Added support for _POSIX_MAPPED_FILES if defined in source (#564)

Getting rid of them fixed GA Ubuntu, but broke GA MacOS. Let's try a
different strategy.

.clang-format

@@ -1,161 +0,0 @@
----
-Language:        Cpp
-AlignAfterOpenBracket: Align
-AlignArrayOfStructures: Left
-AlignConsecutiveAssignments: AcrossComments
-AlignConsecutiveBitFields: AcrossComments
-AlignConsecutiveDeclarations: AcrossComments
-AlignConsecutiveMacros: AcrossComments
-# AlignConsecutiveShortCaseStatements: AcrossComments
-AlignEscapedNewlines: Left # LeftWithLastLine
-AlignOperands:   Align
-AlignTrailingComments:
-  Kind: Always
-  OverEmptyLines: 1
-AllowAllArgumentsOnNextLine: true
-AllowAllParametersOfDeclarationOnNextLine: false
-# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen
-AllowShortBlocksOnASingleLine: Never
-AllowShortCaseLabelsOnASingleLine: false
-AllowShortFunctionsOnASingleLine: Inline
-AllowShortIfStatementsOnASingleLine: Never
-AllowShortLambdasOnASingleLine: Inline
-AllowShortLoopsOnASingleLine: false
-AlwaysBreakBeforeMultilineStrings: true
-BinPackArguments: true
-BinPackParameters: true # OnePerLine
-BitFieldColonSpacing: Both
-BreakBeforeBraces: Custom # Attach
-BraceWrapping:
-  AfterCaseLabel:  true
-  AfterClass:      false
-  AfterControlStatement: false
-  AfterEnum:       false
-  AfterFunction:   false
-  AfterNamespace:  false
-  AfterObjCDeclaration: false
-  AfterStruct:     false
-  AfterUnion:      false
-  AfterExternBlock: false
-  BeforeCatch:     false
-  BeforeElse:      false
-  BeforeLambdaBody: false
-  BeforeWhile: false
-  IndentBraces:    false
-  SplitEmptyFunction: false
-  SplitEmptyRecord: false
-  SplitEmptyNamespace: false
-# BreakAdjacentStringLiterals: true
-BreakAfterAttributes: Never
-BreakBeforeBinaryOperators: None
-BreakBeforeInlineASMColon: OnlyMultiline
-BreakBeforeTernaryOperators: false
-# BreakBinaryOperations: Never
-BreakConstructorInitializers: AfterColon
-# BreakFunctionDefinitionParameters: false
-BreakInheritanceList: AfterComma
-BreakStringLiterals: true
-# BreakTemplateDeclarations: Yes
-ColumnLimit:     120
-CommentPragmas:  '^ IWYU pragma:'
-CompactNamespaces: false
-ConstructorInitializerIndentWidth: 4
-ContinuationIndentWidth: 4
-Cpp11BracedListStyle: false
-DerivePointerAlignment: false
-DisableFormat:   false
-EmptyLineBeforeAccessModifier: Leave
-EmptyLineAfterAccessModifier: Never
-ExperimentalAutoDetectBinPacking: false
-FixNamespaceComments: true
-IncludeBlocks:   Regroup
-IncludeCategories:
-  - Regex:           '^<.*\.h>'
-    Priority:        1
-    SortPriority:    0
-  - Regex:           '^<.*'
-    Priority:        2
-    SortPriority:    0
-  - Regex:           '.*'
-    Priority:        3
-    SortPriority:    0
-IncludeIsMainRegex: '([-_](test|unittest))?$'
-IncludeIsMainSourceRegex: ''
-IndentAccessModifiers: false
-IndentCaseBlocks: true
-IndentCaseLabels: true
-IndentExternBlock: NoIndent
-IndentGotoLabels: false
-IndentPPDirectives: AfterHash
-IndentWidth:     4
-IndentWrappedFunctionNames: false
-InsertBraces:    true # NOTE: may lead to incorrect formatting
-InsertNewlineAtEOF: true
-JavaScriptQuotes: Leave
-JavaScriptWrapImports: true
-KeepEmptyLinesAtTheStartOfBlocks: false
-LambdaBodyIndentation: Signature
-LineEnding: LF
-MacroBlockBegin: ''
-MacroBlockEnd:   ''
-MaxEmptyLinesToKeep: 1
-NamespaceIndentation: None
-ObjCBinPackProtocolList: Auto
-ObjCBlockIndentWidth: 4
-ObjCSpaceAfterProperty: true
-ObjCSpaceBeforeProtocolList: true
-PPIndentWidth: -1
-PackConstructorInitializers: CurrentLine
-PenaltyBreakAssignment: 2
-PenaltyBreakBeforeFirstCallParameter: 1
-PenaltyBreakComment: 300
-PenaltyBreakFirstLessLess: 120
-PenaltyBreakString: 1000
-PenaltyBreakTemplateDeclaration: 10
-PenaltyExcessCharacter: 1000000
-PenaltyReturnTypeOnItsOwnLine: 200
-PointerAlignment: Middle
-QualifierAlignment: Left
-#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict']
-RawStringFormats:
-  - Language:        Cpp
-    Delimiters:
-      - cc
-      - CC
-      - cpp
-      - Cpp
-      - CPP
-      - 'c++'
-      - 'C++'
-    CanonicalDelimiter: ''
-ReferenceAlignment: Middle
-ReflowComments:  false # IndentOnly
-SeparateDefinitionBlocks: Always
-SortIncludes:    CaseInsensitive
-SortUsingDeclarations: LexicographicNumeric
-SpaceAfterCStyleCast: true
-SpaceAfterLogicalNot: false
-SpaceAfterTemplateKeyword: true
-SpaceBeforeAssignmentOperators: true
-SpaceBeforeCpp11BracedList: false
-SpaceBeforeCtorInitializerColon: true
-SpaceBeforeInheritanceColon: true
-SpaceBeforeParens: ControlStatements
-SpaceBeforeRangeBasedForLoopColon: true
-SpaceInEmptyBlock: false
-SpaceInEmptyParentheses: false
-SpacesBeforeTrailingComments: 2
-SpacesInAngles:  Never
-SpacesInContainerLiterals: true
-SpacesInLineCommentPrefix:
-  Minimum: 1
-  Maximum: -1
-SpacesInParentheses: false
-SpacesInSquareBrackets: false
-SpaceBeforeSquareBrackets: false
-Standard:        c++17
-TabWidth:        4
-UseTab:          Never
-WhitespaceSensitiveMacros: ['STRINGIZE']
-...
-

.clang-tidy

@@ -1,26 +0,0 @@
----
-Checks: >
-    bugprone-*,
-    -bugprone-easily-swappable-parameters,
-    -bugprone-implicit-widening-of-multiplication-result,
-    -bugprone-misplaced-widening-cast,
-    -bugprone-narrowing-conversions,
-    readability-*,
-    -readability-avoid-unconditional-preprocessor-if,
-    -readability-function-cognitive-complexity,
-    -readability-identifier-length,
-    -readability-implicit-bool-conversion,
-    -readability-magic-numbers,
-    -readability-uppercase-literal-suffix,
-    -readability-simplify-boolean-expr,
-    clang-analyzer-*,
-    -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
-    performance-*,
-    portability-*,
-    -portability-simd-intrinsics,
-    misc-*,
-    -misc-const-correctness,
-    -misc-non-private-member-variables-in-classes,
-    -misc-no-recursion,
-    -misc-use-anonymous-namespace,
-FormatStyle: none

.devops/cloud-v-pipeline

@@ -1,22 +0,0 @@
-node('x86_runner1'){            // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries
-    stage('Cleanup'){
-        cleanWs()               // Cleaning previous CI build in workspace
-    }
-    stage('checkout repo'){
-        retry(5){               // Retry if the cloning fails due to some reason
-            checkout scm        // Clone the repo on Runner
-        }
-    }
-    stage('Compiling llama.cpp'){
-        sh'''#!/bin/bash
-            make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V
-        '''
-    }
-    stage('Running llama.cpp'){
-        sh'''#!/bin/bash
-            module load gnu-bin2/0.1            # loading latest versions of vector qemu and vector gcc
-            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
-            cat llama_log.txt                   # Printing results
-        '''
-    }
-}

.devops/cpu.Dockerfile

@@ -1,92 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-ARG TARGETARCH
-
-ARG GGML_CPU_ARM_ARCH=armv8-a
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
-    elif [ "$TARGETARCH" = "arm64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
-    else \
-        echo "Unsupported architecture"; \
-        exit 1; \
-    fi && \
-    cmake --build build -j $(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ubuntu:$UBUNTU_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/cuda.Dockerfile

@@ -1,94 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.6.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_CUDA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/intel.Dockerfile

@@ -1,91 +0,0 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
-
-## Build Image
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" \
-        && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with dynamic libs" && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]
-

.devops/llama-cli-cann.Dockerfile

@@ -1,44 +0,0 @@
-ARG ASCEND_VERSION=8.0.rc2.alpha003-910b-openeuler22.03-py3.8
-
-FROM ascendai/cann:$ASCEND_VERSION AS build
-
-WORKDIR /app
-
-COPY . .
-
-RUN yum install -y gcc g++ cmake make
-ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
-ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH
-ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
-ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH}
-ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH}
-ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME}
-ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
-ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit
-ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME}
-
-# find libascend_hal.so, because the drive hasn`t been mounted.
-ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
-
-RUN echo "Building with static libs" && \
-    source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF  && \
-    cmake --build build --config Release --target llama-cli
-
-# TODO: use image with NNRT
-FROM ascendai/cann:$ASCEND_VERSION AS runtime
-COPY --from=build /app/build/bin/llama-cli /llama-cli
-
-ENV LC_ALL=C.utf8
-
-ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
-ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH
-ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
-ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH}
-ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH}
-ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME}
-ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
-ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit
-ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME}
-
-ENTRYPOINT ["/llama-cli" ]

.devops/llama-cpp-cuda.srpm.spec

@@ -1,83 +0,0 @@
-# SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
-# Built and maintained by John Boero - boeroboy@gmail.com
-# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
-
-# Notes for llama.cpp:
-# 1. Tags are currently based on hash - which will not sort asciibetically.
-#    We need to declare standard versioning if people want to sort latest releases.
-# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
-# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
-#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
-# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
-#    It is up to the user to install the correct vendor-specific support.
-
-Name:           llama.cpp-cuda
-Version:        %( date "+%%Y%%m%%d" )
-Release:        1%{?dist}
-Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
-License:        MIT
-Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
-BuildRequires:  coreutils make gcc-c++ git cuda-toolkit
-Requires:       cuda-toolkit
-URL:            https://github.com/ggerganov/llama.cpp
-
-%define debug_package %{nil}
-%define source_date_epoch_from_changelog 0
-
-%description
-CPU inference for Meta's Lllama2 models using default options.
-
-%prep
-%setup -n llama.cpp-master
-
-%build
-make -j GGML_CUDA=1
-
-%install
-mkdir -p %{buildroot}%{_bindir}/
-cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli
-cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server
-cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple
-
-mkdir -p %{buildroot}/usr/lib/systemd/system
-%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacuda.service
-[Unit]
-Description=Llama.cpp server, CPU only (no GPU support in this build).
-After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
-
-[Service]
-Type=simple
-EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS
-ExecReload=/bin/kill -s HUP $MAINPID
-Restart=never
-
-[Install]
-WantedBy=default.target
-EOF
-
-mkdir -p %{buildroot}/etc/sysconfig
-%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
-LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
-EOF
-
-%clean
-rm -rf %{buildroot}
-rm -rf %{_builddir}/*
-
-%files
-%{_bindir}/llama-cuda-cli
-%{_bindir}/llama-cuda-server
-%{_bindir}/llama-cuda-simple
-/usr/lib/systemd/system/llamacuda.service
-%config /etc/sysconfig/llama
-
-%pre
-
-%post
-
-%preun
-%postun
-
-%changelog

.devops/llama-cpp.srpm.spec

@@ -1,85 +0,0 @@
-# SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
-# Built and maintained by John Boero - boeroboy@gmail.com
-# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
-
-# Notes for llama.cpp:
-# 1. Tags are currently based on hash - which will not sort asciibetically.
-#    We need to declare standard versioning if people want to sort latest releases.
-#    In the meantime, YYYYMMDD format will be used.
-# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
-# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
-#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
-# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
-#    It is up to the user to install the correct vendor-specific support.
-
-Name:           llama.cpp
-Version:        %( date "+%%Y%%m%%d" )
-Release:        1%{?dist}
-Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
-License:        MIT
-Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
-BuildRequires:  coreutils make gcc-c++ git libstdc++-devel
-Requires:       libstdc++
-URL:            https://github.com/ggerganov/llama.cpp
-
-%define debug_package %{nil}
-%define source_date_epoch_from_changelog 0
-
-%description
-CPU inference for Meta's Lllama2 models using default options.
-Models are not included in this package and must be downloaded separately.
-
-%prep
-%setup -n llama.cpp-master
-
-%build
-make -j
-
-%install
-mkdir -p %{buildroot}%{_bindir}/
-cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
-cp -p llama-server %{buildroot}%{_bindir}/llama-server
-cp -p llama-simple %{buildroot}%{_bindir}/llama-simple
-
-mkdir -p %{buildroot}/usr/lib/systemd/system
-%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llama.service
-[Unit]
-Description=Llama.cpp server, CPU only (no GPU support in this build).
-After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
-
-[Service]
-Type=simple
-EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llama-server $LLAMA_ARGS
-ExecReload=/bin/kill -s HUP $MAINPID
-Restart=never
-
-[Install]
-WantedBy=default.target
-EOF
-
-mkdir -p %{buildroot}/etc/sysconfig
-%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
-LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
-EOF
-
-%clean
-rm -rf %{buildroot}
-rm -rf %{_builddir}/*
-
-%files
-%{_bindir}/llama-cli
-%{_bindir}/llama-server
-%{_bindir}/llama-simple
-/usr/lib/systemd/system/llama.service
-%config /etc/sysconfig/llama
-
-%pre
-
-%post
-
-%preun
-%postun
-
-%changelog

.devops/musa.Dockerfile

@@ -1,108 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
-# Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_MUSA_DEV_CONTAINER} AS build
-
-# MUSA architecture to build for (defaults to all supported archs)
-ARG MUSA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y \
-    build-essential \
-    cmake \
-    python3 \
-    python3-pip \
-    git \
-    libcurl4-openssl-dev \
-    libgomp1
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default MUSA archs if not specified
-RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_MUSA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/nix/apps.nix

@@ -1,21 +0,0 @@
-{
-  perSystem =
-    { config, lib, ... }:
-    {
-      apps =
-        let
-          inherit (config.packages) default;
-          binaries = [
-            "llama-cli"
-            "llama-embedding"
-            "llama-server"
-            "llama-quantize"
-          ];
-          mkApp = name: {
-            type = "app";
-            program = "${default}/bin/${name}";
-          };
-        in
-        lib.genAttrs binaries mkApp;
-    };
-}

.devops/nix/devshells.nix

@@ -1,52 +0,0 @@
-{ inputs, ... }:
-
-{
-  perSystem =
-    {
-      config,
-      lib,
-      system,
-      ...
-    }:
-    {
-      devShells =
-        let
-          pkgs = import inputs.nixpkgs { inherit system; };
-          stdenv = pkgs.stdenv;
-          scripts = config.packages.python-scripts;
-        in
-        lib.pipe (config.packages) [
-          (lib.concatMapAttrs (
-            name: package: {
-              ${name} = pkgs.mkShell {
-                name = "${name}";
-                inputsFrom = [ package ];
-                shellHook = ''
-                  echo "Entering ${name} devShell"
-                '';
-              };
-              "${name}-extra" =
-                if (name == "python-scripts") then
-                  null
-                else
-                  pkgs.mkShell {
-                    name = "${name}-extra";
-                    inputsFrom = [
-                      package
-                      scripts
-                    ];
-                    # Extra packages that *may* be used by some scripts
-                    packages = [
-                        pkgs.python3Packages.tiktoken
-                    ];
-                    shellHook = ''
-                      echo "Entering ${name} devShell"
-                      addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib stdenv.cc.cc}/lib"
-                    '';
-                  };
-            }
-          ))
-          (lib.filterAttrs (name: value: value != null))
-        ];
-    };
-}

.devops/nix/docker.nix

@@ -1,37 +0,0 @@
-{
-  lib,
-  dockerTools,
-  buildEnv,
-  llama-cpp,
-  interactive ? true,
-  coreutils,
-}:
-
-# A tar that can be fed into `docker load`:
-#
-# $ nix build .#llamaPackages.docker
-# $ docker load < result
-
-# For details and variations cf.
-# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage
-# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922
-# - https://nixery.dev/
-
-# Approximate (compressed) sizes, at the time of writing, are:
-#
-# .#llamaPackages.docker: 125M;
-# .#llamaPackagesCuda.docker: 537M;
-# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M.
-
-dockerTools.buildLayeredImage {
-  name = llama-cpp.pname;
-  tag = "latest";
-
-  contents =
-    [ llama-cpp ]
-    ++ lib.optionals interactive [
-      coreutils
-      dockerTools.binSh
-      dockerTools.caCertificates
-    ];
-}

.devops/nix/jetson-support.nix

@@ -1,39 +0,0 @@
-{ inputs, ... }:
-{
-  perSystem =
-    {
-      config,
-      system,
-      lib,
-      pkgsCuda,
-      ...
-    }:
-    {
-      legacyPackages =
-        let
-          caps.llamaPackagesXavier = "7.2";
-          caps.llamaPackagesOrin = "8.7";
-          caps.llamaPackagesTX2 = "6.2";
-          caps.llamaPackagesNano = "5.3";
-
-          pkgsFor =
-            cap:
-            import inputs.nixpkgs {
-              inherit system;
-              config = {
-                cudaSupport = true;
-                cudaCapabilities = [ cap ];
-                cudaEnableForwardCompat = false;
-                inherit (pkgsCuda.config) allowUnfreePredicate;
-              };
-            };
-        in
-        builtins.mapAttrs (name: cap: (pkgsFor cap).callPackage ./scope.nix { }) caps;
-
-      packages = lib.optionalAttrs (system == "aarch64-linux") {
-        jetson-xavier = config.legacyPackages.llamaPackagesXavier.llama-cpp;
-        jetson-orin = config.legacyPackages.llamaPackagesOrin.llama-cpp;
-        jetson-nano = config.legacyPackages.llamaPackagesNano.llama-cpp;
-      };
-    };
-}

.devops/nix/nixpkgs-instances.nix

@@ -1,45 +0,0 @@
-{ inputs, ... }:
-{
-  # The _module.args definitions are passed on to modules as arguments. E.g.
-  # the module `{ pkgs ... }: { /* config */ }` implicitly uses
-  # `_module.args.pkgs` (defined in this case by flake-parts).
-  perSystem =
-    { system, ... }:
-    {
-      _module.args = {
-        # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs
-        # again, the below creates several nixpkgs instances which the
-        # flake-centric CLI will be forced to evaluate e.g. on `nix flake show`.
-        #
-        # This is currently "slow" and "expensive", on a certain scale.
-        # This also isn't "right" in that this hinders dependency injection at
-        # the level of flake inputs. This might get removed in the foreseeable
-        # future.
-        #
-        # Note that you can use these expressions without Nix
-        # (`pkgs.callPackage ./devops/nix/scope.nix { }` is the entry point).
-
-        pkgsCuda = import inputs.nixpkgs {
-          inherit system;
-          # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc,
-          # and ucx are built with CUDA support)
-          config.cudaSupport = true;
-          config.allowUnfreePredicate =
-            p:
-            builtins.all (
-              license:
-              license.free
-              || builtins.elem license.shortName [
-                "CUDA EULA"
-                "cuDNN EULA"
-              ]
-            ) (p.meta.licenses or [ p.meta.license ]);
-        };
-        # Ensure dependencies use ROCm consistently
-        pkgsRocm = import inputs.nixpkgs {
-          inherit system;
-          config.rocmSupport = true;
-        };
-      };
-    };
-}

.devops/nix/package-gguf-py.nix

@@ -1,36 +0,0 @@
-{
-  lib,
-  llamaVersion,
-  numpy,
-  tqdm,
-  sentencepiece,
-  pyyaml,
-  poetry-core,
-  buildPythonPackage,
-  pytestCheckHook,
-}:
-
-buildPythonPackage {
-  pname = "gguf";
-  version = llamaVersion;
-  pyproject = true;
-  nativeBuildInputs = [ poetry-core ];
-  propagatedBuildInputs = [
-    numpy
-    tqdm
-    sentencepiece
-    pyyaml
-  ];
-  src = lib.cleanSource ../../gguf-py;
-  pythonImportsCheck = [
-    "numpy"
-    "gguf"
-  ];
-  nativeCheckInputs = [ pytestCheckHook ];
-  doCheck = true;
-  meta = with lib; {
-    description = "Python package for writing binary files in the GGUF format";
-    license = licenses.mit;
-    maintainers = [ maintainers.ditsuke ];
-  };
-}

.devops/nix/package.nix

@@ -1,247 +0,0 @@
-{
-  lib,
-  glibc,
-  config,
-  stdenv,
-  runCommand,
-  cmake,
-  ninja,
-  pkg-config,
-  git,
-  mpi,
-  blas,
-  cudaPackages,
-  autoAddDriverRunpath,
-  darwin,
-  rocmPackages,
-  vulkan-headers,
-  vulkan-loader,
-  curl,
-  shaderc,
-  useBlas ?
-    builtins.all (x: !x) [
-      useCuda
-      useMetalKit
-      useRocm
-      useVulkan
-    ]
-    && blas.meta.available,
-  useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
-  # Increases the runtime closure size by ~700M
-  useMpi ? false,
-  useRocm ? config.rocmSupport,
-  rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
-  enableCurl ? true,
-  useVulkan ? false,
-  llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
-
-  # It's necessary to consistently use backendStdenv when building with CUDA support,
-  # otherwise we get libstdc++ errors downstream.
-  effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
-  enableStatic ? effectiveStdenv.hostPlatform.isStatic,
-  precompileMetalShaders ? false,
-}:
-
-let
-  inherit (lib)
-    cmakeBool
-    cmakeFeature
-    optionals
-    strings
-    ;
-
-  stdenv = throw "Use effectiveStdenv instead";
-
-  suffices =
-    lib.optionals useBlas [ "BLAS" ]
-    ++ lib.optionals useCuda [ "CUDA" ]
-    ++ lib.optionals useMetalKit [ "MetalKit" ]
-    ++ lib.optionals useMpi [ "MPI" ]
-    ++ lib.optionals useRocm [ "ROCm" ]
-    ++ lib.optionals useVulkan [ "Vulkan" ];
-
-  pnameSuffix =
-    strings.optionalString (suffices != [ ])
-      "-${strings.concatMapStringsSep "-" strings.toLower suffices}";
-  descriptionSuffix = strings.optionalString (
-    suffices != [ ]
-  ) ", accelerated with ${strings.concatStringsSep ", " suffices}";
-
-  xcrunHost = runCommand "xcrunHost" { } ''
-    mkdir -p $out/bin
-    ln -s /usr/bin/xcrun $out/bin
-  '';
-
-  # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64
-  # separately
-  darwinBuildInputs =
-    with darwin.apple_sdk.frameworks;
-    [
-      Accelerate
-      CoreVideo
-      CoreGraphics
-    ]
-    ++ optionals useMetalKit [ MetalKit ];
-
-  cudaBuildInputs = with cudaPackages; [
-    cuda_cudart
-    cuda_cccl # <nv/target>
-    libcublas
-  ];
-
-  rocmBuildInputs = with rocmPackages; [
-    clr
-    hipblas
-    rocblas
-  ];
-
-  vulkanBuildInputs = [
-    vulkan-headers
-    vulkan-loader
-    shaderc
-  ];
-in
-
-effectiveStdenv.mkDerivation (finalAttrs: {
-  pname = "llama-cpp${pnameSuffix}";
-  version = llamaVersion;
-
-  # Note: none of the files discarded here are visible in the sandbox or
-  # affect the output hash. This also means they can be modified without
-  # triggering a rebuild.
-  src = lib.cleanSourceWith {
-    filter =
-      name: type:
-      let
-        noneOf = builtins.all (x: !x);
-        baseName = baseNameOf name;
-      in
-      noneOf [
-        (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths
-        (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths
-        (lib.hasPrefix "." baseName) # Skip hidden files and directories
-        (baseName == "flake.lock")
-      ];
-    src = lib.cleanSource ../../.;
-  };
-
-  postPatch = ''
-    substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
-      --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-    substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
-      --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
-  '';
-
-  # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
-  # `default.metallib` may be compiled with Metal compiler from XCode
-  # and we need to escape sandbox on MacOS to access Metal compiler.
-  # `xcrun` is used find the path of the Metal compiler, which is varible
-  # and not on $PATH
-  # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
-  __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
-
-  nativeBuildInputs =
-    [
-      cmake
-      ninja
-      pkg-config
-      git
-    ]
-    ++ optionals useCuda [
-      cudaPackages.cuda_nvcc
-
-      autoAddDriverRunpath
-    ]
-    ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ]
-    ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];
-
-  buildInputs =
-    optionals effectiveStdenv.isDarwin darwinBuildInputs
-    ++ optionals useCuda cudaBuildInputs
-    ++ optionals useMpi [ mpi ]
-    ++ optionals useRocm rocmBuildInputs
-    ++ optionals useBlas [ blas ]
-    ++ optionals useVulkan vulkanBuildInputs
-    ++ optionals enableCurl [ curl ];
-
-  cmakeFlags =
-    [
-      (cmakeBool "LLAMA_BUILD_SERVER" true)
-      (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
-      (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
-      (cmakeBool "LLAMA_CURL" enableCurl)
-      (cmakeBool "GGML_NATIVE" false)
-      (cmakeBool "GGML_BLAS" useBlas)
-      (cmakeBool "GGML_CUDA" useCuda)
-      (cmakeBool "GGML_HIP" useRocm)
-      (cmakeBool "GGML_METAL" useMetalKit)
-      (cmakeBool "GGML_VULKAN" useVulkan)
-      (cmakeBool "GGML_STATIC" enableStatic)
-    ]
-    ++ optionals useCuda [
-      (
-        with cudaPackages.flags;
-        cmakeFeature "CMAKE_CUDA_ARCHITECTURES" (
-          builtins.concatStringsSep ";" (map dropDot cudaCapabilities)
-        )
-      )
-    ]
-    ++ optionals useRocm [
-      (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets)
-    ]
-    ++ optionals useMetalKit [
-      (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
-      (cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
-    ];
-
-  # Environment variables needed for ROCm
-  env = optionals useRocm {
-    ROCM_PATH = "${rocmPackages.clr}";
-    HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode";
-  };
-
-  # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
-  # if they haven't been added yet.
-  postInstall = ''
-    mkdir -p $out/include
-    cp $src/include/llama.h $out/include/
-  '';
-
-  meta = {
-    # Configurations we don't want even the CI to evaluate. Results in the
-    # "unsupported platform" messages. This is mostly a no-op, because
-    # cudaPackages would've refused to evaluate anyway.
-    badPlatforms = optionals useCuda lib.platforms.darwin;
-
-    # Configurations that are known to result in build failures. Can be
-    # overridden by importing Nixpkgs with `allowBroken = true`.
-    broken = (useMetalKit && !effectiveStdenv.isDarwin);
-
-    description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
-    homepage = "https://github.com/ggerganov/llama.cpp/";
-    license = lib.licenses.mit;
-
-    # Accommodates `nix run` and `lib.getExe`
-    mainProgram = "llama-cli";
-
-    # These people might respond, on the best effort basis, if you ping them
-    # in case of Nix-specific regressions or for reviewing Nix-specific PRs.
-    # Consider adding yourself to this list if you want to ensure this flake
-    # stays maintained and you're willing to invest your time. Do not add
-    # other people without their consent. Consider removing people after
-    # they've been unreachable for long periods of time.
-
-    # Note that lib.maintainers is defined in Nixpkgs, but you may just add
-    # an attrset following the same format as in
-    # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix
-    maintainers = with lib.maintainers; [
-      philiptaron
-      SomeoneSerge
-    ];
-
-    # Extend `badPlatforms` instead
-    platforms = lib.platforms.all;
-  };
-})

.devops/nix/python-scripts.nix

@@ -1,66 +0,0 @@
-{
-  lib,
-  stdenv,
-  buildPythonPackage,
-  poetry-core,
-  mkShell,
-  python3Packages,
-  gguf-py,
-}@inputs:
-
-let
-  llama-python-deps = with python3Packages; [
-    numpy
-    sentencepiece
-    transformers
-    protobuf
-    torchWithoutCuda
-    gguf-py
-    tqdm
-
-    # for scripts/compare-llama-bench.py
-    gitpython
-    tabulate
-
-    # for examples/pydantic-models-to-grammar-examples.py
-    docstring-parser
-    pydantic
-
-  ];
-
-  llama-python-test-deps = with python3Packages; [
-    # Server bench
-    matplotlib
-
-    # server tests
-    openai
-    pytest
-    prometheus-client
-  ];
-in
-
-buildPythonPackage ({
-  pname = "llama-scripts";
-  version = "0.0.0";
-  pyproject = true;
-
-  # NOTE: The files filtered out here are not visible in the build sandbox, neither
-  # do they affect the output hash. They can be modified without triggering a rebuild.
-  src = lib.cleanSourceWith {
-    filter =
-      name: type:
-      let
-        any = builtins.any (x: x);
-        baseName = builtins.baseNameOf name;
-      in
-      any [
-        (lib.hasSuffix ".py" name)
-        (baseName == "README.md")
-        (baseName == "pyproject.toml")
-      ];
-    src = lib.cleanSource ../../.;
-  };
-  nativeBuildInputs = [ poetry-core ];
-  nativeCheckInputs = llama-python-test-deps;
-  dependencies = llama-python-deps;
-})

.devops/nix/scope.nix

@@ -1,41 +0,0 @@
-{
-  lib,
-  newScope,
-  python3,
-  llamaVersion ? "0.0.0",
-}:
-
-let
-  pythonPackages = python3.pkgs;
-  buildPythonPackage = pythonPackages.buildPythonPackage;
-  numpy = pythonPackages.numpy;
-  tqdm = pythonPackages.tqdm;
-  sentencepiece = pythonPackages.sentencepiece;
-  pyyaml = pythonPackages.pyyaml;
-  poetry-core = pythonPackages.poetry-core;
-  pytestCheckHook = pythonPackages.pytestCheckHook;
-in
-
-# We're using `makeScope` instead of just writing out an attrset
-# because it allows users to apply overlays later using `overrideScope'`.
-# Cf. https://noogle.dev/f/lib/makeScope
-
-lib.makeScope newScope (self: {
-  inherit llamaVersion;
-  gguf-py = self.callPackage ./package-gguf-py.nix {
-    inherit
-      buildPythonPackage
-      numpy
-      tqdm
-      sentencepiece
-      poetry-core
-      pyyaml
-      pytestCheckHook
-      ;
-  };
-  python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; };
-  llama-cpp = self.callPackage ./package.nix { };
-  docker = self.callPackage ./docker.nix { };
-  docker-min = self.callPackage ./docker.nix { interactive = false; };
-  sif = self.callPackage ./sif.nix { };
-})

.devops/nix/sif.nix

@@ -1,27 +0,0 @@
-{
-  lib,
-  singularity-tools,
-  llama-cpp,
-  bashInteractive,
-  interactive ? false,
-}:
-
-let
-  optionalInt = cond: x: if cond then x else 0;
-in
-singularity-tools.buildImage rec {
-  inherit (llama-cpp) name;
-  contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ];
-
-  # These are excessive (but safe) for most variants. Building singularity
-  # images requires superuser privileges, so we build them inside a VM in a
-  # writable image of pre-determined size.
-  #
-  # ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846
-  #
-  # Expected image sizes:
-  # - cpu/blas: 150M,
-  # - cuda, all gencodes: 560M,
-  diskSize = 4096 + optionalInt llama-cpp.useRocm 16384;
-  memSize = diskSize;
-}

.devops/rocm.Dockerfile

@@ -1,113 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-
-# This needs to generally match the container host's environment.
-ARG ROCM_VERSION=6.3
-ARG AMDGPU_VERSION=6.3
-
-# Target the CUDA build image
-ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
-
-### Build image
-FROM ${BASE_ROCM_DEV_CONTAINER} AS build
-
-# Unless otherwise specified, we make a fat build.
-# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
-# This is mostly tied to rocBLAS supported archs.
-# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
-# gfx906 is deprecated
-#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html
-
-#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
-ARG ROCM_DOCKER_ARCH=gfx1100
-
-# Set nvcc architectured
-ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
-# Enable ROCm
-# ENV CC=/opt/rocm/llvm/bin/clang
-# ENV CXX=/opt/rocm/llvm/bin/clang++
-
-RUN apt-get update \
-    && apt-get install -y \
-    build-essential \
-    cmake \
-    git \
-    libcurl4-openssl-dev \
-    curl \
-    libgomp1
-
-WORKDIR /app
-
-COPY . .
-
-RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-    && cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib \
-    && find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_ROCM_DEV_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3-pip \
-    python3 \
-    python3-wheel\
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/tools.sh

@@ -1,49 +0,0 @@
-#!/bin/bash
-set -e
-
-# Read the first argument into a variable
-arg1="$1"
-
-# Shift the arguments to remove the first one
-shift
-
-if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    exec python3 ./convert_hf_to_gguf.py "$@"
-elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    exec ./llama-quantize "$@"
-elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    exec ./llama-cli "$@"
-elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
-    exec ./llama-bench "$@"
-elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
-    exec ./llama-perplexity "$@"
-elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
-    echo "Converting PTH to GGML..."
-    for i in $(ls $1/$2/ggml-model-f16.bin*); do
-        if [ -f "${i/f16/q4_0}" ]; then
-            echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
-        else
-            echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
-        fi
-    done
-elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    exec ./llama-server "$@"
-else
-    echo "Unknown command: $arg1"
-    echo "Available commands: "
-    echo "  --run (-r): Run a model previously converted into ggml"
-    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
-    echo "  --bench (-b): Benchmark the performance of the inference for various parameters."
-    echo "              ex: -m model.gguf"
-    echo "  --perplexity (-p): Measure the perplexity of a model over a given text."
-    echo "              ex: -m model.gguf -f file.txt"
-    echo "  --convert (-c): Convert a llama model into ggml"
-    echo "              ex: --outtype f16 \"/models/7B/\" "
-    echo "  --quantize (-q): Optimize with quantization process ggml"
-    echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
-    echo "  --all-in-one (-a): Execute --convert & --quantize"
-    echo "              ex: \"/models/\" 7B"
-    echo "  --server (-s): Run a model on the server"
-    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -c 2048 -ngl 43 -mg 1 --port 8080"
-fi

.devops/vulkan.Dockerfile

@@ -1,89 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-# Install build tools
-RUN apt update && apt install -y git build-essential cmake wget
-
-# Install Vulkan SDK and cURL
-RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
-    apt update -y && \
-    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
-
-# Build it
-WORKDIR /app
-
-COPY . .
-
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ubuntu:$UBUNTU_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl libvulkan-dev \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    python3-wheel \
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.dockerignore

@@ -1,20 +0,0 @@
-*.o
-*.a
-.cache/
-# Do not ignore .git directory, otherwise the reported build number will always be 0
-.github/
-.gitignore
-.vs/
-.vscode/
-.DS_Store
-
-build*/
-
-models/*
-
-/llama-cli
-/llama-quantize
-
-arm_neon.h
-compile_commands.json
-Dockerfile

.ecrc

@@ -1,6 +0,0 @@
-{
-  "Exclude": ["^\\.gitmodules$", "stb_image\\.h"],
-  "Disable": {
-    "IndentSize": true
-  }
-}

.editorconfig

@@ -1,50 +0,0 @@
-# https://EditorConfig.org
-
-# Top-most EditorConfig file
-root = true
-
-# Unix-style newlines with a newline ending every file, utf-8 charset
-[*]
-end_of_line = lf
-insert_final_newline = true
-trim_trailing_whitespace = true
-charset = utf-8
-indent_style = space
-indent_size = 4
-
-[Makefile]
-indent_style = tab
-
-[scripts/*.mk]
-indent_style = tab
-
-[prompts/*.txt]
-insert_final_newline = unset
-
-[examples/server/public/*]
-indent_size = 2
-
-[examples/server/public/deps_*]
-trim_trailing_whitespace = unset
-indent_style = unset
-indent_size = unset
-
-[examples/server/deps_*]
-trim_trailing_whitespace = unset
-indent_style = unset
-indent_size = unset
-
-[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
-indent_style = tab
-
-[examples/cvector-generator/*.txt]
-trim_trailing_whitespace = unset
-insert_final_newline = unset
-
-[models/templates/*.jinja]
-indent_style = unset
-indent_size = unset
-end_of_line = unset
-charset = unset
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.flake8

@@ -1,17 +0,0 @@
-[flake8]
-max-line-length = 125
-ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
-exclude =
-    # Do not traverse examples
-    examples,
-    # Do not include package initializers
-    __init__.py,
-    # No need to traverse our git directory
-    .git,
-    # There's no value in checking cache directories
-    __pycache__,
-    # No need to include the build path
-    build,
-    # This contains builds that we don't want to check
-    dist  # This is generated with `python build .` for package releases
-# max-complexity = 10

.github/ISSUE_TEMPLATE/010-bug-compilation.yml

@@ -1,87 +0,0 @@
-name: Bug (compilation)
-description: Something goes wrong when trying to compile llama.cpp.
-title: "Compile bug: "
-labels: ["bug-unconfirmed", "compilation"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thanks for taking the time to fill out this bug report!
-        This issue template is intended for bug reports where the compilation of llama.cpp fails.
-        Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`.
-        If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
-        by clearing `~/.cache/ccache` (on Linux).
-  - type: textarea
-    id: commit
-    attributes:
-      label: Git commit
-      description: Which commit are you trying to compile?
-      placeholder: |
-        $git rev-parse HEAD
-        84a07a17b1b08cf2b9747c633a2372782848a27f
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: Operating systems
-      description: Which operating systems do you know to be affected?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: true
-  - type: dropdown
-    id: backends
-    attributes:
-        label: GGML backends
-        description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan]
-        multiple: true
-    validations:
-      required: true
-  - type: textarea
-    id: info
-    attributes:
-      label: Problem description & steps to reproduce
-      description: >
-        Please give us a summary of the problem and tell us how to reproduce it.
-        If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us.
-      placeholder: >
-        I'm trying to compile llama.cpp with CUDA support on a fresh install of Ubuntu and get error XY.
-        Here are the exact commands that I used: ...
-    validations:
-      required: true
-  - type: textarea
-    id: first_bad_commit
-    attributes:
-      label: First Bad Commit
-      description: >
-        If the bug was not present on an earlier version: when did it start appearing?
-        If possible, please do a git bisect and identify the exact commit that introduced the bug.
-    validations:
-      required: false
-  - type: textarea
-    id: command
-    attributes:
-      label: Compile command
-      description: >
-        Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`.
-        This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: true
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: >
-          Please copy and paste any relevant log output, including any generated text.
-          This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/011-bug-results.yml

@@ -1,101 +0,0 @@
-name: Bug (model use)
-description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module).
-title: "Eval bug: "
-labels: ["bug-unconfirmed", "model evaluation"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thanks for taking the time to fill out this bug report!
-        This issue template is intended for bug reports where the model evaluation results
-        (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation.
-        If you encountered the issue while using an external UI (e.g. ollama),
-        please reproduce your issue using one of the examples/binaries in this repository.
-        The `llama-cli` binary can be used for simple and reproducible model inference.
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which version of our software are you running? (use `--version` to get a version string)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: Operating systems
-      description: Which operating systems do you know to be affected?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: true
-  - type: dropdown
-    id: backends
-    attributes:
-        label: GGML backends
-        description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan]
-        multiple: true
-    validations:
-      required: true
-  - type: textarea
-    id: hardware
-    attributes:
-      label: Hardware
-      description: Which CPUs/GPUs are you using?
-      placeholder: >
-        e.g. Ryzen 5950X + 2x RTX 4090
-    validations:
-      required: true
-  - type: textarea
-    id: model
-    attributes:
-      label: Models
-      description: >
-        Which model(s) at which quantization were you using when encountering the bug?
-        If you downloaded a GGUF file off of Huggingface, please provide a link.
-      placeholder: >
-        e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M
-    validations:
-      required: false
-  - type: textarea
-    id: info
-    attributes:
-      label: Problem description & steps to reproduce
-      description: >
-        Please give us a summary of the problem and tell us how to reproduce it.
-        If you can narrow down the bug to specific hardware, compile flags, or command line arguments,
-        that information would be very much appreciated by us.
-      placeholder: >
-        e.g. when I run llama-cli with -ngl 99 I get garbled outputs.
-        When I use -ngl 0 it works correctly.
-        Here are the exact commands that I used: ...
-    validations:
-      required: true
-  - type: textarea
-    id: first_bad_commit
-    attributes:
-      label: First Bad Commit
-      description: >
-        If the bug was not present on an earlier version: when did it start appearing?
-        If possible, please do a git bisect and identify the exact commit that introduced the bug.
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: >
-          Please copy and paste any relevant log output, including the command that you entered and any generated text.
-          This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/019-bug-misc.yml

@@ -1,91 +0,0 @@
-name: Bug (misc.)
-description: Something is not working the way it should (and it's not covered by any of the above cases).
-title: "Misc. bug: "
-labels: ["bug-unconfirmed"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thanks for taking the time to fill out this bug report!
-        This issue template is intended for miscellaneous bugs that don't fit into any other category.
-        If you encountered the issue while using an external UI (e.g. ollama),
-        please reproduce your issue using one of the examples/binaries in this repository.
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which version of our software is affected? (You can use `--version` to get a version string.)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: Operating systems
-      description: Which operating systems do you know to be affected?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: false
-  - type: dropdown
-    id: module
-    attributes:
-      label: Which llama.cpp modules do you know to be affected?
-      multiple: true
-      options:
-        - Documentation/Github
-        - libllama (core library)
-        - llama-cli
-        - llama-server
-        - llama-bench
-        - llama-quantize
-        - Python/Bash scripts
-        - Test code
-        - Other (Please specify in the next section)
-    validations:
-      required: false
-  - type: textarea
-    id: command
-    attributes:
-      label: Command line
-      description: >
-        Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc.
-        This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: false
-  - type: textarea
-    id: info
-    attributes:
-      label: Problem description & steps to reproduce
-      description: >
-        Please give us a summary of the problem and tell us how to reproduce it (if applicable).
-    validations:
-      required: true
-  - type: textarea
-    id: first_bad_commit
-    attributes:
-      label: First Bad Commit
-      description: >
-        If the bug was not present on an earlier version and it's not trivial to track down: when did it start appearing?
-        If possible, please do a git bisect and identify the exact commit that introduced the bug.
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: >
-          If applicable, please copy and paste any relevant log output, including any generated text.
-          This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/020-enhancement.yml

@@ -1,51 +0,0 @@
-name: Enhancement
-description: Used to request enhancements for llama.cpp.
-title: "Feature Request: "
-labels: ["enhancement"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas)
-
-  - type: checkboxes
-    id: prerequisites
-    attributes:
-      label: Prerequisites
-      description: Please confirm the following before submitting your enhancement request.
-      options:
-        - label: I am running the latest code. Mention the version if possible as well.
-          required: true
-        - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
-          required: true
-        - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
-          required: true
-        - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share.
-          required: true
-
-  - type: textarea
-    id: feature-description
-    attributes:
-      label: Feature Description
-      description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
-      placeholder: Detailed description of the enhancement
-    validations:
-      required: true
-
-  - type: textarea
-    id: motivation
-    attributes:
-      label: Motivation
-      description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
-      placeholder: Explanation of why this feature is needed and its benefits
-    validations:
-      required: true
-
-  - type: textarea
-    id: possible-implementation
-    attributes:
-      label: Possible Implementation
-      description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
-      placeholder: Detailed description of potential implementation
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/030-research.yml

@@ -1,52 +0,0 @@
-name: Research
-description: Track new technical research area.
-title: "Research: "
-labels: ["research 🔬"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
-
-  - type: checkboxes
-    id: research-stage
-    attributes:
-      label: Research Stage
-      description: Track general state of this research ticket
-      options:
-        - label: Background Research (Let's try to avoid reinventing the wheel)
-        - label: Hypothesis Formed (How do you think this will work and it's effect?)
-        - label: Strategy / Implementation Forming
-        - label: Analysis of results
-        - label: Debrief / Documentation (So people in the future can learn from us)
-
-  - type: textarea
-    id: background
-    attributes:
-      label: Previous existing literature and research
-      description: Whats the current state of the art and whats the motivation for this research?
-
-  - type: textarea
-    id: hypothesis
-    attributes:
-      label: Hypothesis
-      description: How do you think this will work and it's effect?
-
-  - type: textarea
-    id: implementation
-    attributes:
-      label: Implementation
-      description: Got an approach? e.g. a PR ready to go?
-
-  - type: textarea
-    id: analysis
-    attributes:
-      label: Analysis
-      description: How does the proposed implementation behave?
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/040-refactor.yml

@@ -1,28 +0,0 @@
-name: Refactor (Maintainers)
-description: Used to track refactoring opportunities.
-title: "Refactor: "
-labels: ["refactor"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
-        Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
-
-  - type: textarea
-    id: background-description
-    attributes:
-      label: Background Description
-      description: Please provide a detailed written description of the pain points you are trying to solve.
-      placeholder: Detailed description behind your motivation to request refactor
-    validations:
-      required: true
-
-  - type: textarea
-    id: possible-approaches
-    attributes:
-      label: Possible Refactor Approaches
-      description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list.
-      placeholder: Your idea of possible refactoring opportunity/approaches
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -1,11 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Got an idea?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
-    about: Pop it there. It may then become an enhancement ticket.
-  - name: Got a question?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
-    about: Ask a question there!
-  - name: Want to contribute?
-    url: https://github.com/ggerganov/llama.cpp/wiki/contribute
-    about: Head to the contribution guide page of the wiki for areas you can help with

.github/labeler.yml

@@ -1,86 +0,0 @@
-# https://github.com/actions/labeler
-Kompute:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-kompute.h
-            - ggml/src/ggml-kompute/**
-            - README-kompute.md
-Apple Metal:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-metal.h
-            - ggml/src/ggml-metal/**
-            - README-metal.md
-SYCL:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-sycl.h
-            - ggml/src/ggml-sycl/**
-            - docs/backend/SYCL.md
-            - examples/sycl/**
-Nvidia GPU:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-cuda.h
-            - ggml/src/ggml-cuda/**
-Vulkan:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-vulkan.h
-            - ggml/src/ggml-vulkan/**
-documentation:
-    - changed-files:
-        - any-glob-to-any-file:
-            - docs/**
-            - media/**
-testing:
-    - changed-files:
-        - any-glob-to-any-file:
-            - tests/**
-build:
-    - changed-files:
-        - any-glob-to-any-file:
-            - cmake/**
-            - CMakeLists.txt
-            - CMakePresets.json
-examples:
-    - changed-files:
-        - any-glob-to-any-file: examples/**
-devops:
-    - changed-files:
-        - any-glob-to-any-file:
-            - .devops/**
-            - .github/**
-            - ci/**
-python:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.py"
-            - requirements/**
-            - gguf-py/**
-            - .flake8
-script:
-    - changed-files:
-        - any-glob-to-any-file:
-            - scripts/**
-android:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/llama.android/**
-server:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/server/**
-ggml:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/**
-nix:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.nix"
-            - .github/workflows/nix-*.yml
-            - .devops/nix/nixpkgs-instances.nix
-embedding:
-    - changed-files:
-        - any-glob-to-any-file: examples/embedding/

.github/pull_request_template.md

@@ -1 +0,0 @@
-*Make sure to read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR*

.github/workflows/bench.yml.disabled

@@ -1,315 +0,0 @@
-# TODO: there have been some issues with the workflow, so disabling for now
-#       https://github.com/ggerganov/llama.cpp/issues/7893
-#
-# Benchmark
-name: Benchmark
-
-on:
-  workflow_dispatch:
-    inputs:
-      gpu-series:
-        description: 'Azure GPU series to run with'
-        required: true
-        type: choice
-        options:
-          - Standard_NC4as_T4_v3
-          - Standard_NC24ads_A100_v4
-          - Standard_NC80adis_H100_v5
-      sha:
-        description: 'Commit SHA1 to build'
-        required: false
-        type: string
-      duration:
-        description: 'Duration of the bench'
-        type: string
-        default: 10m
-
-  push:
-    branches:
-      - master
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
-  pull_request_target:
-    types: [opened, synchronize, reopened]
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
-  schedule:
-    -  cron: '04 2 * * *'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }}
-  cancel-in-progress: true
-
-jobs:
-  bench-server-baseline:
-    runs-on: Standard_NC4as_T4_v3
-    env:
-      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
-      N_USERS: 8
-      DURATION: 10m
-
-    strategy:
-      matrix:
-        model: [phi-2]
-        ftype: [q4_0, q8_0, f16]
-        include:
-          - model: phi-2
-            ftype: q4_0
-            pr_comment_enabled: "true"
-
-    if: |
-      inputs.gpu-series == 'Standard_NC4as_T4_v3'
-      || (
-        github.event_name == 'schedule'
-        && github.ref_name == 'master'
-        && github.repository_owner == 'ggerganov'
-      )
-      || github.event_name == 'pull_request_target'
-      || (
-        github.event_name == 'push'
-        && github.event.ref == 'refs/heads/master'
-        && github.repository_owner == 'ggerganov'
-      )
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Install python env
-        id: pipenv
-        run: |
-          cd examples/server/bench
-          python3 -m venv venv
-          source venv/bin/activate
-          pip install -r requirements.txt
-
-      - name: Prometheus
-        id: install_prometheus
-        run: |
-          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
-          tar xzf prometheus*.tar.gz --strip-components=1
-          ./prometheus --config.file=examples/server/bench/prometheus.yml &
-          while ! nc -z localhost 9090; do
-            sleep 0.1
-          done
-
-      - name: Set up Go
-        uses: actions/setup-go@v5
-        with:
-          go-version: '1.21'
-
-      - name: Install k6 and xk6-sse
-        id: k6_installation
-        run: |
-          cd examples/server/bench
-          go install go.k6.io/xk6/cmd/xk6@latest
-          xk6 build master \
-              --with github.com/phymbert/xk6-sse
-
-      - name: Build
-        id: cmake_build
-        run: |
-          set -eux
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DLLAMA_CUBLAS=ON \
-              -DCUDAToolkit_ROOT=/usr/local/cuda \
-              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
-              -DCMAKE_CUDA_ARCHITECTURES=75 \
-              -DLLAMA_FATAL_WARNINGS=OFF \
-              -DLLAMA_ALL_WARNINGS=OFF \
-              -DCMAKE_BUILD_TYPE=Release;
-          cmake --build build --config Release -j $(nproc) --target llama-server
-
-      - name: Download the dataset
-        id: download_dataset
-        run: |
-          cd examples/server/bench
-          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-      - name: Server bench
-        id: server_bench
-        env:
-            HEAD_REF: ${{ github.head_ref || github.ref_name }}
-        run: |
-          set -eux
-
-          cd examples/server/bench
-          source venv/bin/activate
-          python bench.py \
-              --runner-label ${{ env.RUNNER_LABEL }} \
-              --name ${{ github.job }} \
-              --branch $HEAD_REF \
-              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
-              --scenario script.js \
-              --duration ${{ github.event.inputs.duration || env.DURATION }} \
-              --hf-repo ggml-org/models	 \
-              --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \
-              --model-path-prefix /models \
-              --parallel ${{ env.N_USERS }} \
-              -ngl 33 \
-              --batch-size 2048 \
-              --ubatch-size	256 \
-              --ctx-size 16384 \
-              --n-prompts 1000 \
-              --max-prompt-tokens 1024 \
-              --max-tokens 2048
-
-          cat results.github.env >> $GITHUB_ENV
-
-          # Remove dataset as we do not want it in the artefact
-          rm ShareGPT_V3_unfiltered_cleaned_split.json
-
-      - uses: actions/upload-artifact@v4
-        with:
-          name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          compression-level: 9
-          path: |
-            examples/server/bench/*.jpg
-            examples/server/bench/*.json
-            examples/server/bench/*.log
-
-      - name: Commit status
-        uses: Sibz/github-status-action@v1
-        with:
-          authToken: ${{secrets.GITHUB_TOKEN}}
-          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
-          context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          description: |
-            ${{ env.BENCH_RESULTS }}
-          state: 'success'
-
-      - name: Upload benchmark images
-        uses: devicons/public-upload-to-imgur@v2.2.2
-        continue-on-error: true # Important as it looks unstable: 503
-        id: imgur_step
-        with:
-          client_id: ${{secrets.IMGUR_CLIENT_ID}}
-          path: |
-            examples/server/bench/prompt_tokens_seconds.jpg
-            examples/server/bench/predicted_tokens_seconds.jpg
-            examples/server/bench/kv_cache_usage_ratio.jpg
-            examples/server/bench/requests_processing.jpg
-
-      - name: Extract mermaid
-        id: set_mermaid
-        run: |
-          set -eux
-
-          cd examples/server/bench
-          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
-          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
-          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
-          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
-          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
-          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
-          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
-          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
-          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-      - name: Extract image url
-        id: extract_image_url
-        continue-on-error: true
-        run: |
-          set -eux
-
-          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
-          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
-          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
-          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
-
-      - name: Comment PR
-        uses: mshick/add-pr-comment@v2
-        id: comment_pr
-        if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }}
-        with:
-          message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          message: |
-            <p align="center">
-
-            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
-
-            </p>
-
-            <details>
-
-            <summary>Expand details for performance related PR only</summary>
-
-            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
-            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
-            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s
-            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s
-            - ${{ env.BENCH_GRAPH_XLABEL }}
-
-
-            <p align="center">
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
-
-            <details>
-
-            <summary>More</summary>
-
-            ```mermaid
-            ${{ env.PROMPT_TOKENS_SECONDS }}
-            ```
-
-            </details>
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.PREDICTED_TOKENS_SECONDS }}
-            ```
-
-            </details>
-
-            </p>
-
-            <details>
-
-            <summary>Details</summary>
-
-            <p align="center">
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.KV_CACHE_USAGE_RATIO }}
-            ```
-
-            </details>
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.REQUESTS_PROCESSING }}
-            ```
-
-            </details>
-
-            </p>
-            </details>
-            </details>

.github/workflows/close-issue.yml

@@ -1,28 +0,0 @@
-name: Close inactive issues
-on:
-  schedule:
-    - cron: "42 0 * * *"
-
-# Fine-grant permission
-# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
-permissions:
-  issues: write
-
-jobs:
-  close-issues:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/stale@v5
-        with:
-          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
-          days-before-issue-stale: 30
-          days-before-issue-close: 14
-          stale-issue-label: "stale"
-          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
-          days-before-pr-stale: -1
-          days-before-pr-close: -1
-          operations-per-run: 10000
-          repo-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/docker.yml

@@ -1,173 +0,0 @@
-# This workflow uses actions that are not certified by GitHub.
-# They are provided by a third-party and are governed by
-# separate terms of service, privacy policy, and support
-# documentation.
-
-# GitHub recommends pinning actions to a commit SHA.
-# To get a newer version, you will need to update the SHA.
-# You can also reference a tag or branch, but the action may change without warning.
-
-name: Publish Docker image
-
-on:
-  workflow_dispatch: # allows manual triggering
-  schedule:
-    # Rebuild daily rather than on every push because it is expensive
-    - cron: '12 4 * * *'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-# Fine-grant permission
-# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
-permissions:
-  packages: write
-
-jobs:
-  push_to_registry:
-    name: Push Docker image to Docker Hub
-
-    runs-on: ubuntu-22.04
-    env:
-      COMMIT_SHA: ${{ github.sha }}
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
-          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true }
-    steps:
-      - name: Check out the repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0 # preserve git history, so we can determine the build number
-
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v2
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Determine tag name
-        id: tag
-        shell: bash
-        run: |
-          BUILD_NUMBER="$(git rev-list --count HEAD)"
-          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
-          REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
-          REPO_NAME="${{ github.event.repository.name }}"
-
-          # determine tag name postfix (build number, commit hash)
-          if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
-            TAG_POSTFIX="-b${BUILD_NUMBER}"
-          else
-            SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
-            TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}"
-          fi
-          # list all tags possible
-          if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
-              TYPE=""
-          else
-              TYPE="-${{ matrix.config.tag }}"
-          fi
-          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
-          FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}"
-          LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}"
-          SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}"
-          echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
-          echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
-          echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
-          echo "full_output_tags=$FULLTAGS"  # print out for debugging
-          echo "light_output_tags=$LIGHTTAGS"  # print out for debugging
-          echo "server_output_tags=$SERVERTAGS"  # print out for debugging
-        env:
-          GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
-
-      - name: Free Disk Space (Ubuntu)
-        if: ${{ matrix.config.free_disk_space == true }}
-        uses: ggml-org/free-disk-space@v1.3.1
-        with:
-          # this might remove tools that are actually needed,
-          # if set to "true" but frees about 6 GB
-          tool-cache: false
-
-          # all of these default to true, but feel free to set to
-          # "false" if necessary for your workflow
-          android: true
-          dotnet: true
-          haskell: true
-          large-packages: true
-          docker-images: true
-          swap-storage: true
-
-      - name: Build and push Full Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.full_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: full
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-
-      - name: Build and push Light Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.light_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: light
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-
-      - name: Build and push Server Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.server_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: server
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache

.github/workflows/editorconfig.yml

@@ -1,29 +0,0 @@
-name: EditorConfig Checker
-
-on:
-  workflow_dispatch: # allows manual triggering
-    inputs:
-      create_release:
-        description: 'Create new release'
-        required: true
-        type: boolean
-  push:
-    branches:
-      - master
-  pull_request:
-    branches:
-      - master
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  editorconfig:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: editorconfig-checker/action-editorconfig-checker@v2
-        with:
-          version: v3.0.3
-      - run: editorconfig-checker

.github/workflows/gguf-publish.yml

@@ -1,44 +0,0 @@
-# This workflow will upload a Python Package using Twine when a GGUF release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-# See `gguf-py/README.md` for how to make a release.
-
-# This workflow uses actions that are not certified by GitHub.
-# They are provided by a third-party and are governed by
-# separate terms of service, privacy policy, and support
-# documentation.
-
-name: Upload Python Package
-
-on:
-  workflow_dispatch:
-  push:
-    # Pattern matched against refs/tags
-    tags:
-      - 'gguf-v*'           # Push events to every version tag
-
-
-jobs:
-  deploy:
-
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version: '3.9.x'
-    - name: Install dependencies
-      run: |
-        cd gguf-py
-        python -m pip install poetry
-        poetry install
-
-    - name: Build package
-      run: cd gguf-py && poetry build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        password: ${{ secrets.PYPI_API_TOKEN }}
-        packages-dir: gguf-py/dist

.github/workflows/labeler.yml

@@ -1,17 +0,0 @@
-name: "Pull Request Labeler"
-on:
-- pull_request_target
-
-jobs:
-  labeler:
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        repository: "ggerganov/llama.cpp"
-    - uses: actions/labeler@v5
-      with:
-        configuration-path: '.github/labeler.yml'

.github/workflows/python-check-requirements.yml

@@ -1,33 +0,0 @@
-name: Python check requirements.txt
-
-on:
-  push:
-    paths:
-      - '.github/workflows/python-check-requirements.yml'
-      - 'scripts/check-requirements.sh'
-      - 'convert*.py'
-      - '**/requirements*.txt'
-  pull_request:
-    paths:
-      - '.github/workflows/python-check-requirements.yml'
-      - 'scripts/check-requirements.sh'
-      - 'convert*.py'
-      - '**/requirements*.txt'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  python-check-requirements:
-    runs-on: ubuntu-latest
-    name: check-requirements
-    steps:
-      - name: Check out source repository
-        uses: actions/checkout@v4
-      - name: Set up Python environment
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Run check-requirements.sh script
-        run:  bash scripts/check-requirements.sh

.github/workflows/python-lint.yml

@@ -1,30 +0,0 @@
-name: flake8 Lint
-
-on:
-  push:
-    branches:
-      - master
-    paths: ['.github/workflows/python-lint.yml', '**/*.py']
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/python-lint.yml', '**/*.py']
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  flake8-lint:
-    runs-on: ubuntu-latest
-    name: Lint
-    steps:
-      - name: Check out source repository
-        uses: actions/checkout@v4
-      - name: Set up Python environment
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: flake8 Lint
-        uses: py-actions/flake8@v2
-        with:
-            plugins: "flake8-no-print"

.github/workflows/python-type-check.yml

@@ -1,40 +0,0 @@
-name: Python Type-Check
-
-on:
-  push:
-    paths:
-      - '.github/workflows/python-type-check.yml'
-      - 'pyrightconfig.json'
-      - '**.py'
-      - '**/requirements*.txt'
-  pull_request:
-    paths:
-      - '.github/workflows/python-type-check.yml'
-      - 'pyrightconfig.json'
-      - '**.py'
-      - '**/requirements*.txt'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  python-type-check:
-    runs-on: ubuntu-latest
-    name: pyright type-check
-    steps:
-      - name: Check out source repository
-        uses: actions/checkout@v4
-      - name: Set up Python environment
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install Python dependencies
-        # TODO: use a venv
-        run: pip install -r requirements/requirements-all.txt
-      - name: Type-check with Pyright
-        uses: jakebailey/pyright-action@v2
-        with:
-          version: 1.1.382
-          level: warning
-          warnings: true

.github/workflows/server.yml

@@ -1,239 +0,0 @@
-# Server build and tests
-name: Server
-
-on:
-  workflow_dispatch: # allows manual triggering
-    inputs:
-      sha:
-        description: 'Commit SHA1 to build'
-        required: false
-        type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
-  push:
-    branches:
-      - master
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-
-env:
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-  LLAMA_LOG_VERBOSITY: 10
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  server:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libcurl4-openssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r examples/server/tests/requirements.txt
-
-      # Setup nodejs (to be used for verifying bundled index.html)
-      - uses: actions/setup-node@v4
-        with:
-          node-version: '22.11.0'
-
-      - name: WebUI - Install dependencies
-        id: webui_lint
-        run: |
-          cd examples/server/webui
-          npm ci
-
-      - name: WebUI - Check code format
-        id: webui_format
-        run: |
-          git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
-          git status
-
-          npm run format
-          git status
-          modified_files="$(git status -s)"
-          echo "Modified files: ${modified_files}"
-          if [ -n "${modified_files}" ]; then
-            echo "Files do not follow coding style. To fix: npm run format"
-            echo "${modified_files}"
-            exit 1
-          fi
-
-      - name: Verify bundled index.html
-        id: verify_server_index_html
-        run: |
-          git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
-          git status
-
-          npm run build
-          git status
-          modified_files="$(git status -s)"
-          echo "Modified files: ${modified_files}"
-          if [ -n "${modified_files}" ]; then
-            echo "Repository is dirty or server/webui is not built as expected"
-            echo "Hint: You may need to follow Web UI build guide in server/README.md"
-            echo "${modified_files}"
-            exit 1
-          fi
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cd examples/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd examples/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd examples/server/tests
-          SLOW_TESTS=1 ./tests.sh
-
-
-  server-windows:
-    runs-on: windows-2019
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: libCURL
-        id: get_libcurl
-        env:
-          CURL_VERSION: 8.6.0_6
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
-          mkdir $env:RUNNER_TEMP/libcurl
-          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
-          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r examples/server/tests/requirements.txt
-
-      - name: Copy Libcurl
-        id: prepare_libcurl
-        run: |
-          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
-        run: |
-          cd examples/server/tests
-          $env:PYTHONIOENCODING = ":replace"
-          pytest -v -x -m "not slow"
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd examples/server/tests
-          $env:SLOW_TESTS = "1"
-          pytest -v -x

.gitignore

@@ -1,145 +1,26 @@
-# Extensions
-
-*.a
-*.bat
-*.bin
-*.d
-*.dll
-*.dot
-*.etag
-*.exe
-*.gcda
-*.gcno
-*.gcov
-*.gguf
-*.gguf.json
-*.lastModified
-*.log
-*.metallib
 *.o
-*.so
-*.swp
-*.tmp
-
-# IDE / OS
-
+*.a
 .cache/
-.ccls-cache/
-.direnv/
-.DS_Store
-.envrc
-.idea/
-.swiftpm
 .vs/
 .vscode/
-nppBackup
+.DS_Store
 
-
-# Coverage
-
-gcovr-report/
-lcov-report/
-
-# Build Artifacts
-
-tags
-.build/
-build*
-!build-info.cmake
-!build-info.cpp.in
-!build-info.sh
-!build.zig
-!docs/build.md
-/libllama.so
-/llama-*
-/vulkan-shaders-gen
-android-ndk-*
-arm_neon.h
-cmake-build-*
-CMakeSettings.json
-compile_commands.json
-ggml-metal-embed.metal
-llama-batched-swift
-/rpc-server
-out/
-tmp/
-autogen-*.md
-
-# Deprecated
-
-/main
-/server
-
-# CI
-
-!.github/workflows/*.yml
-
-# Models
+build/
+build-em/
+build-debug/
+build-release/
+build-static/
+build-no-accel/
+build-sanitize-addr/
+build-sanitize-thread/
 
 models/*
-models-mnt
-!models/.editorconfig
-!models/ggml-vocab-*.gguf*
 
-# Zig
-zig-out/
-zig-cache/
+/main
+/quantize
+/magic.dat
 
-# Logs
-
-ppl-*.txt
-qnt-*.txt
-perf-*.txt
-
-# Examples
-
-examples/jeopardy/results.txt
-examples/server/*.css.hpp
-examples/server/*.html.hpp
-examples/server/*.js.hpp
-examples/server/*.mjs.hpp
-!build_64.sh
-!examples/*.bat
-!examples/*/*.kts
-!examples/*/*/*.kts
-!examples/sycl/*.bat
-!examples/sycl/*.sh
-
-# Server Web UI temporary files
-node_modules
-examples/server/webui/dist
-
-# Python
-
-/.venv
-__pycache__/
-*/poetry.lock
-poetry.toml
-
-# Nix
-/result
-
-# Test binaries
-/tests/test-backend-ops
-/tests/test-double-float
-/tests/test-grad0
-/tests/test-grammar-parser
-/tests/test-llama-grammar
-/tests/test-opt
-/tests/test-quantize-fns
-/tests/test-quantize-perf
-/tests/test-rope
-/tests/test-sampling
-/tests/test-tokenizer-0
-/tests/test-tokenizer-1-bpe
-/tests/test-tokenizer-1-spm
-
-# Scripts
-!/scripts/install-oneapi.bat
-
-# Test models for lora adapters
-/lora-tests
-
-# Local scripts
-/run-vim.sh
-/run-chat.sh
+arm_neon.h
+compile_commands.json
+CMakeFiles/
+CMakeCache.txt

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "kompute"]
-	path = ggml/src/ggml-kompute/kompute
-	url = https://github.com/nomic-ai/kompute.git

.pre-commit-config.yaml

@@ -1,16 +0,0 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
-exclude: prompts/.*.txt
-repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.6.0
-  hooks:
-  - id: trailing-whitespace
-  - id: end-of-file-fixer
-  - id: check-yaml
-  - id: check-added-large-files
-- repo: https://github.com/PyCQA/flake8
-  rev: 7.0.0
-  hooks:
-  -   id: flake8
-      additional_dependencies: [flake8-no-print]

CMakeLists.txt

@@ -1,236 +1,130 @@
-cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
-project("llama.cpp" C CXX)
-include(CheckIncludeFileCXX)
+cmake_minimum_required(VERSION 3.8)
+project("llama.cpp")
 
-#set(CMAKE_WARN_DEPRECATED YES)
-set(CMAKE_WARN_UNUSED_CLI YES)
-
-set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+set(CMAKE_C_STANDARD 11)
 
 if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
     set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()
 
-# Add path to modules
-list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
+option(LLAMA_ALL_WARNINGS            "llama: enable all compiler warnings"                   ON)
+option(LLAMA_ALL_WARNINGS_3RD_PARTY  "llama: enable all compiler warnings in 3rd party libs" OFF)
 
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
-set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+option(LLAMA_SANITIZE_THREAD         "llama: enable thread sanitizer"    OFF)
+option(LLAMA_SANITIZE_ADDRESS        "llama: enable address sanitizer"   OFF)
+option(LLAMA_SANITIZE_UNDEFINED      "llama: enable undefined sanitizer" OFF)
 
-if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
-    set(LLAMA_STANDALONE ON)
-
-    include(git-vars)
-
-    # configure project version
-    # TODO
-else()
-    set(LLAMA_STANDALONE OFF)
+if (APPLE)
+    option(LLAMA_NO_ACCELERATE       "llama: disable Accelerate framework" OFF)
+    option(LLAMA_NO_AVX              "llama: disable AVX" OFF)
+    option(LLAMA_NO_AVX2             "llama: disable AVX2" OFF)
+    option(LLAMA_NO_FMA              "llama: disable FMA" OFF)
 endif()
 
-if (EMSCRIPTEN)
-    set(BUILD_SHARED_LIBS_DEFAULT OFF)
-
-    option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON)
-else()
-    if (MINGW)
-        set(BUILD_SHARED_LIBS_DEFAULT OFF)
-    else()
-        set(BUILD_SHARED_LIBS_DEFAULT ON)
-    endif()
-endif()
-
-option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
-
-if (WIN32)
-    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-endif()
-
-if (MSVC)
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
-endif()
-
-#
-# option list
-#
-
-# debug
-option(LLAMA_ALL_WARNINGS           "llama: enable all compiler warnings"                   ON)
-option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
-
-# build
-option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF)
-
-# sanitizers
-option(LLAMA_SANITIZE_THREAD    "llama: enable thread sanitizer"    OFF)
-option(LLAMA_SANITIZE_ADDRESS   "llama: enable address sanitizer"   OFF)
-option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
-
-# utils
-option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
-
-# extra artifacts
-option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
-
-# 3rd party libs
-option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
-option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
-
-# Required for relocatable CMake package
-include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
-include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
-
-# override ggml options
-set(GGML_ALL_WARNINGS   ${LLAMA_ALL_WARNINGS})
-set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
-
-# change the default for these ggml options
-if (NOT DEFINED GGML_LLAMAFILE)
-    set(GGML_LLAMAFILE_DEFAULT ON)
-endif()
-
-if (NOT DEFINED GGML_CUDA_GRAPHS)
-    set(GGML_CUDA_GRAPHS_DEFAULT ON)
-endif()
-
-# transition helpers
-function (llama_option_depr TYPE OLD NEW)
-    if (${OLD})
-        message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n")
-        set(${NEW} ON PARENT_SCOPE)
-    endif()
-endfunction()
-
-llama_option_depr(FATAL_ERROR LLAMA_CUBLAS              GGML_CUDA)
-llama_option_depr(WARNING     LLAMA_CUDA                GGML_CUDA)
-llama_option_depr(WARNING     LLAMA_KOMPUTE             GGML_KOMPUTE)
-llama_option_depr(WARNING     LLAMA_METAL               GGML_METAL)
-llama_option_depr(WARNING     LLAMA_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY)
-llama_option_depr(WARNING     LLAMA_NATIVE              GGML_NATIVE)
-llama_option_depr(WARNING     LLAMA_RPC                 GGML_RPC)
-llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
-llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
-llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
-
 if (NOT MSVC)
     if (LLAMA_SANITIZE_THREAD)
-        message(STATUS "Using -fsanitize=thread")
-
-        add_compile_options(-fsanitize=thread)
-        link_libraries     (-fsanitize=thread)
+        set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -fsanitize=thread")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
     endif()
 
     if (LLAMA_SANITIZE_ADDRESS)
-        message(STATUS "Using -fsanitize=address")
-
-        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries     (-fsanitize=address)
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
     endif()
 
     if (LLAMA_SANITIZE_UNDEFINED)
-        message(STATUS "Using -fsanitize=undefined")
-
-        add_compile_options(-fsanitize=undefined)
-        link_libraries     (-fsanitize=undefined)
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
     endif()
 endif()
 
-#
-# 3rd-party
-#
+if (APPLE AND NOT LLAMA_NO_ACCELERATE)
+    find_library(ACCELERATE_FRAMEWORK Accelerate)
+    if (ACCELERATE_FRAMEWORK)
+        message(STATUS "Accelerate framework found")
 
-if (NOT TARGET ggml)
-    add_subdirectory(ggml)
-    # ... otherwise assume ggml is added by a parent CMakeLists.txt
+        set(LLAMA_EXTRA_LIBS  ${LLAMA_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
+        set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
+    else()
+        message(WARNING "Accelerate framework not found")
+    endif()
 endif()
 
-#
-# build the library
-#
-
-add_subdirectory(src)
-
-#
-# utils, programs, examples and tests
-#
-
-if (LLAMA_BUILD_COMMON)
-    add_subdirectory(common)
+if (LLAMA_ALL_WARNINGS)
+    if (NOT MSVC)
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
+            -Wall                           \
+            -Wextra                         \
+            -Wpedantic                      \
+            -Wshadow                        \
+            -Wcast-qual                     \
+            -Wstrict-prototypes             \
+            -Wpointer-arith                 \
+            -Wno-unused-function            \
+        ")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
+            -Wall                           \
+            -Wextra                         \
+            -Wpedantic                      \
+            -Wcast-qual                     \
+        ")
+    else()
+        # todo : msvc
+    endif()
 endif()
 
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
-    include(CTest)
-    add_subdirectory(tests)
+message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
+
+if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
+    message(STATUS "ARM detected")
+else()
+    message(STATUS "x86 detected")
+    if (MSVC)
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
+        set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
+    else()
+        if(NOT LLAMA_NO_AVX)
+            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
+        endif()
+        if(NOT LLAMA_NO_AVX2)
+            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
+        endif()
+        if(NOT LLAMA_NO_FMA)
+            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
+        endif()
+        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
+    endif()
 endif()
 
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
-    add_subdirectory(examples)
-    add_subdirectory(pocs)
-endif()
+# if (LLAMA_PERF)
+#     set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_PERF)
+# endif()
 
-#
-# install
-#
+add_executable(llama
+    main.cpp
+    utils.cpp
+    utils.h
+    mmap.c
+    mmap.h)
 
-include(GNUInstallDirs)
-include(CMakePackageConfigHelpers)
+add_executable(quantize
+    quantize.cpp
+    utils.cpp
+    utils.h)
 
-set(LLAMA_BUILD_NUMBER        ${BUILD_NUMBER})
-set(LLAMA_BUILD_COMMIT        ${BUILD_COMMIT})
-set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})
+add_library(ggml
+    ggml.c
+    ggml.h)
 
-set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header  files")
-set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
-set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
+target_compile_definitions(ggml PUBLIC ${LLAMA_EXTRA_FLAGS})
+target_compile_definitions(llama PUBLIC ${LLAMA_EXTRA_FLAGS})
+target_compile_definitions(quantize PUBLIC ${LLAMA_EXTRA_FLAGS})
 
-set(LLAMA_PUBLIC_HEADERS
-    ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h
-    ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h)
-
-set_target_properties(llama
-    PROPERTIES
-        PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
-
-install(TARGETS llama LIBRARY PUBLIC_HEADER)
-
-configure_package_config_file(
-        ${CMAKE_CURRENT_SOURCE_DIR}/cmake/llama-config.cmake.in
-        ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
-    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama
-    PATH_VARS LLAMA_INCLUDE_INSTALL_DIR
-              LLAMA_LIB_INSTALL_DIR
-              LLAMA_BIN_INSTALL_DIR )
-
-write_basic_package_version_file(
-        ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
-    VERSION ${LLAMA_INSTALL_VERSION}
-    COMPATIBILITY SameMajorVersion)
-
-install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
-              ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
-        DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
-
-install(
-    FILES convert_hf_to_gguf.py
-    PERMISSIONS
-        OWNER_READ
-        OWNER_WRITE
-        OWNER_EXECUTE
-        GROUP_READ
-        GROUP_EXECUTE
-        WORLD_READ
-        WORLD_EXECUTE
-    DESTINATION ${CMAKE_INSTALL_BINDIR})
-
-configure_file(cmake/llama.pc.in
-        "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        @ONLY)
-
-install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
+target_link_libraries(ggml PRIVATE ${LLAMA_EXTRA_LIBS})
+target_include_directories(ggml PUBLIC .)
+target_link_libraries(quantize PRIVATE ggml)
+target_link_libraries(llama PRIVATE ggml)

CMakePresets.json

@@ -1,97 +0,0 @@
-{
-  "version": 4,
-  "configurePresets": [
-    {
-        "name":  "base",
-        "hidden": true,
-        "generator":   "Ninja",
-        "binaryDir":   "${sourceDir}/build-${presetName}",
-        "cacheVariables": {
-            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
-            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
-        }
-    },
-    {
-        "name": "sycl-base",
-        "hidden": true,
-        "generator": "Ninja",
-        "binaryDir": "${sourceDir}/build-${presetName}",
-        "cacheVariables": {
-            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
-            "CMAKE_CXX_COMPILER": "icx",
-            "CMAKE_C_COMPILER": "cl",
-            "GGML_SYCL": "ON",
-            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
-        }
-    },
-    { "name": "debug",    "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
-    { "name": "release",  "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
-    { "name": "reldbg",   "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
-    { "name": "static",   "hidden": true, "cacheVariables": { "GGML_STATIC":      "ON" } },
-    { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16":    "ON" } },
-    { "name": "vulkan",   "hidden": true, "cacheVariables": { "GGML_VULKAN":      "ON" } },
-
-    {
-        "name": "x64-windows-llvm", "hidden": true,
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake"
-        }
-    },
-
-    {
-        "name": "arm64-windows-msvc", "hidden": true,
-        "architecture": { "value": "arm64",    "strategy": "external" },
-        "toolset":      { "value": "host=x64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake"
-        }
-    },
-
-    {
-        "name": "arm64-windows-llvm", "hidden": true,
-        "architecture": { "value": "arm64",    "strategy": "external" },
-        "toolset":      { "value": "host=x64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake"
-        }
-    },
-
-    {
-        "name": "arm64-apple-clang", "hidden": true,
-        "architecture": { "value": "arm64",    "strategy": "external" },
-        "toolset":      { "value": "host=x64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake"
-        }
-    },
-
-    { "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] },
-    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] },
-    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] },
-
-    { "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] },
-    { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
-    { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang",  "reldbg", "static" ] },
-
-    { "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc",  "debug"   ] },
-    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
-    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },
-
-    { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] },
-    { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] },
-    { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] },
-    { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] },
-
-    { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
-    { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
-    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
-
-    { "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] },
-    { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
-    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
-    { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] },
-
-    { "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] },
-    { "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] }
-  ]
-}

CODEOWNERS

@@ -1,11 +0,0 @@
-# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
-
-/ci/ @ggerganov
-/.devops/*.Dockerfile @ngxson
-/examples/server/ @ngxson
-/ggml/src/ggml-cuda/fattn* @JohannesGaessler
-/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
-/ggml/src/ggml-cuda/mmv.* @JohannesGaessler
-/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
-/ggml/src/ggml-opt.cpp @JohannesGaessler
-/ggml/src/gguf.cpp @JohannesGaessler

CONTRIBUTING.md

@@ -1,125 +0,0 @@
-# Pull requests (for contributors)
-
-- Test your changes:
-    - Execute [the full CI locally on your machine](ci/README.md) before publishing
-    - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
-    - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
-    - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
-- Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
-
-# Pull requests (for collaborators)
-
-- Squash-merge PRs
-- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
-- Optionally pick a `<module>` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules
-- Consider adding yourself to [CODEOWNERS](CODEOWNERS)
-
-# Coding guidelines
-
-- Avoid adding third-party dependencies, extra files, extra headers, etc.
-- Always consider cross-compatibility with other operating systems and architectures
-- Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
-- Vertical alignment makes things more readable and easier to batch edit
-- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets
-- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
-    - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
-    ```cpp
-    // OK
-    llama_context * ctx;
-    const llama_rope_type rope_type;
-
-    // not OK
-    struct llama_context * ctx;
-    const enum llama_rope_type rope_type;
-    ```
-
-    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
-
-- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code
-- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
-- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
-- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
-
-![matmul](media/matmul.png)
-
-# Naming guidelines
-
-- Use `snake_case` for function, variable and type names
-- Naming usually optimizes for longest common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
-
-    ```cpp
-    // not OK
-    int small_number;
-    int big_number;
-
-    // OK
-    int number_small;
-    int number_big;
-    ```
-
-- Enum values are always in upper case and prefixed with the enum name
-
-    ```cpp
-    enum llama_vocab_type {
-        LLAMA_VOCAB_TYPE_NONE = 0,
-        LLAMA_VOCAB_TYPE_SPM  = 1,
-        LLAMA_VOCAB_TYPE_BPE  = 2,
-        LLAMA_VOCAB_TYPE_WPM  = 3,
-        LLAMA_VOCAB_TYPE_UGM  = 4,
-        LLAMA_VOCAB_TYPE_RWKV = 5,
-    };
-    ```
-
-- The general naming pattern is `<class>_<method>`, with `<method>` being `<action>_<noun>`
-
-    ```cpp
-    llama_model_init();           // class: "llama_model",         method: "init"
-    llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
-    llama_sampler_get_seed();     // class: "llama_sampler",       method: "get_seed"
-    llama_set_embeddings();       // class: "llama_context",       method: "set_embeddings"
-    llama_n_threads();            // class: "llama_context",       method: "n_threads"
-    llama_adapter_lora_free();    // class: "llama_adapter_lora",  method: "free"
-    ```
-
-    - The `get` `<action>` can be omitted
-    - The `<noun>` can be omitted if not necessary
-    - The `_context` suffix of the `<class>` is optional. Use it to disambiguate symbols when needed
-    - Use `init`/`free` for constructor/destructor `<action>`
-
-- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
-
-    ```cpp
-    typedef struct llama_context * llama_context_t;
-
-    enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
-    ```
-
-    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_
-
-- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension
-- Python filenames are all lowercase with underscores
-
-- _(TODO: abbreviations usage)_
-
-# Preprocessor directives
-
-- _(TODO: add guidelines with examples and apply them to the codebase)_
-
-    ```cpp
-    #ifdef FOO
-    #endif // FOO
-    ```
-
-# Documentation
-
-- Documentation is a community effort
-- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
-- When you notice incorrect or outdated documentation, please update it
-
-# Resources
-
-The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:
-
-https://github.com/ggerganov/llama.cpp/projects

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023-2024 The ggml authors
+Copyright (c) 2023 Georgi Gerganov
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Package.swift

@@ -1,19 +0,0 @@
-// swift-tools-version:5.5
-
-import PackageDescription
-
-let package = Package(
-    name: "llama",
-    platforms: [
-        .macOS(.v12),
-        .iOS(.v14),
-        .watchOS(.v4),
-        .tvOS(.v14)
-    ],
-    products: [
-        .library(name: "llama", targets: ["llama"]),
-    ],
-    targets: [
-        .systemLibrary(name: "llama", pkgConfig: "llama"),
-    ]
-)

README.md

@@ -1,522 +1,223 @@
 # llama.cpp
 
-![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
-
+[![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
-[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
 
-[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
+Inference of [Facebook's LLaMA](https://github.com/facebookresearch/llama) model in pure C/C++
 
-Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
+**Hot topics:**
 
-## Recent API changes
-
-- [Changelog for `libllama` API](https://github.com/ggerganov/llama.cpp/issues/9289)
-- [Changelog for `llama-server` REST API](https://github.com/ggerganov/llama.cpp/issues/9291)
-
-## Hot topics
-
-- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
-- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
-- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639
-- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
-- Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
-- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
-- Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
-
-----
+- Cache input prompts for faster initialization: https://github.com/ggerganov/llama.cpp/issues/64
+- Create a `llama.cpp` logo: https://github.com/ggerganov/llama.cpp/issues/105
 
 ## Description
 
-The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
-range of hardware - locally and in the cloud.
-
-- Plain C/C++ implementation without any dependencies
-- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
-- AVX, AVX2, AVX512 and AMX support for x86 architectures
-- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
-- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
-- Vulkan and SYCL backend support
-- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
-
-The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
-
-<details>
-<summary>Models</summary>
-
-Typically finetunes of the base models below are supported as well.
-
-Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)
-
-#### Text-only
-
-- [X] LLaMA 🦙
-- [x] LLaMA 2 🦙🦙
-- [x] LLaMA 3 🦙🦙🦙
-- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
-- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
-- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
-- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
-- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
-- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423)
-- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
-- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
-- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
-- [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
-- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
-- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
-- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
-- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
-- [X] [StableLM models](https://huggingface.co/stabilityai)
-- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
-- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
-- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
-- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
-- [x] [PhiMoE](https://github.com/ggerganov/llama.cpp/pull/11003)
-- [x] [GPT-2](https://huggingface.co/gpt2)
-- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
-- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
-- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
-- [x] [Gemma](https://ai.google.dev/gemma)
-- [x] [Mamba](https://github.com/state-spaces/mamba)
-- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
-- [x] [Xverse](https://huggingface.co/models?search=xverse)
-- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
-- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
-- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
-- [x] [OLMo](https://allenai.org/olmo)
-- [x] [OLMo 2](https://allenai.org/olmo)
-- [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924)
-- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
-- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
-- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
-- [x] [Smaug](https://huggingface.co/models?search=Smaug)
-- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
-- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
-- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
-- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
-- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
-- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
-- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
-- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
-- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
-- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
-- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
-- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
-- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
-
-#### Multimodal
-
-- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
-- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
-- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
-- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
-- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
-- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
-- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
-- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
-- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
-- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
-- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
-
-</details>
-
-<details>
-<summary>Bindings</summary>
-
-- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
-- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
-- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
-- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
-- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli)
-- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
-- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
-- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
-- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
-- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
-- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
-- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
-- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
-- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
-- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
-- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
-- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
-- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
-- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
-- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
-- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
-- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
-- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
-- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
-- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
-
-</details>
-
-<details>
-<summary>UIs</summary>
-
-*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
-
-- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
-- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
-- [Dot](https://github.com/alexpinel/Dot) (GPL)
-- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
-- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
-- [janhq/jan](https://github.com/janhq/jan) (AGPL)
-- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
-- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
-- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
-- [LARS](https://github.com/abgulati/LARS) (AGPL)
-- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
-- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
-- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
-- [LMStudio](https://lmstudio.ai/) (proprietary)
-- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
-- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
-- [MindMac](https://mindmac.app) (proprietary)
-- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
-- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
-- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
-- [nat/openplayground](https://github.com/nat/openplayground) (MIT)
-- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT)
-- [ollama/ollama](https://github.com/ollama/ollama) (MIT)
-- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
-- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
-- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT)
-- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT)
-- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
-- [ramalama](https://github.com/containers/ramalama) (MIT)
-- [semperai/amica](https://github.com/semperai/amica) (MIT)
-- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
-- [Autopen](https://github.com/blackhole89/autopen) (GPL)
-
-</details>
-
-<details>
-<summary>Tools</summary>
-
-- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
-- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
-- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
-- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
-- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
-
-</details>
-
-<details>
-<summary>Infrastructure</summary>
-
-- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
-- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
-- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
-- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
-- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
-
-</details>
-
-<details>
-<summary>Games</summary>
-
-- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
-
-</details>
-
-## Supported backends
-
-| Backend | Target devices |
-| --- | --- |
-| [Metal](docs/build.md#metal-build) | Apple Silicon |
-| [BLAS](docs/build.md#blas-build) | All |
-| [BLIS](docs/backend/BLIS.md) | All |
-| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
-| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
-| [CUDA](docs/build.md#cuda) | Nvidia GPU |
-| [HIP](docs/build.md#hip) | AMD GPU |
-| [Vulkan](docs/build.md#vulkan) | GPU |
-| [CANN](docs/build.md#cann) | Ascend NPU |
+The main goal is to run the model using 4-bit quantization on a MacBook
+
+- Plain C/C++ implementation without dependencies
+- Apple silicon first-class citizen - optimized via ARM NEON
+- AVX2 support for x86 architectures
+- Mixed F16 / F32 precision
+- 4-bit quantization support
+- Runs on the CPU
+
+This was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022) - I have no idea if it works correctly.
+Please do not make conclusions about the models based on the results from this implementation.
+For all I know, it can be completely wrong. This project is for educational purposes.
+New features will probably be added mostly through community contributions.
+
+Supported platforms:
+
+- [X] Mac OS
+- [X] Linux
+- [X] Windows (via CMake)
+
+---
+
+Here is a typical run using LLaMA-7B:
+
+```java
+make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -t 8 -n 512
+I llama.cpp build info:
+I UNAME_S:  Darwin
+I UNAME_P:  arm
+I UNAME_M:  arm64
+I CFLAGS:   -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE
+I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread
+I LDFLAGS:   -framework Accelerate
+I CC:       Apple clang version 14.0.0 (clang-1400.0.29.202)
+I CXX:      Apple clang version 14.0.0 (clang-1400.0.29.202)
+
+make: Nothing to be done for `default'.
+main: seed = 1678486056
+llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
+llama_model_load: n_vocab = 32000
+llama_model_load: n_ctx   = 512
+llama_model_load: n_embd  = 4096
+llama_model_load: n_mult  = 256
+llama_model_load: n_head  = 32
+llama_model_load: n_layer = 32
+llama_model_load: n_rot   = 128
+llama_model_load: f16     = 2
+llama_model_load: n_ff    = 11008
+llama_model_load: ggml ctx size = 4529.34 MB
+llama_model_load: memory_size =   512.00 MB, n_mem = 16384
+llama_model_load: .................................... done
+llama_model_load: model size =  4017.27 MB / num tensors = 291
+
+main: prompt: 'Building a website can be done in 10 simple steps:'
+main: number of tokens in prompt = 15
+     1 -> ''
+  8893 -> 'Build'
+   292 -> 'ing'
+   263 -> ' a'
+  4700 -> ' website'
+   508 -> ' can'
+   367 -> ' be'
+  2309 -> ' done'
+   297 -> ' in'
+ 29871 -> ' '
+ 29896 -> '1'
+ 29900 -> '0'
+  2560 -> ' simple'
+  6576 -> ' steps'
+ 29901 -> ':'
+
+sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000
+
+
+Building a website can be done in 10 simple steps:
+1) Select a domain name and web hosting plan
+2) Complete a sitemap
+3) List your products
+4) Write product descriptions
+5) Create a user account
+6) Build the template
+7) Start building the website
+8) Advertise the website
+9) Provide email support
+10) Submit the website to search engines
+A website is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
+The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user's browser.
+The web pages are stored in a web server. The web server is also called a host. When the website is accessed, it is retrieved from the server and displayed on the user's computer.
+A website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
+A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user's screen.
+A website can also be viewed on different devices such as desktops, tablets and smartphones.
+Hence, to have a website displayed on a browser, the website must be hosted.
+A domain name is an address of a website. It is the name of the website.
+The website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
+A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user’s screen.
+A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted.
+A domain name is an address of a website. It is the name of the website.
+A website is an address of a website. It is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
+The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user’s browser.
+A website is known as a website when it is hosted
+
+main: mem per token = 14434244 bytes
+main:     load time =  1332.48 ms
+main:   sample time =  1081.40 ms
+main:  predict time = 31378.77 ms / 61.41 ms per token
+main:    total time = 34036.74 ms
+```
+
+And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
+
+https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
+
+## Usage
+
+Here are the step for the LLaMA-7B model:
+
+```bash
+# build this repo
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+make
+
+# obtain the original LLaMA model weights and place them in ./models
+ls ./models
+65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
+
+# install Python dependencies
+python3 -m pip install torch numpy sentencepiece
+
+# convert the 7B model to ggml FP16 format
+python3 convert-pth-to-ggml.py models/7B/ 1
+
+# quantize the model to 4-bits
+./quantize.sh 7B
+
+# run the inference
+./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
+```
+
+When running the larger models, make sure you have enough disk space to store all the intermediate files.
+
+TODO: add model disk/mem requirements
+
+### Interactive mode
+
+If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
+In this mode, you can always interrupt generation by pressing Ctrl+C and enter one or more lines of text which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt which makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
+
+Here is an example few-shot interaction, invoked with the command
+```
+./main -m ./models/13B/ggml-model-q4_0.bin -t 8 -n 256 --repeat_penalty 1.0 --color -i -r "User:" \
+                                           -p \
+"Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
+
+User: Hello, Bob.
+Bob: Hello. How may I help you today?
+User: Please tell me the largest city in Europe.
+Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
+User:"
+
+```
+Note the use of `--color` to distinguish between user input and generated text.
+
+![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
+
+### Android
+
+You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux).
+First, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
+```
+$ mkdir build-android
+$ cd build-android
+$ export NDK=<your_ndk_directory>
+$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
+$ make
+```
+Install [termux](https://play.google.com/store/apps/details?id=com.termux) on your device and run `termux-setup-storage` to get access to your SD card.
+Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
+
+https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
+
+
+## Limitations
+
+- We don't know yet how much the quantization affects the quality of the generated text
+- Probably the token sampling can be improved
+- The Accelerate framework is actually currently unused since I found that for tensor shapes typical for the Decoder,
+  there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simlpy don't
+  know how to utilize it properly. But in any case, you can even disable it with `LLAMA_NO_ACCELERATE=1 make` and the
+  performance will be the same, since no BLAS calls are invoked by the current implementation
 
-## Building the project
-
-The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
-The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:
-
-- Clone this repository and build locally, see [how to build](docs/build.md)
-- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md)
-- Use a Docker image, see [documentation for Docker](docs/docker.md)
-- Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases)
-
-## Obtaining and quantizing models
-
-The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
-
-- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
-- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
-
-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf <user>/<model>[:quant]`
-
-After downloading a model, use the CLI tools to run it locally - see below.
-
-`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
-
-The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
-
-- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
-- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggerganov/llama.cpp/discussions/10123)
-- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggerganov/llama.cpp/discussions/9268)
-- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggerganov/llama.cpp/discussions/9669)
-
-To learn more about model quantization, [read this documentation](examples/quantize/README.md)
-
-## [`llama-cli`](examples/main)
-
-#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
-
-- <details open>
-    <summary>Run in conversation mode</summary>
-
-    Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
-
-    ```bash
-    llama-cli -m model.gguf
-
-    # > hi, who are you?
-    # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
-    #
-    # > what is 1+1?
-    # Easy peasy! The answer to 1+1 is... 2!
-    ```
-
-    </details>
-
-- <details>
-    <summary>Run in conversation mode with custom chat template</summary>
-
-    ```bash
-    # use the "chatml" template (use -h to see the list of supported templates)
-    llama-cli -m model.gguf -cnv --chat-template chatml
-
-    # use a custom template
-    llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
-    ```
-
-    </details>
-
-- <details>
-    <summary>Run simple text completion</summary>
-
-    To disable conversation mode explicitly, use `-no-cnv`
-
-    ```bash
-    llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv
-
-    # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
-    ```
-
-    </details>
-
-- <details>
-    <summary>Constrain the output with a custom grammar</summary>
-
-    ```bash
-    llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
-
-    # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
-    ```
-
-    The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md).
-
-    For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/
-
-    </details>
-
-
-## [`llama-server`](examples/server)
-
-#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
-
-- <details open>
-    <summary>Start a local HTTP server with default configuration on port 8080</summary>
-
-    ```bash
-    llama-server -m model.gguf --port 8080
-
-    # Basic web UI can be accessed via browser: http://localhost:8080
-    # Chat completion endpoint: http://localhost:8080/v1/chat/completions
-    ```
-
-    </details>
-
-- <details>
-    <summary>Support multiple-users and parallel decoding</summary>
-
-    ```bash
-    # up to 4 concurrent requests, each with 4096 max context
-    llama-server -m model.gguf -c 16384 -np 4
-    ```
-
-    </details>
-
-- <details>
-    <summary>Enable speculative decoding</summary>
-
-    ```bash
-    # the draft.gguf model should be a small variant of the target model.gguf
-    llama-server -m model.gguf -md draft.gguf
-    ```
-
-    </details>
-
-- <details>
-    <summary>Serve an embedding model</summary>
-
-    ```bash
-    # use the /embedding endpoint
-    llama-server -m model.gguf --embedding --pooling cls -ub 8192
-    ```
-
-    </details>
-
-- <details>
-    <summary>Serve a reranking model</summary>
-
-    ```bash
-    # use the /reranking endpoint
-    llama-server -m model.gguf --reranking
-    ```
-
-    </details>
-
-- <details>
-    <summary>Constrain all outputs with a grammar</summary>
-
-    ```bash
-    # custom grammar
-    llama-server -m model.gguf --grammar-file grammar.gbnf
-
-    # JSON
-    llama-server -m model.gguf --grammar-file grammars/json.gbnf
-    ```
-
-    </details>
-
-
-## [`llama-perplexity`](examples/perplexity)
-
-#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text.
-
-- <details open>
-    <summary>Measure the perplexity over a text file</summary>
-
-    ```bash
-    llama-perplexity -m model.gguf -f file.txt
-
-    # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
-    # Final estimate: PPL = 5.4007 +/- 0.67339
-    ```
-
-    </details>
-
-- <details>
-    <summary>Measure KL divergence</summary>
-
-    ```bash
-    # TODO
-    ```
-
-    </details>
-
-[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
-[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
-
-## [`llama-bench`](examples/llama-bench)
-
-#### Benchmark the performance of the inference for various parameters.
-
-- <details open>
-    <summary>Run default benchmark</summary>
-
-    ```bash
-    llama-bench -m model.gguf
-
-    # Output:
-    # | model               |       size |     params | backend    | threads |          test |                  t/s |
-    # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
-    #
-    # build: 3e0ba0e60 (4229)
-    ```
-
-    </details>
-
-## [`llama-run`](examples/run)
-
-#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
-
-- <details>
-    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
-
-    ```bash
-    llama-run granite-code
-    ```
-
-    </details>
-
-[^3]: [RamaLama](https://github.com/containers/ramalama)
-
-## [`llama-simple`](examples/simple)
-
-#### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
-
-- <details>
-    <summary>Basic text completion</summary>
-
-    ```bash
-    llama-simple -m model.gguf
-
-    # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
-    ```
-
-    </details>
-
-
-## Contributing
+### Contributing
 
 - Contributors can open PRs
-- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
+- Collaborators can push to branches in the `llama.cpp` repo
 - Collaborators will be invited based on contributions
-- Any help with managing issues, PRs and projects is very appreciated!
+
+### Coding guidelines
+
+- Avoid adding third-party dependencies, extra files, extra headers, etc.
+- Always consider cross-compatibility with other operating systems and architectures
+- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
+- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
+- Clean-up any trailing whitespaces, use 4 spaces indentation, brackets on same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
-- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
-- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
-- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
 
-## Other documentation
-
-- [main (cli)](examples/main/README.md)
-- [server](examples/server/README.md)
-- [GBNF grammars](grammars/README.md)
-
-#### Development documentation
-
-- [How to build](docs/build.md)
-- [Running on Docker](docs/docker.md)
-- [Build on Android](docs/android.md)
-- [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
-- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
-
-#### Seminal papers and background on the models
-
-If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
-- LLaMA:
-    - [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
-    - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
-- GPT-3
-    - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
-- GPT-3.5 / InstructGPT / ChatGPT:
-    - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
-    - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
-
-#### References
+### Misc
 
+- Practice your C++ typing skills: https://typing-battles.ggerganov.com

SECURITY.md

@@ -1,67 +0,0 @@
-# Security Policy
-
- - [**Using llama.cpp securely**](#using-llamacpp-securely)
-   - [Untrusted models](#untrusted-models)
-   - [Untrusted inputs](#untrusted-inputs)
-   - [Data privacy](#data-privacy)
-   - [Untrusted environments or networks](#untrusted-environments-or-networks)
-   - [Multi-Tenant environments](#multi-tenant-environments)
- - [**Reporting a vulnerability**](#reporting-a-vulnerability)
-
-## Using llama.cpp securely
-
-### Untrusted models
-Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
-
-*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
-
-> [!NOTE]
-> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
-
-### Untrusted inputs
-
-Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
-
-For maximum security when handling untrusted inputs, you may need to employ the following:
-
-* Sandboxing: Isolate the environment where the inference happens.
-* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
-* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
-* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
-    * Validation: Enforce strict rules on allowed characters and data types.
-    * Filtering: Remove potentially malicious scripts or code fragments.
-    * Encoding: Convert special characters into safe representations.
-    * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
-
-### Data privacy
-
-To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
-
-### Untrusted environments or networks
-
-If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
-* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
-* Encrypt your data if sending it over the network.
-
-### Multi-Tenant environments
-
-If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
-
-1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
-
-2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
-
-3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
-
-4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
-
-## Reporting a vulnerability
-
-Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
-
-<!-- normal version -->
-However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new).
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

Sources/llama/llama.h

@@ -1,4 +0,0 @@
-#pragma once
-
-#include <llama.h>
-

Sources/llama/module.modulemap

@@ -1,5 +0,0 @@
-module llama [system] {
-    header "llama.h"
-    link "llama"
-    export *
-}

ci/README.md

@@ -1,29 +0,0 @@
-# CI
-
-In addition to [Github Actions](https://github.com/ggerganov/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
-
-https://github.com/ggml-org/ci
-
-It monitors the `master` branch for new commits and runs the
-[ci/run.sh](https://github.com/ggerganov/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
-to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
-to cover various hardware architectures, including GPU and Apple Silicon instances.
-
-Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
-Only the branches of this repo are monitored for this keyword.
-
-It is a good practice, before publishing changes to execute the full CI locally on your machine:
-
-```bash
-mkdir tmp
-
-# CPU-only build
-bash ./ci/run.sh ./tmp/results ./tmp/mnt
-
-# with CUDA support
-GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-
-# with SYCL support
-source /opt/intel/oneapi/setvars.sh
-GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-```

ci/run.sh

@@ -1,854 +0,0 @@
-#!/bin/bash
-#
-# sample usage:
-#
-# mkdir tmp
-#
-# # CPU-only build
-# bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-# # with CUDA support
-# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-# # with SYCL support
-# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-# # with VULKAN support
-# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-
-if [ -z "$2" ]; then
-    echo "usage: $0 <output-dir> <mnt-dir>"
-    exit 1
-fi
-
-mkdir -p "$1"
-mkdir -p "$2"
-
-OUT=$(realpath "$1")
-MNT=$(realpath "$2")
-
-rm -f "$OUT/*.log"
-rm -f "$OUT/*.exit"
-rm -f "$OUT/*.md"
-
-sd=`dirname $0`
-cd $sd/../
-SRC=`pwd`
-
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
-
-if [ ! -z ${GG_BUILD_METAL} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON"
-fi
-
-if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native"
-fi
-
-if [ ! -z ${GG_BUILD_SYCL} ]; then
-    if [ -z ${ONEAPI_ROOT} ]; then
-        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
-        echo "source /opt/intel/oneapi/setvars.sh"
-        exit 1
-    fi
-
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
-fi
-
-if [ ! -z ${GG_BUILD_VULKAN} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
-fi
-## helpers
-
-# download a file if it does not exist or if it is outdated
-function gg_wget {
-    local out=$1
-    local url=$2
-
-    local cwd=`pwd`
-
-    mkdir -p $out
-    cd $out
-
-    # should not re-download if file is the same
-    wget -nv -N $url
-
-    cd $cwd
-}
-
-function gg_printf {
-    printf -- "$@" >> $OUT/README.md
-}
-
-function gg_run {
-    ci=$1
-
-    set -o pipefail
-    set -x
-
-    gg_run_$ci | tee $OUT/$ci.log
-    cur=$?
-    echo "$cur" > $OUT/$ci.exit
-
-    set +x
-    set +o pipefail
-
-    gg_sum_$ci
-
-    ret=$((ret | cur))
-}
-
-## ci
-
-# ctest_debug
-
-function gg_run_ctest_debug {
-    cd ${SRC}
-
-    rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
-
-    set -e
-
-    # Check cmake, make and ctest are installed
-    gg_check_build_requirements
-
-    (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                  ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
-
-    set +e
-}
-
-function gg_sum_ctest_debug {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs ctest in debug mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
-    gg_printf '```\n'
-    gg_printf '\n'
-}
-
-# ctest_release
-
-function gg_run_ctest_release {
-    cd ${SRC}
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    # Check cmake, make and ctest are installed
-    gg_check_build_requirements
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    if [ -z ${GG_BUILD_LOW_PERF} ]; then
-        (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log
-    else
-        (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
-    fi
-
-    set +e
-}
-
-function gg_sum_ctest_release {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs ctest in release mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
-    gg_printf '```\n'
-}
-
-# test_scripts_debug
-
-function gg_run_test_scripts_debug {
-    cd ${SRC}
-
-    set -e
-
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-
-    set +e
-}
-
-function gg_sum_test_scripts_debug {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs test scripts in debug mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
-    gg_printf '```\n'
-    gg_printf '\n'
-}
-
-# test_scripts_release
-
-function gg_run_test_scripts_release {
-    cd ${SRC}
-
-    set -e
-
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-
-    set +e
-}
-
-function gg_sum_test_scripts_release {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs test scripts in release mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
-    gg_printf '```\n'
-    gg_printf '\n'
-}
-
-function gg_get_model {
-    local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf"
-    local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf"
-    local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
-    if [[ -s $gguf_0 ]]; then
-        echo -n "$gguf_0"
-    elif [[ -s $gguf_1 ]]; then
-        echo -n "$gguf_1"
-    elif [[ -s $gguf_2 ]]; then
-        echo -n "$gguf_2"
-    else
-        echo >&2 "No model found. Can't run gg_run_ctest_with_model."
-        exit 1
-    fi
-}
-
-function gg_run_ctest_with_model_debug {
-    cd ${SRC}
-
-    local model; model=$(gg_get_model)
-    cd build-ci-debug
-    set -e
-    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
-    set +e
-    cd ..
-}
-
-function gg_run_ctest_with_model_release {
-    cd ${SRC}
-
-    local model; model=$(gg_get_model)
-    cd build-ci-release
-    set -e
-    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
-    set +e
-    cd ..
-}
-
-function gg_sum_ctest_with_model_debug {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs ctest with model files in debug mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
-    gg_printf '```\n'
-}
-
-function gg_sum_ctest_with_model_release {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs ctest with model files in release mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
-    gg_printf '```\n'
-}
-
-# open_llama_7b_v2
-
-function gg_run_open_llama_7b_v2 {
-    cd ${SRC}
-
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
-
-    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
-
-    path_models="../models-mnt/open-llama/7B-v2"
-    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
-    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
-    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
-    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
-    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
-    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
-    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
-    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
-    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
-    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
-
-    wiki_test="${path_wiki}/wiki.test.raw"
-
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-
-    function check_ppl {
-        qnt="$1"
-        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
-            return 20
-        fi
-
-        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
-        return 0
-    }
-
-    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-
-    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
-
-    set +e
-}
-
-function gg_sum_open_llama_7b_v2 {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'OpenLLaMA 7B-v2:\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
-    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
-    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
-    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
-    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
-    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
-    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
-    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
-    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
-    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
-    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
-    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
-    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-}
-
-# pythia_1.4b
-
-function gg_run_pythia_1_4b {
-    cd ${SRC}
-
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin
-
-    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
-    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
-
-    path_models="../models-mnt/pythia/1.4B"
-    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
-    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
-    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
-    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
-    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
-    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
-    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
-    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
-    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
-    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
-
-    wiki_test_60="${path_wiki}/wiki.test-60.raw"
-
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-
-    function check_ppl {
-        qnt="$1"
-        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
-            return 20
-        fi
-
-        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
-        return 0
-    }
-
-    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
-    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-
-    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
-
-    set +e
-}
-
-function gg_sum_pythia_1_4b {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Pythia 1.4B:\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
-    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
-    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
-    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
-    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
-    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
-    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
-    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
-    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
-    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
-    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
-    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
-    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-}
-
-# pythia_2_8b
-
-function gg_run_pythia_2_8b {
-    cd ${SRC}
-
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin
-
-    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
-
-    path_models="../models-mnt/pythia/2.8B"
-    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
-    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
-    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
-    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
-    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
-    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
-    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
-    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
-    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
-    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
-
-    wiki_test="${path_wiki}/wiki.test.raw"
-
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-
-    function check_ppl {
-        qnt="$1"
-        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
-            return 20
-        fi
-
-        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
-        return 0
-    }
-
-    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
-    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-
-    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
-
-    set +e
-}
-
-function gg_sum_pythia_2_8b {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Pythia 2.8B:\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
-    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
-    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
-    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
-    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
-    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
-    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
-    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
-    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
-    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
-    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
-    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
-    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-}
-
-# bge-small
-
-function gg_run_embd_bge_small {
-    cd ${SRC}
-
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/modules.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
-
-    gg_wget models-mnt/bge-small/1_Pooling https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json
-
-    path_models="../models-mnt/bge-small"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
-
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-
-    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-
-    set +e
-}
-
-function gg_sum_embd_bge_small {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'BGE Small (BERT):\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
-    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
-}
-
-# rerank_tiny
-
-function gg_run_rerank_tiny {
-    cd ${SRC}
-
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/sentence_bert_config.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.txt
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/modules.json
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
-
-    gg_wget models-mnt/rerank-tiny/1_Pooling https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/1_Pooling/config.json
-
-    path_models="../models-mnt/rerank-tiny"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-
-    # for this model, the SEP token is "</s>"
-    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?</s></s>hi\nwhat is panda?</s></s>it's a bear\nwhat is panda?</s></s>The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
-
-    # sample output
-    # rerank score 0:    0.029
-    # rerank score 1:    0.029
-    # rerank score 2:    0.135
-
-    # check that the score is in the range [$3, $4]
-    function check_score {
-        qnt="$1"
-        score=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
-
-        if [ $(echo "$score < $3" | bc) -eq 1 ] || [ $(echo "$score > $4" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: score not in range [%s, %s])\n' "$qnt" "$score" "$3" "$4"
-            return 20
-        fi
-
-        printf '  - %s @ %s OK\n' "$qnt" "$score"
-        return 0
-    }
-
-    check_score "rerank score 0" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 0")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
-    check_score "rerank score 1" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 1")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
-    check_score "rerank score 2" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 2")" "0.10" "0.30" | tee -a $OUT/${ci}-rk-f16.log
-
-    set +e
-}
-
-function gg_sum_rerank_tiny {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Rerank Tiny (Jina):\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)"
-}
-
-function gg_check_build_requirements {
-    if ! command -v cmake &> /dev/null; then
-        gg_printf 'cmake not found, please install'
-    fi
-
-    if ! command -v make &> /dev/null; then
-        gg_printf 'make not found, please install'
-    fi
-
-    if ! command -v ctest &> /dev/null; then
-        gg_printf 'ctest not found, please install'
-    fi
-}
-
-## main
-
-export LLAMA_LOG_PREFIX=1
-export LLAMA_LOG_TIMESTAMPS=1
-
-if [ -z ${GG_BUILD_LOW_PERF} ]; then
-    # Create symlink: ./llama.cpp/models-mnt -> $MNT/models/models-mnt
-    rm -rf ${SRC}/models-mnt
-    mnt_models=${MNT}/models
-    mkdir -p ${mnt_models}
-    ln -sfn ${mnt_models} ${SRC}/models-mnt
-
-    # Create a fresh python3 venv and enter it
-    if ! python3 -m venv "$MNT/venv"; then
-        echo "Error: Failed to create Python virtual environment at $MNT/venv."
-        exit 1
-    fi
-    source "$MNT/venv/bin/activate"
-
-    pip install -r ${SRC}/requirements.txt --disable-pip-version-check
-    pip install --editable gguf-py --disable-pip-version-check
-fi
-
-ret=0
-
-test $ret -eq 0 && gg_run ctest_debug
-test $ret -eq 0 && gg_run ctest_release
-
-if [ -z ${GG_BUILD_LOW_PERF} ]; then
-    test $ret -eq 0 && gg_run embd_bge_small
-    test $ret -eq 0 && gg_run rerank_tiny
-
-    if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
-        test $ret -eq 0 && gg_run test_scripts_debug
-        test $ret -eq 0 && gg_run test_scripts_release
-    fi
-
-    if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
-        if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then
-            test $ret -eq 0 && gg_run pythia_1_4b
-        else
-            test $ret -eq 0 && gg_run pythia_2_8b
-            #test $ret -eq 0 && gg_run open_llama_7b_v2
-        fi
-        test $ret -eq 0 && gg_run ctest_with_model_debug
-        test $ret -eq 0 && gg_run ctest_with_model_release
-    fi
-fi
-
-exit $ret

cmake/arm64-apple-clang.cmake

@@ -1,16 +0,0 @@
-set( CMAKE_SYSTEM_NAME Darwin )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-apple-darwin-macho )
-
-set( CMAKE_C_COMPILER    clang )
-set( CMAKE_CXX_COMPILER  clang++ )
-
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )
-
-set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
-set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )

cmake/arm64-windows-llvm.cmake

@@ -1,16 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-pc-windows-msvc )
-
-set( CMAKE_C_COMPILER    clang )
-set( CMAKE_CXX_COMPILER  clang++ )
-
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )
-
-set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
-set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )

cmake/arm64-windows-msvc.cmake

@@ -1,6 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-pc-windows-msvc )
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )

cmake/build-info.cmake

@@ -1,58 +0,0 @@
-set(BUILD_NUMBER 0)
-set(BUILD_COMMIT "unknown")
-set(BUILD_COMPILER "unknown")
-set(BUILD_TARGET "unknown")
-
-# Look for git
-find_package(Git)
-if(NOT Git_FOUND)
-    find_program(GIT_EXECUTABLE NAMES git git.exe)
-    if(GIT_EXECUTABLE)
-        set(Git_FOUND TRUE)
-        message(STATUS "Found Git: ${GIT_EXECUTABLE}")
-    else()
-        message(WARNING "Git not found. Build info will not be accurate.")
-    endif()
-endif()
-
-# Get the commit count and hash
-if(Git_FOUND)
-    execute_process(
-        COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-        OUTPUT_VARIABLE HEAD
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-        RESULT_VARIABLE RES
-    )
-    if (RES EQUAL 0)
-        set(BUILD_COMMIT ${HEAD})
-    endif()
-    execute_process(
-        COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-        OUTPUT_VARIABLE COUNT
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-        RESULT_VARIABLE RES
-    )
-    if (RES EQUAL 0)
-        set(BUILD_NUMBER ${COUNT})
-    endif()
-endif()
-
-if(MSVC)
-    set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
-    set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
-else()
-    execute_process(
-        COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER}
-        OUTPUT_VARIABLE OUT
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-    )
-    set(BUILD_COMPILER ${OUT})
-    execute_process(
-        COMMAND ${CMAKE_C_COMPILER} -dumpmachine
-        OUTPUT_VARIABLE OUT
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-    )
-    set(BUILD_TARGET ${OUT})
-endif()

cmake/common.cmake

@@ -1,33 +0,0 @@
-function(llama_add_compile_flags)
-    if (LLAMA_FATAL_WARNINGS)
-        if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
-            list(APPEND C_FLAGS   -Werror)
-            list(APPEND CXX_FLAGS -Werror)
-        elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-            add_compile_options(/WX)
-        endif()
-    endif()
-
-    if (LLAMA_ALL_WARNINGS)
-        if (NOT MSVC)
-            list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-                                -Werror=implicit-int -Werror=implicit-function-declaration)
-
-            list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn)
-
-            list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-
-            list(APPEND C_FLAGS   ${WARNING_FLAGS})
-            list(APPEND CXX_FLAGS ${WARNING_FLAGS})
-
-            ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
-
-            add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
-                                "$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
-        else()
-            # todo : msvc
-            set(C_FLAGS   "" PARENT_SCOPE)
-            set(CXX_FLAGS "" PARENT_SCOPE)
-        endif()
-    endif()
-endfunction()

cmake/git-vars.cmake

@@ -1,22 +0,0 @@
-find_package(Git)
-
-# the commit's SHA1
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_SHA1
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the date of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_DATE
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the subject of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%s
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

cmake/llama-config.cmake.in

@@ -1,30 +0,0 @@
-set(LLAMA_VERSION      @LLAMA_INSTALL_VERSION@)
-set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
-set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
-set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)
-
-@PACKAGE_INIT@
-
-set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
-set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
-set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
-
-find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
-
-find_library(llama_LIBRARY llama
-    REQUIRED
-    HINTS ${LLAMA_LIB_DIR}
-    NO_CMAKE_FIND_ROOT_PATH
-)
-
-add_library(llama UNKNOWN IMPORTED)
-set_target_properties(llama
-    PROPERTIES
-        INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
-        INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
-        IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-        IMPORTED_LOCATION "${llama_LIBRARY}"
-        INTERFACE_COMPILE_FEATURES c_std_90
-        POSITION_INDEPENDENT_CODE ON)
-
-check_required_components(Llama)

cmake/llama.pc.in

@@ -1,10 +0,0 @@
-prefix=@CMAKE_INSTALL_PREFIX@
-exec_prefix=@CMAKE_INSTALL_PREFIX@
-libdir=@CMAKE_INSTALL_FULL_LIBDIR@
-includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
-
-Name: llama
-Description: Port of Facebook's LLaMA model in C/C++
-Version: @LLAMA_INSTALL_VERSION@
-Libs: -L${libdir} -lggml -lggml-base -lllama
-Cflags: -I${includedir}

cmake/x64-windows-llvm.cmake

@@ -1,11 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR x86_64 )
-
-set( CMAKE_C_COMPILER    clang )
-set( CMAKE_CXX_COMPILER  clang++ )
-
-set( arch_c_flags "-march=native" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" )
-

common/CMakeLists.txt

@@ -1,124 +0,0 @@
-# common
-
-find_package(Threads REQUIRED)
-
-llama_add_compile_flags()
-
-# Build info header
-#
-
-if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
-    set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
-
-    # Is git submodule
-    if(NOT IS_DIRECTORY "${GIT_DIR}")
-        file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
-        string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
-        string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
-        if (SLASH_POS EQUAL 0)
-            set(GIT_DIR "${REAL_GIT_DIR}")
-        else()
-            set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
-        endif()
-    endif()
-
-    if(EXISTS "${GIT_DIR}/index")
-        set(GIT_INDEX "${GIT_DIR}/index")
-    else()
-        message(WARNING "Git index not found in git repository.")
-        set(GIT_INDEX "")
-    endif()
-else()
-    message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
-    set(GIT_INDEX "")
-endif()
-
-# Add a custom command to rebuild build-info.cpp when .git/index changes
-add_custom_command(
-    OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp"
-    COMMENT "Generating build details from Git"
-    COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
-            -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
-    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
-    DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
-    VERBATIM
-)
-set(TARGET build_info)
-add_library(${TARGET} OBJECT build-info.cpp)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-endif()
-
-set(TARGET common)
-
-add_library(${TARGET} STATIC
-    arg.cpp
-    arg.h
-    base64.hpp
-    chat.cpp
-    chat.hpp
-    chat-template.hpp
-    common.cpp
-    common.h
-    console.cpp
-    console.h
-    json-schema-to-grammar.cpp
-    json.hpp
-    llguidance.cpp
-    log.cpp
-    log.h
-    minja.hpp
-    ngram-cache.cpp
-    ngram-cache.h
-    sampling.cpp
-    sampling.h
-    speculative.cpp
-    speculative.h
-    )
-
-if (BUILD_SHARED_LIBS)
-    set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-endif()
-
-set(LLAMA_COMMON_EXTRA_LIBS build_info)
-
-# Use curl to download model url
-if (LLAMA_CURL)
-    find_package(CURL REQUIRED)
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
-    include_directories(${CURL_INCLUDE_DIRS})
-    find_library(CURL_LIBRARY curl REQUIRED)
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
-endif ()
-
-if (LLAMA_LLGUIDANCE)
-    include(ExternalProject)
-    set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
-    set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
-    ExternalProject_Add(llguidance_ext
-        GIT_REPOSITORY https://github.com/guidance-ai/llguidance
-        # v0.6.12:
-        GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
-        PREFIX ${CMAKE_BINARY_DIR}/llguidance
-        SOURCE_DIR ${LLGUIDANCE_SRC}
-        BUILD_IN_SOURCE TRUE
-        CONFIGURE_COMMAND ""
-        BUILD_COMMAND cargo build --release
-        INSTALL_COMMAND ""
-        BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h
-        UPDATE_COMMAND ""
-    )
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
-
-    add_library(llguidance STATIC IMPORTED)
-    set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a)
-    add_dependencies(llguidance llguidance_ext)
-
-    target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance)
-endif ()
-
-target_include_directories(${TARGET} PUBLIC .)
-target_compile_features   (${TARGET} PUBLIC cxx_std_17)
-target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)

common/arg.h

@@ -1,80 +0,0 @@
-#pragma once
-
-#include "common.h"
-
-#include <set>
-#include <string>
-#include <vector>
-
-//
-// CLI argument parsing
-//
-
-struct common_arg {
-    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
-    std::set<enum llama_example> excludes = {};
-    std::vector<const char *> args;
-    const char * value_hint   = nullptr; // help text or example for arg value
-    const char * value_hint_2 = nullptr; // for second arg value
-    const char * env          = nullptr;
-    std::string help;
-    bool is_sparam = false; // is current arg a sampling param?
-    void (*handler_void)   (common_params & params) = nullptr;
-    void (*handler_string) (common_params & params, const std::string &) = nullptr;
-    void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
-    void (*handler_int)    (common_params & params, int) = nullptr;
-
-    common_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(common_params & params, const std::string &)
-    ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
-
-    common_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(common_params & params, int)
-    ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
-
-    common_arg(
-        const std::initializer_list<const char *> & args,
-        const std::string & help,
-        void (*handler)(common_params & params)
-    ) : args(args), help(help), handler_void(handler) {}
-
-    // support 2 values for arg
-    common_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const char * value_hint_2,
-        const std::string & help,
-        void (*handler)(common_params & params, const std::string &, const std::string &)
-    ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
-
-    common_arg & set_examples(std::initializer_list<enum llama_example> examples);
-    common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
-    common_arg & set_env(const char * env);
-    common_arg & set_sparam();
-    bool in_example(enum llama_example ex);
-    bool is_exclude(enum llama_example ex);
-    bool get_value_from_env(std::string & output);
-    bool has_value_from_env();
-    std::string to_string();
-};
-
-struct common_params_context {
-    enum llama_example ex = LLAMA_EXAMPLE_COMMON;
-    common_params & params;
-    std::vector<common_arg> options;
-    void(*print_usage)(int, char **) = nullptr;
-    common_params_context(common_params & params) : params(params) {}
-};
-
-// parse input arguments from CLI
-// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
-bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
-
-// function to be used by test-arg-parser
-common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

common/base64.hpp

@@ -1,392 +0,0 @@
-/*
-This is free and unencumbered software released into the public domain.
-
-Anyone is free to copy, modify, publish, use, compile, sell, or
-distribute this software, either in source code form or as a compiled
-binary, for any purpose, commercial or non-commercial, and by any
-means.
-
-In jurisdictions that recognize copyright laws, the author or authors
-of this software dedicate any and all copyright interest in the
-software to the public domain. We make this dedication for the benefit
-of the public at large and to the detriment of our heirs and
-successors. We intend this dedication to be an overt act of
-relinquishment in perpetuity of all present and future rights to this
-software under copyright law.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
-OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-OTHER DEALINGS IN THE SOFTWARE.
-
-For more information, please refer to <http://unlicense.org>
-*/
-
-#ifndef PUBLIC_DOMAIN_BASE64_HPP_
-#define PUBLIC_DOMAIN_BASE64_HPP_
-
-#include <cstdint>
-#include <iterator>
-#include <stdexcept>
-#include <string>
-
-class base64_error : public std::runtime_error
-{
-public:
-    using std::runtime_error::runtime_error;
-};
-
-class base64
-{
-public:
-    enum class alphabet
-    {
-        /** the alphabet is detected automatically */
-        auto_,
-        /** the standard base64 alphabet is used */
-        standard,
-        /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/
-        url_filename_safe
-    };
-
-    enum class decoding_behavior
-    {
-        /** if the input is not padded, the remaining bits are ignored */
-        moderate,
-        /** if a padding character is encounter decoding is finished */
-        loose
-    };
-
-    /**
-     Encodes all the elements from `in_begin` to `in_end` to `out`.
-
-     @warning The source and destination cannot overlap. The destination must be able to hold at least
-     `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator.
-
-     @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than
-     8 bits
-     @tparam Output_iterator the destination; the elements written to it are from the type `char`
-     @param in_begin the beginning of the source
-     @param in_end the ending of the source
-     @param out the destination iterator
-     @param alphabet which alphabet should be used
-     @returns the iterator to the next element past the last element copied
-     @throws see `Input_iterator` and `Output_iterator`
-    */
-    template<typename Input_iterator, typename Output_iterator>
-    static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
-                                  alphabet alphabet = alphabet::standard)
-    {
-        constexpr auto pad = '=';
-        const char* alpha  = alphabet == alphabet::url_filename_safe
-                                ? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_"
-                                : "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
-
-        while (in_begin != in_end) {
-            std::uint8_t i0 = 0, i1 = 0, i2 = 0;
-
-            // first character
-            i0 = static_cast<std::uint8_t>(*in_begin);
-            ++in_begin;
-
-            *out = alpha[i0 >> 2 & 0x3f];
-            ++out;
-
-            // part of first character and second
-            if (in_begin != in_end) {
-                i1 = static_cast<std::uint8_t>(*in_begin);
-                ++in_begin;
-
-                *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)];
-                ++out;
-            } else {
-                *out = alpha[(i0 & 0x3) << 4];
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                break;
-            }
-
-            // part of second character and third
-            if (in_begin != in_end) {
-                i2 = static_cast<std::uint8_t>(*in_begin);
-                ++in_begin;
-
-                *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)];
-                ++out;
-            } else {
-                *out = alpha[(i1 & 0xf) << 2];
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                break;
-            }
-
-            // rest of third
-            *out = alpha[i2 & 0x3f];
-            ++out;
-        }
-
-        return out;
-    }
-    /**
-     Encodes a string.
-
-     @param str the string that should be encoded
-     @param alphabet which alphabet should be used
-     @returns the encoded base64 string
-     @throws see base64::encode()
-    */
-    static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard)
-    {
-        std::string result;
-
-        result.reserve(required_encode_size(str.length()) + 1);
-
-        encode(str.begin(), str.end(), std::back_inserter(result), alphabet);
-
-        return result;
-    }
-    /**
-     Encodes a char array.
-
-     @param buffer the char array
-     @param size the size of the array
-     @param alphabet which alphabet should be used
-     @returns the encoded string
-    */
-    static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard)
-    {
-        std::string result;
-
-        result.reserve(required_encode_size(size) + 1);
-
-        encode(buffer, buffer + size, std::back_inserter(result), alphabet);
-
-        return result;
-    }
-    /**
-     Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`,
-     in other words: inplace decoding is possible.
-
-     @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`,
-     otherwise the behavior depends on the output iterator.
-
-     @tparam Input_iterator the source; the returned elements are cast to `char`
-     @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t`
-     @param in_begin the beginning of the source
-     @param in_end the ending of the source
-     @param out the destination iterator
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the iterator to the next element past the last element copied
-     @throws base64_error depending on the set behavior
-     @throws see `Input_iterator` and `Output_iterator`
-    */
-    template<typename Input_iterator, typename Output_iterator>
-    static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
-                                  alphabet alphabet          = alphabet::auto_,
-                                  decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        //constexpr auto pad = '=';
-        std::uint8_t last  = 0;
-        auto bits          = 0;
-
-        while (in_begin != in_end) {
-            auto c = *in_begin;
-            ++in_begin;
-
-            if (c == '=') {
-                break;
-            }
-
-            auto part = _base64_value(alphabet, c);
-
-            // enough bits for one byte
-            if (bits + 6 >= 8) {
-                *out = (last << (8 - bits)) | (part >> (bits - 2));
-                ++out;
-
-                bits -= 2;
-            } else {
-                bits += 6;
-            }
-
-            last = part;
-        }
-
-        // check padding
-        if (behavior != decoding_behavior::loose) {
-            while (in_begin != in_end) {
-                auto c = *in_begin;
-                ++in_begin;
-
-                if (c != '=') {
-                    throw base64_error("invalid base64 character.");
-                }
-            }
-        }
-
-        return out;
-    }
-    /**
-     Decodes a string.
-
-     @param str the base64 encoded string
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the decoded string
-     @throws see base64::decode()
-    */
-    static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_,
-                              decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        std::string result;
-
-        result.reserve(max_decode_size(str.length()));
-
-        decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior);
-
-        return result;
-    }
-    /**
-     Decodes a string.
-
-     @param buffer the base64 encoded buffer
-     @param size the size of the buffer
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the decoded string
-     @throws see base64::decode()
-    */
-    static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_,
-                              decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        std::string result;
-
-        result.reserve(max_decode_size(size));
-
-        decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior);
-
-        return result;
-    }
-    /**
-     Decodes a string inplace.
-
-     @param[in,out] str the base64 encoded string
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @throws base64::decode_inplace()
-    */
-    static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_,
-                               decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin());
-    }
-    /**
-     Decodes a char array inplace.
-
-     @param[in,out] str the string array
-     @param size the length of the array
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the pointer to the next element past the last element decoded
-     @throws base64::decode_inplace()
-    */
-    static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_,
-                                decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        return decode(str, str + size, str, alphabet, behavior);
-    }
-    /**
-     Returns the required decoding size for a given size. The value is calculated with the following formula:
-
-     $$
-     \lceil \frac{size}{4} \rceil \cdot 3
-     $$
-
-     @param size the size of the encoded input
-     @returns the size of the resulting decoded buffer; this the absolute maximum
-    */
-    static std::size_t max_decode_size(std::size_t size) noexcept
-    {
-        return (size / 4 + (size % 4 ? 1 : 0)) * 3;
-    }
-    /**
-     Returns the required encoding size for a given size. The value is calculated with the following formula:
-
-     $$
-     \lceil \frac{size}{3} \rceil \cdot 4
-     $$
-
-     @param size the size of the decoded input
-     @returns the size of the resulting encoded buffer
-    */
-    static std::size_t required_encode_size(std::size_t size) noexcept
-    {
-        return (size / 3 + (size % 3 ? 1 : 0)) * 4;
-    }
-
-private:
-    static std::uint8_t _base64_value(alphabet& alphabet, char c)
-    {
-        if (c >= 'A' && c <= 'Z') {
-            return c - 'A';
-        } else if (c >= 'a' && c <= 'z') {
-            return c - 'a' + 26;
-        } else if (c >= '0' && c <= '9') {
-            return c - '0' + 52;
-        }
-
-        // comes down to alphabet
-        if (alphabet == alphabet::standard) {
-            if (c == '+') {
-                return 62;
-            } else if (c == '/') {
-                return 63;
-            }
-        } else if (alphabet == alphabet::url_filename_safe) {
-            if (c == '-') {
-                return 62;
-            } else if (c == '_') {
-                return 63;
-            }
-        } // auto detect
-        else {
-            if (c == '+') {
-                alphabet = alphabet::standard;
-
-                return 62;
-            } else if (c == '/') {
-                alphabet = alphabet::standard;
-
-                return 63;
-            } else if (c == '-') {
-                alphabet = alphabet::url_filename_safe;
-
-                return 62;
-            } else if (c == '_') {
-                alphabet = alphabet::url_filename_safe;
-
-                return 63;
-            }
-        }
-
-        throw base64_error("invalid base64 character.");
-    }
-};
-
-#endif // !PUBLIC_DOMAIN_BASE64_HPP_

common/build-info.cpp.in

@@ -1,4 +0,0 @@
-int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@;
-char const *LLAMA_COMMIT = "@BUILD_COMMIT@";
-char const *LLAMA_COMPILER = "@BUILD_COMPILER@";
-char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@";

common/chat-template.hpp

@@ -1,529 +0,0 @@
-/*
-    Copyright 2024 Google LLC
-
-    Use of this source code is governed by an MIT-style
-    license that can be found in the LICENSE file or at
-    https://opensource.org/licenses/MIT.
-*/
-// SPDX-License-Identifier: MIT
-#pragma once
-
-#include "minja.hpp"
-#include <json.hpp>
-#include <string>
-#include <vector>
-
-using json = nlohmann::ordered_json;
-
-namespace minja {
-
-struct chat_template_caps {
-    bool supports_tools = false;
-    bool supports_tool_calls = false;
-    bool supports_tool_responses = false;
-    bool supports_system_role = false;
-    bool supports_parallel_tool_calls = false;
-    bool supports_tool_call_id = false;
-    // meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object.
-    // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
-    bool requires_object_arguments = false;
-    // CohereForAI/c4ai-command-r-plus simple variant
-    bool requires_non_null_content = false;
-    // MiniMaxAI/MiniMax-Text-01 special
-    bool requires_typed_content = false;
-};
-
-struct chat_template_inputs {
-    nlohmann::ordered_json messages;
-    nlohmann::ordered_json tools;
-    bool add_generation_prompt = true;
-    nlohmann::ordered_json extra_context;
-    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
-};
-
-struct chat_template_options {
-    bool apply_polyfills = true;
-    bool use_bos_token = true;
-    bool use_eos_token = true;
-    bool define_strftime_now = true;
-
-    bool polyfill_tools = true;
-    bool polyfill_tool_call_examples = true;
-    bool polyfill_tool_calls = true;
-    bool polyfill_tool_responses = true;
-    bool polyfill_system_role = true;
-    bool polyfill_object_arguments = true;
-    bool polyfill_typed_content = true;
-};
-
-class chat_template {
-
-  private:
-    chat_template_caps caps_;
-    std::string source_;
-    std::string bos_token_;
-    std::string eos_token_;
-    std::shared_ptr<minja::TemplateNode> template_root_;
-    std::string tool_call_example_;
-
-    std::string try_raw_render(
-        const nlohmann::ordered_json & messages,
-        const nlohmann::ordered_json & tools,
-        bool add_generation_prompt,
-        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
-    {
-        try {
-            chat_template_inputs inputs;
-            inputs.messages = messages;
-            inputs.tools = tools;
-            inputs.add_generation_prompt = add_generation_prompt;
-            inputs.extra_context = extra_context;
-            // Use fixed date for tests
-            inputs.now = std::chrono::system_clock::from_time_t(0);
-
-            chat_template_options opts;
-            opts.apply_polyfills = false;
-
-            auto prompt = apply(inputs, opts);
-            // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
-            return prompt;
-        } catch (const std::exception & e) {
-            // fprintf(stderr, "try_raw_render error: %s\n", e.what());
-            return "";
-        }
-    }
-
-  public:
-
-    chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
-        : source_(source), bos_token_(bos_token), eos_token_(eos_token)
-    {
-        template_root_ = minja::Parser::parse(source_, {
-            /* .trim_blocks = */ true,
-            /* .lstrip_blocks = */ true,
-            /* .keep_trailing_newline = */ false,
-        });
-
-        auto contains = [](const std::string & haystack, const std::string & needle) {
-            return haystack.find(needle) != std::string::npos;
-        };
-
-        const std::string user_needle = "<User Needle>";
-        const std::string sys_needle = "<System Needle>";
-        const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}};
-        const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}};
-
-        caps_.requires_typed_content =
-            !contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle)
-            && contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle);
-
-        const auto dummy_user_msg = caps_.requires_typed_content
-            ? dummy_typed_user_msg
-            : dummy_str_user_msg;
-        const json needle_system_msg = {
-            {"role", "system"},
-            {"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)},
-        };
-
-        caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle);
-
-        auto out = try_raw_render(json::array({
-            dummy_user_msg
-        }), json::array({
-            {
-                {"name", "some_tool"},
-                {"type", "function"},
-                {"function", {
-                    {"name", "some_tool"},
-                    {"description", "Some tool."},
-                    {"parameters", {
-                        {"type", "object"},
-                        {"properties", {
-                            {"arg", {
-                                {"type", "string"},
-                                {"description", "Some argument."},
-                            }},
-                        }},
-                        {"required", json::array({ "arg" })},
-                    }},
-                }},
-            },
-        }), false);
-        caps_.supports_tools = contains(out, "some_tool");
-
-        auto make_tool_calls_msg = [&](const json & tool_calls) {
-            return json {
-                {"role", "assistant"},
-                {"content", nullptr},
-                {"tool_calls", tool_calls},
-            };
-        };
-        auto make_tool_call = [](const std::string & tool_name, const json & arguments) {
-            return json {
-                {"id", "call_1___"},
-                {"type", "function"},
-                {"function", {
-                    {"arguments", arguments},
-                    {"name", tool_name},
-                }},
-            };
-        };
-        const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}};
-
-        // Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want.
-        out = try_raw_render(json::array({
-            dummy_user_msg,
-            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
-        }), {}, false);
-        auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
-        out = try_raw_render(json::array({
-            dummy_user_msg,
-            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
-        }), {}, false);
-        auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
-
-        caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
-        caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
-        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
-        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
-        caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
-
-        if (caps_.supports_tool_calls) {
-            auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
-            auto tc1 = make_tool_call("test_tool1", dummy_args);
-            auto tc2 = make_tool_call("test_tool2", dummy_args);
-            auto out = try_raw_render(json::array({
-                dummy_user_msg,
-                make_tool_calls_msg(json::array({tc1, tc2})),
-            }), {}, false);
-            caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2");
-
-            out = try_raw_render(json::array({
-                dummy_user_msg,
-                make_tool_calls_msg(json::array({tc1})),
-                {
-                    {"role", "tool"},
-                    {"name", "test_tool1"},
-                    {"content", "Some response!"},
-                    {"tool_call_id", "call_911_"},
-                }
-            }), {}, false);
-            caps_.supports_tool_responses = contains(out, "Some response!");
-            caps_.supports_tool_call_id = contains(out, "call_911_");
-        }
-
-        try {
-            if (!caps_.supports_tools) {
-                const json user_msg {
-                    {"role", "user"},
-                    {"content", "Hey"},
-                };
-                const json args {
-                    {"arg1", "some_value"},
-                };
-                const json tool_call_msg {
-                    {"role", "assistant"},
-                    {"content", nullptr},
-                    {"tool_calls", json::array({
-                        {
-                            // TODO: detect if requires numerical id or fixed length == 6 like Nemo
-                            {"id", "call_1___"},
-                            {"type", "function"},
-                            {"function", {
-                                {"name", "tool_name"},
-                                {"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))},
-                            }},
-                        },
-                    })},
-                };
-                std::string prefix, full;
-                {
-                    chat_template_inputs inputs;
-                    inputs.messages = json::array({user_msg});
-                    inputs.add_generation_prompt = true;
-                    prefix = apply(inputs);
-                }
-                {
-                    chat_template_inputs inputs;
-                    inputs.messages = json::array({user_msg, tool_call_msg});
-                    inputs.add_generation_prompt = false;
-                    full = apply(inputs);
-                }
-                auto eos_pos_last = full.rfind(eos_token_);
-                if (eos_pos_last == prefix.size() - eos_token_.size() ||
-                      (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) {
-                    full = full.substr(0, eos_pos_last);
-                }
-                size_t common_prefix_length = 0;
-                for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) {
-                    if (prefix[i] != full[i]) {
-                        break;
-                    }
-                    if (prefix[i] == '<') {
-                        // DeepSeek R1's template (as of 20250209) adds a trailing <think> if add_generation_prompt,
-                        // but it removes thinking tags for past messages.
-                        // The prefix and full strings diverge at <think> vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <.
-                        continue;
-                    }
-                    common_prefix_length = i + 1;
-                }
-                auto example = full.substr(common_prefix_length);
-                if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) {
-                    fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
-                } else {
-                    tool_call_example_ = example;
-                }
-            }
-        } catch (const std::exception & e) {
-            fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
-        }
-    }
-
-    const std::string & source() const { return source_; }
-    const std::string & bos_token() const { return bos_token_; }
-    const std::string & eos_token() const { return eos_token_; }
-    const chat_template_caps & original_caps() const { return caps_; }
-
-    // Deprecated, please use the form with chat_template_inputs and chat_template_options
-    std::string apply(
-        const nlohmann::ordered_json & messages,
-        const nlohmann::ordered_json & tools,
-        bool add_generation_prompt,
-        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
-        bool apply_polyfills = true)
-    {
-        fprintf(stderr, "[%s] Deprecated!\n", __func__);
-        chat_template_inputs inputs;
-        inputs.messages = messages;
-        inputs.tools = tools;
-        inputs.add_generation_prompt = add_generation_prompt;
-        inputs.extra_context = extra_context;
-        inputs.now = std::chrono::system_clock::now();
-
-        chat_template_options opts;
-        opts.apply_polyfills = apply_polyfills;
-
-        return apply(inputs, opts);
-    }
-
-    std::string apply(
-        const chat_template_inputs & inputs,
-        const chat_template_options & opts = chat_template_options()) const
-    {
-        json actual_messages;
-
-        auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
-        auto has_tool_calls = false;
-        auto has_tool_responses = false;
-        auto has_string_content = false;
-        for (const auto & message : inputs.messages) {
-            if (message.contains("tool_calls") && !message["tool_calls"].is_null()) {
-                has_tool_calls = true;
-            }
-            if (message.contains("role") && message["role"] == "tool") {
-                has_tool_responses = true;
-            }
-            if (message.contains("content") && message["content"].is_string()) {
-                has_string_content = true;
-            }
-        }
-
-        auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
-        auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
-        auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
-        auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
-        auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
-        auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
-        auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
-
-        auto needs_polyfills = opts.apply_polyfills && (false
-            || polyfill_system_role
-            || polyfill_tools
-            || polyfill_tool_calls
-            || polyfill_tool_responses
-            || polyfill_object_arguments
-            || polyfill_typed_content
-        );
-
-        if (needs_polyfills) {
-            actual_messages = json::array();
-
-            auto add_message = [&](const json & msg) {
-                if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
-                    actual_messages.push_back({
-                        {"role", msg.at("role")},
-                        {"content", {{
-                            {"type", "text"},
-                            {"text", msg.at("content")},
-                        }}},
-                    });
-                } else {
-                    actual_messages.push_back(msg);
-                }
-            };
-
-            std::string pending_system;
-            auto flush_sys = [&]() {
-                if (!pending_system.empty()) {
-                    add_message({
-                        {"role", "user"},
-                        {"content", pending_system},
-                    });
-                    pending_system.clear();
-                }
-            };
-
-            json adjusted_messages;
-            if (polyfill_tools) {
-                adjusted_messages = add_system(inputs.messages,
-                    "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
-                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n"));
-            } else {
-                adjusted_messages = inputs.messages;
-            }
-
-            for (const auto & message_ : adjusted_messages) {
-                auto message = message_;
-                if (!message.contains("role") || !message.contains("content")) {
-                    throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
-                }
-                std::string role = message.at("role");
-
-                if (message.contains("tool_calls")) {
-                    if (polyfill_object_arguments || polyfill_tool_calls) {
-                        for (auto & tool_call : message.at("tool_calls")) {
-                            if (tool_call["type"] == "function") {
-                                auto & function = tool_call.at("function");
-                                auto & arguments = function.at("arguments");
-                                if (arguments.is_string()) {
-                                    try {
-                                        arguments = json::parse(arguments.get<std::string>());
-                                    } catch (const std::exception & ecvt) {
-                                        fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what());
-                                    }
-                                }
-                            }
-                        }
-                    }
-                    if (polyfill_tool_calls) {
-                        auto content = message.at("content");
-                        auto tool_calls = json::array();
-                        for (const auto & tool_call : message.at("tool_calls")) {
-                            if (tool_call.at("type") != "function") {
-                                continue;
-                            }
-                            const auto & function = tool_call.at("function");
-                            auto tc = json {
-                                {"name", function.at("name")},
-                                {"arguments", function.at("arguments")},
-                            };
-                            if (tool_call.contains("id")) {
-                                tc["id"] = tool_call["id"];
-                            }
-                            tool_calls.push_back(tc);
-                        }
-                        auto obj = json {
-                            {"tool_calls", tool_calls},
-                        };
-                        if (!content.is_null() && content != "") {
-                            obj["content"] = content;
-                        }
-                        message["content"] = obj.dump(2);
-                        message.erase("tool_calls");
-                    }
-                }
-                if (polyfill_tool_responses && role == "tool") {
-                    message["role"] = "user";
-                    auto obj = json {
-                        {"tool_response", {
-                            {"content", message.at("content")},
-                        }},
-                    };
-                    if (message.contains("name")) {
-                        obj["tool_response"]["name"] = message.at("name");
-                    }
-                    if (message.contains("tool_call_id")) {
-                        obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
-                    }
-                    message["content"] = obj.dump(2);
-                    message.erase("name");
-                }
-
-                if (!message["content"].is_null() && polyfill_system_role) {
-                    std::string content = message.at("content");
-                    if (role == "system") {
-                        if (!pending_system.empty()) pending_system += "\n";
-                        pending_system += content;
-                        continue;
-                    } else {
-                        if (role == "user") {
-                            if (!pending_system.empty()) {
-                                message["content"] = pending_system + (content.empty() ? "" : "\n" + content);
-                                pending_system.clear();
-                            }
-                        } else {
-                            flush_sys();
-                        }
-                    }
-                }
-                add_message(message);
-            }
-            flush_sys();
-        } else {
-            actual_messages = inputs.messages;
-        }
-
-        auto context = minja::Context::make(json({
-            {"messages", actual_messages},
-            {"add_generation_prompt", inputs.add_generation_prompt},
-        }));
-        context->set("bos_token", opts.use_bos_token ? bos_token_ : "");
-        context->set("eos_token", opts.use_eos_token ? eos_token_ : "");
-        if (opts.define_strftime_now) {
-            auto now = inputs.now;
-            context->set("strftime_now", Value::callable([now](const std::shared_ptr<minja::Context> &, minja::ArgumentsValue & args) {
-                args.expectArgs("strftime_now", {1, 1}, {0, 0});
-                auto format = args.args[0].get<std::string>();
-
-                auto time = std::chrono::system_clock::to_time_t(now);
-                auto local_time = *std::localtime(&time);
-                std::ostringstream ss;
-                ss << std::put_time(&local_time, format.c_str());
-                return ss.str();
-            }));
-        }
-        if (!inputs.tools.is_null()) {
-            context->set("tools", minja::Value(inputs.tools));
-        }
-        if (!inputs.extra_context.is_null()) {
-            for (auto & kv : inputs.extra_context.items()) {
-                context->set(kv.key(), minja::Value(kv.value()));
-            }
-        }
-
-        auto ret = template_root_->render(context);
-        // fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str());
-        // fprintf(stderr, "apply: %s\n\n", ret.c_str());
-        return ret;
-    }
-
-    static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) {
-        json messages_with_system = messages;
-
-        if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") {
-            std::string existing_system = messages_with_system.at(0).at("content");
-            messages_with_system[0] = json {
-                {"role", "system"},
-                {"content", existing_system + "\n\n" + system_prompt},
-            };
-        } else {
-            messages_with_system.insert(messages_with_system.begin(), json {
-                {"role", "system"},
-                {"content", system_prompt},
-            });
-        }
-        return messages_with_system;
-    }
-};
-
-}  // namespace minja

common/chat.cpp

@@ -1,966 +0,0 @@
-#include "chat.hpp"
-#include "chat-template.hpp"
-#include "json-schema-to-grammar.h"
-#include "log.h"
-#include "minja.hpp"
-
-std::string common_chat_format_name(common_chat_format format) {
-    switch (format) {
-        case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
-        case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
-        case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo";
-        case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x";
-        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools";
-        case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1";
-        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
-        case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
-        case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
-        default:
-            throw std::runtime_error("Unknown chat format");
-    }
-}
-
-const common_grammar_options grammar_options {
-    /* .dotall = */ false,
-    /* .compact_spaces = */ false,
-    // /* .compact_spaces = */ true,
-};
-
-static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) {
-    // // https://json.nlohmann.me/features/parsing/sax_interface/
-    struct json_error_locator : public nlohmann::json_sax<json> {
-        std::size_t position;
-        bool found_error;
-
-        json_error_locator() : position(0), found_error(false) {}
-
-        bool parse_error(std::size_t position, const std::string &, const json::exception &) override {
-            this->position = position - 1;
-            this->found_error = true;
-            return false;
-        }
-        bool null() override { return true; }
-        bool boolean(bool) override { return true; }
-        bool number_integer(number_integer_t) override { return true; }
-        bool number_unsigned(number_unsigned_t) override { return true; }
-        bool number_float(number_float_t, const string_t &) override { return true; }
-        bool string(string_t &) override { return true; }
-        bool binary(binary_t &) override { return true; }
-        bool start_object(std::size_t) override { return true; }
-        bool key(string_t &) override { return true; }
-        bool end_object() override { return true; }
-        bool start_array(std::size_t) override { return true; }
-        bool end_array() override { return true; }
-    };
-    json_error_locator err_loc;
-    json::sax_parse(it, end, &err_loc);
-
-    std::string::const_iterator temptative_end;
-    if (err_loc.found_error) {
-        temptative_end = it + err_loc.position;
-    } else {
-        temptative_end = end;
-    }
-    std::string json_sub {it, temptative_end};
-    try {
-        out = json::parse(json_sub);
-        it = temptative_end;
-        return true;
-    } catch (const std::exception &) {
-        return false;
-    }
-}
-
-
-/**
- * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
- * Aggregates the prefix, suffix and in-between text into the content.
- */
-static common_chat_msg parse_json_tool_calls(
-    const std::string& input,
-    const std::optional<std::regex> & trigger_opt,
-    const std::regex & function_regex,
-    const std::regex & close_regex) {
-    std::smatch match;
-
-    common_chat_msg result;
-    result.role = "assistant";
-
-
-    auto end = input.end();
-    auto it = input.begin();
-
-    if (trigger_opt) {
-        if (!std::regex_search(it, end, match, *trigger_opt)) {
-            result.content = input;
-            return result;
-        }
-        result.content = match.prefix().str();
-        it = match.suffix().first;
-    }
-
-    while (it != end) {
-        std::sregex_iterator rend;
-        std::sregex_iterator rit(it, end, function_regex);
-        if (rit == rend) {
-            fprintf(stderr, "No more tool calls found\n");
-            result.content += std::string(it, end);
-            break;
-        }
-        auto name = rit->str(1);
-        result.content += std::string(it, rit->prefix().second);
-        it = rit->suffix().first;
-
-        json arguments;
-        if (!parse_json(it, end, arguments)) {
-            throw std::runtime_error("Failed to parse json tool call arguments");
-        }
-        if (!std::regex_search(it, end, match, close_regex)) {
-            throw std::runtime_error("Malformed input, missing closing pattern");
-        }
-        it = match.suffix().first;
-        result.tool_calls.push_back({name, arguments.is_string() ? arguments.get<std::string>() : arguments.dump(), /* id= */ ""});
-    }
-    return result;
-}
-
-static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) {
-    auto content_end = input.find(prefix);
-    size_t tc_start = std::string::npos;
-
-    common_chat_msg result;
-    result.role = "assistant";
-    const auto process_tool_calls = [&](const json & tool_calls) {
-        for (const auto & tool_call : tool_calls) {
-            const auto & arguments = tool_call["arguments"];
-            result.tool_calls.push_back({
-                tool_call["name"],
-                arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
-                tool_call.contains("id") ? tool_call["id"] : "",
-            });
-        }
-    };
-    if (content_end == std::string::npos) {
-        result.content = input;
-    } else {
-        tc_start = content_end + prefix.size() - rstrip_prefix;
-        result.content = input.substr(0, content_end);
-        auto tool_calls = json::parse(input.substr(tc_start));
-        process_tool_calls(tool_calls);
-    }
-    return result;
-}
-
-static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
-    for (const auto & tool : tools) {
-        if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) {
-            LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
-            continue;
-        }
-        fn(tool);
-    }
-}
-
-static std::string apply(
-    const common_chat_template & tmpl,
-    const nlohmann::ordered_json & messages,
-    const nlohmann::ordered_json & tools,
-    bool add_generation_prompt,
-    const nlohmann::ordered_json & extra_context = nlohmann::ordered_json())
-{
-    minja::chat_template_inputs tmpl_inputs;
-    tmpl_inputs.messages = messages;
-    tmpl_inputs.tools = tools;
-    tmpl_inputs.add_generation_prompt = add_generation_prompt;
-    tmpl_inputs.extra_context = extra_context;
-    // TODO: add flag to control date/time, if only for testing purposes.
-    // tmpl_inputs.now = std::chrono::system_clock::now();
-
-    minja::chat_template_options tmpl_opts;
-    tmpl_opts.use_bos_token = false;
-    tmpl_opts.use_eos_token = false;
-
-    return tmpl.apply(tmpl_inputs, tmpl_opts);
-}
-
-static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-
-    auto tool_call_schemas = json::array();
-    foreach_function(inputs.tools, [&](const json & tool) {
-        const auto & function = tool["function"];
-        auto tool_schema = json {
-            {"type", "object"},
-            {"properties", {
-                {"name", {
-                    {"type", "string"},
-                    {"const", function["name"]},
-                }},
-                {"arguments", function["parameters"]},
-            }},
-            {"required", json::array({"name", "arguments"})},
-        };
-        if (function.contains("description")) {
-            tool_schema["description"] = function["description"];
-        }
-        if (inputs.parallel_tool_calls) {
-            tool_schema["properties"]["id"] = {
-                {"type", "string"},
-                {"minLength", 4},
-            };
-            tool_schema["required"].push_back("id");
-        }
-        tool_call_schemas.emplace_back(tool_schema);
-    });
-    const auto tool_call =
-        inputs.parallel_tool_calls
-            ? json {
-                {"type", "object"},
-                {"properties", {
-                    {"tool_calls", {
-                        {"type", "array"},
-                        {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
-                            {"anyOf", tool_call_schemas},
-                        }},
-                        {"minItems", 1},
-                    }},
-                }},
-                {"required", json::array({"tool_calls"})},
-            }
-            : json {
-                {"type", "object"},
-                {"properties", {
-                    {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
-                        {"anyOf", tool_call_schemas},
-                    }},
-                }},
-                {"required", json::array({"tool_call"})},
-            };
-    const auto schema =
-        inputs.tool_choice != "required"
-            ? json {
-                {"anyOf", json::array({
-                    tool_call,
-                    {
-                        {"type", "object"},
-                        {"properties", {
-                            {"response", inputs.json_schema.is_null()
-                                ? json {{"type", "string"}}
-                                : inputs.json_schema
-                            },
-                        }},
-                        {"required", json::array({"response"})},
-                    },
-                })}
-            }
-            : tool_call;
-
-    data.grammar_lazy = false;
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        builder.add_schema("root", schema);
-    }, grammar_options);
-
-    auto tweaked_messages = common_chat_template::add_system(
-        inputs.messages,
-        "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
-
-    data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_GENERIC;
-    return data;
-}
-static common_chat_msg common_chat_parse_generic(const std::string & input) {
-    json data = json::parse(input);
-    common_chat_msg result;
-    result.role = "assistant";
-    if (data.contains("tool_calls")) {
-        for (const auto & tool_call : data["tool_calls"]) {
-            result.tool_calls.push_back({
-                tool_call["name"],
-                tool_call["arguments"].dump(),
-                tool_call.contains("id") ? tool_call["id"] : "",
-            });
-        }
-    } else if (data.contains("tool_call")) {
-        result.tool_calls.push_back({
-            data["tool_call"]["name"],
-            data["tool_call"]["arguments"].dump(),
-            /* id= */ "",
-        });
-    } else if (data.contains("response")) {
-        const auto & response = data["response"];
-        result.content = response.is_string() ? response.get<std::string>() : response.dump(2);
-    }
-    return result;
-}
-
-static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        auto schemas = json::array();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            schemas.push_back({
-                {"type", "object"},
-                {"properties", {
-                    // Important note: the model is probably trained to take a JSON stringified arguments value.
-                    // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object.
-                    {"name", {
-                        {"type", "string"},
-                        {"const", function["name"]},
-                    }},
-                    {"arguments", function["parameters"]},
-                    {"id", {
-                        {"type", "string"},
-                        // Nemo's template expects a 9-character alphanumeric ID.
-                        {"pattern", "^[a-zA-Z0-9]{9}$"},
-                    }},
-                }},
-                {"required", json::array({"name", "arguments", "id"})},
-            });
-        });
-        auto schema = json {
-            {"type", "array"},
-            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-            {"minItems", 1},
-        };
-        if (!inputs.parallel_tool_calls) {
-            schema["maxItems"] = 1;
-        }
-        builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
-    }, grammar_options);
-    data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
-    return data;
-}
-static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) {
-    return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
-}
-
-static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        auto schemas = json::array();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            schemas.push_back({
-                {"type", "object"},
-                {"properties", {
-                    {"tool_call_id", {
-                        {"type", "string"},
-                        // Command-R's template expects an integer string.
-                        {"pattern", "^[0-9]{1,10}$"},
-                    }},
-                    {"tool_name", {
-                        {"type", "string"},
-                        {"const", function["name"]},
-                    }},
-                    {"parameters", function["parameters"]},
-                }},
-                {"required", json::array({"tool_call_id", "tool_name", "parameters"})},
-            });
-        });
-        auto schema = json {
-            {"type", "array"},
-            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-            {"minItems", 1},
-        };
-        if (!inputs.parallel_tool_calls) {
-            schema["maxItems"] = 1;
-        }
-        builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\"");
-    }, grammar_options);
-    data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false});
-    data.preserved_tokens = {
-        "<|START_RESPONSE|>",
-        "<|END_RESPONSE|>",
-        "<|START_THINKING|>",
-        "<|END_THINKING|>",
-        "<|END_ACTION|>",
-    };
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
-    return data;
-}
-static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
-    static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>");
-    static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
-    std::smatch match;
-
-    common_chat_msg result;
-    result.role = "assistant";
-    if (std::regex_match(input, match, response_regex)) {
-        result.content = match[1].str();
-    } else if (std::regex_match(input, match, thought_action_regex)) {
-        result.tool_plan = match[1].str();
-        auto actions_str = match[2].str();
-        auto actions = json::parse(actions_str);
-        for (const auto & action : actions) {
-            result.tool_calls.push_back({
-                /* .name = */      action["tool_name"],
-                /* .arguments = */ action["parameters"].dump(),
-                /* .id = */        action["tool_call_id"],
-            });
-        }
-    } else {
-        LOG_ERR("Failed to parse command_r output");
-        result.content = input;
-    }
-    return result;
-}
-
-static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
-    if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
-        throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
-    }
-    const auto & parameters_properties = parameters.at("properties");
-    const auto & parameters_required = parameters.at("required");
-    for (const auto & prop : expected_properties) {
-        if (!parameters_properties.contains(prop)) {
-            throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop);
-        }
-        if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) {
-            throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop);
-        }
-    }
-    if (parameters_properties.size() != expected_properties.size()) {
-        throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", "));
-    }
-}
-
-static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) {
-    auto builtin_tools = json::array();
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-
-        auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
-            if (name == "wolfram_alpha") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
-                expect_tool_parameters(name, parameters, {"query"});
-            } else if (name == "web_search" || name == "brave_search") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
-                expect_tool_parameters(name, parameters, {"query"});
-            } else if (name == "python" || name == "code_interpreter") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
-                expect_tool_parameters(name, parameters, {"code"});
-            } else {
-                return false;
-            }
-
-            std::vector<std::string> kvs;
-            for (const auto & [key, value] : parameters.at("properties").items()) {
-                kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value));
-            }
-
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
-            builtin_tools.push_back(name);
-
-            return true;
-        };
-
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            builder.resolve_refs(parameters);
-
-            // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
-            if (allow_python_tag_builtin_tools) {
-                handle_builtin_tool(name, parameters);
-            }
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"{\" space "
-                    "( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? "
-                    "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
-                        builder.add_schema(name + "-args", parameters) +
-                    " \"}\""));
-            data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
-        });
-        data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n  \"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n    \"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n  \"type\": \"function\"", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n    \"type\": \"function\"", /* .at_start = */ true});
-        if (!builtin_tools.empty()) {
-            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
-        }
-        builder.add_rule("root", string_join(tool_rules, " | "));
-    }, grammar_options);
-    data.additional_stops.push_back("<|eom_id|>");
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
-        {"tools_in_user_message", false},
-        {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
-    });
-    data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
-        ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
-        : COMMON_CHAT_FORMAT_LLAMA_3_X;
-    return data;
-}
-static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
-    // TODO: tighten & simplify the parser, don't accept leading text context.
-    static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": ");
-    static std::regex close_regex("\\}");
-    static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)");
-
-    if (with_builtin_tools) {
-        std::smatch match;
-        if (std::regex_match(input, match, builtin_call_regex)) {
-            auto name = match[1].str();
-            auto raw_args = match[2].str();
-
-            // TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing.
-            auto it_eq = raw_args.find('=');
-            auto arg_name = raw_args.substr(0, it_eq);
-            auto arg_value_str = raw_args.substr(it_eq + 1);
-            auto arg_value = json::parse(arg_value_str);
-
-            return {
-                /* .role = */ "assistant",
-                /* .content = */ match.prefix().str(),
-                /* .tool_calls = */ {
-                    {
-                        /* .name = */ match[1],
-                        /* .arguments = */ (json {
-                            {arg_name, arg_value},
-                        }).dump(),
-                        /* .id = */ "",
-                    },
-                },
-            };
-        }
-    }
-    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            auto args_rule = builder.add_schema(name + "-args", parameters);
-            tool_rules.push_back(builder.add_rule(name + "-call",
-                "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
-        });
-        data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
-        data.preserved_tokens = {
-            "<｜tool▁sep｜>",
-            "<｜tool▁call▁end｜>",
-        };
-        builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
-    }, grammar_options);
-    auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.prompt = prompt;
-    data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
-    return data;
-}
-static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) {
-    static std::regex trigger_regex("<｜tool▁calls▁begin｜>");
-    static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
-    static std::regex close_regex("```<｜tool▁call▁end｜>");
-    return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    fprintf(stderr, "%s\n", __func__);
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
-        {"datetime", "Jan 29 2025 13:00:00 GMT"},
-        {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
-    });
-    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != "required";
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            auto schemas = json::array();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool["function"];
-                schemas.push_back({
-                    {"type", "object"},
-                    {"properties", {
-                        {"name", {
-                            {"type", "string"},
-                            {"const", function["name"]},
-                        }},
-                        {"arguments", function["parameters"]},
-                    }},
-                    {"required", json::array({"name", "arguments", "id"})},
-                });
-            });
-            auto schema = json {
-                {"type", "array"},
-                {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-                {"minItems", 1},
-            };
-            if (!inputs.parallel_tool_calls) {
-                schema["maxItems"] = 1;
-            }
-            builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema));
-        }, grammar_options);
-        data.grammar_triggers.push_back({" functools[", /* .at_start = */ false});
-        data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2;
-    } else {
-        data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    }
-    return data;
-}
-static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) {
-    return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1);
-}
-
-static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
-    // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
-    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != "required";
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> first_tool_rules;
-            std::vector<std::string> subsequent_tool_rules;
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool["function"];
-                std::string name = function["name"];
-                auto parameters = function["parameters"];
-                auto args_rule = builder.add_schema(name + "-args", parameters);
-                first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule));
-                subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule));
-                data.grammar_triggers.push_back({name, /* .at_start = */ true});
-                data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false});
-            });
-            auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space";
-            if (inputs.parallel_tool_calls) {
-                auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space";
-                builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*");
-            } else {
-                builder.add_rule("root", first_rule);
-            }
-
-        }, grammar_options);
-    }
-    return data;
-}
-
-static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) {
-    auto expected_it = expected.begin();
-    auto tmp_it = it;
-    while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) {
-        ++tmp_it;
-        ++expected_it;
-    }
-    if (expected_it == expected.end()) {
-        it = tmp_it;
-        return true;
-    }
-    return false;
-}
-
-static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
-    static std::regex function_regex(R"((?:>>>)?(\w+)\n)");
-    static std::regex close_regex(R"($|(?=>>>))");
-
-    std::string content;
-    auto it = input.begin();
-    const auto end = input.end();
-
-    if (consume(it, end, "all\n")) {
-        std::smatch match;
-        if (std::regex_search(it, end, match, function_regex)) {
-            auto fun_it = match.prefix().second;
-            content = std::string(it, fun_it);
-            it = fun_it;
-        } else {
-            common_chat_msg res;
-            res.role = "assistant";
-            res.content = std::string(it, end);
-            return res;
-        }
-    }
-    // TODO: tighten & simplify.
-    try {
-        auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
-        res.content = content + res.content;
-        return res;
-    } catch (const std::exception & e) {
-        LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what());
-        common_chat_msg res;
-        res.role = "assistant";
-        res.content = input;
-        return res;
-    }
-}
-
-static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
-    common_chat_params data;
-    json tools = inputs.tools.is_null() ? inputs.tools : json::array();
-    std::string python_code_argument_name;
-    auto has_raw_python = false;
-
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            const auto & parameters = function["parameters"];
-            std::string name = function["name"];
-            if (name == "python" || name == "ipython") {
-                if (!parameters.contains("type")) {
-                    throw std::runtime_error("Missing type in python tool");
-                }
-                has_raw_python = true;
-                auto type = parameters.at("type");
-                if (type == "object") {
-                    auto properties = parameters.at("properties");
-                    for (auto it = properties.begin(); it != properties.end(); ++it) {
-                        if (it.value().at("type") == "string") {
-                            if (!python_code_argument_name.empty()) {
-                                throw std::runtime_error("Multiple string arguments found in python tool");
-                            }
-                            python_code_argument_name = it.key();
-                        }
-                    }
-                    if (python_code_argument_name.empty()) {
-                        throw std::runtime_error("No string argument found in python tool");
-                    }
-                } else if (type != "string") {
-                    throw std::runtime_error("Invalid type in python tool: " + type.dump());
-                }
-            }
-            tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
-        });
-        if (has_raw_python) {
-            tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
-            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
-        }
-        auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
-        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
-        data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
-    }, grammar_options);
-
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    // TODO: if (has_raw_python)
-    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
-    return data;
-}
-static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
-    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
-    static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
-    std::smatch match;
-    if (std::regex_search(input, match, python_tag_regex)) {
-        auto code = match[1].str();
-        return {
-            /* .role = */ "assistant",
-            /* .content = */ match.prefix().str(),
-            /* .tool_calls = */ {
-                {
-                    /* .name = */ "python",
-                    /* .arguments = */ (json {{"code", code}}).dump(),
-                    /* .id = */ "",
-                },
-            }
-        };
-    }
-    static std::regex function_regex(R"(<function=(\w+)>)");
-    static std::regex close_regex(R"(</function>)");
-    // TODO: tighten & simplify.
-    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    // (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            builder.resolve_refs(parameters);
-            tool_rules.push_back(builder.add_schema(name + "-call", {
-                {"type", "object"},
-                {"properties", json {
-                    {"name", json {{"const", name}}},
-                    {"arguments", parameters},
-                }},
-                {"required", json::array({"name", "arguments"})},
-            }));
-        });
-        auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
-        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
-        data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
-        data.preserved_tokens = { "</tool_call>" };
-    }, grammar_options);
-
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
-    return data;
-}
-static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) {
-    try {
-        std::regex start_pattern(R"([\n\s]*<tool_call>)");
-        std::regex middle_pattern(R"([\n\s]*</tool_call>[\n\s]*<tool_call>)");
-        std::regex end_pattern(R"([\n\s]*</tool_call>[\n\s]*$)");
-
-        auto end = input.end();
-        std::sregex_iterator rend;
-        std::sregex_iterator rit(input.begin(), end, start_pattern);
-        if (rit == rend) {
-            return {
-                /* .role = */ "assistant",
-                /* .content = */ input,
-                /* .tool_calls = */ {},
-            };
-        }
-
-        common_chat_msg result;
-        result.role = "assistant";
-        result.content = rit->prefix();
-
-        auto it = rit->suffix().first;
-        while (it != end) {
-            json call;
-            if (!parse_json(it, end, call)) {
-                throw std::runtime_error("Failed to parse json tool call");
-            }
-            const auto & arguments = call["arguments"];
-            result.tool_calls.push_back({
-                call["name"],
-                arguments.dump(),
-                // arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
-                /* id= */ "",
-            });
-            rit = {it, end, middle_pattern};
-            if (rit != rend) {
-                it = rit->suffix().first;
-            } else {
-                rit = {it, end, end_pattern};
-                if (rit == rend) {
-                    throw std::runtime_error("Malformed input, missing </tool_call>");
-                }
-                break;
-            }
-        }
-        return result;
-    } catch (const std::exception & e) {
-        return {
-            /* .role = */ "assistant",
-            /* .content = */ input,
-            /* .tool_calls = */ {},
-        };
-    }
-}
-
-static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    data.grammar_lazy = false;
-    if (!inputs.json_schema.is_null()) {
-        if (!inputs.grammar.empty()) {
-            throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
-        }
-        data.grammar = json_schema_to_grammar(inputs.json_schema);
-    } else {
-        data.grammar = inputs.grammar.empty();
-    }
-    return data;
-}
-
-common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none";
-    LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false");
-
-    if (has_tools && !inputs.grammar.empty()) {
-        throw std::runtime_error("Cannot specify grammar with tools");
-    }
-
-    const auto & src = tmpl.source();
-    if (src.find(">>>all") != std::string::npos) {
-        // Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when
-        return common_chat_params_init_functionary_v3_2(tmpl, inputs);
-    }
-    if (src.find(" functools[") != std::string::npos) {
-        // Firefunction v2 requires datetime and functions in the context, even w/o tools.
-        return common_chat_params_init_firefunction_v2(tmpl, inputs);
-    }
-
-    if (!has_tools) {
-        return common_chat_params_init_without_tools(tmpl, inputs);
-    }
-
-    if (src.find("<tool_call>") != std::string::npos) {
-        return common_chat_params_init_hermes_2_pro(tmpl, inputs);
-    }
-    if (src.find("<|start_header_id|>") != std::string::npos
-        && src.find("<function=") != std::string::npos) {
-        return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, inputs);
-    }
-    if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
-        auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
-        return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools);
-    }
-    if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) {
-        return common_chat_params_init_deepseek_r1(tmpl, inputs);
-    }
-    if (src.find("[TOOL_CALLS]") != std::string::npos) {
-        return common_chat_params_init_mistral_nemo(tmpl, inputs);
-    }
-    if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) {
-        return common_chat_params_init_command_r7b(tmpl, inputs);
-    }
-    return common_chat_params_init_generic(tmpl, inputs);
-}
-
-static common_chat_msg common_chat_parse_content_only(const std::string & input) {
-    return {
-        /* .role = */ "assistant",
-        /* .content = */ input,
-        /* .tool_calls = */ {},
-    };
-}
-
-common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) {
-    switch (format) {
-        case COMMON_CHAT_FORMAT_CONTENT_ONLY:
-            return common_chat_parse_content_only(input);
-        case COMMON_CHAT_FORMAT_GENERIC:
-            return common_chat_parse_generic(input);
-        case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
-            return common_chat_parse_mistral_nemo(input);
-        case COMMON_CHAT_FORMAT_LLAMA_3_X:
-            return common_chat_parse_llama_3_1(input);
-        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
-            return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true);
-        case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
-            return common_chat_parse_deepseek_r1(input);
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
-            return common_chat_parse_functionary_v3_2(input);
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
-            return common_chat_parse_functionary_v3_1_llama_3_1(input);
-        case COMMON_CHAT_FORMAT_HERMES_2_PRO:
-            return common_chat_parse_hermes_2_pro(input);
-        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
-            return common_chat_parse_firefunction_v2(input);
-        case COMMON_CHAT_FORMAT_COMMAND_R7B:
-            return common_chat_parse_command_r7b(input);
-        default:
-            throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
-    }
-}

common/chat.hpp

@@ -1,52 +0,0 @@
-// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
-
-#pragma once
-
-#include "common.h"
-#include <json.hpp>
-#include <optional>
-#include <string>
-#include <vector>
-
-using json = nlohmann::ordered_json;
-
-struct common_chat_inputs {
-    json messages;
-    json tools;
-    json tool_choice;
-    json json_schema;
-    bool parallel_tool_calls;
-    bool stream;
-    std::string grammar;
-    bool add_generation_prompt = true;
-};
-
-enum common_chat_format {
-    COMMON_CHAT_FORMAT_CONTENT_ONLY,
-    COMMON_CHAT_FORMAT_GENERIC,
-    COMMON_CHAT_FORMAT_MISTRAL_NEMO,
-    COMMON_CHAT_FORMAT_LLAMA_3_X,
-    COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
-    COMMON_CHAT_FORMAT_DEEPSEEK_R1,
-    COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
-    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
-    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
-    COMMON_CHAT_FORMAT_HERMES_2_PRO,
-    COMMON_CHAT_FORMAT_COMMAND_R7B,
-
-    COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
-};
-
-struct common_chat_params {
-    common_chat_format                  format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    json                                prompt;
-    std::string                         grammar;
-    bool                                grammar_lazy = false;
-    std::vector<common_grammar_trigger> grammar_triggers;
-    std::vector<std::string>            preserved_tokens;
-    std::vector<std::string>            additional_stops;
-};
-
-struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params);
-std::string               common_chat_format_name(common_chat_format format);
-common_chat_msg           common_chat_parse(      const std::string & input, common_chat_format format);

common/cmake/build-info-gen-cpp.cmake

@@ -1,24 +0,0 @@
-include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
-
-set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
-set(OUTPUT_FILE   "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
-
-# Only write the build info if it changed
-if(EXISTS ${OUTPUT_FILE})
-    file(READ ${OUTPUT_FILE} CONTENTS)
-    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMMIT ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMPILER ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_TARGET ${CMAKE_MATCH_1})
-    if (
-        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
-        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
-        NOT OLD_TARGET   STREQUAL BUILD_TARGET
-    )
-        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-    endif()
-else()
-    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-endif()

common/common.h

@@ -1,717 +0,0 @@
-// Various helper functions and utilities
-
-#pragma once
-
-#include "llama-cpp.h"
-
-#include <set>
-#include <string>
-#include <vector>
-#include <sstream>
-
-#ifdef _WIN32
-#define DIRECTORY_SEPARATOR '\\'
-#else
-#define DIRECTORY_SEPARATOR '/'
-#endif // _WIN32
-
-#define die(msg)          do { fputs("error: " msg "\n", stderr);                exit(1); } while (0)
-#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
-
-#define print_build_info() do {                                                                     \
-    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);      \
-    fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
-} while(0)
-
-#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
-
-struct common_adapter_lora_info {
-    std::string path;
-    float scale;
-
-    struct llama_adapter_lora * ptr;
-};
-
-using llama_tokens = std::vector<llama_token>;
-
-// build info
-extern int LLAMA_BUILD_NUMBER;
-extern const char * LLAMA_COMMIT;
-extern const char * LLAMA_COMPILER;
-extern const char * LLAMA_BUILD_TARGET;
-
-struct common_control_vector_load_info;
-
-//
-// CPU utils
-//
-
-struct cpu_params {
-    int      n_threads                   = -1;
-    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
-    bool     mask_valid                  = false;   // Default: any CPU
-    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
-    bool     strict_cpu                  = false;   // Use strict CPU placement
-    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
-};
-
-int32_t cpu_get_num_physical_cores();
-int32_t cpu_get_num_math();
-
-//
-// Common params
-//
-
-enum llama_example {
-    LLAMA_EXAMPLE_COMMON,
-    LLAMA_EXAMPLE_SPECULATIVE,
-    LLAMA_EXAMPLE_MAIN,
-    LLAMA_EXAMPLE_INFILL,
-    LLAMA_EXAMPLE_EMBEDDING,
-    LLAMA_EXAMPLE_PERPLEXITY,
-    LLAMA_EXAMPLE_RETRIEVAL,
-    LLAMA_EXAMPLE_PASSKEY,
-    LLAMA_EXAMPLE_IMATRIX,
-    LLAMA_EXAMPLE_BENCH,
-    LLAMA_EXAMPLE_SERVER,
-    LLAMA_EXAMPLE_CVECTOR_GENERATOR,
-    LLAMA_EXAMPLE_EXPORT_LORA,
-    LLAMA_EXAMPLE_LLAVA,
-    LLAMA_EXAMPLE_LOOKUP,
-    LLAMA_EXAMPLE_PARALLEL,
-    LLAMA_EXAMPLE_TTS,
-
-    LLAMA_EXAMPLE_COUNT,
-};
-
-enum common_sampler_type {
-    COMMON_SAMPLER_TYPE_NONE        = 0,
-    COMMON_SAMPLER_TYPE_DRY         = 1,
-    COMMON_SAMPLER_TYPE_TOP_K       = 2,
-    COMMON_SAMPLER_TYPE_TOP_P       = 3,
-    COMMON_SAMPLER_TYPE_MIN_P       = 4,
-  //COMMON_SAMPLER_TYPE_TFS_Z       = 5,
-    COMMON_SAMPLER_TYPE_TYPICAL_P   = 6,
-    COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
-    COMMON_SAMPLER_TYPE_XTC         = 8,
-    COMMON_SAMPLER_TYPE_INFILL      = 9,
-    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
-};
-
-// dimensionality reduction methods, used by cvector-generator
-enum dimre_method {
-    DIMRE_METHOD_PCA,
-    DIMRE_METHOD_MEAN,
-};
-
-enum common_conversation_mode {
-    COMMON_CONVERSATION_MODE_DISABLED = 0,
-    COMMON_CONVERSATION_MODE_ENABLED  = 1,
-    COMMON_CONVERSATION_MODE_AUTO     = 2,
-};
-
-struct common_grammar_trigger {
-    std::string word;
-    bool at_start;
-};
-
-// sampling parameters
-struct common_params_sampling {
-    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
-
-    int32_t n_prev             = 64;    // number of previous tokens to remember
-    int32_t n_probs            = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep           = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t top_k              = 40;    // <= 0 to use vocab size
-    float   top_p              = 0.95f; // 1.0 = disabled
-    float   min_p              = 0.05f; // 0.0 = disabled
-    float   xtc_probability    = 0.00f; // 0.0 = disabled
-    float   xtc_threshold      = 0.10f; // > 0.5 disables XTC
-    float   typ_p              = 1.00f; // typical_p, 1.0 = disabled
-    float   temp               = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float   dynatemp_range     = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent  = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat     = 1.00f; // 1.0 = disabled
-    float   penalty_freq       = 0.00f; // 0.0 = disabled
-    float   penalty_present    = 0.00f; // 0.0 = disabled
-    float   dry_multiplier     = 0.0f;  // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
-    float   dry_base           = 1.75f; // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
-    int32_t dry_allowed_length = 2;     // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;    // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
-    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   mirostat_tau       = 5.00f; // target entropy
-    float   mirostat_eta       = 0.10f; // learning rate
-    bool    ignore_eos         = false;
-    bool    no_perf            = false; // disable performance metrics
-    bool    timing_per_token   = false;
-
-    std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
-
-
-    std::vector<enum common_sampler_type> samplers = {
-        COMMON_SAMPLER_TYPE_PENALTIES,
-        COMMON_SAMPLER_TYPE_DRY,
-        COMMON_SAMPLER_TYPE_TOP_K,
-        COMMON_SAMPLER_TYPE_TYPICAL_P,
-        COMMON_SAMPLER_TYPE_TOP_P,
-        COMMON_SAMPLER_TYPE_MIN_P,
-        COMMON_SAMPLER_TYPE_XTC,
-        COMMON_SAMPLER_TYPE_TEMPERATURE,
-    };
-
-    std::string                         grammar; // optional BNF-like grammar to constrain sampling
-    bool                                grammar_lazy = false;
-    std::vector<common_grammar_trigger> grammar_trigger_words;  // optional trigger words to trigger lazy grammar
-    std::vector<llama_token>            grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
-    std::set<llama_token>               preserved_tokens;
-
-    std::vector<llama_logit_bias> logit_bias; // logit biases to apply
-
-    // print the parameters into a string
-    std::string print() const;
-};
-
-struct common_params_speculative {
-    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
-
-    int32_t n_ctx        =     0; // draft context size
-    int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min        =     5; // minimum number of draft tokens to use for speculative decoding
-    int32_t n_gpu_layers =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
-    float   p_split      =  0.1f; // speculative decoding split probability
-    float   p_min        =  0.9f; // minimum speculative decoding probability (greedy)
-
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
-
-    std::string hf_repo = ""; // HF repo                                                     // NOLINT
-    std::string hf_file = ""; // HF file                                                     // NOLINT
-
-    std::string model = "";     // draft model for speculative decoding                      // NOLINT
-    std::string model_url = ""; // model url to download                                     // NOLINT
-};
-
-struct common_params_vocoder {
-    std::string hf_repo = ""; // HF repo                                                     // NOLINT
-    std::string hf_file = ""; // HF file                                                     // NOLINT
-
-    std::string model     = ""; // model path                                                // NOLINT
-    std::string model_url = ""; // model url to download                                     // NOLINT
-
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
-};
-
-struct common_params {
-    int32_t n_predict             =    -1; // new tokens to predict
-    int32_t n_ctx                 =  4096; // context size
-    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
-    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel            =     1; // number of parallel sequences to decode
-    int32_t n_sequences           =     1; // number of sequences to decode
-    int32_t grp_attn_n            =     1; // group-attention factor
-    int32_t grp_attn_w            =   512; // group-attention width
-    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
-    float   rope_freq_base        =  0.0f; // RoPE base frequency
-    float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
-    float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
-    float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
-    int32_t yarn_orig_ctx         =     0; // YaRN original context length
-    float   defrag_thold          =  0.1f; // KV cache defragmentation threshold
-
-    // offload params
-    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
-
-    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
-    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
-
-    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
-
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
-
-    ggml_backend_sched_eval_callback cb_eval = nullptr;
-    void * cb_eval_user_data                 = nullptr;
-
-    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
-
-    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
-    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
-
-    struct common_params_sampling    sampling;
-    struct common_params_speculative speculative;
-    struct common_params_vocoder     vocoder;
-
-    std::string model                = ""; // model path                                                    // NOLINT
-    std::string model_alias          = ""; // model alias                                                   // NOLINT
-    std::string model_url            = ""; // model url to download                                         // NOLINT
-    std::string hf_token             = ""; // HF token                                                      // NOLINT
-    std::string hf_repo              = ""; // HF repo                                                       // NOLINT
-    std::string hf_file              = ""; // HF file                                                       // NOLINT
-    std::string prompt               = "";                                                                  // NOLINT
-    std::string prompt_file          = ""; // store the external prompt file name                           // NOLINT
-    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state             // NOLINT
-    std::string input_prefix         = ""; // string to prefix user inputs with                             // NOLINT
-    std::string input_suffix         = ""; // string to suffix user inputs with                             // NOLINT
-    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
-    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
-    std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
-
-    std::vector<std::string> in_files;   // all input files
-    std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
-    std::vector<llama_model_kv_override> kv_overrides;
-
-    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
-    std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale
-
-    std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
-
-    int32_t verbosity                  = 0;
-    int32_t control_vector_layer_start = -1; // layer range for control vector
-    int32_t control_vector_layer_end   = -1; // layer range for control vector
-
-    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
-    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
-                                     //                                       (which is more convenient to use for plotting)
-                                     //
-    bool   hellaswag        = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
-    size_t hellaswag_tasks  = 400;   // number of tasks to use when computing the HellaSwag score
-
-    bool   winogrande       = false; // compute Winogrande score over random tasks from datafile supplied in prompt
-    size_t winogrande_tasks = 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
-
-    bool   multiple_choice  = false;  // compute TruthfulQA score over random tasks from datafile supplied in prompt
-    size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
-
-    bool   kl_divergence    = false; // compute KL divergence
-
-    bool usage             = false; // print usage
-    bool use_color         = false; // use color to distinguish generations and inputs
-    bool special           = false; // enable special token output
-    bool interactive       = false; // interactive mode
-    bool interactive_first = false; // wait for user input immediately
-    bool prompt_cache_all  = false; // save user input and generations to prompt cache
-    bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
-
-    bool escape            = true;  // escape "\n", "\r", "\t", "\'", "\"", and "\\"
-    bool multiline_input   = false; // reverse the usage of `\`
-    bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
-    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
-    bool flash_attn        = false; // flash attention
-    bool no_perf           = false; // disable performance metrics
-    bool ctx_shift         = true;  // context shift on inifinite text generation
-
-    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool logits_all        = false; // return logits for all tokens in the batch
-    bool use_mmap          = true;  // use mmap for faster loads
-    bool use_mlock         = false; // use mlock to keep model in memory
-    bool verbose_prompt    = false; // print prompt tokens before generation
-    bool display_prompt    = true;  // print prompt before generation
-    bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
-    bool no_kv_offload     = false; // disable KV offloading
-    bool warmup            = true;  // warmup run
-    bool check_tensors     = false; // validate tensor data
-
-    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
-    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
-
-    common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
-
-    // multimodal models (see examples/llava)
-    std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
-    std::vector<std::string> image; // path to image file(s)
-
-    // embedding
-    bool embedding         = false; // get only sentence embedding
-    int32_t embd_normalize = 2;     // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
-    std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
-    std::string embd_sep   = "\n";  // separator of embeddings
-    bool reranking         = false; // enable reranking support on server
-
-    // server params
-    int32_t port           = 8080;         // server listens on this network port
-    int32_t timeout_read   = 600;          // http read timeout in seconds
-    int32_t timeout_write  = timeout_read; // http write timeout in seconds
-    int32_t n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
-    int32_t n_cache_reuse  = 0;            // min chunk size to reuse from the cache via KV shifting
-
-    std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";                                                                         // NOLINT
-    std::string chat_template = "";                                                                         // NOLINT
-    bool use_jinja = false;                                                                                 // NOLINT
-    bool enable_chat_template = true;
-
-    std::vector<std::string> api_keys;
-
-    std::string ssl_file_key  = "";                                                                         // NOLINT
-    std::string ssl_file_cert = "";                                                                         // NOLINT
-
-    // "advanced" endpoints are disabled by default for better security
-    bool webui            = true;
-    bool endpoint_slots   = false;
-    bool endpoint_props   = false; // only control POST requests, not GET
-    bool endpoint_metrics = false;
-
-    bool log_json = false;
-
-    std::string slot_save_path;
-
-    float slot_prompt_similarity = 0.5f;
-
-    // batched-bench params
-    bool is_pp_shared = false;
-
-    std::vector<int32_t> n_pp;
-    std::vector<int32_t> n_tg;
-    std::vector<int32_t> n_pl;
-
-    // retrieval params
-    std::vector<std::string> context_files; // context files to embed
-
-    int32_t chunk_size = 64; // chunk size for context embedding
-
-    std::string chunk_separator = "\n"; // chunk separator for context embedding
-
-    // passkey params
-    int32_t n_junk = 250; // number of times to repeat the junk text
-    int32_t i_pos  = -1;  // position of the passkey in the junk text
-
-    // imatrix params
-    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
-
-    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
-    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
-    int32_t i_chunk     =  0; // start processing from this chunk
-
-    bool process_output = false; // collect data for the output tensor
-    bool compute_ppl    = true;  // whether to compute perplexity
-
-    // cvector-generator params
-    int n_pca_batch = 100;
-    int n_pca_iterations = 1000;
-    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
-    std::string cvector_outfile       = "control_vector.gguf";
-    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
-    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
-
-    bool spm_infill = false; // suffix/prefix/middle pattern for infill
-
-    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
-
-    // batched-bench params
-    bool batched_bench_output_jsonl = false;
-};
-
-// call once at the start of a program if it uses libcommon
-// initializes the logging system and prints info about the build
-void common_init();
-
-std::string common_params_get_system_info(const common_params & params);
-
-bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
-bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
-void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
-bool set_process_priority(enum ggml_sched_priority prio);
-
-//
-// String utils
-//
-
-#ifdef __GNUC__
-#ifdef __MINGW32__
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
-#endif
-
-LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
-std::string string_format(const char * fmt, ...);
-
-std::string string_strip(const std::string & str);
-std::string string_get_sortable_timestamp();
-
-std::string string_join(const std::vector<std::string> & values, const std::string & separator);
-std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
-std::string string_repeat(const std::string & str, size_t n);
-
-void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
-
-template<class T>
-static std::vector<T> string_split(const std::string & str, char delim) {
-    static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string");
-    std::vector<T> values;
-    std::istringstream str_stream(str);
-    std::string token;
-    while (std::getline(str_stream, token, delim)) {
-        T value;
-        std::istringstream token_stream(token);
-        token_stream >> value;
-        values.push_back(value);
-    }
-    return values;
-}
-
-template<>
-std::vector<std::string> string_split<std::string>(const std::string & input, char separator)
-{
-    std::vector<std::string> parts;
-    size_t begin_pos = 0;
-    size_t separator_pos = input.find(separator);
-    while (separator_pos != std::string::npos) {
-        std::string part = input.substr(begin_pos, separator_pos - begin_pos);
-        parts.emplace_back(part);
-        begin_pos = separator_pos + 1;
-        separator_pos = input.find(separator, begin_pos);
-    }
-    parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos));
-    return parts;
-}
-
-static bool string_starts_with(const std::string & str,
-                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
-    return str.rfind(prefix, 0) == 0;
-}
-
-static bool string_ends_with(const std::string & str,
-                               const std::string & suffix) {  // While we wait for C++20's std::string::ends_with...
-    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
-}
-
-bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
-void string_process_escapes(std::string & input);
-
-std::string string_from(bool value);
-std::string string_from(const std::vector<int> & values);
-std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens);
-std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch);
-
-//
-// Filesystem utils
-//
-
-bool fs_validate_filename(const std::string & filename);
-bool fs_create_directory_with_parents(const std::string & path);
-
-std::string fs_get_cache_directory();
-std::string fs_get_cache_file(const std::string & filename);
-
-//
-// Model utils
-//
-
-// note: defines object's lifetime
-struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
-
-    std::vector<llama_adapter_lora_ptr> lora;
-};
-
-struct common_init_result     common_init_from_params(common_params & params);
-
-struct llama_model_params     common_model_params_to_llama  (      common_params & params);
-struct llama_context_params   common_context_params_to_llama(const common_params & params);
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
-
-struct llama_model * common_load_model_from_url(
-    const std::string & model_url,
-    const std::string & local_path,
-    const std::string & hf_token,
-    const struct llama_model_params & params);
-
-struct llama_model * common_load_model_from_hf(
-    const std::string & repo,
-    const std::string & remote_path,
-    const std::string & local_path,
-    const std::string & hf_token,
-    const struct llama_model_params & params);
-
-std::pair<std::string, std::string> common_get_hf_file(
-    const std::string & hf_repo_with_tag,
-    const std::string & hf_token);
-
-// clear LoRA adapters from context, then apply new list of adapters
-void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
-
-//
-// Batch utils
-//
-
-void common_batch_clear(struct llama_batch & batch);
-
-void common_batch_add(
-                 struct llama_batch & batch,
-                        llama_token   id,
-                          llama_pos   pos,
-    const std::vector<llama_seq_id> & seq_ids,
-                               bool   logits);
-
-//
-// Token utils
-//
-
-// longest common prefix
-size_t common_lcp(const llama_tokens & a, const llama_tokens & b);
-
-// longet common subsequence
-size_t common_lcs(const llama_tokens & a, const llama_tokens & b);
-
-//
-// Vocab utils
-//
-
-// tokenizes a string into a vector of tokens
-// should work similar to Python's `tokenizer.encode`
-std::vector<llama_token> common_tokenize(
-  const struct llama_context * ctx,
-           const std::string & text,
-                        bool   add_special,
-                        bool   parse_special = false);
-
-std::vector<llama_token> common_tokenize(
-    const struct llama_vocab * vocab,
-           const std::string & text,
-                        bool   add_special,
-                        bool   parse_special = false);
-
-// tokenizes a token into a piece, optionally renders special/control tokens
-// should work similar to Python's `tokenizer.id_to_piece`
-std::string common_token_to_piece(
-        const struct llama_context * ctx,
-                       llama_token   token,
-                       bool          special = true);
-
-std::string common_token_to_piece(
-          const struct llama_vocab * vocab,
-                       llama_token   token,
-                       bool          special = true);
-
-// detokenizes a vector of tokens into a string
-// should work similar to Python's `tokenizer.decode`
-// optionally renders special/control tokens
-std::string common_detokenize(
-            const struct llama_context * ctx,
-        const std::vector<llama_token> & tokens,
-                                  bool   special = true);
-
-std::string common_detokenize(
-              const struct llama_vocab * vocab,
-        const std::vector<llama_token> & tokens,
-                                  bool   special = true);
-
-//
-// Chat template utils
-//
-
-struct common_tool_call {
-    std::string name;
-    std::string arguments;
-    std::string id;
-};
-
-// same with llama_chat_message, but uses std::string
-struct common_chat_msg {
-    std::string role;
-    std::string content;
-    std::vector<common_tool_call> tool_calls;
-    std::string tool_plan = "";
-};
-
-// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
-
-namespace minja {
-    class chat_template;
-}
-
-typedef minja::chat_template common_chat_template;
-
-struct common_chat_templates {
-    bool has_explicit_template; // Model had builtin template or template overridde was specified.
-    std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
-    std::unique_ptr<common_chat_template> template_tool_use;
-};
-
-// CPP wrapper for llama_chat_apply_template
-// If the built-in template is not supported, we default to chatml
-// If the custom "tmpl" is not supported, we throw an error
-std::string common_chat_apply_template(
-        const common_chat_template & tmpl,
-        const std::vector<common_chat_msg> & chat,
-        bool add_ass,
-        bool use_jinja);
-
-// Format single message, while taking into account the position of that message in chat history
-std::string common_chat_format_single(
-        const common_chat_template & tmpl,
-        const std::vector<common_chat_msg> & past_msg,
-        const common_chat_msg & new_msg,
-        bool add_ass,
-        bool use_jinja);
-
-// Returns an example of formatted chat
-std::string common_chat_format_example(
-    const common_chat_template & tmpl, bool use_jinja);
-
-common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override);
-
-//
-// KV cache utils
-//
-
-// Dump the KV cache view with the number of sequences per cell.
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
-
-// Dump the KV cache view showing individual sequences in each cell (long output).
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
-
-//
-// Embedding utils
-//
-
-// TODO: repace embd_norm with an enum
-void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);
-
-float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
-
-//
-// Control vector utils
-//
-
-struct common_control_vector_data {
-    int n_embd;
-
-    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
-    std::vector<float> data;
-};
-
-struct common_control_vector_load_info {
-    float strength;
-
-    std::string fname;
-};
-
-// Load control vectors, scale each by strength, and add them together.
-// On error, returns {-1, empty}
-common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos);
-
-//
-// Split utils
-//
-
-namespace {
-
-const char * const LLM_KV_SPLIT_NO            = "split.no";
-const char * const LLM_KV_SPLIT_COUNT         = "split.count";
-const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
-
-}

common/console.cpp

@@ -1,504 +0,0 @@
-#include "console.h"
-#include <vector>
-#include <iostream>
-
-#if defined(_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-#define NOMINMAX
-#endif
-#include <windows.h>
-#include <fcntl.h>
-#include <io.h>
-#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING
-#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004
-#endif
-#else
-#include <climits>
-#include <sys/ioctl.h>
-#include <unistd.h>
-#include <wchar.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <signal.h>
-#include <termios.h>
-#endif
-
-#define ANSI_COLOR_RED     "\x1b[31m"
-#define ANSI_COLOR_GREEN   "\x1b[32m"
-#define ANSI_COLOR_YELLOW  "\x1b[33m"
-#define ANSI_COLOR_BLUE    "\x1b[34m"
-#define ANSI_COLOR_MAGENTA "\x1b[35m"
-#define ANSI_COLOR_CYAN    "\x1b[36m"
-#define ANSI_COLOR_RESET   "\x1b[0m"
-#define ANSI_BOLD          "\x1b[1m"
-
-namespace console {
-
-    //
-    // Console state
-    //
-
-    static bool      advanced_display = false;
-    static bool      simple_io        = true;
-    static display_t current_display  = reset;
-
-    static FILE*     out              = stdout;
-
-#if defined (_WIN32)
-    static void*     hConsole;
-#else
-    static FILE*     tty              = nullptr;
-    static termios   initial_state;
-#endif
-
-    //
-    // Init and cleanup
-    //
-
-    void init(bool use_simple_io, bool use_advanced_display) {
-        advanced_display = use_advanced_display;
-        simple_io = use_simple_io;
-#if defined(_WIN32)
-        // Windows-specific console initialization
-        DWORD dwMode = 0;
-        hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
-        if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
-            hConsole = GetStdHandle(STD_ERROR_HANDLE);
-            if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) {
-                hConsole = nullptr;
-                simple_io = true;
-            }
-        }
-        if (hConsole) {
-            // Check conditions combined to reduce nesting
-            if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) &&
-                !SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
-                advanced_display = false;
-            }
-            // Set console output codepage to UTF8
-            SetConsoleOutputCP(CP_UTF8);
-        }
-        HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
-        if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
-            // Set console input codepage to UTF16
-            _setmode(_fileno(stdin), _O_WTEXT);
-
-            // Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
-            if (simple_io) {
-                dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;
-            } else {
-                dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
-            }
-            if (!SetConsoleMode(hConIn, dwMode)) {
-                simple_io = true;
-            }
-        }
-        if (simple_io) {
-            _setmode(_fileno(stdin), _O_U8TEXT);
-        }
-#else
-        // POSIX-specific console initialization
-        if (!simple_io) {
-            struct termios new_termios;
-            tcgetattr(STDIN_FILENO, &initial_state);
-            new_termios = initial_state;
-            new_termios.c_lflag &= ~(ICANON | ECHO);
-            new_termios.c_cc[VMIN] = 1;
-            new_termios.c_cc[VTIME] = 0;
-            tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
-
-            tty = fopen("/dev/tty", "w+");
-            if (tty != nullptr) {
-                out = tty;
-            }
-        }
-
-        setlocale(LC_ALL, "");
-#endif
-    }
-
-    void cleanup() {
-        // Reset console display
-        set_display(reset);
-
-#if !defined(_WIN32)
-        // Restore settings on POSIX systems
-        if (!simple_io) {
-            if (tty != nullptr) {
-                out = stdout;
-                fclose(tty);
-                tty = nullptr;
-            }
-            tcsetattr(STDIN_FILENO, TCSANOW, &initial_state);
-        }
-#endif
-    }
-
-    //
-    // Display and IO
-    //
-
-    // Keep track of current display and only emit ANSI code if it changes
-    void set_display(display_t display) {
-        if (advanced_display && current_display != display) {
-            fflush(stdout);
-            switch(display) {
-                case reset:
-                    fprintf(out, ANSI_COLOR_RESET);
-                    break;
-                case prompt:
-                    fprintf(out, ANSI_COLOR_YELLOW);
-                    break;
-                case user_input:
-                    fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);
-                    break;
-                case error:
-                    fprintf(out, ANSI_BOLD ANSI_COLOR_RED);
-            }
-            current_display = display;
-            fflush(out);
-        }
-    }
-
-    static char32_t getchar32() {
-#if defined(_WIN32)
-        HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);
-        wchar_t high_surrogate = 0;
-
-        while (true) {
-            INPUT_RECORD record;
-            DWORD count;
-            if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {
-                return WEOF;
-            }
-
-            if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
-                wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
-                if (wc == 0) {
-                    continue;
-                }
-
-                if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
-                    high_surrogate = wc;
-                    continue;
-                }
-                if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate
-                    if (high_surrogate != 0) { // Check if we have a high surrogate
-                        return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;
-                    }
-                }
-
-                high_surrogate = 0; // Reset the high surrogate
-                return static_cast<char32_t>(wc);
-            }
-        }
-#else
-        wchar_t wc = getwchar();
-        if (static_cast<wint_t>(wc) == WEOF) {
-            return WEOF;
-        }
-
-#if WCHAR_MAX == 0xFFFF
-        if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
-            wchar_t low_surrogate = getwchar();
-            if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
-                return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
-            }
-        }
-        if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
-            return 0xFFFD; // Return the replacement character U+FFFD
-        }
-#endif
-
-        return static_cast<char32_t>(wc);
-#endif
-    }
-
-    static void pop_cursor() {
-#if defined(_WIN32)
-        if (hConsole != NULL) {
-            CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
-            GetConsoleScreenBufferInfo(hConsole, &bufferInfo);
-
-            COORD newCursorPosition = bufferInfo.dwCursorPosition;
-            if (newCursorPosition.X == 0) {
-                newCursorPosition.X = bufferInfo.dwSize.X - 1;
-                newCursorPosition.Y -= 1;
-            } else {
-                newCursorPosition.X -= 1;
-            }
-
-            SetConsoleCursorPosition(hConsole, newCursorPosition);
-            return;
-        }
-#endif
-        putc('\b', out);
-    }
-
-    static int estimateWidth(char32_t codepoint) {
-#if defined(_WIN32)
-        (void)codepoint;
-        return 1;
-#else
-        return wcwidth(codepoint);
-#endif
-    }
-
-    static int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) {
-#if defined(_WIN32)
-        CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
-        if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) {
-            // go with the default
-            return expectedWidth;
-        }
-        COORD initialPosition = bufferInfo.dwCursorPosition;
-        DWORD nNumberOfChars = length;
-        WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
-
-        CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
-        GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
-
-        // Figure out our real position if we're in the last column
-        if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
-            DWORD nNumberOfChars;
-            WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL);
-            GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
-        }
-
-        int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
-        if (width < 0) {
-            width += newBufferInfo.dwSize.X;
-        }
-        return width;
-#else
-        // We can trust expectedWidth if we've got one
-        if (expectedWidth >= 0 || tty == nullptr) {
-            fwrite(utf8_codepoint, length, 1, out);
-            return expectedWidth;
-        }
-
-        fputs("\033[6n", tty); // Query cursor position
-        int x1;
-        int y1;
-        int x2;
-        int y2;
-        int results = 0;
-        results = fscanf(tty, "\033[%d;%dR", &y1, &x1);
-
-        fwrite(utf8_codepoint, length, 1, tty);
-
-        fputs("\033[6n", tty); // Query cursor position
-        results += fscanf(tty, "\033[%d;%dR", &y2, &x2);
-
-        if (results != 4) {
-            return expectedWidth;
-        }
-
-        int width = x2 - x1;
-        if (width < 0) {
-            // Calculate the width considering text wrapping
-            struct winsize w;
-            ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
-            width += w.ws_col;
-        }
-        return width;
-#endif
-    }
-
-    static void replace_last(char ch) {
-#if defined(_WIN32)
-        pop_cursor();
-        put_codepoint(&ch, 1, 1);
-#else
-        fprintf(out, "\b%c", ch);
-#endif
-    }
-
-    static void append_utf8(char32_t ch, std::string & out) {
-        if (ch <= 0x7F) {
-            out.push_back(static_cast<unsigned char>(ch));
-        } else if (ch <= 0x7FF) {
-            out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
-            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-        } else if (ch <= 0xFFFF) {
-            out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
-            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
-            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-        } else if (ch <= 0x10FFFF) {
-            out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
-            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
-            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
-            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-        } else {
-            // Invalid Unicode code point
-        }
-    }
-
-    // Helper function to remove the last UTF-8 character from a string
-    static void pop_back_utf8_char(std::string & line) {
-        if (line.empty()) {
-            return;
-        }
-
-        size_t pos = line.length() - 1;
-
-        // Find the start of the last UTF-8 character (checking up to 4 bytes back)
-        for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
-            if ((line[pos] & 0xC0) != 0x80) {
-                break; // Found the start of the character
-            }
-        }
-        line.erase(pos);
-    }
-
-    static bool readline_advanced(std::string & line, bool multiline_input) {
-        if (out != stdout) {
-            fflush(stdout);
-        }
-
-        line.clear();
-        std::vector<int> widths;
-        bool is_special_char = false;
-        bool end_of_stream = false;
-
-        char32_t input_char;
-        while (true) {
-            fflush(out); // Ensure all output is displayed before waiting for input
-            input_char = getchar32();
-
-            if (input_char == '\r' || input_char == '\n') {
-                break;
-            }
-
-            if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
-                end_of_stream = true;
-                break;
-            }
-
-            if (is_special_char) {
-                set_display(user_input);
-                replace_last(line.back());
-                is_special_char = false;
-            }
-
-            if (input_char == '\033') { // Escape sequence
-                char32_t code = getchar32();
-                if (code == '[' || code == 0x1B) {
-                    // Discard the rest of the escape sequence
-                    while ((code = getchar32()) != (char32_t) WEOF) {
-                        if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
-                            break;
-                        }
-                    }
-                }
-            } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
-                if (!widths.empty()) {
-                    int count;
-                    do {
-                        count = widths.back();
-                        widths.pop_back();
-                        // Move cursor back, print space, and move cursor back again
-                        for (int i = 0; i < count; i++) {
-                            replace_last(' ');
-                            pop_cursor();
-                        }
-                        pop_back_utf8_char(line);
-                    } while (count == 0 && !widths.empty());
-                }
-            } else {
-                int offset = line.length();
-                append_utf8(input_char, line);
-                int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
-                if (width < 0) {
-                    width = 0;
-                }
-                widths.push_back(width);
-            }
-
-            if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
-                set_display(prompt);
-                replace_last(line.back());
-                is_special_char = true;
-            }
-        }
-
-        bool has_more = multiline_input;
-        if (is_special_char) {
-            replace_last(' ');
-            pop_cursor();
-
-            char last = line.back();
-            line.pop_back();
-            if (last == '\\') {
-                line += '\n';
-                fputc('\n', out);
-                has_more = !has_more;
-            } else {
-                // llama will just eat the single space, it won't act as a space
-                if (line.length() == 1 && line.back() == ' ') {
-                    line.clear();
-                    pop_cursor();
-                }
-                has_more = false;
-            }
-        } else {
-            if (end_of_stream) {
-                has_more = false;
-            } else {
-                line += '\n';
-                fputc('\n', out);
-            }
-        }
-
-        fflush(out);
-        return has_more;
-    }
-
-    static bool readline_simple(std::string & line, bool multiline_input) {
-#if defined(_WIN32)
-        std::wstring wline;
-        if (!std::getline(std::wcin, wline)) {
-            // Input stream is bad or EOF received
-            line.clear();
-            GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0);
-            return false;
-        }
-
-        int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);
-        line.resize(size_needed);
-        WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);
-#else
-        if (!std::getline(std::cin, line)) {
-            // Input stream is bad or EOF received
-            line.clear();
-            return false;
-        }
-#endif
-        if (!line.empty()) {
-            char last = line.back();
-            if (last == '/') { // Always return control on '/' symbol
-                line.pop_back();
-                return false;
-            }
-            if (last == '\\') { // '\\' changes the default action
-                line.pop_back();
-                multiline_input = !multiline_input;
-            }
-        }
-        line += '\n';
-
-        // By default, continue input if multiline_input is set
-        return multiline_input;
-    }
-
-    bool readline(std::string & line, bool multiline_input) {
-        set_display(user_input);
-
-        if (simple_io) {
-            return readline_simple(line, multiline_input);
-        }
-        return readline_advanced(line, multiline_input);
-    }
-
-}

common/console.h

@@ -1,19 +0,0 @@
-// Console functions
-
-#pragma once
-
-#include <string>
-
-namespace console {
-    enum display_t {
-        reset = 0,
-        prompt,
-        user_input,
-        error
-    };
-
-    void init(bool use_simple_io, bool use_advanced_display);
-    void cleanup();
-    void set_display(display_t display);
-    bool readline(std::string & line, bool multiline_input);
-}

common/json-schema-to-grammar.h

@@ -1,22 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-// Change JSON_ASSERT from assert() to GGML_ASSERT:
-#define JSON_ASSERT GGML_ASSERT
-#include "json.hpp"
-
-std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
-                                   bool force_gbnf = false);
-
-struct common_grammar_builder {
-    std::function<std::string(const std::string &, const std::string &)> add_rule;
-    std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
-    std::function<void(nlohmann::ordered_json &)> resolve_refs;
-};
-
-struct common_grammar_options {
-    bool dotall = false;
-    bool compact_spaces = false;
-};
-
-std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});

common/llguidance.cpp

@@ -1,270 +0,0 @@
-#include "sampling.h"
-#include "log.h"
-
-#ifdef LLAMA_USE_LLGUIDANCE
-
-#    include "llguidance.h"
-#    include <cmath>
-
-struct llama_sampler_llg {
-    const llama_vocab * vocab;
-    std::string         grammar_kind;
-    std::string         grammar_data;
-    LlgTokenizer *      tokenizer;
-    LlgConstraint *     grammar;
-    LlgMaskResult       llg_res;
-    bool                has_llg_res;
-};
-
-static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
-                                             const char * grammar_data) {
-    LlgConstraintInit cinit;
-    llg_constraint_init_set_defaults(&cinit, tokenizer);
-    const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
-    if (log_level && *log_level) {
-        cinit.log_stderr_level = atoi(log_level);
-    }
-    auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
-    if (llg_get_error(c)) {
-        LOG_ERR("llg error: %s\n", llg_get_error(c));
-        llg_free_constraint(c);
-        return nullptr;
-    }
-    return c;
-}
-
-static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
-    return "llguidance";
-}
-
-static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (ctx->grammar) {
-        LlgCommitResult res;
-        llg_commit_token(ctx->grammar, token, &res);
-        ctx->has_llg_res = false;
-    }
-}
-
-static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (ctx->grammar) {
-        if (!ctx->has_llg_res) {
-            if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
-                ctx->has_llg_res = true;
-            } else {
-                LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
-                llg_free_constraint(ctx->grammar);
-                ctx->grammar = nullptr;
-            }
-        }
-        if (ctx->has_llg_res) {
-            if (ctx->llg_res.is_stop) {
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
-            } else {
-                const uint32_t * mask = ctx->llg_res.sample_mask;
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    auto token = cur_p->data[i].id;
-                    if ((mask[token / 32] & (1 << (token % 32))) == 0) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-}
-
-static void llama_sampler_llg_reset(llama_sampler * smpl) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (!ctx->grammar) {
-        return;
-    }
-
-    auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
-    llg_free_constraint(ctx->grammar);
-    ctx->grammar     = grammar_new;
-    ctx->has_llg_res = false;
-}
-
-static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
-    const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
-
-    auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
-
-    // copy the state
-    {
-        auto * result_ctx = (llama_sampler_llg *) result->ctx;
-
-        if (ctx->grammar) {
-            result_ctx->grammar_kind = ctx->grammar_kind;
-            result_ctx->grammar_data = ctx->grammar_data;
-            result_ctx->grammar      = llg_clone_constraint(ctx->grammar);
-            result_ctx->tokenizer    = llg_clone_tokenizer(ctx->tokenizer);
-        }
-    }
-
-    return result;
-}
-
-static void llama_sampler_llg_free(llama_sampler * smpl) {
-    const auto * ctx = (llama_sampler_llg *) smpl->ctx;
-
-    if (ctx->grammar) {
-        llg_free_constraint(ctx->grammar);
-        llg_free_tokenizer(ctx->tokenizer);
-    }
-
-    delete ctx;
-}
-
-static llama_sampler_i llama_sampler_llg_i = {
-    /* .name   = */ llama_sampler_llg_name,
-    /* .accept = */ llama_sampler_llg_accept_impl,
-    /* .apply  = */ llama_sampler_llg_apply,
-    /* .reset  = */ llama_sampler_llg_reset,
-    /* .clone  = */ llama_sampler_llg_clone,
-    /* .free   = */ llama_sampler_llg_free,
-};
-
-static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
-                                            uint32_t * output_tokens, size_t output_tokens_len) {
-    const llama_vocab * vocab = (const llama_vocab *) user_data;
-    int                 r     = 0;
-    try {
-        r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
-                           true);
-    } catch (const std::exception & e) {
-        GGML_ABORT("llama_tokenize failed: %s\n", e.what());
-    }
-    if (r < 0) {
-        return -r;
-    }
-    return r;
-}
-
-static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
-    // TODO store the tokenizer in the vocab somehow
-    static const llama_vocab * vocab_cache;
-    static LlgTokenizer *      tokenizer_cache;
-
-    if (vocab_cache == vocab) {
-        return llg_clone_tokenizer(tokenizer_cache);
-    }
-
-    auto tok_eos = llama_vocab_eot(vocab);
-    if (tok_eos == LLAMA_TOKEN_NULL) {
-        tok_eos = llama_vocab_eos(vocab);
-    }
-
-    size_t vocab_size = llama_vocab_n_tokens(vocab);
-
-    auto token_lens       = new uint32_t[vocab_size];
-    // we typically have ~7 bytes per token; let's go on the safe side here
-    auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
-    auto token_bytes      = new uint8_t[token_bytes_size];
-
-    size_t offset = 0;
-    for (size_t i = 0; i < vocab_size; i++) {
-        size_t max_token = 1024;
-        if (token_bytes_size - offset < max_token) {
-            GGML_ABORT("token_bytes buffer too small\n");
-        }
-
-        llama_token token = i;
-        auto        dp    = (char *) token_bytes + offset;
-        auto        size  = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
-        if (size < 0) {
-            GGML_ABORT("llama_detokenize failed\n");
-        }
-        if (size == 0) {
-            size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
-            if (size < 0) {
-                GGML_ABORT("llama_detokenize failed\n");
-            }
-            if (size != 0) {
-                *dp = '\xff';  // special token prefix marker
-                size += 1;
-            }
-        }
-
-        token_lens[i] = size;
-        offset += size;
-    }
-
-    LlgTokenizerInit tinit = {
-        /* .vocab_size                         = */ (uint32_t) vocab_size,
-        /* .tok_eos                            = */ (uint32_t) tok_eos,
-        /* .token_lens                         = */ token_lens,
-        /* .token_bytes                        = */ token_bytes,
-        /* .tokenizer_json                     = */ nullptr,
-        /* .tokenize_assumes_string            = */ true,
-        /* .tokenize_fn                        = */ llama_sampler_llg_tokenize_fn,
-        /* .use_approximate_greedy_tokenize_fn = */ false,
-        /* .tokenize_user_data                 = */ vocab,
-    };
-
-    char           error_buffer[1024];
-    LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
-
-    delete[] token_bytes;
-    delete[] token_lens;
-
-    if (tokenizer == nullptr) {
-        LOG_ERR("llg tokenizer error: %s\n", error_buffer);
-        return tokenizer;
-    }
-
-    if (tokenizer_cache) {
-        llg_free_tokenizer(tokenizer_cache);
-    }
-    vocab_cache     = vocab;
-    tokenizer_cache = tokenizer;
-
-    return llg_clone_tokenizer(tokenizer_cache);
-}
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
-                                       const char * grammar_data) {
-    auto * ctx = new llama_sampler_llg;
-
-    if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
-        auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
-        *ctx           = {
-            /* .vocab        = */ vocab,
-            /* .grammar_kind = */ grammar_kind,
-            /* .grammar_data = */ grammar_data,
-            /* .tokenizer    = */ tokenizer,
-            /* .grammar      = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
-        };
-    } else {
-        *ctx = {
-            /* .vocab        = */ vocab,
-            /* .grammar_kind = */ {},
-            /* .grammar_data = */ {},
-            /* .tokenizer    = */ nullptr,
-            /* .grammar      = */ nullptr,
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
-        };
-    }
-
-    return llama_sampler_init(
-        /* .iface = */ &llama_sampler_llg_i,
-        /* .ctx   = */ ctx
-    );
-}
-
-#else
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
-    LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
-    return nullptr;
-}
-
-#endif  // LLAMA_USE_LLGUIDANCE

common/log.cpp

@@ -1,392 +0,0 @@
-#include "log.h"
-
-#include <condition_variable>
-#include <cstdarg>
-#include <cstdio>
-#include <mutex>
-#include <sstream>
-#include <thread>
-#include <vector>
-
-int common_log_verbosity_thold = LOG_DEFAULT_LLAMA;
-
-void common_log_set_verbosity_thold(int verbosity) {
-    common_log_verbosity_thold = verbosity;
-}
-
-static int64_t t_us() {
-    return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
-}
-
-// colors
-enum common_log_col : int {
-    COMMON_LOG_COL_DEFAULT = 0,
-    COMMON_LOG_COL_BOLD,
-    COMMON_LOG_COL_RED,
-    COMMON_LOG_COL_GREEN,
-    COMMON_LOG_COL_YELLOW,
-    COMMON_LOG_COL_BLUE,
-    COMMON_LOG_COL_MAGENTA,
-    COMMON_LOG_COL_CYAN,
-    COMMON_LOG_COL_WHITE,
-};
-
-// disable colors by default
-static std::vector<const char *> g_col = {
-    "",
-    "",
-    "",
-    "",
-    "",
-    "",
-    "",
-    "",
-    "",
-};
-
-struct common_log_entry {
-    enum ggml_log_level level;
-
-    bool prefix;
-
-    int64_t timestamp;
-
-    std::vector<char> msg;
-
-    // signals the worker thread to stop
-    bool is_end;
-
-    void print(FILE * file = nullptr) const {
-        FILE * fcur = file;
-        if (!fcur) {
-            // stderr displays DBG messages only when their verbosity level is not higher than the threshold
-            // these messages will still be logged to a file
-            if (level == GGML_LOG_LEVEL_DEBUG && common_log_verbosity_thold < LOG_DEFAULT_DEBUG) {
-                return;
-            }
-
-            fcur = stdout;
-
-            if (level != GGML_LOG_LEVEL_NONE) {
-                fcur = stderr;
-            }
-        }
-
-        if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) {
-            if (timestamp) {
-                // [M.s.ms.us]
-                fprintf(fcur, "%s%d.%02d.%03d.%03d%s ",
-                        g_col[COMMON_LOG_COL_BLUE],
-                        (int) (timestamp / 1000000 / 60),
-                        (int) (timestamp / 1000000 % 60),
-                        (int) (timestamp / 1000 % 1000),
-                        (int) (timestamp % 1000),
-                        g_col[COMMON_LOG_COL_DEFAULT]);
-            }
-
-            switch (level) {
-                case GGML_LOG_LEVEL_INFO:  fprintf(fcur, "%sI %s", g_col[COMMON_LOG_COL_GREEN],   g_col[COMMON_LOG_COL_DEFAULT]); break;
-                case GGML_LOG_LEVEL_WARN:  fprintf(fcur, "%sW %s", g_col[COMMON_LOG_COL_MAGENTA], ""                        ); break;
-                case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[COMMON_LOG_COL_RED],     ""                        ); break;
-                case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[COMMON_LOG_COL_YELLOW],  ""                        ); break;
-                default:
-                    break;
-            }
-        }
-
-        fprintf(fcur, "%s", msg.data());
-
-        if (level == GGML_LOG_LEVEL_WARN || level == GGML_LOG_LEVEL_ERROR || level == GGML_LOG_LEVEL_DEBUG) {
-            fprintf(fcur, "%s", g_col[COMMON_LOG_COL_DEFAULT]);
-        }
-
-        fflush(fcur);
-    }
-};
-
-struct common_log {
-    // default capacity - will be expanded if needed
-    common_log() : common_log(256) {}
-
-    common_log(size_t capacity) {
-        file = nullptr;
-        prefix = false;
-        timestamps = false;
-        running = false;
-        t_start = t_us();
-
-        // initial message size - will be expanded if longer messages arrive
-        entries.resize(capacity);
-        for (auto & entry : entries) {
-            entry.msg.resize(256);
-        }
-
-        head = 0;
-        tail = 0;
-
-        resume();
-    }
-
-    ~common_log() {
-        pause();
-        if (file) {
-            fclose(file);
-        }
-    }
-
-private:
-    std::mutex mtx;
-    std::thread thrd;
-    std::condition_variable cv;
-
-    FILE * file;
-
-    bool prefix;
-    bool timestamps;
-    bool running;
-
-    int64_t t_start;
-
-    // ring buffer of entries
-    std::vector<common_log_entry> entries;
-    size_t head;
-    size_t tail;
-
-    // worker thread copies into this
-    common_log_entry cur;
-
-public:
-    void add(enum ggml_log_level level, const char * fmt, va_list args) {
-        std::lock_guard<std::mutex> lock(mtx);
-
-        if (!running) {
-            // discard messages while the worker thread is paused
-            return;
-        }
-
-        auto & entry = entries[tail];
-
-        {
-            // cannot use args twice, so make a copy in case we need to expand the buffer
-            va_list args_copy;
-            va_copy(args_copy, args);
-
-#if 1
-            const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args);
-            if (n >= entry.msg.size()) {
-                entry.msg.resize(n + 1);
-                vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args_copy);
-            }
-#else
-            // hack for bolding arguments
-
-            std::stringstream ss;
-            for (int i = 0; fmt[i] != 0; i++) {
-                if (fmt[i] == '%') {
-                    ss << LOG_COL_BOLD;
-                    while (fmt[i] != ' ' && fmt[i] != ')' && fmt[i] != ']' && fmt[i] != 0) ss << fmt[i++];
-                    ss << LOG_COL_DEFAULT;
-                    if (fmt[i] == 0) break;
-                }
-                ss << fmt[i];
-            }
-            const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args);
-            if (n >= entry.msg.size()) {
-                entry.msg.resize(n + 1);
-                vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
-            }
-#endif
-            va_end(args_copy);
-        }
-
-        entry.level = level;
-        entry.prefix = prefix;
-        entry.timestamp = 0;
-        if (timestamps) {
-            entry.timestamp = t_us() - t_start;
-        }
-        entry.is_end = false;
-
-        tail = (tail + 1) % entries.size();
-        if (tail == head) {
-            // expand the buffer
-            std::vector<common_log_entry> new_entries(2*entries.size());
-
-            size_t new_tail = 0;
-
-            do {
-                new_entries[new_tail] = std::move(entries[head]);
-
-                head     = (head     + 1) % entries.size();
-                new_tail = (new_tail + 1);
-            } while (head != tail);
-
-            head = 0;
-            tail = new_tail;
-
-            for (size_t i = tail; i < new_entries.size(); i++) {
-                new_entries[i].msg.resize(256);
-            }
-
-            entries = std::move(new_entries);
-        }
-
-        cv.notify_one();
-    }
-
-    void resume() {
-        std::lock_guard<std::mutex> lock(mtx);
-
-        if (running) {
-            return;
-        }
-
-        running = true;
-
-        thrd = std::thread([this]() {
-            while (true) {
-                {
-                    std::unique_lock<std::mutex> lock(mtx);
-                    cv.wait(lock, [this]() { return head != tail; });
-
-                    cur = entries[head];
-
-                    head = (head + 1) % entries.size();
-                }
-
-                if (cur.is_end) {
-                    break;
-                }
-
-                cur.print(); // stdout and stderr
-
-                if (file) {
-                    cur.print(file);
-                }
-            }
-        });
-    }
-
-    void pause() {
-        {
-            std::lock_guard<std::mutex> lock(mtx);
-
-            if (!running) {
-                return;
-            }
-
-            running = false;
-
-            // push an entry to signal the worker thread to stop
-            {
-                auto & entry = entries[tail];
-                entry.is_end = true;
-
-                tail = (tail + 1) % entries.size();
-            }
-
-            cv.notify_one();
-        }
-
-        thrd.join();
-    }
-
-    void set_file(const char * path) {
-        pause();
-
-        if (file) {
-            fclose(file);
-        }
-
-        if (path) {
-            file = fopen(path, "w");
-        } else {
-            file = nullptr;
-        }
-
-        resume();
-    }
-
-    void set_colors(bool colors) {
-        pause();
-
-        if (colors) {
-            g_col[COMMON_LOG_COL_DEFAULT] = LOG_COL_DEFAULT;
-            g_col[COMMON_LOG_COL_BOLD]    = LOG_COL_BOLD;
-            g_col[COMMON_LOG_COL_RED]     = LOG_COL_RED;
-            g_col[COMMON_LOG_COL_GREEN]   = LOG_COL_GREEN;
-            g_col[COMMON_LOG_COL_YELLOW]  = LOG_COL_YELLOW;
-            g_col[COMMON_LOG_COL_BLUE]    = LOG_COL_BLUE;
-            g_col[COMMON_LOG_COL_MAGENTA] = LOG_COL_MAGENTA;
-            g_col[COMMON_LOG_COL_CYAN]    = LOG_COL_CYAN;
-            g_col[COMMON_LOG_COL_WHITE]   = LOG_COL_WHITE;
-        } else {
-            for (size_t i = 0; i < g_col.size(); i++) {
-                g_col[i] = "";
-            }
-        }
-
-        resume();
-    }
-
-    void set_prefix(bool prefix) {
-        std::lock_guard<std::mutex> lock(mtx);
-
-        this->prefix = prefix;
-    }
-
-    void set_timestamps(bool timestamps) {
-        std::lock_guard<std::mutex> lock(mtx);
-
-        this->timestamps = timestamps;
-    }
-};
-
-//
-// public API
-//
-
-struct common_log * common_log_init() {
-    return new common_log;
-}
-
-struct common_log * common_log_main() {
-    static struct common_log log;
-
-    return &log;
-}
-
-void common_log_pause(struct common_log * log) {
-    log->pause();
-}
-
-void common_log_resume(struct common_log * log) {
-    log->resume();
-}
-
-void common_log_free(struct common_log * log) {
-    delete log;
-}
-
-void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...) {
-    va_list args;
-    va_start(args, fmt);
-    log->add(level, fmt, args);
-    va_end(args);
-}
-
-void common_log_set_file(struct common_log * log, const char * file) {
-    log->set_file(file);
-}
-
-void common_log_set_colors(struct common_log * log, bool colors) {
-    log->set_colors(colors);
-}
-
-void common_log_set_prefix(struct common_log * log, bool prefix) {
-    log->set_prefix(prefix);
-}
-
-void common_log_set_timestamps(struct common_log * log, bool timestamps) {
-    log->set_timestamps(timestamps);
-}

common/log.h

@@ -1,103 +0,0 @@
-#pragma once
-
-#include "ggml.h" // for ggml_log_level
-
-#define LOG_CLR_TO_EOL  "\033[K\r"
-#define LOG_COL_DEFAULT "\033[0m"
-#define LOG_COL_BOLD    "\033[1m"
-#define LOG_COL_RED     "\033[31m"
-#define LOG_COL_GREEN   "\033[32m"
-#define LOG_COL_YELLOW  "\033[33m"
-#define LOG_COL_BLUE    "\033[34m"
-#define LOG_COL_MAGENTA "\033[35m"
-#define LOG_COL_CYAN    "\033[36m"
-#define LOG_COL_WHITE   "\033[37m"
-
-#ifndef __GNUC__
-#    define LOG_ATTRIBUTE_FORMAT(...)
-#elif defined(__MINGW32__)
-#    define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#    define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-
-#define LOG_DEFAULT_DEBUG 1
-#define LOG_DEFAULT_LLAMA 0
-
-// needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower
-// set via common_log_set_verbosity()
-extern int common_log_verbosity_thold;
-
-void common_log_set_verbosity_thold(int verbosity); // not thread-safe
-
-// the common_log uses an internal worker thread to print/write log messages
-// when the worker thread is paused, incoming log messages are discarded
-struct common_log;
-
-struct common_log * common_log_init();
-struct common_log * common_log_main(); // singleton, automatically destroys itself on exit
-void                common_log_pause (struct common_log * log); // pause  the worker thread, not thread-safe
-void                common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe
-void                common_log_free  (struct common_log * log);
-
-LOG_ATTRIBUTE_FORMAT(3, 4)
-void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...);
-
-// defaults: file = NULL, colors = false, prefix = false, timestamps = false
-//
-// regular log output:
-//
-//   ggml_backend_metal_log_allocated_size: allocated buffer, size =  6695.84 MiB, ( 6695.91 / 21845.34)
-//   llm_load_tensors: ggml ctx size =    0.27 MiB
-//   llm_load_tensors: offloading 32 repeating layers to GPU
-//   llm_load_tensors: offloading non-repeating layers to GPU
-//
-// with prefix = true, timestamps = true, the log output will look like this:
-//
-//   0.00.035.060 D ggml_backend_metal_log_allocated_size: allocated buffer, size =  6695.84 MiB, ( 6695.91 / 21845.34)
-//   0.00.035.064 I llm_load_tensors: ggml ctx size =    0.27 MiB
-//   0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU
-//   0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU
-//
-// I - info    (stdout, V = 0)
-// W - warning (stderr, V = 0)
-// E - error   (stderr, V = 0)
-// D - debug   (stderr, V = LOG_DEFAULT_DEBUG)
-//
-
-void common_log_set_file      (struct common_log * log, const char * file);       // not thread-safe
-void common_log_set_colors    (struct common_log * log,       bool   colors);     // not thread-safe
-void common_log_set_prefix    (struct common_log * log,       bool   prefix);     // whether to output prefix to each log
-void common_log_set_timestamps(struct common_log * log,       bool   timestamps); // whether to output timestamps in the prefix
-
-// helper macros for logging
-// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold
-//
-// for example:
-//
-//   LOG_DBG("this is a debug message: %d\n", expensive_function());
-//
-// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold
-//
-
-#define LOG_TMPL(level, verbosity, ...) \
-    do { \
-        if ((verbosity) <= common_log_verbosity_thold) { \
-            common_log_add(common_log_main(), (level), __VA_ARGS__); \
-        } \
-    } while (0)
-
-#define LOG(...)             LOG_TMPL(GGML_LOG_LEVEL_NONE, 0,         __VA_ARGS__)
-#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
-
-#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  0,                 __VA_ARGS__)
-#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  0,                 __VA_ARGS__)
-#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0,                 __VA_ARGS__)
-#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__)
-#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  0,                 __VA_ARGS__)
-
-#define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  verbosity, __VA_ARGS__)
-#define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)
-#define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__)
-#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__)
-#define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  verbosity, __VA_ARGS__)

common/ngram-cache.cpp

@@ -1,285 +0,0 @@
-#include "ngram-cache.h"
-#include "common.h"
-#include "log.h"
-
-#include <cinttypes>
-#include <cstdint>
-#include <cstdio>
-#include <fstream>
-#include <thread>
-
-void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
-                              std::vector<llama_token> & inp, int nnew, bool print_progress) {
-    const int64_t t_start_ms = ggml_time_ms();
-    const int64_t inp_size = inp.size();
-
-    const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
-    int64_t n_done = 0;
-
-    for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
-        const int64_t i_start = std::max(inp_size - nnew, ngram_size);
-        for (int64_t i = i_start; i < inp_size; ++i) {
-            const int64_t ngram_start = i - ngram_size;
-            common_ngram ngram(&inp[ngram_start], ngram_size);
-            const llama_token token = inp[i];
-
-            common_ngram_cache::iterator part_it = ngram_cache.find(ngram);
-            if (part_it == ngram_cache.end()) {
-                common_ngram_cache_part part;
-                part.emplace(token, 1);
-                ngram_cache.emplace(ngram, part);
-            } else {
-                common_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
-                if (token_count_it == part_it->second.end()) {
-                    part_it->second.emplace(token, 1);
-                } else {
-                    token_count_it->second++;
-                }
-            }
-            ++n_done;
-
-            if (print_progress && n_done % 10000000 == 0) {
-                const int64_t t_now_ms = ggml_time_ms();
-                const int64_t eta_ms   = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
-                const int64_t eta_min  = eta_ms / (60*1000);
-                const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
-
-                fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
-            }
-        }
-    }
-}
-
-// Helper function to get a token from the combined, speculative sequence of inp and draft.
-static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
-    return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
-}
-
-// If sample size or percentage are below these thresholds the draft is aborted early:
-constexpr int    draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2,  2,  1,  1};
-constexpr int        draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
-constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4,  3,  2,  2};
-constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
-
-// Helper function that tries to draft a token from only the static ngram cache:
-static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
-    common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
-    if (part_static_it == nc_static.end()) {
-        return LLAMA_TOKEN_NULL;
-    }
-    const common_ngram_cache_part part_static = part_static_it->second;
-
-    int max_count_static  = 0;
-    int sum_count_static  = 0;
-    llama_token max_token = LLAMA_TOKEN_NULL;
-
-    for (std::pair<llama_token, int> token_count_static : part_static) {
-        const llama_token token = token_count_static.first;
-        const int32_t count_static  = token_count_static.second;
-
-        if (count_static > max_count_static) {
-            max_token        = token;
-            max_count_static = count_static;
-        }
-        sum_count_static += count_static;
-    }
-
-    if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
-        return LLAMA_TOKEN_NULL;
-    }
-    if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
-        return LLAMA_TOKEN_NULL;
-    }
-    return max_token;
-}
-
-// Try to draft a token from primary cache (context/dynamic), validate with static cache:
-static llama_token try_draft(
-    common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
-    const int * min_sample_size, const int * min_percent) {
-
-    llama_token drafted_token = LLAMA_TOKEN_NULL;
-
-    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
-        const common_ngram ngram_primary = ngrams_primary[i];
-
-        common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
-        if (part_primary_it == nc_primary.end()) {
-            continue;
-        }
-        const common_ngram_cache_part part_primary = part_primary_it->second;
-
-        int max_count_primary = 0;
-        int max_count_static  = 0;
-        int sum_count_primary = 0;
-        llama_token max_token = LLAMA_TOKEN_NULL;
-
-        for (std::pair<llama_token, int> token_count_primary : part_primary) {
-            const llama_token token = token_count_primary.first;
-
-            common_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
-
-            const int32_t count_primary = token_count_primary.second;
-            const int32_t count_static  = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
-
-            if (count_primary*count_static > max_count_primary*max_count_static) {
-                max_token         = token;
-                max_count_primary = count_primary;
-                max_count_static  = count_static;
-            }
-            sum_count_primary += count_primary;
-        }
-
-        if (sum_count_primary < min_sample_size[i]) {
-            continue;
-        }
-        if (100*max_count_primary < min_percent[i]*sum_count_primary) {
-            continue;;
-        }
-        drafted_token = max_token;
-    }
-
-    return drafted_token;
-}
-
-void common_ngram_cache_draft(
-    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
-    common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static
-) {
-    GGML_ASSERT(draft.size() == 1);
-    const int inp_size = inp.size();
-
-    if (inp_size < LLAMA_NGRAM_STATIC) {
-        return;
-    }
-
-    while ((int) draft.size()-1 < n_draft) {
-        llama_token drafted_token = LLAMA_TOKEN_NULL;
-
-        const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
-        common_ngram ngram_static;
-        for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
-            ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
-        }
-        common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
-        common_ngram_cache_part part_static;
-        if (part_static_it != nc_static.end()) {
-            part_static = part_static_it->second;
-        }
-
-        // cd = context + dynamic
-        std::vector<common_ngram> ngrams_cd;
-        for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
-            const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
-            common_ngram ngram_cd;
-            for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
-                ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
-            }
-            ngrams_cd.push_back(ngram_cd);
-        }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
-            drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
-        }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
-            drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
-        }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
-            drafted_token = try_draft(nc_static, ngram_static);
-        }
-
-        if (drafted_token == LLAMA_TOKEN_NULL) {
-            break;
-        }
-
-        LOG(" - draft candidate: token=%d\n", drafted_token);
-        draft.push_back(drafted_token);
-    }
-}
-
-void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) {
-    std::ofstream file_out(filename, std::ios::binary);
-    for (std::pair<common_ngram, common_ngram_cache_part> item : ngram_cache) {
-        const common_ngram      ngram        = item.first;
-        common_ngram_cache_part token_counts = item.second;
-        GGML_ASSERT(!token_counts.empty());
-        const int32_t ntokens = token_counts.size();
-        GGML_ASSERT(ntokens > 0);
-
-        file_out.write(reinterpret_cast<const char *>(&ngram),   sizeof(common_ngram));
-        file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
-        for (std::pair<llama_token, int32_t> item2 : token_counts) {
-            const llama_token token = item2.first;
-            const int32_t     count = item2.second;
-            GGML_ASSERT(count > 0);
-
-            file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
-            file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
-        }
-    }
-
-}
-
-common_ngram_cache common_ngram_cache_load(std::string & filename) {
-    std::ifstream hashmap_file(filename, std::ios::binary);
-    if (!hashmap_file) {
-        throw std::ifstream::failure("Unable to open file " + filename);
-    }
-    common_ngram_cache ngram_cache;
-
-    common_ngram ngram;
-    int32_t     ntokens;
-    llama_token token;
-    int32_t     count;
-
-    char * ngramc   = reinterpret_cast<char*>(&ngram);
-    char * ntokensc = reinterpret_cast<char*>(&ntokens);
-    char * tokenc   = reinterpret_cast<char*>(&token);
-    char * countc   = reinterpret_cast<char*>(&count);
-    while(hashmap_file.read(ngramc, sizeof(common_ngram))) {
-        GGML_ASSERT(!hashmap_file.eof());
-        GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
-        GGML_ASSERT(ntokens > 0);
-        common_ngram_cache_part token_counts;
-
-        for (int i = 0; i < ntokens; ++i) {
-            GGML_ASSERT(!hashmap_file.eof());
-            GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
-            GGML_ASSERT(!hashmap_file.eof());
-            GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
-            GGML_ASSERT(count > 0);
-            token_counts.emplace(token, count);
-        }
-
-        ngram_cache.emplace(ngram, token_counts);
-    }
-    GGML_ASSERT(hashmap_file.eof());
-
-    return ngram_cache;
-}
-
-void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) {
-    for (std::pair<common_ngram, common_ngram_cache_part> ngram_part : ngram_cache_add) {
-        const common_ngram      ngram = ngram_part.first;
-        common_ngram_cache_part  part = ngram_part.second;
-
-        common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
-        if (part_merged_it == ngram_cache_target.end()) {
-            ngram_cache_target.emplace(ngram, part);
-            continue;
-        }
-
-        for (std::pair<llama_token, int32_t> token_count : part) {
-            const llama_token token = token_count.first;
-            const int32_t     count = token_count.second;
-            GGML_ASSERT(count > 0);
-
-            common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
-            if (token_count_merged_it == part_merged_it->second.end()) {
-                part_merged_it->second.emplace(token, count);
-                continue;
-            }
-
-            token_count_merged_it->second += count;
-        }
-    }
-}

common/ngram-cache.h

@@ -1,101 +0,0 @@
-#pragma once
-
-#include "llama.h"
-
-#include <unordered_map>
-#include <string>
-#include <vector>
-
-#define LLAMA_NGRAM_MIN    1
-#define LLAMA_NGRAM_MAX    4
-#define LLAMA_NGRAM_STATIC 2
-
-// Data structures to map n-grams to empirical token probabilities:
-
-struct common_ngram {
-    llama_token tokens[LLAMA_NGRAM_MAX];
-
-    common_ngram() {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = LLAMA_TOKEN_NULL;
-        }
-    }
-
-    common_ngram(const llama_token * input, const int ngram_size) {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
-        }
-    }
-
-    bool operator==(const common_ngram & other) const {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            if (tokens[i] != other.tokens[i]) {
-                return false;
-            }
-        }
-        return true;
-    }
-};
-
-struct common_token_hash_function {
-    size_t operator()(const llama_token token) const {
-        // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
-        return token * 11400714819323198485llu;
-    }
-};
-
-struct common_ngram_hash_function {
-    size_t operator()(const common_ngram & ngram) const {
-        size_t hash = common_token_hash_function{}(ngram.tokens[0]);
-        for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
-            hash ^= common_token_hash_function{}(ngram.tokens[i]);
-        }
-        return hash;
-    }
-};
-
-// token -> number of times token has been seen
-typedef std::unordered_map<llama_token, int32_t> common_ngram_cache_part;
-
-// n-gram -> empirical distribution of following tokens
-typedef std::unordered_map<common_ngram, common_ngram_cache_part, common_ngram_hash_function> common_ngram_cache;
-
-
-// Update an ngram cache with tokens.
-// ngram_cache:         the cache to modify.
-// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
-// inp_data:            the token sequence with which to update ngram_cache.
-// nnew:                how many new tokens have been appended to inp_data since the last call to this function.
-// print_progress:      whether to print progress to stderr.
-//
-// In order to get correct results inp_data can ONLY BE APPENDED TO.
-// Changes in the middle need a complete rebuild.
-void common_ngram_cache_update(
-    common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
-
-// Try to draft tokens from ngram caches.
-// inp:                the tokens generated so far.
-// draft:              the token sequence to draft. Expected to initially contain the previously sampled token.
-// n_draft:            maximum number of tokens to add to draft.
-// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
-// nc_context:         ngram cache based on current context.
-// nc_dynamic:         ngram cache based on previous user generations.
-// nc_static:          ngram cache generated from a large text corpus, used for validation.
-void common_ngram_cache_draft(
-    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
-    common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static);
-
-// Save an ngram cache to a file.
-// ngram_cache: the ngram cache to save.
-// filename:    the path under which to save the ngram cache.
-void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename);
-
-// Load an ngram cache saved with common_ngram_cache_save.
-// filename: the path from which to load the ngram cache.
-// returns:  an ngram cache containing the information saved to filename.
-common_ngram_cache common_ngram_cache_load(std::string & filename);
-
-// Merge two ngram caches.
-// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
-// ngram_cache_add:    the ngram cache to add to ngram_cache_target.
-void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add);

common/sampling.cpp

@@ -1,526 +0,0 @@
-#include "sampling.h"
-
-#include "common.h"
-
-#include <cmath>
-#include <unordered_map>
-
-// the ring buffer works similarly to std::deque, but with a fixed capacity
-// TODO: deduplicate with llama-impl.h
-template<typename T>
-struct ring_buffer {
-    ring_buffer(size_t cap) : capacity(cap), data(cap) {}
-
-    T & front() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[first];
-    }
-
-    const T & front() const {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[first];
-    }
-
-    T & back() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[pos];
-    }
-
-    const T & back() const {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        return data[pos];
-    }
-
-    void push_back(const T & value) {
-        if (sz == capacity) {
-            // advance the start when buffer is full
-            first = (first + 1) % capacity;
-        } else {
-            sz++;
-        }
-        data[pos] = value;
-        pos = (pos + 1) % capacity;
-    }
-
-    T pop_front() {
-        if (sz == 0) {
-            throw std::runtime_error("ring buffer is empty");
-        }
-        T value = data[first];
-        first = (first + 1) % capacity;
-        sz--;
-        return value;
-    }
-
-    const T & rat(size_t i) const {
-        if (i >= sz) {
-            throw std::runtime_error("ring buffer: index out of bounds");
-        }
-        return data[(first + sz - i - 1) % capacity];
-    }
-
-    std::vector<T> to_vector() const {
-        std::vector<T> result;
-        result.reserve(sz);
-        for (size_t i = 0; i < sz; i++) {
-            result.push_back(data[(first + i) % capacity]);
-        }
-        return result;
-    }
-
-    void clear() {
-        // here only reset the status of the buffer
-        sz = 0;
-        first = 0;
-        pos = 0;
-    }
-
-    bool empty() const {
-        return sz == 0;
-    }
-
-    size_t size() const {
-        return sz;
-    }
-
-    size_t capacity = 0;
-    size_t sz = 0;
-    size_t first = 0;
-    size_t pos = 0;
-    std::vector<T> data;
-};
-
-struct common_sampler {
-    common_params_sampling params;
-
-    struct llama_sampler * grmr;
-    struct llama_sampler * chain;
-
-    ring_buffer<llama_token> prev;
-
-    std::vector<llama_token_data> cur;
-
-    llama_token_data_array cur_p;
-
-    void set_logits(struct llama_context * ctx, int idx) {
-        const auto * logits = llama_get_logits_ith(ctx, idx);
-
-        const llama_model * model = llama_get_model(ctx);
-        const llama_vocab * vocab = llama_model_get_vocab(model);
-
-        const int n_vocab = llama_vocab_n_tokens(vocab);
-
-        cur.resize(n_vocab);
-
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
-        }
-
-        cur_p = { cur.data(), cur.size(), -1, false };
-    }
-};
-
-std::string common_params_sampling::print() const {
-    char result[1024];
-
-    snprintf(result, sizeof(result),
-            "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
-            "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
-            "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
-            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
-            penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
-            dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
-            top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
-            mirostat, mirostat_eta, mirostat_tau);
-
-    return std::string(result);
-}
-
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
-
-    lparams.no_perf = params.no_perf;
-
-    std::vector<const char *> trigger_words;
-    trigger_words.reserve(params.grammar_trigger_words.size());
-    for (const auto & str : params.grammar_trigger_words) {
-        trigger_words.push_back(str.word.c_str());
-    }
-
-    struct llama_sampler * grmr;
-    if (params.grammar.compare(0, 11, "%llguidance") == 0) {
-#ifdef LLAMA_USE_LLGUIDANCE
-        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
-#else
-        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
-#endif // LLAMA_USE_LLGUIDANCE
-    } else {
-        grmr = params.grammar_lazy
-             ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
-                                               trigger_words.data(), trigger_words.size(),
-                                               params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
-             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
-    }
-
-    auto * result = new common_sampler {
-        /* .params = */ params,
-        /* .grmr   = */ grmr,
-        /* .chain  = */ llama_sampler_chain_init(lparams),
-        /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
-        /* .cur    = */ {},
-        /* .cur_p  = */ {},
-    };
-
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
-                params.logit_bias.size(),
-                params.logit_bias.data()));
-
-    if (params.mirostat == 0) {
-        for (const auto & cnstr : params.samplers) {
-            switch (cnstr) {
-                case COMMON_SAMPLER_TYPE_DRY:
-                    {
-                        std::vector<const char *> c_breakers;
-                        c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto & str : params.dry_sequence_breakers) {
-                            c_breakers.push_back(str.c_str());
-                        }
-
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
-                    }
-                    break;
-                case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
-                    break;
-                case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p    (params.top_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p    (params.min_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_XTC:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
-                    break;
-                case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical  (params.typ_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
-                    break;
-                case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (vocab));
-                    break;
-                case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
-                    break;
-                default:
-                    GGML_ASSERT(false && "unknown sampler type");
-            }
-        }
-        llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
-    } else if (params.mirostat == 1) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
-    } else if (params.mirostat == 2) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
-    } else {
-        GGML_ASSERT(false && "unknown mirostat version");
-    }
-
-    return result;
-}
-
-void common_sampler_free(struct common_sampler * gsmpl) {
-    if (gsmpl) {
-        llama_sampler_free(gsmpl->grmr);
-
-        llama_sampler_free(gsmpl->chain);
-
-        delete gsmpl;
-    }
-}
-
-void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
-    if (accept_grammar) {
-        llama_sampler_accept(gsmpl->grmr, token);
-    }
-
-    llama_sampler_accept(gsmpl->chain, token);
-
-    gsmpl->prev.push_back(token);
-}
-
-void common_sampler_reset(struct common_sampler * gsmpl) {
-    llama_sampler_reset(gsmpl->grmr);
-
-    llama_sampler_reset(gsmpl->chain);
-}
-
-struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
-    return new common_sampler {
-        /* .params = */ gsmpl->params,
-        /* .grmr   = */ llama_sampler_clone(gsmpl->grmr),
-        /* .chain  = */ llama_sampler_clone(gsmpl->chain),
-        /* .prev   = */ gsmpl->prev,
-        /* .cur    = */ gsmpl->cur,
-        /* .cur_p  = */ gsmpl->cur_p,
-    };
-}
-
-void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
-    // TODO: measure grammar performance
-
-    if (gsmpl) {
-        llama_perf_sampler_print(gsmpl->chain);
-    }
-    if (ctx) {
-        llama_perf_context_print(ctx);
-    }
-}
-
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
-    gsmpl->set_logits(ctx, idx);
-
-    auto & grmr  = gsmpl->grmr;
-    auto & chain = gsmpl->chain;
-    auto & cur_p = gsmpl->cur_p; // initialized by set_logits
-
-    if (grammar_first) {
-        llama_sampler_apply(grmr, &cur_p);
-    }
-
-    llama_sampler_apply(chain, &cur_p);
-
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
-
-    const llama_token id = cur_p.data[cur_p.selected].id;
-
-    if (grammar_first) {
-        return id;
-    }
-
-    // check if it the sampled token fits the grammar
-    {
-        llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
-        llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
-
-        llama_sampler_apply(grmr, &single_token_data_array);
-
-        const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
-        if (is_valid) {
-            return id;
-        }
-    }
-
-    // resampling:
-    // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
-    gsmpl->set_logits(ctx, idx);
-
-    llama_sampler_apply(grmr,  &cur_p);
-    llama_sampler_apply(chain, &cur_p);
-
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
-
-    return cur_p.data[cur_p.selected].id;
-}
-
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
-    GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
-
-    std::vector<llama_token> result;
-    result.reserve(idxs.size());
-
-    size_t i = 0;
-    for (; i < draft.size(); i++) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
-
-        common_sampler_accept(gsmpl, id, true);
-
-        result.push_back(id);
-
-        if (draft[i] != id) {
-            break;
-        }
-    }
-
-    if (i == draft.size()) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
-
-        common_sampler_accept(gsmpl, id, true);
-
-        result.push_back(id);
-    }
-
-    return result;
-}
-
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
-    std::vector<int> idxs(draft.size() + 1);
-    for (size_t i = 0; i < idxs.size(); ++i) {
-        idxs[i] = i;
-    }
-
-    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
-}
-
-uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
-    return llama_sampler_get_seed(gsmpl->chain);
-}
-
-// helpers
-
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
-    return &gsmpl->cur_p;
-}
-
-llama_token common_sampler_last(const struct common_sampler * gsmpl) {
-    return gsmpl->prev.rat(0);
-}
-
-std::string common_sampler_print(const struct common_sampler * gsmpl) {
-    std::string result = "logits ";
-
-    for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
-        const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
-        result += std::string("-> ") + llama_sampler_name(smpl) + " ";
-    }
-
-    return result;
-}
-
-std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) {
-    n = std::min(n, (int) gsmpl->prev.size());
-
-    if (n <= 0) {
-        return "";
-    }
-
-    std::string result;
-    result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab
-
-    for (int i = n - 1; i >= 0; i--) {
-        const llama_token id = gsmpl->prev.rat(i);
-
-        GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
-
-        result += common_token_to_piece(ctx_main, id);
-    }
-
-    return result;
-}
-
-char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
-    switch (cnstr) {
-        case COMMON_SAMPLER_TYPE_DRY:         return 'd';
-        case COMMON_SAMPLER_TYPE_TOP_K:       return 'k';
-        case COMMON_SAMPLER_TYPE_TYPICAL_P:   return 'y';
-        case COMMON_SAMPLER_TYPE_TOP_P:       return 'p';
-        case COMMON_SAMPLER_TYPE_MIN_P:       return 'm';
-        case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
-        case COMMON_SAMPLER_TYPE_XTC:         return 'x';
-        case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
-        default : return '?';
-    }
-}
-
-std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
-    switch (cnstr) {
-        case COMMON_SAMPLER_TYPE_DRY:         return "dry";
-        case COMMON_SAMPLER_TYPE_TOP_K:       return "top_k";
-        case COMMON_SAMPLER_TYPE_TYPICAL_P:   return "typ_p";
-        case COMMON_SAMPLER_TYPE_TOP_P:       return "top_p";
-        case COMMON_SAMPLER_TYPE_MIN_P:       return "min_p";
-        case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
-        case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
-        case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
-        default : return "";
-    }
-}
-
-std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
-    std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
-        { "dry",         COMMON_SAMPLER_TYPE_DRY },
-        { "top_k",       COMMON_SAMPLER_TYPE_TOP_K },
-        { "top_p",       COMMON_SAMPLER_TYPE_TOP_P },
-        { "typ_p",       COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { "min_p",       COMMON_SAMPLER_TYPE_MIN_P },
-        { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
-        { "xtc",         COMMON_SAMPLER_TYPE_XTC },
-        { "infill",      COMMON_SAMPLER_TYPE_INFILL },
-        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
-    };
-
-    // since samplers names are written multiple ways
-    // make it ready for both system names and input names
-    std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
-        { "top-k",       COMMON_SAMPLER_TYPE_TOP_K },
-        { "top-p",       COMMON_SAMPLER_TYPE_TOP_P },
-        { "nucleus",     COMMON_SAMPLER_TYPE_TOP_P },
-        { "typical-p",   COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { "typical",     COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { "typ-p",       COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { "typ",         COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { "min-p",       COMMON_SAMPLER_TYPE_MIN_P },
-        { "temp",        COMMON_SAMPLER_TYPE_TEMPERATURE },
-    };
-
-    std::vector<common_sampler_type> samplers;
-    samplers.reserve(names.size());
-
-    for (const auto & name : names) {
-        auto sampler = sampler_canonical_name_map.find(name);
-        if (sampler != sampler_canonical_name_map.end()) {
-            samplers.push_back(sampler->second);
-        } else {
-            if (allow_alt_names) {
-                sampler = sampler_alt_name_map.find(name);
-                if (sampler != sampler_alt_name_map.end()) {
-                    samplers.push_back(sampler->second);
-                }
-            }
-        }
-    }
-
-    return samplers;
-}
-
-std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
-    std::unordered_map<char, common_sampler_type> sampler_name_map = {
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY),         COMMON_SAMPLER_TYPE_DRY },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K),       COMMON_SAMPLER_TYPE_TOP_K },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P),   COMMON_SAMPLER_TYPE_TYPICAL_P },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P),       COMMON_SAMPLER_TYPE_TOP_P },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P),       COMMON_SAMPLER_TYPE_MIN_P },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
-    };
-
-    std::vector<common_sampler_type> samplers;
-    samplers.reserve(chars.size());
-
-    for (const auto & c : chars) {
-        const auto sampler = sampler_name_map.find(c);
-        if (sampler != sampler_name_map.end()) {
-            samplers.push_back(sampler->second);
-        }
-    }
-
-    return samplers;
-}

common/sampling.h

@@ -1,107 +0,0 @@
-#pragma once
-
-#include "llama.h"
-
-#include "common.h"
-
-#include <string>
-#include <vector>
-
-// common_sampler extends llama_sampler with additional functionality:
-//
-//  - grammar support
-//  - custom sampler logic based on the parameters
-//  - history of the last accepted tokens
-//  - performance metrics
-//
-// This goal is to have a common implementation of the sampling logic shared across the examples.
-// For example, depending on the temperature, the sampling chain can be very simple (greedy) or more
-// complex (top-k, top-p, etc).
-//
-// Another example is related to the grammar. In general, the grammar constraints applied on the full
-// vocabulary can be very taxing. To improve performance, the grammar can be applied only to the sampled
-// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the
-// grammar constraints are applied to the full vocabulary and the token is resampled.
-//
-// The common_sampler also maintains a container with the last accepted tokens. In the future, this can
-// be moved into the core llama library.
-//
-// For convenience, the common_sampler also maintains a container with the current candidate tokens.
-// This can be used to access the probabilities of the rest of the non-sampled tokens.
-//
-// TODO: measure grammar performance
-//
-
-struct common_sampler;
-
-// llama_sampler API overloads
-
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
-
-void common_sampler_free(struct common_sampler * gsmpl);
-
-// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
-void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
-void                    common_sampler_reset (struct common_sampler * gsmpl);
-struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
-
-// arguments can be nullptr to skip printing
-void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
-
-// extended sampling implementation:
-//
-// - set logits
-// - apply the configured sampler chain
-// - check if the token fits the grammar (if any)
-// - if not: resample by first applying the grammar constraints and then sampling again (slower path)
-//
-// if grammar_first is true, the grammar is applied before the samplers (slower)
-// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
-//
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
-
-// generalized version of common_sampler_sample
-//
-// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match
-// if the sampler disagrees at some point, we stop and return the accepted tokens up to now
-//
-//      common_sampler_sample_n(gsmpl, ctx, { idx }, {});
-//
-// is equivalent to
-//
-//      common_sampler_sample(gsmpl, ctx, idx);
-//      common_sampler_accept(gsmpl, token, true);
-//
-// requires: idxs.size() == draft.size() + 1
-//
-// returns at least 1 token, up to idxs.size()
-//
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
-
-// assume idxs == [ 0, 1, 2, ..., draft.size() ]
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
-
-uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
-
-// helpers
-
-// access the internal list of current candidate tokens
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl);
-
-// get the last accepted token
-llama_token common_sampler_last(const struct common_sampler * gsmpl);
-
-// print the sampler chain into a string
-std::string common_sampler_print(const struct common_sampler * gsmpl);
-
-// get a string representation of the last accepted tokens
-std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n);
-
-char        common_sampler_type_to_chr(enum common_sampler_type cnstr);
-std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
-
-std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
-std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
-                const char * grammar_kind, const char * grammar_data);

common/speculative.cpp

@@ -1,277 +0,0 @@
-#include "speculative.h"
-
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-
-#include <cstring>
-
-#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  128
-#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
-
-struct common_speculative {
-    struct llama_context * ctx;
-    struct common_sampler * smpl;
-
-    llama_batch batch;
-    llama_tokens prompt;
-};
-
-struct common_speculative * common_speculative_init(
-        struct llama_context * ctx_dft) {
-    auto * result = new common_speculative {
-        /* .ctx    = */ ctx_dft,
-        /* .smpl   = */ nullptr,
-        /* .batch  = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
-        /* .prompt = */ {},
-    };
-
-    // TODO: optimize or pass from outside?
-#if 0
-    {
-        common_params_sampling params;
-        params.no_perf = false;
-
-        params.top_k = 40;
-        params.top_p = 0.9;
-
-        params.samplers = {
-            COMMON_SAMPLER_TYPE_TOP_K,
-            COMMON_SAMPLER_TYPE_TOP_P,
-            COMMON_SAMPLER_TYPE_INFILL,
-        };
-
-        result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
-    }
-#else
-    {
-        common_params_sampling params;
-        params.no_perf = false;
-
-        params.top_k = 10;
-
-        params.samplers = {
-            COMMON_SAMPLER_TYPE_TOP_K,
-        };
-
-        result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
-    }
-#endif
-
-    return result;
-}
-
-void common_speculative_free(struct common_speculative * spec) {
-    if (spec == nullptr) {
-        return;
-    }
-
-    common_sampler_free(spec->smpl);
-
-    llama_batch_free(spec->batch);
-
-    delete spec;
-}
-
-bool common_speculative_are_compatible(
-        const struct llama_context * ctx_tgt,
-        const struct llama_context * ctx_dft) {
-    const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
-    const struct llama_model * model_dft = llama_get_model(ctx_dft);
-
-    const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
-    const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
-
-    const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
-    LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
-
-    const bool vocab_type_dft = llama_vocab_type(vocab_dft);
-    LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
-
-    if (vocab_type_tgt != vocab_type_dft) {
-        LOG_ERR("%s: draft model vocab type must match target model to use speculation but "
-                     "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
-        return false;
-    }
-
-    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
-        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
-        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
-        LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
-        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
-        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
-        return false;
-    }
-
-    {
-        const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
-        const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
-
-        const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
-
-        if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
-            LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
-                         "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
-            return false;
-        }
-
-        for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
-            const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
-            const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
-            if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
-                             "token %d content differs - target '%s', draft '%s'\n", __func__, i,
-                        common_token_to_piece(ctx_tgt, i).c_str(),
-                        common_token_to_piece(ctx_dft, i).c_str());
-                return false;
-            }
-        }
-    }
-
-    return true;
-}
-
-llama_tokens common_speculative_gen_draft(
-        struct common_speculative * spec,
-        struct common_speculative_params params,
-        const llama_tokens & prompt_tgt,
-        llama_token id_last) {
-    auto & batch  = spec->batch;
-    auto & ctx    = spec->ctx;
-    auto & smpl   = spec->smpl;
-    auto & prompt = spec->prompt;
-
-    int reuse_i = 0;
-    int reuse_n = 0;
-
-    const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
-
-    const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
-
-    // reuse as much as possible from the old draft context
-    // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
-    for (int i = 0; i < (int) prompt.size(); ++i) {
-        int cur = 0;
-        while (i_start + cur < (int) prompt_tgt.size() &&
-               i       + cur < (int) prompt.size() &&
-               prompt_tgt[i_start + cur] == prompt[i + cur]) {
-            cur++;
-        }
-
-        if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) {
-            reuse_i = i;
-            reuse_n = cur;
-        }
-    }
-
-    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size());
-
-    llama_tokens result;
-    result.reserve(params.n_draft);
-
-    if (reuse_n == 0) {
-        llama_kv_cache_clear(ctx);
-
-        prompt.clear();
-    } else {
-        // this happens when a previous draft has been discarded (for example, due to being too small), but the
-        // target model agreed with it. in this case, we simply pass back the previous results to save compute
-        if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) {
-            for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) {
-                result.push_back(prompt[i]);
-
-                if (params.n_draft <= (int) result.size()) {
-                    break;
-                }
-            }
-
-            return result;
-        }
-
-        if (reuse_i > 0) {
-            llama_kv_cache_seq_rm (ctx, 0, 0, reuse_i);
-            llama_kv_cache_seq_add(ctx, 0, reuse_i, -1, -reuse_i);
-
-            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
-        }
-
-        if (reuse_n < (int) prompt.size()) {
-            llama_kv_cache_seq_rm (ctx, 0, reuse_n, -1);
-
-            prompt.erase(prompt.begin() + reuse_n, prompt.end());
-        }
-    }
-
-    // prepare a batch to evaluate any new tokens in the prompt
-    common_batch_clear(batch);
-
-    for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) {
-        //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
-        common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false);
-
-        prompt.push_back(prompt_tgt[i]);
-    }
-
-    // we should rarely end-up here during normal decoding
-    if (batch.n_tokens > 0) {
-        //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
-
-        llama_decode(ctx, batch);
-    }
-
-    const llama_pos n_past = prompt.size();
-
-    LOG_DBG("%s: n_past = %d\n", __func__, n_past);
-
-    common_batch_clear(batch);
-    common_batch_add  (batch, id_last, n_past, { 0 }, true);
-
-    prompt.push_back(id_last);
-
-    //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str());
-
-    llama_decode(ctx, batch);
-
-    common_sampler_reset(smpl);
-
-    // sample n_draft tokens from the draft model
-    for (int i = 0; i < params.n_draft; ++i) {
-        common_batch_clear(batch);
-
-        common_sampler_sample(smpl, ctx, 0, true);
-
-        const auto * cur_p = common_sampler_get_candidates(smpl);
-
-        for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-            LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
-        }
-
-        // add drafted token for each sequence
-        const llama_token id = cur_p->data[0].id;
-
-        // only collect very high-confidence draft tokens
-        if (cur_p->data[0].p < params.p_min) {
-            break;
-        }
-
-        common_sampler_accept(smpl, id, true);
-
-        result.push_back(id);
-
-        if (params.n_draft <= (int) result.size()) {
-            break;
-        }
-
-        common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
-
-        // evaluate the drafted tokens on the draft model
-        llama_decode(ctx, batch);
-
-        prompt.push_back(id);
-    }
-
-    return result;
-}

common/speculative.h

@@ -1,28 +0,0 @@
-#pragma once
-
-#include "llama.h"
-#include "common.h"
-
-struct common_speculative;
-
-struct common_speculative_params {
-    int n_draft = 16;  // max drafted tokens
-    int n_reuse = 256;
-
-    float p_min = 0.9f; // min probabiliy required to accept a token in the draft
-};
-
-struct common_speculative * common_speculative_init(struct llama_context * ctx_dft);
-
-void common_speculative_free(struct common_speculative * spec);
-
-bool common_speculative_are_compatible(
-        const struct llama_context * ctx_tgt,
-        const struct llama_context * ctx_dft);
-
-// sample up to n_draft tokens and add them to the batch using the draft model
-llama_tokens common_speculative_gen_draft(
-               struct common_speculative * spec,
-        struct common_speculative_params   params,
-                      const llama_tokens & prompt,
-                             llama_token   id_last);