Merge branch 'master' into server-probs

2023-08-25 06:20:32 +08:00 · 2023-08-25 06:20:32 +08:00 · db29d68db5
commit db29d68db5
parent 3f436ea3f3 2e5f70a25f
52 changed files with 2570 additions and 1143 deletions
--- a/.devops/lamma-cpp-clblast.srpm.spec
+++ b/.devops/lamma-cpp-clblast.srpm.spec
@ -0,0 +1,58 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# 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-clblast
+Version:        master
+Release:        1%{?dist}
+Summary:        OpenCL Inference of LLaMA model in pure C/C++
+License:        MIT
+Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
+BuildRequires:  coreutils make gcc-c++ git mesa-libOpenCL-devel
+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 LLAMA_CLBLAST=1
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llamacppclblast
+cp -p server %{buildroot}%{_bindir}/llamacppclblastserver
+cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llamacppclblast
+%{_bindir}/llamacppclblastserver
+%{_bindir}/llamacppclblastsimple
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog
--- a/.devops/lamma-cpp-cublas.srpm.spec
+++ b/.devops/lamma-cpp-cublas.srpm.spec
@ -0,0 +1,59 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# 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-cublas
+Version:        master
+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 LLAMA_CUBLAS=1
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llamacppcublas
+cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
+cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llamacppcublas
+%{_bindir}/llamacppcublasserver
+%{_bindir}/llamacppcublassimple
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@ -0,0 +1,58 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# 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
+Version:        master
+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
+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
+%autosetup
+
+%build
+make -j
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llamacpp
+cp -p server %{buildroot}%{_bindir}/llamacppserver
+cp -p simple %{buildroot}%{_bindir}/llamacppsimple
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llamacpp
+%{_bindir}/llamacppserver
+%{_bindir}/llamacppsimple
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@ -291,24 +291,32 @@ jobs:
          cd build
          ctest -C Release --verbose --timeout 900

-      - name: Get commit hash
-        id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi

      - name: Pack artifacts
        id: pack_artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        run: |
          Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
-          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        uses: actions/upload-artifact@v3
        with:
          path: |
-            llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip
+            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip

  windows-latest-cmake-cublas:
    runs-on: windows-latest
@ -338,23 +346,31 @@ jobs:
          cmake .. -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON
          cmake --build . --config Release

-      - name: Get commit hash
-        id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi

      - name: Pack artifacts
        id: pack_artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        run: |
-          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\build\bin\Release\*
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\build\bin\Release\*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        uses: actions/upload-artifact@v3
        with:
          path: |
-            llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip

      - name: Copy and pack Cuda runtime
        if: ${{ matrix.cuda == '12.1.0' }}
@ -400,21 +416,34 @@ jobs:
      - windows-latest-cmake-cublas

    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v1
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
      - name: Download artifacts
        id: download-artifact
        uses: actions/download-artifact@v3

-      - name: Get commit hash
-        id: commit
-        uses: pr-mpt/actions-commit-hash@v2
-
      - name: Create release
        id: create_release
        uses: anzz1/action-create-release@v1
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
-          tag_name: ${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}
+          tag_name: ${{ steps.tag.outputs.name }}

      - name: Upload release
        id: upload_release
--- a/.gitignore
+++ b/.gitignore
@ -3,6 +3,8 @@
 *.so
 *.gguf
 *.bin
+*.exe
+*.dll
 .DS_Store
 .build/
 .cache/
@ -81,4 +83,3 @@ tests/test-quantize-fns
 tests/test-quantize-perf
 tests/test-sampling
 tests/test-tokenizer-0
-
--- a/README.md
+++ b/README.md
@ -11,15 +11,17 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++

 ### Hot topics

-A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)
+- Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717

-Last revision compatible with the old format: [dadbed9](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
+- A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)

-### Current `master` should be considered in Beta - expect some issues for a few days!
+  Last revision compatible with the old format: [dadbed9](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)

-### Be prepared to re-convert and / or re-quantize your GGUF models while this notice is up!
+  ### Current `master` should be considered in Beta - expect some issues for a few days!

-### Issues with non-GGUF models will be considered with low priority!
+  ### Be prepared to re-convert and / or re-quantize your GGUF models while this notice is up!
+
+  ### Issues with non-GGUF models will be considered with low priority!

 ----

@ -66,12 +68,11 @@ The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quant
 - Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2 and AVX512 support for x86 architectures
 - Mixed F16 / F32 precision
- 4-bit, 5-bit and 8-bit integer quantization support
- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
- cuBLAS and CLBlast support
+- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support
+- CUDA, Metal and OpenCL GPU backend support

 The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
-Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves
+Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves
 as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.

 **Supported platforms:**
@ -85,6 +86,7 @@ as the main playground for developing new features for the [ggml](https://github

 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [X] Falcon
 - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
 - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
 - [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)
@ -115,90 +117,84 @@ as the main playground for developing new features for the [ggml](https://github

 ---

-Here is a typical run using LLaMA-7B:
+Here is a typical run using LLaMA v2 13B on M2 Ultra:

 ```java
-make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
+$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
 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 CFLAGS:   -I.            -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
+I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
 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)
+I CC:       Apple clang version 14.0.3 (clang-1403.0.22.14.1)
+I CXX:      Apple clang version 14.0.3 (clang-1403.0.22.14.1)

 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: build = 1041 (cf658ad)
+main: seed  = 1692823051
+llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
+llama_model_loader: - type  f32:   81 tensors
+llama_model_loader: - type q4_0:  281 tensors
+llama_model_loader: - type q6_K:    1 tensors
+llm_load_print_meta: format         = GGUF V1 (latest)
+llm_load_print_meta: arch           = llama
+llm_load_print_meta: vocab type     = SPM
+llm_load_print_meta: n_vocab        = 32000
+llm_load_print_meta: n_merges       = 0
+llm_load_print_meta: n_ctx_train    = 4096
+llm_load_print_meta: n_ctx          = 512
+llm_load_print_meta: n_embd         = 5120
+llm_load_print_meta: n_head         = 40
+llm_load_print_meta: n_head_kv      = 40
+llm_load_print_meta: n_layer        = 40
+llm_load_print_meta: n_rot          = 128
+llm_load_print_meta: n_gqa          = 1
+llm_load_print_meta: f_norm_eps     = 1.0e-05
+llm_load_print_meta: f_norm_rms_eps = 1.0e-05
+llm_load_print_meta: n_ff           = 13824
+llm_load_print_meta: freq_base      = 10000.0
+llm_load_print_meta: freq_scale     = 1
+llm_load_print_meta: model type     = 13B
+llm_load_print_meta: model ftype    = mostly Q4_0
+llm_load_print_meta: model size     = 13.02 B
+llm_load_print_meta: general.name   = LLaMA v2
+llm_load_print_meta: BOS token = 1 '<s>'
+llm_load_print_meta: EOS token = 2 '</s>'
+llm_load_print_meta: UNK token = 0 '<unk>'
+llm_load_print_meta: LF token  = 13 '<0x0A>'
+llm_load_tensors: ggml ctx size =    0.11 MB
+llm_load_tensors: mem required  = 7024.01 MB (+  400.00 MB per state)
+...................................................................................................
+llama_new_context_with_model: kv self size  =  400.00 MB
+llama_new_context_with_model: compute buffer total size =   75.41 MB

-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
+system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
+sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
+generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0


-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
+ Building a website can be done in 10 simple steps:
+Step 1: Find the right website platform.
+Step 2: Choose your domain name and hosting plan.
+Step 3: Design your website layout.
+Step 4: Write your website content and add images.
+Step 5: Install security features to protect your site from hackers or spammers
+Step 6: Test your website on multiple browsers, mobile devices, operating systems etc…
+Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine!
+Step 8: Start marketing and promoting the website via social media channels or paid ads
+Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc…
+Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further!
+How does a Website Work?
+A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable!
+The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking.
+How to
+llama_print_timings:        load time =   576.45 ms
+llama_print_timings:      sample time =   283.10 ms /   400 runs   (    0.71 ms per token,  1412.91 tokens per second)
+llama_print_timings: prompt eval time =   599.83 ms /    19 tokens (   31.57 ms per token,    31.68 tokens per second)
+llama_print_timings:        eval time = 24513.59 ms /   399 runs   (   61.44 ms per token,    16.28 tokens per second)
+llama_print_timings:       total time = 25431.49 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:
@ -543,6 +539,8 @@ As the models are currently fully loaded into memory, you will need adequate dis

 Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

+*(outdated)*
+
 | Model | Measure      | F16    | Q4_0   | Q4_1   | Q5_0   | Q5_1   | Q8_0   |
 |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
 |    7B | perplexity   | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
--- a/ci/run.sh
+++ b/ci/run.sh
--- a/common/common.cpp
+++ b/common/common.cpp
@ -417,6 +417,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
            params.antiprompt.push_back(argv[i]);
        } else if (arg == "--perplexity") {
            params.perplexity = true;
+        } else if (arg == "--ppl-stride") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.ppl_stride = std::stoi(argv[i]);
+        } else if (arg == "--ppl-output-type") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.ppl_output_type = std::stoi(argv[i]);
        } else if (arg == "--hellaswag") {
            params.hellaswag = true;
        } else if (arg == "--hellaswag-tasks") {
@ -732,35 +744,3 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok

    return std::string(result.data(), result.size());
 }
-
-std::vector<llama_token> llama_tokenize_bpe(
-        struct llama_context * ctx,
-           const std::string & text,
-                        bool   add_bos) {
-    int n_tokens = text.length() + add_bos;
-    std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
-    }
-    return result;
-}
-
-std::string llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token) {
-    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        const int check = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
-    }
-
-    return std::string(result.data(), result.size());
-}
-
--- a/common/common.h
+++ b/common/common.h
@ -64,6 +64,10 @@ struct gpt_params {
    std::string lora_adapter = "";  // lora adapter path
    std::string lora_base    = "";  // base model path for the lora adapter

+    int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
+    int  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

@ -116,15 +120,6 @@ std::vector<llama_token> llama_tokenize(
           const std::string & text,
                        bool   add_bos);

-std::vector<llama_token> llama_tokenize_bpe(
-        struct llama_context * ctx,
-           const std::string & text,
-                        bool   add_bos);
-
 std::string llama_token_to_str(
        const struct llama_context * ctx,
                       llama_token   token);
-
-std::string llama_token_to_str_bpe(
-    const struct llama_context * ctx,
-                   llama_token   token);
--- a/convert-falcon-hf-to-gguf.py
+++ b/convert-falcon-hf-to-gguf.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF falcon--> gguf conversion

 import gguf
@ -94,21 +95,27 @@ print("gguf: get model metadata")

 block_count = hparams["n_layer"]

-gguf_writer.add_name(last_dir)
+gguf_writer.add_name("Falcon")
 gguf_writer.add_context_length(2048) # not in config.json
 gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
 gguf_writer.add_embedding_length(hparams["hidden_size"])
 gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
 gguf_writer.add_block_count(block_count)
 gguf_writer.add_head_count(hparams["n_head"])
-if "n_head_kv" in hparams: gguf_writer.add_head_count_kv(hparams["n_head_kv"])
+if "n_head_kv" in hparams:
+    gguf_writer.add_head_count_kv(hparams["n_head_kv"])
+else:
+    gguf_writer.add_head_count_kv(1)
 gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)

 # TOKENIZATION

 print("gguf: get tokenizer metadata")

 tokens: List[str] = []
+scores: List[float] = []
+toktypes: List[int] = []
 merges: List[str] = []


@ -152,41 +159,30 @@ if Path(dir_model + "/tokenizer.json").is_file():
            text = bytearray(pad_token)

        tokens.append(text)
+        scores.append(0.0)                      # dymmy
+        toktypes.append(gguf.TokenType.NORMAL)  # dummy

    gguf_writer.add_token_list(tokens)
+    gguf_writer.add_token_scores(scores)
+    gguf_writer.add_token_types(toktypes)

-    if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
-        print("gguf: get special token ids")
+print("gguf: get special token ids")
+# Look for special tokens in config.json

-        with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
+if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
+    gguf_writer.add_bos_token_id(hparams["bos_token_id"])

-        # find special token ids
+if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
+    gguf_writer.add_eos_token_id(hparams["eos_token_id"])

-        if "bos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["bos_token"]:
-                    gguf_writer.add_bos_token_id(key["id"])
+if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
+    gguf_writer.add_unk_token_id(hparams["unk_token_id"])

-        if "eos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["eos_token"]:
-                    gguf_writer.add_eos_token_id(key["id"])
+if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
+    gguf_writer.add_sep_token_id(hparams["sep_token_id"])

-        if "unk_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["unk_token"]:
-                    gguf_writer.add_unk_token_id(key["id"])
-
-        if "sep_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["sep_token"]:
-                    gguf_writer.add_sep_token_id(key["id"])
-
-        if "pad_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["pad_token"]:
-                    gguf_writer.add_pad_token_id(key["id"])
+if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
+    gguf_writer.add_pad_token_id(hparams["pad_token_id"])


 # TENSORS
@ -194,8 +190,9 @@ if Path(dir_model + "/tokenizer.json").is_file():
 tensor_map = gguf.get_tensor_name_map(ARCH,block_count)

 # params for qkv transform
-n_head = hparams["n_head"]
+n_head    = hparams["n_head"]
 n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
+
 head_dim = hparams["hidden_size"] // n_head

 # tensor info
--- a/convert-gptneox-hf-to-gguf.py
+++ b/convert-gptneox-hf-to-gguf.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF gptneox--> gguf conversion

 import gguf
--- a/convert-llama-7b-pth-to-gguf.py
+++ b/convert-llama-7b-pth-to-gguf.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # 7b pth llama --> gguf conversion
 # Only models with a single datafile are supported, like 7B
 # HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
--- a/convert-llama-ggmlv3-to-gguf.py
+++ b/convert-llama-ggmlv3-to-gguf.py
@ -1,12 +1,11 @@
-import sys, struct, math, argparse, warnings
+#!/usr/bin/env python3
+import sys, struct, math, argparse
 from pathlib import Path

 import numpy as np

 import gguf

-warnings.filterwarnings('error')
-
 # Note: Does not support GGML_QKK_64
 QK_K = 256
 # Items here are (block size, type size)
@ -95,7 +94,7 @@ class Tensor:
        pad = ((offset + 31) & ~31) - offset
        offset += pad
        n_elems = np.prod(self.dims)
-        n_bytes = (n_elems * tysize) // blksize
+        n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
        self.start_offset = offset
        self.len_bytes = n_bytes
        offset += n_bytes
@ -327,11 +326,7 @@ def main():
    data = np.memmap(cfg.input, mode = 'r')
    model = GGMLV3Model()
    print('* Scanning GGML input file')
-    try:
-        offset = model.load(data, 0)
-    except OverflowError:
-        print(f'!!! Caught overflow loading tensors. The most likely issue is running on Windows but not in WSL. Try running in WSL if possible.', file = sys.stderr)
-        raise
+    offset = model.load(data, 0)
    print(f'* GGML model hyperparameters: {model.hyperparameters}')
    vocab_override = None
    params_override = None
--- a/convert-llama-hf-to-gguf.py
+++ b/convert-llama-hf-to-gguf.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF llama --> gguf conversion

 import gguf
--- a/convert-lora-to-ggml.py
+++ b/convert-lora-to-ggml.py
@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 import json
 import os
 import re
@ -6,23 +6,22 @@ import struct
 import sys
 from typing import Any, Dict, Sequence, TextIO

+import numpy as np
 import torch

-from convert import DATA_TYPE_TO_FTYPE, NUMPY_TYPE_TO_DATA_TYPE, DataType
+NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1}
+

 HF_SUBLAYER_TO_GGML = {
-    "self_attn.q_proj": "attention.wq",
-    "self_attn.k_proj": "attention.wk",
-    "self_attn.v_proj": "attention.wv",
-    "self_attn.o_proj": "attention.wo",
-    "mlp.gate_proj": "feed_forward.w1",
-    "mlp.down_proj": "feed_forward.w2",
-    "mlp.up_proj": "feed_forward.w3",
-    "input_layernorm": "attention_norm",
+    "self_attn.q_proj": "attn_q",
+    "self_attn.k_proj": "attn_k",
+    "self_attn.v_proj": "attn_v",
+    "self_attn.o_proj": "attn_output",
+    "mlp.gate_proj": "ffn_gate",
+    "mlp.down_proj": "ffn_down",
+    "mlp.up_proj": "ffn_up",
+    "input_layernorm": "attn_norm",
    "post_attention_layernorm": "ffn_norm",
-    # "norm": "norm",
-    # "embed_tokens": "tok_embeddings",
-    # "lm_head": "output",
 }


@ -39,7 +38,7 @@ def translate_tensor_name(t: str) -> str:
            sys.exit(1)

        output_string = (
-            f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
+            f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
        )
        return output_string
    else:
@ -54,12 +53,14 @@ def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
    # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
    # but some models ship a float value instead
    # let's convert to int, but fail if lossless conversion is not possible
-    assert int(params["lora_alpha"]) == params["lora_alpha"], "cannot convert float to int losslessly"
+    assert (
+        int(params["lora_alpha"]) == params["lora_alpha"]
+    ), "cannot convert float to int losslessly"
    fout.write(struct.pack("i", int(params["lora_alpha"])))


 def write_tensor_header(
-    self, name: str, shape: Sequence[int], data_type: DataType
+    self, name: str, shape: Sequence[int], data_type: np.dtype
 ) -> None:
    sname = name.encode("utf-8")
    fout.write(
@ -67,7 +68,7 @@ def write_tensor_header(
            "iii",
            len(shape),
            len(sname),
-            DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]],
+            NUMPY_TYPE_TO_FTYPE[data_type.name],
        )
    )
    fout.write(struct.pack("i" * len(shape), *shape[::-1]))
--- a/convert.py
+++ b/convert.py
@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3

 import gguf
 import argparse
@ -104,8 +104,13 @@ class Params:
    n_head_kv:  int
    f_norm_eps: float

+    f_rope_freq_base: Optional[float] = None
+
    ftype: Optional[GGMLFileType] = None

+    # path to the directory containing the model files
+    path_model: Optional['Path'] = None
+
    @staticmethod
    def find_n_mult(n_ff: int, n_embd: int) -> int:
        # hardcoded magic range
@ -191,15 +196,26 @@ class Params:
    def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
        config = json.load(open(config_path))

-        n_vocab    = config["vocab_size"]
-        n_embd     = config["dim"]
-        n_layer    = config["n_layers"]
-        n_mult     = config["multiple_of"]
-        n_ctx      = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
-        n_ff       = -1
-        n_head     = config["n_heads"]
-        n_head_kv  = config["n_kv_heads"] if "n_kv_heads" in config else n_head
-        f_norm_eps = config["norm_eps"]
+        n_vocab          = config["vocab_size"] if "vocab_size" in config else -1
+        n_embd           = config["dim"]
+        n_layer          = config["n_layers"]
+        n_mult           = config["multiple_of"]
+        n_ff             = -1
+        n_head           = config["n_heads"]
+        n_head_kv        = config["n_kv_heads"] if "n_kv_heads" in config else n_head
+        f_norm_eps       = config["norm_eps"]
+        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
+
+        # hack to determine LLaMA v1 vs v2 vs CodeLlama
+        if f_rope_freq_base and f_rope_freq_base == 1000000:
+            # CodeLlama
+            n_ctx = 16384
+        elif config["norm_eps"] == 1e-05:
+            # LLaMA v2
+            n_ctx = 4096
+        else:
+            # LLaMA v1
+            n_ctx = 2048

        if n_vocab == -1:
            n_vocab = model["tok_embeddings.weight"].shape[0]
@ -208,15 +224,16 @@ class Params:
            n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]

        return Params(
-            n_vocab    = n_vocab,
-            n_embd     = n_embd,
-            n_mult     = n_mult,
-            n_layer    = n_layer,
-            n_ctx      = n_ctx,
-            n_ff       = n_ff,
-            n_head     = n_head,
-            n_head_kv  = n_head_kv,
-            f_norm_eps = f_norm_eps,
+            n_vocab          = n_vocab,
+            n_embd           = n_embd,
+            n_mult           = n_mult,
+            n_layer          = n_layer,
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = n_head,
+            n_head_kv        = n_head_kv,
+            f_norm_eps       = f_norm_eps,
+            f_rope_freq_base = f_rope_freq_base,
        )

    @staticmethod
@ -231,6 +248,8 @@ class Params:
        else:
            params = Params.guessed(model_plus.model)

+        params.path_model = model_plus.paths[0].parent
+
        return params


@ -733,7 +752,13 @@ class OutputFile:
        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])

    def add_meta_arch(self, params: Params) -> None:
-        self.gguf.add_name                ("LLaMA")
+        name = "LLaMA"
+        if (params.n_ctx == 4096):
+            name = "LLaMA v2"
+            if params.path_model:
+                name = str(params.path_model.parent).split('/')[-1]
+
+        self.gguf.add_name                (name)
        self.gguf.add_context_length      (params.n_ctx)
        self.gguf.add_embedding_length    (params.n_embd)
        self.gguf.add_block_count         (params.n_layer)
@ -743,6 +768,9 @@ class OutputFile:
        self.gguf.add_head_count_kv       (params.n_head_kv)
        self.gguf.add_layer_norm_rms_eps  (params.f_norm_eps)

+        if params.f_rope_freq_base:
+            self.gguf.add_rope_freq_base(params.f_rope_freq_base)
+
        if params.ftype:
            self.gguf.add_file_type(params.ftype)

--- a/examples/convert-llama2c-to-ggml/README.md
+++ b/examples/convert-llama2c-to-ggml/README.md
@ -12,15 +12,19 @@ usage: ./convert-llama2c-to-ggml [options]

 options:
  -h, --help                       show this help message and exit
-  --copy-vocab-from-model FNAME    model path from which to copy vocab (default 'models/ggml-vocab.bin')
+  --copy-vocab-from-model FNAME    model path from which to copy vocab (default 'tokenizer.bin')
  --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model
  --llama2c-output-model FNAME     model path to save the converted llama2.c model (default ak_llama_model.bin')
 ```

-An example command is as follows:
+An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:

-`$ ./convert-llama2c-to-ggml --copy-vocab-from-model <ggml-vocab.bin> --llama2c-model <llama2.c model path> --llama2c-output-model <ggml output model path>`
+`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin`

-Now you can use the model with command like:
+For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually:

-`$ ./main -m <ggml output model path> -p "One day, Lily met a Shoggoth" -n 500 -c 256 -eps 1e-5`
+`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin`
+
+Now you can use the model with a command like:
+
+`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@ -17,6 +17,9 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif

+#define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
+#define LLAMA_FILE_VERSION_GGJT_V3   3
+
 //////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
 typedef struct {
    int dim; // transformer dimension
@ -49,10 +52,10 @@ typedef struct {
    // float* freq_cis_real; // (seq_len, dim/2)
    // float* freq_cis_imag; // (seq_len, dim/2)
    // (optional) classifier weights for the logits, on the last layer
-    //float* wcls;
+    float* wcls;
 } TransformerWeights;

-void malloc_weights(TransformerWeights* w, Config* p) {
+void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
    // we calloc instead of malloc to keep valgrind happy
    w->token_embedding_table = new float[p->vocab_size * p->dim]();
    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
@ -86,9 +89,16 @@ void malloc_weights(TransformerWeights* w, Config* p) {

    w->rms_final_weight = new float[p->dim]();
    printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
+
+    if (shared_weights) {
+        w->wcls = NULL;
+    } else {
+        w->wcls = new float[p->vocab_size * p->dim]();
+        printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+    }
 }

-int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
+int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
    if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
    if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
    if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
@ -100,6 +110,22 @@ int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
    if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
    if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
    if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
+
+    // Skip freq_cis_real & freq_cis_imag
+    int head_size = p->dim / p->n_heads;
+    fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
+
+    if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
+
+    // Check we didn't forget to read anything
+    auto curr = ftell(f);
+    fseek(f, 0, SEEK_END);
+    auto end = ftell(f);
+    if (curr != end) {
+        printf("Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", curr, end);
+        return 1;
+    }
+
    return 0;
 }

@ -115,6 +141,7 @@ void free_weights(TransformerWeights* w) {
    delete w->w2;
    delete w->w3;
    delete w->rms_final_weight;
+    if (w->wcls) delete w->wcls;
 }

 void print_sample_weights(TransformerWeights *w){
@ -131,6 +158,7 @@ void print_sample_weights(TransformerWeights *w){
    printf("%f\n", w->w2[0]);
    printf("%f\n", w->w3[0]);
    printf("%f\n", w->rms_att_weight[0]);
+    if (w->wcls) printf("%f\n", w->wcls[0]);
 }
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////

@ -509,26 +537,28 @@ bool is_ggml_file(const char *filename) {
 }

 void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
-    // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
-    if (is_ggml_file(filename)) {
-
-        struct llama_context_params llama_params = llama_context_default_params();
-        llama_params.vocab_only = true;
-
-        struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
-        struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
-
-        const int n_vocab = llama_n_vocab(lctx);
-        vocab->id_to_token.resize(n_vocab);
-        for (int i=0; i<n_vocab; ++i) {
-            vocab->id_to_token[i].text  = llama_token_get_text(lctx, i);
-            vocab->id_to_token[i].score = llama_token_get_score(lctx, i);
-            vocab->id_to_token[i].type  = llama_token_get_type(lctx, i);
-            vocab->token_to_id.emplace(vocab->id_to_token[i].text, i);
-        }
-        llama_free(lctx);
-        llama_free_model(lmodel);
-    } else { // assume llama2.c vocabulary
+#pragma message("TODO: implement reading vocabulary using gguf")
+//    // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
+//    if (is_ggml_file(filename)) {
+//
+//        struct llama_context_params llama_params = llama_context_default_params();
+//        llama_params.vocab_only = true;
+//
+//        struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
+//        struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
+//
+//        const int n_vocab = llama_n_vocab(lctx);
+//        vocab->id_to_token.resize(n_vocab);
+//        for (int i=0; i<n_vocab; ++i) {
+//            vocab->id_to_token[i].text  = llama_token_get_text(lctx, i);
+//            vocab->id_to_token[i].score = llama_token_get_score(lctx, i);
+//            vocab->id_to_token[i].type  = llama_token_get_type(lctx, i);
+//            vocab->token_to_id.emplace(vocab->id_to_token[i].text, i);
+//        }
+//        llama_free(lctx);
+//        llama_free_model(lmodel);
+//    } else
+    { // assume llama2.c vocabulary
        printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
        llama_file file(filename, "rb");
        const int  n_vocab = config->vocab_size;
@ -538,6 +568,12 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab)
            float_t score = file.read_f32();
            uint32_t len = file.read_u32();
            std::string text = file.read_string(len);
+            // Special-case handling of <0xXX> single byte tokens.
+            char byte_val;
+            if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
+                char cstr[2] = { byte_val, 0 };
+                text = cstr;
+            }
            vocab->id_to_token[i].text = text;
            vocab->id_to_token[i].score = score;
            vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED;
@ -589,83 +625,80 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
    }

 #pragma message("TODO: implement file saving using gguf")
-    (void) vocab;
-    (void) model;
-    (void) w;
-//    // write_magic
-//    file.write_u32(LLAMA_FILE_MAGIC);   // magic
-//    file.write_u32(LLAMA_FILE_VERSION); // version
-//    // write_hparams
-//    file.write_u32(model->hparams.n_vocab);
-//    file.write_u32(model->hparams.n_embd);
-//    file.write_u32(model->hparams.n_mult);
-//    file.write_u32(model->hparams.n_head);
-//    file.write_u32(model->hparams.n_layer);
-//    file.write_u32(model->hparams.n_rot);
-//    file.write_u32(LLAMA_FTYPE_ALL_F32);
-//
-//    // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
-//    uint32_t n_vocab = model->hparams.n_vocab;
-//    for (uint32_t i = 0; i < n_vocab; i++) {
-//        const auto & token_data = vocab->id_to_token.at(i);
-//        file.write_u32((uint32_t) token_data.tok.size());
-//        file.write_raw(token_data.tok.data(), token_data.tok.size());
-//        file.write_raw(&token_data.score, sizeof(token_data.score));
-//    }
-//
-//    // stuff AK weights into GG weights one by one.
-//    // w->token_embedding_table -> model->tok_embeddings
-//    // float*                   -> struct ggml_tensor
-//    stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
-//    stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
-//
-//    stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
-//    //print_row(model->norm, 0);
-//
-//    // for rms-att-weight
-//    int row_length = model->hparams.n_embd;
-//    const auto & hparams = model->hparams;
-//    //int n_ff = model->hparams.n_embd;
-//    int n_ff = get_n_ff(&hparams);
-//
-//    for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
-//        auto & layer = model->layers[i];
-//        // 1d
-//        stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.ffn_norm      , &w->rms_ffn_weight[i*row_length]);
-//
-//        // from 3d matrix layer x dim x dim to 2d matrix dim x dim
-//        stuff_karpathy_weights_into_gg(layer.wq            , &w->wq[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wk            , &w->wk[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wv            , &w->wv[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wo            , &w->wo[i*row_length*row_length]);
-//
-//        stuff_karpathy_weights_into_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
-//        stuff_karpathy_weights_into_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.w3            , &w->w3[i*row_length*n_ff]);
-//    }
-//    // write tensors
-//    write_tensor(&file, model->tok_embeddings);
-//    write_tensor(&file, model->norm);
-//    write_tensor(&file, model->output); // ?
-//    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-//        auto & layer = model->layers[i];
-//
-//        write_tensor(&file, layer.attention_norm);
-//        write_tensor(&file, layer.wq);
-//        write_tensor(&file, layer.wk);
-//        write_tensor(&file, layer.wv);
-//        write_tensor(&file, layer.wo);
-//        write_tensor(&file, layer.ffn_norm);
-//        write_tensor(&file, layer.w1);
-//        write_tensor(&file, layer.w2);
-//        write_tensor(&file, layer.w3);
-//    }
+    // write_magic
+    file.write_u32(LLAMA_FILE_MAGIC_GGJT);   // magic
+    file.write_u32(LLAMA_FILE_VERSION_GGJT_V3); // version
+    // write_hparams
+    file.write_u32(model->hparams.n_vocab);
+    file.write_u32(model->hparams.n_embd);
+    file.write_u32(model->hparams.n_mult);
+    file.write_u32(model->hparams.n_head);
+    file.write_u32(model->hparams.n_layer);
+    file.write_u32(model->hparams.n_rot);
+    file.write_u32(LLAMA_FTYPE_ALL_F32);
+
+    // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
+    uint32_t n_vocab = model->hparams.n_vocab;
+    for (uint32_t i = 0; i < n_vocab; i++) {
+        const auto & token_data = vocab->id_to_token.at(i);
+        file.write_u32((uint32_t) token_data.text.size());
+        file.write_raw(token_data.text.data(), token_data.text.size());
+        file.write_raw(&token_data.score, sizeof(token_data.score));
+    }
+
+    // stuff AK weights into GG weights one by one.
+    // w->token_embedding_table -> model->tok_embeddings
+    // float*                   -> struct ggml_tensor
+    stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
+    stuff_karpathy_weights_into_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
+
+    stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
+    //print_row(model->norm, 0);
+
+    // for rms-att-weight
+    int row_length = model->hparams.n_embd;
+    const auto & hparams = model->hparams;
+    //int n_ff = model->hparams.n_embd;
+    int n_ff = get_n_ff(&hparams);
+
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
+        auto & layer = model->layers[i];
+        // 1d
+        stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
+        stuff_karpathy_weights_into_gg(layer.ffn_norm      , &w->rms_ffn_weight[i*row_length]);
+
+        // from 3d matrix layer x dim x dim to 2d matrix dim x dim
+        stuff_karpathy_weights_into_gg(layer.wq            , &w->wq[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wk            , &w->wk[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wv            , &w->wv[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wo            , &w->wo[i*row_length*row_length]);
+
+        stuff_karpathy_weights_into_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
+        stuff_karpathy_weights_into_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
+        stuff_karpathy_weights_into_gg(layer.w3            , &w->w3[i*row_length*n_ff]);
+    }
+    // write tensors
+    write_tensor(&file, model->tok_embeddings);
+    write_tensor(&file, model->norm);
+    write_tensor(&file, model->output); // ?
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
+        auto & layer = model->layers[i];
+
+        write_tensor(&file, layer.attention_norm);
+        write_tensor(&file, layer.wq);
+        write_tensor(&file, layer.wk);
+        write_tensor(&file, layer.wv);
+        write_tensor(&file, layer.wo);
+        write_tensor(&file, layer.ffn_norm);
+        write_tensor(&file, layer.w1);
+        write_tensor(&file, layer.w2);
+        write_tensor(&file, layer.w3);
+    }
 }

 struct train_params get_default_train_params() {
    struct train_params params;
-    params.fn_vocab_model    = "models/ggml-vocab.bin";
+    params.fn_vocab_model    = "tokenizer.bin";
    params.fn_llama2c_output_model = "ak_llama_model.bin";
    params.fn_train_data     = "shakespeare.txt";
    params.fn_checkpoint_in  = "checkpoint.bin";
@ -718,7 +751,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
    fprintf(stderr, "\n");
    fprintf(stderr, "options:\n");
    fprintf(stderr, "  -h, --help                       show this help message and exit\n");
-    fprintf(stderr, "  --copy-vocab-from-model FNAME    llama2.c vocabulary or ggml model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
+    fprintf(stderr, "  --copy-vocab-from-model FNAME    llama2.c vocabulary or ggmlv3 model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
    fprintf(stderr, "  --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
    fprintf(stderr, "  --llama2c-output-model FNAME     model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
    fprintf(stderr, "\n");
@ -791,9 +824,12 @@ int main(int argc, char ** argv) {
        if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
        // read in the config header
        if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
+        auto shared_weights = config.vocab_size > 0;
+        config.vocab_size = abs(config.vocab_size);
+
        // read in the Transformer weights
-        malloc_weights(&weights, &config);
-        if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
+        malloc_weights(&weights, &config, shared_weights);
+        if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }
        fclose(file);
    }

--- a/examples/embd-input/embd_input.py
+++ b/examples/embd-input/embd_input.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import ctypes
 from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
 import numpy as np
--- a/examples/embd-input/llava.py
+++ b/examples/embd-input/llava.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))
--- a/examples/embd-input/minigpt4.py
+++ b/examples/embd-input/minigpt4.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))
--- a/examples/embd-input/panda_gpt.py
+++ b/examples/embd-input/panda_gpt.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))
--- a/examples/jeopardy/graph.py
+++ b/examples/jeopardy/graph.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import matplotlib.pyplot as plt
 import os
 import csv
--- a/examples/jeopardy/jeopardy.sh
+++ b/examples/jeopardy/jeopardy.sh
--- a/examples/json-schema-to-grammar.py
+++ b/examples/json-schema-to-grammar.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import argparse
 import json
 import re
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@ -43,7 +43,7 @@ static bool is_interacting = false;
 void sigint_handler(int signo) {
    if (signo == SIGINT) {
        if (!is_interacting) {
-            is_interacting=true;
+            is_interacting = true;
        } else {
            console::cleanup();
            printf("\n");
@ -189,23 +189,30 @@ int main(int argc, char ** argv) {
        }
    }

+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+
    // tokenize the prompt
    std::vector<llama_token> embd_inp;
    if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
-        embd_inp = ::llama_tokenize(ctx, params.prompt, true);
+        embd_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
    } else {
        embd_inp = session_tokens;
    }

+    // Should not run without any tokens
+    if (embd_inp.empty()) {
+        embd_inp.push_back(llama_token_bos(ctx));
+    }
+
    // Tokenize negative prompt
    std::vector<llama_token> guidance_inp;
    int guidance_offset = 0;
    int original_prompt_len = 0;
    if (ctx_guidance) {
        params.cfg_negative_prompt.insert(0, 1, ' ');
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, true);
+        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, is_spm);

-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
        original_prompt_len = original_inp.size();
        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
    }
@ -252,8 +259,8 @@ int main(int argc, char ** argv) {
    }

    // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
-    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", is_spm);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false);

    // in instruct mode, we inject a prefix and a suffix to each input by the user
    if (params.instruct) {
@ -791,7 +798,8 @@ int main(int argc, char ** argv) {
        }

        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
-        if (params.interactive && n_remain <= 0 && params.n_predict != -1) {
+        // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
+        if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
            n_remain = params.n_predict;
            is_interacting = true;
        }
--- a/examples/make-ggml.py
+++ b/examples/make-ggml.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """
 This script converts Hugging Face llama models to GGML and quantizes them.

--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@ -27,12 +27,136 @@ std::vector<float> softmax(const std::vector<float>& logits) {
    return probs;
 }

-void perplexity(llama_context * ctx, const gpt_params & params) {
+void perplexity_v2(llama_context * ctx, const gpt_params & params) {
    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
    // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
    // Output: `perplexity: 13.5106 [114/114]`
    // BOS tokens will be added for each chunk before eval
-    auto tokens = ::llama_tokenize(ctx, params.prompt, true);
+
+    if (params.ppl_stride <= 0) {
+        fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
+        return;
+    }
+
+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = is_spm;
+
+    fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+    auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+    const int calc_chunk = params.n_ctx;
+
+    fprintf(stderr, "%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk);
+
+    if (int(tokens.size()) <= calc_chunk) {
+        fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
+                tokens.size(), params.n_ctx, params.ppl_stride);
+        return;
+    }
+
+    const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1)  / params.ppl_stride;
+
+    const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
+    const int n_vocab = llama_n_vocab(ctx);
+    const int n_batch = params.n_batch;
+
+    int count = 0;
+    double nll = 0.0;
+
+    fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
+
+    for (int i = 0; i < n_chunk; ++i) {
+        const int start =     i * params.ppl_stride;
+        const int end   = start + calc_chunk;
+
+        const int num_batches = (calc_chunk + n_batch - 1) / n_batch;
+        //fprintf(stderr, "%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches);
+
+        std::vector<float> logits;
+
+        const auto t_start = std::chrono::high_resolution_clock::now();
+
+        for (int j = 0; j < num_batches; ++j) {
+            const int batch_start = start + j * n_batch;
+            const int batch_size  = std::min(end - batch_start, n_batch);
+
+            //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
+            if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
+                //fprintf(stderr, "%s : failed to eval\n", __func__);
+                return;
+            }
+
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
+
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_token_bos(ctx);
+            }
+
+            const auto batch_logits = llama_get_logits(ctx);
+            logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+
+            if (j == 0) {
+                tokens[batch_start] = token_org;
+            }
+        }
+
+        const auto t_end = std::chrono::high_resolution_clock::now();
+
+        if (i == 0) {
+            const float t_total = std::chrono::duration<float>(t_end - t_start).count();
+            fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
+            int total_seconds = (int)(t_total * n_chunk);
+            if (total_seconds >= 60*60) {
+                fprintf(stderr, "%d hours ", total_seconds / (60*60));
+                total_seconds = total_seconds % (60*60);
+            }
+            fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
+        }
+
+        //fprintf(stderr, "%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
+        for (int j = params.n_ctx - params.ppl_stride - 1; j < params.n_ctx - 1; ++j) {
+
+            // Calculate probability of next token, given the previous ones.
+            const std::vector<float> tok_logits(
+                logits.begin() + (j + 0) * n_vocab,
+                logits.begin() + (j + 1) * n_vocab);
+
+            const float prob = softmax(tok_logits)[tokens[start + j + 1]];
+
+            nll += -std::log(prob);
+            ++count;
+        }
+        // perplexity is e^(average negative log-likelihood)
+        if (params.ppl_output_type == 0) {
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        } else {
+            printf("%8d  %.4lf\n", i*params.ppl_stride, std::exp(nll / count));
+        }
+        fflush(stdout);
+    }
+    printf("\n");
+}
+
+void perplexity(llama_context * ctx, const gpt_params & params) {
+    if (params.ppl_stride > 0) {
+        perplexity_v2(ctx, params);
+        return;
+    }
+
+    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+    // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+    // Output: `perplexity: 13.5106 [114/114]`
+    // BOS tokens will be added for each chunk before eval
+
+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = is_spm;
+
+    fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+    auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);

    const int n_chunk_max = tokens.size() / params.n_ctx;

@ -63,7 +187,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
            const auto token_org = tokens[batch_start];

            // add BOS token for the first batch of each chunk
-            if (j == 0) {
+            if (add_bos && j == 0) {
                tokens[batch_start] = llama_token_bos(ctx);
            }

@ -116,7 +240,11 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
            ++count;
        }
        // perplexity is e^(average negative log-likelihood)
-        printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        if (params.ppl_output_type == 0) {
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        } else {
+            printf("%8d  %.4lf\n", i*params.n_ctx, std::exp(nll / count));
+        }
        fflush(stdout);
    }
    printf("\n");
@ -177,8 +305,10 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
    size_t hs_task_count = prompt_lines.size()/6;
    fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);

+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+
    // This is needed as usual for LLaMA models
-    bool prepend_bos = true;
+    const bool add_bos = is_spm;

    // Number of tasks to use when computing the score
    if ( params.hellaswag_tasks < hs_task_count  ) {
@ -234,14 +364,13 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
    std::vector<float> tok_logits(n_vocab);

    for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
-
        // Tokenize the context to count tokens
-        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
+        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
        size_t context_size = context_embd.size();

        // Do the 1st ending
        // In this case we include the context when evaluating
-        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
+        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
        auto query_size = query_embd.size();
        //printf("First query: %d\n",(int)query_size);

@ -369,6 +498,12 @@ int main(int argc, char ** argv) {
    params.perplexity = true;
    params.n_batch = std::min(params.n_batch, params.n_ctx);

+    if (params.ppl_stride > 0) {
+        fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
+                params.n_ctx, params.n_ctx + params.ppl_stride/2);
+        params.n_ctx += params.ppl_stride/2;
+    }
+
    if (params.n_ctx > 2048) {
        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                "expect poor results\n", __func__, params.n_ctx);
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@ -14,25 +14,25 @@ struct quant_option {
 };

 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.50G, +0.2499 ppl @ 7B", },
-    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1846 ppl @ 7B", },
-    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.30G, +0.0796 ppl @ 7B", },
-    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0415 ppl @ 7B", },
+    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.56G, +0.2166 ppl @ LLaMA-v1-7B", },
+    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
+    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
+    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
 #ifdef GGML_USE_K_QUANTS
-    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.67G, +0.8698 ppl @ 7B", },
+    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
    { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
-    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5505 ppl @ 7B", },
-    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.06G, +0.2437 ppl @ 7B", },
-    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1803 ppl @ 7B", },
+    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
+    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
+    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
    { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
-    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.56G, +0.1149 ppl @ 7B", },
-    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0535 ppl @ 7B", },
+    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
+    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
    { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
-    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0353 ppl @ 7B", },
-    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0142 ppl @ 7B", },
-    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0044 ppl @ 7B", },
+    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
+    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
+    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", },
 #endif
-    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ 7B", },
+    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
    { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
 };
--- a/examples/reason-act.sh
+++ b/examples/reason-act.sh
@ -1,4 +1,3 @@
-
 #!/bin/bash

 cd `dirname $0`
--- a/examples/server-llama2-13B.sh
+++ b/examples/server-llama2-13B.sh
--- a/examples/server/api_like_OAI.py
+++ b/examples/server/api_like_OAI.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import argparse
 from flask import Flask, jsonify, request, Response
 import urllib.parse
--- a/examples/server/chat-llama2.sh
+++ b/examples/server/chat-llama2.sh
--- a/examples/server/chat.sh
+++ b/examples/server/chat.sh
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@ -68,7 +68,7 @@ struct ggml_allocr {
    size_t max_size;
    bool measure;
    int parse_seq[GGML_MAX_NODES];
-    bool has_parse_seq;
+    int parse_seq_len;

 #ifdef GGML_ALLOCATOR_DEBUG
    struct ggml_tensor * allocated_tensors[1024];
@ -238,15 +238,11 @@ static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_t
    alloc->n_free_blocks++;
 }

-void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n) {
-    int pos = 0;
+void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) {
    for (int i = 0; i < n; i++) {
-        if (list[i] != -1) {
-            alloc->parse_seq[pos] = list[i];
-            pos++;
-        }
+        alloc->parse_seq[i] = list[i];
    }
-    alloc->has_parse_seq = true;
+    alloc->parse_seq_len = n;
 }

 void ggml_allocr_reset(struct ggml_allocr * alloc) {
@ -269,7 +265,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
        /*.max_size      = */ 0,
        /*.measure       = */ false,
        /*.parse_seq     = */ {0},
-        /*.has_parse_seq = */ false,
+        /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
        /*.allocated_tensors = */ = {0},
 #endif
@ -298,7 +294,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
        /*.max_size      = */ 0,
        /*.measure       = */ true,
        /*.parse_seq     = */ {0},
-        /*.has_parse_seq = */ false,
+        /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
        /*.allocated_tensors = */ = {0},
 #endif
@ -445,8 +441,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                        else {
                            AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
                            node->data = parent->data;
+                            return;
                        }
-                        return;
                    }
                }
            }
@ -497,69 +493,86 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                allocate_node(alloc, input);
            }
        }
-        for (int ind = 0; ind < gf->n_nodes; ind++) {
-            int i;
-            if (alloc->has_parse_seq) {
-                i = alloc->parse_seq[ind];
-            } else {
-                i = ind;
-            }
-            struct ggml_tensor * node = gf->nodes[i];
+        // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
+        int last_barrier_pos = 0;
+        int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes;

-            // allocate parents (leafs)
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
+        for (int ind = 0; ind < n_nodes; ind++) {
+            // allocate a node if there is no parse_seq or this is not a barrier
+            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) {
+                int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind;
+                struct ggml_tensor * node = gf->nodes[i];
+
+                // allocate parents (leafs)
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    allocate_node(alloc, parent);
                }
-                allocate_node(alloc, parent);
+
+                // allocate node
+                allocate_node(alloc, node);
+
+                AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    AT_PRINTF("%s", parent->name);
+                    if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
+                        AT_PRINTF(", ");
+                    }
+                }
+                AT_PRINTF("\n");
            }

-            // allocate node
-            allocate_node(alloc, node);
-
-            AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                AT_PRINTF("%s", parent->name);
-                if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
-                    AT_PRINTF(", ");
-                }
-            }
-            AT_PRINTF("\n");

            // update parents
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
+            // update immediately if there is no parse_seq
+            // update only at barriers if there is parse_seq
+            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) {
+                int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
+                int update_end   = alloc->parse_seq_len ? ind              : ind + 1;
+                for (int i = update_start; i < update_end; i++) {
+                    int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i;
+                    struct ggml_tensor * node = gf->nodes[node_i];
+
+                    for (int j = 0; j < GGML_MAX_SRC; j++) {
+                        struct ggml_tensor * parent = node->src[j];
+                        if (parent == NULL) {
+                            break;
+                        }
+                        struct hash_node * p_hn = hash_get(ht, parent);
+                        p_hn->n_children -= 1;
+
+                        //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
+
+                        if (p_hn->n_children == 0 && p_hn->n_views == 0) {
+                            if (ggml_is_view(parent)) {
+                                struct ggml_tensor * view_src = get_view_source(parent);
+                                struct hash_node * view_src_hn = hash_get(ht, view_src);
+                                view_src_hn->n_views -= 1;
+                                AT_PRINTF("view_src %s\n", view_src->name);
+                                if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
+                                    ggml_allocator_free_tensor(alloc, view_src);
+                                }
+                            }
+                            else {
+                                if (parent->data != node->data) {
+                                    ggml_allocator_free_tensor(alloc, parent);
+                                }
+                            }
+                        }
+                    }
                }
-                struct hash_node * p_hn = hash_get(ht, parent);
-                p_hn->n_children -= 1;
-
-                //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
-
-                if (p_hn->n_children == 0 && p_hn->n_views == 0) {
-                    if (ggml_is_view(parent)) {
-                        struct ggml_tensor * view_src = get_view_source(parent);
-                        struct hash_node * view_src_hn = hash_get(ht, view_src);
-                        view_src_hn->n_views -= 1;
-                        AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
-                        if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, view_src);
-                        }
-                    }
-                    else {
-                        if (parent->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, parent);
-                        }
-                    }
+                AT_PRINTF("\n");
+                if (alloc->parse_seq_len) {
+                    last_barrier_pos = ind + 1;
                }
            }
-            AT_PRINTF("\n");
        }
        // free graph outputs here that wouldn't be freed otherwise because they have no children
        if (outputs != NULL && outputs[g] != NULL) {
--- a/ggml-alloc.h
+++ b/ggml-alloc.h
@ -12,7 +12,7 @@ GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);

 // tell the allocator to parse nodes following the order described in the list
 // you should call this if your graph are optimized to execute out-of-order
-GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n);
+GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);

 GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
 GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@ -3907,6 +3907,29 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
    dst[i + 1] = x0*sin_theta + x1*cos_theta;
 }

+// TODO: this implementation is wrong!
+//static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
+//                                const float p_delta, const int p_delta_rows, const float theta_scale) {
+//    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+//
+//    if (col >= ncols) {
+//        return;
+//    }
+//
+//    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+//    const int i = row*ncols + col/2;
+//
+//    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
+//    const float sin_theta = sinf(theta);
+//    const float cos_theta = cosf(theta);
+//
+//    const float x0 = x[i + 0];
+//    const float x1 = x[i + ncols/2];
+//
+//    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
+//    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
+//}
+
 static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p, const float block_p, const float theta_scale) {
    const int col = blockDim.x*blockIdx.x + threadIdx.x;
    const int half_n_dims = ncols/4;
@ -5515,7 +5538,8 @@ inline void ggml_cuda_op_rope(

    const float theta_scale = powf(freq_base, -2.0f/n_dims);

-    const bool is_glm = mode & 4;
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;

    // compute
    if (is_glm) {
@ -5523,6 +5547,9 @@ inline void ggml_cuda_op_rope(
        const float id_p = min(p, n_ctx - 2.f);
        const float block_p = max(p - (n_ctx - 2.f), 0.f);
        rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
+    } else if (is_neox) {
+        GGML_ASSERT(false && "RoPE NeoX not implemented yet");
+#pragma message("TODO: implement RoPE NeoX for CUDA")
    } else {
        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
        rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
--- a/ggml-metal.m
+++ b/ggml-metal.m
@ -63,6 +63,7 @@ struct ggml_metal_context {
    GGML_METAL_DECL_KERNEL(get_rows_f16);
    GGML_METAL_DECL_KERNEL(get_rows_q4_0);
    GGML_METAL_DECL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DECL_KERNEL(get_rows_q8_0);
    GGML_METAL_DECL_KERNEL(get_rows_q2_K);
    GGML_METAL_DECL_KERNEL(get_rows_q3_K);
    GGML_METAL_DECL_KERNEL(get_rows_q4_K);
@ -73,6 +74,7 @@ struct ggml_metal_context {
    GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
    GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
    GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32);
    GGML_METAL_DECL_KERNEL(mul_mat_q2_K_f32);
    GGML_METAL_DECL_KERNEL(mul_mat_q3_K_f32);
    GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
@ -81,6 +83,7 @@ struct ggml_metal_context {
    GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
    GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
    GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32);
    GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
    GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
    GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
@ -167,7 +170,9 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #define GGML_METAL_ADD_KERNEL(name) \
        ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
        ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
-        fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name); \
+        fprintf(stderr, "%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
+                (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
+                (int) ctx->pipeline_##name.threadExecutionWidth); \
        if (error) { \
            fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
            return NULL; \
@ -186,6 +191,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
        GGML_METAL_ADD_KERNEL(get_rows_f16);
        GGML_METAL_ADD_KERNEL(get_rows_q4_0);
        GGML_METAL_ADD_KERNEL(get_rows_q4_1);
+        GGML_METAL_ADD_KERNEL(get_rows_q8_0);
        GGML_METAL_ADD_KERNEL(get_rows_q2_K);
        GGML_METAL_ADD_KERNEL(get_rows_q3_K);
        GGML_METAL_ADD_KERNEL(get_rows_q4_K);
@ -196,6 +202,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
        GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
        GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
        GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
+        GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32);
        GGML_METAL_ADD_KERNEL(mul_mat_q2_K_f32);
        GGML_METAL_ADD_KERNEL(mul_mat_q3_K_f32);
        GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
@ -203,6 +210,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
        GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
        GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
        GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
        GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
        GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
        GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
@ -218,12 +226,12 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #undef GGML_METAL_ADD_KERNEL
    }

-    fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
-    fprintf(stderr, "%s: hasUnifiedMemory             = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
+    fprintf(stderr, "%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
+    fprintf(stderr, "%s: hasUnifiedMemory              = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
    if (ctx->device.maxTransferRate != 0) {
-        fprintf(stderr, "%s: maxTransferRate              = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
+        fprintf(stderr, "%s: maxTransferRate               = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
    } else {
-        fprintf(stderr, "%s: maxTransferRate              = built-in GPU\n", __func__);
+        fprintf(stderr, "%s: maxTransferRate               = built-in GPU\n", __func__);
    }

    return ctx;
@ -537,8 +545,8 @@ void ggml_metal_graph_compute(

            id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];

-            const int node_start =                                  (cb_idx + 0) * n_nodes_per_cb;
-            const int node_end   = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
+            const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
+            const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);

            for (int ind = node_start; ind < node_end; ++ind) {
                const int i = has_concur ? ctx->concur_list[ind] : ind;
@ -744,32 +752,32 @@ void ggml_metal_graph_compute(
                                [ctx->device supportsFamily:MTLGPUFamilyApple7] &&
                                ne00%32 == 0 &&
                                ne11 > 1) {
-                                    switch (src0->type) {
-                                        case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
-                                        case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
-                                        case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
-                                        case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
-                                        case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
-                                        case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
-                                        case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
-                                        case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
-                                        default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
-                                    }
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                                    [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                                    [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                                    [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5];
-                                    [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6];
-                                    [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
-                                    [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:8];
-                                    [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:9];
-                                    [encoder setBytes:&gqa length:sizeof(gqa) atIndex:10];
-                                    [encoder setThreadgroupMemoryLength:8192 atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                                switch (src0->type) {
+                                    case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
+                                    case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
+                                    case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
+                                    case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break;
+                                    case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
+                                    case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
+                                    case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
+                                    case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
+                                    case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
+                                    default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
                                }
-                            else {
+                                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
+                                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
+                                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
+                                [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:3];
+                                [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:4];
+                                [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
+                                [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
+                                [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
+                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:8];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:9];
+                                [encoder setBytes:&gqa     length:sizeof(gqa)  atIndex:10];
+                                [encoder setThreadgroupMemoryLength:8192 atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                            } else {
                                int nth0 = 32;
                                int nth1 = 1;

@ -799,6 +807,15 @@ void ggml_metal_graph_compute(
                                            nth1 = 8;
                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_1_f32];
                                        } break;
+                                    case GGML_TYPE_Q8_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q8_0_f32];
+                                        } break;
                                    case GGML_TYPE_Q2_K:
                                        {
                                            GGML_ASSERT(ne02 == 1);
@ -868,24 +885,24 @@ void ggml_metal_graph_compute(
                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
                                [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
-                                [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
+                                [encoder setBytes:&gqa  length:sizeof(gqa)  atIndex:17];

-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
                                    src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                }
                                else if (src0t == GGML_TYPE_Q3_K) {
 #ifdef GGML_QKK_64
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #else
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #endif
                                }
                                else if (src0t == GGML_TYPE_Q5_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                }
                                else if (src0t == GGML_TYPE_Q6_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                } else {
                                    [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -895,9 +912,10 @@ void ggml_metal_graph_compute(
                    case GGML_OP_GET_ROWS:
                        {
                            switch (src0->type) {
-                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
+                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
                                case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
                                case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
+                                case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break;
                                case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
                                case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
                                case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
@ -938,16 +956,17 @@ void ggml_metal_graph_compute(
                        } break;
                    case GGML_OP_NORM:
                        {
-                            const float eps = 1e-5f;
+                            float eps;
+                            memcpy(&eps, dst->op_params, sizeof(float));

                            const int nth = 256;

                            [encoder setComputePipelineState:ctx->pipeline_norm];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
-                            [encoder setBytes:&eps  length:sizeof(   float) atIndex:4];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
+                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
                            [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];

                            const int64_t nrows = ggml_nrows(src0);
@ -990,7 +1009,9 @@ void ggml_metal_graph_compute(
                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
                            [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
                            [encoder setBytes:&m0  length:sizeof(    float) atIndex:18];
+
                            const int nth = 32;
+
                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                        } break;
                    case GGML_OP_ROPE:
@ -1005,8 +1026,8 @@ void ggml_metal_graph_compute(
                            memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));

                            [encoder setComputePipelineState:ctx->pipeline_rope];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
@ -1057,24 +1078,24 @@ void ggml_metal_graph_compute(
                                default: GGML_ASSERT(false && "not implemented");
                            }

-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
-                            [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
-                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];

                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                        } break;
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@ -18,6 +18,12 @@ typedef struct {
    uint8_t qs[QK4_1 / 2];  // nibbles / quants
 } block_q4_1;

+#define QK8_0 32
+typedef struct {
+    half    d;         // delta
+    int8_t  qs[QK8_0]; // quants
+} block_q8_0;
+
 kernel void kernel_add(
        device const float * src0,
        device const float * src1,
@ -87,7 +93,12 @@ kernel void kernel_gelu(
    device       float * dst,
    uint tpig[[thread_position_in_grid]]) {
    float x = src0[tpig];
-    dst[tpig] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+
+    // BEWARE !!!
+    // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
+    // This was observed with Falcon 7B and 40B models
+    //
+    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
 }

 kernel void kernel_soft_max(
@ -352,7 +363,7 @@ void mul_vec_q_n_f32(device const void * src0, device const float * src1, device
    const int first_row = (r0 * nsg + sgitg) * nr;
    const uint offset0 = first_row * nb + im/gqa*(nb*ne0);
    device const block_q_type * x = (device const block_q_type *) src0 + offset0;
-    device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
    float yl[16];       // src1 vector cache
    float sumf[nr]={0.f};

@ -424,6 +435,68 @@ kernel void kernel_mul_mat_q4_1_f32(
     mul_vec_q_n_f32<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
 }

+kernel void kernel_mul_mat_q8_0_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01[[buffer(4)]],
+        constant   int64_t & ne02[[buffer(5)]],
+        constant   int64_t & ne10[[buffer(9)]],
+        constant   int64_t & ne12[[buffer(11)]],
+        constant   int64_t & ne0[[buffer(15)]],
+        constant   int64_t & ne1[[buffer(16)]],
+        constant   uint    & gqa[[buffer(17)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int nr  = N_DST;
+    const int nsg = N_SIMDGROUP;
+    const int nw  = N_SIMDWIDTH;
+
+    const int nb = ne00/QK8_0;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * nsg + sgitg) * nr;
+    const uint offset0 = first_row * nb + im/gqa*(nb*ne0);
+    device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0;
+    device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];
+    float sumf[nr]={0.f};
+
+    const int ix = tiisg/2;
+    const int il = tiisg%2;
+
+    device const float * yb = y + ix * QK8_0 + 16*il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += nw/2) {
+        for (int i = 0; i < 16; ++i) {
+            yl[i] = yb[i];
+        }
+
+        for (int row = 0; row < nr; row++) {
+            device const int8_t * qs = x[ib+row*nb].qs + 16*il;
+            float sumq = 0.f;
+            for (int iq = 0; iq < 16; ++iq) {
+                sumq += qs[iq] * yl[iq];
+            }
+            sumf[row] += sumq*x[ib+row*nb].d;
+        }
+
+        yb += QK8_0 * 16;
+    }
+
+    for (int row = 0; row < nr; ++row) {
+        const float tot = simd_sum(sumf[row]);
+        if (tiisg == 0 && first_row + row < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot;
+        }
+    }
+}
+
 kernel void kernel_mul_mat_f16_f32(
        device const  char * src0,
        device const  char * src1,
@ -475,7 +548,6 @@ kernel void kernel_mul_mat_f16_f32(
    }
 }

-
 kernel void kernel_alibi_f32(
        device const float * src0,
        device       float * dst,
@ -571,7 +643,25 @@ kernel void kernel_rope(
            dst_data[1] = x0*sin_theta + x1*cos_theta;
        }
    } else {
-        // TODO: implement
+        for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+            for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                const float cos_theta = cos(theta);
+                const float sin_theta = sin(theta);
+
+                theta *= theta_scale;
+
+                const int64_t i0 = ib*n_dims + ic/2;
+
+                device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                device       float * dst_data  = (device float *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                const float x0 = src[0];
+                const float x1 = src[n_dims/2];
+
+                dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+            }
+        }
    }
 }

@ -1598,12 +1688,12 @@ template <typename type4x4>
 void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
    device const uint16_t * qs = ((device const uint16_t *)xb + 1);
    const half d = il ? (xb->d / 16.h) : xb->d;
-    const half m = il ? (-8.h * 16.h) : -8.h;
+    const half m = il ? ( -8.h * 16.h) : -8.h;
    const ushort mask0 = il ? 0x00F0 : 0x000F;
    const ushort mask1 = il ? 0xF000 : 0x0F00;

    for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)] = (((qs[i] & mask0)) + m) * d;
+        reg[i/2][2*(i%2)]   = (((qs[i] & mask0)     ) + m) * d;
        reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) + m) * d;
    }
 }
@ -1617,11 +1707,21 @@ void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg
    const ushort mask1 = il ? 0xF000 : 0x0F00;

    for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)] = (((qs[i] & mask0)) * d) + m;
+        reg[i/2][2*(i%2)]   = (((qs[i] & mask0)     ) * d) + m;
        reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) * d) + m;
    }
 }

+template <typename type4x4>
+void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
+    device const int8_t * qs = ((device const int8_t *)xb->qs);
+    const half d = xb->d;
+
+    for (int i=0;i<16;i++) {
+        reg[i/4][i%4] = (qs[i + 16*il] * d);
+    }
+}
+
 template <typename type4x4>
 void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
    const half d = xb->d;
@ -1924,9 +2024,10 @@ kernel void kernel_mul_mm(device const  uchar * src0,
 typedef void (get_rows_t)(device const void *, device const int *, device float *, constant int64_t &, \
                          constant uint64_t &, constant uint64_t &, uint, uint, uint);

-template [[host_name("kernel_get_rows_f16")]] kernel get_rows_t kernel_get_rows<half4x4, 1, dequantize_f16>;
+template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
 template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows<block_q4_K, QK_NL, dequantize_q4_K>;
@ -1937,9 +2038,10 @@ typedef void (mat_mm_t)(device const uchar *, device const float *, device float
                             constant int64_t &, constant int64_t &, constant int64_t &, constant int64_t &, \
                             constant int64_t &, constant int64_t &, constant uint &, threadgroup uchar *, uint3, uint, uint);

-template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm<half4x4, 1, dequantize_f16>;
+template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2, dequantize_q8_0>;
 template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_K, QK_NL, dequantize_q4_K>;
--- a/ggml.c
+++ b/ggml.c
@ -3554,9 +3554,9 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) {
 inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }

-static const float GELU_COEF_A    = 0.044715f;
-static const float GELU_QUICK_COEF    = -1.702f;
-static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+static const float GELU_COEF_A     = 0.044715f;
+static const float GELU_QUICK_COEF = -1.702f;
+static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;

 inline static float ggml_gelu_f32(float x) {
    return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
@ -5555,10 +5555,6 @@ struct ggml_tensor * ggml_repeat(
        is_node = true;
    }

-    if (ggml_are_same_shape(a, b) && !is_node) {
-        return a;
-    }
-
    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);

    result->op   = GGML_OP_REPEAT;
@ -5789,6 +5785,7 @@ struct ggml_tensor * ggml_silu_back(
 static struct ggml_tensor * ggml_norm_impl(
        struct ggml_context * ctx,
        struct ggml_tensor  * a,
+        float eps,
        bool inplace) {
    bool is_node = false;

@ -5799,7 +5796,7 @@ static struct ggml_tensor * ggml_norm_impl(

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

-    // TODO: maybe store epsilon here?
+    ggml_set_op_params(result, &eps, sizeof(eps));

    result->op   = GGML_OP_NORM;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -5810,14 +5807,16 @@ static struct ggml_tensor * ggml_norm_impl(

 struct ggml_tensor * ggml_norm(
        struct ggml_context * ctx,
-        struct ggml_tensor  * a) {
-    return ggml_norm_impl(ctx, a, false);
+        struct ggml_tensor  * a,
+        float eps) {
+    return ggml_norm_impl(ctx, a, eps, false);
 }

 struct ggml_tensor * ggml_norm_inplace(
        struct ggml_context * ctx,
-        struct ggml_tensor  * a) {
-    return ggml_norm_impl(ctx, a, true);
+        struct ggml_tensor  * a,
+        float eps) {
+    return ggml_norm_impl(ctx, a, eps, true);
 }

 // ggml_rms_norm
@ -10619,7 +10618,8 @@ static void ggml_compute_forward_norm_f32(

    GGML_TENSOR_UNARY_OP_LOCALS;

-    const float eps = 1e-5f; // TODO: make this a parameter
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));

    // TODO: optimize
    for (int64_t i03 = 0; i03 < ne03; i03++) {
@ -12537,7 +12537,7 @@ static void ggml_compute_forward_rope_f32(
                        dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
                    }
                } else {
-                    // TODO: this is probably wrong, but I can't figure it out ..
+                    // TODO: this might be wrong for ne0 != n_dims - need double check
                    // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
@ -12666,7 +12666,7 @@ static void ggml_compute_forward_rope_f16(
                        dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
                    }
                } else {
-                    // TODO: this is probably wrong, but I can't figure it out ..
+                    // TODO: this might be wrong for ne0 != n_dims - need double check
                    // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
--- a/ggml.h
+++ b/ggml.h
@ -909,14 +909,15 @@ extern "C" {
            struct ggml_tensor  * b);

    // normalize along rows
-    // TODO: eps is hardcoded to 1e-5 for now
    GGML_API struct ggml_tensor * ggml_norm(
            struct ggml_context * ctx,
-            struct ggml_tensor  * a);
+            struct ggml_tensor  * a,
+            float                 eps);

    GGML_API struct ggml_tensor * ggml_norm_inplace(
            struct ggml_context * ctx,
-            struct ggml_tensor  * a);
+            struct ggml_tensor  * a,
+            float                 eps);

    GGML_API struct ggml_tensor * ggml_rms_norm(
            struct ggml_context * ctx,
--- a/gguf.py
+++ b/gguf.py
@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import shutil
 import sys
 import struct
@ -29,12 +30,12 @@ KEY_GENERAL_SOURCE_HF_REPO       = "general.source.hugginface.repository"
 KEY_GENERAL_FILE_TYPE            = "general.file_type"

 # LLM
-KEY_LLM_CONTEXT_LENGTH        = "{arch}.context_length"
-KEY_LLM_EMBEDDING_LENGTH      = "{arch}.embedding_length"
-KEY_LLM_BLOCK_COUNT           = "{arch}.block_count"
-KEY_LLM_FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
-KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
-KEY_LLM_TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
+KEY_CONTEXT_LENGTH        = "{arch}.context_length"
+KEY_EMBEDDING_LENGTH      = "{arch}.embedding_length"
+KEY_BLOCK_COUNT           = "{arch}.block_count"
+KEY_FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
+KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
+KEY_TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"

 # attention
 KEY_ATTENTION_HEAD_COUNT        = "{arch}.attention.head_count"
@ -46,6 +47,7 @@ KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"

 # RoPE
 KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
+KEY_ROPE_FREQ_BASE       = "{arch}.rope.freq_base"
 KEY_ROPE_SCALE_LINEAR    = "{arch}.rope.scale_linear"

 # tokenization
@ -582,7 +584,7 @@ class GGUFWriter:
        self.add_string(KEY_GENERAL_AUTHOR, author)

    def add_tensor_data_layout(self, layout: str):
-        self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
+        self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)

    def add_url(self, url: str):
        self.add_string(KEY_GENERAL_URL, url)
@ -612,27 +614,27 @@ class GGUFWriter:

    def add_context_length(self, length: int):
        self.add_uint32(
-            KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
+            KEY_CONTEXT_LENGTH.format(arch=self.arch), length)

    def add_embedding_length(self, length: int):
        self.add_uint32(
-            KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
+            KEY_EMBEDDING_LENGTH.format(arch=self.arch), length)

    def add_block_count(self, length: int):
        self.add_uint32(
-            KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
+            KEY_BLOCK_COUNT.format(arch=self.arch), length)

    def add_feed_forward_length(self, length: int):
        self.add_uint32(
-            KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+            KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length)

    def add_parallel_residual(self, use: bool):
        self.add_bool(
-            KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
+            KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)

    def add_tensor_data_layout(self, layout: str):
        self.add_string(
-            KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
+            KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)

    def add_head_count(self, count: int):
        self.add_uint32(
@ -662,7 +664,10 @@ class GGUFWriter:
        self.add_uint32(
            KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)

-    def add_rope_scale_linear(self, value:  float):
+    def add_rope_freq_base(self, value: float):
+        self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value)
+
+    def add_rope_scale_linear(self, value: float):
        self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)

    def add_tokenizer_model(self, model: str):
--- a/llama.cpp
+++ b/llama.cpp
--- a/llama.h
+++ b/llama.h
@ -247,6 +247,8 @@ extern "C" {
    LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
    LLAMA_API int llama_n_embd (const struct llama_context * ctx);

+    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
+
    LLAMA_API int llama_model_n_vocab(const struct llama_model * model);
    LLAMA_API int llama_model_n_ctx  (const struct llama_model * model);
    LLAMA_API int llama_model_n_embd (const struct llama_model * model);
@ -346,7 +348,7 @@ extern "C" {

    LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);

-    LLAMA_API llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);

    // Special tokens
    LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
@ -368,13 +370,6 @@ extern "C" {
                             int   n_max_tokens,
                            bool   add_bos);

-    LLAMA_API int llama_tokenize_bpe(
-            struct llama_context * ctx,
-                      const char * text,
-                     llama_token * tokens,
-                             int   n_max_tokens,
-                            bool   add_bos);
-
    LLAMA_API int llama_tokenize_with_model(
        const struct llama_model * model,
                      const char * text,
@ -390,12 +385,6 @@ extern "C" {
                                  char * buf,
                                  int    length);

-    LLAMA_API int llama_token_to_str_bpe(
-            const struct llama_context * ctx,
-                           llama_token   token,
-                                  char * buf,
-                                  int    length);
-
    LLAMA_API int llama_token_to_str_with_model(
              const struct llama_model * model,
                           llama_token   token,
--- a/scripts/get-wikitext-2.sh
+++ b/scripts/get-wikitext-2.sh
--- a/scripts/perf-run-all.sh
+++ b/scripts/perf-run-all.sh
@ -1,93 +0,0 @@
-#!/bin/bash
-#
-# Measure the performance (time per token) of the various quantization techniques
-#
-
-QUANTIZE=0
-if [ "$1" != "" ]; then
-    echo "Quantizing"
-    QUANTIZE=1
-fi
-
-if [ "$QUANTIZE" != "0" ]; then
-    #
-    # quantize
-    #
-
-    # 7B
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-7b-q4_0.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-7b-q4_1.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-7b-q5_0.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-7b-q5_1.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-7b-q8_0.txt
-
-    # 13B
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-13b-q4_0.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-13b-q4_1.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-13b-q5_0.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-13b-q5_1.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-13b-q8_0.txt
-fi
-
-#
-# perf
-# run each command twice
-#
-
-set -x
-
-# 7B - 4 threads
-     ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q8_0.txt | grep llama_print_timings
-
-# 7B - 8 threads
-     ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q8_0.txt | grep llama_print_timings
-
-# 13B - 4 threads
-     ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q8_0.txt | grep llama_print_timings
-
-# 13B - 8 threads
-     ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q8_0.txt | grep llama_print_timings
--- a/scripts/ppl-run-all.sh
+++ b/scripts/ppl-run-all.sh
@ -1,39 +0,0 @@
-#!/bin/bash
-
-#
-# quantize
-#
-
-# 7B
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-7b-q4_0.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-7b-q4_1.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-7b-q5_0.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-7b-q5_1.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-7b-q8_0.txt
-
-# 13B
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-13b-q4_0.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-13b-q4_1.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-13b-q5_0.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-13b-q5_1.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-13b-q8_0.txt
-
-#
-# perplexity
-#
-
-# 7B
-time ./bin/perplexity -m ../models/7B/ggml-model-f16.bin  -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-f16.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_0.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_1.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_0.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_1.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q8_0.txt
-
-# 13B
-time ./bin/perplexity -m ../models/13B/ggml-model-f16.bin  -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-f16.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_0.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_1.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_0.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_1.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q8_0.txt
--- a/scripts/qnt-all.sh
+++ b/scripts/qnt-all.sh
@ -0,0 +1,27 @@
+#!/bin/bash
+
+qnt=(q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args=""
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+model="$1"
+out="../tmp/results-${model}"
+
+mkdir -p ${out}
+
+for q in ${qnt[@]}; do
+    time ./bin/quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
+done
--- a/scripts/run-all-perf.sh
+++ b/scripts/run-all-perf.sh
@ -0,0 +1,31 @@
+#!/bin/bash
+
+qnt=(f16 q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args="-ngl 999 -n 64 -p 512"
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+model="$1"
+out="../tmp/results-${model}"
+
+mkdir -p ${out}
+
+mstr=""
+
+for q in ${qnt[@]}; do
+    mstr="${mstr} -m ../models/${model}/ggml-model-${q}.gguf"
+done
+
+./bin/llama-bench ${mstr} ${args} 2> /dev/null
--- a/scripts/run-all-ppl.sh
+++ b/scripts/run-all-ppl.sh
@ -0,0 +1,27 @@
+#!/bin/bash
+
+qnt=(f16 q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args="-ngl 999 -t 8"
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+model="$1"
+out="../tmp/results-${model}"
+
+mkdir -p ${out}
+
+for q in ${qnt[@]}; do
+    time ./bin/perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
+done
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@ -28,7 +28,8 @@ llama_build_and_test_executable(test-sampling.cpp)
 llama_build_executable(test-tokenizer-0.cpp)
 llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
 llama_build_executable(test-tokenizer-1.cpp)
-llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+# test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
+#llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 #llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
 llama_build_and_test_executable(test-grammar-parser.cpp)
 llama_build_and_test_executable(test-llama-grammar.cpp)
--- a/tests/test-tokenizer-1.cpp
+++ b/tests/test-tokenizer-1.cpp
@ -67,11 +67,13 @@ int main(int argc, char **argv) {
        }
    }

+    GGML_ASSERT(llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_BPE);
+
    const int n_vocab = llama_n_vocab(ctx);

    for (int i = 0; i < n_vocab; ++i) {
-        std::string forward = llama_token_to_str_bpe(ctx, i);
-        std::vector<llama_token> tokens = llama_tokenize_bpe(ctx, forward, false);
+        std::string forward = llama_token_to_str(ctx, i);
+        std::vector<llama_token> tokens = llama_tokenize(ctx, forward, false);
        if (tokens.size() == 1) {
            if (i != tokens[0]) {
                std::string backward = llama_token_to_str(ctx, tokens[0]);
@ -79,16 +81,6 @@ int main(int argc, char **argv) {
                    __func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
                return 2;
            }
-        } else {
-            llama_token_type type = llama_token_get_type(ctx, i);
-            if (type == LLAMA_TOKEN_TYPE_UNKNOWN || type == LLAMA_TOKEN_TYPE_CONTROL || type == LLAMA_TOKEN_TYPE_BYTE) {
-                fprintf(stderr, "%s : info: token %d is string %s and bpe returns tokens %s\n",
-                    __func__, i, llama_token_to_str(ctx, i).c_str(), unescape_whitespace(ctx, tokens).c_str());
-            } else {
-                fprintf(stderr, "%s : error: token %d is string %s but bpe returns tokens %s\n",
-                    __func__, i, llama_token_to_str(ctx, i).c_str(), unescape_whitespace(ctx, tokens).c_str());
-                return 2;
-            }
        }
    }