 'System Message (will be disabled if left empty)',
+        key: 'systemMessage',
+      },
+      ...BASIC_KEYS.map(
+        (key) =>
+          ({
+            type: SettingInputType.SHORT_INPUT,
+            label: key,
+            key,
+          }) as SettingFieldInput
+      ),
+    ],
+  },
+  {
+    title: (
+      <>
+        <FunnelIcon className={ICON_CLASSNAME} />
+        Samplers
+      </>
+    ),
+    fields: [
+      {
+        type: SettingInputType.SHORT_INPUT,
+        label: 'Samplers queue',
+        key: 'samplers',
+      },
+      ...SAMPLER_KEYS.map(
+        (key) =>
+          ({
+            type: SettingInputType.SHORT_INPUT,
+            label: key,
+            key,
+          }) as SettingFieldInput
+      ),
+    ],
+  },
+  {
+    title: (
+      <>
+        <HandRaisedIcon className={ICON_CLASSNAME} />
+        Penalties
+      </>
+    ),
+    fields: PENALTY_KEYS.map((key) => ({
+      type: SettingInputType.SHORT_INPUT,
+      label: key,
+      key,
+    })),
+  },
+  {
+    title: (
+      <>
+        <ChatBubbleOvalLeftEllipsisIcon className={ICON_CLASSNAME} />
+        Reasoning
+      </>
+    ),
+    fields: [
+      {
+        type: SettingInputType.CHECKBOX,
+        label: 'Expand though process by default for generating message',
+        key: 'showThoughtInProgress',
+      },
+      {
+        type: SettingInputType.CHECKBOX,
+        label:
+          'Exclude thought process when sending request to API (Recommended for DeepSeek-R1)',
+        key: 'excludeThoughtOnReq',
+      },
+    ],
+  },
+  {
+    title: (
+      <>
+        <SquaresPlusIcon className={ICON_CLASSNAME} />
+        Advanced
+      </>
+    ),
+    fields: [
+      {
+        type: SettingInputType.CUSTOM,
+        key: 'custom', // dummy key, won't be used
+        component: () => {
+          const debugImportDemoConv = async () => {
+            const res = await fetch('/demo-conversation.json');
+            const demoConv = await res.json();
+            StorageUtils.remove(demoConv.id);
+            for (const msg of demoConv.messages) {
+              StorageUtils.appendMsg(demoConv.id, msg);
+            }
+          };
+          return (
+            <button className="btn" onClick={debugImportDemoConv}>
+              (debug) Import demo conversation
+            </button>
+          );
+        },
+      },
+      {
+        type: SettingInputType.CHECKBOX,
+        label: 'Show tokens per second',
+        key: 'showTokensPerSecond',
+      },
+      {
+        type: SettingInputType.LONG_INPUT,
+        label: (
+          <>
+            Custom JSON config (For more info, refer to{' '}
+            <OpenInNewTab href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md">
+              server documentation
+            </OpenInNewTab>
+            )
+          </>
+        ),
+        key: 'custom',
+      },
+    ],
+  },
+  {
+    title: (
+      <>
+        <BeakerIcon className={ICON_CLASSNAME} />
+        Experimental
+      </>
+    ),
+    fields: [
+      {
+        type: SettingInputType.CUSTOM,
+        key: 'custom', // dummy key, won't be used
+        component: () => (
+          <>
+            <p className="mb-8">
+              Experimental features are not guaranteed to work correctly.
+              <br />
+              <br />
+              If you encounter any problems, create a{' '}
+              <OpenInNewTab href="https://github.com/ggerganov/llama.cpp/issues/new?template=019-bug-misc.yml">
+                Bug (misc.)
+              </OpenInNewTab>{' '}
+              report on Github. Please also specify <b>webui/experimental</b> on
+              the report title and include screenshots.
+              <br />
+              <br />
+              Some features may require packages downloaded from CDN, so they
+              need internet connection.
+            </p>
+          </>
+        ),
+      },
+      {
+        type: SettingInputType.CHECKBOX,
+        label: (
+          <>
+            <b>Enable Python interpreter</b>
+            <br />
+            <small className="text-xs">
+              This feature uses{' '}
+              <OpenInNewTab href="https://pyodide.org">pyodide</OpenInNewTab>,
+              downloaded from CDN. To use this feature, ask the LLM to generate
+              python code inside a markdown code block. You will see a "Run"
+              button on the code block, near the "Copy" button.
+            </small>
+          </>
+        ),
+        key: 'pyIntepreterEnabled',
+      },
+    ],
+  },
+];
+
 export default function SettingDialog({
   show,
   onClose,
@@ -40,6 +266,7 @@ export default function SettingDialog({
   onClose: () => void;
 }) {
   const { config, saveConfig } = useAppContext();
+  const [sectionIdx, setSectionIdx] = useState(0);
 
   // clone the config object to prevent direct mutation
   const [localConfig, setLocalConfig] = useState<typeof CONFIG_DEFAULT>(
@@ -92,181 +319,109 @@ export default function SettingDialog({
     onClose();
   };
 
-  const debugImportDemoConv = async () => {
-    const res = await fetch('/demo-conversation.json');
-    const demoConv = await res.json();
-    StorageUtils.remove(demoConv.id);
-    for (const msg of demoConv.messages) {
-      StorageUtils.appendMsg(demoConv.id, msg);
-    }
-    onClose();
-  };
-
   const onChange = (key: SettKey) => (value: string | boolean) => {
     // note: we do not perform validation here, because we may get incomplete value as user is still typing it
     setLocalConfig({ ...localConfig, [key]: value });
   };
 
   return (
-    <dialog className={`modal ${show ? 'modal-open' : ''}`}>
-      <div className="modal-box">
+    <dialog className={classNames({ modal: true, 'modal-open': show })}>
+      <div className="modal-box w-11/12 max-w-3xl">
         <h3 className="text-lg font-bold mb-6">Settings</h3>
-        <div className="h-[calc(90vh-12rem)] overflow-y-auto">
-          <p className="opacity-40 mb-6">
-            Settings below are saved in browser's localStorage
-          </p>
-
-          <SettingsModalShortInput
-            configKey="apiKey"
-            configDefault={CONFIG_DEFAULT}
-            value={localConfig.apiKey}
-            onChange={onChange('apiKey')}
-          />
-
-          <label className="form-control mb-2">
-            <div className="label">
-              System Message (will be disabled if left empty)
-            </div>
-            <textarea
-              className="textarea textarea-bordered h-24"
-              placeholder={`Default: ${CONFIG_DEFAULT.systemMessage}`}
-              value={localConfig.systemMessage}
-              onChange={(e) => onChange('systemMessage')(e.target.value)}
-            />
-          </label>
-
-          {COMMON_SAMPLER_KEYS.map((key) => (
-            <SettingsModalShortInput
-              key={key}
-              configKey={key}
-              configDefault={CONFIG_DEFAULT}
-              value={localConfig[key]}
-              onChange={onChange(key)}
-            />
-          ))}
-
-          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary className="collapse-title font-bold">
-              Other sampler settings
-            </summary>
-            <div className="collapse-content">
-              <SettingsModalShortInput
-                label="Samplers queue"
-                configKey="samplers"
-                configDefault={CONFIG_DEFAULT}
-                value={localConfig.samplers}
-                onChange={onChange('samplers')}
-              />
-              {OTHER_SAMPLER_KEYS.map((key) => (
-                <SettingsModalShortInput
-                  key={key}
-                  configKey={key}
-                  configDefault={CONFIG_DEFAULT}
-                  value={localConfig[key]}
-                  onChange={onChange(key)}
-                />
-              ))}
-            </div>
-          </details>
-
-          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary className="collapse-title font-bold">
-              Penalties settings
-            </summary>
-            <div className="collapse-content">
-              {PENALTY_KEYS.map((key) => (
-                <SettingsModalShortInput
-                  key={key}
-                  configKey={key}
-                  configDefault={CONFIG_DEFAULT}
-                  value={localConfig[key]}
-                  onChange={onChange(key)}
-                />
-              ))}
-            </div>
-          </details>
-
-          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary className="collapse-title font-bold">
-              Reasoning models
-            </summary>
-            <div className="collapse-content">
-              <div className="flex flex-row items-center mb-2">
-                <input
-                  type="checkbox"
-                  className="checkbox"
-                  checked={localConfig.showThoughtInProgress}
-                  onChange={(e) =>
-                    onChange('showThoughtInProgress')(e.target.checked)
-                  }
-                />
-                <span className="ml-4">
-                  Expand though process by default for generating message
-                </span>
+        <div className="flex flex-col md:flex-row h-[calc(90vh-12rem)]">
+          {/* Left panel, showing sections - Desktop version */}
+          <div className="hidden md:flex flex-col items-stretch pr-4 mr-4 border-r-2 border-base-200">
+            {SETTING_SECTIONS.map((section, idx) => (
+              <div
+                key={idx}
+                className={classNames({
+                  'btn btn-ghost justify-start font-normal w-44 mb-1': true,
+                  'btn-active': sectionIdx === idx,
+                })}
+                onClick={() => setSectionIdx(idx)}
+                dir="auto"
+              >
+                {section.title}
               </div>
-              <div className="flex flex-row items-center mb-2">
-                <input
-                  type="checkbox"
-                  className="checkbox"
-                  checked={localConfig.excludeThoughtOnReq}
-                  onChange={(e) =>
-                    onChange('excludeThoughtOnReq')(e.target.checked)
-                  }
-                />
-                <span className="ml-4">
-                  Exclude thought process when sending request to API
-                  (Recommended for DeepSeek-R1)
-                </span>
-              </div>
-            </div>
-          </details>
+            ))}
+          </div>
 
-          <details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary className="collapse-title font-bold">
-              Advanced config
-            </summary>
-            <div className="collapse-content">
-              {/* this button only shows in dev mode, used to import a demo conversation to test message rendering */}
-              {isDev && (
-                <div className="flex flex-row items-center mb-2">
-                  <button className="btn" onClick={debugImportDemoConv}>
-                    (debug) Import demo conversation
-                  </button>
-                </div>
-              )}
-              <div className="flex flex-row items-center mb-2">
-                <input
-                  type="checkbox"
-                  className="checkbox"
-                  checked={localConfig.showTokensPerSecond}
-                  onChange={(e) =>
-                    onChange('showTokensPerSecond')(e.target.checked)
-                  }
-                />
-                <span className="ml-4">Show tokens per second</span>
-              </div>
-              <label className="form-control mb-2">
-                <div className="label inline">
-                  Custom JSON config (For more info, refer to{' '}
-                  <a
-                    className="underline"
-                    href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md"
-                    target="_blank"
-                    rel="noopener noreferrer"
+          {/* Left panel, showing sections - Mobile version */}
+          <div className="md:hidden flex flex-row gap-2 mb-4">
+            <details className="dropdown">
+              <summary className="btn bt-sm w-full m-1">
+                {SETTING_SECTIONS[sectionIdx].title}
+              </summary>
+              <ul className="menu dropdown-content bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
+                {SETTING_SECTIONS.map((section, idx) => (
+                  <div
+                    key={idx}
+                    className={classNames({
+                      'btn btn-ghost justify-start font-normal': true,
+                      'btn-active': sectionIdx === idx,
+                    })}
+                    onClick={() => setSectionIdx(idx)}
+                    dir="auto"
                   >
-                    server documentation
-                  </a>
-                  )
-                </div>
-                <textarea
-                  className="textarea textarea-bordered h-24"
-                  placeholder='Example: { "mirostat": 1, "min_p": 0.1 }'
-                  value={localConfig.custom}
-                  onChange={(e) => onChange('custom')(e.target.value)}
-                />
-              </label>
-            </div>
-          </details>
+                    {section.title}
+                  </div>
+                ))}
+              </ul>
+            </details>
+          </div>
+
+          {/* Right panel, showing setting fields */}
+          <div className="grow overflow-y-auto px-4">
+            {SETTING_SECTIONS[sectionIdx].fields.map((field, idx) => {
+              const key = `${sectionIdx}-${idx}`;
+              if (field.type === SettingInputType.SHORT_INPUT) {
+                return (
+                  <SettingsModalShortInput
+                    key={key}
+                    configKey={field.key}
+                    value={localConfig[field.key]}
+                    onChange={onChange(field.key)}
+                    label={field.label as string}
+                  />
+                );
+              } else if (field.type === SettingInputType.LONG_INPUT) {
+                return (
+                  <SettingsModalLongInput
+                    key={key}
+                    configKey={field.key}
+                    value={localConfig[field.key].toString()}
+                    onChange={onChange(field.key)}
+                    label={field.label as string}
+                  />
+                );
+              } else if (field.type === SettingInputType.CHECKBOX) {
+                return (
+                  <SettingsModalCheckbox
+                    key={key}
+                    configKey={field.key}
+                    value={!!localConfig[field.key]}
+                    onChange={onChange(field.key)}
+                    label={field.label as string}
+                  />
+                );
+              } else if (field.type === SettingInputType.CUSTOM) {
+                return (
+                  <div key={key} className="mb-2">
+                    {typeof field.component === 'string'
+                      ? field.component
+                      : field.component({
+                          value: localConfig[field.key],
+                          onChange: onChange(field.key),
+                        })}
+                  </div>
+                );
+              }
+            })}
+
+            <p className="opacity-40 mb-6 text-sm mt-8">
+              Settings are saved in browser's localStorage
+            </p>
+          </div>
         </div>
 
         <div className="modal-action">
@@ -285,37 +440,97 @@ export default function SettingDialog({
   );
 }
 
-function SettingsModalShortInput({
+function SettingsModalLongInput({
   configKey,
-  configDefault,
   value,
   onChange,
   label,
 }: {
   configKey: SettKey;
-  configDefault: typeof CONFIG_DEFAULT;
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  value: any;
+  value: string;
   onChange: (value: string) => void;
   label?: string;
 }) {
   return (
-    <label className="input input-bordered join-item grow flex items-center gap-2 mb-2">
-      <div className="dropdown dropdown-hover">
-        <div tabIndex={0} role="button" className="font-bold">
-          {label || configKey}
-        </div>
-        <div className="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
-          {CONFIG_INFO[configKey] ?? '(no help message available)'}
-        </div>
-      </div>
-      <input
-        type="text"
-        className="grow"
-        placeholder={`Default: ${configDefault[configKey] || 'none'}`}
+    <label className="form-control mb-2">
+      <div className="label inline">{label || configKey}</div>
+      <textarea
+        className="textarea textarea-bordered h-24"
+        placeholder={`Default: ${CONFIG_DEFAULT[configKey] || 'none'}`}
         value={value}
         onChange={(e) => onChange(e.target.value)}
       />
     </label>
   );
 }
+
+function SettingsModalShortInput({
+  configKey,
+  value,
+  onChange,
+  label,
+}: {
+  configKey: SettKey;
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  value: any;
+  onChange: (value: string) => void;
+  label?: string;
+}) {
+  const helpMsg = CONFIG_INFO[configKey];
+
+  return (
+    <>
+      {/* on mobile, we simply show the help message here */}
+      {helpMsg && (
+        <div className="block md:hidden mb-1">
+          <b>{label || configKey}</b>
+          <br />
+          <p className="text-xs">{helpMsg}</p>
+        </div>
+      )}
+      <label className="input input-bordered join-item grow flex items-center gap-2 mb-2">
+        <div className="dropdown dropdown-hover">
+          <div tabIndex={0} role="button" className="font-bold hidden md:block">
+            {label || configKey}
+          </div>
+          {helpMsg && (
+            <div className="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
+              {helpMsg}
+            </div>
+          )}
+        </div>
+        <input
+          type="text"
+          className="grow"
+          placeholder={`Default: ${CONFIG_DEFAULT[configKey] || 'none'}`}
+          value={value}
+          onChange={(e) => onChange(e.target.value)}
+        />
+      </label>
+    </>
+  );
+}
+
+function SettingsModalCheckbox({
+  configKey,
+  value,
+  onChange,
+  label,
+}: {
+  configKey: SettKey;
+  value: boolean;
+  onChange: (value: boolean) => void;
+  label: string;
+}) {
+  return (
+    <div className="flex flex-row items-center mb-2">
+      <input
+        type="checkbox"
+        className="toggle"
+        checked={value}
+        onChange={(e) => onChange(e.target.checked)}
+      />
+      <span className="ml-4">{label || configKey}</span>
+    </div>
+  );
+}
diff --git a/examples/server/webui/src/index.scss b/examples/server/webui/src/index.scss
index 9253b6a02..314b68232 100644
--- a/examples/server/webui/src/index.scss
+++ b/examples/server/webui/src/index.scss
@@ -45,6 +45,9 @@
 /* Highlight.js */
 [data-color-scheme='light'] {
   @include meta.load-css('highlight.js/styles/stackoverflow-light');
+  .dark-color {
+    @apply bg-base-content text-base-100;
+  }
 }
 [data-color-scheme='dark'] {
   @include meta.load-css('highlight.js/styles/stackoverflow-dark');
@@ -52,6 +55,9 @@
 [data-color-scheme='auto'] {
   @media (prefers-color-scheme: light) {
     @include meta.load-css('highlight.js/styles/stackoverflow-light');
+    .dark-color {
+      @apply bg-base-content text-base-100;
+    }
   }
   @media (prefers-color-scheme: dark) {
     @include meta.load-css('highlight.js/styles/stackoverflow-dark');
diff --git a/examples/server/webui/src/utils/app.context.tsx b/examples/server/webui/src/utils/app.context.tsx
index d50f825c3..af6bd885f 100644
--- a/examples/server/webui/src/utils/app.context.tsx
+++ b/examples/server/webui/src/utils/app.context.tsx
@@ -1,5 +1,11 @@
 import React, { createContext, useContext, useEffect, useState } from 'react';
-import { APIMessage, Conversation, Message, PendingMessage } from './types';
+import {
+  APIMessage,
+  CanvasData,
+  Conversation,
+  Message,
+  PendingMessage,
+} from './types';
 import StorageUtils from './storage';
 import {
   filterThoughtFromMsgs,
@@ -10,6 +16,7 @@ import { BASE_URL, CONFIG_DEFAULT, isDev } from '../Config';
 import { matchPath, useLocation } from 'react-router';
 
 interface AppContextValue {
+  // conversations and messages
   viewingConversation: Conversation | null;
   pendingMessages: Record<Conversation['id'], PendingMessage>;
   isGenerating: (convId: string) => boolean;
@@ -26,8 +33,15 @@ interface AppContextValue {
     onChunk?: CallbackGeneratedChunk
   ) => Promise<void>;
 
+  // canvas
+  canvasData: CanvasData | null;
+  setCanvasData: (data: CanvasData | null) => void;
+
+  // config
   config: typeof CONFIG_DEFAULT;
   saveConfig: (config: typeof CONFIG_DEFAULT) => void;
+  showSettings: boolean;
+  setShowSettings: (show: boolean) => void;
 }
 
 // for now, this callback is only used for scrolling to the bottom of the chat
@@ -54,8 +68,13 @@ export const AppContextProvider = ({
     Record<Conversation['id'], AbortController>
   >({});
   const [config, setConfig] = useState(StorageUtils.getConfig());
+  const [canvasData, setCanvasData] = useState<CanvasData | null>(null);
+  const [showSettings, setShowSettings] = useState(false);
 
+  // handle change when the convId from URL is changed
   useEffect(() => {
+    // also reset the canvas data
+    setCanvasData(null);
     const handleConversationChange = (changedConvId: string) => {
       if (changedConvId !== convId) return;
       setViewingConversation(StorageUtils.getOneConversation(convId));
@@ -292,8 +311,12 @@ export const AppContextProvider = ({
         sendMessage,
         stopGenerating,
         replaceMessageAndGenerate,
+        canvasData,
+        setCanvasData,
         config,
         saveConfig,
+        showSettings,
+        setShowSettings,
       }}
     >
       {children}
diff --git a/examples/server/webui/src/utils/common.tsx b/examples/server/webui/src/utils/common.tsx
new file mode 100644
index 000000000..09b08b5c9
--- /dev/null
+++ b/examples/server/webui/src/utils/common.tsx
@@ -0,0 +1,38 @@
+export const XCloseButton: React.ElementType<
+  React.ClassAttributes<HTMLButtonElement> &
+    React.HTMLAttributes<HTMLButtonElement>
+> = ({ className, ...props }) => (
+  <button className={`btn btn-square btn-sm ${className ?? ''}`} {...props}>
+    <svg
+      xmlns="http://www.w3.org/2000/svg"
+      className="h-6 w-6"
+      fill="none"
+      viewBox="0 0 24 24"
+      stroke="currentColor"
+    >
+      <path
+        strokeLinecap="round"
+        strokeLinejoin="round"
+        strokeWidth="2"
+        d="M6 18L18 6M6 6l12 12"
+      />
+    </svg>
+  </button>
+);
+
+export const OpenInNewTab = ({
+  href,
+  children,
+}: {
+  href: string;
+  children: string;
+}) => (
+  <a
+    className="underline"
+    href={href}
+    target="_blank"
+    rel="noopener noreferrer"
+  >
+    {children}
+  </a>
+);
diff --git a/examples/server/webui/src/utils/misc.ts b/examples/server/webui/src/utils/misc.ts
index 0c887ee83..e3153fff5 100644
--- a/examples/server/webui/src/utils/misc.ts
+++ b/examples/server/webui/src/utils/misc.ts
@@ -85,3 +85,6 @@ export function classNames(classes: Record<string, boolean>): string {
     .map(([key, _]) => key)
     .join(' ');
 }
+
+export const delay = (ms: number) =>
+  new Promise((resolve) => setTimeout(resolve, ms));
diff --git a/examples/server/webui/src/utils/types.ts b/examples/server/webui/src/utils/types.ts
index d04c6e3dc..7cd12b40a 100644
--- a/examples/server/webui/src/utils/types.ts
+++ b/examples/server/webui/src/utils/types.ts
@@ -23,3 +23,14 @@ export interface Conversation {
 export type PendingMessage = Omit<Message, 'content'> & {
   content: string | null;
 };
+
+export enum CanvasType {
+  PY_INTERPRETER,
+}
+
+export interface CanvasPyInterpreter {
+  type: CanvasType.PY_INTERPRETER;
+  content: string;
+}
+
+export type CanvasData = CanvasPyInterpreter;
diff --git a/examples/server/webui/vite.config.ts b/examples/server/webui/vite.config.ts
index 184fdc6b9..b8a0f03d9 100644
--- a/examples/server/webui/vite.config.ts
+++ b/examples/server/webui/vite.config.ts
@@ -72,5 +72,9 @@ export default defineConfig({
     proxy: {
       '/v1': 'http://localhost:8080',
     },
+    headers: {
+      'Cross-Origin-Embedder-Policy': 'require-corp',
+      'Cross-Origin-Opener-Policy': 'same-origin',
+    },
   },
 });

From 98f6b0fd1ea88ce33f4fcd1775d9a463067156ac Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 9 Feb 2025 01:43:51 -0600
Subject: [PATCH 371/376] vulkan: account for lookup tables when checking
 shared memory size (#11502)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 610 +++++++++++++--------------
 1 file changed, 303 insertions(+), 307 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4c962fde9..d32ba4efb 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -184,12 +184,12 @@ struct vk_device_struct {
 
     size_t idx;
 
-    bool mul_mat_l;
-    bool mul_mat_m;
-    bool mul_mat_s;
-    bool mul_mat_id_l;
-    bool mul_mat_id_m;
-    bool mul_mat_id_s;
+    bool mul_mat_l[GGML_TYPE_COUNT];
+    bool mul_mat_m[GGML_TYPE_COUNT];
+    bool mul_mat_s[GGML_TYPE_COUNT];
+    bool mul_mat_id_l[GGML_TYPE_COUNT];
+    bool mul_mat_id_m[GGML_TYPE_COUNT];
+    bool mul_mat_id_s[GGML_TYPE_COUNT];
 
     // set to true to indicate that some shaders need to be compiled after the dryrun
     bool need_compiles {};
@@ -1378,7 +1378,33 @@ static std::array<uint32_t, 2> fa_rows_cols(uint32_t D, uint32_t clamp, ggml_typ
     return {64, 64};
 };
 
-static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id) {
+static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {
+
+    uint32_t lut_size = 0;
+    switch (src0_type) {
+    case GGML_TYPE_IQ2_XXS:
+        lut_size = 8*256;
+        break;
+    case GGML_TYPE_IQ2_XS:
+        lut_size = 8*512;
+        break;
+    case GGML_TYPE_IQ2_S:
+        lut_size = 8*1024;
+        break;
+    case GGML_TYPE_IQ3_XXS:
+        lut_size = 4*256;
+        break;
+    case GGML_TYPE_IQ3_S:
+        lut_size = 4*512;
+        break;
+    case GGML_TYPE_IQ4_NL:
+    case GGML_TYPE_IQ4_XS:
+        lut_size = 4*16;
+        break;
+    default:
+        break;
+    }
+
     // Needs to be kept up to date on shader changes
     const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
     const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
@@ -1388,7 +1414,13 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     const uint32_t mmid_row_ids = mul_mat_id ? 3072 * sizeof(uint32_t) : 0;
     const uint32_t coopmat_stage = device->coopmat_support ? warptile[7] * warptile[8] / warps * sizeof(float) : 0;
 
-    return (load_bufs + mmid_row_ids + coopmat_stage) <= device->properties.limits.maxComputeSharedMemorySize;
+    const uint32_t total_size = load_bufs + mmid_row_ids + coopmat_stage + lut_size;
+    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
+
+    VK_LOG_DEBUG("ggml_vk_matmul_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), "
+                 "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", supported=" << supported);
+
+    return supported;
 }
 
 static void ggml_vk_load_shaders(vk_device& device) {
@@ -1472,62 +1504,32 @@ static void ggml_vk_load_shaders(vk_device& device) {
         m_align =  64;
         s_align =  32;
 
-        // Fallback to smaller sizes if there's not enough shared memory. Given the current shaders
-        // and tile sizes, this should handle 16KB, 32KB, and 48KB+.
-        // This logic doesn't explicitly account for the 12KB row_ids in the mul_mat_mat_id shaders.
-        // But the numbers happen to work out for 32KB shared memory size that when using the medium
-        // size there's enough room for everything, and we assert for this.
-        uint32_t shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
-        if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
-            l_warptile = m_warptile;
-            l_wg_denoms = m_wg_denoms;
-            shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
-            GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
-        }
-        if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
-            // assert mul_mat_mat_id shaders will fit.
-            GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
-        }
-
-        shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float);
-        if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
-            if (device->properties.limits.maxComputeSharedMemorySize == 32768) {
-                l_warptile_mmq = m_warptile_mmq;
-                l_mmq_wg_denoms = m_mmq_wg_denoms;
-            } else {
-                l_warptile_mmq = s_warptile_mmq;
-                l_mmq_wg_denoms = s_mmq_wg_denoms;
+        for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) {
+            ggml_type t = (ggml_type)i;
+            // Disable medium and large matrix multiplication if not enough shared memory is available
+            // Check mmq warptiles as the largest configuration
+            // Throw an error if not enough for any matrix multiplication is available
+            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, false, t)) {
+                std::cerr << "ggml_vulkan: Error: Shared memory size too small for matrix multiplication." << std::endl;
+                throw std::runtime_error("Shared memory size too small for matrix multiplication.");
+            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, false, t)) {
+                device->mul_mat_m[i] = false;
+                device->mul_mat_l[i] = false;
+            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, false, t)) {
+                device->mul_mat_l[i] = false;
             }
-            shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float);
-            GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
-        }
-        if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
-            // assert mul_mat_mat_id shaders will fit.
-            GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
-        }
-        // Disable medium and large matrix multiplication if not enough shared memory is available
-        // Check mmq warptiles as the largest configuration
-        // Throw an error if not enough for any matrix multiplication is available
-        if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, false)) {
-            std::cerr << "ggml_vulkan: Error: Shared memory size too small for matrix multiplication." << std::endl;
-            throw std::runtime_error("Shared memory size too small for matrix multiplication.");
-        } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, false)) {
-            device->mul_mat_m = false;
-            device->mul_mat_l = false;
-        } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, false)) {
-            device->mul_mat_l = false;
-        }
 
-        // Disable mul_mat_id if not enough shared memory is available
-        if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, true)) {
-            device->mul_mat_id_s = false;
-            device->mul_mat_id_m = false;
-            device->mul_mat_id_l = false;
-        } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, true)) {
-            device->mul_mat_id_m = false;
-            device->mul_mat_id_l = false;
-        } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, true)) {
-            device->mul_mat_id_l = false;
+            // Disable mul_mat_id if not enough shared memory is available
+            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, true, t)) {
+                device->mul_mat_id_s[i] = false;
+                device->mul_mat_id_m[i] = false;
+                device->mul_mat_id_l[i] = false;
+            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, true, t)) {
+                device->mul_mat_id_m[i] = false;
+                device->mul_mat_id_l[i] = false;
+            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, true, t)) {
+                device->mul_mat_id_l[i] = false;
+            }
         }
     }
 
@@ -1684,119 +1686,116 @@ static void ggml_vk_load_shaders(vk_device& device) {
 #if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     if (device->coopmat_support) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
-#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        if (device->mul_mat ## ID ## _l) \
+#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
-        if (device->mul_mat ## ID ## _l) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \
 
         // Create 2 variants, {f16,f32} accumulator
-#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->coopmat_acc_f16_support) { \
-            CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+            CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         } \
         if (device->coopmat_acc_f32_support) { \
-            CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+            CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         } \
 
-        CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
 
         if (device->coopmat_acc_f16_support) {
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         } else {
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-            CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
-        // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
-        if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
-            CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM2(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 
-            if (device->coopmat_acc_f16_support) {
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        if (device->coopmat_acc_f16_support) {
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            } else {
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        } else {
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-                CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            }
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
         }
 #undef CREATE_MM2
 #undef CREATE_MM
@@ -1804,141 +1803,135 @@ static void ggml_vk_load_shaders(vk_device& device) {
 #endif  // defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     if (device->fp16) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
-#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        if (device->mul_mat ## ID ## _l) \
+#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _l) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
 
         // Create 2 variants, {f16,f32} accumulator
-#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
 
-        CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f16acc, matmul_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f16acc,  matmul_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f16acc,   matmul_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f16acc, matmul_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f16acc,   matmul_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f16acc,  matmul_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f16acc,  matmul_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-        // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
-        if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
-            CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM2(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        }
+        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MM
     } else {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
-#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        if (device->mul_mat ## ID ## _l) \
+#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _l) \
+        if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align);   \
-        if (device->mul_mat ## ID ## _m) \
+        if (device->mul_mat ## ID ## _m[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align);   \
-        if (device->mul_mat ## ID ## _s) \
+        if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
 
-        CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
-        // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines.
-        if (device->mul_mat_id_s || device->mul_mat_id_m || device->mul_mat_id_l) {
-            CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM(pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-            CREATE_MM(pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        }
+        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM
     }
 
@@ -2623,34 +2616,36 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         // Shaders
         // Disable matmul tile sizes early if performance low or not supported
-        switch (device->vendor_id) {
+        for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) {
+            switch (device->vendor_id) {
 #ifndef GGML_VULKAN_RUN_TESTS
-        case VK_VENDOR_ID_AMD:
-        case VK_VENDOR_ID_INTEL:
-            device->mul_mat_l = false;
-            device->mul_mat_m = true;
-            device->mul_mat_s = true;
-            device->mul_mat_id_l = false;
-            device->mul_mat_id_m = true;
-            device->mul_mat_id_s = true;
-            break;
-        case VK_VENDOR_ID_APPLE:
-            device->mul_mat_l = false;
-            device->mul_mat_m = true;
-            device->mul_mat_s = false;
-            device->mul_mat_id_l = false;
-            device->mul_mat_id_m = true;
-            device->mul_mat_id_s = false;
-            break;
+            case VK_VENDOR_ID_AMD:
+            case VK_VENDOR_ID_INTEL:
+                device->mul_mat_l[i] = false;
+                device->mul_mat_m[i] = true;
+                device->mul_mat_s[i] = true;
+                device->mul_mat_id_l[i] = false;
+                device->mul_mat_id_m[i] = true;
+                device->mul_mat_id_s[i] = true;
+                break;
+            case VK_VENDOR_ID_APPLE:
+                device->mul_mat_l[i] = false;
+                device->mul_mat_m[i] = true;
+                device->mul_mat_s[i] = false;
+                device->mul_mat_id_l[i] = false;
+                device->mul_mat_id_m[i] = true;
+                device->mul_mat_id_s[i] = false;
+                break;
 #endif
-        default:
-            device->mul_mat_l = true;
-            device->mul_mat_m = true;
-            device->mul_mat_s = true;
-            device->mul_mat_id_l = true;
-            device->mul_mat_id_m = true;
-            device->mul_mat_id_s = true;
-            break;
+            default:
+                device->mul_mat_l[i] = true;
+                device->mul_mat_m[i] = true;
+                device->mul_mat_s[i] = true;
+                device->mul_mat_id_l[i] = true;
+                device->mul_mat_id_m[i] = true;
+                device->mul_mat_id_s[i] = true;
+                break;
+            }
         }
 
         ggml_vk_load_shaders(device);
@@ -3756,31 +3751,31 @@ static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, int m, int
     return split_k;
 }
 
-static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")");
+static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned, ggml_type src0_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ")");
 
     if (ctx->device->coopmat2) {
-        if ((ctx->device->mul_mat_l && (m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) || (!ctx->device->mul_mat_m && !ctx->device->mul_mat_s)) {
+        if ((ctx->device->mul_mat_l[src0_type] && (m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
             return aligned ? mmp->a_l : mmp->l;
         }
-        if ((ctx->device->mul_mat_m && (m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) || !ctx->device->mul_mat_s) {
+        if ((ctx->device->mul_mat_m[src0_type] && (m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) || !ctx->device->mul_mat_s[src0_type]) {
             return aligned ? mmp->a_m : mmp->m;
         }
         return aligned ? mmp->a_s : mmp->s;
     }
 
-    if ((ctx->device->mul_mat_s && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_m && !ctx->device->mul_mat_l)) {
+    if ((ctx->device->mul_mat_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_l[src0_type])) {
         return aligned ? mmp->a_s : mmp->s;
     }
-    if ((ctx->device->mul_mat_m && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_l) {
+    if ((ctx->device->mul_mat_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_l[src0_type]) {
         return aligned ? mmp->a_m : mmp->m;
     }
     return aligned ? mmp->a_l : mmp->l;
 }
 
-static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")");
-    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align;
+static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ")");
+    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true, src0_type)->align;
 }
 
 static void ggml_vk_matmul(
@@ -3807,31 +3802,31 @@ static void ggml_vk_matmul(
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
 }
 
-static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")");
+static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned, ggml_type src0_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ")");
 
     if (ctx->device->coopmat2) {
-        if ((ctx->device->mul_mat_id_l && (m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) || (!ctx->device->mul_mat_id_m && !ctx->device->mul_mat_id_s)) {
+        if ((ctx->device->mul_mat_id_l[src0_type] && (m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_s[src0_type])) {
             return aligned ? mmp->a_l : mmp->l;
         }
-        if ((ctx->device->mul_mat_id_m && (m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) || !ctx->device->mul_mat_id_s) {
+        if ((ctx->device->mul_mat_id_m[src0_type] && (m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) || !ctx->device->mul_mat_id_s[src0_type]) {
             return aligned ? mmp->a_m : mmp->m;
         }
         return aligned ? mmp->a_s : mmp->s;
     }
 
-    if ((ctx->device->mul_mat_id_s && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_id_m && !ctx->device->mul_mat_id_l)) {
+    if ((ctx->device->mul_mat_id_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_l[src0_type])) {
         return aligned ? mmp->a_s : mmp->s;
     }
-    if ((ctx->device->mul_mat_id_m && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_id_l) {
+    if ((ctx->device->mul_mat_id_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_id_l[src0_type]) {
         return aligned ? mmp->a_m : mmp->m;
     }
     return aligned ? mmp->a_l : mmp->l;
 }
 
-static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")");
-    return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true)->align;
+static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ")");
+    return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type)->align;
 }
 
 static void ggml_vk_matmul_id(
@@ -4012,10 +4007,10 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     const int y_ne = ne11 * ne10;
     const int d_ne = ne11 * ne01;
 
-    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? GGML_TYPE_F16 : src0->type));
     const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
 
-    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? GGML_TYPE_F16 : src0->type);
 
     const uint32_t split_k = ggml_vk_guess_split_k(ctx, ne01, ne11, ne10, pipeline);
 
@@ -4594,10 +4589,10 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     const uint64_t y_ne = ne11 * ne10;
     const uint64_t d_ne = ne21 * ne20;
 
-    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1));
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? GGML_TYPE_F16 : src0->type));
     const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 8;
 
-    vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned);
+    vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? GGML_TYPE_F16 : src0->type);
 
     const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
     const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
@@ -8036,13 +8031,14 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             {
+                ggml_type src0_type = op->src[0]->type;
                 ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
                 const vk_device& device = ggml_vk_get_device(ctx->device);
-                if (op->op == GGML_OP_MUL_MAT_ID && !device->mul_mat_id_s && !device->mul_mat_id_m && !device->mul_mat_id_l) {
+                if (op->op == GGML_OP_MUL_MAT_ID && !device->mul_mat_id_s[src0_type] && !device->mul_mat_id_m[src0_type] && !device->mul_mat_id_l[src0_type]) {
                     // If there's not enough shared memory for row_ids and the result tile, fallback to CPU
                     return false;
                 }
-                switch (op->src[0]->type) {
+                switch (src0_type) {
                     case GGML_TYPE_F32:
                     case GGML_TYPE_F16:
                     case GGML_TYPE_Q4_0:

From 19d3c8293b1f61acbe2dab1d49a17950fd788a4a Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Sun, 9 Feb 2025 10:34:49 +0000
Subject: [PATCH 372/376] There's a better way of clearing lines (#11756)

Use the ANSI escape code for clearing a line.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
---
 common/log.h         |  1 +
 examples/run/run.cpp | 15 ++++-----------
 2 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/common/log.h b/common/log.h
index 85dd4393b..4ebc6314b 100644
--- a/common/log.h
+++ b/common/log.h
@@ -2,6 +2,7 @@
 
 #include "ggml.h" // for ggml_log_level
 
+#define LOG_CLR_TO_EOL  "\033[K\r"
 #define LOG_COL_DEFAULT "\033[0m"
 #define LOG_COL_BOLD    "\033[1m"
 #define LOG_COL_RED     "\033[31m"
diff --git a/examples/run/run.cpp b/examples/run/run.cpp
index eab60cad1..9362da220 100644
--- a/examples/run/run.cpp
+++ b/examples/run/run.cpp
@@ -535,8 +535,7 @@ class HttpClient {
 
     static void print_progress(const std::string & progress_prefix, const std::string & progress_bar,
                                const std::string & progress_suffix) {
-        printe("\r%*s\r%s%s| %s", get_terminal_width(), " ", progress_prefix.c_str(), progress_bar.c_str(),
-               progress_suffix.c_str());
+        printe("\r" LOG_CLR_TO_EOL "%s%s| %s", progress_prefix.c_str(), progress_bar.c_str(), progress_suffix.c_str());
     }
     // Function to write data to a file
     static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
@@ -797,16 +796,13 @@ class LlamaData {
     llama_model_ptr initialize_model(Opt & opt) {
         ggml_backend_load_all();
         resolve_model(opt.model_);
-        printe(
-            "\r%*s"
-            "\rLoading model",
-            get_terminal_width(), " ");
+        printe("\r" LOG_CLR_TO_EOL "Loading model");
         llama_model_ptr model(llama_model_load_from_file(opt.model_.c_str(), opt.model_params));
         if (!model) {
             printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
 
-        printe("\r%*s\r", static_cast<int>(sizeof("Loading model")), " ");
+        printe("\r" LOG_CLR_TO_EOL);
         return model;
     }
 
@@ -969,10 +965,7 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
 static int read_user_input(std::string & user_input) {
     static const char * prompt_prefix = "> ";
 #ifdef WIN32
-    printf(
-        "\r%*s"
-        "\r" LOG_COL_DEFAULT "%s",
-        get_terminal_width(), " ", prompt_prefix);
+    printf("\r" LOG_CLR_TO_EOL LOG_COL_DEFAULT "%s", prompt_prefix);
 
     std::getline(std::cin, user_input);
     if (std::cin.eof()) {

From b044a0fe3ca0cbef9dd041edce3ebda8c501fae4 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Mon, 10 Feb 2025 03:08:22 -0300
Subject: [PATCH 373/376] vulkan: add environment variable
 GGML_VK_PREFER_HOST_MEMORY to avoid VRAM allocation (#11592)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index d32ba4efb..512d3341e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -167,6 +167,7 @@ struct vk_device_struct {
     uint32_t subgroup_size;
     uint32_t shader_core_count;
     bool uma;
+    bool prefer_host_memory;
     bool float_controls_rte_fp16;
 
     bool subgroup_size_control;
@@ -1294,7 +1295,9 @@ static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk:
 static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
     vk_buffer buf;
     try {
-        if (device->uma) {
+        if (device->prefer_host_memory) {
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        } else if (device->uma) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
         } else {
@@ -2199,6 +2202,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device->physical_device = physical_devices[dev_num];
         const std::vector<vk::ExtensionProperties> ext_props = device->physical_device.enumerateDeviceExtensionProperties();
 
+        const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY");
+        device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr;
+
         bool fp16_storage = false;
         bool fp16_compute = false;
         bool maintenance4_support = false;

From c2a67efe38e6782c0217e1de3edc68de6951612b Mon Sep 17 00:00:00 2001
From: Danny Milosavljevic <dannym@friendly-machines.com>
Date: Mon, 10 Feb 2025 07:17:21 +0100
Subject: [PATCH 374/376] vulkan: Make Vulkan optional at runtime (#11493).
 (#11494)

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
---
 ggml/include/ggml-vulkan.h           |  2 --
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 16 ++++++++++------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h
index 53cdba072..ed5ea5f79 100644
--- a/ggml/include/ggml-vulkan.h
+++ b/ggml/include/ggml-vulkan.h
@@ -10,8 +10,6 @@ extern "C" {
 #define GGML_VK_NAME "Vulkan"
 #define GGML_VK_MAX_DEVICES 16
 
-GGML_BACKEND_API void ggml_vk_instance_init(void);
-
 // backend API
 GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num);
 
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 512d3341e..bffe95086 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2793,14 +2793,12 @@ static void ggml_vk_print_gpu_info(size_t idx) {
 static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
 
-void ggml_vk_instance_init() {
+static void ggml_vk_instance_init() {
     if (vk_instance_initialized) {
         return;
     }
     VK_LOG_DEBUG("ggml_vk_instance_init()");
 
-    vk_instance_initialized = true;
-
     uint32_t api_version = vk::enumerateInstanceVersion();
 
     if (api_version < VK_API_VERSION_1_2) {
@@ -2851,6 +2849,7 @@ void ggml_vk_instance_init() {
         GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n");
     }
     vk_instance.instance = vk::createInstance(instance_create_info);
+    vk_instance_initialized = true;
 
     size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
 
@@ -2875,7 +2874,7 @@ void ggml_vk_instance_init() {
         // Make sure at least one device exists
         if (devices.empty()) {
             std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
-            GGML_ABORT("fatal error");
+            return;
         }
 
         // Default to using all dedicated GPUs
@@ -8350,8 +8349,13 @@ ggml_backend_reg_t ggml_backend_vk_reg() {
         /* .iface       = */ ggml_backend_vk_reg_i,
         /* .context     = */ nullptr,
     };
-
-    return &reg;
+    try {
+        ggml_vk_instance_init();
+        return &reg;
+    } catch (const vk::SystemError& e) {
+        VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: System error: " << e.what());
+        return nullptr;
+    }
 }
 
 // Extension availability

From 9ac3457b39c78c5eb103280e08fb6163642484ff Mon Sep 17 00:00:00 2001
From: pascal-lc <49066376+pascal-lc@users.noreply.github.com>
Date: Mon, 10 Feb 2025 16:05:57 +0800
Subject: [PATCH 375/376] Update README.md [no ci] (#11781)

typo: `\` -> `/`
Change the UNIX path separator to` \`.
---
 examples/main/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/main/README.md b/examples/main/README.md
index 46f92eb7a..ceaed42f6 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -37,7 +37,7 @@ Once downloaded, place your model in the models folder in llama.cpp.
 
 ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 ```bash
-./llama-cli -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
 ### Windows:

From d7b31a9d84297b493a61c9a8ee3a458e7ccc64a7 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Mon, 10 Feb 2025 09:34:09 +0000
Subject: [PATCH 376/376] sync: minja
 (https://github.com/google/minja/commit/a72057e5190de2c612d4598bb10b4bfd0f53011f)
 (#11774)

---
 common/chat-template.hpp | 28 +++++++++++++++++++++-------
 common/minja.hpp         | 33 ++++++++++++++++++++++++---------
 2 files changed, 45 insertions(+), 16 deletions(-)

diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index 0e88fb361..882ba41bd 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -249,16 +249,30 @@ class chat_template {
                     inputs.add_generation_prompt = false;
                     full = apply(inputs);
                 }
-
-                if (full.find(prefix) != 0) {
-                    if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
-                        prefix = prefix.substr(0, prefix.size() - eos_token_.size());
+                auto eos_pos_last = full.rfind(eos_token_);
+                if (eos_pos_last == prefix.size() - eos_token_.size() ||
+                      (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) {
+                    full = full.substr(0, eos_pos_last);
+                }
+                size_t common_prefix_length = 0;
+                for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) {
+                    if (prefix[i] != full[i]) {
+                        break;
                     }
+                    if (prefix[i] == '<') {
+                        // DeepSeek R1's template (as of 20250209) adds a trailing <think> if add_generation_prompt,
+                        // but it removes thinking tags for past messages.
+                        // The prefix and full strings diverge at <think> vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <.
+                        continue;
+                    }
+                    common_prefix_length = i + 1;
                 }
-                if (full.find(prefix) != 0) {
+                auto example = full.substr(common_prefix_length);
+                if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) {
                     fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
+                } else {
+                    tool_call_example_ = example;
                 }
-                tool_call_example_ = full.substr(prefix.size());
             }
         } catch (const std::exception & e) {
             fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
@@ -363,7 +377,7 @@ class chat_template {
             if (polyfill_tools) {
                 adjusted_messages = add_system(inputs.messages,
                     "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
-                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
+                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n"));
             } else {
                 adjusted_messages = inputs.messages;
             }
diff --git a/common/minja.hpp b/common/minja.hpp
index c304b5c66..c58dd66e0 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -1385,6 +1385,13 @@ static std::string strip(const std::string & s) {
   return s.substr(start, end - start + 1);
 }
 
+static std::string capitalize(const std::string & s) {
+  if (s.empty()) return s;
+  auto result = s;
+  result[0] = std::toupper(result[0]);
+  return result;
+}
+
 static std::string html_escape(const std::string & s) {
   std::string result;
   result.reserve(s.size());
@@ -1462,6 +1469,9 @@ public:
           if (method->get_name() == "strip") {
             vargs.expectArgs("strip method", {0, 0}, {0, 0});
             return Value(strip(str));
+          } else if (method->get_name() == "capitalize") {
+            vargs.expectArgs("capitalize method", {0, 0}, {0, 0});
+            return Value(capitalize(str));
           } else if (method->get_name() == "endswith") {
             vargs.expectArgs("endswith method", {1, 1}, {0, 0});
             auto suffix = vargs.args[0].get<std::string>();
@@ -1792,7 +1802,7 @@ private:
         auto left = parseStringConcat();
         if (!left) throw std::runtime_error("Expected left side of 'logical compare' expression");
 
-        static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not[\r\n\s]+in\b)");
+        static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not\s+in\b)");
         static std::regex not_tok(R"(not\b)");
         std::string op_str;
         while (!(op_str = consumeToken(compare_tok)).empty()) {
@@ -2171,7 +2181,7 @@ private:
     using TemplateTokenIterator = TemplateTokenVector::const_iterator;
 
     std::vector<std::string> parseVarNames() {
-      static std::regex varnames_regex(R"(((?:\w+)(?:[\r\n\s]*,[\r\n\s]*(?:\w+))*)[\r\n\s]*)");
+      static std::regex varnames_regex(R"(((?:\w+)(?:\s*,\s*(?:\w+))*)\s*)");
 
       std::vector<std::string> group;
       if ((group = consumeTokenGroups(varnames_regex)).empty()) throw std::runtime_error("Expected variable names");
@@ -2194,13 +2204,13 @@ private:
     }
 
     TemplateTokenVector tokenize() {
-      static std::regex comment_tok(R"(\{#([-~]?)([\s\S\r\n]*?)([-~]?)#\})");
+      static std::regex comment_tok(R"(\{#([-~]?)([\s\S]*?)([-~]?)#\})");
       static std::regex expr_open_regex(R"(\{\{([-~])?)");
-      static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
+      static std::regex block_open_regex(R"(^\{%([-~])?\s*)");
       static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)");
       static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
-      static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
-      static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
+      static std::regex expr_close_regex(R"(\s*([-~])?\}\})");
+      static std::regex block_close_regex(R"(\s*([-~])?%\})");
 
       TemplateTokenVector tokens;
       std::vector<std::string> group;
@@ -2284,7 +2294,7 @@ private:
               auto post_space = parseBlockClose();
               tokens.push_back(std::make_unique<EndGenerationTemplateToken>(location, pre_space, post_space));
             } else if (keyword == "set") {
-              static std::regex namespaced_var_regex(R"((\w+)[\s\n\r]*\.[\s\n\r]*(\w+))");
+              static std::regex namespaced_var_regex(R"((\w+)\s*\.\s*(\w+))");
 
               std::string ns;
               std::vector<std::string> var_names;
@@ -2336,6 +2346,11 @@ private:
               throw std::runtime_error("Unexpected block: " + keyword);
             }
           } else if (std::regex_search(it, end, match, non_text_open_regex)) {
+            if (!match.position()) {
+                if (match[0] != "{#")
+                    throw std::runtime_error("Internal error: Expected a comment");
+                throw std::runtime_error("Missing end of comment tag");
+            }
             auto text_end = it + match.position();
             text = std::string(it, text_end);
             it = text_end;
@@ -2400,7 +2415,7 @@ private:
 
               auto text = text_token->text;
               if (post_space == SpaceHandling::Strip) {
-                static std::regex trailing_space_regex(R"((\s|\r|\n)+$)");
+                static std::regex trailing_space_regex(R"(\s+$)");
                 text = std::regex_replace(text, trailing_space_regex, "");
               } else if (options.lstrip_blocks && it != end) {
                 auto i = text.size();
@@ -2410,7 +2425,7 @@ private:
                 }
               }
               if (pre_space == SpaceHandling::Strip) {
-                static std::regex leading_space_regex(R"(^(\s|\r|\n)+)");
+                static std::regex leading_space_regex(R"(^\s+)");
                 text = std::regex_replace(text, leading_space_regex, "");
               } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast<ExpressionTemplateToken*>((*(it - 2)).get())) {
                 if (text.length() > 0 && text[0] == '\n') {